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+{
+    "title": "Detangling electrolyte chemical dynamics in lithium sulfur batteries by operando monitoring with optical resonance combs",
+    "pre_title": "Detangling electrolyte chemical dynamics and evolution in Li-S batteries by operando monitoring with optical resonance combs",
+    "journal": "Nature Communications",
+    "published": "14 November 2023",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-43110-8/MediaObjects/41467_2023_43110_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-43110-8/MediaObjects/41467_2023_43110_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-43110-8/MediaObjects/41467_2023_43110_MOESM3_ESM.docx"
+        },
+        {
+            "label": "Supplementary Movie",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-43110-8/MediaObjects/41467_2023_43110_MOESM4_ESM.mp4"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-43110-8/MediaObjects/41467_2023_43110_MOESM5_ESM.zip"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-023-43110-8#Sec17"
+    ],
+    "code": [],
+    "subject": [
+        "Batteries"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3192096/v1.pdf?c=1700053937000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3192096/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-023-43110-8.pdf",
+    "preprint_posted": "03 Aug, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Challenges in enabling next-generation rechargeable batteries with lower cost, higher energy density, and longer cycling life stem not only from combining appropriate materials, but from optimally using cell components given their respective evolutions. One-size-fits-all approaches to operational cycling and monitoring are limited in improving sustainability if they cannot utilize and capture essential chemical dynamics and states of electrodes and electrolytes. Herein we describe and show how the use of tilted fiber Bragg grating (TFBG) sensors to track, via the monitoring of both temperature and refractive index metrics, electrolyte-electrode coupled changes that fundamentally control lithium sulfur batteries. Through quantitative sensing of the sulfur concentration in the electrolyte, we demonstrate that the nucleation pathway and crystallization of Li2S and sulfur governs the cycling performance. With this technique, a critical milestone is achieved, not only towards developing chemistry-wise cells (in terms of smart battery sensing leading to improved safety and health diagnostics), but further towards demonstrating that the coupling of sensing and cycling can revitalize known cell chemistries and break open new directions for their development.Physical sciences/Materials science/Materials for energy and catalysis/Electrochemistry/BatteriesPhysical sciences/Energy science and technology/Energy storage/Batteries",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "Supportinformation.pdfSupport informationLiSbatterysensing.mp4Li-S battery sensing",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Challenges in enabling next-generation rechargeable batteries with lower cost, higher energy density, and longer cycling life stem not only from combining appropriate materials, but from optimally using cell components. One-size-fits-all approaches to operational cycling and monitoring are limited in improving sustainability if they cannot utilize and capture essential chemical dynamics and states of electrodes and electrolytes. Herein we describe and show how the use of tilted fiber Bragg grating (TFBG) sensors to track, via the monitoring of both temperature and refractive index metrics, electrolyte-electrode coupled changes that fundamentally control lithium sulfur batteries. Through quantitative sensing of the sulfur concentration in the electrolyte, we demonstrate that the nucleation pathway and crystallization of Li2S and sulfur govern the cycling performance. With this technique, a critical milestone is achieved, not only towards developing chemistry-wise cells (in terms of smart battery sensing leading to improved safety and health diagnostics), but further towards demonstrating that the coupling of sensing and cycling can revitalize known cell chemistries and break open new directions for their development.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Wide-scale utilization of renewable energy sources is essential to supplementing and perhaps replacing the carbon-based energy supply responsible for climate change. The recent success of electric vehicles made possible by lithium-ion battery technology, attributed to both improved reliability and cost reductions, demonstrates that new breakthrough chemistries may not be necessary if known electrochemical cell pairings can be mastered. Included among these chemistries are resurgent lithium sulfur batteries (LSB), which, in spite of their appeal in terms of theoretical specific energy (~2600\u2009Wh/kg), are still not commercialized. This can be attributed to a number of unresolved challenges, including the insulating nature of sulfur and lithium sulfides, large volume expansion (80%) of the solid sulfur cathode during the formation of Li2S, and the shuttle effect caused by soluble polysulfide in electrolyte1,2.\n\nNumerous characterization techniques have been deployed to clarify the underlying science of LSBs during operation, which have contributed significantly to a better understanding of the kinetics and thermodynamics of the dissolution/precipitation of polysulfides, whose critical role in LSBs has been known for nearly 50\u2009years3. Since then, methods such as X-ray diffraction (XRD)4,5, electrochemical tests6,7,8, and spectroscopic techniques9,10,11,12,13,14,15,16 have been used to provide valuable information regarding identification of polysulfide species and reaction kinetics. However, it is experimentally challenging to isolate the individual polysulfides due to the propensity of disproportionation, and these analytical techniques rely on special equipment and cell designs that cannot be directly deployed for long cycling periods. Recently, optical fiber sensors have attracted attention in battery sensing due to their low cost, compactness, remote sensing capabilities, and simple integration into batteries without interfering with their internal chemistry17. Among the fiber sensor family, the most commercialized Fiber Bragg grating (FBG) sensors have been well integrated inside Na (Li)-ion batteries for monitoring heat and pressure18 or inside the solid-state batteries for tracking the stress dynamics19. Indeed, recently Ziyun et al. demonstrated that the cathode stress evolution of LSB can be in-situ monitored by FBG sensors for understanding the chemo-mechanics20. Nevertheless, tracking polysulfides with FBGs is still limited, owing to the fact that the sensing signals are totally confined inside the fiber core and cannot sense the electrolyte surrounding the fiber surface.\n\nIn order to investigate the external medium of fiber, tilted fiber Bragg gratings (TFBGs, same structure as FBG without physical structure modification21, but rotating the grating plane to a specific angle) have been proposed to excite hundreds of discrete cladding mode resonances that are sensitive to the external medium refractive index perturbation via evanescent fields22, hence serving as an optical comb. This has led to the development of high-performance sensors used in various areas, including biomedicine23, magnetic detection24, and gas monitoring25. Recently, TFBGs have been integrated into commercial batteries to detect chemical dynamics/state of electrolytes related to chemical evolution26. Interestingly, some TFBG-assisted surface plasmon resonance (TFBG-SPR) sensors with higher sensing sensitivity have also been developed for Zn-ion batteries to offer an alternative way of probing ion transport kinetics27. Overall, TFBG sensors provide new opportunities to deal with the challenge of battery sensing as they combine direct optical chemical sensing, as well as physio-mechanical parameters via the confined optical modes.\n\nHerein, TFBG sensors, enabling measurements with a wide array of parameters including refractive index, temperature and strain, are proposed to operando track the chemical dynamics/states of the LSB via electrolyte sulfur concentration. We demonstrate that the capacity fading is strongly correlated with the dissolution/precipitation of polysulfides throughout cycling and hence, with respect to cycling rates. By exploiting the kinetic and thermodynamic responses of soluble sulfur in the electrolyte, the nonlinear net transport flux clarifies the invisible disproportionation process and the origins of its dynamic evolution. With this understanding, we show that altering the nucleation pathway of the crystalline Li2S and sulfur can be attributed to real improvements in cell cycling performance. Subsequently, it is noted that TFBGs have the ability to obtain key chemical-physical-thermal metrics in operando with notable time and spatial resolution that may extend beyond LSBs.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Prior to in operando battery inspection, it is appropriate to first briefly visit the suitability of TFBG sensing for such chemistries, as related to fundamental principles of their operation. TFBGs, immersed in an electrolyte (Fig.\u00a01a), were made in the core of the commercial single-mode fiber by ultraviolet pulse laser to induce periodically permanent refractive modulation. They obey a phase matching condition by enhancing the coupling between fundamental core mode and backward-propagation cladding modes22 (Fig.\u00a01b):\n\nwhere \u03bb is the cladding mode resonance wavelength, \\({n}_{11}(\\lambda )\\) is the effective index of core mode, and \\({n}_{{lm}}(\\lambda )\\) is the effective index of cladding mode with azimuthal order \\({l}\\) and radial order \\(m\\). \u039b is the period of grating along the fiber axis, and \\(\\theta\\) is the grating tilt angle. The experimental spectra are presented in Fig.\u00a01c, where the core mode resonance (i.e., Bragg resonance) is located at the longest wavelength around 1590\u2009nm (sensitive to temperature and strain (T, \u03b5))22. The cladding mode resonances guided by the fiber cladding (beside T, \u03b5, also sensitive to refractive index (RI) of the surrounding media) are shown on the left of Bragg resonances. The leaky modes are located at the region where there is a discontinuity in the cladding mode envelope, indicating the loss of total internal reflection at the point where the cladding mode effective index becomes equal to or smaller than the surrounding RI. Therefore, with respect to soluble polysulfides which perturb electrolyte density, and hence the refractive-index, we focus on the cutoff guided cladding mode near the leaky mode region (around 1560\u2009nm wavelength) which is insensitive to unpolarized input light (i.e. can be probed without a polarizer, which simplifies sensing system and still ensures that detection is both stable and repeatable) and shows the highest refractive index sensitivity22.\n\na Schematic of a fiber optic sensor immersed in electrolyte for in-situ detection of sulfur concentration originating from the generated dissolved polysulfide and their transport activities (i.e., shuttle effect). b Backward-propagation guided modes inside fiber for sensing (Supplementary information Fig.\u00a0S1). c Experimental spectra response to polysulfide. d The wavelength shifts of cladding mode resonance at ~1560\u2009nm to 100\u2009mM polysulfide Li2Sx (x\u2009=\u20091, 2, 3, \u2026, 8), shaded in green; (e) to concentration variation of Li2S4 and Li2S8 from 0\u2009mM to 100\u2009mM; (f) to same sulfur concentration of polysulfide Li2Sx (x\u2009=\u20094, 5, 6, 7, 8). The error bars represent the measurement error (test 3 times continuously) resulting from the surrounding temperature change and electrolyte solvent evaporation.\n\nTo investigate the response of TFBG to polysulfides, depicted in Fig.\u00a01c, it was thoroughly immersed in a series of 100\u2009mM polysulfide containing electrolytes in a modified Swagelok cell. Bearing this in mind, the Bragg resonance remains stable because any strain and temperature variation were eliminated during the measurements, indicating that the cladding mode wavelength shift is only related to refractive index variation. When the chain length of polysulfides is increased while keeping the polysulfide concentration the same, the guided modes on the left side of cladding mode at 1560\u2009nm become leaky due to the increased refractive index. This is a result of the number sulfur atoms in solution becoming larger and perturbing the corresponding mode effective refractive index, while guided modes on its right side are linearly shifted to longer wavelength (Fig.\u00a01d, e and Supplementary Fig.\u00a0S2a, b). Noteworthy is the fact that the refractive index tested by TFBG sensor is an average effect of all the pertinent refractive indices of lithium polysulfide solutions. Following this, dilution of the 100\u2009mM polysulfide Li2Sx (x\u2009=\u20094, 5, 6, 7, 8) to an equivalent concentration of sulfur (Supplementary Fig.\u00a0S2c) yields an equivalent optical effect, stemming from the refractive index of polysulfide solutions converging to the same density, (Fig.\u00a01f and Supplementary Fig.\u00a0S2d, e). Therefore, rather than recognizing the specific species inside the electrolyte, the TFBG sensor will distinctly reveal the electrolyte sulfur concentration evolution of LSB cell.\n\nGiven the promising proof-of-concept of sulfur concentration measurement in electrolyte, we explored the capability of operando chemical dynamics/states testing by putting a 2\u2009mm thick, 12.8\u2009mm diameter polyether ether ketone (PEEK) ring (such that a 1\u2009cm long TFBG can pass through) in the middle of the Swagelok assembly to separate the cathode (sulfur and Super P carbon composite (60/40 wt.%)) and anode (lithium). This configuration ensures that the fiber sensor would not touch either electrode (Supplementary Fig.\u00a0S3), and thereby avoiding the strain induced by cathode, which are known to exhibit around 80% volume changes during cycling20. Meanwhile, any thermal effect stemming from the electrolyte background environment has been calibrated and compensated by the method detailed in Supplementary Fig.\u00a0S4. Filling the PEEK ring with electrolyte (250 \u03bcL, 1\u2009M LiTFSI, 0.5\u2009M LiNO3 in DOL/DME (1:1, v/v)) where the sensor is immersed, the effect of ion concertation gradient of electrolyte including Li+, TFSI- and NO3- in DOL and DME can be safely neglected. It should be noted that prior to LSB investigations, a control experiment was executed with lithium iron phosphate (LFP) as the cathode. Here it was found that the corresponding RI of electrolyte variation is 20 times smaller than that in LSB in which dissolved polysulfide is formed (Supplementary Fig.\u00a0S5). Therefore, the use of a TFBG can provide the possibility of measuring sulfur concentration in the electrolyte during cell operation, and to a large extent, the measurement will be irrespective of the cell\u2019s state of charge or state of health.\n\nBased on the aforementioned concept, we measured the electrolyte sulfur concentration variation with a TFBG sensor while simultaneously deploying in operando XRD to track the phase transition of the composite electrode (Fig.\u00a02a). At the upper voltage plateau around 2.4\u2009V, the highest sulfur concentration in the electrolyte was monitored (left panel of Fig.\u00a02a) and found to be accompanied by a decrease in the sulfur peak intensity (XRD pattern) resulting from a series of phase transformations, i.e., from solid sulfur to soluble intermediate polysulfides. On the other hand, when the carbonate-based electrolyte was used as a reference (i.e., LP30, right panel of Fig.\u00a02a), no concentration gradient was observed in the electrolyte and remained nearly stable due to the fact that no soluble polysulfide intermediates were formed. Instead, this resulted in the formation of insoluble and undetected products, since it is known that there is a nucleophilic reaction between sulfur radical and ethylene carbonate of LP30 to form thiocarbonate-like solid electrolyte interphase28,29 (Supplementary Fig.\u00a0S6). Turning to the lower voltage plateau around 2.1\u2009V (Fig.\u00a02a), the concentration of dissolved sulfur decreases as a result of the reduction of long-chain polysulfide into shorter chains, leading to insoluble Li2S compound in the cathode (Supplementary Fig.\u00a0S7a) and verified by XRD30,31. Upon charging, the sulfur concentration indicates reversible recovery consistent with the decay of Li2S peaks until complete disappearance at the voltage ~2.4\u2009V, where crystallization of sulfur starts and thus sulfur concentration in electrolyte drops again, even though the deposited solid sulfur in the cathode is featureless by XRD31. To identify the sulfur at the end of charge, the cathode powder was recovered in the glovebox by washing and drying to remove any soluble polysulfide as well as remaining electrolyte salts (Fig. 2b, c), confirming that recrystallized sulfur was detected and its surface topography was unchanged (i.e., presence of amorphous sulfur)31. Furthermore, when setting the 15-h open circuit voltage (OCV) after charging, the sulfur concentration increases and reaches a plateau within 9\u2009h (Fig. 2c), whereas, on the other hand, no sulfur concentration changes were observed during rest periods applied at the end of discharge (Supplementary Fig.\u00a0S7b). It is most likely explained by comproportionation reactions during the rest period when the recrystallized sulfur from the end of charging is transformed to soluble lower-order polysulfide via reacting with high-order polysulfide32. This is also supported by the beginning of 2\u2009h rest (first cycle before discharging) demonstrating relatively little variation of electrolyte since fresh electrolyte contains a minimum amount of high order polysulfide and the comproportionation reactions are thus not possible (Supplementary Fig.\u00a0S7c). The crystalline sulfur at the end of charge is related to the cut-off potential that the sulfur recrystallization process disappears4 (disappearance of sulfur concentration valley at the end of charge in Fig.\u00a02d) if setting the potential below 2.4\u2009V (indicated that less sulfur suppresses the related comproportionation reactions, also detailed in Supplementary Fig.\u00a0S7c).\n\na by TFBG and XRD at C/20 (0.275\u2009mA left panel): polysulfide dissolution allowed (electrolyte of 1\u2009M LiTFSI, 0.5\u2009M LiNO3 in DOL/DME (1:1, v/v)); right panel: polysulfide dissolution prohibited (electrolyte of LP30: 1\u2009M LiPF6 in EC/DMC) of sulfur and Super P carbon composite (60/40 wt.%) electrode. b Morphology (SEM) of cathode before cycling and after charge, and content of elemental sulfur and carbon (energy-dispersive X-ray spectroscopy, EDX) of the cathode after charge. c The quantitative analysis of sulfur before cycling, end of first plateau of discharge and end of charge. d The recrystallized sulfur governed by comproportionation reactions and cutoff voltage (C/10, 0. 533\u2009mA). The shaded region in blue stands for 15\u2009h of rest (OCV mode) starting at the end of charge demonstrating that the re-crystallized sulfur (marked by green asterisk \u201c*\u201d) dissolves into the electrolyte in the form of soluble polysulfide through comproportionation reactions. All the data has been duplicated at least two or three times prior to being reported herein (Supplementary Fig.\u00a0S7). The C-rate is defined by the speed at which a battery is fully charged or discharged. In order to better understand the C-rate, the absolute current with every instance of C-rate used was provided.\n\nAltogether, the dynamic of sulfur concentration of electrolyte decoded by TFBG sensor supports the simplified chemical reaction process: during discharge the sulfur receives electrons and transfers them first to soluble Li2S4 at the high voltage plateau. This is followed by formation of insoluble Li2S at the low voltage plateau and vice versa for the charging process, indicating that the consumption rate of sulfur under such galvanostatic conditions can be expressed as a ratio. According to the linear sulfur concentration variation rate, calculated from monitoring the slope (mM/h) on each plateau tested by the sensor during the discharge and charge steps (Supplementary Fig.\u00a0S5a), it was observed that the ratio on the upper and lower plateaus of the discharge step is 3.88, while value obtained during charging is 0.84. It suggests that the rate of sulfur transformation to/from soluble polysulfide is 3.88 times faster than that to/from insoluble Li2S and the respective polysulfides for discharge (0.84 times for charge).\n\nThe disproportionation and association reactions of likely intermediates are mesmerizing, but despite an awareness of existence their mysterious dynamics make LSBs seemingly incomprehensible. Nevertheless, the real-time quantification of soluble sulfur afforded by TFBGs provides a convincing way to straighten their story when combined with GITT (Fig.\u00a03). As depicted in Fig.\u00a03d, the overall profile of sulfur concentration variation matches well with dissolution/precipitation of polysulfides and sulfur already confirmed in Fig.\u00a02, and we focus on the temporal response of the electrolyte to the current pulse and respective rest period. According to Fig.\u00a03a\u2013c, the tiny variation of sulfur concentration from A to B (moving from the rest to cycling mode) is the electrolyte instantaneous response to the leading edge of the current pulse (i.e. strong electric field gradient), originating from polysulfide redistribution driven by the sudden electrical field33. Meanwhile, Fig.\u00a03a, b that concentration variation from A to B is opposite to that in Fig.\u00a03c, this should be attributed to the change of the concentration gradient direction during discharge. We also note that the amplitude of concentration variation from A to B is much smaller than that in Fig.\u00a03c, resulting from the fact that the fiber sensor is physically/geometrically closer to sulfur electrode during assembly process, and therefore it leads to an asymmetric concentration variation. Overall, this effect trends in the opposite direction as polysulfide diffusion in the region from B to C, which relates to the current pulse (referred to herein as the kinetic process). The reader may note a discrepancy between voltage and concentration from C to D during rest, which is attributed to the delay between the electrochemical reaction at the electrode surface and the position of the sensor in the cell. Hence a small lag exists even if removing the current pulse. Regarding the OCV relaxation (3\u2009h rest period) and movement towards equilibrium (herein coined as the thermodynamic process) in the region from C to E, the sulfur concentration fluctuation is most likely explained by polysulfide disproportionation process (i.e. Li2S8\u2009\u2194\u2009Li2S6\u2009+\u20091/4S8)9 since the two best-remaining hypotheses, dissociation (i.e. Li2S6\u2009\u2194\u2009Li+ + LiS6\u2212 or Li2S6\u2009\u2194\u20092LiS3)34 and non-uniform polysulfide distribution can be excluded: the dissociation of polysulfide including anions and radicals are rare while the neutral lithium polysulfide is dominant in the electrolyte34; the polysulfide distribution reaches equilibrium within 1\u2009min (time interval of spectra recording) that is nearly synchronous to electrochemistry (Fig.\u00a02a), not matching the rest period situation with 3\u2009h of continuous soluble sulfur consumption or generation. After careful deliberation, we move forward with the idea that electrochemical and disproportionation processes can be extracted respectively by temporal response of electrolyte based on sulfur concentration decoded by TFBG sensor.\n\na\u2013d The temporal voltage (black curve) and decoded electrolyte sulfur concentration (red curve) by GITT test (the capacity and optical spectra are given in supplementary Fig.\u00a0S8). a\u2013c Detailed view of electrolyte response to current pulse (kinetic process) and rest (thermodynamic process). e Net transport flux of soluble sulfur based on current pulse (kinetics process, green triangle, Dk\u2009=\u2009VBC/(S\\(\\times\\)t)) and rest (thermodynamic process, blue square, Dt\u2009=\u2009VDE/(S\\(\\times\\)t)), where S is across section area of electrolyte that sensor is immersed in, t is corresponding time. It indicates the slope of soluble sulfur consumption (negative) or formation (positive) in electrolyte, which is also plotted by arrows in bottom (arrow up: sulfur increasing, arrow down: sulfur decreasing). The normalized ratio (red sphere) represents the sulfur consumption of rest (thermodynamic process) by \\({Ratio}=\\left|{D}_{t}\\right|/(\\left|{D}_{t}\\right|+\\left|{D}_{k}\\right|)\\).\n\nEncouraged by the results mentioned above, we have next attempted to build a quantitative relation between kinetic and thermodynamic processes through a primitive estimation of net transport flux of sulfur in the electrolyte (Fig.\u00a03e). In STAGE I, on the higher voltage plateau, solid sulfur was continuously consumed upon discharge to form long chain polysulfide, Li2S8 (green triangle), together with the rapid disproportionation process \\({{Li}}_{2}{S}_{8}\\leftrightarrow {{Li}}_{2}{S}_{6}+{1/4S}_{8}\\)9,35,36,37 leading to soluble sulfur consumption during relaxation (blue square). At the same time the normalized ratio (red sphere) between the rest and current pulse process nearly remains the same and fixed at 0.1 (shaded in light green color), meaning that there is a competition reaction between the soluble long chain polysulfide species formation and the S8 reprecipitation in the beginning of the discharge step. In STAGE II, regarding the first-to-second plateau transition whereby a voltage slope forms between 2.3 and 2.1\u2009V, the shorter chain polysulfide Li2S4 is expected to be generated38,39. This is accompanied by a reduction in the rate of formation of soluble sulfur in the electrolyte and hence, the sulfur concentration during rest keeps increasing while the generation of fresh long chain polysulfides winds down and the kinetic/thermodynamic ratio can reach 0.89. This indicates that polysulfide species formation via disproportionation is dominant due to fact that dissolved sulfur continues to react with polysulfide presenting in the electrolyte during rest38,40. Undoubtedly an enriched concentration of sulfur in the electrolyte contributes significantly towards driving the formation of more polysulfides. In STAGE III where the lower voltage plateau marks the conversion between Li2S4 to shorter chain Li2S2 and Li2S forms, the potential disproportionation process \\({{Li}}_{2}{S}_{2}\\leftrightarrow {1/3{Li}}_{2}{S}_{4}+{2/3{Li}}_{2}S\\)40,41 is highlighted from the middle of the second plateau, and raises the sulfur concentration to about a normalized ratio of 0.2 which follows until the end of the half-cycle. Upon charging (STAGE IV and STAGE V), it is evident that the process is not a fully reversible one, as seen with STAGE IV where Li2S4 and Li2S6 reappear and the push towards thermodynamic equilibrium necessitates disproportionation processes leading to consumption of sulfur during rest periods, but an overall concentration increase. In STAGE V, the sulfur concentration in electrolyte drops very sharply, caused by the recrystallization of sulfur during current pulsing, even though that is not visible in the operando XRD studies shown in Fig.\u00a02a. Interestingly, the rise of soluble sulfur during these late-stage rest periods suggests nucleation and/or growth limitations of the recrystallized sulfur, which will be addressed here later. Altogether, the quantitative disproportionation process decoupled by the fiber sensor based on GITT provides meaningful details to understand micro-mechanisms of complicated kinetics processes.\n\nInspired by aforementioned exploration of internal mechanisms of LSBs, we decide to further investigate the operando monitoring over cycling and cycling rates (Fig.\u00a04a, b). Bearing in mind that the temperature (blue curve in Fig.\u00a04b), decoded by Bragg resonance located at 1589 nm26, initially rises to 25\u2009\u00b0C and keeps a constant afterward due to the shipping from glovebox to oven to reach thermal equilibrium. The periodic sulfur concentration evolution (red curve in Fig.\u00a04b), decoded by the wavelength shift of cladding mode located at ~1559.5\u2009nm in Fig.\u00a04a, indicates the reversible dissolution/precipitation of polysulfides and sulfur. Noteworthy here is the feasibility of observing the amplitude of soluble sulfur variation (supplementary Fig.\u00a0S9) that matches the cycling behavior associated with capacity fading owning to less and less Li2S and solid sulfur crystallization over cycles (Fig. 4c), which could be reasonably attributed to the high electrolyte to sulfur ratio (E/S ratio\u2009>\u2009100\u2009\u03bcL/mg), thereby inducing stronger polysulfide shuttle effect with less sulfur utilization. Regarding a sulfur concentration response to the cycling rate depicted in Fig.\u00a04d, a lower cycling rate (C/15) leads to the largest sulfur concentration change, strongly supporting the idea that the most soluble sulfur in electrolyte is transformed to solid species (Li2S) when given adequate time for completion of the redox process. Accelerating the cycling to C/10, C/5 and C/3, partially soluble sulfur transformation is seen (i.e., background of sulfur concentration rises) together with less sulfur crystallization (i.e., the dip of soluble concentration at the end of charge).\n\na Spectra contour of TFBG cladding mode resonances response (C/15, 0.361\u2009mA, details are given in Supplementary Movie\u00a01). b Temporal voltage (gray line), decoded sulfur concentration (red line), and temperature of electrolyte (blue line) over the cycling. Note that there is 10\u2009h of data missing in 12th cycle because of data recording failure of the integrator software. c Capacity variation of (b) upon time. d Soluble sulfur concentration dynamic related to cycling rate (C/15, 0.368\u2009mA; C/10, 0.553\u2009mA; C/5, 1.106\u2009mA; C/3, 1.502\u2009mA). e Soluble sulfur concentration and capacity variations related to cycling rate of first/second plateau. The concentration drop through the re-crystallization of sulfur at the end of charge is marked by green asterisk \u201c*\u201d. With Mode I, the cycling rate was set by upper plateau (C/20, 0.271\u2009mA) and lower plateau (C/5, 1.081\u2009mA); Mode II by upper plateau (C/5, 1.081\u2009mA) and lower plateau (C/20, 0. 271\u2009mA) and Mode III by upper plateau (C/10, 0.541\u2009mA) and lower plateau (C/10, 0. 541\u2009mA).\n\nFollowing the evidence thus far, it is then reasonable to conclude that regular galvanostatic operating conditions might not be perfect for LSBs, considering the various competing mechanisms explored above which are evident at different stages of charge and discharge. With this in mind, different modes of cycling protocols were also pursued to investigate the possibility of tuning cycling performance based on sulfur consumption and capacity. For instance, when setting a relatively low (high) cycling rate for the upper (lower) plateau depicted in Fig.\u00a04e (Mode I), only half of the soluble sulfur inside electrolyte was transformed to Li2S due to high cycling rate of lower plateau, together with high rate of capacity variation. On the upper voltage plateau, the solid sulfur dissolution or recrystallization was 100% successful and which can be readily attributed to the low cycling rate in this voltage range. Further confirmation of the delicate cycling nature of LSBs was seen in Mode II as 80% of the soluble sulfur inside electrolyte was transformed to Li2S due to long cycling in the lower plateau (C/20) but with less solid sulfur recrystallization at the end of charge (C/5). Overall, the quantitative relation of soluble sulfur evolution and cycling (e.g. current density) implies that the efficiency of solid sulfur and Li2S formation governs the cycling performance and significant new progress for LSBs might be made through cycling condition optimization alone.\n\nExtensive work has been conducted over the years to improve the LSB performance through variety of means: the use of high-conductivity sulfur electrodes, strong polysulfide binding to suppress the shuttling phenomenon, surface chemistry to control Li2S nucleation or dissolution, design of electrode architectures that are elastic to withstand volume expansion, and optimized electrolytes enabling high sulfur utilization42. Considering the strategy of trapping lithium polysulfides, it includes physical (spatial) entrapment by confining polysulfide in the pores of non-polar carbon materials43 or designing a sulfur host material that exhibits stronger chemical interaction such as dipolar configurations based on polar surfaces44, metal-sulfur bonding45 and surface chemistry for polysulfide grafting and catenation46, leading to long cycling without strong capacity fading. Nevertheless, the fundamental problem (regardless of approach) is that polysulfides dissolution into the electrolyte is essential for LSB cells to function, and yet, the same polysulfide intermediates are responsible for deleterious parasitic reactions. Thus, a smart sensor that can reliably track the varying concentration of soluble sulfur in real time should be of particular value.\n\nIn this respect, in addition to the less porous Super P carbon discussed above, Ketjen black (KB) carbon, with a BET surface area >1200\u2009m2/g, is utilized as physical nonpolar sulfur confinement host (KB/S, 40/60 wt.%), and the electrolyte was simultaneously monitored by a TFBG senor together with XRD to characterize the composite electrode phase transitions in operando (left panel of Fig.\u00a05a). After the melt-diffusion treatment process, part of sulfur penetrates into the nanostructure of KB (Supplementary Fig.\u00a0S10); however, during cycling the nonpolar physical adsorption or confinement of polysulfide is very limited and massive soluble polysulfide is dissolved and detected. Surprisingly, the capability of Li2S crystallization is enhanced since >91% soluble sulfur disappears and converts to solid short chain polysulfide at second plateau as indicated by the fiber sensor. Meanwhile, unlike linearly sulfur concentration change with super P carbon (BET surface area: 62\u2009m\u00b2/g) substrate cell in Fig.\u00a02a, KB containing cell becomes nonlinear with a higher rate, indicating that the nucleation rate of Li2S is increasing. It could be reasonably assigned by instantaneous nucleation that depletion of the nucleation site of KB occurs at a very early stage, following the nonlinear kinetics of the nucleation pathway: N\u2009=\u2009No[1-exp(-At)] where N is the density of nuclei, No is the density of available nucleation site, and A is the nucleation rate47. In contrast, considering less porous Super P carbon containing cell in Fig.\u00a02a with a lower nucleation rate, the initial density of nuclei increases linearly with time: N\u2009=\u2009NoAt (i.e. progressive nucleation)48,49. Moreover, the porous structure of KB also accelerates the dissolution and re-crystallization of sulfur, which results in a strong sulfur concentration saturation at the transient stage relating to semisolid Li2S4 generation (keeping a balance between electrochemical and disproportionation process) and enhanced soluble sulfur reduction to solid sulfur at the end of charging (Supplementary Fig.\u00a0S11). The dynamic response of sulfur concentration to potential and cycling rate is consistent with the Super P substrate, detailed in Supplementary Fig.\u00a0S12.\n\na Operando measurement of LSB by TFBG and XRD at C/20 (0.26\u2009mA, left panel: nonpolar physical adsorption of polysulfide by KB carbon; right panel: polar adsorption of polysulfide by MOF-801(Zr)). b In-situ detection of polysulfide adsorption by MOF-801(Zr). c XRD pattern for MOF-801(Zr) before and after adsorption of Li2S6. d The temporal voltage (C/15, 0.375\u2009mA, gray line), decoded sulfur concentration (red line) of cathode composite based on MOF-801(Zr), and decoded sulfur concentration (blue line) considering the cathode with KB substrate (same amount of active material, used as a reference without showing the temporal voltage).\n\nTo further extend the use of porous materials as host matrix for sulfur confinement, Metal-Organic Frameworks (MOFs) can be considered as efficient candidates for the selective adsorption of polysulfide species45,50. We have considered MOF-801(Zr), a microporous zirconium fumarate with pores of about 5\u201312\u2009\u00c5 and a high specific surface area (1020 (\u00b120) m2/g), to be considered as an efficient candidate for the adsorption of polysulfide species. Its 3D cubic structure is built-up from Zr6O4(OH)4 oxo-clusters linked to fumarate ligands and exhibits abundant missing linkers defect sites and polar reactive terminal \u2013OH groups. Due to the Lewis acidic character of the Zr nodes and the high reactivity of these terminal groups, the polysulfide species (soft Lewis bases) are expected to interact strongly with the framework51,52. Depicted in Fig.\u00a05b, the 0.5\u2009ml 100\u2009mM polysulfide Li2Sx (x\u2009=\u20094, 6, 8) was monitored in real time by fiber sensor during adsorption, which is evidently finished within 1\u2009h ensuring efficient adsorption inside the cell. After fully adsorbing 100\u2009mM Li2S6 (Fig.\u00a05c), the XRD pattern shows the appearance of new peaks in addition to the ones of MOF-801(Zr), matching with the peaks of pure solid sulfur (Supplementary Fig.\u00a0S13) consistent with the yellow color of the powder. This explains why the operando test starts with the peaks of sulfur (XRD pattern on the right panel of Fig.\u00a05a). The subsequent electrochemical reaction is the same as the cathode substrate with KB including concentration saturation in the transition region between the first and the second plateau, nonlinear sulfur consumption rate with instantaneous nucleation pathway and enhanced sulfur crystallization at the end of charge. However, a key point and difference is that the sulfur concentration inside the electrolyte dramatically decreases by 80.8 % with the same amount of active material due to the enhanced adsorption and localization of polysulfides through abundant polar sites in Lewis acid-base chemical interaction by MOF-801(Zr) (Fig. 5d and Supplementary Fig.\u00a0S14).\n\nTo evaluate the performance of cathode considering the three composites (with Super P, KB, MOF-801(Zr)), we decided to compare their long cycling performance together with capabilities of Li2S nucleation (h1) and sulfur crystallization (h2) (Fig.\u00a06a). Depicted in Fig.\u00a06b, the KB and MOF-801(Zr) cell both experienced better capacity retention over 30 cycles at C/15 with a fade rate as 0.99 %, 0.88% per cycles, respectively, while the super P cell is fading with 4.7% per cycles, which is consistent with the capability of crystallization of Li2S and sulfur shown in Fig.\u00a06c, d. Surprisingly, the KB and MOF-801(Zr) cells have the similar efficiency of solid species crystallization (also supported by Fig.\u00a06f) by taking the merit of high surface area, whereas the MOF-801(Zr) exhibits the best capacity retention due to its strong chemical interaction for polysulfide which also well explains the capability to trap and recrystallize sulfur (Fig.\u00a06g). Meanwhile, less sulfur utilization will be induced as well by intrinsic adsorption together with its low conductivity nature, leading to the lower capacity of MOF-801(Zr) than that of KB cell over cycling. On the other hand, the Super P cell is fading fast because of high electrolyte volume with the E/S ratio over 100 \u03bcL/mg, presumably due to shuttle phenomenon enhancement. However, this problem does not affect the performance of KB and MOF-801(Zr) cells, revealing that perhaps the most important parameter for cycling performance is related to the capability of crystallization of Li2S and sulfur, which relies on a comprehensive balance with all involved factors such as high surface area, sulfur anion bonding and conductivity.\n\na Content of Li2S nucleation (h1) and solid sulfur crystallization (h2). b Cycling performance of cathode composite at C/15 (0.371\u2009mA) over 30 cycles. c, d The corresponding ratio of Li2S nucleation (c) and solid sulfur crystallization (d). e Cycling rate performance of cathode composite (C/15, 0.368\u2009mA; C/10, 0.553\u2009mA; C/5, 1.106\u2009mA; C/3, 1.502\u2009mA). f, g The corresponding ratio of Li2S nucleation (f) and solid sulfur crystallization (g). All the cycling cell were pursued in the presence of TFBG fiber, and the Li2S (h1) and solid sulfur (h2) is normalized by comparing the consumption sulfur in current cycle to that of sulfur fully dissolved in first cycle.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "Herein, TFBG sensors with low cost, easy integration into batteries, and long cycling capabilities during cell operation were demonstrated for operando testing of electrolyte sulfur concentration evolution of the LSB, which reveals the correlated relationship between the capacity fading and dynamic of dissolution/precipitation of polysulfides over cycling and at different cycling rates. Meanwhile, the chemical kinetics and thermodynamic response of soluble polysulfide in electrolyte were decoded with GITT experiments, allowing for the dynamic disproportionation process to be linked to the net transport flux of soluble species. Moreover, the cycling performance is well improved by designing the sulfur composite electrode via porous carbon as nonpolar physical sulfur confinement and MOF-801(Zr) as polar adsorption of polysulfide, through a host-guest chemical interaction. These substrates indicate that the nucleation kinetics and growth of Li2S are changing from a progressive to an instantaneous pathway due to enhanced soluble sulfur consumption rate, ultimately leading to the improvement of crystallization capability of Li2S and sulfur.\n\nDespite the encouraging insights supported by TFBG technology, a limitation of our operando testing stems from the refractive index which is an average effect of all species inside electrolyte. Thus, somewhat complicated data inference is required with critical external inputs, as comparing to direct evidence and measurement of species by infrared fiber operando measurement53 and Raman spectroscopy based on hollow-core optical fiber54. This could might be solved in future by machine learning algorithms to simplify analysis and hence lead to more efficient data treatment. Second, the optical interrogator used here is both expensive and bulky (volume), so future efforts may warrant an optimized integrator system capable of capturing a sensing signal by energy (amplitude) instead of wavelength55, but intensity-based measurements may present other technical challenges. Finally, the cycling based on Swagelok is achieved with high E/S ratio which enhances the shuttle effect of polysulfide, leading to acceleration of capacity fading. Therefore, long-term cycling configurations such as coin or 18650 cells integrated with fiber sensors might reveal other prominent performance-governing mechanisms. Nevertheless, TFBG sensors still provide fruitful details of the chemical dynamics of polysulfide as a diagnostic technique to monitor the state of health of cells in real-time.\n\nConsidering the specific properties of TFBG (multi-resonance-peaks, high refractive index sensitivity, and ultrafast response), new opportunities of battery sensing can be envisioned such that more than two gratings can be integrated inside the cell to map the sulfur concentration gradient, ultrathin solid electrolyte interphase (SEI) films could be characterized by sensitivity enhanced surface plasmon resonance based TFBG via surface and bulk refractive index discrimination56, the dynamic of electrons and phonon coupling inside cathode could be probed by ultrafast measurement through the pump-probe configuration of TFBG57. Overall, the non-disruptive diagnostic techniques based on the TFBG sensor allow us to monitor chemical-physical-thermal metrics in operando with notable time and spatial resolution. Therefore, with the use of these combs it becomes possible to detangle hidden high-value information such as states of charge, health estimations, and operational guidance along with non-electrochemical early-failure indicators, leading to straighter pathways to improving battery reliability, service life, and safety.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "10.86\u2009mmol of ZrOCl2\u20228H2O, 7.624\u2009mmol of fumaric acid, 9\u2009mL of formic acid and 40\u2009mL of deionized water were mixed in the reactor58, following 5\u2009h stirring when the solution becomes cloudy. The ultimate product was collected by centrifugation, abundantly washed with water and ethanol, and dried under vacuum.\n\nThe 100\u2009mM lithium polysulfides solution Li2Sx, (x\u2009=\u20092, 3, \u2026, 8) were prepared by mixing lithium sulfide (99.9 % Li2S, Sigma Aldrich) and sulfur (S, Sigma Aldrich) in stoichiometric ratio to organic electrolyte (1\u2009M LiTFSI, 0.5\u2009M LiNO3 in DOL/DME (1:1, v/v)), and the solution was continuously stirred with additional heating process at 55\u2009\u00b0C for 4\u2009days in argon filled glovebox.\n\nSulfur and Super P conductive carbon (Ketjen-black carbon) with a ratio 60/40\u2009wt% were mixed by hand-grinding, followed by a heat-treatment (160\u2009\u00b0C during 8\u2009h under air). The 5 % wt Poly(tetrafluoroethylene) dispersion (PTFE, Alfa Aesar) is mixed with the cathode composite, rolled to a film and punched into disks (1.2\u2009cm diameter) with sulfur loading around 5.3\u2009mg/cm2, and dried under vacuum at 80\u2009\u00b0C overnight. To make the cathode composite with MOF-801(Zr), the thoroughly adsorption of 100\u2009mM Li2S6 in DOL/DME (1:1, v/v) was achieved by MOF-801(Zr) with a stoichiometric ratio of 1\u2009mL/40\u2009mg, followed by washing the powder twice by DOL and tried in vacuum overnight. The sulfur contained MOF-801(Zr) (40% sulfur) was mixed with Super P conductive carbon with 50/50\u2009wt% by hand-grinding without heat-treatment.\n\nEach 10\u2009mm-long, 556.015\u2009nm period TFBG with 7o internal tilt angle was inscribed in hydrogen-loaded CORNING SMF-28 fiber (core diameter: 8.2\u2009\u03bcm; clad diameter: 62.5 \u03bcm, attenuation: 0.05\u2009dB/km at 1550\u2009nm wavelength) by laser irradiation based on phase-mask method22. Hydrogen loading of the fibers, enhancing their photosensitivity to ultraviolet light, was performed at room temperature and a pressure of 15.2\u2009MPa for 14\u2009days. The input light from KrF pulsed excimer laser (model PM-848 from Light Machinery, Inc., emitting at 248\u2009nm and 100 pulse/s) was cylindrically focused along the fiber axis with energy of ~40\u2009mJ over the grating region and also having passed through a 1078.4\u2009nm period phase mask to produce a permanent periodic refractive index modulation in the core of the fiber. Rotating the fiber and phase mask, the tilt of grating fringes was obtained at an angle in the core as 7\u00b0.\n\nThe transmission spectra simulations were carried out based on three-layer cylindrical waveguide using analytical method57. First, the simulated spectra is calibrated by the experimental spectra in air; Second, the refractive index of electrolyte were obtained by increasing the simulation surrounding RI (third layer) manually to match the experimental spectra, which is 1.3858 at 1559\u2009nm wavelength considering the dispersion (supplementary Fig.\u00a0S2d, e); Finally, mode intensity profiles were simulated by a complex finite-difference vectoral simulation tool (FIMMWAVE, by Photon Design), consisting of a three-layer waveguide: 8.2\u2009\u03bcm core diameter with refractive index 1.449311, 125\u2009\u03bcm cladding diameter with refractive index 1.444078, 80\u2009\u03bcm diameter medium of electrolyte (refractive index\u2009=\u20091.3858).\n\nA ring made of PEEK (12.8\u2009mm diameter, 2\u2009mm thick to fit 10\u2009mm length fiber sensor, storing 250 \u03bcL of electrolyte for immersion and electrochemical testing) is fixed in the middle of 19\u2009mm diameter Swagelok cell where fiber sensor can go through by drilling two holes. The Li metal foil (0.38\u2009mm thickness, 14\u2009mm diameter) is attached to one side of PEEK ring as anode, and on the other side of ring there is a steel grid to hold one Whatman separator beneath cathode composite. The cells were assembled in an argon-filled glovebox.\n\nThe electrochemical performances of Swagelok cell were evaluated by BCS-810 or MPG2 potentiostat (Biologic, France) at 25\u2009\u00b0C degree inside temperature-controlled oven (Memmert, \u00b10.1\u2009\u00b0C). The galvanostatic discharge\u2013charge cycling was carried out with the voltage range of 1.7 V-2.8\u2009V.\n\nTo achieve TFBG sensor operando measurement, the optical transmission spectra were recorded (1\u2009min/spectra) by an optical integrator (CTP10, EXFO SOLUTIONS) with a resolution of 1\u2009pm for wavelength ranging from 1500\u2009nm\u20131600\u2009nm. Considering XRD operando measurement, it was performed on a D8 Advance diffractometer (Bruker) using a Cu K\u03b1 X-ray source (\u03bbK\u03b11\u2009=\u20091.54056\u2009\u00c5, \u03bbK\u03b12\u2009=\u20091.54439\u2009\u00c5) and a LynxEye XE detector. The XRD pattern and electrochemical data are simultaneously recorded by a custom designed airtight cell with a beryllium window producing a full pattern every 20\u2009min.\n\nThe samples were prepared by washing the cathode powder twice by DOL to remove any dissolved lithium polysulfide species and lithium salt, and dried in vacuum chamber overnight. Then the powder was coated with gold (plasma sputtering coater (GSL-1100X-SPC-12, MTI)) for SEM (FEI Magellan) equipped with an energy-dispersive X-ray spectroscopy detector (Oxford Instruments) performed under an acceleration voltage of 20\u2009kV.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The origin data generated in this study are provided in the Source data file. Extra data are available on request from the corresponding author.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "J.-M.T. acknowledges the International Balzan Prize Foundation and the LABEX STOREXII for funding. F.Liu and J.-M.T. acknowledge the European Project \u201cInnovative physical/virtual sensor platform for battery cell\u201d (INSTABAT) (European Union\u2019s Horizon 2020 research and innovation program under grant agreement No 955930). W. Lu acknowledges the support of the CSC scholarship (201906880002). R.D.-C. is thankful to the French Embassy for the Visiting Researcher Fellowship (135694\u2009V). We thank Dr. J. Forero-Saboya for his assistance of scanning electron microscopy images. We thank Dr. F. Betermier for her assistance of preparing cathode. We thank Prof. J. Albert from Carleton University (Ottawa, Canada) for the fabrication of fiber sensors and assistance of simulation software. Finally, we gladly thank Dr. W. He, Dr. Y. Wang, Dr. X. Gao, Dr. B. Li, Dr. C. Gervilli\u00e9-Mouravieff and Mr. C. Leau for extensive and valuable discussion and comments.",
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+            "section_name": "Author information",
+            "section_text": "Coll\u00e8ge de France, Chimie du Solide et de l\u2019Energie\u2014UMR 8260 CNRS, Paris, France\n\nFu Liu\u00a0&\u00a0Jean-Marie Tarascon\n\nR\u00e9seau sur le Stockage Electrochimique de l\u2019Energie (RS2E)\u2014FR CNRS 3459, Amiens, France\n\nFu Liu\u00a0&\u00a0Jean-Marie Tarascon\n\nInstitut des Mat\u00e9riaux Poreux de Paris (IMAP), ESPCI Paris, Ecole Normale Sup\u00e9rieure, CNRS, PSL University, Paris, France\n\nWenqing Lu\u00a0&\u00a0Vanessa Pimenta\n\nThe Hong Kong University of Science and Technology (Guangzhou), Sustainable Energy and Environment Thrust, Nansha, Guangzhou, Guangdong, 511400, P. R. China\n\nJiaqiang Huang\n\nDepartment of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway\n\nSteven Boles\n\nInstitute of Nanotechnology, Gebze Technical University, Kocaeli, 41400, Turkey\n\nRezan Demir-Cakan\n\nDepartment of Chemical Engineering, Gebze Technical University, Kocaeli, 41400, Turkey\n\nRezan Demir-Cakan\n\nSorbonne Universit\u00e9\u2013Universit\u00e9 Pierre-et-Marie-Curie Paris (UPMC), Paris, France\n\nJean-Marie Tarascon\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nF.L., J.H., R.D.-C. and J.-M.T. conceived the idea and designed the experiments. F.L. performed the experiments. F.L., J.H., R.D.-C. and J.-M.T. performed the data analysis. W.L. and V.P. provided the MOF-801(Zr). Finally, F.L., S.B., R.D.-C. and J.-M.T. wrote the paper with contributions from all authors.\n\nCorrespondence to\n                Rezan Demir-Cakan or Jean-Marie Tarascon.",
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+            "section_text": "Nature Communications thanks Zhen Li and Yifei Yu for their contribution to the peer review of this work. A peer review file is available.",
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+            "section_text": "Liu, F., Lu, W., Huang, J. et al. Detangling electrolyte chemical dynamics in lithium sulfur batteries by operando monitoring with optical resonance combs.\n                    Nat Commun 14, 7350 (2023). https://doi.org/10.1038/s41467-023-43110-8\n\nDownload citation\n\nReceived: 21 July 2023\n\nAccepted: 31 October 2023\n\nPublished: 14 November 2023\n\nVersion of record: 14 November 2023\n\nDOI: https://doi.org/10.1038/s41467-023-43110-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            "section_name": "This article is cited by",
+            "section_text": "Nature Communications (2025)\n\nDiscover Chemical Engineering (2024)\n\nNano-Micro Letters (2024)",
+            "section_image": []
+        },
+        {
+            "section_name": "Associated content",
+            "section_text": "Focus",
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+        }
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+{
+    "title": "Capacitive tendency concept alongside supervised machine-learning toward classifying electrochemical behavior of battery and pseudocapacitor materials",
+    "pre_title": "A Novel Approach for Classifying Battery and Pseudocapacitor Materials Using Capacitive Tendency and Supervised Machine Learning",
+    "journal": "Nature Communications",
+    "published": "07 February 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45394-w/MediaObjects/41467_2024_45394_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45394-w/MediaObjects/41467_2024_45394_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [],
+    "code": [
+        "/articles/s41467-024-45394-w#ref-CR27"
+    ],
+    "subject": [
+        "Batteries",
+        "Computational science",
+        "Electrochemistry",
+        "Energy storage"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-2930525/v1.pdf?c=1707858372000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-2930525/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-45394-w.pdf",
+    "preprint_posted": "28 May, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "In recent decades, there have been more than 100,000 scientific articles dedicated to developing electrode materials for supercapacitors and batteries. A heated debate nonetheless persists surrounding the standards for determining electrochemical behavior involving faradaic reactions, since the electrochemical signals produced by the various electrode materials and their different physicochemical properties often complicate matters. The difficulty lies in determining which group these materials fall into through simple binary classification as there can be an overlap between battery and pseudocapacitor signals and because both materials are faradaic in origin. To solve this conundrum, we applied supervised machine-learning toward a statistical analysis of electrochemical signals, and consequently developed a new standard which we called capacitive tendency. This predictor not only surpasses the limitations of human-based classification but also provides statistical tendencies regarding electrochemical behavior. Notably, and of particular importance to the electrochemical energy storage community publishing over a hundred articles weekly, we have created an online tool for easy classification of their data.Physical sciences/Chemistry/Electrochemistry/BatteriesPhysical sciences/Energy science and technology/Energy storagePhysical sciences/Chemistry/ElectrochemistryPhysical sciences/Mathematics and computing/Computational science",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "ESISUB1.docxSupplementary info",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "In recent decades, more than 100,000 scientific articles have been devoted to the development of electrode materials for supercapacitors and batteries. However, there is still intense debate surrounding the criteria for determining the electrochemical behavior involved in Faradaic reactions, as the issue is often complicated by the electrochemical signals produced by various electrode materials and their different physicochemical properties. The difficulty lies in the inability to determine which electrode type (battery vs. pseudocapacitor) these materials belong to via simple binary classification. To overcome this difficulty, we apply supervised machine learning for image classification to electrochemical shape analysis (over 5500 Cyclic Voltammetry curves and 2900 Galvanostatic Charge-Discharge curves), with the predicted confidence percentage reflecting the shape trend of the curve and thus defined as a manufacturer. It\u2019s called \u201ccapacitive tendency\u201d. This predictor not only transcends the limitations of human-based classification but also provides statistical trends regarding electrochemical behavior. Of note, and of particular importance to the electrochemical energy storage community, which publishes over a hundred articles per week, we have created an online tool to easily categorize their data.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "In the energy storage research field, batteries are one of the most studied types of devices owing to their use in a wide range of applications including electronic equipment, electric vehicles and for medical and military purposes1. On the other hand, pseudocapacitive electrodes have attracted a considerable amount of attention due to their superior power capability2. Both of these energy storage systems are generally composed of various types of electrode materials exhibiting electrochemical signals that may or may not resemble one another3.\n\nIt is common knowledge that electric double layer capacitors (EDLCs) rely on a non-faradaic process without any electron transfer, whereas batteries and pseudocapacitors are governed by faradaic reactions4. The latter processes are generally depicted by peaks on Cyclic Voltammograms (CVs) and plateaus on Galvanostatic Charge-Discharge (GCD) curves (Fig.\u00a01)5. However, some faradaic electrode materials including pseudocapacitors display electrochemical signals similar to those of EDLCs, such as the rectangular/quasi-rectangular CV and the sloping GCD curves6,7, found in a variety of transition metal oxides (RuO28, MnO29,10), conducting polymers (poly(3,4-ethylenedioxythiophene)11,12, polyaniline13,14), and carbides (MXene)15. Currently, owing to the vast amounts of materials studied, guidelines for distinguishing between the two are still largely inadequate, with some studies even contradicting the conventional definition of Conway et al., as later supported by Brousse et al. and other researchers in the field7.\n\nIllustration of a experimental CVs and GCDs of different electrode materials including MnO239, V2C40, RuOx41, LaMnO342, Ti3C2Tx15, H2TiNb6O1843, Ag1-3xLax\u25a12xNbO344, Nb2O545, nano-MnS246, bulk-MoS246, TiO247 and NaFePO448, theoretical b CVs and c GCDs undergoing different electrochemical processes.\n\nIndeed, electrochemical signals are numerous and complex, varying according to the choice of electrode materials, as shown in Fig.\u00a01, hence the difficulty in identifying and categorizing these materials based on electrochemical signals. Recently, Fleischmann\u200a et al.16, emphasized on the importance of a unified understanding when it comes to the electrochemical signals found in supercapacitors and batteries. The authors proposed the concept of the \u2018continuum transition\u2019 where the overlapping of electrochemical signals (between battery and supercapacitor) lies in this region depending on the electrolyte confinement stage. It signifies that understanding this overlapping transition essentially requires a clear-cut classification of electrode material types based on their electrochemical behaviors (in CV and GCD). Unfortunately, this qualitative concept of \u2018continuum spectrum\u2019 is urgently required an informative transformation to obtain the quantitative value of it. In order to complete this concept of \u2018continuum spectrum\u2019 and to provide the real quantitative value to it, we analyze the electrochemical signals with the help of supervised machine-learning for achieving the descriptor, \u201ccapacitive tendency\u201d that allows our community to quantify this important spectrum.\n\nTo date, computing techniques have been used as somewhat satisfactory tools toward ascertaining the charge storage mechanism behind various electrochemical signatures17,18,19,20. Recently, text-mining algorithms have been developed to efficiently extract various specific information of the materials from the article such as BatteryDataExtractor using bidirectional-encoder representations from transformers (BERT)21, and Li-ion battery annotated corpus (LIBAC) based on Machine Learning (ML), natural language processing (NLP), Named Entity Recognition (NER)22,23,24. However, the direct interpretation of the image data from figures remain difficult using the above method of data-mining from image. Machine learning has been used to predict the electrochemical mechanism involved in the reaction that expresses through a cyclic voltammogram (CV). Deep learning has also been used to distinguish the mechanism of the electrochemical reaction from CV based on residual neural network (ResNet) architecture and focused on analytical or fundamental electrochemistry. However, the application of machine learning to analyze electrochemical signals in the field of energy storage is still in its early stages.\n\nThis study presents that electrochemical signal analysis (CV and GCD) has been performed using a machine learning (ML) approach based on image classification. This approach is well-suited for unlabeled data, noise-tolerant, and capable of handling complex data. Ultimately, this led to the determination of the capacitive behavior of electrode materials from thousands of scientific papers. The crux of this work lies in its use of machine learning (ML) to quickly and accurately interpret electrochemical signal images and transform them into accurate values. This is made possible by the large database of electrochemical energy storage images that is available to the ML model. This approach overcomes the limitations of human ability to interpret data, which can be too complicated in most cases (Fig.\u00a01). So, by this approach, we propose the definition call \u201ccapacitive tendency\u201d based on the percentage confidence of the classification between box shaped and peak shaped CV, implying the capacitive behavior of electrode materials. In addition to this, we provide an online tool kit which uses supervised machine-learning to easily classify materials. Our work thus serves to put forward a new concept toward understanding and labeling the various electrochemical signatures of energy storage devices.\n\nImage recognition is used in many fields, such as facial recognition, cancer detection and autonomous cars. All these models have been trained using a supervised or semi-supervised deep learning approach, in order to teach the model, the pattern best suited to the situation. The difference between the techniques lies in the choice of neural network, which must be adapted to the specific problem. In our case, the main difficulty was to differentiate the figures representing a CV and a GCD from the other graphs.\n\nOverview of our study is shown in Fig.\u00a02. In this study, these CVs and GCDs were analyzed via supervised ML trained with datasets extracted from over 4000 scientific papers. In the following section, various Convolutional Neural Network architectures are validated and selected based on the evaluations explained in the experimental section, by applying the theoretical CV and GCD curves.\n\nIllustration of a Image extraction from scientific papers followed by CV and GCD classifications based on ResNet50 architecture, b representatives of training datasets, and c representative of testing datasets.\n\nAlthough the application of machine learning in scientific research was not uncommon before, the analysis of the shape of electrochemical signals has never appeared before. For example, Puthongkham et al. wrote a mini-review summarizing the latest applications of machine learning and experimental design in electroanalytical chemistry25. Khosravinia et al. used machine learning to select the best precursor to predict the specific capacitance26. But none of these papers focused on shape changes in electrochemical signals. The innovation of this work is to explore the shape changes between curves with the assistance of artificial intelligence, so as to find the change rules between electrochemical signals.\n\nIn comparison to other studies, the capacitive tendency analyses the shape of the electrochemical signal. Unfortunately, the capacitive tendency doesn\u2019t provide the surface contribution or the diffusional contribution inside the cyclic voltammetry.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-45394-w/MediaObjects/41467_2024_45394_Fig1_HTML.png",
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-45394-w/MediaObjects/41467_2024_45394_Fig2_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Dataset construction",
+            "section_text": "In the present paper, all datasets are in the form of images extracted using PyMuPDF library in Python language from >3300 scientific papers. The first dataset, or Output 1, was obtained by figures extracting using OpenCV which provides (2979) GCD, (5598) CV and other images such as crystal structure image (which will not be used in the further classification steps). In the training process of GCD (process 2) and CV (process 3) classification, CV and GCD images were firstly labeled as belonging to one of two classes, namely battery or pseudocapacitor following the criteria of non-ambiguous signal shape (which can be put into four categories: (1) Box shaped CV, (2) Peak shaped CV, (3) Triangular GCD, and (4) Plateau GCD) for 80% of total data, where 20% of total data was used as testing data. These training processes is based on binary classification of electrochemical signal, such as the box vs peak shaped CV, and the triangular vs plateau shaped GCD, as represented in Supplementary Fig.\u00a06, where all image datasets used are available on Github27.\n\nFrom Process 3, Output 3 was obtained and categorized into three types of training sets: 100% battery, 50% battery/pseudocapacitor, and 100% pseudocapacitor. This output was then further refined in Processes 4 and 5, as illustrated in Fig.\u00a03b. We used three data sources for their study of CV and GCD images. The first source was a large dataset of over 5500 CVs and 2900 GCDs extracted from scientific papers. The second source was theoretical CVs and GCDs generated using electrochemical equations. The third source was experimental CVs and GCDs from co-authors. Moreover, cross-validation was performed with the experts in the field to generate the different training datasets for the optimizing of the classification performance.\n\na CV and GCD datasets obtained after classification by Process 1, splitting them into training and testing datasets for further GCD and CV classification in Process 2 and Process 3, respectively. b The outputs from Process 3 are used in this final classification step (process 4 and 5) to obtain the capacitive tendency based on percentage confidence rating of the prediction. c Table of processes, inputs and outputs performed/used to obtain these results.\n\nHowever, text-mining was not used in this present study since we would like to propose the simple alternative tool focusing image classification of electrochemical signals.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-45394-w/MediaObjects/41467_2024_45394_Fig3_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Validation of classification architectures",
+            "section_text": "In this work, Convolutional Neural Networks (CNNs) were selected for use as the image classification architectures28. Benchmarking was conducted on five different CNN models, including ResNet5029, MobileNetV230, VGG1631, Xception32 and 8-Layer CNN28 (see Supplementary Figs.\u00a01, 2), to compare model performance. It was carried out based on five metrics, including: Accuracy, Sensitivity, Specificity, Precision, and F-Score33 (see Supplementary Fig.\u00a03 and Supplementary Eqs.\u00a01\u20135). During the model training cycles, the number of training and validation iterations can impact the accuracy of the prediction since this is related to the experience gained over time by the ML model. Moreover, binary cross entropy (BCE) loss34, calculated from the prediction error as shown in Eq.\u00a01, was minimized along the number of training iterations to optimize predictor performance.\n\nWhere \\({y}_{i}\\) is the ground truth label (0 or 1, in this case battery or pseudocapacitor), \\(\\hat{y}\\) is the predicted value, and n is the output size34.",
+            "section_image": []
+        },
+        {
+            "section_name": "Machine-learning for CV/GCD classification procedures",
+            "section_text": "The ML architecture displaying the best performance after the validation step (further explained in the Results and Discussion section) was selected for use in this work as will be supervised during classification processes. ResNet50 was exploited in different steps denoted as Processes 1, 2, 3, 4, and 5 (as summarized in Fig.\u00a03c) according to the types of inputs and outputs. All the images extracted from scientific papers were then categorized by Process 1 (ResNet50 model) which yielded Output 1, comprising GCDs, CVs and other images (such as optical image). GCDs from Output 1 were then classified using Process 2, and CVs were separately classified by Process 3, thereby providing the resulting prediction (Output 2: classified GCDs, and Output 3: classified CVs) of either battery or pseudocapacitor with a percentage confidence rating of 0\u2013100%, while the errors were monitored and minimized to improve the prediction. Here, the capacitive tendency (0\u2013100%) was first defined by the percentage confidence value, indicating the probability of CV shape as peak (0% capacitive tendency) and box shape (100% capacitive tendency). In the final step (Fig.\u00a03b), the classified CVs (in Output 3) were labeled according to four percentage confidence classes\u2014100% battery, 50% battery, 50% pseudocapacitor and 100% pseudocapacitor\u2014before being further modeled in Processes 4 and 5 to provide the capacitive tendency based on a percentage confidence of 0\u2013100%.\n\nAn alternative way to understand the definition of capacitive tendency is to analyse it as the deviation from the ideal of the purely capacitive signal (is easy to recognize). When the trained model is confident that the curve is close to a rectangle (for CV) or a triangle (for GCD), then this implies that the curve is close to an ideal capacitive signal. On the contrary, a curve whose confidence value is close to zero means that the curve has a different contribution. Basically, the capacitive tendency reflects the analysis of the signal shape. It is information based on a geometric shape. Of course, alternatives could be used. However, the use of the classical formalism, as indicated in the \u201cideal CVs\u201d area in Fig.\u00a01a, is impossible when the shape of the electrochemical signal deviates from this ideal. In the purely mathematical domain, the possibility of adding a rectangle to a closed geometric shape (a CV is a closed geometric shape) is a complex mathematical situation. Thus, our data science-driven by supervised deep learning approach is a suitable alternative.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results and discussion",
+            "section_text": "This section explains how the models for CV and GCD classification were established for this specific dataset through the validation of different CNN architectures. The selection was based on well-known parameters including Accuracy, Sensitivity, Specificity, Accuracy, and F-Score. Moreover, the most accurate model was developed for use as the descriptor in order to determine the capacitive tendency of the various electrochemical behaviors, by applying the experimental data of various electrode materials. Ultimately, the selected model is destined for use by electrochemists as a tool for determining the nature of their materials.\n\nTo select the Convolutional Neural Network architecture best suited to our datasets, the validation of a total of five models (ResNet50, MobileNetV2, VGG16, Xception, and 8-Layer CNN) was first performed using Processes 2 and 3 with different types of input and output (Table\u00a01). These architectures were chosen based on the reported accuracy ranking ascribed to the models\u2019 performance from ImageNet validation35,36. In this step, the prediction was governed by binary classification to obtain only two different outputs, namely (i) battery or (ii) pseudocapacitor, since the model had been trained and supervised with CV and GCD datasets without ambiguity. ResNet50 was found to be the most accurate and precise one out of all the models (Table 1, Supplementary Figs.\u00a07\u201311) and was thus selected to further prediction in the next step. Moreover, ResNet50 is more adapted to the variety of data that will be input by the users, for example, plot with different frame and font styles and different color curves.\n\nTo demonstrate the efficiency of the model, 5598 CVs and 2979 GCDs were randomly selected and entered the classifier according to Processes 2 and 3. Supplementary Fig.\u00a012 clearly demonstrates that the majority of predicted datasets showed a 100% confidence rating, which would suggest that our ML model displays a high level of precision and reliability with a negligible risk of error.\n\nIn this part, the simulations of CV and GCD images were done using basic equations from theoretical electrochemistry including Faradaic process with peak-shaped CV37, and EDLC with box-shaped CV which relies on Eqs.\u00a02 and 3. The simulated images were then classified by the trained model (process 4\u20135). The equation for CVs showing redox peaks is given as follows:\n\nwhere \\(\\frac{i}{{i}_{\\max }}\\) is the normalized current of the peak current function, \\(F\\) is the Faraday constant, \\(R\\) is the gas constant, \\(T\\) is the temperature, \\(E\\) is the applied potential and \\({E}_{{peak}}^{0}\\) is the peak potential. The box-shaped EDLC current function is given by:\n\nwhere \\(C\\) is the capacitance, \\(R\\) is the resistance and \\(t\\) is the charging period38. It was shown that capacitive behavior is more pronounced the further the CV shape deviates from peaked to rectangular (Fig.\u00a04a).\n\nThe illustration of (a) classified theoretical CVs with Gaussian and box shapes as the components, and (b) classified theoretical galvanostatic charge (I) and discharge (II) curves obtained by using Eq.\u00a04. with a varying M parameter. The color of each curve is related to the probability of being battery (purple gradient bar) or capacitive material (blue gradient bar).\n\nFurthermore, simulating number of theoretical GCD images with the transition in curvature from straight to plateau feature could be applied with the classification model (process 2) in order to see the region of ambiguity. Using Eq.\u00a04 by varying M parameter:\n\nwhere \\(E\\) is the potential, n is the number of electron transfers, t is the charging/discharging time, \u03c4 is the time constant, E\u03c4/4 is the quarter-wave potential and M is the mathematical factor permitting the manipulation of the galvanostatic curve to show either a plateau feature (as found in battery material measurements) or straight line (as in supercapacitor material measurements), the continuum GCD curves were obtained, as shown in Fig.\u00a04b (blue, gray, and purple lines).\n\nFigure\u00a04b(I) shows that a battery-type signature was found to apply for an M value range of between 1.6 and 7 (purple zone, with a 90-100% confidence rating), whereas the prediction point to a pseudocapacitor-type for M values of between 7.1 and 19.6 (blue zone, with a 70-100% confidence rating). Similarly, this result was also observed for theoretical discharging profiles, as shown in Fig.\u00a04b(II). However, in the gray zone when M is around 7.0 during charge and 9.4 during discharge, respectively, the predictor was hesitant to define the signal type, suggesting that a certain ambiguity occurs when the curvature of the GCD signal is somewhere between a straight line and a plateau, as has already been observed and which is consistent with experimental measurements related to pseudocapacitive materials (Fig.\u00a05c). The most pertinent conclusion that can be drawn from this calculation is that our model demonstrated the transition region of GCD signals in accordance with the continuum transition concept as proposed by Fleischmann\u200a et al.16. Our model clearly demonstrates the source of the confusion for both humans and computers, which stems from the fact that these behaviors all originate from faradaic processes where electron transfer is the elementary step. This explains why the results of theoretical studies only hold true for basic scenarios. More complex behaviors, however, are frequently observed in experimental measurements and account for vast amounts of data.\n\nThe capacitive tendency prediction of experimental voltammograms of (a) the well-known pseudocapacitor and battery electrode materials MnO249, and NMC50, compared with the ambiguous CVs of Ag1-3xLax\u25a12xNbO344, and H2TiNbO1843. The predicted (b) CVs and (c) GCDs of other electrode materials from the literature, as mentioned in Fig.\u00a01.\n\nIn accordance with the main purpose of this study, namely overcoming human limitations when it comes to understanding electrochemical signals, the objective in this section concerned clarifying the behavior of faradaic electrode materials. To this end, experimental CVs from Fig.\u00a01 were applied to the model to predict the capacitive tendency behavior of various electrode materials that conventionally can be calculated from dQ/dV\u2009=\u2009constant in only simple cases such as supercapacitor materials but could be too complex to apply for pseudocapacitors. Well-known pseudocapacitive and battery materials from the literature, such as MnO2 and NMC, were compared not only to separate the signals produced by Processes 2 and 3 according to the conventional binary classification, but also to establish a new standard that we called capacitive tendency. Processes 4 and 5 broadened the classification range to create a statistical tendency representing an interpretable value: in the range of 0% denoting a battery, to 100% being a pseudocapacitor. Finally, we were able to predict the capacitive behavior of various electrode materials from experimental data, as demonstrated in Fig.\u00a05.\n\nAs previously mentioned, the exemplary rectangular and peak shapes are unfortunately not often present when it comes to systems exhibiting fast charge/discharge behavior or when pseudocapacitive materials are investigated. Electrochemists thus find it difficult to analyze the voltammograms correctly in the face of such a variety of shapes, with even the CVs of V2C, Nb2O5 and nano-MoS2 electrode materials (Fig.\u00a05b) displaying a similar capacitive tendency of around 52\u201353%. This finding served to emphasize the necessity of using machine-learning as a decisive tool for interpreting CV signals displaying a complexity that is beyond human discernment. The understanding of the origin of the electrochemical behavior is the key point for the deep knowledge and for the future development of the electrode materials. TB robots have been used to study the physicochemical features of a variety of electrode materials, including carbon electrodes, MOFs, COFs, graphite, NMC, and MXenes materials, determining the capacitive tendency of the CV in \u2018continuum region from the recent papers (as shown in Supplementary Fig.\u00a021).\n\nDuring this phase of our research, numerous scientific articles containing the keyword \u201cbattery\u201d (2011 articles) or \u201cpseudocapacitor\u201d (1346 articles) were analyzed using our supervised ML model to provide a statistical analysis of the number of papers containing a keyword that was in contradiction to their signals (used articles outside the training dataset). Briefly, the articles were randomly selected and their relevant CV and GCD signals were extracted and then simply classified into either battery or pseudocapacitive type using only Processes 2 and 3. The outputs in Fig.\u00a06 depict that around 67% of the papers with a \u201cpseudocapacitor\u201d keyword are consistent with their experimental observations. Unexpectedly, however, nearly 50% of the articles with a \u201cbattery\u201d keyword displayed contradicting signals. These results serve to reinforce the fact that human-based interpretation could greatly benefit from being replaced with computing techniques such as ML. Apparently, our machine-learning classification technique showed the significant portion of the articles using binary keywords (battery or pseudocapacitor) that contradict (mismatched) with their electrochemical signal (Supplementary Information).\n\na The methodology behind the title classification of papers as either a battery or pseudocapacitor, followed by b CV and GCD extraction and then c the matched/mismatched outputs using our classifiers (Processes 1, 2 and 3). The percentage correlation between titles for pseudocapacitor and battery materials vs. correctly classified CVs and GCDs.\n\nThis result shows perfectly the limit of the binary approach in the field. Because analysing a binary classification leads to this misclassification by the authors. Our approach, using capacitive tendency, allows a unification of the measurements, by including them in a \u201cspectrum\u201d as proposed by Fleischmann et al.16.\n\nIn order to facilitate the task of users worldwide when it comes to classifying the electrochemical behaviors (battery or pseudocapacitor) of their experimental data (CVs and GCDs), we have launched an online tool for analyzing these signals and providing an output in the form of a capacitive trend (or percentage confidence rating). It is publicly available at http://supercapacitor-battery-artificialintelligence.vistec.ac.th, and details are also provided in the Supplementary Information part\u00a08---the website description.\n\nThe training database boundary is fixed using only scientific data with the pseudocapacitor or battery keywords associated. It is recommended for reader to use the present model to compare signal associated to EDLC, pseudocapacitor and Metal-ion battery. The present model isn\u2019t adapted to redox flow battery, and fuel-cells. Moreover, the present model doesn\u2019t provide any performance predictions. The typical useful application is to compare the same family of materials (i.e, MOF, NMC, MXene) but presenting a different electrochemical behavior. That is the generality and universality of this study.\n\nThe research presented herein has successfully managed to resolve the decades-old conundrum concerning the interpretation of electrochemical signals from CVs and GCDs by making full use of advanced computing technology in order to classify the behavior of materials as battery-like or pseudocapacitor-like. Specifically, we demonstrated that supervised ML is a powerful and accurate way to distinguish between these often complex signals. Our study also highlights the recurrent issue of the titles of scientific papers often contradicting the results of their own data, especially when it comes to those articles with \u201cbattery\u201d in the title. This demonstrates the superiority of machine learning over human-based analysis for the interpretation of electrochemical signal images. Machine-learning is able to quickly and accurately transform the shape information of images into predicted values, while human-based analysis is far slower and more subjective. This is due to the fact that machine learning algorithms are able to learn from large datasets of images and extract patterns that are not visible to the human eye. As a result, machine learning is a more reliable and objective approach to the analysis of electrochemical signal images. As a major contribution to our peers in the electrochemical energy storage community, we are delighted to announce a first online tool based on our model toward simple CV and GCD image classification via our precise marker, called capacitance tendency (quantitative information presented in percentage), affording them the possibility of a quick and easy standard to refer to when attempting to determine the nature of their new materials. Using the present program, all experimental user will be able to correlate chemical information to capacitive tendencies, as the scan rate, the current density. However, a potential drawback of the current classifier is that it can only predict the resistive tendency of electrochemical signals based on CV/GCD image data. A more comprehensive classifier by featuring text-mining of material information of a hidden information such as labels, scan rate, electrolytes in the figure could be an ultimate strategy for future perspectives on artificial intelligence for energy storage technology.",
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+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The figures, tables and literatures data of capacitive performance generated in this study are provided in the Supplementary Information.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "Machine-learning models and datasets are made publicly available at GitHub repository27. The instruction is provided in both supporting information and on Github repository.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "O.F thanks Institut Universitaire de France for the financial support. Y.Z, J.D., and O.F. thank Chengdu University for the collaboration support. Website hosting is supported by VISTEC server. This work is supported by funding from Thailand Science Research and Innovation (TSRI) (Grant No. FRB660004/0457). VISTEC thanks for the impressive technical support of Assistant Researcher Konthee Boonmeeprakob in the Python program. O.F. gives a special thanks to Fred Favier, Institut Charles Gheradht Montpellier, for the long discussion about the concept of pseudocapacitor.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Molecular Electrochemistry for Energy laboratory, VISTEC, Institute of Science and Technology, Rayong, 21210, Thailand\n\nSiraprapha Deebansok\u00a0&\u00a0Olivier Fontaine\n\nInstitute for Advanced Study & College of Food and Biological Engineering, Chengdu University, Chengdu, 610106, China\n\nJie Deng\n\nNantes Universit\u00e9, CNRS, Institut des Mat\u00e9riaux de Nantes Jean Rouxel, IMN, 44000, Nantes, France\n\nEtienne Le Calvez,\u00a0Olivier Crosnier\u00a0&\u00a0Thierry Brousse\n\nR\u00e9seau sur le Stockage \u00c9lectrochimique de l\u2019\u00c9nergie (RS2E), CNRS FR 3459, 33 rue Saint Leu, 80039, Amiens, France\n\nEtienne Le Calvez,\u00a0Olivier Crosnier\u00a0&\u00a0Thierry Brousse\n\nICGM, Universit\u00e9 de Montpellier, CNRS, 34293, Montpellier, France\n\nYachao Zhu\n\nInstitut Universitaire de France, 75005, Paris, France\n\nOlivier Fontaine\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.D., Y.Z. had centralized the dataset of scientific papers, checked the output, and tested the trained model. E.C., O.C., T.B. tested the trained model, checked the dataset, and sent the experimental data. S.D., O.F. write the papers. S.D. programed Python and trained the model.\n\nCorrespondence to\n                Olivier Fontaine.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Yuekuan Zhou, and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional information",
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+            "section_text": "Deebansok, S., Deng, J., Le Calvez, E. et al. Capacitive tendency concept alongside supervised machine-learning toward classifying electrochemical behavior of battery and pseudocapacitor materials.\n                    Nat Commun 15, 1133 (2024). https://doi.org/10.1038/s41467-024-45394-w\n\nDownload citation\n\nReceived: 13 May 2023\n\nAccepted: 19 January 2024\n\nPublished: 07 February 2024\n\nVersion of record: 07 February 2024\n\nDOI: https://doi.org/10.1038/s41467-024-45394-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Journal of Materials Science: Materials in Electronics (2024)",
+            "section_image": []
+        }
+    ]
+}

0358cbd719cad92a5106a1d23b164c7a9ee9af38881e700bf32a1c27078c4f3f/metadata.json

@@ -0,0 +1,134 @@
+{
+    "title": "Red-light-mediated copper-catalyzed photoredox catalysis promotes regioselectivity switch in the difunctionalization of alkenes",
+    "pre_title": "Red Light-Mediated Photoredox Catalysis Promotes Regioselective Switch in the Difunctionalization of Alkenes",
+    "journal": "Nature Communications",
+    "published": "18 June 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49514-4/MediaObjects/41467_2024_49514_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-49514-4/MediaObjects/41467_2024_49514_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [],
+    "code": [],
+    "subject": [
+        "Synthetic chemistry methodology",
+        "Photocatalysis",
+        "Photochemistry"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3910735/v1.pdf?c=1718795354000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3910735/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-49514-4.pdf",
+    "preprint_posted": "12 Feb, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Controlling regioselectivity during difunctionalization of alkenes represents significant challenges, particularly when the installation of both functional groups is involved in radical processes. In this aspect, several functionalized trifluoromethylated (-CF3) compounds have been accomplished via difunctionalization reactions due to their wide importance in the pharmaceutical sectors, however, all these existing reports are limited to afford the corresponding \u03b2-trifluoromethylated products. The main reason for this limitation arises from the fact that -CF3 group served as an initiator in those reactions and predominantly preferred to be installed at the terminal (\u03b2) position of an alkene. In contrary, functionalization of the -CF3 group at the internal (\u03b1) position of alkenes provides valuable products but a meticulous approach is necessary to win this regioselectivity switch. Intrigued by this challenge, we have developed an efficient and highly regioselective strategy where -CF3 group is installed at the \u03b1-position of an alkene and at the end, molecular complexity is achieved via the simultaneous insertion of a sulfonyl fragment (-SO2R) at the \u03b2-position. This strategy provides the simultane-ous installation of two important functional groups such as -CF3 and -SO2R groups and both of these functional groups are the key units to attain or to enhance the bioactivity in organic molecules. A precisely regu-lated sequence of radical generation using red light-mediated photocatalysis facilitates this regioselective switch from the terminal (\u03b2) position to the internal (\u03b1) position. Furthermore, this approach demonstrates dis-tinctive regioselectivity, broad substrate scope and industrial potential for the synthesis of pharmaceuticals under mild reaction conditions.Physical sciences/Chemistry/Catalysis/PhotocatalysisPhysical sciences/Chemistry/Organic chemistry",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "Supportinginformation5.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Controlling regioselectivity during difunctionalization of alkenes remains a significant challenge, particularly when the installation of both functional groups involves radical processes. In this aspect, methodologies to install trifluoromethane (\u2212CF3) via difunctionalization have been explored, due to the importance of this moiety in the pharmaceutical sectors; however, these existing reports are limited, most of which affording only the corresponding \u03b2-trifluoromethylated products. The main reason for this limitation arises from the fact that \u2212CF3 group served as an initiator in those reactions and predominantly preferred to be installed at the terminal (\u03b2) position of an alkene. On the contrary, functionalization of the \u2212CF3 group at the internal (\u03b1) position of alkenes would provide valuable products, but a meticulous approach is necessary to win this regioselectivity switch. Intrigued by this challenge, we here develop an efficient and regioselective strategy where the \u2212CF3 group is installed at the \u03b1-position of an alkene. Molecular complexity is achieved via the simultaneous insertion of a sulfonyl fragment (\u2212SO2R) at the \u03b2-position. A precisely regulated sequence of radical generation using red light-mediated photocatalysis facilitates this regioselective switch from the terminal (\u03b2) position to the internal (\u03b1) position. Furthermore, this approach demonstrates broad substrate scope and industrial potential for the synthesis of pharmaceuticals under mild reaction conditions.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Recently, photoredox catalysis has gained tremendous attention in achieving unique synthetic targets under mild reaction conditions1. In most of these cases, short-wavelength light regions (\u03bbmax\u2009<\u2009460\u2009nm) were utilized to achieve these reactions successfully. However, short-wavelength light regions have severe limitations of potential health risks such as photooxidative damage to the retina. Furthermore, they can lead to generating undesired side products and thereby, lower the atom economy of that reaction2,3,4. Additionally, lower penetration power of short-wavelength light regions causes concern for the scale-up of that particular reaction5. All these limitations have encouraged scientists to move forward to the longer-wavelength regions such as red light or near-infrared (NIR) regions since these are associated with low health risk factors, generate fewer side products due to their lower energy and have high penetration power in the solution which in turn assist to scale up the reaction6,7,8,9,10. In longer-wavelength regions, the photocatalysts will be activated by the low-energy. Consequently, their corresponding redox windows are narrower, and that, in turn, assists in exercising finer control in chemical processes, permitting only specific reactions to take place under defined conditions. Inspired by this, the groups of MacMillan and Rovis have independently developed inspiring photocatalytic strategies for the activation of aryl azide via red light-mediated photoredox catalysis, which have been utilized for proximity labeling11,12. Additionally, the utilization of red light-mediated photocatalysis has been increasingly applied across multiple domains to enhance the control of chemical reactions13,14,15,16. Thus, it is very clear that red light-mediated photoredox catalysis can uniquely attain many unsolved processes that were impossible by the irradiation of ultraviolet (UV) or blue light and that leads to the growing surge of interest in this field, however, it is imperative to acknowledge that still the applications of red light-mediated strategies in organic synthesis are in the early stage of development.\n\nDifunctionalization of alkenes is a powerful synthetic strategy to attain molecular complexity from readily available starting materials17,18,19,20,21,22,23. In this approach, simultaneously, two different functional groups are installed across an olefin by the introduction of two new C\u2013C or C\u2013X bonds. Along this direction, tremendous catalytic efforts have been paid to attain molecular complexity to design pharmaceutically relevant compounds24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50. However, the simultaneous introduction of the trifluoromethyl (\u2212CF3) and the sulfonyl fragment (\u2212SO2R) via difunctionalization is highly challenging due to the intricate difficulty in circumventing undesired side reactions, therefore, rarely has this challenge been solved in organic synthesis. On the other hand, these two functional groups (\u2212CF3 and \u2212SO2R) are highly demanding due to their intrinsic capability to enhance the stability, membrane permeability, and metabolism in bioactive molecules and that is reflected in their wide presence as common pharmaceuticals such as CJ-17493 and eletriptan which are served as an NK-1 receptor antagonist, and as a medication for migraine headaches respectively (Fig.\u00a01a)51,52,53,54,55,56. To the best of our knowledge, only a single report has been published for the simultaneous introduction of these two functional groups across the alkene moiety, however, the position of the \u2212CF3 group was always in the terminal position (\u03b2-position)51. Along the same direction, it should be clearly noted that the difunctionalization of alkenes via the introduction of a \u2212CF3 group has frequently been employed. However, \u2212CF3 group mainly acted as an initiator via the formation of a radical and was always installed to the terminal (\u03b2) position of an alkene (as depicted by the solid frame in Fig.\u00a01b). Followed by this terminal addition, subsequent coupling with other functional groups such as -chloro, -chlorosulfonyl, -amino, -carboxylic acid groups were performed to achieve the difunctionalized products57,58,59,60,61,62. On the contrary, reverse regioselectivity of the \u2212CF3 group at the internal position (\u03b1) in the difunctionalized olefins (indicated by the dashed frame in Fig.\u00a01b) is very rare, although this will allow the achievement of important pharmaceuticals such as CJ-17493, apinocaltamide and many more. To the best of our knowledge, only the group of Li presented an elegant thermocatalytic strategy by involving copper/N-fluorobenzenesulfonimide (NFSI) for the introduction of \u2212CF3 group at the internal position of an alkene (Fig.\u00a01b)32. In this approach, the N-centered radical, derived from an electrophilic NFSI, served as an initiator to facilitate the addition to the -\u03b2 position of the olefin and the (bpy)Zn(CF3)2 complex was employed as a nucleophilic \u2212CF3 reagent.\n\na Selective drug molecules containing trifluoromethyl and sulfonyl groups. b Site-selective trifluoromethylation of olefin. c The requirements for the control of two distinct radicals. d Red light-mediated sulfonyltrifluoromethylation of olefin and optimizations (This work). NFSI, N-fluorobenzenesulfonimide; PC, photocatalyst; DCE, 1,2-dichloroethane; rt, room temperature; bpy, 2,2\u2032-bipyridine; 1,10-phen, 1,10-phenanthroline.\n\nIn this work, inspired by all this information, we became interested in designing a photoredox system that should install both the \u2212CF3 and \u2212SO2R groups simultaneously in alkenes where the \u2212CF3 group should be positioned at the internal position (\u03b1) in the difunctionalized product.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-49514-4/MediaObjects/41467_2024_49514_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results and Discussion",
+            "section_text": "To effectively control the site selectivity, meticulous design of the photoredox strategy during the coupling of two distinct functional groups is inevitable. In the case of Li\u2019s protocol, the approach was distinctly different, as they worked with only one radical (N-centered radical) in attaining the difunctionalized products32. Specifically, when both the \u2212CF3 and \u2212SO2R radicals coexist, the \u2212CF3 radical demonstrates a higher propensity to attach to the olefin first39,59. To overcome this obstacle, we argued to ensure: (1) the formation of the \u2212CF3 radical should occur to the subsequent formation of \u2212SO2R radical, which will readily initiate the addition to olefins; (2) we also argued to utilize a copper salt as a catalyst to capture the free \u2212CF3 radical since copper-based salts are well known for simultaneous cross-coupling reactions by involving \u2212CF3 radical27,28. To fulfill these requirements, we attempted to employ a photocatalyst that should be activated by the red light to attain the sulfonyltrifluoromethylated product (Fig.\u00a01c)63,64. The reason behind our rationale to use the red light in our reaction was due to the lower energy of the red light compared to the blue light, photocatalysts activated by the red light are expected to exhibit a narrower redox window, enabling a precisely control of radical generation, thereby should facilitate regioselectivity during the addition of two distinct radicals on alkenes. Owing to the narrower redox window of the red light-activated photocatalyst, it was essential to ensure that the excited state of the photocatalyst (PC*) should undergo reduction solely through the sulfinate salts via reductive quenching pathway46,64. The resulting sulfonyl radical should then be added to the alkene, leading to the formation of the desired carbon-centered radical. At last, the desired product will be achieved by the carbon-centered radical and Cu\u2212CF3 complex via Cu-catalyzed cross-coupling reaction27,28. In contrast, we rationalized to avoid the oxidative quenching pathway of the PC* since this would have generated free \u2212CF3 radical, which would result in the undesired trifluoromethylated side products (\u2212CF3 group at the terminal (\u03b2) position)39,59. To accomplish this, the photocatalyst was carefully selected based on the redox potentials of sulfinate salts and \u2212CF3 reagents and the redox potentials should have fulfilled: Eox(RSO2\u2212)\u2009<\u2009E(PC*/PC\u2022\u2212), Ered(CF3+)\u2009<\u2009E(PC*/PC\u2022+) and E(PC0/PC\u2022\u2212)\u2009<\u2009Ered(CF3+) (Fig.\u00a01c).\n\nAt the outset of the reaction, 4-vinyl-1,1\u2032-biphenyl (1\u2009equiv.), Os(bptpy)2(PF6)2 (0.8\u2009mol%), NaSO2Ph (3\u2009equiv.) and TTCF3+OTF- (2\u2009equiv.) were employed as the model substrate, photocatalyst, sulfinate salt and \u2212CF3 reagent in the presence of copper chloride (CuCl2, 20\u2009mol%) in dichloromethane (DCM, 0.1\u2009M) to afford the sulfonyltrifluoromethylated product (Fig.\u00a01d, details see Supplementary Table\u00a01\u20133)5,63,64. We carefully chosen these reagents (Os(bptpy)2(PF6)2, sodium benzenesulfinate (NaSO2Ph) and trifluoromethyl thianthrenium triflate (TTCF3+OTF-)) based on their redox potential values to match with our scientific rationale: E([Os]II*/I)\u2009=\u2009+0.93\u2009V vs. Ag/AgCl (3\u2009M KCl), E(\u2009[Os]II*/III)\u2009=\u2009\u22120.67\u2009V vs. Ag/AgCl (3\u2009M KCl))5, Eox(NaSO2Ph)\u2009=\u2009+0.6\u2009V vs. Ag/AgCl (3\u2009M KCl))59,60, Ered(TTCF3+OTF\u2212)\u2009=\u2009\u22120.69\u2009V vs. Ag/AgCl (3\u2009M KCl))65. As expected, the performance of the reaction under these conditions did not generate any trifluoromethylated side products (at the terminal position) and only provided the desired product with 73% of yield. It was also observed that reducing the quantities of NaSO2Ph and TTCF3+OTF\u2212, led to a decrease in the yield of the final product (Fig.\u00a01d, entries 2\u20133). It was necessary to use the excess quantity of sulfinate salts to ensure the faster oxidation of sulfinate salt to the \u2212SO2R radical. In addition, due to the lower solubility in DCM, the use of the excess quantity of sulfinate salts was highly necessary as well as the presence of an excess quantity of \u2212CF3 reagent accelerated the reaction rate25,63,64. Furthermore, the addition of ligands such as 2,2\u2032-bipyridine (bpy) and 1,10-phenanthroline (1,10-phen) exerted deleterious effects in the reaction, giving no product under these conditions (Fig.\u00a01d, entries 4\u20135). We assumed that the presence of ligands occupied the coordination sites for \u2212CF3 radical or hindered the binding of \u2212CF3 radical to the Cu-center27. To verify the importance of the appropriate \u2212CF3 reagent, alternative electrophilic \u2212CF3 sources such as Togni\u2019s reagent, Umemoto\u2019s reagent, and Cu(CF3)3bpy were also applied, albeit substantially lower or negligible yield of the desired product was obtained (Fig.\u00a01d, entries 6\u201310). The rationale behind this could be ascribed to their unsuitable redox potentials, which did not align with Os(bptpy)2(PF6)2 and consequently, failed to meet the requirements. Furthermore, alternative Cu-salts and solvents were also investigated, but lower or negligible yields of the products were obtained (Fig.\u00a01d, entries 11\u201313). Finally, control experiments revealed that the presence of the photocatalyst, Cu-salts and red light were essential for this reaction (Fig.\u00a01d, entries 14\u201316).\n\nIn order to exhibit the red light-mediated regioselective gain for this reaction, reaction conditions under the irradiation of blue light were also compared. Similar to the \u2018red light system\u2019, the crucial combination of the photocatalyst, sulfinate salt and \u2212CF3 reagent was determined, namely [Ru(bpz)3](PF6)2, NaSO2Ph and 5-(trifluoromethyl)dibenzothiophenium triflate (Fig.\u00a02b). However, after extensive optimizations via the investigation of each crucial component of this reaction, the highest yield of the desired product reached to 42% and this could be due to the fact that free \u2212CF3 radical was generated faster under these conditions (see SI 1.3.2). In addition, the generation of free \u2212CF3 radicals could also be attributed to the more powerful blue light. Subsequently, the \u2212CF3 radical underwent an addition reaction with styrene, resulted in the formation of the undesired \u03b2-substituted trifluoromethylated byproduct and the contrast was notably evident in the 19F NMR spectra (Fig.\u00a02c). The \u2018blue light system\u2019 exhibited numerous peaks of side products while the spectrum of the \u2018red light system\u2019 appeared significantly cleaner and mainly contained the \u2212CF3 reagent and the desired product. This significant difference highlighted the pronounced regioselectivity gain in the sulfonyltrifluoromethylation of alkenes via the red light-mediated photocatalysis.\n\na Reaction under optimized conditions with the irradiation of red light. b Reaction under optimized conditions with the irradiation of blue light. c 19F NMR spectra of the reaction under blue and red light conditions.\n\nWith these optimized reaction conditions in hand, we started to evaluate the scope of the sulfonyltrifluoromethylation of alkenes. As shown in the Fig.\u00a03, an array of para-substituted styrenes containing diverse electron-donating groups (EDGs) like -methyl, -acetoxy, and -tert-butyl, as well as electron-withdrawing groups (EWGs) such as -halogens provided the corresponding sulfonyltrifluoromethylated products in moderate to excellent yield (Fig.\u00a03, 1\u20138). Specifically, 4-bromostyrene and 4-chlorostyrene were tolerant under our optimized conditions to provide the desired products (6 and 7), thereby, demonstrated the potential for subsequent functionalization via cross-coupling reactions32. Furthermore, the reaction demonstrated compatibility with 2- and 3-substituted styrenes (10\u201313), leading to the formation of products in satisfactory yield, regardless of the presence of -EDGs or -EWGs. In comparison, electron-deficient alkenes (9 and 14) exhibited decreased efficiency, however, the use of p-chlorophenyl sulfinate led to an improvement in the reaction. In general, the difunctionalization of \u03b2-substituted styrenes represents increased difficulty due to the hindrance caused by these \u03b2-substituents and this hindrance can impede the addition of initiators, such as sulfonyl radicals in this work32. However, under our optimized reaction conditions, (E)-\u03b2-methylstyrene (15) and indene (16) underwent the difunctionalization reaction smoothly and provided a yield of 46% and 78%, respectively. However, unactivated alkenes have not successfully yielded the desired sulfonyltrifluoromethylated products (see SI 1.4.5).\n\naYields are reported as isolated yield. bdr value was determined by 1H NMR.\n\nEncouraged by these results, an extensive exploration of sulfinate salts was conducted within the optimized reaction conditions. To our delight, a diverse array of p-substituted phenyl sulfinates, encompassing -methyl, -chloro, -bromo, -nitro, and -cyano groups, demonstrated excellent tolerance, yielding the desired products in yields from good to excellent (17-21). Furthermore, aliphatic sulfinates (22 and 23) also proved to be compatible which exhibited strong application potentials in pharmaceutical area such as the modification of azidothymidine which is known as an anti-HIV drug66. The adaptability of our methodology extended further to sulfinates bearing biphenyl-, cyclopropane-, and thiophene-groups. These substrates smoothly underwent difunctionalization reactions under the irradiation of red light, yielding products in the range of 35-93% (24-26). This exhibited wide generality of our system to afford various sulfones-containing chemicals, thereby making significant contributions to the field of pharmaceuticals, agrochemicals, and it should be also noted that the synthesis of sulfones-containing chemicals is of paramount importance in organic chemistry46,47,48.\n\nRecently, the focus on late-stage modification has garnered significant interest due to its direct and efficient approach in synthesizing functionalized complex molecules67,68,69,70,71. The expedite synthesis of highly-functionalized molecules holds strong promise for its potential utility in various scientific disciplines including drug discovery, materials science, and molecular imaging71. To evaluate the application of our method on complex molecules, a series of drug molecules and natural products derivatives such as estrone, (S)-(+)-naproxen, dexibuprofen, (1S)-(\u2212)-camphanic acid, indomethacin and adapalene were applied (27-32). Under our experimental conditions, these diverse drug derivatives, encompassing a variety of functional groups, exhibited excellent tolerance and compatibility. The resulting products were obtained in yields from 66% to 88%, indicating high reaction efficiency. This demonstrated the potential of our methodology in facilitating the synthesis of more complex sulfonyltrifluoromethylated molecules. We strongly believe that the -trifluoromethyl and -sulfonyl groups in functionalized drug molecules and natural products should not only improve their inherent properties but should also provide the opportunity for further transformation.\n\nTo further examine the application potential, a 4\u2009mmol-scale reaction was carried out which proceeded smoothly in 4\u2009hours and yielded 0.85\u2009grams of the desired product (Fig.\u00a04a). In addition, product 6 synthesized from 1-bromo-4-vinylbenzene could smoothly give 33 with p-tolylboronic acid via Suzuki-coupling reaction (Fig.\u00a04b)72. Due to the superior light penetration of red light, it became feasible to directly conduct the upscaling of the reaction within a batch reaction system5. To further demonstrate the synthetic utility of our strategy, the elimination of the -sulfonyl group was achieved through a straightforward strategy by using a mixture of Cs2CO3 and 7-methyl-1,5,7-triazabicyclo(4.4.0)dec-5-ene (MTBD), resulting in the production of \u03b1-trifluoromethyl styrene (34) with a yield of 90% (Fig.\u00a04c)64. The mixture of base facilitated the deprotonation and desulfonylation of the sulfonyltrifluoromethylated styrenes to form the \u03b1-trifluoromethyl styrenes. In general, \u03b1-trifluoromethyl styrene derivatives are highly important as versatile synthetic intermediates for the construction of complex fluorinated compounds, which are synthesized through methylenation of trifluoromethylketones (Wittig reaction) or via transition metal-catalyzed cross-coupling reactions73,74. However, compared to these approaches, our strategy enabled the direct synthesis of \u03b1-trifluoromethyl styrene derivatives from styrene, eliminating the requirement of Wittig reagents as well as -borylated or -halide reagents in the processes to improve the atom economy. Additionally, the obtained \u03b1-trifluoromethyl styrene was further transformed into gem-difluoroalkenes (35) in 86% yield and these fluorinated compounds have strong potential to act as a ketone mimic in pharmaceuticals75,76,77. In fact, substitution of the carbonyl group by the gem-difluoroalkene moiety has been shown to enhance the oral bioavailability of therapeutic agents75. Furthermore, our strategy generated a key intermediate (36) for the synthesis of apinocaltamide (38), T-type calcium channel blocker from 4-bromostyrene (Fig.\u00a04d)78,79. All these approaches clearly demonstrate the strong potential of our strategy for further applications in designing or modifying pharmaceuticals.\n\na Gram scale reaction. b Suzuki-coupling reaction. c Elimination of sulfonyl group and followed by defluorination. d Key intermediate generation for synthesis of Apinocaltamide. DMF, dimethylformamide; MTBD, 7-methyl-1,5,7-triazabicyclo(4.4.0)dec-5-ene.\n\nInspired by all these outcomes, we became interested in validating the reaction mechanism of this unique reaction strategy and a series of mechanistic experiments were conducted to validate our mechanistic proposal (Fig.\u00a05). At first, (2,2,6,6-Tetramethylpiperidin-1-yl)oxyl (TEMPO) was added as a radical quenching reagent under the optimized reaction conditions. As expected, a trace quantity of the product was obtained and a carbon-centered radical (III) was captured by TEMPO which was detected by the high-resolution mass spectrometry (HRMS) (Fig.\u00a05a), indicating that the radical process was involved. To further support the involvement of radicals during the addition of the sulfonyl radical, a radical probe experiment was conducted where the model styrene (40) yielded the ring-opening product 41 (Fig.\u00a05b). Upon the addition of sulfonyl radical to 40, a cyclopropylmethyl radical moiety was formed, followed by the rapid ring opening rearrangement relieved the ring strain and finally, resulted in the final ring-opening product (41). Additionally, Stern\u2212Volmer fluorescence quenching experiments were conducted, revealing that the sodium sulfinate salt exhibited the highest potential as a quencher for the excited state of the Os-photocatalyst, which was also corroborated by the electrochemical measurements for redox potentials (Fig.\u00a05c, see SI 1.4.1)5. In Fig.\u00a05c, it is demonstrated that as the concentration of sulfinate salt was increased, there was a notable reduction in fluorescence intensity. However, minimal alterations were detected in the case of the \u2212CF3 reagent, styrene, and CuCl2. This observation was aligned with the anticipated reductive quenching pathway and supported our design that the generation of -sulfonyl radical was prior to the generation of \u2212CF3 radical in the reaction, indicating that no free \u2212CF3 radical was generated and ensuring the high regioselectivity switch in this reaction. Furthermore, the formation of Cu\u2212CF3 active species was also investigated and to analyze the possible Cu\u2212CF3 active species, various control experiments were carried out (Fig.\u00a05d). Initially, we attempted to detect the active species in the absence of styrene under model reaction conditions. No new peak corresponding to CuII\u2212CF3 was observed in 1\u20134\u2009h, however, we observed the presence of the CuIII(CF3)4 anion peak (Fig.\u00a06a). Due to the potential instability of the CuII\u2212CF3 complex, we further attempted the addition of the bpy ligand to detect the potential existence of the CuII\u2212CF3 in Fig.\u00a06a. However, only peak of TTCF3+OTF- was observed in 19F NMR (Fig.\u00a06b). The presence of ligands either occupied the available coordination sites of \u2212CF3 radical or impeded the binding of \u2212CF3 radical to the Cu-center27. To further verify the CuIII(CF3)4 anionic complex, we synthesized stable Me4NCuIII(CF3)4 complex by following the reference article80. However, no product was obtained by using Me4NCuIII(CF3)4 complex instead of CuCl2 under our optimized reaction conditions (Fig.\u00a06c). Similarly, to verify the possibility of CuI\u2212CF3 complex as active species, the model reaction was carried out by replacing CuCl2 with fresh copper powder (Cu0) and as expected, no product was obtained under this condition (Fig.\u00a06d). By analyzing all these experiments, we could assume that the active species Cu\u2212CF3 were not in the form of CuIII\u2212CF3 or CuI\u2212CF3 complexes but possibly were in the form of CuII\u2212CF3 complex, which was also corroborated by the electron paramagnetic resonance (EPR) analysis of reactions (see Supplementary Fig.\u00a09).\n\na Quenching experiments with TEMPO. b Radical probe experiment via ring-opening reaction. c Fluorescence quenching experiments. d Analysis of Cu\u2212CF3 active species. e Proposed mechanism of this work. TEMPO, (2,2,6,6-Tetramethylpiperidin-1-yl)oxyl; bpy, 2,2\u2032-bipyridine.\n\na Model reaction in the absence of styrene after 1\u2009h and 4\u2009h. b Experiment A with the addition of bpy (0.5 or 1.5\u2009equiv.) as ligand. c Model reaction by replacing CuCl2 with Me4NCuIII(CF3)4 complex. d Model reaction by replacing CuCl2 with fresh Cu powder. bpy, 2,2\u2032-bipyridine.\n\nBased on all these mechanistic studies, we proposed a possible mechanism for the overall reaction system (Fig.\u00a05e). The excited state of the photocatalyst [OsII]* (EII*/I\u2009=\u2009+0.93\u2009V vs. Ag/AgCl (3\u2009M KCl), EII*/III\u2009=\u2009\u22120.67\u2009V vs. Ag/AgCl (3\u2009M KCl))5 was activated by the red light and exclusively underwent reduction by the sulfinate salts, I (Eox\u2009=\u2009+0.4\u2009\u2212\u20090.6\u2009V vs. Ag/AgCl (3\u2009M KCl))63,64 to form the sulfonyl radical II (Path A) rather than oxidation by TTCF3+OTF- IV (Ered\u2009=\u2009\u22120.69\u2009V vs. Ag/AgCl (3\u2009M KCl))65 to generate the free \u2212CF3 radical V (Path B), which was consistent with the result of fluorescence quenching experiments. The formed sulfonyl radical II was added to the alkene to generate a carbon-centered radical III, which was verified by the TEMPO quenching experiment and the radical probe experiment. Later, the CuI-species captured the free \u2212CF3 radical V, generated through the reduction of IV by [OsI] (EII/I\u2009=\u2009\u22120.82\u2009V vs. Ag/AgCl (3\u2009M KCl))5, resulted in the formation of the CuII\u2500CF3 complex VI. At last, the final product VII was delivered via the cross-coupling reaction between III and VI. In addition, this reaction process is a closed catalytic cycle according to the calculation of quantum yield (see SI 1.4.4).\n\nIn summary, we have developed a unique protocol where red light-mediated photocatalysis triggered a regioselective switch during the sulfonyltrifluoromethylation of olefins. This strategy has effectively addressed the challenges associated with regioselective addition of radicals onto alkenes. The broad substrate scope and late-stage transformation demonstrated the high efficiency of these reactions and also proved the excellent tolerance of functional groups. Furthermore, post-functionalization studies highlighted the significant industrial potential of the sulfonyltrifluoromethylated product. Additionally, detailed mechanistic investigations revealed a sequential generation of radicals, followed by Cu-catalyzed cross-coupling reactions. We believe that this strategy will strongly contribute to the regioselective functionalizations and will further inspire the development of additional methods in this field.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "A dried reaction vial with a magnetic stirring bar was charged with Os(bptpy)2(PF6)2 (0.0008\u2009mmol, 0.8\u2009mol%), CuCl2 (0.02\u2009mmol, 20\u2009mol%), TT\u2212CF3+OTF\u2212 (0.2\u2009mmol, 2\u2009equiv.) and sodium sulfinate (0.3\u2009mmol, 3\u2009equiv.). After charging all these reagents, the vessel was evacuated by using Schlenk techniques and flushed with N2 three times. Under nitrogen gas flow, olefin (0.1\u2009mmol, 1\u2009equiv.) (if liquid, otherwise added before flushing cycle) and dry DCM (0.1\u2009M) were added by using a syringe which was flushed with inert gas. The resulting mixture was stirred for 3\u20134\u2009h under the irradiation of red LED light (EvoluChem\u2122 LED 650PF HCK1012-XX-014 650\u2009nm 20\u2009mW/cm\u00b2) in the EvoluChem PhotoRedOx Box. After the completion of the reaction, the reaction mixture was quenched by adding distilled water (2\u2009mL). The organic phase was extracted and concentrated in vacuo. 1,1,1-Trifluorotoluene was added as an internal standard to determine the NMR yield of the functionalized product through 19F NMR. Purification proceeded via flash column chromatography.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The materials, reaction optimization, mechanism investigation, general procedure of reactions, characterization of substrates and spectra of products, as well as all other supporting data generated in this study are provided in this manuscript and in the Supplementary Information. Any additional data that support the findings of this study are available from the corresponding authors upon request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "S.D. thanks the Francqui start up grant from the University of Antwerp, Belgium, for the financial support. T.Z. thanks FWO SB PhD fellowship for their financial assistance to finish this work. We thank Dr. Rakesh Maiti from University of Bayreuth for helpful discussions. We also thank Mr. Glenn Van Haesendonck from UAntwerpen, Belgium for HRMS measurements.",
+            "section_image": []
+        },
+        {
+            "section_name": "Funding",
+            "section_text": "Open Access funding enabled and organized by Projekt DEAL.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Department of Chemistry, University of Antwerp, Antwerp, Belgium\n\nTong Zhang\u00a0&\u00a0Shoubhik Das\n\nLeibniz-Institut f\u00fcr Katalyse e.V. an der Universit\u00e4t Rostock (LIKAT), Rostock, Germany\n\nJabor Rabeah\n\nState Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Lanzhou Institute of Chemical Physics (LICP), Chinese Academy of Sciences, Lanzhou, P. R. China\n\nJabor Rabeah\n\nDepartment of Chemistry, University of Bayreuth, Bayreuth, Germany\n\nShoubhik Das\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nT.Z. and S.D. designed the project. T.Z. developed the reaction, investigated the substrate scope, examined the applications, and studied the reaction mechanism. J.R. did an EPR analysis. Finally, T.Z. and S.D. wrote the manuscript.\n\nCorrespondence to\n                Shoubhik Das.",
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+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Zhang, T., Rabeah, J. & Das, S. Red-light-mediated copper-catalyzed photoredox catalysis promotes regioselectivity switch in the difunctionalization of alkenes.\n                    Nat Commun 15, 5208 (2024). https://doi.org/10.1038/s41467-024-49514-4\n\nDownload citation\n\nReceived: 30 January 2024\n\nAccepted: 05 June 2024\n\nPublished: 18 June 2024\n\nVersion of record: 18 June 2024\n\nDOI: https://doi.org/10.1038/s41467-024-49514-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Nature Reviews Chemistry (2024)",
+            "section_image": []
+        }
+    ]
+}

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+{
+    "title": "Probing charge redistribution at the interface of self-assembled cyclo-P5 pentamers on Ag(111)",
+    "pre_title": "Probing charge redistribution at the interface of self-assembled cyclo-P5 pentamers on Ag(111)",
+    "journal": "Nature Communications",
+    "published": "02 August 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50862-4/MediaObjects/41467_2024_50862_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50862-4/MediaObjects/41467_2024_50862_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-024-50862-4#ref-CR52"
+    ],
+    "code": [],
+    "subject": [
+        "Electronic properties and materials",
+        "Structural properties",
+        "Two-dimensional materials"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3777510/v1.pdf?c=1722683273000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3777510/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-50862-4.pdf",
+    "preprint_posted": "29 Jan, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Phosphorus pentamer (cyclo-P5-) ions are unstable in nature but can be synthesized at the Ag(111) surface. Unlike monolayer black phosphorous, little is known about their electronic properties when in contact with metal electrodes, although this is crucial for future applications. Here we characterize the atomic structure of cyclo-P5 assembled on Ag(111) using atomic force microscopy with functionalized tips and density functional theory. Combining force and tunneling spectroscopy, we find that a strong charge transfer induces an inward dipole moment at the cyclo-P5/Ag interface as well as the formation of an interface state. We probe the image potential states by field-effect resonant tunneling and quantify the increase of the local change of work function of 0.46 eV at the cyclo-P5 assembly. Our results suggest that the high-quality of the cyclo-P5/Ag interface might serve as a prototypical system for electric contacts in phosphorus-based semiconductor devices.Physical sciences/Nanoscience and technology/Nanoscale materials/Electronic properties and materialsPhysical sciences/Materials science/Techniques and instrumentation/Microscopy/Scanning probe microscopyPhysical sciences/Nanoscience and technology/Nanoscale materials/Two-dimensional materialscyclo-P\u2212 5 pentamerwork functionatomic force microscopyscanning tunneling microscopyfield-emission resonance spectroscopydensity functional theory",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Phosphorus pentamers (cyclo-P5) are unstable in nature but can be synthesized at the Ag(111) surface. Unlike monolayer black phosphorous, little is known about their electronic properties when in contact with metal electrodes, although this is crucial for future applications. Here, we characterize the atomic structure of cyclo-P5 assembled on Ag(111) using atomic force microscopy with functionalized tips and density functional theory. Combining force and tunneling spectroscopy, we find that a strong charge transfer induces an inward dipole moment at the cyclo-P5/Ag interface as well as the formation of an interface state. We probe the image potential states by field-effect resonant tunneling and quantify the increase of the local change of work function of 0.46 eV at the cyclo-P5 assembly. Our experimental approach suggest that the cyclo-P5/Ag interface has the characteristic ingredients of a p-type semiconductor-metal Schottky junction with potential applications in field-effect transistors, diodes, or solar cells.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Elemental phosphorus (P) is not only ubiquitous in human life, it is also one of the most fascinating areas of chemistry as it can exist in a large diversity of allotropes1,2,3, in various cluster configurations4,5, or in organic compounds6. The phosphorous polymorphism is even multiplied on the atomic scale when using a surface to constrain the reaction in two dimensions (2D). As in the field of on-surface chemistry producing complex nanographene structures in ultra-high vacuum (UHV)7,8, surface-assisted phosphorus reactions on metals have synthesized blue phosphorus9, P chains10, or even planar cyclo-P5 rings11,12. Since then, phosphorus allotropes have emerged as a promising one-atom thick 2D material beyond graphene, due to its moderate direct band gap (0.3 to 2.0 eV)13 suitable for nanoelectronics and nanophotonics applications14,15. However, allotropic configurations, their atomic buckling, defects, or potential alloy formation can be detrimental to the semiconducting character. In addition, the interaction of 2D materials with delocalized electrons of metal, as well as the dynamical charge transfer between the two media, are key factors in fostering gate-tunable functionalities such as superconductivity16,17. Experimental study of these aspects at the fundamental level is therefore essential for future quantum applications where metallic electrical contacts are required18.\n\nLow-temperature scanning probe microscopy is an incontrovertible tool for assessing atomic structures in contact with metals and characterizing their electronic properties with high spectral resolution in UHV. Atomic force microscopy (AFM) with functionalized tips19,20 has demonstrated real-space imaging with improved lateral and vertical resolution of aromatic molecules and cyclo-carbons21. Recently, AFM imaging and spectroscopy have also tackled monoelemental 2D materials demonstrating a precise quantification of the atomic buckling in these structures22,23. Not only restricted to structural characterization, charge distributions, and work function changes are also accessible at the nanometer scale using Kelvin probe force microscopy (KPFM)24,25,26,27. In addition, the investigation of the local density of states (LDOS) of 2D materials near the Fermi level is readily achieved by means of STM and scanning tunneling spectroscopy (STS). Tunneling spectroscopy can also probe the image potential states (IPS) of 2D synthetic materials, as demonstrated in the case of graphene28, germanene29, or borophene30,31. Quantifying these Stark-shifted unoccupied states lying below the vacuum level gives not only access to the fundamental physical processes involved in charge carrier dynamics but also to quantify local modulations of the work function at the interface between 2D materials and metals.\n\nBy applying this methodology, we determine here the structure of phosphorus chains and self-assembled cyclo-P5 pentamers on Ag(111) using low-temperature (4.5 K) AFM imaging with CO-terminated tips. KPFM spectroscopic measurements indicate the formation of an inwards dipole moment at the P5/Ag interface, which results from the charge transfer from the Ag substrate to the network, as confirmed by DFT calculations. This charge transfer leads to a complex charge redistribution and the formation of an interfacial hybridized state (IS). Through FERT and STS spectroscopy, we determined the energy position of the IS and the series of IPS at the cyclo-P5 assembly as compared to pristine Ag, confirming an increase of the local work function of \u00a0\u2248\u20090.46\u2009eV. We found that the P5/Ag system behaves as a prototypical p-type semiconductor-metal junction with a Schottky barrier built at the interface, opening prospects for its use in field-effect transistors, diodes, or solar cells. Given the general interest in tailoring the physical characteristics of monoelemental 2D materials contacted to a metal, we think that our experimental approach might serve as a powerful asset for deciphering the energy level alignment of future devices involving electric contacts.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Phosphorus atoms were sublimed in UHV onto the Ag(111) substrate, kept at about 423\u2009K (see \u201cMethods\u201d). Figure\u00a01a shows an STM overview image of the resulting structures for a relative coverage of less than 0.3 monolayer (ML). Similar to refs. 10,11, extended 1D chains aligned along the \\([1\\overline{1}0]\\) directions of Ag(111) (marked 1) in Fig.\u00a01a) coexist with domains of P5 molecules (2). The inset of Fig.\u00a01a further shows a STM image of the double and triple chains, which depends on the P deposition rate11.\n\na STM topographic image after sublimation of phosphorus atoms on Ag(111) leading to P chains (1) and cyclo-P5 domains (2), (It\u2009=\u20091 pA, Vs\u2009=\u20090.15\u2009mV). The inset shows an STM image of the single, double, and triple chains, respectively. b\u2013d Series of AFM images with CO-terminated tip revealing the armchair structure of single, double, and triple P chains, (f0\u2009=\u200926\u2009kHz, A\u2009=\u200950\u2009pm). Scale bars are 0.5\u2009nm. e Atomic configurations of the triple armchair chains obtained by DFT calculations. Phosphorus and silver atoms are shown in orang and gray, respectively. f Corresponding AFM simulation using the DFT coordinates.\n\nTo precisely determine their atomic configurations, we employed AFM imaging with CO-terminated tips (see \u201cMethods\u201d, Fig.\u00a01c, d)19. A common AFM contrast is observed for all configurations assigned to an armchair structure of the chain, which resembles that of hydrocarbon chains32. In each configuration, the chain has always an apparent width by AFM of 6.0 \u00c5 with a minimum distance between them of 0.25 \u00c5. Thus, double and triple chains have apparent widths of 14.5 and 23 \u00c5, respectively. The relaxed structure of the triple chain configuration calculated by DFT is shown in Fig.\u00a01e. Phosphorus atoms colored in orange sit on bridge sites of the Ag lattice (gray) and are aligned along one \\([1\\bar{1}0]\\) direction in accordance with the experimental data. Based on the DFT coordinates, we simulated the AFM image (see \u201cMethods\u201d, Fig.\u00a01f). The excellent agreement with the experimental image of Fig.\u00a01d confirms the armchair structure of the P chains on Ag(111), similar to ref. 10.\n\nIncreasing the P coverage to about 0.4\u2009ML while keeping the substrate at 423\u2009K leads to the formation of large islands of cyclo-P5 pentamers relative to the chains (Fig.\u00a02a)12. In Fig.\u00a02b, the close-up STM image reveals the structure of the self-assembled domains consisting of a hexagonal lattice with parameters a1\u2009=\u2009b1\u2009=\u20097.6 \u00c5. Each bright protrusion corresponds to one cyclo-P5 molecule as schematized by the black dashed pentagons. Domains of cyclo-P5 rings exhibit a superstructure characterized by stripes separated by \u00a0\u2248\u20094.56\u2009nm (i.e., six P5 rows), as shown by black dotted lines in Fig.\u00a02a. These lines are rotated by 19\u00b0 as compared to the \\([1\\bar{1}0]\\) directions of the Ag(111) substrate, which agrees with previous experimental works11 as well as the relaxed structure obtained by DFT calculations of Fig.\u00a02d. We thus confirm that the cyclo-P5 assembly adopts a (\\(\\sqrt{7}\\times 6\\sqrt{7}\\))R19\u00b0 unit cell with respect to the Ag(111) surface lattice as previously reported by Zhang et al.11.\n\na STM image of the self-assembled pentamers on Ag(111), (It\u2009=\u20091 pA, Vs\u2009= 0.15\u2009mV). Islands systematically show a superlattice of bright lines rotated by 19\u2218 with respect to the \\([1\\overline{1}0]\\) directions of Ag(111) (dotted lines). b Close-up STM topography showing the P5 pentamers depicted by dashed lines. c Corresponding AFM image revealing the P5 chemical structure, (f0\u2009=\u200926\u2009kHz, A\u2009=\u200950\u2009pm). Each cyclo-P5 ring is composed of three short bonds (blue) and two longer ones (red), respectively. d Atomic configurations of the pentamer assembly on Ag(111) obtained by DFT. Phosphorus and silver atoms are shown in orange and gray, respectively. e Corresponding AFM simulation using the DFT coordinates. f Site-dependent \u0394f(Z) spectroscopic curves acquired at two neighboring P atoms of a P5 molecule (orange and brown), between two P5 molecules (gray) and on Ag(111) (black), respectively. The local minima of the \u0394f(Z) curves indicate the relative height of the phosphorus atoms.\n\nA deeper insight into the chemical structure of the cyclo-P5 molecules is provided by the AFM image in Fig.\u00a02c. We also simulated the AFM image based on DFT coordinates, allowing us to confirm the exact position and structure of the P5 molecules in their self-assembly in registry with the Ag(111). The P-P bond length within the cyclo-P5 pentamer extracted by AFM varies from 2.5 to 2.7 \u00c5, which is always larger by about 8% than that of DFT calculations for the pentamer on Ag(111) (Fig.\u00a02d and Supplementary Fig.\u00a01) or in the gas phase (2.185 \u00c5)33. It is well-established that this overestimation of apparent bond lengths is induced by the tilting of the CO molecule attached to the AFM tip upon scanning, as shown for planar polyaromatic hydrocarbons or P3N3 molecules34,35. Moreover, each cyclo-P5 ring is composed of three short apparent bonds (2.5 \u00c5, blue bonds in Fig.\u00a02c) and two longer ones (2.7 \u00c5, red bonds). This particular bond order, also confirmed in the relaxed structure calculated by DFT (Fig.\u00a02d), is likely induced by the small buckling of the structure when adsorbed on Ag(111).\n\nTo accurately quantify the atomic corrugation within the cyclo-P5 structure, we acquired a series of site-dependent \u0394f(Z) spectroscopic curves (Fig.\u00a02f) at the locations marked in the inset AFM image. The black and gray curves were obtained on Ag and between two pentamers, respectively. On top of neighboring atoms of a cyclo-P5 (orange and brown curves), spectra exhibit a characteristic dip arising from the interaction between the front-end oxygen atom of the CO-terminated tip with the phosphorus atom. The dashed vertical lines indicate the Z position of their bottoms and are the signature of the relative atomic Z height23. The difference \u0394Z of \u00a0\u2248\u200920\u201330\u2009pm thus represents the intrinsic atomic corrugation within the cyclo-P5 pentagonal structure5, which is comparable with atomic corrugations in graphene36 or planar molecules37. Thus, this confirms the planarity of the cyclo-P5 structure5, as reflected in the constant-height AFM image of Fig.\u00a02e.\n\nThrough DFT calculations, it has been determined that the cyclic P5 pentamer exhibits a higher binding energy on the Ag(111) surface, amounting to \u2212\u20090.90\u2009eV per atom (Supplementary Fig.\u00a02). This larger energy value is primarily facilitated through a charge transfer mechanism, promoting the stability of the cyclic P5 structure. To provide insights into the charge distribution at the cyclo-P5 interface, we performed force versus voltage spectroscopic measurements (see \u201cMethods\u201d). Experimentally, the frequency shift \u0394f as a function of the sample bias Vs is measured at a constant tip height Z, providing in the \u0394f(V) curve a parabola due to the electric force acting between tip and sample. The voltage V* at the top of the parabola represents the local contact potential difference (LCPD) between tip and sample, which allows one to image charge distributions and work function changes with nanoscale resolution24,25,26,27. Figure\u00a03a shows a \u0394f(V) cross-section acquired across a P5 domain (see STM inset of Fig.\u00a03a). Single \u0394f(V) point-spectra on top of the P5 network (orange) and on Ag(111) (black) are plotted in Fig.\u00a03b, respectively. The dashed lines in Fig.\u00a03a, b refer to the V* position. The LCPD value systematically shifts towards positive values (\u0394V*\u00a0\u2248\u20090.22\u2009V) for the pentamer assembly as compared to the pristine Ag substrate. This indicates the accumulation of charges at the P5 network as compared to the Ag substrate.\n\na Frequency shift \u0394f as a function of sample bias voltage Vs, measured across a pentamer domain shown in the STM image by a dashed line, (parameters : f0\u2009=\u200926\u2009kHz, A\u2009=\u200980\u2009pm). b Single \u0394f(V) curves at the pentamer assembly (orange) as compared to the Ag(111) (black). Dashed lines mark the top of the parabola allowing the extract an LCPD shift \u0394V*\u2009=\u20090.22\u2009V. c Top and side views of the charge redistribution between pentamers and Ag(111). Blue (red) areas show electron accumulation (depletion). The white and black dashed lines refer to the position of the P5 assembly and the last Ag layer, respectively. The isosurface level of the plot is set to \u00a0\u00b1\u200913\u2009\u00d7\u200910\u22123 e \u00c5\u22123. d Schematic illustration of the charge redistribution at the P5/Ag(111) interface leading to an inward surface dipole (D) moment and a local work function change (\\({\\phi }_{{{{{\\rm{P}}}}}_{5}/{{{\\rm{Ag}}}}}\\)). The cyclo-P5 layer is colored orange. \u0394V* refers to the LCPD change.\n\nTo better rationalize this, we calculated the charge redistribution at the cyclo-P5/Ag interface (see \u201cMethods\u201d), whose top and side views of isosurfaces of electron accumulation (blue, +13\u2009\u00d7\u200910\u22123 e \u00c5\u22123) and depletion (red, \u221213\u2009\u00d7\u200910\u22123 e \u00c5\u22123) are displayed in Fig.\u00a03c. An electron transfer from the Ag(111) substrate to the P atoms of pentamers is observed as a negative charge accumulation located at the cyclo-P5 ring (red). In the P5/Ag gap (marked by white and black dashed lines in the side view in Fig.\u00a03c), charge accumulation/depletion layers emerge below each cyclo-P5 structure, which supports the formation of a hybridized state30,31. We emphasize that such an interface state is not restricted to 2D Xenes on metals since it has been observed for organic/metal systems38.\n\nBetween cyclo-P5 rings, we note the absence of in-plane charge redistribution. Considering that the last Ag layer is depleted (red) while each cyclo-P5 has an excess of negative charges (blue), the P5 assembly can be approximated to a lattice of surface dipole moments of D\u2009=\u20091.42 Debye pointing towards the substrate (see arrow in Fig.\u00a03c). The observation of such surface dipole moments is consistent with an increase of the LWF at the P5/Ag interface, fixed to an arbitrary value \u0394\u03d5 in the diagram of Fig.\u00a03d. While the LWF increase agrees with the LCPD shift to more positive values in force spectroscopy, it is important to note that the LCPD variations are a qualitative indicate of the LWF changes at the atomic scale since it can have a strong distance-dependence on metal substrate39,40. Indeed, the \u0394V* cannot directly account for the difference of work function \u0394\u03d5\u2009= \\({\\phi }_{{{{{\\rm{P}}}}}_{5}/{{{\\rm{Ag}}}}}\\) - \u03d5Ag shown in Fig.\u00a03d due to the averaging effects of the electrostatic interactions between tip and sample and the uncertainty in the work function of the tip. As will be discussed later, a quantitative experimental value of LWF (0.46\u2009eV) can be obtained by the analysis of IPS spectra.\n\nFinally, the planar cyclo-P5 structure has, in principle, an unpaired electron leading to an anionic state (i.e., cyclo-\\({{{{\\rm{P}}}}}_{5}^{-}\\)), which has been identified by nuclear magnetic resonance (NMR) in the gas phase or as a ligand41,42. Upon adsorption on Ag(111), the cyclo-\\({{{{\\rm{P}}}}}_{5}^{-}\\) anion can coordinate with the Ag atoms below it, leading to a charge redistribution at the interface and the formation of an interfacial state. This charge transfer modifies the amount of charge of the pentamer away from an integer, as confirmed by the Bader charge analysis showing an accumulation of electrons on P atoms (\u22120.115 e) and an electron depletion (+0.061 e) of the depleted Ag layer. We, therefore, conclude that cyclo-P5 molecules do not retain their anionic character on Ag(111). This conclusion is further corroborated by the absence of a Kondo resonance or spin excitations in dI/dV spectra acquired near the Fermi level43.\n\nTo shed more light on the electronic properties at the P5/Ag interface, we next performed differential conductance measurements (dI/dV) across a P5 domain (see \u201cMethods\u201d). Figure\u00a04a shows the typical dI/dV point-spectra spectra of the network (orange) as compared to Ag(111) (black). We marked with dashed lines the valence band edge maximum (VBE) at \u22120.5\u2009eV and the conduction band edge minimum (CBE) at 0.3\u2009eV, providing a gap Eg of the P5 assembly to about 0.8\u2009eV. The spectra also show a strong resonance at 2.5\u2009V, which we attribute to tunneling into an interface state (IS). dI/dV maps (Fig.\u00a04b) further reveal the density of states at the valence band at Vs\u2009=\u2009\u22120.5\u2009V. This atomic feature evolves to a stripe pattern at Vs\u2009=\u2009+2.5\u2009V (Fig.\u00a04c, bottom), revealing the spatial modulation of the IS (Fig.\u00a04a) similar to the superstructure shown in STM topographic image of Fig.\u00a02a.\n\na dI/dV point-spectra acquired above the P5 assembly (orange) and on Ag(111) (black), whose locations are shown in the STM inset. (parameters: It\u2009=\u20091\u2009pA, Vs\u2009=\u2009500\u2009mV, \\({A}_{{{{\\rm{mod}}}}}\\) =\u200910\u2009mV, f\u2009=\u2009511\u2009Hz). b dI/dV maps at Vs\u2009=\u2009\u2212\u20091.25 and 2.5\u2009V, respectively. c STM topographic image of three P5 domains and the corresponding dI/dV maps of the IS modulation. d Scheme of the band alignment and the formation of Stark-shifted IPS (orange lines). e FERT cross-section acquired across the P5 assembly along the dashed line in (a), (Set-points: It\u2009=\u20091 pA, Vs\u2009=\u2009500\u2009mV, \\({A}_{{{{\\rm{mod}}}}}\\) =\u200935\u2009mV, f\u2009=\u2009511\u2009Hz). f Single FERT spectra of the P5 assembly and the Ag(111) substrate, showing the series of nth IPS. g Extracted IPS peak voltages as a function of n2/3.\n\nTo quantify the local change of work function (LWF), we acquired field-effect resonant tunneling (FERT) spectra in order to probe IPS between the cyclo-P5 assembly and silver. Experimentally, FERT spectra (also called dZ/dV spectroscopy) are obtained by sweeping the sample voltage Vs while keeping constant the tunneling current with the STM feedback loop. When the voltage exceeds the local work function, resonant tunneling through the tip-sample junction occurs from the tip into the image potential states, giving rise to a series of peaks in the FERT spectra. From a quasi-classical approximation (Fig.\u00a04d), tunneling resonances occur when the Fermi level of the tip aligns with the Stark-shifted IPS states, see Eq. (1).\n\nwhere Vn is the sample voltage for the nth IPS, \u03d5 is the work function of the sample, m is the free electron mass, and E is the electric field. Interestingly, the comparison of FERT spectra between tip positions enables us to distinguish interfacial charge transfer and quantify the local change of work function at the nanometer scale28,29,30,31.\n\nFigure\u00a04e, f shows the series of IPS states obtained above the cyclo-P5 self-assembly as compared to Ag(111), respectively. The resonance at Vs\u2009=\u20092.2\u2009eV of the orange spectrum of Fig.\u00a04f, which is absent for the Ag one (black), corresponds to the IS. The peaks noted n\u2009=\u20091 to 6 of the black spectra are the IPS states of the pristine Ag substrate. On the P5 assembly, IPS states are clearly shifted to higher voltage when ramping up the electric field (i.e., Vs), which is the signature of the increase of LWF30,31.\n\nA quantitative estimation of the LWF can be obtained from Eq. (1). In Fig.\u00a04g, we plot the voltage position Vn of the IPS states as a function of n2/3 for both the cyclo-P5 network (orange squares) and the Ag(111) substrate (black triangles). By fitting the linear progression of each dataset, we extract the LWF value corresponding to the y-intercepts to \u03d5Ag\u2009=\u20094.49\u2009eV and \\({\\phi }_{{{{{\\rm{P}}}}}_{5}}\\) =\u20094.95\u2009eV, respectively. Considering that our experimental estimate of \u03d5Ag is in agreement with that obtained by ultraviolet photoelectron spectroscopy (UPS)44, we quantify the increase of LWF of \u0394\u03d5\u2009=\u20090.46\u2009eV induced by the cyclo-P5 assembly adsorbed on Ag(111). Altogether, the observation of the IS and the shift of IPS resonances in tunneling spectroscopy point to a charge transfer from the Ag substrate to the cyclo-P5 network, leading to the creation of inwards electric dipoles at the interface.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "In summary, we synthesized phosphorus chains and cyclo-P5 pentamers by depositing phosphorus atoms on atomically flat Ag(111) in an ultra-high vacuum. Using low-temperature AFM with CO-terminated tips, armchair P chains, and cyclo-P5 rings are resolved with atomic precision. DFT calculations support a substantial charge transfer from the Ag substrate to P5 pentamers, which results in a complex charge redistribution at the P5/Ag interface. This further leads to the emergence of an interface state as observed by dI/dV spectroscopy. Using force-voltage spectroscopic measurements, we infer the local increase of work function at the P5 network in comparison to the bare metal substrate and determine the direction of the induced surface dipole moments. We corroborated these measurements with FERT spectroscopy to quantify the LWF increase of 0.46\u2009eV at the P5/Ag interface. Based on our experimental estimates, we summarize in Supplementary Fig.\u00a03 the energy level alignment at the cyclo-P5/Ag interface. Considering that the Ag metal is depleted while the P5 assembly is negatively charged, we conclude that the P5/Ag system behaves as a prototypical p-type semiconductor-metal junction with a Schottky barrier built at the interface. It further shows that this system could have potential applications in field-effect transistors, diodes, or solar cells. Finally, by exploring the fundamental characteristics of the prototypical cyclo-P5/metal interface, our methodology (applicable to other emerging 2D materials and related quantum materials) not only showcases the importance of scanning probe microscopy as a powerful technique to study structural and electronic properties at the atomic scale but also provides insights for improved performances of phosphorus-based devices.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The Ag(111) substrate purchased from Mateck GmbH was sputtered by Ar+ ions and annealed at 770\u2009K to eliminate any surface contaminations. Phosphorus atoms were sublimed by heating up a black phosphorus crystal contained in a Knudsen cell in an ultra-high vacuum (UHV). The P flux was estimated using a quartz microbalance. To obtain the phosphorus chains and P5 domains, we annealed the Ag(111) substrate during deposition at temperatures described in ref. 9.\n\nAFM measurements were performed with commercially available tuning-fork sensors in the qPlus configuration45 equipped with a tungsten tip (f0\u2009=\u200926 kHz, Q\u2009=\u200910,000\u201325,000, nominal spring constant k\u2009=\u20091800 N m\u22121, oscillation amplitude A\u00a0\u2248\u200950\u2009pm. Constant-height AFM images were obtained using tips terminated with a single CO in the non-contact frequency-modulated AFM (FMAFM) mode at zero voltage19,46. CO molecules were adsorbed on the sample maintained at below 20\u2009K. Before its functionalization, the apex was sharpened by gentle indentations into the Ag surface. A single CO molecule was carefully attached to the tip following the procedure of ref. 47. Simulations of the AFM images based on the DFT coordinates were carried out using the probe-particle model48. Site-dependent \u0394f(Z) spectroscopic measurements to determine the atomic buckling of phosphorus pentamers were obtained with CO-terminated tips. The \u0394f(V) cross-section of 1\u2009\u00d7\u200985 pixels was acquired with Ag-coated metallic tips (tunneling setpoints: It\u2009=\u20091\u2009pA, Vs\u2009=\u2009800\u2009mV, Zoffset\u2009=\u2009+80\u2009pm).\n\nAll DFT calculations were carried out in the Vienna ab initio simulation package (VASP)49 with the projector augmented wave (PAW) method. The generalized gradient approximation (GGA) in the framework of Perdew-Burke-Ernzerhof (PBE)50 was chosen with the plane-wave cutoff energy set at 400\u2009eV for all calculations. The DFT-D351 method of Grimme was employed to describe the van der Waals (vdW) interactions. The geometries of the structures were relaxed until the force on each atom was less than 0.02 eV \u00c5\u22121, and the energy convergence criterion of 1\u2009\u00d7\u200910\u22124\u2009eV was met. The Brillouin zone was sampled using Gamma k-mesh with a separation criterion of 0.03. Metal slabs with 3 atomic layers were adopted as the substrate, and the bottom layer was fixed to simulate the bulk. The vacuum spacing between neighboring images was set at least 15 \u00c5 along the non-periodic directions to avoid a periodic interaction.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The data that support the findings of this study are available from Zenodo52 and from the corresponding authors upon request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "E.M. and R.P. acknowledge funding from the Swiss Nanoscience Institute (SNI), the European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation program (ULTRADISS grant agreement No 834402 and support as a part of NCCR SPIN, a National Center of Competence (or Excellence) in Research, funded by the SNF (grant number 51NF40-180604). E.M. and T.G. acknowledge the Sinergia Project funded by the SNF (CRSII5_213533). E.M., T.G., and R.P. acknowledge the SNF grant (200021_228403). T.G. acknowledges the FET-Open program (Q-AFM grant agreement No 828966) of the European Commission. J.-C.L. acknowledges funding from the European Union\u2019s Horizon 2020 research and innovation program under the Marie Sk\u0142odowska-Curie grant agreement number 847471. C.L. and E.M. acknowledge the Georg H. Endress Foundation.",
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+        },
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+            "section_name": "Author information",
+            "section_text": "Department of Physics, University of Basel, Basel, Switzerland\n\nOuthmane Chahib,\u00a0Jung-Ching Liu,\u00a0Chao Li,\u00a0Thilo Glatzel,\u00a0Ernst Meyer\u00a0&\u00a0R\u00e9my Pawlak\n\nInstitute of Technology for Carbon Neutrality, Shenzhen Institute of Advanced Technology, Shenzhen, People\u2019s Republic of China\n\nYuling Yin\u00a0&\u00a0Feng Ding\n\nFaculty of Materials Science and Energy Engineering, Shenzhen University of Advanced Technology, Shenzhen, People\u2019s Republic of China\n\nYuling Yin\u00a0&\u00a0Feng Ding\n\nState Key Laboratory of Precision Spectroscopy, School of Physics and Electronic Science, East China Normal University, Shanghai, People\u2019s Republic of China\n\nQinghong Yuan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nR.P. and E.M. conceived the experiments. O.C. and R.P. performed the STM/AFM measurements with support from J.-C.L. and C.L. Y.Y., F.D., and Q.Y. performed DFT calculations. O.C. and R.P. analyzed the data. R.P. wrote the manuscript. O.C., Y.Y., J.-C.L., C.L., T.G., F.D., Q.Y., E.M., and R.P. discussed the results and revised the manuscript.\n\nCorrespondence to\n                Ernst Meyer or R\u00e9my Pawlak.",
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+            "section_text": "Chahib, O., Yin, Y., Liu, JC. et al. Probing charge redistribution at the interface of self-assembled cyclo-P5 pentamers on Ag(111).\n                    Nat Commun 15, 6542 (2024). https://doi.org/10.1038/s41467-024-50862-4\n\nDownload citation\n\nReceived: 10 January 2024\n\nAccepted: 23 July 2024\n\nPublished: 02 August 2024\n\nVersion of record: 02 August 2024\n\nDOI: https://doi.org/10.1038/s41467-024-50862-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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03b0b6a014cc46268783cf3d9b76467437f21edb16a2598fe060abe63f57631f/metadata.json

@@ -0,0 +1,155 @@
+{
+    "title": "Regulation of leptin signaling and diet-induced obesity by SEL1L-HRD1 ER-associated degradation in POMC expressing neurons",
+    "pre_title": "SEL1L-HRD1 ER-associated degradation regulates leptin receptor maturation and signaling in POMC neurons in diet-induced obesity",
+    "journal": "Nature Communications",
+    "published": "29 September 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52743-2/MediaObjects/41467_2024_52743_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52743-2/MediaObjects/41467_2024_52743_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52743-2/MediaObjects/41467_2024_52743_MOESM3_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52743-2/MediaObjects/41467_2024_52743_MOESM4_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "https://alphafold.ebi.ac.uk/entry/P48356",
+        "/articles/s41467-024-52743-2#Sec28"
+    ],
+    "code": [],
+    "subject": [
+        "Endoplasmic reticulum",
+        "Hormone receptors",
+        "Obesity",
+        "Protein aggregation"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3768472/v1.pdf?c=1727694380000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3768472/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-52743-2.pdf",
+    "preprint_posted": "11 Jan, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Endoplasmic reticulum (ER) homeostasis in the hypothalamus has been implicated in the pathogenesis of certain patho-physiological conditions such as diet-induced obesity (DIO) and type 2 diabetes; however, the significance of ER quality control mechanism(s) and its underlying mechanism remain largely unclear and highly controversial in some cases. Moreover, how the biogenesis of nascent leptin receptor in the ER is regulated remains largely unexplored. Here we report that the SEL1L-HRD1 protein complex of the highly conserved ER-associated protein degradation (ERAD) machinery in POMC neurons is indispensable for leptin signaling in diet-induced obesity. SEL1L-HRD1 ERAD is constitutively expressed in hypothalamic POMC neurons. Loss of SEL1L in POMC neurons attenuates leptin signaling and predisposes mice to HFD-associated pathologies including leptin resistance. Mechanistically, newly synthesized leptin receptors, both wildtype and disease-associated human mutant Cys604Ser (Cys602Ser in mice), are misfolding prone and bona fide substrates of SEL1L-HRD1 ERAD. Indeed, defects in SEL1L-HRD1 ERAD markedly impair the maturation of these receptors and causes their ER retention. This study not only uncovers a new role of SEL1L-HRD1 ERAD in the pathogenesis of diet-induced obesity and central leptin resistance, but a new regulatory mechanism for leptin signaling.Biological sciences/Molecular biology/Protein folding/Endoplasmic reticulumBiological sciences/Physiology/Metabolism/Feeding behaviour/ObesityBiological sciences/Molecular biology/Protein folding/Protein aggregationBiological sciences/Cell biology/Cell signalling/Hormone receptorsSEL1L-HRD1 ERADPOMCdiet-induced obesityleptin signalingleptin receptorparabiosis",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "HFDPKOSupplementaryNC.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Endoplasmic reticulum (ER) homeostasis in the hypothalamus has been implicated in the pathogenesis of diet-induced obesity (DIO) and type 2 diabetes; however, the underlying molecular mechanism remain vague and debatable. Here we report that SEL1L-HRD1 protein complex of the highly conserved ER-associated protein degradation (ERAD) machinery in POMC-expressing neurons ameliorates diet-induced obesity and its associated complications, partly by regulating the turnover of the long isoform of Leptin receptors (LepRb). Loss of SEL1L in POMC-expressing neurons attenuates leptin signaling and predisposes mice to HFD-associated pathologies including fatty liver, glucose intolerance, insulin and leptin resistance. Mechanistically, nascent LepRb, both wildtype and disease-associated Cys604Ser variant, are misfolding prone and bona fide substrates of SEL1L-HRD1 ERAD. In the absence of SEL1L-HRD1 ERAD, LepRb are largely retained in the ER, in an ER stress-independent manner. This study uncovers an important role of SEL1L-HRD1 ERAD in the pathogenesis of central leptin resistance and leptin signaling.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Hypothalamic neurons play key roles in regulating metabolic processes and energy balance, adapting to changes in diet and body weight1. In conditions such as diet-induced obesity (DIO), the hypothalamus may undergo maladaptive changes that exacerbate weight gain and insulin resistance, contributing to the development and progression of type-2 diabetes2,3,4,5,6,7,8. Proteostasis within the endoplasmic reticulum (ER) is essential for maintaining cellular function and overall physiological homeostasis9,10,11,12,13,14,15. It has been proposed that the hypothalamic unfolded protein response (UPR), an ER quality-control mechanism activated by the buildup of misfolded proteins in the ER, may significantly contribute to the development of DIO and type-2 diabetes through the regulation of inflammation and leptin resistance16,17,18,19,20,21,22. However, other studies have reported that UPR might have a protective role in similar experimental settings23,24. Consequently, the significance and underlying mechanisms of ER quality control pathways in DIO remain a subject of debate.\n\nIn addition to the UPR, ER-associated degradation (ERAD) is a constitutively active and highly conserved mechanism that targets unfolded or misfolded proteins in the ER for degradation by the cytosolic proteasomes25,26,27,28,29,30,31,32. Among the many putative ERAD complexes, the SEL1L-HRD1 protein complex represents the most evolutionarily conserved branch26,28,32,33,34. In this complex, SEL1L is an obligatory cofactor for the E3 ligase HRD1 by mediating substrate recruitment and stabilizing HRD1 ERAD complex29,30,31,35,36,37. Recent studies using cell type-specific SEL1L or HRD1 knockout mouse models have highlighted the (patho-)physiological importance of the SEL1L-HRD1 ERAD pathway in a substrate-specific manner38,39,40,41,42,43,44,45. Particularly relevant to this study, we previously showed that SEL1L-HRD1 ERAD regulates water balance and food intake by controlling the maturation of prohormones proAVP and POMC, respectively42,43. POMC neuron-specific Sel1L deletion results in hyperphagia and age-associated obesity starting around 13 weeks of age when fed a low-fat chow diet42. Given the importance of POMC neurons in maintaining energy homeostasis under various nutritional status, one outstanding question is the relevance and significance of SEL1L-HRD1 ERAD in POMC neurons under pathophysiological conditions, such as DIO.\n\nHere, we show that SEL1L-HRD1 ERAD in POMC-expressing neurons at the arcuate nucleus (ARC) of the hypothalamus controls DIO pathogenesis and leptin sensitivity via regulating the turnover of the long isoform of LepR (LepRb), responsible for leptin signaling46,47,48,49. POMC-specific Sel1L deficient (Sel1LPOMC) mice are hypersensitive to high fat diet (HFD) feeding. We further show that nascent LepRb protein is unstable and degraded by SEL1L-HRD1 ERAD, a process required for functional LepRb to reach the cell surface.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Given that the SEL1L-HRD1 protein complex is constitutively expressed in the ARC of the hypothalamus42, we investigated whether its expression in this region is regulated in response to overnutrition. To this end, we placed transgenic mice expressing the eGFP transgene under the control of POMC gene promoter42,50 on 60% HFD consisting of 60% calories derived from fat for 1 or 8 weeks. As expected, HFD feeding reduced the expression of Pomc, Npy and Agrp genes (Supplementary Fig.\u00a01a) and increased the protein levels of POMC derivatives \u03b2-Endorphin and \u03b1-melanocyte-stimulating hormone (\u03b1-MSH) (Supplementary Fig.\u00a01b\u2013e). Moreover, HFD feeding enhanced neuronal activity in hypothalamic paraventricular nucleus (PVH) region as measured by nuclear c-FOS following both 1- and 8-week HFD (Supplementary Fig.\u00a01d, e).\n\nOne-week HFD significantly induced Hrd1 mRNA level in the ARC, but not Sel1L mRNA level, while 8-week HFD feeding had no such effect (Fig.\u00a01a). At the protein levels, both SEL1L and HRD1 proteins, were elevated in the ARC after 1-week HFD, and returned to the basal levels after 8-week HFD (Fig.\u00a01b, c). Confocal microscopic analysis further showed the upregulation of SEL1L and HRD1 expression in the POMC-expressing neurons upon 1-week HFD, while returned to the basal level following 8-week HFD (Fig.\u00a01d\u2013g). In non-POMC neurons, HRD1 protein level was transiently upregulated, but not SEL1L (Fig.\u00a01d\u2013g). Hence, SEL1L-HRD1 expression in POMC-expressing neurons are responsive to acute, but not chronic, HFD feeding.\n\na Quantitative PCR (qPCR) analysis of Sel1L and Hrd1 mRNA levels in the ARC of the C57BL/6\u2009J male mice fed on NCD, 1w- and 8w-HFD (n\u2009=\u20094 mice per group). b, c Representative images (b) and quantitation (c) of Western blot of SEL1L and HRD1 proteins in the ARC of the C57BL/6\u2009J male mice fed on NCD, 1w- and 8w-HFD (n\u2009=\u200913 mice per group). d\u2013g Representative image and quantitation of IF staining of SEL1L (d, e) and HRD1 (f, g) in the ARC of C57BL/6\u2009J POMC-eGFP transgenic male mice fed on NCD, 1w- and 8w-HFD. Yellow arrows, GFP+ POMC neurons; white arrowheads, non-POMC neurons. e NCD, n\u2009=\u2009372, 280 for POMC and non-POMC neurons; 1w-HFD, n\u2009=\u2009335, 210 for POMC and non-POMC neurons; 8w-HFD: n\u2009=\u2009350, 210 for POMC and non-POMC neurons. g NCD, n\u2009=\u2009199, 376 for POMC and non-POMC neurons; 1w-HFD, n\u2009=\u2009301, 284 for POMC and non-POMC neurons; 8w-HFD: n\u2009=\u2009225, 211 for POMC and non-POMC neurons. NCD for normal chow diet; HFD for high fat diet; ARC, arcuate nucleus. arb. units, arbitrary units. Values, mean\u2009\u00b1\u2009SEM. n.s., not significant; p values are as indicated unless ****p\u2009<\u20090.0001 by one-way ANOVA followed by Tukey\u2019s multiple comparisons test (a, c, e, g). Source data are provided as a Source Data file.\n\nTo delineate the importance of hypothalamic ERAD in DIO, we next examined the phenotypes of Sel1LPOMC mice generated by crossing Sel1Lf/f with the POMC-Cre mouse line42. The mice were subjected to an 8-week HFD or chow diet starting from 5 weeks of age. In line with our previous findings, Sel1LPOMC mice grew comparably to WT littermates on chow diet for the first 13 weeks of age42 (Fig.\u00a02a). By contrast, soon after placed on HFD, Sel1LPOMC mice, both sexes, gained significantly more body weight than WT littermates (Fig.\u00a02a). Body composition analysis showed that fat content was significantly increased in Sel1LPOMC mice, reaching over 50% of body mass after 8-week HFD with more lipid deposition in the livers, and both white and brown adipose tissues (WAT and BAT) (Fig.\u00a02b, c and Supplementary Fig.\u00a02a). HFD-fed Sel1LPOMC mice became highly glucose intolerant and insulin resistant (Fig.\u00a02d, e), with elevated ad libitum and fasting blood glucose (Fig.\u00a02f) as well as ad libitum insulin levels (Fig.\u00a02g). In addition, Sel1LPOMC mice exhibited elevated levels of glucagon and corticosterone, while their rectal temperature 2 degrees Celsius lower compared to WT littermates (Supplementary Fig.\u00a02b\u2013d). Hence, we concluded that ERAD-deficient mice in POMC-expressing neurons are susceptible to DIO and its pathologies.\n\na Growth curve of Sel1Lf/f and Sel1LPOMC male (left) and female (right) mice fed NCD or HFD (NCD: n\u2009=\u200924 male and 16 female Sel1Lf/f mice, n\u2009=\u20099 male and 9 female Sel1LPOMC mice; HFD: n\u2009=\u200921 male and 10 female Sel1Lf/f mice, n\u2009=\u200918 male and 9 female Sel1LPOMC mice). b Body composition of Sel1Lf/f and Sel1LPOMC male mice after 8w-HFD (n\u2009=\u20094 Sel1Lf/f and n\u2009=\u20097 Sel1LPOMC). c H&E images of peripheral tissues from male mice fed HFD for 8 weeks (n\u2009=\u20093 mice per group). iWAT inguinal white adipose tissue, gWAT gonadal white adipose tissue, BAT brown adipose tissues. d, e GTT (d) and ITT (e) in 8w-HFD male mice fasted for 16 or 6\u2009hrs prior to glucose (2\u2009g/kg body weight) or insulin (1\u2009unit/kg body weight) injections, respectively (n\u2009=\u20096 mice per group). f Serum glucose levels in 8w-HFD male mice either under ad-lib or 6h-fasting (ad-lib: n\u2009=\u200910 mice per group; fasting: n\u2009=\u20097,8 mice for Sel1Lf/f and Sel1LPOMC). g Ad-lib insulin levels in 8w-HFD male mice (n\u2009=\u20096, 5 mice for Sel1Lf/f and Sel1LPOMC). NCD for normal chow diet; HFD for high fat diet. Values, mean\u2009\u00b1\u2009SEM. n.s. not significant; p values are as indicated unless ****p\u2009<\u20090.0001 by two-way ANOVA followed by Tukey\u2019s multiple comparisons test (a) or Bonferroni\u2019s multiple comparisons (b, d\u2013f), or two-tailed Student\u2019s t-test (g). Source data are provided as a Source Data file.\n\nWe next explored the possible mechanism underlying the susceptibility to DIO in Sel1LPOMC mice. Sel1LPOMC mice consumed ~ 40% more food daily, i.e., hyperphagia, at both 1- and 8-week HFD (Fig.\u00a03a). To demonstrate the causal link between food intake and weight gain, we performed pair feeding (providing the same amount of the food consumed by WT littermates) after 8 weeks of ad libitum HFD feeding. Sel1LPOMC mice showed rapid weight gain under ad libitum; however, their weight gain significantly slowed down with pair feeding and returned to previous levels when placed back on ad libitum HFD (Fig.\u00a03b). Moreover, weight gain of Sel1LPOMC mice was comparable to that of WT littermates with pair-feeding at the beginning of HFD feeding at 5 weeks of age (Fig.\u00a03c). We then tested whether hyperphagia of Sel1LPOMC mice is caused by leptin resistance. Hyperleptinemia via leptin injection induces body weight loss in WT mice, but not Sel1LPOMC mice (Fig.\u00a03d, e). Indeed, unlike WT mice, Sel1LPOMC mice continued to gain body weight following leptin injection (Fig.\u00a03e). This difference in body weight gain was likely due to the differences in food intake in response to leptin injection (Fig.\u00a03f), pointing to leptin resistance in Sel1LPOMC mice. Indeed, Sel1LPOMC mice developed increasingly severe hyperleptinemia with HFD feeding (Fig.\u00a03g). Hence, we concluded that ERAD deficiency in POMC-expressing neurons triggers hyperphagia and leptin resistance.\n\na Daily food intake of Sel1Lf/f and Sel1LPOMC male mice at 1w- and 8w-HFD (1w-HFD: n\u2009=\u200911, 9 mice for Sel1Lf/f and Sel1LPOMC; 8w-HFD: n\u2009=\u20099, 11 mice for Sel1Lf/f and Sel1LPOMC). b Growth of male Sel1LPOMC mice under 8-week ad libitum, 8-week pair feeding and 1-week ad libitum on HFD (n\u2009=\u20093 mice, blue dots). Male Sel1Lf/f mice fed ad libitum with the same diets were included as controls (n\u2009=\u20093 mice, black dots). c Growth of Sel1Lf/f male mice ad libitum and Sel1LPOMC male mice either ad libitum or pair-feeding of HFD starting from 5 weeks of age (ad libitum: n\u2009=\u200911, 8 mice for Sel1Lf/f and Sel1LPOMC; pair feeding: n\u2009=\u20093 Sel1LPOMC). d, e, f Diagram of leptin sensitivity test (d). 12-week-old male mice on HFD were injected daily i.p. with vehicle PBS and then PBS or leptin (2\u2009mg/kg body weight) for 3 days. e, f Body weight change (e), average daily food intake (f) following 3 daily vehicle or leptin injections of the male mice (PBS: n\u2009=\u20094 mice per group; leptin: n\u2009=\u20096, 4 mice for Sel1Lf/f and Sel1LPOMC). Body weight change was calculated by end point body weights minus starting point body weights. g Serum leptin levels in male mice fed on NCD, 1w- and 8w-HFD (NCD: n\u2009=\u200913, 11 mice for Sel1Lf/f and Sel1LPOMC; 1w-HFD: n\u2009=\u20097, 8 mice for Sel1Lf/f and Sel1LPOMC; 8w-HFD: n\u2009=\u20095, 8 mice for Sel1Lf/f and Sel1LPOMC). NCD for normal chow diet; HFD for high fat diet. Values, mean\u2009\u00b1\u2009SEM. n.s. not significant; p values are as indicated unless ****p\u2009<\u20090.0001 by two-way ANOVA followed by Bonferroni\u2019s multiple comparisons test (a, b, e, f) or Tukey\u2019s multiple comparisons test (c, g). Source data are provided as a Source Data file.\n\nTo further establish the effect of leptin resistance in ERAD deficiency-associated DIO, we next performed parabiosis where two littermates were surgically stitched together to allow the sharing of the circulation (Fig.\u00a04a). Following two weeks of recovery on chow diet, the parabionts were placed on HFD for 8 weeks (Fig.\u00a04a). Two control parabionts, WT:WT (Group I) and Sel1LPOMC:Sel1LPOMC (Group II), gained weight as expected with the latter pair becoming obese (Fig.\u00a04a, b). By contrast, in WT:Sel1LPOMC (Group III) parabionts, body weight gain for WT mice was attenuated compared to WT mice in WT:WT (Group I) parabionts, while body weight gain for Sel1LPOMC mice was comparable to that of Sel1LPOMC:Sel1LPOMC parabionts (Group II) (Fig.\u00a04a, b). Body compositions (i.e., lean vs. fat) in parabionts were not affected by the partner (Fig.\u00a04c). Moreover, serum leptin and insulin levels were highly elevated in Sel1LPOMC mice, but unaltered in WT mice regardless of the partners (Fig.\u00a04d, e), likely due to short half-life of circulating hormones as previously reported51,52. Overall, these data suggested that Sel1LPOMC mice are defective in responding to circulating leptin.\n\na Schematic diagram for the experimental design (left) and pictures of Sel1Lf/f and Sel1LPOMC female parabionts following 8-week HFD (n\u2009=\u20093, 1, 5 pairs in groups I-III). b\u2013e Body weights (b), body composition (c), serum leptin (d) and insulin (e) levels in female parabionts before and after parabiosis and 8-week HFD (n\u2009=\u20096, 2, 5 mice in groups I\u2013III). NCD for normal chow diet; HFD for high fat diet. Values, mean\u2009\u00b1\u2009SEM. n.s. not significant; p values are as indicated unless ****p\u2009<\u20090.0001 by two-way ANOVA followed by Bonferroni\u2019s multiple comparisons test (b) or Tukey\u2019s multiple comparisons test (c\u2013e). Source data are provided as a Source Data file.\n\nWe next asked how POMC-specific SEL1L-HRD1 ERAD regulates leptin sensitivity. As leptin induces phosphorylation of STAT3 (pSTAT3), we examined the levels of pSTAT3 in POMC neurons following leptin challenge. To visualize POMC neurons, we generated Sel1LPOMC mice with POMC-eGFP reporter (Sel1LPOMC;POMC-eGFP). HFD feeding progressively blunted leptin-induced pSTAT3 in POMC neurons of the ARC region of WT mice, but to a much greater extent, in Sel1LPOMC mice (Fig.\u00a05a\u2013d and Supplementary Fig.\u00a03). In keeping with the notion that pSTAT3 a critical transcription factor for the Pomc gene53, hypothalamic Pomc mRNA expression was markedly decreased in Sel1LPOMC mice with HFD compared to WT littermates (Fig.\u00a05e). Western blot analysis of pSTAT3 of the ARC region of Sel1LPOMC mice also showed a greater reduction of the percent of pSTAT3 following HFD feeding (Fig.\u00a05f, g). Thus, our data suggested that SEL1L-HRD1 ERAD is required for leptin signaling in POMC-expressing neurons.\n\na\u2013d Representative immunofluorescence (IF) staining of pSTAT3 (red) and eGFP (green) in Sel1Lf/f;Pomc-eGFP and Sel1LPOMC;Pomc-eGFP mice under NCD (a), 1w-HFD (b) and 8w-HFD (c). Mice were fasted overnight followed by i.p. injection with leptin (2\u2009mg/kg body weight), with quantitation of percentage of pSTAT3+ POMC neurons shown in (d). Mice injected with PBS were included as negative controls (Supplemental Fig.\u00a03). Yellow arrows, pSTAT3+ POMC neurons; white arrowheads, pSTAT3- POMC neurons. (NCD: n\u2009=\u20094 mice per genotype; 1w-, 8w-HFD and PBS: n\u2009=\u20093 mice per genotype). e Quantitative PCR (qPCR) analysis of Pomc mRNA levels in ARC of Sel1Lf/f and Sel1LPOMC male mice at 8w-HFD (n\u2009=\u20093 mice per group). f, g Representative Western blot analysis (f) and quantitation (g) of leptin-induced pSTAT3 in ARC of Sel1Lf/f and Sel1LPOMC mice under NCD or 8w-HFD (NCD: n\u2009=\u20092 mice per genotype for PBS, n\u2009=\u20094 mice per genotype for leptin; 8w-HFD: n\u2009=\u20094 mice per group). NCD for normal chow diet; HFD for high fat diet; ARC, arcuate nucleus. arb. units, arbitrary units. Values, mean\u2009\u00b1\u2009SEM. n.s. not significant; p values are as indicated unless ****p\u2009<\u20090.0001 by two-way ANOVA followed by Bonferroni\u2019s multiple comparisons test (d, e, g). Source data are provided as a Source Data file.\n\nGiven the reported role of UPR in DIO pathogenesis16,18,19,20,21, we next asked whether ERAD deficiency activates UPR and if so, to what extent. There was no detectable activation of the PERK pathway as measured by phosphorylation of PERK and its downstream phosphorylation of eIF2\u03b1 in the ARC of Sel1LPOMC mice (Fig.\u00a06a and Supplementary Fig.\u00a04a). Phosphorylation of IRE1\u03b1, on the other hand, was moderately elevated in the ARC of Sel1LPOMC mice, so was the splicing of Xbp1 mRNA (a downstream effector of IRE1\u03b1) as well as the ER chaperone BiP (Fig.\u00a06b, c and Supplementary Fig.\u00a04a\u2013d). In vitro, treatment with an ER stress inducer thapsigargin (Tg) induced strong ER stress, but failed to affect leptin signaling in WT HEK293T cells transfected with mouse LepRb (mLepRb) (Fig.\u00a06d and Supplementary Fig.\u00a04e), indicating that UPR is not sufficient to induce leptin resistance. This finding contrasts with previous reports that have causally linked UPR to leptin resistance16,18,19,20,21.\n\na\u2013c Representative Western blot analysis and quantitation of the PERK pathway (a, n\u2009=\u20096 mice per group), and phostag-gel (P-T) analysis of IRE1\u03b1 phosphorylation (b, n\u2009=\u20093 mice per group), BiP levels (b, n\u2009=\u20096 mice per group), and reverse transcriptase PCR (RT-PCR) analysis and quantitation of Xbp1 mRNA splicing (c, n\u2009=\u20095, 4 mice for Sel1Lf/f and Sel1LPOMC) in the ARC of Sel1Lf/f and Sel1LPOMC mice on 8w-HFD. Livers from male mice treated with tunicamycin (TM, 1\u2009mg/kg, i.p.) for 24 hrs (Liver_TM), liver lysates treated with Lambda protein phosphatase (Liver_TM w/ \u03bbPP), and livers under basal condition (Liver_CON) were included as controls. d Representative Western blot and quantitation showing the lack of effect of ER stress on leptin signaling. WT HEK293T transfected with mLepRb were treated with thapsigargin (Tg) at the indicated concentration for 4\u2009hrs and leptin for 30\u2009min in serum free medium, followed by Western blot and RT-PCR analyses of UPR activation (n\u2009=\u20095, 2, 2 independent cell samples per group for Western blot, P-T gel, and RT-PCR). e Representative confocal images and quantitation of the number of GFP+ POMC neurons in Sel1Lf/f;POMC-eGFP and Sel1LPOMC;POMC-eGFP mice on 8w-HFD (n\u2009=\u20096, 9 mice for Sel1Lf/f;Pomc-eGFP and Sel1LPOMC;Pomc-eGFP). f, g Representative Western blot analysis (f) and quantitation (g) of inflammatory markers in the ARC of Sel1Lf/f and Sel1LPOMC male mice fed on 8w-HFD (n\u2009=\u20093 mice per group). arb. units, arbitrary units. HFD for high fat diet; ARC, arcuate nucleus. arb. units, arbitrary units. Values, mean\u2009\u00b1\u2009SEM. n.s., not significant; p values are as indicated by two-way ANOVA followed by Tukey\u2019s multiple comparisons test (d) or two-tailed Student\u2019s t-test (a\u2013c, e, g). Source data are provided as a Source Data file.\n\nWe found no significant POMC neuronal loss in the ARC of Sel1LPOMC;POMC-eGFP mice compared to WT littermates following 8-week HFD (Fig.\u00a06e). Inflammatory markers were largely comparable in the ARC of Sel1LPOMC mice vs. WT littermates as measured by phosphorylation and protein levels of c-Jun N-terminal Kinase (JNK) as well as protein levels of I kappa B alpha (I\u03baB\u03b1) (Fig.\u00a06f\u2013g). Astrogliosis in the ARC regions was comparable between WT and Sel1LPOMC mice, as demonstrate by Western blot and immunofluorescence staining of the astrocyte marker Glial Fibrillary acidic protein (GFAP) and/or microglia marker Ionized calcium-binding adaptor molecule 1 (IBA1) (Fig.\u00a06f, g and Supplementary Fig.\u00a04f). Taken together, we concluded that Sel1L deficiency in POMC-expressing neurons triggers leptin resistance independently of UPR, neuronal cell death, and inflammation.\n\nThe aforementioned data suggested that SEL1L-HRD1 ERAD regulates leptin sensitivity upstream of STAT3. To further explore the underlying mechanism, we generated leptin-responsive LepRb-expressing HEK293T cells. In line with decreased leptin sensitivity in vivo, HRD1\u2212/\u2212 HEK293T cells exhibited impaired phosphorylation of JAK2 and STAT3 compared to those in WT cells in response to leptin stimulation (Fig.\u00a07a, b), despite having much higher LepRb protein levels (Fig.\u00a07a\u2013c). Moreover, SEL1L interaction with LepRb in LepRb-transfected cells was markedly enhanced in HRD1\u2212/\u2212 cells compared to that in WT cells (Fig.\u00a07d, e). LepRb was ubiquitinated in an HRD1-dependent manner (Fig.\u00a07f) and was significantly stabilized in HRD1\u2212/\u2212 cells compared to that in WT cells (Fig.\u00a07g). As LepRb is a glycosylated protein, we next performed endoglycosidase H (EndoH) digestion assay to separate the fraction of LepRb that have exited the ER (endoH resistant) or retained in the ER (endoH sensitive). Intriguingly, EndoH resistant fraction of LepRb were significantly reduced by ~40% in HRD1\u2212/\u2212 HEK293T cells compared to WT cells (Fig.\u00a08a). This result was further confirmed using the surface biotinylation assay followed by immunoprecipitation with streptavidin-beads, which also showed reduced amount of surface LepRb in total protein levels by 50% in HRD1\u2212/\u2212 cells compared to that of WT cells (Fig.\u00a08b, c). Moreover, confocal microscopy following immunofluorescence staining showed an altered distribution of LepRb with increased intracellular, but decreased surface, expression in ERAD-deficient cells (Fig.\u00a08d and Supplementary Fig.\u00a05). In the absence of SEL1L-HRD1, LepRb protein was prone to form high molecular weight (HMW) aggregates via disulfide bonds (Fig.\u00a08e). Taken together, our data showed that nascent LepRb protein is unstable and degraded by SEL1L-HRD1 ERAD, a process required for functional LepRb to reach the cell surface.\n\na, b Representative Western blot (a) and quantitation (b) for leptin (100\u2009nM, 30\u2009min)-induced phosphorylation of JAK2-STAT3 in HEK293T transfected with mLepRb-3xFlag in serum-free medium. (n\u2009=\u20095,7,4 independent samples per group for pJAK2, pSTAT3 and mLepRb-3xFLAG). arb. units, arbitrary units. c Representative Western blot and quantitation of mLepRb-3xFLAG protein levels in transfected HEK293T in complete medium (DMEM w/ 10% serum) (n\u2009=\u20098 independent samples per group). arb. units, arbitrary units. d, e Immunoblot analysis following immunoprecipitation (IP) of SEL1L (d) or FLAG (e) from lysates of WT or HRD1-/- HEK293T transfected with mLepRb-3xFlag (n\u2009=\u20092, 3 independent samples for SEL1L-IP and FLAG-IP). f Immunoblot analysis of Ub following denaturing immunoprecipitation (IP) of Flag from lysates of HEK293T transfected with mLepRb-3xFlag (n\u2009=\u20093 independent samples per group). arb. units, arbitrary units. g Protein decay analysis and quantitation (below) of LepRb protein levels in LepRb-3xFlag-expressing HEK293T cells treated with Brefeldin-A and cycloheximide (CHX) for the indicated time periods (n\u2009=\u20094 independent samples per group). Values, mean\u2009\u00b1\u2009SEM. n.s., not significant; p values are as indicated unless ****p\u2009<\u20090.0001 by two-tailed Student\u2019s t-test (c, f) or two-way ANOVA followed by Bonferroni\u2019s multiple comparisons test (b, g). Source data are provided as a Source Data file.\n\na Representative Western blot and quantitation (right) of LepRb protein following treatment with EndoH or PNGase in HEK 293T transfected mLepRb-3xFlag (n\u2009=\u20093 independent samples per group). arb. units, arbitrary units. b, c Measurement of surface mLepRb levels in HEK293T transfected with mLepRb-3xFlag in DMEM with 10% serum (b), or serum-free DMEM treated with leptin for 30\u2009min (c) followed by biotinylation for membrane surface proteins followed by immunoprecipitation by streptavidin-beads (b, n\u2009=\u20093 independent samples per group; c, n\u2009=\u20092 independent samples per group). arb. units, arbitrary units. d Representative confocal IF images of mLepRb-3xFlag in transfected HEK293T cells in DMEM with 10% serum or serum-free DMEM treated with 100\u2009nM leptin for 30\u2009min. Quantitation of percent of surface signals over total shown on the right (10% serum: n\u2009=\u200952 WT and 57 HRD1\u2212/\u2212 cells; serum-free w/leptin: n\u2009=\u200928 cells per genotype). arb. units, arbitrary units. Split channels are shown in Supplementary Fig.\u00a05. e Western blot analyses and quantitation (right) of mLepRb-3xFlag protein in transfected HEK293T cells on reducing and nonreducing SDS-PAGE (n\u2009=\u20093 independent samples per group). arb. units, arbitrary units. Values, mean\u2009\u00b1\u2009SEM. n.s., not significant; p values are as indicated unless ****p\u2009<\u20090.0001 by two-tailed Student\u2019s t-test (a, b, d, e). Source data are provided as a Source Data file.\n\nTo demonstrate the clinical relevance of our findings, we examined whether a subset of LepRb disease variants are SEL1L-HRD1 ERAD substrates. Here, we focused on LepRb mutant C604S, a recessive point mutation identified in two brothers aged 1 and 5 years, both with severe obesity54,55, with unclear mechanism54,56,57,58. C604-C674 forms a disulfide bond in human LepRb corresponding to C602-C672 in mouse (Supplementary Fig.\u00a06a, b)56,57,58. Indeed, leptin treatment triggered very subtle pSTAT3 in WT HEK293T cells expressing C602S mLepRb compared to those expressing WT mLepRb, which was further reduced in HRD1\u2212/\u2212 cells (Supplementary Fig.\u00a06c). Similar to WT mLepRb, C602S mLepRb was stabilized in the absence of HRD1 (Supplementary Fig.\u00a06d). Notably, C602S mLepRb readily formed HMW aggregates in WT HEK293T cells, and to a much greater extent in HRD1\u2212/\u2212 cells (Supplementary Fig.\u00a06e). Hence, we concluded that C604S LepRb variant is also a substrate of SEL1L-HRD1 ERAD.",
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "This study reports an important role of hypothalamic ERAD in maintaining energy homeostasis under nutrient overload conditions. SEL1L-HRD1 ERAD defects in POMC neurons predispose mice to DIO and its pathologies, largely due to leptin resistance and hyperphagia. Moreover, this study identifies a regulatory mechanism for LepRb signaling. Our data show that native LepRb is an endogenous substrate of SEL1L-HRD1 ERAD. This degradation event is crucial for the production of functional LepRb proteins at the cell surface as ERAD deficiency results in LepRb being retained in the ER (Fig.\u00a09). Pointing to the clinical relevance of our findings, human LepRb C604S variant is trapped in the ER and degraded by SEL1L-HRD1 ERAD, leading to reduced expression at the cell surface. Taken together, this study uncovers the importance of SEL1L-HRD1 ERAD in leptin signaling and leptin biology.\n\na In POMC-expressing neurons, SEL1L-HRD1 ERAD constitutively degrades misfolded LepRb, a process to ensure the production of functional LepRb at the cell surface. b The absence of SEL1L-HRD1 ERAD in POMC-expressing neurons causes the accumulation and aggregation of misfolded LepRb, which disrupts the proper folding of nascent LepRb. This impairment in LepRb biogenesis results in severe leptin resistance in mice when on a HFD. Created in BioRender. Mao, H. (2024) BioRender.com/a66y170.\n\nIn our Sel1LPOMC mouse model, the POMC promoter is active at embryonic day 10.5 (E10.5)59, resulting in Sel1L deletion in POMC-expressing progenitors. As these progenitors differentiate into various neuronal types, including LepRb-expressing POMC neurons as well as non-POMC neurons1,60,61,62,63, the impact of SEL1L-HRD1 ERAD in Sel1LPOMC mice may reflect effects on both POMC and non-POMC neurons. Notably, non-POMC neurons, such as nearly a quarter of the hypothalamic AgRP/NPY neurons60 or functionally distinct POMC neurons61,62, could also influence leptin signaling and food intake. Earlier studies have shown that mice with LepRb deficiency in POMC-expressing neurons (using the same POMC-cre line as ours) gain significantly higher body weight in both sexes at 4\u20136 weeks of age on a regular chow diet64,65 or HFD65. However, another study using the inducible POMC Cre-ERT2 line to delete LepRb in POMC neurons of adult mice found systemic insulin and leptin resistance but no significant body weight gain four weeks after tamoxifen administration66. The discrepancies may be attributed to the possible involvement of other non-POMC neurons in the studies using the original POMC-cre line64,65. Therefore, future research is needed to explore the role of SEL1L-HRD1 ERAD in other types of hypothalamic neurons.\n\nSel1L deficiency in POMC neurons is associated with minimal, if any, UPR activation, cell death or inflammation, which is in line with many recent studies of different cell and tissue types40,42,43,44,45,67. Hence, we conclude that the effect of SEL1L-HRD1 ERAD is independent of the IRE1\u03b1 -XBP1 pathway of the UPR and cell death. Furthermore, we recently identified several hypomorphic variants of SEL1L and HRD1 in patients with ERAD-associated neurodevelopmental disorders with infancy onset (EDNI) syndrome68,69. Notably, neither patient fibroblasts nor knockin HEK293T cells carrying these variants exhibited any overt UPR. These findings point to cellular adaptation in response to ERAD deficiency and the accumulation of misfolded proteins. Adaptive mechanisms include upregulation of ER chaperones to increase folding efficiency, expansion of ER volume to dilute misfolded protein concentration, enhanced aggregation and sequestration of misfolded proteins to reduce proteotoxicity, and/or activation of ER-phagy to clear misfolded protein aggregates or damaged ER26,28,32,34,39,70.\n\nPrevious reports have suggested that UPR may play a causal role in leptin resistance due to impaired leptin signaling16,18,71. These studies used ER stress inducers like tunicamycin and thapsigargin which can be fraught with artefacts. Tunicamycin, for example, inhibits glycosylation of glycoproteins72, including LepRb, which may impair leptin signaling directly due to defective glycosylation and functionality of LepRb, rather than UPR activation. Additionally, the high doses of ER stress inducers used in these studies may not be physiologically relevant16,18,71. In our study, thapsigargin treatment induced ER stress responses in a dose-dependent manner but did not alter leptin signaling in WT HEK293T cells transfected with mLepRb, even at a very high level. Thus, these findings suggest that the UPR is unlikely to play a key role in leptin signaling. Further studies are needed to test this model.\n\nThis study demonstrates an important role of SEL1L-HRD1 ERAD in leptin signaling, at least in part via the regulation of the turnover of LepRb protein\u00a0(Fig. 9). We previously showed that SEL1L-HRD1 ERAD is required for the posttranslational maturation of the prohormone POMC in mice on chow diet and that Sel1L deficiency in POMC neurons causes age-associated obesity in mice on chow diet due to the ER retention of POMC42. In DIO mouse models, we found defects in Sel1LPOMC mice occurring upstream of Pomc gene transcription as leptin-induced STAT3 phosphorylation is impaired in the absence of SEL1L-HRD1 ERAD. Further mechanistic studies identified that partial loss-of-function of LepRb results from impaired ER exit of nascent LepRb protein in SEL1L-HRD1 ERAD deficient cells. This study suggests that nascent LepRb proteins are prone to misfolding in the ER, likely due to the formation of multiple disulfide bonds and glycosylation. If not cleared efficiently, misfolded LepRb with highly reactive cysteine thiols may promote the formation of aggregates with aberrant inter- or intra-molecular disulfide-bonds42,43,44. Consequently, they rely on SEL1L-HRD1 ERAD to create an ER environment conducive for the proper folding and conformation of nascent LepRb\u00a0(Fig. 9). In this study, due to the lack of an antibody for LepRb detection in vivo, we conducted extensive mechanistic investigations in vitro instead. We acknowledge the limitations of the in vitro system used, which may not fully represent in vivo neuronal systems. Nevertheless, the extensive use of in vivo and in vitro approaches in this study provides strong support for our overall conclusions.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "All animal experiments were performed in compliance with University of Michigan (Ann Arbor, MI) and University of Virginia Institutional Animal Care and Use Committee (#PRO00006888 and # 4459) guidelines.\n\nPOMC-specific Sel1L-deficient mice (Sel1LPOMC) and control littermates (Sel1Lf/f) on the C57BL/6\u2009J background were generated by crossing Sel1Lfl/fl mice on the C57BL/6\u2009J background36 with mice on the POMC-promoter\u2013driven Cre line on the C57BL/6\u2009J background (B6.FVB-Tg(Pomc-cre)1Lowl/J, JAX 010714)64. Sel1LPOMC mice were further crossed with Pomc-eGFP reporter mice on the C57BL/6\u2009J background (C57BL/6J-Tg(POMC-EGFP)1Low/J, JAX 009593)73 to generate Sel1LPOMC;Pomc-eGFP and control Sel1Lf/f;Pomc-eGFP littermates. WT B6 mice were purchased from JAX and bred in our mouse facility. Mice were maintained on a normal chow diet (13% fat, 57% carbohydrate and 30% protein, PicoLab Rodent Diet 5L0D) and placed on a high-fat diet (HFD, calories provided by 60% fat, 20% carbohydrate and 20% protein, Research Diet D12492) from 5 weeks of age for 1 or 8 weeks. All mice were housed in a temperature-controlled room (20\u2009\u00b0C\u201323\u2009\u00b0C) with a 12-hr light/12-hr dark cycle and 40\u201360% humidity.\n\nTo perform daily food intake measurement, mice were first acclimatized to single housing 24\u2009h before the experiment. Daily food intake was measured 1\u2009h before the onset of the dark cycle each day. For the pair-feeding at later stage of HFD feeding, Sel1LPOMC and WT littermates had free access to HFD for eight weeks and were then single housed and fed ~2.5\u2009g HFD, which was determined by the average of daily food intake of WT littermates, at the start of the dark cycle. For the pair-feeding at early stage of HFD feeding, 5-week-old Sel1LPOMC mice were split into two groups: One group of Sel1LPOMC and WT littermates had continuous free access to food; the other group of Sel1LPOMC mice (pair-fed) was fed ~2.5\u2009g at the start of dark hrs. Weekly bodyweight gains were monitored.\n\nTwelve-week-old mice were acclimatized to single housing with HFD and intraperitoneal (i.p.) injection (PBS) for three days, followed by three daily leptin injections (2\u2009mg/kg body weight, R&D systems; catalog 498-OB-05M) 1\u2009h before the onset of dark cycle as described42. Body weight and food intake were monitored daily during the treatment period. For pSTAT3 staining, mice were injected i.p. with 2\u2009mg/kg leptin, followed by overnight fasting. Mice were anesthetized by isoflurane for fixation-perfusion 30\u2009min after injection.\n\nParabiosis surgery was performed as previously described74,75. The 7-week-old female mice from the same cage were anesthetized via isoflurane vaporizer and shaved thoroughly from 1\u2009cm above the elbow to 1\u2009cm below the knee on the side to be connected. On the shaved side, a longitudinal skin incision was performed followed by the gentle detachment of skin from the subcutaneous fascia to create free skin with 0.5\u2009cm width. Mice were then joined by tightly attaching the olecranons and the knees. The skin was sutured ventrally from the elbow towards the knee using continuous dorsal stitches. After 2-week recovery, mice were fed on HFD for 8 weeks. Body weights were monitored and serum insulin and leptin levels were measured.\n\nBriefly, blood was collected from anesthetized mice via cardiac puncture, transferred to 1.5\u2009ml microcentrifuge tubes, kept at room temperature for 30\u2009min prior to centrifugation at 2000\u2009g for 15\u2009min. Serum was aliquoted and stored at \u221280\u2009\u00b0C until analysis. For brain microdissection, Adult Mouse Brain Slicer Matrix (BSMAA001-1, Zivic Instruments) was used to collect coronal brain slices containing ARC region with further microdissection to obtain ARC-enriched region. All tissues were snap-frozen in liquid nitrogen and stored at \u221280\u2009\u00b0C before use.\n\nMice were anesthetized with isoflurane, perfused with PBS followed by 4% paraformaldehyde (PFA) (Electron Microscopy Sciences; catalog 19210) for fixation. Brains were then postfixed in 4% PFA for overnight at 4\u2009\u00b0C, dehydrated in 15% sucrose overnight at 4\u2009\u00b0C and then 30% sucrose overnight at 4\u2009\u00b0C, and sectioned (30\u2009\u03bcm) on a cryostat (Microm HM550 Cryostat, Thermo Fisher Scientific). The sections were stored in DEPC-containing anti-freezing media (50% 0.05\u2009M sodium phosphate pH 7.3, 30% ethylene glycol, 20% glycerol) at \u221220\u2009\u00b0C. Different brain regions were identified using the Paxinos and Franklin atlas. Counted as distance from bregma, the following coordinates were used: PVN (\u20130.82\u2009mm to \u20130.94\u2009mm) and ARC (\u20131.58\u2009mm to \u20131.7\u2009mm).\n\nFrozen tissue or cells were homogenized by sonication in lysis buffer (150\u2009mM NaCl, 50\u2009mM Tris pH 7.5, 10\u2009mM EDTA, 1% Triton X-100) with freshly added protease inhibitors (Sigma; catalog P8340), phosphatase inhibitors (Sigma; catalog P5726) and 10\u2009mM N-ethylmaleimide (Thermo Scientific; catalog 23030). Lysates were incubated on ice for 30\u2009min followed by centrifugation (13,000\u2009g, 10\u2009min at 4\u2009\u00b0C). Supernatants were collected and analyzed for protein concentration using Bradford assay (Bio-Rad; catalog 5000006). For denaturing SDS-PAGE, samples were further supplemented with 1\u2009mM DTT and denatured at 95\u2009\u00b0C for 5\u2009min in 5x SDS sample buffer (250\u2009mM Tris-HCl pH 6.8, 10% sodium dodecyl sulfate, 0.05% Bromophenol blue, 50% glycerol, and 1.44\u2009M \u03b2-mercaptoethanol). For non-reducing SDA-PAGE, samples were prepared in 5x non-denaturing sample buffer (250\u2009mM Tris-HCl pH 6.8, 10% sodium dodecyl sulfate, 0.05% bromophenol blue, 50% glycerol). For phostag gel analysis based on phos-tag system as described76,77, SDS-PAGE gel was supplemented with 50\u2009\u03bcM MnCl2 (Sigma) and 25\u2009\u03bcM phostag reagent (NARD Institute; catalog AAL-107). Protein lysates from the livers of mice treated with tunicamycin (TM, 1\u2009mg/kg, i.p.) for 24\u2009h was used as a positive control for UPR. For phosphatase treatment, 100\u2009\u00b5g tissue lysates were incubated with 1\u2009\u00b5l lambda phosphatase (\u03bbPPase, New England BioLabs; catalog P0753S) in 1\u00d7 PMP buffer (New England BioLabs; catalog B0761S) with 1\u2009mM MnCl2 (New England BioLabs; catalog B1761S) at 30\u2009\u00b0C for 30\u2009min. Reaction was stopped by adding 5\u00d7 SDS sample buffer and incubated at 90\u2009\u00b0C for 5\u2009min.\n\nAll samples were incubated in 65\u2009\u00b0C for 10\u2009min and run with 15-30\u2009\u03bcg total lysate on SDS-PAGE gel for separation followed by electrophoretic transfer to PVDF membrane (0.45\u03bcm, Millipore; catalog IPFL00010). The blots were incubated in 2% BSA/Tri-buffered saline tween-20 (TBST) with primary antibodies overnight at 4\u2009\u00b0C, washed with TBST followed by 1\u2009hr incubation with goat anti-rabbit or mouse IgG HRP at room temperature. Blots were scanned by the ChemiDoc imaging system (Bio-Rad #1708265 and #12003153). Band density was quantitated using the Image Lab software (v6.0.1, Bio-Rad). Antibodies for Western blot were as follows: SEL1L (rabbit, 1:8000, Abclonal; catalog E112049), HRD1 (rabbit, 1:2000, ABclonal; catalog E15102), GRP78 BiP (rabbit, 1:5000, Abcam; catalog ab21685), HSP90 (rabbit, 1:5,000, Santa Cruz Biotechnology Inc.; catalog sc-7947), HSP70 (rabbit, 1:1000, Santa Cruz Biotechnology Inc.; catalog sc-1060), FLAG (mouse, 1:2000, Sigma-Aldrich; catalog F-1804), IRE1\u03b1 (rabbit, 1:2,000, Cell Signaling Technology; catalog 3294), p-eIF2\u03b1 (rabbit, 1:2000, Cell Signaling Technology; catalog 3597), eIF2\u03b1 (rabbit, 1:2000, Cell Signaling Technology; catalog 9722), p-JNK (mouse, 1:2000, Cell Signaling Technology; catalog 9255), JNK (rabbit, 1:1000, Cell Signaling Technology; catalog 9252), PERK (Rabbit, 1:1000, Cell Signaling Technology; catalog 3192), pSTAT3 (Tyr705) (rabbit, 1:1000, Cell Signaling Technology; catalog 9131), STAT3 (rabbit, 1:1000, Cell Signaling Technology; catalog 9132), pJAK2 (Tyr1007/1008) (rabbit, 1:1000, Cell Signaling Technology; catalog 3771), JAK2 (rabbit, 1:1000, ABclonal; catalog A19629), Tubulin (mouse, 1:5000, Santa Cruz Biotechnology Inc.; catalog sc-5286), I\u03baB\u03b1 (rabbit, 1:1000, Cell Signaling Technology; catalog 9242) and IBA1 (rabbit, 1:1000, Proteintech; catalog 10904-1-AP). Secondary antibodies for Western blot were goat anti-rabbit IgG HRP and goat anti-mouse IgG HRP at 1:5,000 from Bio-Rad.\n\nHEK293T cells were transfected with LepRb-3xFLAG for 48 hrs before snap-frozen in liquid nitrogen. The sample lysates were prepared in NP-40 lysis buffer (50\u2009mM Tris-HCl at pH7.5, 150\u2009mM NaCl, 1% NP-40, 1\u2009mM EDTA) with protease and phosphatase inhibitors, followed by centrifugation at 16,000\u2009\u00d7\u2009g for 10\u2009min. Protein concentrations of supernatants were measured using Bradford before the addition of 1% SDS and 5\u2009mM DTT at 95\u2009\u00b0C for 10\u2009min. Lysates were diluted 1:10 with NP-40 lysis buffer with the final SDS concentration \u22640.1% and incubated with 20\u2009\u03bcl anti-FLAG agarose overnight at 4\u2009\u00b0C with gently rocking. The agarose beads were washed three times with NP-40 lysis buffer with protease and phosphatase inhibitors. The samples were eluted from beads in the SDS sample buffer at 95\u2009\u00b0C for 5\u2009min prior to be subjected for SDS-PAGE and immunoblotting.\n\nFor immunostaining of free-floating brain sections, samples were picked out of anti-freezing buffer followed by 3 washes with PBS. Free-floating sections were simultaneously incubated with primary antibodies in blocking buffer (0.3% donkey serum and 0.25% Triton X-100 in 0.1\u2009M PBS) overnight at 4\u2009\u00b0C. Following three washes with PBS, sections were incubated with secondary antibodies for 2\u2009h at room temperature. Brain sections were then mounted on gelatin-coated slides (Southern Biotech; catalog SLD01-CS). Counterstaining and mounting were performed with mounting medium containing DAPI (Vector Laboratories; catalog H-1200) and Fisherfinest Premium Cover Glasses (Fisher Scientific; catalog 12-548-5\u2009P). For immunostaining in cells, 24\u2009h after transfection of LepRb-3xFLAG constructs, cells were placed on Poly-L-Lysine (Advanced Biomatrix; catalog 5048) coated Millicell EZ SLIDE 8-well glasses (Millipore; catalog PEZGS0816) for 24\u2009h before treatment and fixation. Permeabilization was included and the overall process were the same as described above. Images were captured using the Nikon A1 confocal microscope at the University of Michigan Morphology and Image Analysis Core. To quantify immunoreactivity, identical acquisition settings were used for imaging each brain section from all groups within an experiment. The numbers of immunoreactivity-positive soma analysis and intensity of immunoreaction were quantified in 3D stack volumes after uniform background subtraction using the NIS Elements AR software (Nikon) and FIJI (ImageJ2 version 2.14.0, National Institute of Health, USA).\n\nAntibodies for immunostaining were as follows: HRD1 (rabbit, 1:500, homemade), GRP78 BiP (rabbit, 1:500, Abcam; catalog ab21685), \u03b1-MSH (sheep, 1:2,000, Millipore; catalog AB5087), \u03b2-endorphin (rabbit, 1:2,000, Phoenix Pharmaceuticals; catalog H-022-33, provided by Carol Elias), and GFP (chicken IgY, 1:300, Abcam; catalog ab13970), p-Y705 STAT3 (rabbit, 1:200, Cell Signaling Technology; catalog 9145), GFAP (rabbit, 1:500, Agilent; catalog\u00a0Z033429-2), FLAG (mouse, 1:500, Sigma-Aldrich; catalog F-1804), KDEL (rabbit, 1:500, Novus Biologicals; catalog NBP2-75549), eIF3\u03b7 (goat, 1:500, Santa Cruz Biotechnology; catalog sc-16377).\n\nSecondary antibodies for fluorescent immunostaining (all 1:500) were as follows: Anti-rabbit IgG Alexa Fluor 647; anti-goat IgG Alexa Fluor 488 & 647; anti-sheep IgG Cy5 were from Jackson ImmunoResearch. Donkey anti-mouse IgG Alexa flour 555 was from Invitrogen (catalog A32773) and goat anti-chicken IgY FITC was from Aves Labs (catalog F-1005).\n\nmLepRb cDNA was provided by Dr. Martin Myers at University of Michigan Medical School. The LepRb coding region was amplified by PCR using a primer set containing HindIII and XbaI restriction site at 5\u2019 and 3\u2019 respectively.\n\nF: 5\u2032- CCG AAGCTT ATGATGTGTCAGAAATTCTATGTGGTT-3\u2032\n\nR: 5\u2032- TGC TCTAGA CACAGTTAAGTCACACATCTTATT-3\u2032\n\nBoth PCR products and the backbone vector p3xFLAG-CMV14 were digested using HindIII and XbaI restriction enzymes in the double digestion system from New England BioLabs. For construction of LepRb point mutants, quick change mutagenesis was performed using PFU DNA polymerase (600140, Agilent). The following primers were used for mutagenesis to construct LepRb-C602S:\n\nF: 5\u2032- CCTGCTGGTGTCAGACCTCAGTGCAGTCTATG-3\u2032\n\nR: 5\u2032- CATAGACTGCACTGAGGTCTGACACCAGCAGG-3\u2032\n\nHEK293T cells were were originally obtained from ATCC (catalog CRL-3216) and cultured at 37\u2009\u00b0C with 5% CO2 in DMEM with 10% fetal bovine serum (Fisher Scientific; catalog FB12999102). To generate HRD1-deficient HEK293T cells, sgRNA oligonucleotides designed for human HRD1 (5\u2032-GGACAAAGGCCTGGATGTAC-3\u2032) was inserted into lentiCRISPR v2 (plasmid 52961, Addgene). Cells transfected with empty plasmids without sgRNA were used as wild type control. Cells grown in 10\u2009cm petri dishes were transfected with indicated plasmids using 5\u2009\u03bcl 1\u2009mg/ml polyethylenimine (PEI, Sigma) per 1\u2009\u03bcg of plasmids for HEK293T cells. Cells were cultured 24\u2009h after transfection in medium containing 2\u2009\u00b5g/ml puromycin for 48\u2009h and then in normal growth media.\n\nResults are expressed as the mean\u2009\u00b1\u2009SEM unless otherwise stated. Statistical analyses were performed in GraphPad Prism version 8.0 (GraphPad Software Inc.). Comparisons between the groups were made by unpaired two-tailed Student\u2019s t test for two groups, or one-way ANOVA or two-way ANOVA followed by multiple comparisons test for more than two groups. P value\u2009<\u20090.05 was considered as statistically significant. No data were excluded from the analyses. Sample size was determined by the formula of the power analysis, N\u2009=\u20098(CV)^2[1\u2009+\u2009(1-PC)^2]/(PC)^2, to reach the error\u2009=\u20090.05, Power\u2009=\u20090.80, percentage change in means (PC)\u2009=\u200920%, co-efficient of variation (CV)\u2009=\u200910\u201315% (varies between the experiments). The exact number of mice and cultures were indicated in the figure legend. All experiments were repeated at least twice and/or performed with several independent biological samples, and representative data are shown. Mice in each group were randomly chosen based on the age, genotype and gender. The Investigators were not blinded to allocation during experiments and outcome assessment.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The materials and reagents used are either commercially available or available upon the request, with detailed information included in Methods. The predicted structure of mLepRb is available at AlphaFold ID AF-P48356-F1 [https://alphafold.ebi.ac.uk/entry/P48356]. All data supporting the findings and materials for the manuscript are available within the article and the Supplementary Information.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "We thank Drs. Carol Elias,\u00a0Richard Wojcikiewicz and Martin Myers for reagents; Drs. Peter Arvan, Carol Elias and Daniel Klionsky for critical comments and suggestions, and members of the Qi and Arvan laboratories for comments and technical assistance. This work was supported by NIH grants 1R01DK137794, 1R35GM130292, 1R01DK120047, and American Diabetes Association Career Development (1-12-CD-04) and Innovative Basic Science (1-19-IBS-23) Awards (L.Q.).",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Geun Hyang Kim\n\nPresent address: Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, New York, NY, 10591, USA\n\nDepartment of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48105, USA\n\nHancheng Mao,\u00a0Geun Hyang Kim\u00a0&\u00a0Ling Qi\n\nDepartment of Molecular Physiology and Biological Physics, University of Virginia, School of Medicine, Charlottesville, VA, 22903, USA\n\nLinxiu Pan\u00a0&\u00a0Ling Qi\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.M. and G.H.K. designed the most of experiments and H.M., with the help of G.H.K. and L.P., performed most of the experiments and data analysis. G.H.K. performed parabiosis. H.M., with the help of G.H.K., wrote the methods and figure legends. L.Q. and H.M. wrote the manuscript. All authors have approved the manuscript.\n\nCorrespondence to\n                Ling Qi.",
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+            "section_text": "Mao, H., Kim, G.H., Pan, L. et al. Regulation of leptin signaling and diet-induced obesity by SEL1L-HRD1 ER-associated degradation in POMC expressing neurons.\n                    Nat Commun 15, 8435 (2024). https://doi.org/10.1038/s41467-024-52743-2\n\nDownload citation\n\nReceived: 17 December 2023\n\nAccepted: 19 September 2024\n\nPublished: 29 September 2024\n\nVersion of record: 29 September 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52743-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Nature Cell Biology (2025)",
+            "section_image": []
+        }
+    ]
+}

03d68cf8e193cb35c9be6144c0f8c2e40f012dc558e3da2c1ab952dea9dac6d5/metadata.json

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+{
+    "title": "Mn-inlaid antiphase boundaries in perovskite structure",
+    "pre_title": "Mn-inlaid antiphase boundaries in perovskite structure",
+    "journal": "Nature Communications",
+    "published": "07 August 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51024-2/MediaObjects/41467_2024_51024_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51024-2/MediaObjects/41467_2024_51024_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-51024-2/MediaObjects/41467_2024_51024_MOESM3_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-024-51024-2#Sec15"
+    ],
+    "code": [],
+    "subject": [
+        "Ferroelectrics and multiferroics",
+        "Transmission electron microscopy"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3884985/v1.pdf?c=1723115294000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3884985/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-51024-2.pdf",
+    "preprint_posted": "04 Feb, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Improvements in the polarization of environmentally-friendly perovskite ferroelectrics have proved to be a challenging task. In contrast to traditional methods by complex chemical composition designs, we successfully formed new Mn-inlaid antiphase boundaries in Mn-doped (K,Na)NbO3 thin films using pulsed laser deposition method. Mono- or bi-atomic layer of Mn has been identified to inlay along the antiphase boundaries to balance the charges originated from the deficiency of alkali ions and to induce out-of-plane tensile strain in the KNN films. Thus, rectangular saturated polarization-electric field hysteresis loops have been achieved, with a significantly improved twice remanent polarization of 114 \u03bcC/cm2, which can be comparable to that of typical PZT thin films. Moreover, the Mn occupation at the A-site of the KNN perovskite structure was directly revealed using atomic-scale microstructure and composition analysis. The Mn-inlaid antiphase boundary can further enrich the understanding of perovskite crystal structure and open up new possibilities for the design and optimization of perovskite materials.Physical sciences/Materials science/Condensed-matter physics/Ferroelectrics and multiferroicsPhysical sciences/Materials science/Techniques and instrumentation/Microscopy/Transmission electron microscopy",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplementaryInformation2.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Improvements in the polarization of environmentally-friendly perovskite ferroelectrics have proved to be a challenging task in order to replace the toxic Pb-based counterparts. In contrast to common methods by complex chemical composition designs, we have formed Mn-inlaid antiphase boundaries in Mn-doped (K,Na)NbO3 thin films using pulsed laser deposition method. Here, we observed that mono- or bi-atomic layer of Mn has been identified to inlay along the antiphase boundaries to balance the charges originated from the deficiency of alkali ions and to induce the strain in the KNN films. Thus, rectangular saturated polarization-electric field hysteresis loops have been achieved, with a significantly improved twice remanent polarization of 114\u2009\u03bcC/cm2 with an applied electric field of 606\u2009kV/cm, which can be comparable to that of the typical Pb-based thin films. Moreover, we directly see the Mn occupation at the A-site of KNN perovskite structure using atomic-scale microstructure and composition analysis. The Mn-inlaid antiphase boundary can further enrich the understanding of perovskite crystal structure and give more possibilities for the design and optimization of perovskite materials.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "High-performance perovskite ferroelectrics are central to various electro-mechanical functional devices1,2. However, the use of toxic Pb-based ferroelectrics in high-end applications is being limited due to environmental concerns and the related legislations3. As an eco-friendly alternative, lead-free perovskite potassium sodium niobate (KxNa1-xNbO3, KNN)-based perovskite ferroelectrics materials have been intensively studied since the discovery of a large piezoelectric coefficient d33 value of 416 pC/N for KNN-based ceramics by Saito et al4,5,6. With advancements in the ferroelectric performances of the KNN-based ceramics and single crystals7,8,9,10, a great deal of efforts have also been made to prepare the KNN-based thin films by various deposition techniques11,12,13,14. However, the volatilization of the alkali ions has been identified as a major issue in obtaining high-quality KNN-based films. The inevitable loss of potassium and sodium elements during the vapor or chemical solution-based depositions changes the stoichiometry, resulting in the formation of undesired alkali-deficient secondary phases and defects. Consequently, the KNN-based films exhibit high electrical conduction and poor ferroelectric response15. Previous strategies focused on chemical composition adjustment to compensate for the elemental volatilization, and construction of morphotropic or polytropic phase boundaries to enhance the dielectric and ferroelectric properties of KNN-based thin films16,17,18,19.\n\nRecently, some other methods, such as interface effects, flexible substrates, and defects engineering have been explored to modulate the lattice strain, displacement polarization and electronic structure11,12,20,21,22,23. All those efforts have led to a gradual improvement in the overall ferroelectric performances, however, which still remain significantly inferior to that of Pb-based films and thereby are far from being suitable for practical applications in micro electro-mechanical devices, ferroelectric field-effect transistors, nonvolatile memories and electro-optic devices, etc1,24,25. Material properties are strongly influenced by the microstructure. In terms of crystal structure, the above-mentioned attempts primarily focused on distorting or tilting the lattice within the basic perovskite framework, which has given rise to limited effects in improving the ferroelectric properties. It has been observed that the physical constraints of the underlying substrate and the large unit cell of oxide perovskite structure can lead to the generation and propagation of out-of-phase boundary defects through the entire thickness of the film, especially with special deposition processes12,26. These charged out-of-phase boundaries, originated from the alkali-deficiency, have been identified and found to play an important role in the piezoelectric performances of the NaNbO3 films12.\n\nIn this work, we also harnessed these inherent characteristics (alkali deficiency and out-of-phase boundaries) to create a nanocolumnar structure and then incorporated another element at the boundaries between the KNN nanocolumns to form a perovskite derivative structure. Specifically, the nanostructured KNN-based thin film consists of perovskite KNN nanocolumns that are interspersed with Mn-inlaid antiphase boundaries. Atomic resolution images revealed that the thickness of perovskite KNN slabs varied from a few to tens of nanometers, while the coherent antiphase boundaries consisted of one or two atomic layers enriched with Mn. This strategic incorporation of Mn not only helped to balance the charges originating from alkali ions deficiency, leading to a reduced leakage current but also induced a noticeable out-of-plane tensile strain in the KNN nanocolumns. This strain promoted a high degree of tetragonality, resulting in an improvement in its ferroelectric polarization with a high twice-remanent polarization value of ~114\u2009\u03bcC/cm2 under an applied electric field of 606\u2009kV/cm.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results and discussion",
+            "section_text": "A ceramic target of K0.5Na0.5NbO3 with 2\u2009wt% MnO2 addition (KNN-M) was used for the deposition of KNN-M films on La0.07Sr0.93SnO3 (LSSO)-coated SrTiO3 (STO) (001) substrates via pulsed laser deposition (PLD) method. High-resolution X-ray diffraction (HRXRD) techniques were employed to assess the crystalline quality of the KNN-M films. Figure\u00a01a shows the XRD 2\u03b8-\u03c9 pattern of a representative KNN-M film. Only (00\u2009l) (l\u2009=\u20091, 2, 3) reflection peaks of KNN-M, LSSO and STO are observed, indicating that the film is epitaxially grown along the c-axis direction with a single phase. The rocking curves shown in Fig.\u00a01b exhibit a full-width-at-half-maximum of approximately 0.18\u00b0 for KNN-M (002) and 0.09\u00b0 for LSSO (002), demonstrating a high crystallinity of the films. To examine the epitaxial relationship and the strain state of the samples, the X-ray reciprocal space mappings (RSMs) around the symmetric (002) and asymmetric (\\(\\bar{1}\\)30) reflections of the film are measured in Fig.\u00a01c and d, respectively. The discrete and clear spots in Fig.\u00a01c and d confirm the orientation relationship between the films and STO substrate. In Fig.\u00a01d, the Qx value of the KNN-M (\\(\\bar{1}\\)30) spot obviously deviates from that of STO and LSSO, suggesting a relaxation of the lattice mismatch strain between the film and substrate. Based on the RSM results, the out-of-plane and in-plane lattice parameters of the KNN-M films were calculated to be 4.02\u2009\u00c5 and 3.95\u2009\u00c5, respectively.\n\na\u2013d The XRD 2\u03b8-\u03c9 pattern, rocking curves around STO (002), LSSO (002) and KNN-M (002) and RSMs around the STO (002) and (\\(\\bar{1}\\)03) reflections. e\u2013g The cross-sectional low magnification HAADF image, SAED pattern and atomic resolution HAADF image of the KNN-M thin film. The red triangle (g) points towards the antiphase boundaries and the red rectangular box (g) represents the overlap of the KNN nanocolumns. h, i The planar-view low-magnification ABF image and corresponding EDS mapping of Mn element for the KNN-M thin film, where the Mn enrichment can be observed at the antiphase boundaries. j The planar-view atomic resolution HAADF image of the KNN-M thin film. The yellow and red frame (j) represented the antiphase boundaries and transition dislocation, respectively. The APB in this figure represents antiphase boundaries.\n\nFigure\u00a01e displays a high-angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) image of the KNN-M film along the [010] zone axis. It is evident that the KNN-M thin film consists of vertically aligned nanocolumns in the out-of-plane direction rather than a homogeneous and smooth cross-section. The X-ray energy dispersive spectroscopy (EDS) analysis confirms the presence of Mn enrichment at the column boundaries along the out-of-plane direction, as seen in Supplementary Fig.\u00a01. Additionally, the selected area electron diffraction (SAED) pattern of the KNN-M thin films along the [010] zone axis, as shown in Fig.\u00a01f, reveals the classic single crystal diffraction spots in the out-of-plane direction, while the streaks along the in-plane direction are attributed to the shape effect. This effect arises from the principle that a small thickness in real space corresponds to a large length in reciprocal space, and vice versa. Thus, the reciprocal diffraction pattern further supports the presence of nanocolumns in the specimens, which is consistent with the HAADF image in real space. Atomic resolution HAADF images reveal a series of nanocolumn grains exhibiting the classic single crystal phase perovskite structure, as shown in Fig.\u00a01g. However, it is observed that the lattices of neighboring perovskite KNN nanocolumns undergo a relative shift by half a unit cell length at the phase boundaries along the out-of-plane direction, indicated by the red triangle in Fig. 1g.\n\nDue to the overlap of the nanocolumnar structure in the cross-sectional observation direction, indicated by the red rectangle in Fig.\u00a01g and Supplementary Fig.\u00a02, we studied the microstructure using a plan-view sample without the substrate. We found that the KNN-M films present the \u2018Tetris-like\u2019 microstructure consisting of dense nanocolumns with sizes ranging from a few to tens of nanometers. Through annular bright field (ABF) imaging, we observed that the nanocolumns predominantly exhibit atomic-scale linear dark contrast along both the [001] and [010] directions, as shown in Fig.\u00a01h and Supplementary Fig.\u00a03. The corresponding EDS composition analysis reveals that the nanocolumn areas are composed of K, Na, Mn, Nb and O elements, with apparent Mn enrichment observed in some linear dark contrast areas, as shown in Fig.\u00a01i and Supplementary Fig.\u00a04. The electron energy loss spectroscopy (EELS) analysis indicates that the Mn ions mainly exhibit bivalence (Mn2+) (Supplementary Fig.\u00a05). In addition, the atomic resolution HAADF imaging reveal that the lattices of adjacent perovskite KNN nanocolumns are coherent, but undergo a half-unit cell shift relative to each other across the atomic-scale antiphase boundaries with Mn enrichment, as marked within the yellow rectangle in Fig.\u00a01j. At the ends of these antiphase boundaries, the lattice misregistry of 1/2 unit cell is gradually reduced to zero with continuous atom deficiency along the boundaries and no more Mn element could be detected with EDS analysis, as shown in the red rectangles in Fig.\u00a01j and Supplementary Fig.\u00a06.\n\nFurthermore, we have identified two different atomic configurations for the Mn-inlaid antiphase boundaries, as illustrated in Fig.\u00a02. Figure\u00a02a displays an atomic resolution HAADF image of one type of antiphase boundary structure (referred to as Type-I). In this image, a coherent antiphase feature is observed, where the A-site lattice of one KNN nanocolumn runs into the B-site lattice of the neighboring nanocolumn across a single atomic column. This single atomic column exhibits a brighter contrast than the A-site K/Na atomic columns, but a darker contrast compared to the B-site Nb/O atom columns. The line-scans of image intensity were performed along the single atomic column layer. The line profile of intensity reveals similar atomic column intensities, as shown in Fig.\u00a02b. Based on the uniform image contrasts and corresponding line profiles, it can be inferred that the atomic columns in the Type-I antiphase boundaries have similar chemical compositions. The intensity of the atomic column in HAADF image is approximately proportional to the square of the atomic number (Z2)27, making the HAADF images suitable for composition analysis. However, due to the much smaller scattering cross-section of oxygen, the light element oxygen cannot be observed in the HAADF image. Therefore, we also obtained atomic resolution integrated differential phase contrast (iDPC) images to visualize the distribution of all atoms, as shown in Fig.\u00a02c. The iDPC image reveals only one atomic column at the antiphase boundary, and no separate oxygen atom columns are observed in the plan-view direction. In addition, we observed that the single atomic layer at the antiphase boundaries can gradually translate to a Ruddlesden-Popper like double atomic layer with in-situ beam irradiation in STEM mode, and vice versa (Fig.\u00a02d, Supplementary Figs.\u00a07 and 8).\n\na In-plane atomic resolution HAADF image of the Type-I antiphase boundaries. The red frames in (a) indicate the unit cells of KNN-M near the antiphase boundary, where 1/2 unit cell shift was observed along the boundary. b Line-profile of the single antiphase atomic columns along the antiphase boundary in (a). c The iDPC image of the Type-I antiphase boundaries. Here, only one atomic column at the antiphase boundary can be observed (yellow rectangle frame). d The HAADF image of Type-I antiphase boundaries with in-situ electron irradiations. e\u2013j In-plane HAADF image of the KNN-M thin film containing Type-I antiphase boundaries and corresponding EDS mapping of the K, Na, O, Mn and Nb elements, respectively. k Composite elemental map with Nb (in blue) and Mn (in yellow), where the Mn occupation at the A-site of KNN perovskite structure is directly observed. l Schematic structural model of the Type-I Mn-inlaid antiphase boundaries. It indicates that the single atom column at the Type-I boundary is mainly composed of Mn and O elements. m In-plane atomic resolution HAADF image of the Type-II antiphase boundaries. The red frames in (m) indicate the unit cells of KNN-M near the Type-II antiphase boundary, where 1/2 unit cell shift was observed along the boundary. n Line-profile of the single antiphase atomic columns along the transition boundary in (m). o The iDPC image of the Type-II antiphase boundaries. Here, the double atomic column at the antiphase boundary can be observed (yellow rectangle frame). p Schematic structural model of Type-II antiphase boundaries.\n\nWe performed the EDS mapping to study the atomic resolution composition distribution in Type-I antiphase boundary regions, as shown in Fig.\u00a02e. The color-coded elemental maps of K, Na, O, Mn and Nb are presented in Fig.\u00a02f\u2013j, respectively. It is evident that all the elements are generally distributed uniformly in the KNN-based nanocolumns. However, the atomic columns at the antiphase boundary layer are primarily composed of the Mn and O elements, with a trace amount of K/Na. The composite map of Nb/Mn in Fig.\u00a02k illustrates the relative positions of the Nb and Mn atoms. Based on the elemental distribution maps, we find that the Mn element occupies the A-site of the perovskite lattice. The theoretical bond valence model study also indicated that the substitution of Mn in A-site was more energy stable than B-site, according to the global instability index calculated using Structure Prediction Diagnostic Software28,29, as shown in Supplementary Figs.\u00a09 and 10. Due to the structural changes at the boundaries during in-situ beam irradiation, as shown in Fig.\u00a02d and Supplementary Fig.\u00a07, the Mn enrichment is observed along the boundary, but the location of Mn signals at the antiphase boundaries does not precisely correspond to the atomic-scale image from the EDS analysis. By analyzing the atomic resolution images in both the planar-view and cross-sectional directions (Supplementary Fig.\u00a02) and conducting composition analysis, we have depicted the schematic structure of Type-I antiphase boundaries in Fig.\u00a02l. This structure consists of nanocolumnar perovskite KNN sheets alternating with a single Mn/O sheet running along the c axis direction and the neighboring perovskite slabs relatively shifting by half a unit cell length in both the in-plane and out-of-plane directions. At Type-I antiphase boundaries, the lattice site corresponds to the A-site of one KNN nanocolumn and the O-site of another KNN nanocolumn. Therefore, both the Mn and O atoms can randomly occupy the atom site at the boundaries theoretically.\n\nThe other antiphase boundary (Type-II) is illustrated in Fig.\u00a02m, where the two adjacent KNN phases coherently transit across a double atom layer, and the distance between the neighboring Nb atomic columns at the boundaries is approximately 3/2-unit cells. We observed distinct contrast variations for the atom columns at the transition layers, which differ from the uniform contrast of the atom columns at Type-I antiphase boundaries. The line profile of intensity also exhibits a noticeable difference for the atomic column along one row of transition atomic columns in Fig.\u00a02m, as shown in Fig.\u00a02n. The observed variations in image contrast or line profiles can be attributed to composition differences in each atomic column at Type-II antiphase boundaries. Through atomic resolution composition analysis, we determined that the atom columns with bright contrast primarily consist of Nb atoms, while the dark atom columns are composed of K/Na/Mn (Supplementary Fig.\u00a011). Additionally, we also observed the presence of Mn enrichment at the boundaries. However, the signals are noticeably weaker compared to the distinct single Mn-rich layer observed at Type-I antiphase boundaries (Supplementary Fig.\u00a012). We also acquired the iDPC images for Type-II antiphase boundaries (Fig.\u00a02o), which reveal separated O atom columns that can bond with neighboring Nb atoms to form oxygen octahedra at the boundaries. Furthermore, we found that Type-II antiphase boundaries remained stable under beam irradiation. Based on the aforementioned experimental results, we constructed the schematic structural model of Type-II antiphase boundaries, as depicted in Fig.\u00a02p. For comparison, we also prepared the pure KNN thin films with a similar deposition process. From the cross-sectional view, serials of nanocolumns can also be observed (Supplementary Fig.\u00a013a). Atomic resolution HAADF images revealed no noticeable lattice shifts between two adjacent nanocolumns in the out-of-plane direction (Supplementary Fig.\u00a013b). However, the plan-view image displayed a high density of stacking faults due to the K/Na deficiency (Supplementary Fig.\u00a013c). This deficiency resulted in a high leakage current and prevented the display of ferroelectricity at room temperature12,14.\n\nWe examined the ferroelectric polarization of this film and found that the out-of-plane phase image of the as-grown film without dc bias exhibits homogeneity across the layer with a weak contrast, as shown in Fig.\u00a03a, indicating an ordered out-of-plane polarization component. This result is also attested to the excellent epitaxial quality of the films. The out-of-plane piezoelectric force microscope (PFM) phase can switch to the opposite direction under a voltage of \u22128 V. When a positive dc bias of +8\u2009V was applied, the PFM phase switched to the same direction as the as-grown film, which confirms that the out-of-plane polarization is directed uniformly toward the bottom of the film. The well-saturated ferroelectric polarization-electric field (P\u2013E) hysteresis loops of the KNN-M film were displayed at room temperature (Fig.\u00a03b). The film demonstrated a enhancement in remanent polarization (Pr) with the 2Pr value of ~114\u2009\u03bcC/cm2 when subjected to an applied electric field of 606\u2009kV/cm. The 2Pr value of the KNN-M thin film is larger than the values so far reported for KNN-based films (7.1\u221264.9\u2009\u03bcC/cm2), and comparable to that of typical PZT ferroelectric thin films24. Furthermore, at the temperature of 80\u2009K, no significant variation in Pr was observed, but the breakdown field strength increased to 2700\u2009kV/cm, as shown in Fig.\u00a03c. Supplementary Fig.\u00a014 shows the temperature-dependent ferroelectric behaviors of the KNN-M film, where well-defined P\u2013E hysteresis loops from 25\u2009\u00b0C to 200\u2009\u00b0C were obtained. Additionally, the polarization of the films with different switching cycles for the KNN-M film measured at 10\u2009kHz with an applied electric field of 200\u2009kV/cm is shown in Fig.\u00a03d, the inset shows the P\u2013E hysteresis loops before and after fatigue. It can be seen that the KNN-M film did not show a noticeable attenuation of the P\u2013E hysteresis loops until 107 switching cycles.\n\na The out-of-plane polarization switching of the KNN-M film captured using PFM imaging technique under a dc bias of \u00b1 8\u2009V. b Room-temperature P\u2013E hysteresis loops displayed under different electric fields. c The P\u2013E hysteresis loops under different electric field for the KNN-M thin films at a low-temperature of 80\u2009K. d Polarization fatigue performance (normalized \u00b1 Pr vs the number of switching cycles) for the KNN-M film measured at 10\u2009kHz with an applied electric field of 200\u2009kV/cm, the inset shows the P\u2013E hysteresis loops before and after fatigue.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "Based on the microstructure analysis and the ferroelectric properties, we delve into the mechanisms behind the formation of a nanocolumnar structure with Mn-inlaid antiphase boundaries and the enhancement of ferroelectric polarization. In the case of pure KNN thin films, the appropriate deposition process led to the formation of vertical nanocolumnar structures aligned along the out-of-plane direction. Through atomic resolution investigation of microstructure, it was observed that the KNN nanocolumns maintained the normal perovskite structure. Significant K/Na deficiencies primarily occurred at the boundaries, resulting in lattice rearrangement and the formation of charged out-of-phase boundaries. These crystallographic out-of-phase boundaries, commonly found in epitaxial perovskite films, tend to propagate throughout the entire thickness of the film. Consequently, nanocolumnar KNN thin films exhibited large leakage currents, as depicted in Supplementary Fig.\u00a015 and ref.12. However, consistent with previous studies11,16,19, our experimental results show that the addition of a small concentration of Mn into KNN film was very effective in reducing the leakage current density. According to the above atomic structure imaging and composition analysis, it is assumed that Mn ions mainly fill and compensate A-site vacancies formed by the volatilization of K+/Na+ ions. Due to the small number of A-site K+/Na+ vacancies in the KNN nanocolumns and the significantly smaller radius of the Mn ion than that of A-site K+/Na+ ions, the Mn doping contents is low in the lattice of KNN nanocolumns. In addition, considering its primary valence, Mn2+ can act as a donor-dopant on the A-site of the perovskite lattice. However, in order to balance the charge, the occupancy of Mn2+ on the A-site vacancies was only partial. As a result, partial A-site deficiency still existed in the KNN nanocolumns18. On the contrary, the serious K/Na volatilizations lead to the high-density A-site vacancies at the boundaries. This means that the Mn has more opportunities to fill the A-site vacancies. Therefore, a high density of Mn was accumulated at the phase boundaries to form the atomic-scale Mn-rich boundaries.\n\nIn the case of Type-I antiphase boundaries, both cation Mn2+ and anion O2- can occupy the atomic site at the single antiphase boundary layer. By adjusting the ratio of Mn/O, we can achieve charge balance in optimum experimental conditions (Supplementary Fig.\u00a016). For Type-II antiphase boundaries, which resemble the Ruddlesden-Popper structure, separate atomic columns of cations and anion O2- were observed. The cation atomic sites at these boundaries can be occupied by the Nb5+, Mn2+, K+ and Na+ ions. The ratios among these four cations could also be adjusted experimentally to maintain charge balance (Supplementary Fig.\u00a017). Consequently, the leakage currents can be significantly reduced, which is crucial for the ferroelectric functionality of the thin film. In our study, we also investigated the microstructure of KNN thin films with the Mn concentration of 1\u2009wt% and 5\u2009wt%. It was observed that the sample with a low Mn doping content, such as 1\u2009wt%, exhibited a vertical nanocolumns structure, but the K/Na deficiencies were not fully compensated (Supplementary Fig.\u00a018). As the Mn doping level increased to 5\u2009wt%, the nanoscale Mn enriched phases were observed, which disrupted the epitaxial growth of the thin films, as shown in Supplementary Fig.\u00a019. Furthermore, we studied the room-temperature P\u2013E hysteresis loops of the KNN thin films samples with the Mn doping concentration of 0, 0.7, 1, 1.5, 3, and 5\u2009wt% as shown in Supplementary Fig.\u00a020. High leakage current and low remanent polarization were observed in the pure KNN thin films. The remanent polarization gradually increased and the leakage current decreased as the Mn doping concentration increased. However, the apparent high leakage current and the poor remanent polarization are again visible in the larger 3 and 5\u2009wt% Mn doping samples. More importantly, the specific atomic configurations of the Mn-inlaid antiphase boundaries induced apparent lattice strains, as evidenced by the annular dark field (ADF) images (Supplementary Fig.\u00a021, Supplementary Fig.\u00a022), where the brighter image contrast in ADF image suggests the existence of lattice strains27. Different from the conventional in-plane heteroepitaxial interfacial strain that gradually relax in nanoscale with the growth of KNN thin films20, the high-density boundaries are organized in an ordered manner on a nanoscale and extend vertically throughout the KNN-M thin films. As a result, the apparent strain can be experienced through the entire thin film.\n\nHere, the intuitive evolution of lattice parameters was studied using the geometry phase analysis method. Figure\u00a04a displays an ADF image of the KNN-M thin film, revealing the presence of two types of boundaries. The corresponding relative lattice strain are plotted in Fig.\u00a04b (Exx), Fig.\u00a04c (Eyy) and Fig.\u00a04d (Exy), respectively. The Exx, Eyy and Exy are relative values representing local lattice displacements from the referenced KNN lattice in horizontal, vertical, and shear directions in Fig.\u00a04a. The positive or negative value of E indicates the measured local lattice parameters being larger or smaller than the reference one, respectively. It can be seen that the boundaries exhibit apparent relative lattice strains, mainly perpendicular to their orientation, with no observable shear strain and the relative strains parallel to the boundaries as shown in Fig.\u00a04b\u2013d and Supplementary Fig.\u00a023. The local areas with dark contrast can be observed at the antiphase boundaries in Fig.\u00a04b c, which attributed to atomic structure transition of the single atomic layer to the Ruddlesden-Popper like double atomic layer at the antiphase boundaries with the beam irradiation shown in Fig.\u00a02d and Supplementary Fig.\u00a024. It is well-known that lattice strain can significantly influence the ferroelectric polarization of thin films. The displacement of the center polar B-site cations relative to the corner A-site cations (\u03b4B-A) was used as a measure of the local polarization. Figure\u00a04e displays a colored arrow map of \u03b4B-A, representing the orientation and magnitude of the polarization. In general, the film exhibited apparent displacement polarization, with the larger polarization observed near the boundaries. The different strains presented at each type of boundary resulted in the formation of multidomain structures with a mixture of various orientations of polarization. To investigate the impact of boundary strain on ferroelectric polarization, we conducted phase field simulations. The phenomenological model and relative lattice strain distributions used in these simulations were obtained from Fig.\u00a04a\u2013d, as shown in Supplementary Fig.\u00a025. Specifically, we examined the different lattice strains, namely \u03b5local (0, 0.1, and 0.2), induced by the Mn accumulated boundaries, to elucidate their effects. Our simulations employed the orthorhombic phase with the [110]-oriented polarization of KNN at room temperature16,30, as illustrated in Supplementary Fig.\u00a026, which shows the detailed microstructural evolution. As the applied external electric field increased, the polarizations transitioned from the orthorhombic phase (represented by different colors in Fig.\u00a04f) to the tetragonal phase (indicated by light blue color in Fig.\u00a04g). Importantly, the lattice strains around the boundaries induced larger polarization in both in-plane and out-of-plane directions, as shown in Fig.\u00a04f and Supplementary Figs.\u00a026 and 27. Therefore, the simulated ferroelectric loop demonstrated an increased ferroelectric polarization with the increase of lattice strain (Fig.\u00a04h). Moreover, the local strain field stabilized the tetragonal phase. Thus, the tetragonal phase domains become more stable with an increase in the strain field (see Supplementary Fig.\u00a027). It becomes more difficult to switch the stabilized domains when changing the external electric field, which increases the coercive electric field strength.\n\na In-plane atomic-resolution ADF image of the KNN-M thin film. b\u2013d Maps depicting the relative lattice strains: (b) Exx (in-plane strain), (c) Eyy (out-of-plane strain) and (d) Exy (shear strain). e Colored arrows map of polarizations (\u03b4Nb-K/Na), indicating the polarization orientation of the KNN nanocolumns in (a). f Polarization of KNN-M obtained from phase-field simulation. g Mapping of the piezoelectric response of the KNN-M film under an applied electric field obtained from phase-field simulation. h Calculated P\u2013E curves with different local strains.\n\nIn summary, we prepared and observed the Mn-inlaid antiphase boundaries structure in perovskites. The high-density vertical Mn-inlaid antiphase boundaries show the positivity effect on charge balance, coherent transition to the neighboring KNN nanocolumns and the induction of large strain for the nanocolumnar KNN lattice in the entire KNN-M thin films. Therefore, higher ferroelectric polarizations have been observed for the KNN-based thin films. The Mn-inlaid antiphase boundaries also give a possible structure frame to modulate the various properties of perovskites.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-51024-2/MediaObjects/41467_2024_51024_Fig4_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The ceramic targets of the (K,Na)NbO3 with 0/0.75/1/2/5\u2009wt% MnO2 were fabricated using the conventional solid-state reaction method. High-purity materials, including K2CO3 (99.0%), Na2CO3 (99.8%), Nb2O5 (99.9%) and MnO2 (98.8%), were obtained from Sinopharm Chemical Reagent Beijing Co. Ltd. Initially, the raw materials were accurately weighed according to the stoichiometric ratio of (K0.5Na0.5)NbO3 and then homogenized in a planetary mill for 24\u2009h using ethyl alcohol as the medium. After calcination at 750\u00b0C for 4\u2009h, the resulting powder mixtures were milled, dried, and sieved. Subsequently, additional MnO2 was incorporated into the powders and homogenized for another 24\u2009h in the planetary mill. The resulting powder mixtures were then dried and sieved. Next, the mixed KNN-M powders were compacted into disks by uniaxial pressing at a pressure of 150\u2009MPa for 2\u2009min. Finally, the KNN-M pellets, with a diameter of 25\u2009mm, were sintered at 1120\u2009\u00b0C for 4\u2009h to prepare the ceramic targets.\n\nThe KNN-M films were deposited on the STO (001) substrate buffered with LSSO layer using the PLD technique with a 248\u2009nm KrF excimer laser. The deposition process involved maintaining the substrate temperature at 735\u2009\u00b0C and the O2 pressure at 15\u2009Pa for the LSSO layer, followed by deposition of the KNN-M films at 700\u2009\u00b0C and 30\u2009Pa. The target-substrate distance was set as 5\u2009cm, and the laser energy were kept at 2\u2009J/cm2. The thicknesses of the KNN-M and LSSO were approximately 300\u2009nm and 30\u2009nm, respectively. After deposition, each film underwent in-situ annealing for 15\u2009minutes before being cooled down to room temperature.\n\nThe crystalline phase of films was characterized by HRXRD using Cu K\u03b11 radiation. The samples were mounted in the diffractometer (PANalytical, X\u2019Pert), for linear scans, rocking curves and RSMs at room temperature.\n\nThe cross-sectional TEM samples were prepared using a focused ion beam (FIB) method (Thermofisher Helios UX5, USA). For the plan-view TEM samples, we mechanically polished the samples to a thickness of approximately 3\u2009\u03bcm using the tripod method (ALLIED Multiprep) and then ion-mill the samples to tens of nanometers using FIB methods (Thermofisher Helios UX5, USA). TEM characterizations, low magnification STEM imaging, and nanoscale EDS composition analysis were performed using the Thermofisher Talos F200X equipped Four (Si-Li) EDS detectors with the accelerating voltage of 200\u2009kV, where the acquiring times about 30\u2009mins were used for each nanoscale EDS mapping. Atomic resolution HAADF and iDPC images were acquired using the Thermofisher Titan Themis Z with an accelerating voltage of 300\u2009kV. Atom resolution ADF image and EDS mapping were obtained using the Thermofisher Titan cubed Themis G2 300 with the accelerating voltage of 300\u2009kV. In experiment, we consecutively acquired 20 frames drift-corrected atomic resolution ADF images, and stacked them for atom displacement polarizations analysis and geometry phase analysis. For atomic resolution EDS mapping, we firstly adjust the experimental conditions of the zone axis [100] of target sample area, beam current (0.1\u2009nA), accelerating voltage (300\u2009kV), etc. and then wait for about 16\u2009h to ensure the stable of TEM sample and microscopy. It takes about 40\u2009min to acquire each atomic resolution EDS mapping with 512\u2009*\u2009512 pixels and 10 dell times. The acquisition of EELS data was carried out using Gatan electron energy loss spectroscopy (EELS) on the FEI Titan G2 80\u2013300 with an accelerating voltage of 300\u2009kV.\n\nPolarization versus electric field (P\u2013E) hysteresis loops were measured through the ferroelectric test system (TF Analyzer 2000E, Germany) at 1\u2009kHz at room temperature. The P\u2013E loops at liquid nitrogen temperature of 80\u2009K were obtained with Cryogenic Probe Station supplied by Lake Shore Company. The ferroelectric domains and polarization reversal behaviors were characterized by piezoresponse force microscopy (PFM, Asylum Cypher), where the chemical mechanical polishing technology was used to decrease the surface roughness of thin films. Here, the non-crystallizing 0.05 micron colloidal silica suspension was used for the surface polishing of thin films about 3\u2009min on the ALLIED Multiprep\u2122 System with 5 revolutions per minute. Subsequently, the ethyl alcohol and acetone were used for the ultrasonic cleaning of the thin films, successively.\n\nA standard peak finding algorithm is employed for the ADF image, which is based on fitting two-dimensional Gaussian functions to the intensity maxima. This algorithm allows us to determine the position and brightness of each column. Using these data, we can calculate the off-center ion displacements between the Nb column and the center of the unit cell. The center of the unit cell is determined by the average coordinate of the four K/Na atomic columns. We use the following formula to calculate the displacements:\n\nwhere i/j indicate the row/column number of each atom column, rij indicates the position of Nb atomic columns, and Rij indicates the position of K/Na atomic columns.\n\nA single crystal considering Cubic (C) to Orthorhombic (O) ferroelectric transition with Mn-inlaid antiphase boundaries has been carried out in phase-field simulations. The total free energy of the ferroelectric system can be described as:\n\nwhere f bulk represents the bulk free energy density,\n\nwhere \\({\\alpha }_{ij}\\) is the coefficient and depends on concentration c and temperature T.\n\nfgrad represents the gradient energy density,\n\nwhere G11 is the gradient energy coefficient.\n\nfcouple represents the couple effect caused by lattice strain \u03b5local.\n\nwhere \\({q}_{11}={C}_{11}{Q}_{11}+2{C}_{12}{Q}_{12}\\), \\({q}_{12}={C}_{11}{Q}_{12}+{C}_{12}({Q}_{11}+{Q}_{12})\\), \\({q}_{44}=2{C}_{44}{Q}_{44}\\),C11, C12, and C44 is the elastic constants in Voigt\u2019s notation and Qij is the electrostrictive coefficients. felas is the long-range elastic interaction energy densities and felec is the electrostatic interaction energy densities.\n\nwhere cijkl is the elastic constant tensor, \u03b5ij the total strain, \u03b50kl the electrostrictive stress-free strain, i.e., \u03b50kl =QijklPkPl.\n\nwhere fdipole is the dipole-dipole interaction caused by polarization, fdepola the depolarization energy density and fappl the energy density caused by applied electric field. The dimensionless parameters used in our simulations:\n\nC11\u2009=\u20091780, C12\u2009=\u2009964, C44\u2009=\u20091220, Q11\u2009=\u20090.1, Q12\u2009=\u2009\u22120.034, Q44\u2009=\u20090.029.\n\nThe temporal evolution of the spontaneous polarization field can be obtained by solving the time dependent Ginzburg Landau (TDGL) equation:\n\nwhere M is the kinetic coefficient, F is the total free energy, and t is time.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data supporting the findings are provided as figures in the article and Supplementary Information. All raw data generated during the current study are available from the corresponding author upon request.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This research was supported by the National Natural Science Foundation of China (Grant No. 51702253) (Z.Y.), (Grant No. 12374095, 11574324) (F.C.), (Grant No. 12204005) (L.X.), the Natural Sciences & Engineering Research Council of Canada (NSERC DG, RGPIN-2023-04416) (Z.Y.), the Natural Science Foundation of Shaanxi Province (grant 2022JQ-325) (C.L.). We also thank helpful discussions and suggestions with C.L. Jia, X.B. Yang, D.M Xu and Y. Chao.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Instrumental Analysis Center, Xi\u2019an Jiaotong University, Xi\u2019an, China\n\nChao Li\u00a0&\u00a0Chuansheng Ma\n\nElectronic Materials Research Laboratory, Key Laboratory of the Ministry of Education, School of Electronic Science and Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China\n\nLingyan Wang,\u00a0Xuerong Ren,\u00a0Yijun Zhang,\u00a0Guohua Dong,\u00a0Haixia Liu,\u00a0Xiaoyong Wei\u00a0&\u00a0Wei Ren\n\nInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China\n\nLiqiang Xu\n\nResearch Center for Advanced Functional Ceramics, Wuzhen Laboratory, Jiaxing, China\n\nFangzhou Yao\u00a0&\u00a0Ke Wang\n\nSchool of Physics and Information Technology, Shaanxi Normal University, Xi\u2019an, China\n\nJiangbo Lu\u00a0&\u00a0Hongmei Jing\n\nFrontier Institute of Science and Technology, Xi\u2019an Jiaotong University, Xi\u2019an, China\n\nDong Wang\n\nSchool of Chemistry, Xi\u2019an Jiaotong University, Xi\u2019an, China\n\nZhongshuai Liang\n\nLaboratory for Complex, Collective and Critical phenomena (L3C), State Key Laboratory for Mechanical Behavior of Materials, Xi\u2019an Jiaotong University, Xi\u2019an, China\n\nPing Huang\n\nSchool of Materials Science and Engineering, Peking University, Beijing, China\n\nShengqiang Wu\n\nThe State Key Laboratory of Materials-Oriented Chemical Engineering, College of Materials Science and Engineering, Nanjing Tech University, Nanjing, China\n\nYinong Lyu\n\nDepartment of Chemistry & 4D LABS, Simon Fraser University, Burnaby, B.C., Canada\n\nZuo-Guang Ye\n\nAnhui Province Key Laboratory of Low-Energy Quantum Materials and Devices, High Magnetic Field Laboratory, HFIPS, Chinese Academy of Sciences, Hefei, China\n\nFeng Chen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nC.L., L.X., F.C., and L.W. conceived and designed the study; F.Y. and K.W. fabricated the ceramic targets; L.X. and F.C. prepared the thin films; L.X., Z.L., L.W., and C.L. carried out the electric testing; G.D., H.L., and C.L. carried out the PFM testing; L.X. and Z.L. performed X-ray diffraction and analyzed the data; C.L. prepared the TEM samples and performed S/TEM and analyzed the data. H.J., S.W, P.H., and C.L. performed aberration-corrected STEM images and analyzed the data; J.L. and C.L. performed atomic resolution EDS mapping and analyzed the data; C.L., C.M., S.W., and Y.L. performed EELS; D.W. performed and analyzed the phase-field simulations; C.L., L.X., L.W., F.C., X.W., W.R., Y.Z., and Z.-G.Y. wrote and modified the manuscript; All authors discussed the results and revised the manuscript.\n\nCorrespondence to\n                Lingyan Wang, Liqiang Xu or Feng Chen.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The Authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional information",
+            "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",
+            "section_image": []
+        },
+        {
+            "section_name": "Source data",
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+            "section_image": []
+        },
+        {
+            "section_name": "Rights and permissions",
+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions",
+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Li, C., Wang, L., Xu, L. et al. Mn-inlaid antiphase boundaries in perovskite structure.\n                    Nat Commun 15, 6735 (2024). https://doi.org/10.1038/s41467-024-51024-2\n\nDownload citation\n\nReceived: 21 January 2024\n\nAccepted: 23 July 2024\n\nPublished: 07 August 2024\n\nVersion of record: 07 August 2024\n\nDOI: https://doi.org/10.1038/s41467-024-51024-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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+        }
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+}

0481817a811ef5943e02c6f71c38db6c4ebc3f3451bbc8d278f2dbfe56469ccf/metadata.json

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+{
+    "title": "Complete biosynthetic pathway of furochromones in Saposhnikovia divaricata and its evolutionary mechanism in Apiaceae plants",
+    "pre_title": "Complete biosynthetic pathway of furochromones and its evolutionary mechanism in Apiaceae plants",
+    "journal": "Nature Communications",
+    "published": "01 April 2025",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58498-8/MediaObjects/41467_2025_58498_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58498-8/MediaObjects/41467_2025_58498_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Supplementary Dataset",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58498-8/MediaObjects/41467_2025_58498_MOESM3_ESM.xlsx"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58498-8/MediaObjects/41467_2025_58498_MOESM4_ESM.pdf"
+        },
+        {
+            "label": "Transparent Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58498-8/MediaObjects/41467_2025_58498_MOESM5_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58498-8/MediaObjects/41467_2025_58498_MOESM6_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-025-58498-8#MOESM1",
+        "https://www.rcsb.org/structure/8ZNK",
+        "https://doi.org/10.6084/m9.figshare.25904887.v1",
+        "https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA036214",
+        "/articles/s41467-025-58498-8#MOESM3",
+        "/articles/s41467-025-58498-8#MOESM3",
+        "/articles/s41467-025-58498-8#Sec29"
+    ],
+    "code": [],
+    "subject": [
+        "Biosynthesis",
+        "Natural product synthesis",
+        "Secondary metabolism"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4779533/v1.pdf?c=1743592088000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4779533/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-025-58498-8.pdf",
+    "preprint_posted": "30 Jul, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Furochromones are bioactive and specific secondary metabolites of many Apiaceae plants. Their biosynthesis remains largely unexplored. In this work, we dissected the complete biosynthetic pathway of major furochromones in the medicinal plant Saposhnikovia divaricata by characterizing novel prenyltransferase, peucenin cyclase, methyltransferase, hydroxylase, and glycosyltransferases. De novo biosynthesis of prim-O-glucosylcimifugin and 5-O-methylvisamminoside was then realized in tobacco leaves. Through comparative genomic and transcriptomic analyses, we further found that proximal duplication and high expression of a pentaketide chromone synthase gene SdPCS, together with the presence of a lineage-specific peucenin cyclase gene SdPC, led to the predominant accumulation of furochromones in the roots of S. divaricata among surveyed Apiaceae plants. This study paves the way for metabolic engineering production of furochromones, and sheds light into evolutionary mechanism of furochromone biosynthesis among Apiaceae plants.Biological sciences/Chemical biology/BiosynthesisBiological sciences/Chemical biology/Natural products/Natural product synthesisBiological sciences/Plant sciences/Secondary metabolism",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplementaryTables.xlsxSupplementary TablesSuppplementaryFigures.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Furochromones are specific bioactive secondary metabolites of many Apiaceae plants. Their biosynthesis remains largely unexplored. In this work, we dissect the complete biosynthetic pathway of major furochromones in the medicinal plant Saposhnikovia divaricata by characterizing prenyltransferase, peucenin cyclase, methyltransferase, hydroxylase, and glycosyltransferases. De novo biosynthesis of prim-O-glucosylcimifugin and 5-O-methylvisamminoside is realized in Nicotiana benthamiana leaves. Through comparative genomic and transcriptomic analyses, we further find that proximal duplication and high expression of a pentaketide chromone synthase gene SdPCS, together with the presence of a lineage-specific peucenin cyclase gene SdPC, lead to the predominant accumulation of furochromones in the roots of S. divaricata among surveyed Apiaceae plants. This study paves the way for metabolic engineering production of furochromones, and sheds light into evolutionary mechanisms of furochromone biosynthesis among Apiaceae plants.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Furochromones are an important class of bioactive natural products. They demonstrate anti-inflammatory1,2, hepatoprotective3, and antiviral4 activities. For instance, khellin was used as a smooth muscle relaxant and cardioprotective drug in Europe5. While chromones are widely present in plants, furochromones have only been reported in a few families including Apiaceae, Ranunculaceae, and Leguminosae6. In Apiaceae, furochromones are the major bioactive compounds of Saposhnikovia divaricata7, Ammi visnaga8, and Cnidium monnieri9. Particularly, S. divaricata contains abundant prim-O-glucosylcimifugin (POG) and 5-O-methylvisamminoside (5-O-MVG), and their total content could be above 0.24% of dry weight10. These two compounds may contribute to the bioactivities of S. divaricata for the treatment of respiratory virus infection11, type I allergy12, colitis13, and aging-impaired endogenous tendon regeneration14.\n\nThe structures of POG and 5-O-MVG feature in the substitution of an isoprenyl group at C-6, which forms a fused dihydrofuran ring15,16 (Supplementary Fig.\u00a01). The biosynthesis of simple chromones has been extensively studied. The chromone skeletons are generated by polyketide synthases, such as PECPS from Aquilaria sinensis and AaPCS from Aloe arborescens17,18. However, the biosynthesis of furochromones remains largely unexplored. In the early 1970s, researchers fed sodium [1-14C] acetate to the shoots of Ammi visnaga, and revealed that peucenin and visamminol were biosynthetic intermediates of furochromones19. For the biosynthesis of POG or 5-O-MVG, a prenyltransferase (PT) is responsible to introduce an isoprenyl group to C-6 of the chromone skeleton20. Thus far, very few enzymes have been reported to catalyze cyclization of an isoprenyl group to form a dihydrofuran ring. Although CYP76F112 from Ficus carica, PpDC and PpOC from Peucedanum praeruptorum, NiDC and NiOC from Notopterygium incisum, as well as AsDC and AsOC from Angelica sinensis have been reported to catalyze similar reactions to produce furocoumarins21,22,23,24, no enzymes have been testified to generate furochromones. On the other hand, glycosyl substitutions at hydroxyl groups linking to the quaternary C-3\u2019 or the secondary C-11 are rare for natural products, and these reactions are usually catalyzed by uridine diphosphate-dependent glycosyltransferases (UGTs)25. Moreover, both POG and 5-O-MVG contain a methoxyl group at C-5, and the methylation reaction was proposed to be catalyzed by an O-methyltransferase (OMT)26. Although a big family of OMTs have been reported from plants, few OMTs could catalyze methylation at the less active 5-OH. Limited examples include the isoflavone 5-O-methyltransferase from Lupinus luteus27 and CdFOMT5 from Citrus depressa28. For POG, a cytochrome P450 (CYP450) enzyme may introduce the extra primary hydroxyl group at C-1129. Based on the above analysis, we hypothesized the biosynthetic pathway of 5-O-MVG (6) and POG (9) (Fig.\u00a01a). While the enzyme categories catalyzing each step seem obvious, the specific enzymes with expected functions are still illusive.\n\na The proposed biosynthetic pathway and catalytic enzymes. PT prenyltransferase, PCS pentaketide chromone synthase, CYP450 cytochrome P450 enzyme, OMT O-methyltransferase, UGT uridine diphosphate-dependent glycosyltransferase. 1, malonyl-CoA; 2, noreugenin; 3, peucenin; 4, visamminol; 5, 5-O-methylvisamminol; 6, 5-O-methylvisamminoside; 7, norcimifugin; 8, cimifugin; 9, prim-O-glucosylcimifugin. b Image of the sampled S. divaricata. c, Total ion currents (TICs) and extracted ion chromatograms (EICs) of the root, petiole, and leave of S. divaricata by LC/MS analysis. EIC mass range: m/z 291.11\u2013291.12\u2009+\u2009293.09\u2013293.10. d Contents of 5, 6, 8 and 9 in different organs, calculated on the basis of dry weight (n\u2009=\u20093, three biologically independent samples were tested). e Genomic statistics of S. divaricata, showing eight chromosomes (Chr1\u2013Chr8). i, pseudochromosomes; ii, gene density; iii, Gypsy LTR density; iv, Copia LRT density; v, Helitron density; vi, GC content.\n\nIn this work, we dissected the biosynthetic pathway of POG and 5-O-MVG in S. divaricata. The functions of seven enzymes were characterized, including SdPCS, SdPT, SdPC, SdCH, SdOMT, SdUGT1, and SdUGT2. Utilizing these gene elements, we realized the complete biosynthesis of POG and 5-O-MVG in Nicotiana benthamiana leaves. Moreover, we unravelled the genetic mechanisms for high abundances of POG and 5-O-MVG in S. divaricata among Apiaceae plants.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58498-8/MediaObjects/41467_2025_58498_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results and discussion",
+            "section_text": "First, we analyzed the chemical constituents of three organs of S. divaricata (leaf, petiole, and root, Fig.\u00a01b\u2013c) by liquid chromatography coupled with mass spectrometry (LC/MS). At least five furochromones (5\u20139) could be detected, which supported the validity of our proposed biosynthetic pathway. Subsequently, the contents of major compounds 5, 6, 8 and 9 in five tissue samples (roots at three developmental stages, petiole, and leaf) were quantitatively determined (Supplementary Figs.\u00a02\u20136). The results indicated the roots contained more abundant furochromones, particularly the glycosides 6 and 9, than the petiole and leaf samples (Fig.\u00a01d).\n\nIn order to obtain a complete list of candidate genes involved in the biosynthesis of POG and 5-O-MVG, we sequenced, assembled, and annotated a chromosome-level genome of S. divaricata. Based on 43.72\u2009Gb PacBio CCS long reads, we assembled the genome to 1.95\u2009Gb (Supplementary Table\u00a01), which was consistent with the estimate by flow cytometry (1.94\u2009\u00b1\u20090.02\u2009Gb) (Supplementary Fig.\u00a07) and the published assembly30. The assembly contig N50 was 2.22\u2009Mb and the Benchmarking Universal Single-Copy Ortholog (BUSCO) score was 96.1%, indicating good genome continuity and completeness (Supplementary Tables\u00a02\u20133). By Hi-C technology, 94.27% contigs were anchored onto eight chromosomes (Fig.\u00a01e, Supplementary Fig.\u00a08 and Supplementary Table\u00a04). Multiple-tissue RNA-Seq data (Supplementary Table\u00a05), ab initio prediction, and homolog protein evidences were combined for genome annotation, which led to the identification of 38,704 high-confidence protein-coding genes and 65,734 transcripts. Finally, a total of 1,751,401 repetitive elements were annotated, accounting for 76.78% of the genome (Supplementary Table\u00a06). With the high-quality genome and multiple-tissue RNA-Seq data, we quantified the gene expression abundance (fragments per kilobase of exon model per million mapped fragments, FPKM) of the five tissue samples mentioned above. Subsequently, we screened candidate genes according to genome annotation or local blastn search, and selected genes whose expression levels were correlated with the contents of downstream secondary metabolites in different organs for functional characterization.\n\nThe first step of the biosynthetic pathway converts malonyl-CoA (1) to noreugenin (2). The pentaketide chromone synthase AaPCS from Aloe arborescens is the only reported enzyme to catalyze this type of reaction18. Thus, we conducted a local blastn search using AaPCS as a query in the transcriptome of S. divaricata, and ten candidate genes with e values\u2009<\u200910\u221221 were discovered. The expression level (FPKM) of one gene, SdPCS, was highly correlated with the furochromones contents with Pearson correlation coefficient (PCC)\u2009>\u20090.95 (Supplementary Table\u00a07). It was sub-cloned into the pET28a (+) vector for protein expression in E. coli BL21 (DE3) cells. The function was characterized by enzyme catalysis reactions with 1 as substrate. According to high-performance liquid chromatography (HPLC) and LC/MS analyses, SdPCS generated a new peak, which was identified as 2 by comparing with a reference standard. SdPCS showed the highest catalytic efficiency at 37\u00b0C in Na2HPO4-NaH2PO4 buffer (pH 6.0). The kcat/Km value of SdPCS generating 2 was 22.84\u2009M\u22121\u00b7s\u22121 (Supplementary Fig.\u00a09). From the genome of S. divaricata, we further discovered and characterized SdPCS2 with the same function (Fig.\u00a02a), though it was initially neglected due to low expression level (Supplementary Data\u00a01). SdPCS2 shows high amino acid sequence identity (91.33%) and short distance in the genome (135,334\u2009bp) with SdPCS (Supplementary Figs.\u00a010\u201311).\n\nFunctional characterization of SdPCS (a), SdPT (b), and SdPC (c). Shown are HPLC/UV chromatograms of enzyme catalysis reactions (\u03bb\u2009=\u2009280\u2009nm), together with (+)-ESI-MS and MS/MS spectra of the products. Control, reaction mixtures incubated with boiled enzymes or microsomes.\n\nTo discover the prenyltransferase (PT) converting 2 to peucenin (3), we obtained one candidate gene SdPT (PCC\u2009>\u20090.95, Supplementary Table\u00a08) among the 20 annotated PT genes. SdPT was sub-cloned to pESC-Leu vector and expressed in yeast WAT11 cells31. When the yeast microsomes were incubated with 2, DMAPP and MgCl2, HPLC analysis showed a new product, which exhibited an [M\u2009+\u2009H]+ ion at m/z 261.11 in LC/MS analysis. The MS/MS spectrum showed an abundant [M-56\u2009+\u2009H]+ fragment at m/z 205.05, indicating a prenyl substitution at C-6 or C-832 (Fig.\u00a02b). SdPT showed the highest catalytic efficiency at 45\u2009\u00b0C in Na2CO3-NaHCO3 buffer (pH 10.0), with a Km value of 29.08 \u03bcM (Supplementary Fig.\u00a012). Then we purified 0.8\u2009mg of the product from scaled-up enzymatic reactions with the most suitable reaction condition. The 1H-NMR spectrum showed two methylene signals at \u03b4H 3.17 (m, H-1\u2032), one olefinic signal at \u03b4H 5.16 (t, J\u2009=\u20096.0\u2009Hz, H-2\u2032), and two methyl signals at \u03b4H 1.61 (H-4\u2032) and 1.71 (H-5\u2032), indicating the presence of an isoprenyl group. The HMBC cross peaks from H-1\u2032 to C-5 (\u03b4C 158.1), C\u22126 (\u03b4C 111.1), and C-7 (\u03b4C 164.8) indicated the isoprenyl group was located at C-6 (Supplementary Figs.\u00a013\u201316). Thus, the product was identified as peucenin (3) (Supplementary Table\u00a09). SdPT represented the first prenyltransferase utilizing chromones as substrate. SdPT2, with an amino acid sequence identity of 85.79% (Supplementary Fig.\u00a017), exhibited the same catalytic function as SdPT.\n\nFew enzymes are known to catalyze the oxidative cyclization of isoprenyl groups, except for several CYP450 enzymes involved in the biosynthesis of furocoumarins22,23,24. Since these enzymes belong to the CYP736 family, we screened candidates from the same family in S. divaricata, and chose four candidate genes whose expression levels were highly correlated with the furochromones contents (PCC\u2009>\u20090.90, Supplementary Table\u00a010). By incubating the microsomes of SdPC recombinant yeast WAT11 cells with 3 and NADPH, a new product was yielded. LC/MS analysis showed an [M\u2009+\u2009H]+ ion at m/z 277, which could be fragmented into m/z 259 and m/z 205. Its structure was proposed to be visamminol (4). As no reference standard was available, we prepared 4 through hydrolysis of visamminol 3\u2019-O-glucoside catalyzed by \u03b2-glucosidase (Supplementary Fig.\u00a018), and confirmed its structure by NMR analysis. The 1H-NMR spectrum showed two methyl signals at \u03b4H 1.13 (s, H-4\u2032) and \u03b4H 1.14 (s, H-5\u2032), a tertiary proton signal at \u03b4H 4.71 (t, J\u2009=\u20098.6\u2009Hz, H-2\u2032), and a methylene signal at \u03b4H 3.02 (d, J\u2009=\u20098.6\u2009Hz, H-1\u2032), indicating the presence of a furan ring. The HMBC cross peaks from H-2\u2032 (\u03b4H 4.71) to C-1\u2032 (\u03b4C 26.6), C-7 (\u03b4C 166.4), and C-6 (\u03b4H 109.5) indicated the furan ring was conjugated with the benzene ring (Supplementary Figs.\u00a019-22, Supplementary Table\u00a09). HPLC and LC/MS analyses indicated the product had the same retention time and mass spectra with 4 (Fig.\u00a02c). As the oxidative cyclization of isoprenyl phenolic compounds by chemical synthesis requires strong oxidizers like m-chloroperbenzoic acid33, SdPC represents an efficient enzyme catalyst for this reaction.\n\nC-11 of compounds 7-9 is hydroxylated, indicating the presence of a CYP450 enzyme. However, very few enzymes have been reported to catalyze a similar reaction, thus no suitable templates are available for gene blast search. By analyzing the transcriptome data, we selected 12 candidate CYP genes, whose expression levels were highly correlated with the total contents of 8 and 9 (PCC\u2009>\u20090.95, Supplementary Table\u00a011). These genes were expressed in yeast WAT11 cells, and the microsomes were incubated with NADPH and 4 or 5 (Tris-HCl buffer, 50\u2009mM) for functional characterization. LC/MS analysis indicated that SdCH could convert 4 and 5 (5-O-methylvisamminol) into 7 (norcimifugin) and 8 (cimifugin), respectively (Fig.\u00a03a, Supplementary Fig.\u00a023).\n\nFunctional characterization of SdCH (a), SdOMT (b), and SdUGT1/2 (c, d). Shown are HPLC/UV chromatograms of the enzyme catalysis reactions (\u03bb\u2009=\u2009280\u2009nm), together with (+)-ESI-MS and MS/MS spectra of the products. e Kinetic parameters of SdUGT1 and SdUGT2. f Simulated binding modes of 8 in the crystal structure of SdUGT2 when catalyzing 11-O-glycosylation (left) and 3\u2032-O-glycosylation (right). Hydrogen bonds and hydrophobic interactions were labeled with blue and orange dashes, respectively. STD, reference standard. Control, reaction mixtures incubated with boiled enzymes or microsomes.\n\nLikewise, we discovered the 5-O-methyltransferase SdOMT which converted 4 and 7 into 5 and 8, respectively (PCC\u2009>\u20090.90, Supplementary Table\u00a012). Its function was characterized by enzymatic reaction and LC/MS analysis (Fig.\u00a03b, Supplementary Fig.\u00a024).\n\nGlycosylation is the final step in the biosynthetic pathway. A total of 8 UGT genes with FPKM\u2009>\u200910 in the roots were chosen as candidate genes, and were cloned and expressed in E. coli BL21(DE3) (Supplementary Table\u00a013). The functions were characterized by enzymatic catalysis with UDP-Glc (UDPG) as sugar donor, and 5 or 8 as sugar acceptor. SdUGT1 (UGT93BA1) could catalyze the glucosylation of 3\u2032-OH of 5 (tertiary alcohol) and 11-OH of 8 (primary alcohol) to produce 6 (5-O-methylvisamminoside, 5-O-MVG) and 9 (prim-O-glucosylcimifugin, POG), respectively. The products could lose 162\u2009Da in the MS/MS spectra, and their structures were identified by comparing with reference standards (Fig.\u00a03c\u2013d). Moreover, we discovered SdUGT2 (UGT93BB1), which exhibited a high amino acid sequence identity (54.93%) with SdUGT1 and showed the same catalytic activities (Supplementary Fig.\u00a025). Interestingly, SdUGT1 and SdUGT2 only catalyzed 11-O-, but not 3\u2032-O-glycosylation of 8. Moreover, they showed 31 and 2.8-fold higher catalytic efficiency (kcat/Km value) with 8 than with 5 as substrate (Fig.\u00a03e, Supplementary Figs.\u00a026\u201327).\n\nTo elucidate mechanisms for the preference towards 11-OH, we acquired the crystal structure of SdUGT2 in complex with UDP through X-ray diffraction (PDB ID: 8ZNK, 1.88\u2009\u00c5) (Fig.\u00a03f, Supplementary Fig.\u00a028, Supplementary Table\u00a014). The structure of SdUGT2 showed a typical GT-B fold with two Rossmann-like \u03b2/\u03b1/\u03b2 domains. The N-terminal domain (NTD, residues 1\u2013261 and 454\u2013480) and the C-terminal domain (CTD, residues 262\u2013453) are primarily responsible for sugar acceptor and sugar donor binding, respectively. Then we simulated the SdUGT2/UDP-Glc complex structure according to the GgCGT/UDP-Glc structure, and docked 8 into the structure in two potential binding modes through AutoDock 4.2 software34,35. Alanine scanning of residues around the binding pocket led to remarkably decreased activities for most mutants, indicating reliability of the docking results (Supplementary Fig.\u00a029). In both binding modes, His32 is close to the glycosylation sites (11-OH or 3\u2032-OH) with a distance below 3.1\u2009\u00c5. Thus, the hydroxyl groups could be easily deprotonated to initiate the glycosylation reaction. However, 8 could form more hydrogen bonds and hydrophobic interactions in the 11-O-glycosylation mode than in the 3\u2032-O-glycosylation mode, as predicted by PLIP 202136. Thus, SdUGT2 preferred to catalyze 11-O-glycosylation of 8. On the other hand, docking of 9 into SdUGT2 showed the distance between 3\u2032-OH and His32 was too far for glycosylation reaction. This result was consistent with the absence of furochromone 3\u2032,11-di-O-glycosides in S. divaricata7 (Supplementary Fig.\u00a030). We also simulated the structure of SdUGT1 using Alphafold237, and docked 8 and UDP-Glc into the structure in the same way as described above. Comparing the binding modes of 8 in SdUGT1 and SdUGT2, we noticed that 8 was more stable in SdUGT1 as a result of extra hydrogen bonds and \u03c0-stacking interactions (Supplementary Fig.\u00a031). This is probably the reason for the higher catalytic efficiency of SdUGT1 than SdUGT2.\n\nThus far, we have identified seven enzymes from S. divaricata catalyzing biosynthesis of the two major furochromones 6 and 9. These genes are located at different chromosomes. Specifically, SdCH and SdUGT1 are located at Chr1, SdPCS and SdPC at Chr2, SdPT at Chr3, SdOMT at Chr6, and SdUGT2 at Chr8 (Fig.\u00a04a). To our knowledge, this is the first report to unravel the complete biosynthetic pathway of furochromones. The expression levels of identified genes, except for SdUGT1 and SdUGT2, are highly correlated with the distribution of major furochromones among different organs of S. divaricata.\n\na Genomic location of biosynthetic genes in S. divaricata. Catalytic functions of biosynthetic genes responsible for formation (b) and post-modification (c) of the furochromone skeleton. Extracted ion chromatograms (EICs) of biosynthetic products in LC/MS analysis are shown. STD, reference standards. EV, agrobacterium-mediated transient expression using a vector without any biosynthetic genes.\n\nPOG and 5-O-MVG are important bioactive compounds in S. divaricata. Their extraction and purification are time and labor-consuming. It is imperative to engineer the biosynthetic pathway in chassis organisms. In this work, we realized de novo biosynthesis of furochromones in Nicotiana benthamiana leaves. Transient expression of the seven genes in N. benthamiana leaves revealed that all genes showed the expected catalytic activities (Fig.\u00a04b-c). When all the seven genes were infiltrated into N. benthamiana leaves, 6 and 9 could be detected. Given the low catalytic efficiency of SdPT, we increased the OD600 value of SdPT strain to 0.40, and 6 and 9 were generated at a yield of 17.48 \u03bcg/g and 3.82 \u03bcg/g (dry weight, Supplementary Fig.\u00a032), respectively, where the yields were calculated with six independent biological replicates.\n\nTo gain deep insights into the evolution of biosynthetic pathway of furochromones in Apiaceae, we incorporated another seven Apiaceous species (Coriandrum sativum, Apium graveolens, Angelica sinensis, Ligusticum chuanxiong, Daucus carota, Bupleurum chinense, Centella asiatica) into metabolic, comparative genomic and transcriptomic analyses (Supplementary Data\u00a02\u20134, Supplementary Table\u00a015). These species represented different evolutionary lineages, including the subfamilies Mackinlayoideae and Apioideae (including the tribe Bupleureae, Apieae, Sinodielsia Clade, Selineae). We first determined and compared the contents of four typical furochromones (5, 6, 8, and 9) among the eight species (Supplementary Figs.\u00a033\u201353). Unexpectedly, the furochromones did not show a stepwise accumulation along the phylogeny backbone but exhibited a drastic enrichment in S. divaricata. The contents of furochromones in the other species were generally low (Fig.\u00a05a, Supplementary Table\u00a016). This result implied substantial differences in furochromone biosynthesis between S. divaricata and the other Apiaceous plants.\n\na Contents of typical furochromones in various organs of Apiaceae plants, and syntenic gene analysis. b Contents of simple chromones in various organs of Apiaceae plants and the hypothesized causes (n\u2009=\u20093, three biologically independent samples were tested, and data are presented as mean values +/\u2212 SD).\n\nTo investigate the evolutionary shift in furochromone biosynthesis from early diverged Apiaceous lineage to S. divaricata, we constructed a maximum likelihood (ML) phylogeny of Apiaceae species based on 398 strict single-copy orthologous genes (Fig.\u00a05a). It revealed that S. divaricata belonged to the latest diverged clade including C. sativum. Then, the conserved syntenic gene blocks containing each furochromone-biosynthetic gene was identified in each Apiaceous species (Supplementary Figs.\u00a054\u201364). The syntenic and homologous genes of SdUGTs and SdCH were detected in all Apiaceous species, while PCSs, OMTs and PTs were limited to Apioideae, indicating a stepwise assembly of the furochromone biosynthetic pathway in Apiaceae (Fig.\u00a05a). Remarkably, the last piece of the puzzle, SdPC, was detected only in S. divaricata (Fig.\u00a05a). This clue motivated us to speculate that most Apiaceous species except S. divaricata may not contain any functional PC, thus leading to low furochromone content.\n\nAdditionally, we found that simple chromones (2 and 3) showed consistent distribution with furochromones (4, 5, 6, 8 and 9) in Apiaceae. They were also drastically enriched in S. divaricata (Fig.\u00a05b, Supplementary Figs.\u00a065\u201388). As 2 and 3 were generated before the catalysis of PC, their absence in other Apiaceous species was more likely caused by blocking of the initial step (PCS catalysis) (Fig.\u00a05b). Moreover, most potential Apiaceous PTs involved in furochromone biosynthesis showed moderate or high expression abundances (Supplementary Table\u00a017). Thus, except for PC, we mainly focused on the upstream PCS, the housekeeping gene in the chromone biosynthetic pathway.\n\nWe retrieved all potential PKS III genes in S. divaricata and the other seven Apiaceae species, and constructed an ML tree. A strongly supporting clade (bootstrap support value (BS)\u2009=\u2009100) containing SdPCS and 20 potential Apiaceous PCSs was identified (Fig.\u00a06a). Most genes in this clade were in the same syntenic region, implying they shared the same ancestor (Supplementary Fig.\u00a063). No PCS was detected in C. asiatica, one of the most basal species in Apiaceae, indicating that PCS may first emerge in the Apioideae subfamily. Then we expressed and characterized all the 20 genes, and compared their functions by enzymatic assays (Fig.\u00a02a, Supplementary Figs.\u00a089\u201394). Most of these enzymes showed similar catalytic abilities by converting 1 to 2 (Fig.\u00a06a). However, the expression level of SdPCS in the root of S. divaricata was remarkably higher than the other homologous PCS genes (FPKM value, 162.05 vs 0\u20135.19) (Fig.\u00a06a).\n\na Phylogenetic relationships, enzymatic activities, and expression abundances of Apiaceous PCSs. The PCS enzyme activity was quantified by HPLC/UV peak area of generated noreugenin (2) (\u03bb\u2009=\u2009280\u2009nm, n\u2009=\u20093, three biologically independent samples were tested, and data are presented as mean values +/\u2212 SD). R/R, L, and P/S represent Root/Rhizome, Leaf, and Petiole/Stem, respectively. b Comparison of the FPKM between SdPCS and other potential Apiaceous PCSs in the genome-wide context. Each red line represents a log2 (FPKM ratio) between SdPCS and a potential Apiaceous PCS. Each grey density plot indicates the log2 (FPKM ratio) distribution of genome-wide orthologous genes of one Apiaceae species. c Syntenic regions containing Apiaceous PCSs. d Expression levels of the three PCS copies in S. divaricata (n\u2009=\u20093, three biologically independent samples were tested, data are presented as mean values +/\u2212 SD).\n\nWe are aware that direct inter-species comparison of FPKM might lead to misinterpretation since the utilization of FPKM value is usually limited to intra-species level. Instead, we compared the FPKM ratio between SdPCS and other potential Apiaceous PCSs against 9470\u201327,214 pairs of orthologous genes as genomic background. The log2 (FPKM ratio) value of >95% genome-wide ortholog pairs between S. divaricata and other Apiaceous species in root/rhizome is <5.00, with a mean near zero (\u22120.005). This result indicates that FPKM values of the investigated species are basically comparable. It is noteworthy that the log2 (FPKM ratio) between SdPCS and other Apiaceous PCSs in root/rhizome, ranging from 6.12 to 20.63 (mean = 14.15), was higher than 95% of genome-wide orthologous gene pairs (Fig.\u00a06b). Thus, we deduced the expression abundance of SdPCS was significantly higher than the other Apiaceous PCSs. As the initial step is usually the rate-limiting step in biosynthetic pathway38, the exceptionally high expression of SdPCS may contribute to the furochromone accumulation in S. divaricata.\n\nMoreover, we traced the origin of SdPCS (SaDchr02G001054), and found it might originate from proximal duplication of the nearby SdPCS2 (SaDchr02G001052) (Fig.\u00a06c). The PCS ML tree revealed the three PCS copies in S. divaricata clustered in the same clade and SdPCS2 diverged earlier, indicating SdPCS was originated from S. divaricata specific duplication event rather than directly inherited from ancestor species (Fig.\u00a06a). Sequence analysis further confirmed this deduction. SdPCS2 showed the highest coding sequence (CDS) identity with other syntenic Apiaceous PCSs among the three SdPCSs (Supplementary Fig.\u00a095). Although the chromosomal position of SdPCS3 (not anchored to any chromosome) is unknown, the lowest CDS identity with other Apiaceous PCSs indicated it was younger than SdPCS and SdPCS2 (Supplementary Fig.\u00a095). Therefore, SdPCS2 is most likely the direct progenitor of SdPCS (Fig.\u00a06c). Notably, we found that SdPCS2 was nearly not expressed in any tissue (Fig.\u00a06d), and its syntenic genes in other Apiaceous species were almost not expressed, either (Supplementary Table\u00a018). Thus, the proximal duplication and high expression of SdPCS profoundly contributed to the biosynthesis of furochromones in the roots of S. divaricata.\n\nAs shown in Fig.\u00a07a, we did not detect syntenic genes of SdPC in the other Apiaceous plants. We retrieved all Apiaceous CYP736s and constructed an ML tree. The reported PpDC was also included22. A robust clade (BS\u2009=\u2009100) containing SdPC and 18 other potential PCs was identified (Fig.\u00a07b, Supplementary Fig.\u00a064). Although DCAR_313054 and As05G02751 were not included in this clade, they were syntenic with several genes including SaDchr05G002066 and LCX7BG003145 (Supplementary Fig.\u00a096). The 11 potential PCs from A. sinensis lost one exon and was likely to lose the cyclization activity, thus they were not further analyzed. Finally, we cloned and characterized the other 10 potential PCs. Strikingly, none of them except for SdPC was effective in producing visamminol (4) (Fig.\u00a07c). This observation confirmed our hypothesis that the absence of PC genes may lead to the low contents of furochromones in most Apiaceous species. However, we cannot exclude the possibility that these or other potential PCs might weakly catalyze the reactions in vivo, as trace furochromones were detected in all Apiaceous plants.\n\na Syntenic regions containing SdPC. The syntenic gene pairs are connected by grey lines. b Phylogenetic relationship and gene structure of Apiaceous PCs. As05G02748 (AsDC) is the same as As05G00644 in the initial annotation version. c HPLC/UV chromatograms showing the in vitro enzymatic activity of potential Apiaceous PCs (\u03bb\u2009=\u2009280\u2009nm).\n\nSince PpDC participated in the generation of furocoumarins in Peucedanum praeruptorum22, we tested the catalytic activities of homologous PCs using demethylisuberosin as substrate. SdPC4, AsDC, LcPC2 and DcPC showed cyclization activities (Supplementary Fig.\u00a097). However, SdPC could not catalyze this reaction despite its high sequence similarity with SdPC4 (Supplementary Fig.\u00a098). Interestingly, these four genes were located at the same syntenic block, which did not include SdPC (Supplementary Fig.\u00a096). Thus, SdPC is a homologous enzyme with novel function, and its evolutionary origin warrants further investigation in the future.\n\nIn conclusion, this work dissected the complete biosynthetic pathway of prim-O-glucosylcimifugin and 5-O-methylvisamminoside, the major bioactive furochromone glucosides in S. divaricata. The functions of seven biosynthetic enzymes were characterized by enzymatic catalysis reactions, and the biosynthetic pathway was verified by de novo biosynthesis of major furochromones in Nicotiana benthamiana. Moreover, we explored the evolutionary mechanisms of furochromones biosynthesis in Apiaceae plants. Through comparative metabolic, genomic, and transcriptomic analyses of eight plant species, we found that proximal duplication and high expression of a pentaketide chromone synthase gene SdPCS, as well as the presence of a lineage-specific peucenin cyclase gene SdPC, contribute to the abundant and specific accumulation of furochromones in the roots of S. divaricata. This work provides critical insights into the biosynthesis of furochromones and serves as a platform for their metabolic engineering production.",
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+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The sources of fresh plants of Saposhnikovia divaricata, Centella asiatica, Bupleurum chinense, Daucus carota, Angelica sinensis, Apium graveolens, and Coriandrum sativum are given in Supplementary Data\u00a02. We sampled leaves, petioles, and roots of Saposhnikovia divaricata for both metabolic analyses and RNA-Seq, with the roots revealing three different growth levels (Supplementary Table\u00a015). For each tissue/stage, three replicates were sampled.\n\nThe chemical reference standards and sugar donors used in this study were purchased from YuanYe Biotechnology Co., Ltd. (Shanghai, China). Methanol and acetonitrile (Thermo Fisher Scientific, USA) were of HPLC grade. The conversion rates were determined by HPLC/UV analysis on an Agilent HPLC 1260 instrument. Samples were separated on a Zorbax SB-C18 column (4.6\u00d7250\u2009mm, 5 \u03bcm, Agilent, USA). The column temperature was 30\u2009\u00b0C. To calculate the conversion rates, peak areas of both substrate and product were integrated by Chromeleon\u00ae at a certain wavelength. LC/MS analysis was performed on a Q-Exactive quadrupole Orbitrap mass spectrometer (Thermo Fisher Scientific, USA).\n\nFor the PacBio library construction, 15\u2009\u03bcg genomic DNA from the leaves of S. divaricata was fragmented into approximately 15\u2009kb using g-TUBEs (Covaris, USA). After removing short fragments and single-strand overhangs, the retained fragments were converted into the proprietary SMRTbell library with the PacBio DNA Template Preparation Kit (Pacific Biosciences, CA, USA). Single Molecule Real Time (SMRT) sequencing was performed on a PacBio Sequel II sequencing platform. For Hi-C library construction, chromatin was first fixed in place with formaldehyde in the nucleus and then extracted. The extracted chromatin was digested with DpnII. The 5\u2019 overhangs of resulting fragments were then filled in with biotinylated nucleotides, and free blunt ends were ligated. After ligation, the DNA was purified from protein and treated following the Illumina Next Generation manufacturer\u2019s instructions. The libraries were subsequently sequenced on Illumina Hiseq X, producing 166.99\u2009Gb 2 \u00d7 150\u2009bp paired-end reads. The raw data of PacBio subreads was filtered to HiFi reads by PBccs (v6.4.0) (https://github.com/PacificBiosciences/ccs), and subsequently assembled with Hifiasm (v0.16.0)39, which generated a pair of haplotype-resolved assemblies, hap1.p_ctg (1.95\u2009Gb) and hap2.p_ctg (1.93\u2009Gb). We selected the slightly larger hap1.p_ctg for subsequent BUSCO assessment, scaffolding, and annotation. The initial assembled contigs were anchored to chromosomes by 3D-DNA pipeline (v201008)40 and further manually adjusted to produce a chromosome-level genome. BUSCO (v5.4.3)41 was used for benchmarking the genome with the \u201cembryophyte_odb10\u201d database.\n\nEDTA (v2.0.0)42 was used to de novo identify, annotate, and classify the repetitive elements in the genome of S. divaricata. Prior to protein-coding gene annotation, the annotated repetitive elements in the genome were soft masked with bedtools (v2.28.0)43. RNA-Seq raw reads of S. divaricata were filtered with fastx-toolkits (v0.0.14) (http://hannonlab.cshl.edu/fastx_toolkit/index.html) and then assembled through Hisat2 (v2.2.1)44 and Stringtie (v2.2.0)45. The raw assembly of transcripts was further validated by PASA (v2.5.1)46, which were then incorporated into the MAKER (v3.01.03)47 pipeline to automatedly identify protein-coding genes. Finally, the gene models identified by MAKER were updated by PASA (v2.5.1)46. Some genes were recognized and annotated by manual examination. Their names were different from the other genes that were annotated and named using software. To reveal the relative locations of a manual-annotated gene, we named it by its nearby upstream gene name plus a suffix of downstream_s1. For example, SaDchr03g003979_downstream_s1 (SdPT2) is a manually annotated gene located downstream region of the SaDchr03g003979. Function annotations of the protein-coding genes were carried out by BLASTP searches against entries in both NCBI non-redundant protein (NR) (https://www.ncbi.nlm.nih.gov/) and Swiss-Prot (https://www.uniprot.org/) databases. The prediction of conserved domains for the genes was performed by InterProScan (v5.11-51.0)48. The annotations of the GO terms (http://geneontology.org/) and KEGG pathways (https://www.genome.jp/kegg/) for the genes were annotated with eggNOG-mapper (v2.1.10-0)49.\n\nThe total RNA was extracted with the TranZolTM kit (Transgen Biotech, China) following the manufacturer\u2019s instructions, and was used to synthesize the first-stranded complementary DNA (cDNA) with TransScript one-step genomic DNA (gDNA) removal and cDNA synthesis SuperMix (Transgen Biotech, China). The transcriptome data of different tissues of S. divaricata were sequenced at Novogene Co., Ltd. (Beijing, China).\n\nThe raw RNA-seq reads were filtered in fastp50 with default parameters and then mapped to the reference genome of S. divaricata by Hisat2 (v2.2.1)44. The counts of reads mapping to exons of each gene were calculated by featureCounts51. The FPKM value of each gene was calculated in R.\n\nHMMER3 (v3.3.2)52 was used to identify 47PKSs, PTs, CYPs, OMTs and UGTs with an e-value of 1e\u22126. The HMMER profiles PF02797 and PF00195 were used for PKS III search. PF01040 and PF00067 was utilized to identify CYPs and PTs. PF00891 and PF08100 were employed to search OMTs, PF00201 was applied for UGT identification. The possible pseudogenes (length of predicted CDS\u2009<\u2009200 amino acids) were discarded. Gene structures of all candidate genes were manually adjusted with IGV-GSAman (https://gitee.com/CJchen/IGV-sRNA).\n\nPearson correlation coefficients and the P values between contents of furochromones and the FPKM of genes among different tissues of S. divaricata were calculated with the corr.test function in R package psych (https://rdocumentation.org/packages/psych/versions/2.3.3). The correlation between the expression abundance of each gene and furochromone content was analyzed based on identical sample composition, involving 15 pairs of strict-matched samples. Those unexpressed genes were not incorporated in correlation analysis.\n\nML phylogeny was constructed based on 398 strict single-copy orthologous genes identified by OrthoFinder (v2.5.4)53 to clarify the phylogenetic relationship among the eight Apiaceous species. The protein sequences were aligned by MUSCLE (v5.1.linux64)54 and subsequently concatenated by Phylosuite (v1.2.2)55. ModelTest-NG (v0.1.7)56 was used to detect the best-fit amino acid substitution model, based on which RAxML-NG (v1.1.0)57 was employed to construct the ML phylogeny with 1,000 bootstrap analyses. The construction of phylogeny of biosynthetic genes follows the same method above.\n\nThe microsyntenic analyses generally followed the methods of Griesmann et al. (2018) and Yang et al. (2023). All vs. all blastp (E-value\u2009=\u20091e\u22125) was conducted for the protein sequences among eight Apiaceous genomes with BLAST (v2.13.0\u2009+\u2009)58. The output protein identity matrix was loaded in JCVI (v1.2.7) to produce collinear gene blocks. Subsequently, we identified the syntenic region containing the furochromone biosynthetic genes (\u2009\u00b1\u2009100\u2009kb) in each species using the genome of S. divaricata as the reference. Because the syntenic retention varied between different species pairs, we compared the syntenic gene pairs for all species pairs and retained those gene pairs demonstrating consistent syntenic relationships. To eliminate the bias induced by mistaken annotation, we manually checked the corrected gene structure in syntenic region and re-organized the microsyntenic gene pairs.\n\nThe full-length candidate genes were amplified from cDNA with TransStart FastPfu DNA Polymerase (Transgen, China). Candidate genes for PCS, OMT and UGT were recombined in the pET-28a (+) vector (Invitrogen, USA) at BamH I site. Candidate genes for PT, PC and CH were cloned into pESC-Leu vector at BamH I (Invitrogen, USA). Sequences of the primers used in this study are listed in Supplementary Table\u00a019.\n\nThe recombinant plasmids for PCS, OMTs and OGTs were introduced into E. coli BL21 (DE3) (Transgen Biotech, China) for heterologous expression. The E. coli cells were grown in 500\u2009mL Luria-Bertani medium (JS0666, JSENB, China) containing kanamycin (50\u2009\u03bcg/mL) at 37\u2009\u00b0C. After OD600 reached 0.4\u20130.6, the cells were induced with 0.1\u2009mM IPTG at 18\u2009\u00b0C. After 18\u201324\u2009h, the cell pellets were harvested by centrifugation (5632 \u00d7 g, 3\u2009min at 4\u2009\u00b0C), and then resuspended in 15\u2009mL lysis buffer (50\u2009mM NaH2PO4 pH 8.0, 300\u2009mM NaCl, 30\u2009mM imidazole, pH 8.0). Then cells were disrupted by sonication on ice, and the cell debris was removed by centrifugation at 5632 \u00d7 g for 50\u2009min at 4\u2009\u00b0C. The supernatant was collected and loaded onto a pre-equilibrated column (His TrapTM HP, 5\u2009mL, GE Healthcare), and eluted with different concentrations of elution buffer (50\u2009mM NaH2PO4, pH 8.0, 300\u2009mM NaCl, 30/300\u2009mM imidazole)59. The purified protein solution was added with approximately 0.5\u2009mL glycerol (25%) and stored at \u221280\u2009\u00b0C.\n\nThe recombinant plasmids for PT, PC and CH were introduced into yeast strain Saccharomyces cerevisiae WAT11 for heterologous expression. The yeast cells were grown in synthetic dropin medium without leucine (SD-Leu). Liquid cultures of the recombinant strains were set up by picking a single colony and growing in 50\u2009mL of SD-Leu medium containing 20\u2009g/L glucose at 28\u2009\u00b0C overnight. The cells were collected by centrifugation (1000\u2009g, 2\u2009min) and resuspended in 25\u2009mL of SD-Leu medium containing 20\u2009g/L galactose to induce target protein expression for 24\u201348\u2009hours at 28\u2009\u00b0C. The microsomes of yeast cells were prepared as reported31.\n\nThe purified proteins and prepared microsomes were used for functional characterization by in vitro enzymatic reactions. The reactions were conducted in 100\u2009\u03bcL Tris-HCl buffer (50\u2009mM, pH 8.0) containing 50\u2009\u03bcg purified enzymes or 20\u2009\u03bcL microsomes. The incubation mixtures include substrates (0.1\u2009mM, malonyl-CoA for PCSs, noreugenin for PTs, peucenin for PCs, visamminol for OMTs and CHs, 5-O-methylvisamminol for CHs and SdUGT1/2, norcimifugin for OMTs, and cimifugin for SdUGT1/2), and donors/cofactors (0.5\u2009mM, dimethylallyl pyrophosphate (DMAPP) and MgCl2 for PTs, nicotinamide adenine dinucleotide phosphate (NADPH) for PCs and CHs, S-adenosylmethionine (SAM) and dithiothreitol (DTT) for OMTs, and uridine diphosphate glucose (UDPG) for SdUGT1/2). The reactions continued in a shaking incubator for 2\u2009hours (37\u2009\u00b0C for OMTs and UGTs, 30\u2009\u00b0C for PCSs, PTs, PCs and CHs). For PCSs, reactions were terminated by adding 10\u2009\u03bcL 20% HCl followed by extraction with 300\u2009\u03bcL ethyl acetate and redissolution in 100\u2009\u03bcL MeOH. The other reactions were terminated by adding 100\u2009\u03bcL ice-cold MeOH. The mixtures were then centrifuged at 21,130 \u00d7 g for 20\u2009min. The supernatants were analyzed by HPLC and LC/MS.\n\nSamples were separated on a Zorbax SB-C18 column (4.6\u2009\u00d7\u2009250\u2009mm, 5 \u03bcm, Agilent, USA). The HPLC methods are shown in Supplementary Table\u00a020. LC/MS analysis was performed on a Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer equipped with a heated ESI source (Thermo Fisher Scientific, USA). The MS parameters were as follows: sheath gas pressure 45 arb, aux gas pressure 10 arb, discharge voltage 4.5\u2009kV, capillary temperature 350\u2009\u00b0C. MS1 resolution was set as 70,000 FWHM, AGC target 1*E6, maximum injection time 50\u2009ms, and scan range m/z 100\u20131000. MS2 resolution was set as 17,500 FWHM, AGC target 1*E5, maximum injection time 100\u2009ms, NCE 35.\n\nTo optimize the pH value, different reaction buffers with pH from 4.0\u20136.0 (citric acid-sodium citrate buffer), 6.0\u20138.0 (Na2HPO4-NaH2PO4 buffer), 7.0\u20139.0 (Tris-HCl buffer), and 9.0\u201311.0 (Na2CO3-NaHCO3 buffer) were tested. To optimize the reaction temperature, the reactions were incubated at 4, 18, 30, 37, 45, or 60\u2009\u00b0C. All enzymatic reactions (100\u2009\u03bcL reaction mixtures, the same as those used for enzyme activity assay) were conducted in three parallel experiments (n\u2009=\u20093). The reactions were terminated and centrifuged at 21,130 \u00d7 g for 20\u2009min for HPLC analysis as described above. The conversion rates in percentage were calculated from peak areas of products and substrates in HPLC/UV chromatograms (Agilent 1260, USA) (The peak area of product divided by the total peak area of product and substrate). The catalytic efficiency of SdPCS was evaluated by the peak area of target product (2).\n\nReactions were conducted in a final volume of 50\u2009\u03bcL with 50\u2009mM reaction buffer, suitable concentration of protein or microsome, 1\u2009mol/L of saturated donors/cofactors (UDPG for SdUGT1/2, DMAPP and MgCl2 for SdPT), and different concentrations of substrate (5 or 8 for SdUGT1/2, 2 for SdPT, 1 for SdPCS) (Supplementary Table\u00a021, Supplementary Fig.\u00a09d, Supplementary Fig.\u00a011d). The reactions were quenched (adding 70\u2009\u03bcL pre-cooled methanol for SdUGT1/2 and SdPT, adding 5\u2009\u03bcL 20% HCl followed by extraction with 300\u2009\u03bcL ethyl acetate and redissolution in 100\u2009\u03bcL MeOH for SdPCS) after incubating at the optimal temperature for a certain reaction time (15\u2009min for SdUGT1/2, 60\u2009min for SdPT and SdPCS), and then centrifuged at 21,130 \u00d7 g for 20\u2009min. The supernatants were used for HPLC analysis. All experiments were performed in triplicate. The conversion rates were calculated as described above. To determine the yields of 2 produced by SdPCS, we acquired its regression equation by testing calibration standard solutions (7.78 \u03bc\u039c reference standard 2 diluted by 2, 4, 8, 16, 32, 64, 128 and 256 folds, Supplementary Fig.\u00a099), and calculated the conversion rates of each reaction according to HPLC/UV analysis. The kinetic parameters were calculated with Michaelis-Menten plot fitted by Graphapad Prism 8.060.\n\nTo prepare the prenylated product, the reaction mixtures contained 100\u2009\u03bcL buffer (50\u2009mM Tris-HCl, pH 8.0), 0.2\u2009mM noreugenin, 1.0\u2009mM DMAPP, 2.0\u2009mM MgCl2, and 20\u2009\u03bcL microsome. A total of 1200 parallel tube reactions were conducted. The reactions were performed at 30\u2009\u00b0C overnight and terminated by extraction with 4-fold volume of ethyl acetate. The organic solvent was removed under reduced pressure. The residue was dissolved in 1.5\u2009mL of methanol. The products were then purified by reversed-phase semi-preparative HPLC. The structures were characterized by HRMS and extensive 1D and 2D NMR analyses.\n\nTo prepare the hydrolyzed product of visamminol-3\u2019-O-glucoside, the reaction mixture contained 20\u2009mL buffer (50\u2009mM NaH2PO4-Na2HPO4, pH 6.0), 0.5\u2009mM visamminol-3\u2019-O-glucoside, and 200\u2009mg \u03b2-glucosidase (Solarbio, Beijing, China). A total of 5 parallel tubes were used. The reactions were performed at 45\u2009\u00b0C for 4\u2009hours and terminated by extraction with 4-fold volume of ethyl acetate. The extract was treated as described above.\n\nThe full-length cDNA of SdUGT2 was cloned into pET-28a (+) vector. The S-tag of pET28a was removed. A TrxA-tag and 6\u00d7His-tag followed by thrombin site were added before the N-terminus of the target protein to facilitate purification. The TrxA-His-thrombin-SdUGT2 protein was expressed in E. coli (DE3) strain and purified by Ni-affinity chromatography (GE Healthcare). After purification, the recombinant protein was digested by thrombin to remove tag (4\u2009\u00b0C, 8\u2009h). The sample was mixed with Ni-NTA affinity beads for the second time to purify the protein. The flow-through was concentrated and then applied to size-exclusion chromatography on a SuperdexTM 200 increase 10/300 GL prepacked column (GE Healthcare) for further purification. The elution buffer was 20\u2009mM Tris-HCl (pH 7.5) and 50\u2009mM NaCl. Fractions containing SdUGT2 were collected and concentrated to 20\u2009mg/mL, flash-frozen on liquid nitrogen, and then stored in a \u221280\u2009\u00b0C freezer. The purified protein was incubated with 6\u2009mM UDP for 2\u2009h. The crystals of SdUGT2 were obtained after 5 days at 16\u2009\u00b0C in hanging drops containing 1\u2009\u03bcL of protein solution and 1\u2009\u03bcL of reservoir solution (0.2\u2009M lithium sulfate monohydrate, 0.1\u2009M Bis-Tris pH 5.25, 28% w/v polyethylene glycol 3,350) (Supplementary Fig.\u00a028). The crystals were flash-frozen in the reservoir solution supplemented with 25% (v/v) glycerol.\n\nThe diffraction data of SdUGT2 crystal were collected at beamlines BL19U1 and BL02U1 Shanghai Synchrotron Radiation Facility (SSRF). The data were processed with XDS. The structures were solved by molecular replacement with Phaser. Crystallographic refinement was performed repeatedly with Phenix and COOT. The refined structures were validated by Phenix and the PDB validation server (https://validate-rcsb-1.wwpdb.org/). The final refined structures were deposited in the Protein Data Bank. The diffraction data and structure refinement statistics are shown in Supplementary Table\u00a014.\n\nSince all the reported UGT structures are highly conserved for the UDP-sugar binding domain, we simulated the SdUGT2/UDPG sugar complex structures by superimposing the UDP parts of UDPG to reported structures. With reference to the docking parameters of UGT71AP2, the axis of grid box for SdUGT1/2 is x\u2009=\u2009\u221231.157, y\u2009=\u2009\u221221.476, and z\u2009=\u2009\u221211.44361. Then we performed Auto-Dock analysis by Lamarckian Genetic Algorithm with default parameters for 250,000 evaluations in 100 cycles, and the other parameters followed the default settings. We selected conformations for further structural analysis according to the binding energies and possibilities for glycosylation reactions to happen. For SdUGT2 in complex with UDPG and 8, we selected two conformations representing 11-O-glucosylation and 3\u2032-O-glycosylation, respectively, among a set of 46 conformations with the lowest binding energy. For SdUGT2 in complex with UDPG and 9, we selected one conformation from a set of two conformations with the second lowest binding energy, since no conformation with lower binding energy was suitable for further 3\u2032-O-glycosylation (Supplementary Fig.\u00a0100).\n\nThe full-length DNA sequences of SdPCS, SdPT, SdPC, SdOMT, SdCH and SdUGT1/2 were amplified with primers given in Supplementary Table\u00a019. The PCR products were sub-cloned into pDonr207 vectors with the Gateway BP Clonase II Enzyme Mix and then cloned into pEAQ-HT-DEST1 vector with the Gateway LR Clonase II Enzyme Mix according to the manufacturer\u2019s instructions62. The recombinant pEAQ-HT-DEST1-target gene vectors were transformed into Agrobacterium tumefaciens strain GV3101 by chemical conversion method. Single colonies were inoculated at 28\u2009\u00b0C and subsequently shaked in LB culture medium (50\u2009\u03bcg/mL kanamycin and 50\u2009\u03bcg/mL rifampicin) until OD600\u2009=\u20090.6. After centrifugation, bacteria were re-suspended in MMA buffer to OD600\u2009=\u20090.2 for each strain. Different strains were mixed for transformation. The infection solution was infiltrated into leaves of 5\u20136 weeks old N. benthamiana. After 7 days, the samples were harvested and freeze-dried. The secondary metabolites were extracted by methanol and analyzed by LC/MS. The contents of compounds 5, 6, 8 and 9 were quantified by regression equations. Reference standards 5, 6, 8 and 9 were respectively dissolved in DMSO to make solutions of 1\u2009mg/mL, which were 1:1 mixed to obtain the mixed stock solution. The stock solution was serially diluted with methanol containing 4\u2009\u03bcg/mL bergenin as internal standard to obtain calibration standard solutions (diluted by 2, 4, 8, 16, 32, 64, 128, 256, 512, 1,024, 2,048, 4,096, 8,192, 16,384, 32,768, 65,536 and 131,072 folds, respectively). The regression equations of 5, 6, 8 and 9 were listed in Supplementary Figs.\u00a0101\u2013104. The LC/MS method parameters are listed in Supplementary Table\u00a020. The data were analyzed using XcaliburTM 4.3 software. The yields of 6 and 9 in each group were the average contents of six independent biological replicates.\n\nThe secondary metabolites of different Apiaceae plants were extracted by methanol, and analyzed by LC/MS following the methods mentioned above.\n\nNo data were excluded from the analyses. The experiments were not randomized.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "Data supporting the findings of this study are available in the article, supplementary materials, or public database. The gene sequence data generated in this study have been deposited in the NCBI database under the accession numbers listed in Supplementary Table\u00a022. The crystal structure in this study has been deposited in the RCSB PDB database under the accession number: SdUGT2 (8ZNK, https://www.rcsb.org/structure/8ZNK). The assembled genome and annotation files of S. divaricata are available at figshare [https://doi.org/10.6084/m9.figshare.25904887.v1]. The raw sequence data for the PacBio HiFi reads, Hi-C reads and RNA-Seq reads of Saposhnikovia divaricata generated in this study have been deposited in the Genome Sequence Archive at the National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation under BioProject number PRJCA036214. The sources of genome data and RNA-seq data of other Apiaceous plants are listed in Supplementary Data\u00a03, 4.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+        },
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+            "section_name": "Acknowledgements",
+            "section_text": "This work was supported by the National Key Research and Development Program of China (No. 2023YFA0914100 to M.Y., and No. 2023YFA0915800 to L.W.), Beijing Natural Science Foundation (No. QY23076 to J.L.Z., and 83001Y0439 to C.X.Z.), and National Natural Science Foundation of China (No. 81725023 to M.Y.). We thank Dr. Rong-shen Wang and Xi-ran Zhang of Ye Lab and Xun-meng Feng and Jiao-jiao Ji of Wang Lab for their technical assistance. We also thank the staff at BL19U1/BL02U1 beamlines at SSRF of the National Facility for Protein Science in Shanghai (NFPS), Shanghai Advanced Research Institute, Chinese Academy of Sciences, for providing technical support in X-ray diffraction data collection and analysis. We use the element syringe from SciDraw website in Fig.\u00a04b (https://scidraw.io/about/).",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Jian-lin Zou, Hong-ye Li, Bao Nie.\n\nState Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Beijing, 100191, China\n\nJian-lin Zou,\u00a0Hong-ye Li,\u00a0Zi-long Wang,\u00a0Chun-xue Zhao,\u00a0Yun-gang Tian,\u00a0Meng Zhang,\u00a0Hao-tian Wang\u00a0&\u00a0Min Ye\n\nShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China\n\nBao Nie,\u00a0Li-qun Lin,\u00a0Zhuang-wei Hou,\u00a0Wen-kai Sun,\u00a0Xiao-xu Han\u00a0&\u00a0Li Wang\n\nCivil Aviation Medicine Center, Civil Aviation Administration of China, A-1 Gaojing, Beijing, 100123, China\n\nWei-zhe Xu\u00a0&\u00a0Qing-yan Li\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nM.Y. and L.W. designed the research. M.Y., L.W., J.L.Z. and C.X.Z. acquired fundings for this study. B.N. contributed to the genome assembling and related bioinformatic analysis. J.L.Z. and H.Y.L. designed and performed the major experiments and data analysis. Z.L.W., Y.G.T., L.Q.L, W.Z.X., Z.W.H., W.K.S., X.X.H., M.Z., H.T.W. and Q.Y.L. assisted with experiments. M.Y., W.L., J.L.Z., H.Y.L. and B.N. wrote the manuscript. All authors have given approval to the final version of the manuscript.\n\nCorrespondence to\n                Li Wang or Min Ye.",
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+        {
+            "section_name": "About this article",
+            "section_text": "Zou, Jl., Li, Hy., Nie, B. et al. Complete biosynthetic pathway of furochromones in Saposhnikovia divaricata and its evolutionary mechanism in Apiaceae plants.\n                    Nat Commun 16, 3109 (2025). https://doi.org/10.1038/s41467-025-58498-8\n\nDownload citation\n\nReceived: 22 July 2024\n\nAccepted: 18 March 2025\n\nPublished: 01 April 2025\n\nVersion of record: 01 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58498-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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@@ -0,0 +1,148 @@
+{
+    "title": "Density-wave-like gap evolution in La3Ni2O7 under high pressure revealed by ultrafast optical spectroscopy",
+    "pre_title": "Density-wave-like gap evolution in La3Ni2O7 under high pressure revealed by ultrafast optical spectroscopy",
+    "journal": "Nature Communications",
+    "published": "29 November 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54518-1/MediaObjects/41467_2024_54518_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Transparent Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54518-1/MediaObjects/41467_2024_54518_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54518-1/MediaObjects/41467_2024_54518_MOESM3_ESM.zip"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-024-54518-1#MOESM1",
+        "/articles/s41467-024-54518-1#Sec11"
+    ],
+    "code": [],
+    "subject": [
+        "Phase transitions and critical phenomena",
+        "Superconducting properties and materials"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4592948/v1.pdf?c=1732972070000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4592948/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-54518-1.pdf",
+    "preprint_posted": "26 Jun, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Density-wave-like (DW) order is believed to be correlated with superconductivity in the recently discovered high-temperature superconductor La3Ni2O7. However, experimental investigations of its evolution under high pressure are still lacking. Here, we investigate the quasiparticle dynamics in bilayer nickelate La3Ni2O7 single crystals using ultrafast optical pump-probe spectroscopy under high pressures up to 34.2 GPa. Near ambient pressure, the temperature-dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of a DW gap at 151 K, with an energy scale of 66 meV as determined by the Rothwarf-Taylor model. With increasing pressure, this phonon bottleneck effect is gradually suppressed and completely disappears around 26 GPa. Remarkably, at pressures above 29.4 GPa, we observe the emergence of a new DW order with a transition temperature of approximately 130 K. Our study provides the first experimental evidence of the evolution of the DW gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in La3Ni2O7. These findings highlight the complex electronic phase transitions in this material and underscore the role of high pressure in tuning its superconducting and DW properties.Physical sciences/Physics/Condensed-matter physics/Superconducting properties and materialsPhysical sciences/Physics/Condensed-matter physics/Phase transitions and critical phenomena",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "supplementaryinformation.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Density wave (DW) order is believed to be correlated with superconductivity in the recently discovered high-temperature superconductor La3Ni2O7. However, experimental investigations of its evolution under high pressure are still lacking. Here, we explore the quasiparticle dynamics in bilayer nickelate La3Ni2O7 single crystals using ultrafast optical pump-probe spectroscopy under high pressures up to 34.2\u2009GPa. At ambient pressure, the temperature-dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of an energy gap around 151\u2009K. The energy scale of the DW-like gap is determined to be 66\u2009meV by the Rothwarf-Taylor model. Combined with recent experiential results, we propose that this DW-like transition at ambient pressure and low temperature is spin density wave (SDW). With increasing pressure, this SDW order is significantly suppressed up to 13.3\u2009GPa before it completely disappears around 26\u2009GPa. Remarkably, at pressures above 29.4\u2009GPa, we observe the emergence of another DW-like order with a transition temperature of approximately 135\u2009K, which is probably related to the predicted charge density wave (CDW) order. Our study provides the experimental evidences of the evolution of the DW-like gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in La3Ni2O7.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Nickel-based superconductors have attracted significant attention since the first member Nd0.8Sr0.2NiO2 was discovered1,2,3,4. They have similar d electron configurations resembling cuprates, suggesting the potential high-temperature superconductivity. This hypothesis was further supported by recent findings, where La3Ni2O7 single crystal was found to show a superconducting transition with TC\u2009\u2248\u200980\u2009K at pressures above 14\u2009GPa5. Many experiments reported its superconducting properties at high pressures6,7,8,9. However, the mechanism of its superconductivity is still unclear and under debate10,11,12,13,14,15,16,17,18,19,20,21,22.\n\nThe interplay between density wave order and superconductivity is a widely investigated topic in high-temperature superconductors since they are expected to be strongly related23. In La3Ni2O7, two DW transitions have been proposed at ambient pressure when the temperature is decreased8,9,24,25,26,27,28,29,30. Various measurements, including resonant inelastic X-ray scattering (RIXS)29, muon spin rotation (\u03bcSR) experiments24,30, Nuclear magnetic resonance (NMR)25,28 have identified the presence of SDW transition around 150\u2009K. While the CDW transition was suggested in either the transport or optical conductivity measurements, with transition temperature varying from 110 to 130\u2009K5,9,27,28,31. The complex DW behaviors in La3Ni2O7, which was proposed to stem from the scattering between the multiple Fermi surface sheet contributed mainly by the two eg Ni \\({d}_{{x}^{2}-{y}^{2}}\\) and \\({d}_{{z}^{2}}\\) orbitals11,12,13,16,32,33,34,35, are significantly influenced by the temperature and pressure6,27,30. Thus, a thoroughly investigation of the evolution of the DW orders under pressure is crucial for unraveling the pairing mechanism of superconductivity in this nickelate.\n\nStudies of the DW orders in La3Ni2O7 at high pressure are currently insufficient due to the limited availability of experimental tools for reliable high-pressure measurements. Currently, most of the reported experimental results of La3Ni2O7 are focused on the transport properties. However, the signature of DW transition in the transport measurements usually becomes indistinguishable starting from 3\u2009GPa6,9,26,27. Recently, there have been a few tentative works to study the DW order at low pressure. For example, a \u03bcSR experiment30 indicates the DW order can persist at least to 2.3\u2009GPa with an enhanced DW transition temperature, but the data under pressure higher than 2.3\u2009GPa is still lacking. On the other hand, the superconducting volume fraction is relatively low and exhibits sample dependence, indicating that the currently available samples are highly inhomogeneous, as evidenced by X-ray diffraction8,36,37 and scanning transmission electron microscopy measurements8,38. The crystal imperfections, such as oxygen vacancies, and the existence of multiple structural phases may obscure the intrinsic properties of the correct phase responsible for superconductivity. Up to now, how the DW orders evolve under high pressure remains unknown and requires further investigation.\n\nHere, we report the evolution of DW-like orders in La3Ni2O7 under high pressure using ultrafast optical spectroscopy. Time-resolved optical pump-probe spectroscopy has been widely employed to study nonequilibrium quasiparticle dynamics in various materials exhibiting superconductivity and density wave phenomena, due to its extreme sensitivity to the presence of energy gap39,40. However, performing pump-probe experiments under high pressure and low temperature is challenging due to the technical difficulties in combining high-pressure equipment with cryogenic systems while maintaining optical access for ultrafast laser pulses41. Despite these challenges, such experiments provide valuable insights into the behavior of materials under extreme conditions42,43,44. In this work, we observed the DW gap opening near ambient pressure below a transition temperature TDW, as evidenced by the phonon bottleneck (PB) effect. The gap fitted by Rothwarf-Taylor (RT) model is \u0394DW\u2009=\u200966\u2009meV. As the pressure is increased from 0 to 13.3\u2009GPa, the low-pressure DW order is significantly suppressed with decreasing TDW. The PB effect is relatively weak at higher pressures and the energy gap decreases slightly with increasing pressure before becoming indistinguishable around 26\u2009GPa. Above 29.4\u2009GPa, another DW phase appears, indicated by the re-emergence of the PB effect and a drastic increase in the transition temperature. Our results report a thorough evolution of the complex DW orders under high pressure, providing key experimental information for understanding the mechanism of superconductivity in nickelate.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Figure\u00a01a shows the time-resolved reflectivity change \u0394R/R in La3Ni2O7 at several selected temperatures near ambient pressure. At high temperature, photoexcitation leads to a quick rise in the reflectivity, followed by a fast decay into a constant offset. The relaxation time exhibits minor variations as the temperature increases to 250\u2009K. Below 151\u2009K, an additional long-lived component with a negative amplitude appears, which relaxes more quickly and increases in amplitude as the temperature decreases further. This transition, where the initial positive change in \u0394R/R turns negative, corresponds to the expected DW-like transition at ambient pressure. Consequently, we attribute the fast decay signal to the electron-phonon thermalization and the slow-decay component to the recombination across the DW gap, as discussed in detail below. Accordingly, we fit the data using a single-component exponential function, \\(\\Delta R/R={{{{\\rm{A}}}}}_{{{{\\rm{f}}}}}{e}^{-t/{\\tau }_{{{{\\rm{f}}}}}}+C\\) above TDW, and two-component decay function, \\(\\Delta R/R={{{{\\rm{A}}}}}_{{{{\\rm{f}}}}}{e}^{-t/{\\tau }_{{{{\\rm{f}}}}}}-{{{{\\rm{A}}}}}_{{{{\\rm{s}}}}}{e}^{-t/{\\tau }_{{{{\\rm{s}}}}}}+C\\) at low temperature, where A and \u03c4 represent the relaxation amplitude and decay time, respectively (Supplementary Note\u00a01). The subscripts (f and s) denote the fast and slow relaxation processes, respectively. C is a constant offset. The experimental data can be fitted quite well as shown in Fig.\u00a01a. The extracted As and \u03c4s as a function of temperature are depicted in Fig.\u00a01b. Below TDW,\u00a0As increases sharply from zero, while \u03c4s shows a continuous divergence. Our subsequent analysis suggests that the anomalous behavior around TDW can be explained by a relaxation bottleneck associated with the opening of a DW-like gap.\n\na \u0394R/R signals at several selected temperatures near ambient pressure. The experiential data can be well fitted by one and two exponential decays above and below 151\u2009K, respectively. The solid lines are the fitting curves. b Temperature dependent amplitude As and relaxation time \u03c4s. As decreases to nearly zero around 151\u2009K, where \u03c4s shows a clear divergence. The solid lines are fitting results according to RT model. Error bars are the standard error in the exponential fitting.\n\nTo explain the slow relaxation process in La3Ni2O7, we employ the RT model45. It is a phenomenological model that was initially proposed to describe the relaxation of photoexcited carriers in superconductors where the formation of a gap in the electronic density of states leads to a relaxation bottleneck. When the energy gap is comparable to the phonon energy, the phonons emitted during quasiparticle relaxation can re-excite the quasiparticles, thereby impeding their relaxation back to equilibrium. The RT model has also been shown to be applicable to other systems with gap opening in the density of states, such as charge/spin density wave, and heavy fermion materials39,40. Based on this model, the thermally quasiparticle density nT is related to the transient reflectivity amplitude A via nT \u221d [A(T)/A(T\u21920)]\u22121\u00a0\u2212\u00a01. Combining the relationship of \\({n}_{T}\\propto \\sqrt{\\Delta (T)T}\\exp [-\\Delta (T)/T]\\), we obtain46:\n\nwhere the \u03a6 is the pump fluence, \u0394(T) is the temperature dependent gap energy, kB is the Boltzmann constant, and \u03b3 is a fitting parameter. In the RT model, the relaxation time near transition temperature is dominated by phonons with frequency \u210f\u03c9\u2009\u2265\u20092\u0394 transferring their energy to lower frequency phonons with \u210f\u03c9\u2009<\u20092\u0394, so the re-excitation of the condensed quasiparticles would stop. The relaxation time \u03c4 near transition temperature is given by46 :\n\nAssuming that \u0394(T) follows BCS temperature dependence \\(\\Delta (T)\\approx \\Delta (0)\\tanh \\left(1.74\\sqrt{\\frac{{T}_{c}}{T}-1}\\right)\\), we fit As and \u03c4s using Eq. (1) and (2). The results, represented by the solid lines in Fig.\u00a01b, yield a transition temperature TDW\u2009~\u2009151\u2009K and a gap energy \u0394(0)\u2009~\u200966\u2009meV which is in good agreement with the values previously reported by NMR25,28 and optical conductivity spectroscopy31. The excellent fit strongly supports our assumption of the formation of a gap in the electric density of states due to the development of DW order below TDW. We notice a similar work in ref. 47 where no PB effect was observed at ambient pressure. This discrepancy is probably due to the inhomogeneous nature of La3Ni2O77,8,8,36,37,38,48, as discussed in Supplementary Notes\u00a02 and 3.\n\nTo further investigate the evolution of DW order in La3Ni2O7 as a function of pressure, we perform ultrafast pump-probe measurements under high pressure up to 34.2\u2009GPa. Figure\u00a02 displays the temperature dependent transient reflectivity data at several selected pressures. The slow relaxation component with negative amplitude observed below TDW persists across all pressures. The same fitting procedures described earlier were applied to the data under various pressures. The extracted parameter \u03c4s is depicted in Fig.\u00a02 as scatter points. It is obvious that \u03c4s diverges around TDW for all pressures except 26 GPa. Above 29.4\u2009GPa, the relaxation time \u03c4s initially decreases slightly with increasing temperature, then sharply increases, exhibiting a quasi-divergent behavior at TDW\u2009~\u2009135\u2009K. This temperature dependence of \u03c4s closely resembles that near ambient pressure, suggesting the re-opening of an energy gap under pressures above 29.4\u2009GPa.\n\na 0\u2009GPa, b 4.2\u2009GPa, c 8.2\u2009GPa, d 13.3\u2009GPa, e 16.7\u2009GPa, f 19.7\u2009GPa, g 26\u2009GPa and h 34.2\u2009GPa. The negative component exists at low temperature, and vanishes at temperature higher than TDW for all pressures. The scatters in each panel are the extracted \u03c4s. Phonon bottleneck effect are clearly observed except for 26\u2009GPa, indicating the suppression of DW orders. Error bars indicate the standard error in the exponential fitting.\n\nIn order to obtain more detailed information on the gap evolution, the \u0394R/R signals as a function of pressure at 20\u2009K are plotted in Fig.\u00a03a. The negative amplitude monotonically reduces with increasing pressure and becomes indistinguishable at 26\u2009GPa, above which the negative signal appears again. Figure\u00a03b displays the fitting parameters As and \u03c4s as a function of pressure at 20\u2009K. As the pressure increases up to 2.2\u2009GPa, As drops dramatically, accompanied by a slight decrease of \u03c4s. Upon further compression, As decreases gradually towards zero while \u03c4s exhibits a quasi-divergence around 26\u2009GPa. According to Eq. (2), the relaxation time increases with the decrease of the gap energy at fixed temperature and vice versa. Therefore, the observed increase in \u03c4s with increasing pressure suggests a progressive suppression of the DW gap in this pressure range. Above 29.4\u2009GPa, the increase of As and the decrease of \u03c4s indicate the DW gap gets promoted again, consistent with the reappearance of the PB effect at higher TDW, as shown in Fig.\u00a02h.\n\na Pump-probe spectra at various pressures at 20\u2009K. The dashed line indicates the existence of negative decay component at 26\u2009GPa. b The extracted amplitude As and decay time \u03c4s as a function of pressure. As decreases with increasing pressure before starts to increase above 26\u2009GPa. \u03c4s shows a quasi-divergent character, indicating the suppression of DW orders around 26\u2009GPa. Error bars are the standard error in the exponential fitting.\n\nThe identical RT analysis was applied to the temperature dependence of the slow relaxation As and \u03c4s at high pressures (Supplementary Note\u00a04). The extracted TDW values are summarized in the Temperature-Pressure phase diagram in Fig.\u00a04. Based on the high pressure results above, the diagram can be divided into two major regions, DW-I, and DW-II, with a critical pressure of 26\u2009GPa. In the low-pressure region, the DW transition is gradually suppressed from 151\u00a0K near ambient pressure to 110\u2009K at 13.3\u2009GPa. TDW rapidly decreases to around 85\u2009K at 16.7\u2009GPa and then decreases slightly with pressure up to 19.7\u2009GPa. Since the PB effect is too weak to distinguish at 26\u2009GPa (Supplementary Note\u00a04), a value of 100\u2009K at which the negative decay component disappears, was added to Fig.\u00a04 as a hollow circle for comparison. Upon further compression, divergent behavior of \u03c4s appears again near 135\u2009K, suggesting the presence of another energy gap in the density of states. The transition temperature increases slightly with further increasing pressure.\n\nThe upper and bottom panels show the extracted energy gap \u0394DW and the DW transition temperature TDW, respectively. The onset temperature of superconductivity TC obtained from resistance measurements5 were indicated as the starts for comparison. The phase diagram is divided into two major regions, DW-I and DW-II, with a critical pressure of 26\u2009GPa. Upon compression, the SDW order in the low region is significantly suppressed up to around 13.3\u2009GPa as indicated by the shaded stripe, before it completely disappears around 26\u2009GPa. The phonon bottleneck effect at 26\u2009GPa is too weak to extract a reasonable energy gap, hence a temperature of 100\u2009K above which the negative decay component disappears is depicted as a hollow circle. Above 29.4\u2009GPa, another DW-like order with a transition temperature of \u00a0~135\u2009K reemerges, which is probably related to the predicted CDW order. The error bars on the upper panel are calculated as the standard error in the RT model fitting of As under pressure.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "The present work provides clear evidences of the presence of DW orders in La3Ni2O7 under high pressure. However, various broken-symmetry states such as superconductivity and spin/charge density wave will develop an energy gap below the phase transition temperature, resulting in a similar PB effect39,40. Combined with the recent RIXS29, \u03bcSR24,30 and NMR25,28 experiments where an SDW transition around 150\u2009K at ambient pressure has been identified, we attributed the DW order in the low-pressure region to the SDW. As shown in the upper panel of Fig.\u00a04, the extracted gap amplitude decreases from approximately 66\u2009meV near ambient pressure to around 20\u2009meV at 13.3\u2009GPa. The gradual suppression of the DW order with increasing pressure is consistent with the transport measurements6,8,9,26. It is worth mentioning that the PB effect above 13.3\u2009GPa is relatively weak and the energy gap is roughly independent of pressure below 26\u2009GPa, suggesting that the PB effect in the intermediate pressure range may have a different origin. Nevertheless, it does not originate from the superconductivity since the transition temperature obtained in present work is higher than the onset temperature of superconductivity in the resistance measurements5, as indicated in Fig.\u00a04. Moreover, the superconducting fraction volume has been demonstrated to be relatively low7, and hence superconductivity can not be captured by the ultrafast optical spectroscopy since it is a bulk-sensitive technique. After the long-range DW order is suppressed by pressure, the short-range order may persist in La3Ni2O79, resembling cuprates49 and iron-based superconductors50. The short-range orders may induce the opening of a small gap in the density of states, as evidenced by the weak PB effect in the pressure range of 13.3 to 26\u2009GPa. Another possibility is the coexistence of multiple structure variants in La3Ni2O7 single crystals, including the majority La3Ni2O7 (327) phase and minority La4Ni3O10 (4310) and La2NiO4 (214) phase, as demonstrated by electron microscopy7,51. The SDW order below 13.3\u2009GPa is unambiguous from the predominant 327 phase, while the weak features between 13.3 and 26\u2009GPa may be contributed by the minor 4310 phase after the SDW in the majority 327 phase was suppressed by pressure above 13.3\u2009GPa. At 26\u2009GPa, the PB effect is too weak to extract a reasonable gap, probably due to the complete suppression of the short-range orders in 327 phase and the DW order in 4310 phase52,53.\n\nRecent theoretical works have indicated that electron-phonon coupling alone is insufficient to trigger superconductivity, suggesting that the Cooper pairing mechanism is unconventional in pressurized La3Ni2O7 and may originate from antiferromagnetic fluctuation14,33,34,35. The suppression of SDW order above 13.3\u2009GPa observed in the present work, coinciding with the onset of superconductivity in the transport measurements5,9, suggests that magnetic fluctuations are particularly critical for understanding the pairing mechanism of superconductivity in this nickelate. Spin fluctuation has been considered to be the pairing mediator in unconventional superconductors, including cuprates54, iron pnictides and chalcogenides55, as well as infinite-layer nickelate56. Our phase diagram based on the ultrafast optical spectroscopic measurements, as shown in Fig.\u00a04, indicates that the La3Ni2O7 share similarity with these superconductors in their paring mechanism.\n\nFirst principle calculations revealed that La3Ni2O7 favors an antiferromagnetic ground state, under which the strong Fermi surface nesting evokes the electronic instability resulting in a potential structure transition from Fmmm symmetry to Cmmm or Cmcm symmetry21. However, the authors in ref. 21 also pointed out that the distortion of Ni-O bound length in the predicted CDW structure is less than 0.1\u2009\u00c5, making its experimental probe very challenging. The sensitivity of our technique to the presence of DW orders is further reinforced by the re-emergence of PB effect above 29.4\u2009GPa. The linear temperature-dependent resistance above TC, characteristic of strange-metal behavior, has been observed to persist up to 30\u2009GPa5,6. The strange-metal behavior does not preclude the existence of DW-II order since the DW-like features in our ultrafast spectra are very clear even under pressure up to 13.3\u2009GPa, while the resistance anomaly related to the DW order usually becomes undistinguishable above 3\u2009GPa5,6. Upon further compression, both the extracted transition temperature and energy gap increase slightly with increasing pressure, agreed nicely with the theoretic prediction21. Therefore, we attribute the DW-II phase to the predicted CDW. Below TDW, the As follows a typical BCS-like temperature dependence, reflecting the behavior of the CDW order parameter (Supplementary Fig.\u00a04). Whether this CDW coexists or competes with superconductivity needs further investigations in the sample with a high superconducting volume fraction.\n\nIn summary, we have presented ultrafast optical pump-probe measurements on recently discovered nickelate superconductor La3Ni2O7 crystal under pressure up to 34.2\u2009GPa. By analyzing the data with RT model, the evolution of DW-like orders under high pressure is revealed and summarized in a phase diagram. With increasing pressure, the SDW order, as demonstrated at ambient pressure, is significantly suppressed up to 13.3\u2009GPa before it completely disappears around 26\u2009GPa. Intriguingly, at pressures above 29.4\u2009GPa, another DW-like order with a transition temperature of approximately 135\u2009K re-emerges, which is probably related to the predicted CDW order. Our results not only provide the experimental evidence of the DW evolution under high pressure, but also offer insight into the underlying correlation between the DW order and superconductivity in pressured La3Ni2O7.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "Single-crystalline La3Ni2O7 samples were grown using a vertical optical-image floating-zone method at an oxygen pressure of 15 bar and a 5-kW Xenon arc lamp (100-bar Model HKZ, SciDre)5,27. A small piece of sample was cut from the crystal to measure the resistivity using standard four-probe method. The sample exhibits metallic behavior at ambient pressure and undergoes a clear drop in resistance around 80\u2009K at 16.7\u2009GPa (Supplementary Note\u00a05). Crystal structures of the samples were investigated by x-ray diffraction (Empyrean, Cu target) at 300\u2009K. The results indicate that the sample is a bilayer structure in Cmcm space group at room temperature and ambient pressure (Supplementary Note\u00a06). The sample with flat surface cut from the same crystal was used for the transient reflectivity measurements.\n\nHigh pressure was generated by screw-pressure-type nonmagnetic Be-Cu alloy diamond anvil cell (DAC) with a 500\u2009\u03bcm culet. Fine KBr powders were used as the pressure transmitting medium, which has been demonstrated could offer quasi-hydrostatic pressure condition for the pump probe measurments44. The sample chamber with a diameter of 300\u2009\u03bcm was made in a Rhenium gasket. La3Ni2O7 crystal with size of 150\u2009\u03bcm was loaded in the center of the chamber and a small ruby ball was placed aside the sample. The DAC was loaded in a continuous flow liquid helium cryostat with temperature varying from 10 to 300\u2009K. An additional thermal sensor was mounted on the force plate of the DAC for precise measurement of sample temperature. The pressure was calibrated using the ruby fluorescence shift at low temperatures for all the pump-probe experiments.\n\nAn achromatic pump-probe system based on a mode-locked Yb:KGW laser system was employed. The laser pulses with wavelength of 800\u2009nm and repetition rate of 50\u2009kHz was generated by the optical parametric amplifier, which was divided into two beams. One served as the probe beam, and another passed through a BBO crystal to generate a 400\u2009nm pump pulses. The pump and probe beams were focused onto the sample surface through a 5\u00a0\u00d7 objective lens. The focused spot diameters of the pump and the probe pulse were 37 and 17\u2009\u03bcm, respectively. The pulse duration, after passing through the cryostat window and diamond anvil, was measured to be 50\u2009fs. In the temperature and pressure dependence measurements, the pump and probe fluences on the sample were kept at 45 and 9\u2009\u03bcJ/cm\u22122, respectively. The pump beam was modulated by a chopper with a frequency of 433\u2009Hz and the reflected pump beam was filtered out. The reflected probe beams traversed through the same objective lens, received by a photo-diode detector and sampled by a lock-in amplifier to enhance the signal-to-noise ratio. The relative change of reflectivity \\(\\Delta R(t)/{R}_{0}=\\left[R(t)-{R}_{0}\\right]/{R}_{0}\\), where R and R0 are the reflectivity of the probe with and without the presence of pump pulses, respectively, was recorded as a function of the time delay between the pump and probe pulses.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data that support the conclusions of this study in are available within the paper and Supporting Information. Raw data generated during the current study are available from the corresponding author upon request.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_name": "Acknowledgements",
+            "section_text": "This work was supported by the National Natural Science Foundation of China (Grants No. 11974414, No. 12375304,\u00a0No. 12374050, No. 12134018, No. 12425404, and No. 12174454), the National Key Research and Development Program of China (Grants No. 2023YFA1608900, No. 2021YFA1400300, No. 2023YFA1406000, No. 2023YFA1406500), the GuangDong Basic and Applied Basic Research Foundation (Grants No. 2024A1515030030 and No. 2024B1515020040), the Shenzhen Science and Technology Program (Grant No. RCYX20231211090245050), and the Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices (Grant No. 2022B1212010008). This work was carried out at the Synergetic Extreme Condition User Facility (SECUF).",
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+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Yanghao Meng, Yi Yang, Hualei Sun.\n\nBeijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China\n\nYanghao Meng,\u00a0Yi Yang,\u00a0Jianlin Luo,\u00a0Liucheng Chen,\u00a0Xiaoli Ma,\u00a0Fang Hong,\u00a0Xinbo Wang\u00a0&\u00a0Xiaohui Yu\n\nSchool of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China\n\nYanghao Meng,\u00a0Jianlin Luo,\u00a0Fang Hong,\u00a0Xinbo Wang\u00a0&\u00a0Xiaohui Yu\n\nKey Laboratory of Education Ministry for Laser and Infrared System Integration Technology, Shandong University, Qingdao, China\n\nYi Yang\u00a0&\u00a0Sasa Zhang\n\nSchool of Science, Sun Yat-sen University, Shenzhen, China\n\nHualei Sun\n\nSchool of Information Science and Engineering, Shandong University, Qingdao, China\n\nSasa Zhang\n\nCenter for Neutron Science and Technology, Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou, China\n\nMeng Wang\n\nSongshan Lake Materials Laboratory, Dongguan, Guangdong, China\n\nFang Hong\u00a0&\u00a0Xiaohui Yu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.M., Y.Y., and H.S. contribute equally to this work. X.W., X.Y., F.H., and M.W. designed the project; Y.M. and Y.Y. performed the optical experiments; S.Z. and J.L. contributed to the development of ultrafast optical system. H.S. synthesized the crystals. H.S. and Y.M. characterized the samples with the assistance of L.C. and X.M.; Y.M., Y.Y., and X.W. analyzed the data and wrote the manuscript. All authors participated in the discussion and comment on the paper.\n\nCorrespondence to\n                Meng Wang, Fang Hong, Xinbo Wang or Xiaohui Yu.",
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+            "section_name": "About this article",
+            "section_text": "Meng, Y., Yang, Y., Sun, H. et al. Density-wave-like gap evolution in La3Ni2O7 under high pressure revealed by ultrafast optical spectroscopy.\n                    Nat Commun 15, 10408 (2024). https://doi.org/10.1038/s41467-024-54518-1\n\nDownload citation\n\nReceived: 17 June 2024\n\nAccepted: 08 November 2024\n\nPublished: 29 November 2024\n\nVersion of record: 29 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54518-1\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+{
+    "title": "MS CETSA deep functional proteomics uncovers DNA repair programs leading to gemcitabine resistance",
+    "pre_title": "MS-CETSA functional proteomics uncovers new DNA-repair programs leading to Gemcitabine resistance",
+    "journal": "Nature Communications",
+    "published": "07 May 2025",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59505-8/MediaObjects/41467_2025_59505_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59505-8/MediaObjects/41467_2025_59505_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Supplementary Data 1",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59505-8/MediaObjects/41467_2025_59505_MOESM3_ESM.xlsx"
+        },
+        {
+            "label": "Supplementary Data 2",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59505-8/MediaObjects/41467_2025_59505_MOESM4_ESM.xlsx"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59505-8/MediaObjects/41467_2025_59505_MOESM5_ESM.pdf"
+        },
+        {
+            "label": "Transparent Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59505-8/MediaObjects/41467_2025_59505_MOESM6_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-59505-8/MediaObjects/41467_2025_59505_MOESM7_ESM.zip"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "http://proteomecentral.proteomexchange.org/",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054912",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054911",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054910",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054909",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054908",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054907",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054903",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054902",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054901",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054854",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054853",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD054852",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD055016",
+        "https://www.ebi.ac.uk/pride/archive/projects/PXD055015",
+        "/articles/s41467-025-59505-8#ref-CR38",
+        "/articles/s41467-025-59505-8#Sec34"
+    ],
+    "code": [],
+    "subject": [
+        "DNA damage and repair",
+        "Protein\u2013protein interaction networks"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4820265/v1.pdf?c=1746702465000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4820265/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-025-59505-8.pdf",
+    "preprint_posted": "19 Aug, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Mechanisms for resistance to cytotoxic cancer drugs are dependent on dynamic changes in the biochemistry of cellular pathways, information which is hard to obtain at the systems level.  Here we use a deep functional proteomics implementation of CETSA (Cellular Thermal Shift Assay) revealing a range of induced biochemical responses to gemcitabine in resistant and sensitive diffuse large B cell lymphoma (DLBCL) cell lines. Initial responses in both, gemcitabine resistant and sensitive cells, reflect known targeted effects by gemcitabine on ribonucleotide reductase and DNA damage responses. However, after 3-5 hours the responses diverge dramatically where sensitive cells show induction of characteristic CETSA signals for early apoptosis, while resistant cells reveal biochemical modulations reflecting transition through a distinct DNA-damage signaling state, including opening of cell cycle checkpoints and induction of translesion DNA synthesis (TLS) programs allowing bypass of damaged DNA-adducts. The data also reveal the induction of a new program, labeled the Auxiliary DNA Damage Repair (ADDR) protein ensemble likely supporting DNA replication at damaged sites. We show that this response can be attenuated in resistant cells by an ATR inhibitor re-establishing gemcitabine sensitivity and demonstrate ATR as a key signaling node of this response.Biological sciences/Cell biology/Mechanisms of diseaseBiological sciences/Chemical biology/Mechanism of actionBiological sciences/Cancer/Tumour biomarkersBiological sciences/Biotechnology/Proteomics/Protein–protein interaction networksMS-CETSAIMPRINTSgemcitabineresistanceDLBCLATRADDRTLS",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "Yes there is potential Competing Interest.\nProf. Nordlund is the inventor of patents related to the CETSA method and is a\r\ncofounder and board member of Pelago Biosciences AB.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplementaryTable1.xlsxSupplementaryTable2.xlsx",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Mechanisms for resistance to cytotoxic cancer drugs are dependent on dynamic changes in the biochemistry of cellular pathways, information which is hard to obtain at the systems level. Here we use a deep functional proteomics implementation of the Cellular Thermal Shift Assay to reveal a range of induced biochemical responses to gemcitabine in resistant and sensitive diffuse large B cell lymphoma cell lines. Initial responses in both, gemcitabine resistant and sensitive cells, reflect known targeted effects by gemcitabine on ribonucleotide reductase and DNA damage responses. However, later responses diverge dramatically where sensitive cells show induction of characteristic CETSA signals for early apoptosis, while resistant cells reveal biochemical modulations reflecting transition through a distinct DNA-damage signaling state, including opening of cell cycle checkpoints and induction of translesion DNA synthesis programs, allowing bypass of damaged DNA-adducts. The results also show the induction of a protein ensemble, labeled the Auxiliary DNA Damage Repair, likely supporting DNA replication at damaged sites that can be attenuated in resistant cells by an ATR inhibitor, thus re-establishing gemcitabine sensitivity and demonstrating ATR as a key signaling node of this response.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Cancer cells evade cytotoxic drugs through activation of resistance mechanisms. While drug-sensitive cancer cells induce cellular programs leading to cell death, a\u00a0wide range of cellular processes have been implied in resistant cells, including modulations of drug transport1 or activation2, induction of apoptosis blockade3, bypass of oncogene inhibition by mutations of\u00a0drug binding site4, activation of parallel driver pathways5, as well as modulation of tumor microenvironment6 and cell-to-cell signaling7,8. It is likely that multiple resistance mechanisms are established simultaneously\u00a0 to overcome drug action.\n\nDetailed insights into which resistance promoting programs are operating in cancers of individual patients at different stages of therapy could arguably be transformative for selection of optimal drug combinations and staging in personalized therapy, as well as for identifying novel drug targets to attenuate resistance responses. However, conclusive\u00a0 elucidation of cancer drug\u00a0resistance mechanisms is often challenging when they can involve complex remodeling of cellular pathways. Typically, resistance mechanisms are addressed using genomic or transcriptomic approaches, most often assessing static differences between cancer patient samples or sensitive and resistant cancer cells in model systems9,10,11. Although such studies can access key mutations and RNA level changes implicative of resistance, cellular pathways and processes are highly regulated at the biochemical level, information only indirectly accessed\u00a0using these methods. Moreover, comparison of static cells does not address drug-induced cellular\u00a0responses which\u00a0can play key roles in resistance to cytotoxic cancer drugs but are normally not activated during ambient cancer cell growth. Notably, some cancer drug-induced resistance responses can be efficiently studied with focused assays, such as the induction of reactive oxygen species, autophagy and chaperone activation. While useful, these studies require a priori knowledge on putative mode of resistance mechanisms, and do not provide an unbiased view on sequences of regulatory events.\n\nHere, we examined the induced modulation of cellular biochemistry leading to resistance towards one of the more commonly used cytotoxic cancer drugs \u2013 gemcitabine, an anti-neoplastic pyrimidine analog that replaces cytidine during DNA replication and inhibits ribonucleotide reductase (RNR)12. In various cancers\u00a0including pancreatic, breast, ovarian, non-small cell lung cancer and lymphoma, gemcitabine is employed either in the first-line or refractory setting. Diffuse large B-cell lymphoma (DLBCL) represents the most frequently occurring and aggressive form of non-Hodgkin\u2019s lymphoma. The anthracycline-based regimen R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) is the standard of care for first-line treatment with ~60% of the patients achieving complete response13. However 20-50% of patients do not respond, or relapse within the first two years of treatment14. Gemcitabine has recently been used in salvage regimens for DLBCL although resistance often develops15. Gemcitabine is a nucleotide prodrug that needs to be metabolized into its active phosphorylated form within cells to exert its effects (Fig.\u00a01A). RNR catalyzes the conversion of ribonucleoside diphosphates to deoxyribonucleoside diphosphates and is a major protein target for gemcitabine16,17. In its diphosphate form, gemcitabine inhibits RNR by forming a covalent adduct to the catalytic subunit (RRM1), or alternatively scavenging the free radical cofactor of RNR, thus depleting dNTP pools18. While genomic and transcriptomic studies have helped identify driver pathways and prognostic gene signatures in DLBCL19,20, this information remains of limited utility in guiding treatment regimens, especially in stratifying patients who may respond to specific salvage therapy agents.\n\nA Structure and thus far known MoA of gemcitabine. B IMPRINTS CETSA experimental workflow. Created in BioRender. Tam, W. (2025) https://BioRender.com/dufluq1. C Interpretation of IMPRINTS CETSA profiles. D IMPRINTS CETSA profiles of RRM1 in two sensitive, OCI-LY3 (orange) and OCI-LY19 (red), and two resistant, HT (dark green) and SUDHL4 (light green), cell lines after 1\u2009h, 3\u2009h, 5\u2009h or 8\u2009h of gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. E 3\u2009h Isothermal Dose Response (ITDR) of RRM1 in OCI-LY19 (red) and SUDHL4 (green) cells with different doses of gemcitabine and at 52\u2009\u00b0C CETSA heating. Data are presented as mean fold change compared to the reference from technical replicates (n\u2009=\u20092). Source data are provided as a Source Data file.\n\nTo better understand how sensitive or resistance biochemical pathways become selectively activated in response to therapeutics in DLBCL cells, we apply a time-dependent implementation of the deep functional proteomics method, IMPRINTS-CETSA (Integrated Modulation of Protein Interaction States - Cellular Thermal Shift Assay) to study gemcitabine-induced programs. CETSA reports on modulations of pathway activation at the biochemical level in intact cells by monitoring changes in protein interaction states, i.e., interactions made by individual proteins to other molecules in live cells reflecting protein activity and functional states21. MS-CETSA (Mass Spectrometry-based CETSA) is the first integrative technology that can directly assess such protein interaction states in intact cells and tissues but has not been used previously for deep characterization of induced drug resistance. In the present study, we reveal comprehensive and distinct information on the time-dependent biochemical responses of gemcitabine in sensitive and resistant DLBCL cells. Initial responses in both cell types reveal similar RNR inhibition and activation of DNA-damage signaling. However, the downstream response in sensitive cells reflects the characteristic CETSA signature for apoptosis induction22, while in resistant cells we observe cell cycle progression, translesion DNA synthesis (TLS) and describe the induction of a protein ensemble that likely support DNA repair. This response provides a rationale for gemcitabine resistance in DLBCL cells, which can be reversed by attenuating the DNA-repair inducing pathway with an ATR (ataxia telangiectasia and Rad3-related protein) inhibitor, and thereby re-establishing gemcitabine sensitivity. This study validates IMPRINTS-CETSA23 as an efficient approach to dissect induced cancer drug resistance pathways at the biochemical level and provide drug targets and biomarkers for combination therapies with potential applications in the clinic.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-59505-8/MediaObjects/41467_2025_59505_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "To study gemcitabine-induced resistance mechanisms we first evaluated the cell viability of a panel of DLBCL cell lines when challenged with a range of gemcitabine doses over 48\u2009h. Of the profiled cell lines, we selected two sensitive (OCI-LY19, IC50\u2009=\u20092.4\u2009nM and OCI-LY3, IC50\u2009=\u200914.4\u2009nM) and two resistant (SUDHL4, IC50 = not defined and HT, IC50\u2009=\u2009not defined) cell lines (Supplementary Fig.\u00a01A) to employ the highly sensitive IMPRINTS-CETSA format, whereby 3 biological replicates of treated cells were labeled together with their vehicle controls. To capture the dynamic cellular response upon drug treatment, sensitive OCI-LY19 and resistant SUDHL4 cells were treated for 4 time points (1\u2009h, 3\u2009h, 5\u2009h and 8\u2009h), while for comparison purposes sensitive OCI-LY3 and resistant HT cells were treated only at 2 timepoints (1\u2009h and 8\u2009h). For informative CETSA responses to be measurable, the drug concentration needs to be sufficiently high to induce molecular perturbations with high stoichiometry. We therefore selected 20X IC50 for the sensitive cells, i.e. 48\u2009nM for OCI-LY19 and 288\u2009nM for OCI-LY3. For both resistant cells we used 500X the concentration in relation to OCI-LY19, i.e. 24\u2009\u00b5M, but have additionally collected CETSA data at lower concentrations (480\u2009nM and 48\u2009nM) to monitor dose-dependent responses. We confirmed that treated cells remained intact and viable, as judged from a trypan blue assay, at the maximum timepoint for the CETSA experiments (Supplementary Fig.\u00a01B). IMPRINTS-CETSA was performed similarly in all cell lines using a 6-temperature protocol (Fig.\u00a01B) with the interpretation of IMPRINTS profiles explained in Fig.\u00a01C. CETSA is based on the biophysical concept that, with increasing temperatures, proteins denature and precipitate out of the soluble fraction resulting in distinct melting profiles for each protein. Changes in the protein interaction states through e.g. binding to a ligand (e.g. drug), interaction with other molecules (e.g. proteins, DNA, RNA, metabolites), or posttranslational modifications, can lead to a shift in the melting profile, which is observed as thermal stabilization or destabilization. An IMPRINTS-CETSA profile illustrates the difference in the abundance of measured soluble protein between vehicle and treatment conditions at a given temperature. The protein coverages and numbers of hits scored using our standard hit selection criteria (described in Materials & Methods) are shown in Supplementary Data\u00a01.\n\nThe association of gemcitabine with RNR was expected to increase protein stability and produce a thermal shift. Indeed, within 1\u2009h, the large and catalytic subunit, RRM1, displayed similar IMPRINTS profiles in both resistant and sensitive DLBCL cells, supporting extensive target engagement and inhibition of de novo deoxyribonucleotide synthesis (Fig.\u00a01D). This thermal stabilization was also seen at subsequent timepoints of 3\u2009h, 5\u2009h, and 8\u2009h. An isothermal dose response (ITDR) experiment also showed comparable dose-dependent stabilization of RRM1, supporting similar target engagement in sensitive and resistant cells (Fig.\u00a01E).\n\nApart from RNR inhibition, gemcitabine acts by being incorporated into DNA, inducing single strand DNA (ssDNA) breaks and stalled replication forks24. Indeed, five proteins - RPA1, RPA2, RPA3, CHEK1 and DNMT1 (Fig.\u00a02) - located at replication sites showed similar CETSA profiles across the various time points and cell lines (Fig.\u00a02). Out of these, 4 belong to the core molecular machinery for sensing and signaling ssDNA damage and stalled replication fork. The replication protein A (RPA) is a heterotrimeric complex consisting of 3 subunits - RPA1, RPA2, and RPA3. Upon genotoxic stress RPA is known to coat ssDNA and is subsequently hyperphosphorylated, initiating downstream DNA-damage response (DDR) pathways25. All three RPA subunits showed a thermal stabilization which likely reflect the ssDNA-bound form of RPA and hyperphosphorylation. Additionally, we observed a thermal destabilization of CHEK1, which was concomitant with its phosphorylation at Ser345 (Supplementary Fig.\u00a02A) and further validates the initiation of DDR pathways. The fact that the thermal shifts are present early in sensitive and resistant cell lines suggest that resistance mechanisms occur downstream of these initial responses. DNMT1 is a major enzyme involved in DNA methylation inheritance and plays a critical role in maintaining genome stability26. Notably, similar to the FDA-approved DNMT1 inhibitor decitabine, gemcitabine is also a cytidine analog. We therefore investigated the possibility of a direct soluble gemcitabine triphosphate-DNMT1 interaction in a cell lysate western blot CETSA experiment but did not see significant thermal stability shifts (Supplementary Fig.\u00a02B). Additionally, gemcitabine treatment did not result in DNMT1 degradation as is described for decitabine (Supplementary Fig. 2C). It instead appeared plausible that the observed CETSA effects report on the modulations of specific protein or DNA interactions with DNMT1 induced at the stalled replication fork.\n\nHypothetical model indicating proteins at the stalled replication fork after gemcitabine-induced DNA damage, and respective IMRPINTS profiles of RPA1, RPA2, RPA3, CHEK1 and DNMT1 in sensitive OCI-LY3 (orange) and OCI-LY19 (red), as well as resistant HT (dark green) and SUDHL4 cells (light green) after 1\u2009h, 3\u2009h, 5\u2009h or 8\u2009h of gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. Created in BioRender. Tam, W. (2025) https://BioRender.com/6fy4aw8.\n\nThrough the use and analyses of several apoptosis-inducing drugs, we recently identified a prototypic CETSA apoptosis response that is dominated by nuclear proteins and reflects very early apoptosis including caspase activation22. This response was characterized by 47 proteins which we termed the core CETSA apoptosis ensemble (CCAE), and this provided the first means for direct assessment of caspase activation in intact cells. When our current data was compared with the ensemble described above, 37 and 34 proteins were measured for the sensitive and resistant cells, respectively (Supplementary Fig. 3). Among those, 23 proteins were identified as hits in sensitive, but only 1 in resistant cells (Fig.\u00a03A). These results unequivocally conclude that apoptosis induction was indeed unique to sensitive cells, despite the far higher gemcitabine concentration used to treat resistant cells. To further verify that apoptosis is only induced in sensitive cells, we looked at PARP1 cleavage, a recognized hallmark of apoptosis, by western blot. Indeed, we only observed cleaved PARP1 in sensitive cells (Supplementary Fig. 4A). Although apoptosis can be initiated by ATR/CHEK1 signaling via p53-activation27, the lack of CETSA shifts in proteins recently defined as p53-regulated proteins in cell death, indicates that the observed processes here are independent of p5328. Our current study furthermore resolved the sequence of early apoptosis events and showed that the emergence of the CETSA apoptosis response in sensitive cells was clearly time dependent with proteins such as PARP1, XRCC5, XRCC6, MATR3, LMNB1, LMNB2, RBMX and ZC3H11A showing distinct thermal stability shifts as early as 3\u2009h and 5\u2009h following gemcitabine exposure (Fig.\u00a03B). For some proteins that are cleaved by caspases, we also described a \u201cregional stabilization due to proteolysis\u201d (RESP) effect, whereby stability changes in regions either N- or C-terminal of caspase cleavage sites were observed. This effect was also measured here in a subset of proteins including known caspase targets such as PARP1, LMNB1, MATR3 and DDX21 (Fig.\u00a03C).\n\nA Venn diagram showing overlap of the hits from sensitive OCI-LY19 (top) and resistant SUDHL4 cells (bottom) with the previously identified CCAE (Core CETSA Apoptosis Ensemble) proteins. B STRING plot showing the overlapping proteins from A with IMPRINTS CETSA profiles for OCI-LY19 (red hues) and SUDHL4 (green hues) after 1\u2009h, 3\u2009h, 5\u2009h and 8\u2009h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. C IMPRINTS profiles of PARP1, MATR3, LMNB1 and DDX21 showing peptides before and after known caspase cleavage sites in sensitive OCI-LY19 cells (red hues) and resistant SUDHL4 cells (green hues) after 1\u2009h, 3\u2009h, 5\u2009h and 8\u2009h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. D IMPRINTS profiles\u00a0of CCNA2, CCNB1, CCNB2 and CDK1 for OCI-LY19 (red hues) and SUDHL4 (green hues) after 1\u2009h, 3\u2009h, 5\u2009h and 8\u2009h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. E Progression of cell cycle and distribution of cells in different cell cycle phases in the sensitive OCI-LY19 and resistant SUDHL4 cells after 8\u2009h and 24\u2009h with and without gemcitabine treatment. A two-way ANOVA was performed, and data are presented as relative percentage of cells in each cycle phase \u00b1SEM from biological replicates (n\u2009=\u20094) with p-values denoting significant changes in G1 phase (blue). Source data are provided as a Source Data file.\n\nIn contrast to the prominent CETSA apoptosis signatures featured in the sensitive cells, we observed cell-cycle regulating processes as one of the dominant features in the response of resistant cells. Contributing to this were significant shifts for cyclins and cyclin-dependent kinases (CDKs), most prominently CCNA2, CCNB1, CCNB2 and CDK1 (Fig.\u00a03D). These proteins showed distinct time-dependent thermal stabilizations or abundance changes with similar IMPRINTS profiles as compared to our previously published cell cycle study23. The shifts indicated increased activation of CDK complexes that promoted G2/M and G1/S phase checkpoint transitions. To rule out that these effects are due to the higher gemcitabine concentration used in treatment of resistant cells, we consulted our additional low dose datasets that also included the same concentration as used for the sensitive cells (48\u2009nM). The findings supported similar modulations of cell cycle checkpoints in resistant cells at lower doses, demonstrating that this induced response spanned over a wide concentration range (Supplementary Fig. 4B). Additionally, in our previous cell cycle study, RB1 phosphorylation during G1/S checkpoint release resulted in a thermal stabilization. Here, we observed the opposite effect, i.e. thermal destabilization and thus dephosphorylation, in gemcitabine sensitive cells (Supplementary Fig. 4C). Analysis of cell cycle distribution using propidium iodide staining confirmed G1 arrest in sensitive cells, while resistant cells underwent normal cycling upon gemcitabine treatment (Fig.\u00a03E).\n\nNext, we sought to explain how resistant cells were able to proceed with the cell cycle, despite the exposure to a DNA synthesis inhibitor.\n\nDDR is dependent on the availability of dNTPs at appropriate levels for accurate DNA synthesis. SAMHD1, which exhibited significant destabilization in resistant cells (Supplementary Fig. 5A), is a regulator of dNTP homeostasis via its dNTPase activity29. Like CHEK1, we tested whether phosphorylation of SAMHD1 is concomitant with its thermal destabilization, but did not detect significant changes (Supplementary Fig.\u00a05B). When we knocked down SAMHD1 (Supplementary Fig.\u00a05C) as an attempt to re-establish gemcitabine sensitivity, we instead observed a slight increase in resistance (Supplementary Fig.\u00a05D). LC/MS measurements of deoxyribonucleotide (and ribonucleotide) pools showed differences between SUDHL4 SAMHD1 WT and KO cells primarily in dGNP and dANP abundance; this is consistent with SAMHD1 being a dNTP hydrolase of purine nucleotides (Supplementary Fig.\u00a05E). In a more compact 3-temperature IMPRINTS-CETSA experiment we found similar gemcitabine responses between SUDHL4 WT and KO cells. However, a notable difference was a much weaker stabilization of RRM1 after gemcitabine treatment in the KO cells (Supplementary Fig. 5F). As gemcitabine-triphosphate is a substrate of SAMHD130,31, we reasoned that the reduced cycling between different phosphorylation states of gemcitabine in the SAMHD1 KO cells affected the cellular concentration of the inhibitory diphosphate form of gemcitabine, thereby causing the reduction of RRM1 engagement. This might subsequently lead to the observed attenuated deoxyribonucleotide pools in the KO cells, explaining the increase in resistance.\n\nInterestingly, \u201ctranslesion synthesis\u201d (TLS) appeared as a prominent pathway only in resistant cells at 8\u2009h (Fig.\u00a04A). TLS is a process that facilitates DNA synthesis over damaged lesions by reorganizing replication complexes through the recruitment of specialized DNA repair polymerases. In addition to subunits of ssDNA binding proteins RPA1, RPA2 and RPA3, we observed pronounced time dependent thermal stabilization and abundance increases of two key proteins associated with TLS: PCNA binding protein (PCLAF) and Denticleless Protein Homolog (DTL). Accompanying these changes, we observed strong thermal destabilization of the core catalytic subunits of the replicative\u00a0DNA polymerase \u03b4 (PolD), POLD1, POLD2 and POLD4, the latter also depicting a decrease in abundance levels. To investigate the possible induction of dedicated TLS polymerases as a putative mechanism to overcome DNA damage and hence gemcitabine resistance, we examined the protein levels of several of the repair/TLS polymerases after 1\u2009h, 3\u2009h, 5\u2009h and 8\u2009h of gemcitabine exposure in both sensitive and resistant cells. POL\u03b7, POL\u03b9, and Rev1 did not show any difference in protein levels (Supplementary Fig. 6A). Notably, however, we observed a time-dependent reduction of POL\u03ba protein abundance only in sensitive cells (Supplementary Fig.\u00a06B), which was restored in the presence of the pan-caspase inhibitor zVAD-FMK (Supplementary Fig.\u00a06C). This suggests an active elimination of DDR mechanisms through caspases upon the irreversible commitment to apoptosis. To further validate that the observed CETSA shifts indeed reported on TLS activation, we employed an orthogonal standard TLS assay, whereby the ubiquitination status of the DNA clamp protein, PCNA, is assessed. Upon DNA damage, mono-ubiquitination of PCNA primes access to DNA by TLS polymerases32. We measured the levels of total versus mono-ubiquitinated PCNA after gemcitabine treatment and indeed only observed TLS activation in resistant cells (Fig.\u00a04B). Next, we sought to explore the effect on gemcitabine resistance by disrupting TLS with a REV7/REV3 interaction inhibitor. We indeed found synergistic effects between gemcitabine and REV7/REV3-In-1 (Fig.\u00a04C). From these findings, we postulate a gemcitabine resistance mechanism that involves the release of replicative DNA polymerase (PolD), indicated by thermal destabilizations, followed by mono-ubiquitination of PCLAF, which facilitates access to TLS polymerases and restart of replication fork to bypass DNA damage-induced replication arrest and apoptosis (Fig.\u00a04D).\n\nA Nodes indicating translesion synthesis pathway as a GO term and IMPRINTS profiles of the involved proteins in OCI-LY19 (red hues) and SUDHL4 (green hues) after 1\u2009h, 3\u2009h, 5\u2009h and 8\u2009h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. B Representative western blot of PCNA and mono-ubiquitinated PCNA (top) and quantification of ubPCNA/PCNA ratio (bottom) in the sensitive OCI-LY19 (red) and resistant SUDHL4 (green) cells after 6\u2009h of gemcitabine (or vehicle) treatment. A two-way ANOVA was performed comparing vehicle versus gemcitabine treatment, and data are presented as mean ubPCNA to total PCNA ratio \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. C ZIP Synergy score of gemcitabine and rev7/3-in-1 concentrations in SUDHL4 cells at 48\u2009h. D Hypothetical model of gemcitabine induced translesion synthesis polymerase switch. Created in BioRender. Tam, W. (2025) https://BioRender.com/0i85r9z.\n\nIn addition to the TLS CETSA protein shifts, an ensemble of 5 proteins exhibited a strong concomitant protein abundance increase following gemcitabine treatment. This ensemble includes: RRM2 (Ribonucleoside diphosphate Reductase subunit M2) and TK1 (Thymidylate Kinase), which are involved in deoxyribonucleotide provision; GMNN (Geminin), which inhibits the formation of a pre-replication complex; SLBP (Stem Loop Binding Protein), which promotes histone transcription, and FBXO5 (F-box only protein 5), a regulator of the anaphase promoting complex. Together with DTL and PCLAF, these proteins were distinctly upregulated in the two resistant, but not sensitive DLBCL cell lines. We termed this protein ensemble, the Auxiliary DNA Damage Repair (ADDR) response proteins (Fig.\u00a05A). We confirm that the ADDR CETSA signature was also present with lower doses of gemcitabine exposures in resistant cells (Supplementary Fig.\u00a07A). We next examined whether the ADDR ensemble is more commonly activated in these cells and indeed found increased abundances upon treatment with other DNA damaging drugs such as cladribine and cytarabine (Fig.\u00a05B). To further validate whether the ADDR response is a conserved mechanism, we utilized a completely different cell system, MDA-MB-231 breast cancer cells, treated with another class of genotoxic drug, the DNA cross-linking agent cisplatin. Strikingly, in the dataset of this model, all 5 proteins of the ADDR response, as well as DTL and PCLAF, were among the strongest shifting proteins which displayed abundance changes (Supplementary Fig.\u00a07B). These observations indicate that the ADDR program may have a broader role in conferring resistance towards a spectrum of DNA damaging agents. Notably, prominent thermal destabilization of PolD subunits (Supplementary Fig.\u00a07C), described above, were also observed in the cisplatin data. However, in contrast to gemcitabine-treated DLBCL cells, there was no shift for proteins in the ssDNA binding RPA complex or CHEK1 (Supplementary Fig.\u00a07D) in cisplatin-treated breast cancer cells. Hence, across both experimental models, our data pointed to a CHEK1-independent mechanism for inducing TLS and ADDR responses. Finally, we confirmed that the gemcitabine-resistant SUDHL4 cells were, in fact, also resistant towards cytarabine, cladribine as well as cisplatin (Fig.\u00a05C).\n\nA IMPRINTS profiles of ADDR protein ensemble in OCI-LY19 (red hues) and SUDHL4 (green hues) after 1\u2009h, 3\u2009h, 5\u2009h and 8\u2009h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. B Quantification of ADDR proteins in SUDHL4 cells after 6\u2009h treatment with cladribine (orange) and cytarabine (red). Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. C Relative viability and IC50 values of OCI-LY19 (dotted lines) and SUDHL4 (continuous lines) cells after 48\u2009h treatment with increasing concentrations of gemcitabine (green), cisplatin (light blue), cytarabine (red) or cladribine (orange). Data are presented as mean relative viability compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. D Relative viability of SUDHL4 cells treated for 72\u2009h with increasing concentrations of gemcitabine, either alone (green) or in combination with 1\u2009\u00b5M AZD6738 (red). Data are presented as mean relative viability compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20099). Source data are provided as a Source Data file. E IMPRINTS profiles of ADDR protein ensemble in gemcitabine resistant SUDHL4 cells after 6\u2009h of treatment of gemcitabine alone, AZD6738 alone or in combination. Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file. F IMPRINTS profiles of POLD1, POLD2, POLD4 in gemcitabine resistant SUDHL4 cells after 6\u2009h of treatment of gemcitabine alone, AZD6738 alone or in combination. Data are presented as mean log2 fold change compared to the reference \u00b1SEM from biological replicates (n\u2009=\u20093). Source data are provided as a Source Data file.\n\nGiven the early CETSA signals of DNA damage sensing proteins upon gemcitabine treatment, and the role of TLS and ADDR responses in resistant cells, we reasoned that preventing the initiation of DDR mechanisms might be exploited to re-establish sensitivity. A key player in sensing DNA damage, together with RPA and CHEK1, is the serine/threonine kinase ATR.The use of inhibitors of ATR kinase (ATRi)\u00a0have shown pre-clinical and clinical synergy with gemcitabine33. Accordingly, we investigated the effects of the ATRi, AZD6738, on the gemcitabine response in resistant SUDHL4 cells. Interestingly, combination treatment resulted in attenuation of gemcitabine resistance as observed by a 100 fold lower IC50 value at 72\u2009h (Fig.\u00a05D).\n\nNext, we investigated whether the resistance signatures in TLS and ADDR responses are affected and thus performed a 3-temperature IMPRINTS experiment in resistant SUDHL4 cells treated with either gemcitabine alone, AZD6738 alone, or in combination. Consistent with our hypothesis, the most prominent effects were seen for the proteins of the ADDR ensemble, as well as DTL and PCLAF from the TLS ensemble, whereby there was a dramatic decrease in abundance (Fig.\u00a05E). The destablisation of POLD1 and POLD2, and level changes of POLD4, were also attenuated by ATRi (Fig.\u00a05F). This strongly supported the notion that the induction of these proteins was indeed ATR-dependent. The re-established sensitivity by ATRi reinforced the conclusion that the induction of the ADDR and TLS (DTL/PCLAF) ensembles was a key prerequisite for establishing resistance to DNA-damaging drugs.\n\nThe quite dramatically decreased abundance of the ADDR ensemble upon combination treatment could be attributed to the relatively fast turnover rates of these proteins in exponentially growing cells, typically accomplished by a high rate of production and degradation. Indeed, in a Molm16 AML cell line protein turnover dataset used in our lab as reference, the 4 measured proteins all have rapid turnover rates (TK1-15 h; RRM2-11h; SLBP-28h; PCLAF-10h) (Supplementary Data\u00a02). Arguably, this design makes these proteins particularly useful for regulating urgent events in cellular processes. However, our current data is not conclusive on whether this is only due to decreased transcriptional activity for the corresponding genes in ATRi treated resistant cells, or whether there are posttranscriptional mechanisms or activation of proteosome degradation components induced. In the future, a more detailed elucidation of the signaling mechanisms post-ATR will be helpful to define contributions from different mechanisms to increased protein levels.\n\nTo test the feasibility of applying MS-CETSA in clinical samples, we performed an ITDR-CETSA experiment on biopsies from two DLBCL patients who have relapsed after first line therapy and have not been previously treated with gemcitabine. Cells were extracted using Ficoll-paque and treated ex vivo for 5\u2009h with increasing doses of gemcitabine (Fig.\u00a06A). By comparing the CETSA signatures of resistant and responding cells, we can conclude that both clinical samples were dominated by shifts in proteins of the CETSA apoptosis ensemble discussed above (Fig.\u00a06B), i.e. still sensitive to gemcitabine.\n\nA Experimental design for MS-CETSA treatment of patient samples from DLBCL patients. Patient samples were treated with different doses of gemcitabine ex vivo for 5\u2009h. The treated samples were CETSA heated and subjected to mass spectrometry. Created in BioRender. Tam, W. (2025) https://BioRender.com/4myrn1e. B MS-CETSA ITDR profiles of selected CCAE proteins in patient samples (top panel) and sensitive OCI-LY19 (bottom panel) cells. Data are presented as mean log2 fold change compared to the reference from technical replicates (n\u2009=\u20092). Source data are provided as a Source Data file.\n\nUsing a time-resolved IMPRINTS CETSA approach we identify a potential candidate drug-induced resistance\u00a0mechanism in DLBCL cells and show that the responses in sensitive and resistant cells were largely mutually exclusive (Supplementary Fig.\u00a08A, B).\n\nWhen most omics studies of drug resistance are performed through profiling cells in the absence of treatment, we wondered whether the stark divergence seen in the late time points of our gemcitabine-induced CETSA data could be captured through such static omics approaches, and if similar informative conclusions could be made on candidate resistance mechanisms. Therefore, we investigated sequence data of the cell lines used from the \u201cCCLE Cell Line Gene Mutation Profiles\u201d database to examine the genetic difference of our cell lines. This shows similar number of mutations in all cell lines except the resistant HT cells (OCI-LY3: 90, OCI-LY19: 77, SUDHL4: 79, HT: 256), which had a significantly higher number of mutations that seem to be cell line specific (Supplementary Fig.\u00a08C). An over-representation analysis (Supplementary Fig.\u00a08D) did not reveal any pathways that are specifically affected in sensitive cells. For resistant cells we noted the term \u201cintrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator\u201d enriched, which could be explained by the mutational status of p53 for these cells. However, according to our data, induction of\u00a0apoptosis in sensitive DLBCL cells is p53 independent.\n\nFurthermore, we profiled the four cell lines on the protein level by quantitative proteomics. In a correlation heatmap we show that the 2 sensitive and resistant cell lines cluster together, respectively, indicating baseline differences that may already reflect their drug response phenotypes (Supplementary Fig.\u00a08E). We specifically compared the resistant SUDHL4 with the sensitive OCI-LY19 cells and detected 111 proteins down regulated, and 150 proteins up regulated. Notably, no difference was observed in most of the above-described proteins related to gemcitabine resistance (Supplementary Fig.\u00a08F) and again, no specific pathways indicating drug resistance mechanisms were overrepresented (Supplementary Fig.\u00a08G, H). Instead, enriched GO terms were linked to general B-cell functions (e.g. receptor signaling, cytokine production) and we therefore concluded that these were the baseline differences reflected in the clustering of sensitive and resistant cells.\n\nTaken together, this analysis of genetic mutations and a quantitative\u00a0proteomic analysis support that baseline profiling of cell lines is, at least in this case, insufficient to predict gemcitabine resistance. Therefore, global MS-CETSA time-resolved characterization of resistance mechanisms will likely in many cases constitute a more informative approache for identifying\u00a0cancer drug resistance mechanisms.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "Non-hypothesis driven system-wide methods have the potential to identify the most prominent molecular processes regulating cellular phenotypes. However, despite cellular biochemistry controlling most molecular processes of the cell, methods for efficient studies of cellular biochemistry at the systems level have been elusive. This has, arguably, also contributed to our relatively fragmented current understanding of the biochemical basis for pathway activation in cancer drug resistance.\n\nCETSA constitutes the first systems-wide method which can report on a range of different types of cellular biochemistry, from protein-protein and protein-DNA/RNA interactions to phosphorylation events and flux through metabolic pathways21. However, so far CETSA studies have predominantly been focused on identifying drug interactions. Although it has been clear that CETSA can report on cellular pathway modulations downstream of drug binding, in our view this has not yet been systematically explored. Limitations of previous approaches have been the use of suboptimal CETSA implementations which don\u2019t allow for robust measurements of small stability shifts typically induced by functional biochemical changes. Furthermore, time-dependent studies have not been systematically explored to dissect sequences of drug-induced activation of cellular processes/pathways. In one study, we have previously applied a 2 time-point MS-CETSA approach to study MoA and resistance to 5-FU, which revealed attenuation of anticipated toxic biochemistry in resistant cells, but no drug-induced resistance response34.\n\nIn the present work we use the highly sensitive IMPRINTS-CETSA implementation in a time-dependent approach to demonstrate applicability of this technology to study the biochemical pathways involved in gemcitabine MoA and resistance mechanisms. By focusing on the overlapping responses in cell pairs of resistant and sensitive cells, a distinct view of the biochemistry of the MoA of gemcitabine in the two cell types is revealed. The initial responses are very similar, reflecting the direct target engagement of RNR, through RRM1 thermal stabilization, as well as the establishment of a DNA-damage signaling hub activated by RPA binding to exposed ssDNA and CHEK1 phosphorylation. The almost identical isothermal dose response CETSA shifts for RRM1 indicate an exclusion of modifications in drug internalization and metabolism as dominant resistant mechanisms in our system. At the 3\u20138\u2009h time-points, sensitive cells rapidly enter apoptosis as judged from the observed shifts in CETSA apoptosis ensemble proteins, while resistant cells show CETSA shifts of CDK complexes supporting open cell cycle checkpoints, consistent with the continued proliferation.\n\nMost notably in the resistant cell CETSA data, distinct responses are seen for proteins related to activation of DNA repair, i.e., the abundance and thermal stability changes of two TLS biomarkers (PCLAF and DTL) as well as prominent destabilization in Pol\u03b4, likely reporting on the induction of TLS. This is further supported by the increased mono-ubiquitination of PCNA only in the resistant cells and the synergistic effects of gemcitabine with REV7/REV3 interaction inhibitor. The induced TLS program explains how resistant cells overcome stalled replication forks by allowing DNA-synthesis over damage lesions. TLS has not been previously implied in resistance to gemcitabine but has been suggested as a mechanism of resistance to cisplatin as derived from over-expression of TLS polymerases in resistant cells35. However, in these cases TLS proteins are assumed to be constitutively expressed and not part of an induced TLS response, as uncovered in the present study.\n\nIn addition to the induction of CDK activation and TLS programs, the induction of the ADDR ensemble of proteins is the most dominating feature of the response in resistant cells. These proteins appear to have functions that can support DNA-repair/replication and could therefore be supportive for TLS, although not previously identified as an ensemble in a DNA repair context. The distinct attenuation of both the induction of TLS proteins DTL and PCLAF, and the ADDR response by an ATR inhibitor strongly support that ATR is a signaling node in this response. However, we conclude that the response is likely not CHEK1 dependent, when ADDR response for cisplatin in MDA-MB-231 breast cancer cells does not coincide with CHEK1 activation. The disparity likely reflected differences in DNA damage mechanisms between the two drugs: cisplatin is a DNA crosslinking agent while gemcitabine induces single strand breaks.\n\nIntriguingly, CHEK1 activation is expected to mediate cell cycle arrest, but in contrast, in gemcitabine resistant cells, the CETSA shifts of CDK complex and cell cycle assessments support the opposite effect, i.e., opening of cell cycle checkpoints. This gives further support for the activation of an alternative signaling pathway for the induction of a pathway downstream of ATR, controlling DNA-repair and cell cycle checkpoints to support cell proliferation during genotoxic challenges, principle of this pathway outlined in Fig.\u00a07. However, despite significant efforts we have not been able to identify additional components of the signaling pathway downstream of ATR, which also might provide additional target proteins for specifically attenuating gemcitabine resistance.\n\nSchematic summary of gemcitabine-induced cellular responses in sensitive (red nodes) versus resistant (green nodes), or both (orange nodes) cells. Dotted line indicates signaling pathway yet to be established in detail. Created in BioRender. Tam, W. (2025) https://BioRender.com/jgju7ac.\n\nIn addition to constituting a pathway for induction of DNA-repair, the fact that ATR inhibition re-sensitized cells to gemcitabine, supports that this response is a key component of the gemcitabine resistance in this system. There have been previous reports of positive results for using ATRi in combination with gemcitabine in pancreatic cancer33 and ovarian cancer36 therapies. In a recent phase 2 trial in platinum-resistant high-grade serous ovarian cancer, a combination of the selective ATR inhibitor berzosertib, and gemcitabine showed significantly prolonged progression-free survival compared to treatment with gemcitabine alone37. The current studies provide a mechanistic rationale for the combination of ATRi and gemcitabine for DLBCL.\n\nNotably, this study emphasizes the importance of monitoring drug-induced responses as an approach to successfully identify resistance mechanisms as analyses of genetic mutations and static proteomic profiling failed to capture the proposed gemcitabine resistance mechanisms. As a future strategy for patient stratification, CETSA could potentially be used to monitor whether this (or other) resistance mechanism(s) is in effect in clinical samples, or if instead the early apoptosis profile are detectable with CETSA, indicating sensitivity. The data from two clinical DLBCL patient samples of gemcitabine na\u00efve patients also support that high quality CETSA information can be obtained from clinical DLBCL samples. In spite of cell heterogeneity in typical tumor samples, we envisage that CETSA can identify the dominant resistance mechanism(s) in the sample, to guide therapy, and as resistance evolves anew, again dominant resistance mechanisms might be detectable by CETSA.\n\nTogether the present study supports that time-dependent IMPRINTS-CETSA constitutes a highly efficient strategy to discover sequences of prominent pathway activation explaining cancer drug MoA and resistance. Therefore, as an alternative to focused studies of cancer drug MoA, which are often limited in their scope by requirement of pathway/protein specific biochemical assays, this work establishes IMPRINTS-CETSA as an efficient strategy for global studies of the biochemistry of cancer drug resistance, where comprehensive insights into effects on many different cellular pathways can be directly accessed using a single method. These studies also provide a repertoire of MoA-based drug resistance biomarkers showing robust responses with potential applicability in the clinic.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "This research complies with all relevant ethical regulations. Collection of patient samples is approved under A*STAR IRB: 2021-140.\n\nKey resource are provided as Supplementary table\u00a01. Further information and requests for resources and reagents should be directed to and will be fulfilled by the corresponding author.\n\nHuman breast adenocarcinoma cell line MDA-MB-231 (CRM-HTB-26) was purchased from ATCC. Human lymphoma cell line SUDHL4 (CRL-2957) was purchased from ATCC, HT cells (CRL-2260) were a gift from the lab of Ernesto Guccione, Icahn School of medicine at Mt Sinai (formerly at IMCB, Singapore), OCI-LY19 and OCI-LY3 was obtained from the lab of Manikandan Lakshmanan at IMCB, Singapore.\n\nAll the DLBCL cell lines and MDA-MB-231 were maintained in RPMI-1640 medium (R8758, Sigma) and L-glutamine, supplemented with 20% fetal bovine serum (FBS), 100 units/ml penicillin and streptomycin in a 37\u2009\u00b0C CO2 incubator.\n\nKnockout was performed using LentiCRISPRv2GFP vector (82416, Addgene). Single-guide RNA encoding SAMHD1 was cloned into LentiCRISPRv2GFP vector. Briefly, lentiviruses were packaged using HEK293T cells via co-transfection of gene of interest, VSVG and delta 8.2 vector, using Lipofectamine 2000 transfection reagent (11668019, Thermo Fisher). Viruses collected were concentrated using Amicon Ultra Centrifugal filters (C2566709, Merck) and spinoculated onto the SUDHL4 cells in the presence of 8\u2009\u03bcg/ml polybrene (sc-134220, Santa Cruz) at 800\u2009g for 30\u2009min at room temperature. The target sequences of the sgRNAs are as follow: SAMHD1 sgRNA-1 forward 5\u2032-CACCGAGGATGTCTAGTTCACGCAC -3\u2032; SAMHD1 sgRNA-1 reverse 5\u2032-AAACGTGCGTGAACTAGACATCCTC -3\u2032. Single cells containing the CRISPR-GFP positive vector were then sorted through FACS and harvested as monoclones.\n\nWe have complied with all relevant ethical regulations. Collection of samples is approved under A*STAR IRB: 2021-140. Informed written consent was obtained from all participants and no compensation was provided. Tumors were collected in MACS Tissue Storage Solution (130-100-08, Miltenyi) and kept on ice for transport. Tissue was cut into equally small pieces using a scalpel. To obtain single cell solutions, the cells were passed through a sterile 70\u2009\u00b5M cell filter mesh (352350, Corning) in RPMI-1640 medium (R8758, Sigma) and L-glutamine, supplemented with 10% fetal bovine serum (FBS), 100 units/ml penicillin and streptomycin. Cells number was determined and MS-CETSA experiment was performed immediately.\n\nGemcitabine, AZD6738 (kindly provided by Prof. Anand Jeyasekharan, CSI, Singapore) and Decitabine were solubilized in water. Cisplatin, Cytarabine, Cladribine, Z-VAD-FMK, MG132 and REV7/REV3L-IN-1 were solubilized in DMSO. All compound stocks were aliquoted and stored at \u221220\u2009\u00b0C.\n\nCell viability was assessed using the MTT assay. Cells were seeded in96-well v-bottom plates at a density of 2\u2009\u00d7\u2009104 cells per well and incubated overnight. After drug treatment, 100\u2009\u00b5L of MTT solution (0.5\u2009mg/mL in PBS) was added to each well and incubated at 37\u2009\u00b0C for 2\u2009h. The resulting formazan crystals were dissolved in DMSO, and absorbance was measured at 540\u2009nm using a microplate reader. Cell viability was expressed as a percentage relative to untreated controls.\n\nFor the in vitro IMPRINTS-CETSA experiments, cell lines were seeded at 0.5\u2009\u00d7\u2009106 cells/ml of media and preconditioned in complete RPMI with 2% FBS for 24\u2009h. The cells were then treated with either vehicle or drug at their respective final concentrations and incubated at 37\u2009\u00b0C and 5% CO2 for indicated time points. Cells were pelleted for 4\u2009min at 400\u2009\u00d7\u2009g, washed with PBS and resuspended in 50\u2009\u00b5l PBS. For the in vitro ITDR-CETSA experiments, cell lines were distributed into 6 tubes at 0.3\u2009\u00d7\u2009106/100\u2009\u00b5l in media, while the total cells of primary DLBCL clinical samples were distributed into 6 tubes in media. Cells were then treated with either vehicle or drug at their respective final concentrations and incubated at 37\u2009\u00b0C and 5% CO2 for indicated time points. Cells were pelleted for 4\u2009min at 400\u2009\u00d7\u2009g, washed with PBS and resuspended in 50\u2009\u00b5l PBS. Harvested cells and lysates were aliquoted into PCR tubes corresponding to each treatment condition and subjected to a 3\u2009min CETSA heating step in a Veriti thermal cycler (Applied Biosystems) with temperatures ranging from 37\u2009\u00b0C to 57\u2009\u00b0C, followed by 3\u2009min cooling at 4\u2009\u00b0C.\n\nFor lysate CETSA experiments 20 \u00d7106 cells/ml were lysed by adding 2X kinase buffer to the final concentration of 50\u2009mM HEPES pH 7.5, 5\u2009mM beta-glycerophosphate, 0.1\u2009mM sodium orthovanadate (Na3VO4), 10\u2009mM MgCl2, 1\u2009mM TCEP (Sinopharm Chemical Reagent Co.), 1x protease inhibitor cocktail (Nacalai Tesque Inc.) and 25U/ml Benzonase. Cells were subjected to five freeze-thaw cycles with liquid nitrogen to release soluble proteins. The suspension was then centrifuged for 20\u2009min at 20,000\u2009\u00d7\u2009g and 4\u2009\u00b0C to remove cell debris and 30\u2009\u00b5l of supernatants were treated with either vehicle or drug at their respective final concentrations for 1\u2009min. Lysates were aliquoted into PCR tubes corresponding to each treatment condition and subjected to a 3\u2009min CETSA heating step in a Veriti thermal cycler (Applied Biosystems) with temperatures ranging from 37\u2009\u00b0C to 57\u2009\u00b0C, followed by 3\u2009min cooling at 4\u2009\u00b0C.\n\nFollowing CETSA heat treatment, the cells were lysed by adding 2X kinase buffer to the final concentration of 50\u2009mM HEPES pH 7.5, 5\u2009mM beta-glycerophosphate, 0.1\u2009mM sodium orthovanadate (Na3VO4), 10\u2009mM MgCl2, 1\u2009mM TCEP (Sinopharm Chemical Reagent Co.), 1x protease inhibitor cocktail (Nacalai Tesque Inc.) and 25U/ml Benzonase. All the samples were subjected to five freeze-thaw cycles with liquid nitrogen to release soluble proteins. For lysate CETSA experiments this step was skipped and immediately proceeded to the next step. The suspension was then centrifuged for 20\u2009min at 20,000\u2009\u00d7\u2009g and 4\u2009\u00b0C to remove cell debris. The supernatants were then analyzed using either LC-MS or western blotting.\n\nFor quantitative proteomic profiling, cells were lysed in 8\u2009M Urea with 1:250 protease inhibitor for 10\u2009min at room temperature, followed by 3x pulse sonication (8\u2009W, 30% amplitude, 30\u2009s on, 10\u2009s off). cell suspension was centrifuged for 20\u2009min at 20,000\u2009\u00d7\u2009g and 4\u2009\u00b0C to remove any remaining cell debris. The supernatants were used for LC-MS or western blotting.\n\nCell lines were seeded at 0.5\u2009\u00d7\u2009106 cells/ml of media and preconditioned in complete RPMI with 2% FBS for 24\u2009h. The cells were then treated with either vehicle or drug at their respective final concentrations and incubated at 37\u2009\u00b0C and 5% CO2 for indicated time points. Cells were pelleted for 4\u2009min at 400\u2009\u00d7\u2009g, washed with PBS. Cells were fixed in 70% ethanol overnight, washed twice with cold PBS, then resuspended in PI staining solution (100\u2009\u00b5g/ml ribonuclease A, 50\u2009\u00b5g/ml PI in PBS) and incubated in the dark for at least 30\u2009min at room temperature, followed by flow cytometric analysis on a LSR II (BD Biosciences, UK) flow cytometer. FlowJo (FlowJo, LLC, USA) was used to analyze the data.\n\nCell lines were seeded at 0.5\u2009\u00d7\u2009106 cells/ml of media and preconditioned in complete RPMI with 2% FBS for 24\u2009h. The cells were then treated with either vehicle or drug at their respective final concentrations and incubated at 37\u2009\u00b0C and 5% CO2 for indicated time points. Cells were pelleted for 4\u2009min at 400\u2009\u00d7\u2009g, washed with PBS. Cell pellets were harvested and snap frozen in liquid nitrogen. The samples were stored in \u221280\u2009\u00b0C until transportation to Creative Proteomics for nucleotide quantification through LC-MS analysis. Each cell sample was resuspended in 500\u2009\u00b5l of 80% methanol and then lysed on a MM 400 mill mixer at a shaking frequency of 30\u2009Hz and with the aid of two metal balls for 2\u2009min. The samples were subsequently sonicated for 1\u2009min in an ice-water bath before centrifugal clarification at 21,000\u2009\u00d7\u2009g and 5\u2009\u00b0C for 10\u2009min. The clear supernatants were collected for the following assay. The precipitated pellets were used for protein assay using a standardized BCA procedure. Serially diluted standard solutions of the targeted nucleotides were prepared in 80% methanol. 100\u2009\u00b5l of each standard solution of the clear supernatant of each sample were dried under a nitrogen gas flow. The residues were dissolved in 100\u2009\u00b5l of a 13C-labeled internal standard solution. 10\u2009\u00b5l aliquots of the resulting solutions were injected into a C18 column (2.1\u2009\u00d7\u2009110\u2009mm, 1.9\u2009\u00b5m) to run UPLC-MRM/MS with (\u2212) ion detection on a Waters Acquity UPLC system coupled to a Sciex QTRAP 6500 Plus MS instrument, with the use of tributylamine buffer (A) and acetonitrile (B) as the mobile phase for gradient elution.\n\nWestern blotting was performed on protein extracts obtained either by freeze-thawing or lysis by RIPA buffer (Thermo Scientific). Protein concentrations for each sample were quantified using bicinchoninic acid (BCA) assay according to manufacturer\u2019s instructions.\n\nProtein extract samples were mixed with NuPAGE loading buffer consisting of NuPAGE LDS sample buffer (NP0008, Life technologies) and reducing agent (NP0009, Life Technologies) and boiled at 95\u2009\u00b0C. Proteins were separated on NuPAGE 4\u201312% Bis-Tris midi gels (WG1403BX10, Invitrogen) for 45\u201355\u2009min at 200\u2009V. Separated proteins were transferred to nitrocellulose membranes using the iBlot system (Invitrogen)onto nitrocellulose membranes. Membranes were blocked in 5% (w/v) non-fat milk (Semper AB) in TBS with 0.05% Tween 20 (Medicago 09-7510-100) (TBS-T) for 1\u2009h with gentle shaking. Incubation with primary antibody was performed overnight at 4\u2009\u00b0C and with gentle shaking. After washing in TBS-T for 3\u2009\u00d7\u200910\u2009min, the membranes were incubated with secondary antibodies for 1\u2009h, washed again 3\u2009\u00d7\u200910\u2009min in TBS-T and developed using ClarityTM Western ECL Substrate (170-5061, BioRad). The chemiluminescent signal was detected using the ChemiDocTM XRS+ imaging system from BioRad and the band intensities were quantified using ImageLabTM software (BioRad).\n\nProtein concentrations were quantified after lysis using the BCA according to manufacturer\u2019s instructions and the same amount of protein was used for sample preparation. Samples were reduced with 25% TFE and 20\u2009mM TCEP at 55\u2009\u00b0C for 20\u2009min, followed by alkylation with 55\u2009mM of 2-chloroacetamide (CAA) (C0267, Sigma) in the dark at room temperature for 30\u2009min. Samples were digested with LysC (1:25 enzyme to protein ratio, Wako Chemicals Ltd), for 4\u20136\u2009h before adding trypsin (1:25, Promega) for overnight digestion at 37\u2009\u00b0C. The samples were dried by a centrifugal vacuum evaporator and desalted with Oasis HLB 96-well plate following the manufacturer\u2019s instructions. The desalted peptides were re-solubilized in 100\u2009mM TEAB to 1\u2009\u00b5g/\u00b5l. All the peptides were labeled with Isobaric Tandem Mass Tags -10plex TMT according to the manufacturer\u2019s protocol (90110, Thermo Scientific). The labeling was done at room temperature for at least 1\u2009h and labeled samples were quenched using 10\u2009\u00b5l of 1\u2009M Tris (pH 7.4) solution. A high pH reverse phase Zorbax 300 Extend C-18 4.6\u2009mm\u2009\u00d7\u2009250\u2009mm (Agilent) column and liquid chromatography AKTA Micro (GE) system was used for offline sample pre-fractionation. The fractions were concatenated into 20 fractions and dried with a centrifugal vacuum evaporator.\n\nThe digested, labeled, and dried peptide sample fractions were resuspended in 0.1% acetonitrile, 0.5% (v/v) acetic acid and 0.06% TFA in water immediately before analysis on LC-MS. Online chromatography was performed using Dionex UltiMate 3000 UPLC system coupled to a Q Exactive mass spectrometer (Thermo Scientific). Each fraction was separated on a 50\u2009cm\u2009\u00d7\u200975\u2009\u03bcm (ID) EASY-Spray analytical column (ES903, Thermo Scientific) in a 80\u2009min gradient of programmed mixture of solvent A (0.1% formic acid in H2O) and solvent B (99.9% acetonitrile, 0.1% formic acid). MS data were acquired using a top 12 data-dependent acquisition method. Full scan MS spectra were acquired in the range of 350\u20131550\u2009m/z at a resolution of 60,000 and AGC target of 3e6; Top 12 dd-MS2 60,000 and 1e5 with isolation window at 1.0\u2009m/z.\n\nProtein identification was performed by Proteome Discoverer 2.5 software (Thermo Scientific), using both Mascot 2.6.0 (Matrix Science) and Sequest HT (Thermo Scientific) search engines to search against reviewed human Uniprot databases (downloaded on 13 Jan 2017, including 42,105 sequence entries and another downloaded on 23 Jul 2018, including 9606 sequence entries). MS precursor mass tolerance was set at 20ppm, fragment mass tolerance 0.05\u2009Da, and maximum missed cleavage sites of 3. Dynamic modifications searched for Oxidation (M), Deamidation (NQ), and Acetylation (N-terminal protein). Static modifications: Carbamidomethyl (C) and TMT10plex (K and peptide N terminus). Only the spectrum peaks with signal-to-noise ratio (S/N)\u2009>\u20094 were chosen for searches. The false discovery rate (FDR) was set to 1% at both PSM and peptide levels. Only the unique and razor peptides were used for protein assignment and abundance quantification. Isotopic correction of the reporter ions in each TMT channel was performed according to the product sheet. Only the master proteins in the protein group were used for downstream analysis. For some datasets, the peptide abundances were obtained from Proteome Discoverer software (version 2.5). Every peptide with another modification than a TMT one was removed. To ensure the accuracy of TMT quantification, reporter S/N threshold was set at 10 and co-isolation threshold at 30%. Then, every peptide dataset has been treated the same way as the protein dataset according to the same method described in Dai et al.23. To illustrate the RESP effect, we summed the non Log2 transformed fold changes from the peptides located before the cleaved site and the ones after the cleaved site. Then those fold changes were Log2 transformed and plotted as bar plots.\n\nQuantified protein/peptide abundances were imported into the R environment (http://www.R-project.org/) to facilitate the data analysis and visualization. Only the proteins with at least two quantifying abundance counts were used for downstream analysis. Data cleaning, normalization, and calculations of protein abundance and thermal stability differences in each condition were performed using the IMPRINTS.CETSA and the IMPRINTS.CETSA.app R packages38.\n\nProtein-protein interaction network for hits was obtained by importing the hitlist Uniprot IDs into Cytoscape v.3.9.1 (http://cytoscape.org). Using the embedded STRING interaction database (http://apps.cytoscape.org/apps/stringApp), a default confidence cut-off score of 0.4 was applied to retrieve the network. Each node represents one hit protein, and edges symbolize protein-protein interactions. Nodes explanation can be found on figure legends. Comparative GO analysis was performed using the ClueGO v2.5.1 plug-in in Cytoscape (http://apps.cytoscape.org/apps/cluego). Hitlist Uniprot IDs were imported to query the GO-Biological Processes database (EBI-QuickGO-GOA-15783 terms/pathways with 17268 available unique genes-20.11.2017). The parameters for analysis were set as follows: Evidence code \u2013 All; Use Go Term Fusion; GO tree interval \u2013 Level 3\u20138; GO Term/Pathway Selection \u2013 Minimum 3 genes and threshold of 4% of genes per term; GO term connectivity threshold (Kappa score) \u2013 0.4; Two-sided hypergeometric test with Bonferroni step down p-value correction. Only GO terms with p-value\u2009<\u20090.05 are shown. GO terms are presented as nodes and clustered together based on term similarity. Node size is proportional to the p-value for GO term enrichment. Node colors are set according to the treatment condition showing the % of visible proteins of a term/pathway.\n\nAll graphs were generated using GraphPad Prism, R environment, cytoscape or Biorender. All data are presented as mean with error bars representing the standard error of the mean (SEM). Error bars that are smaller than the displayed data points are not displayed by the software. Details regarding replicates for each experiment can be found in the figure legends. Sigmoidal curves were fit (where appropriate) using R environment. Unpaired t-tests were performed using GraphPad Prism and the results are displayed in figures and figure legends. The flow cytometry data was analyzed on FlowJo v10.8 and GraphPad Prism was used to represent the data.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All mass spectrometry raw data generated in this study have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/) via the jPOST repository with the dataset identifiers PXD054912, PXD054911, PXD054910, PXD054909, PXD054908, PXD054907, PXD054903, PXD054902, PXD054901, PXD054854, PXD054853, PXD054852, PXD055016, PXD055015. Data cleaning, normalization, and calculations of protein abundance and thermal stability differences in each condition were performed using the IMPRINTS.CETSA and the IMPRINTS.CETSA.app R packages38.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Change history",
+            "section_text": "In the version of the article initially published, affiliation 3 was incorrectly listed as one of Wai Leong Tam's affiliations, and has now been corrected to affiliation 2 (Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome 138672, Singapore). Similarly, affiliation 2 was wrongly included in P\u00e4r Nordlund's affiliations, and has now been corrected to affiliation 3 (Department of Oncology and Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden). These corrections have been made to the HTML and PDF versions of the article.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "We gratefully acknowledge funding from the Swedish Research Council (P.N.), the Swedish Cancer Society (P.N.), Radiumhemmet\u2019s funds (P.N.), the Knut and Alice Wallenberg Foundation (P.N.), Singapore\u2019s National Research Foundation (NRF-NRFI08-2022, NRF-CRP22-2019-0003 (W.L.T., N.P.)), Singapore\u2019s National Medical Research Council (MOH-001332-00 (Y.Y.L.)), Agency for Science, Technology and Research, Singapore, and the Singapore Ministry of Education under its Research Centres of Excellence initiative. We also acknowledge all past members of the P.N. lab.",
+            "section_image": []
+        },
+        {
+            "section_name": "Funding",
+            "section_text": "Open access funding provided by Karolinska Institute.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, 138673, Singapore\n\nYing Yu Liang,\u00a0Khalidah Khalid,\u00a0Hai Van Le,\u00a0P\u00e4r Nordlund\u00a0&\u00a0Nayana Prabhu\n\nGenome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, 138672, Singapore\n\nHui Min Vivian Teo,\u00a0Jane Jia Hui Lee\u00a0&\u00a0Wai Leong Tam\n\nDepartment of Oncology and Pathology, Karolinska Institutet, 171 77, Stockholm, Sweden\n\nMindaugas Raitelaitis,\u00a0Marc-Antoine Gerault,\u00a0Jiawen Lyu\u00a0&\u00a0P\u00e4r Nordlund\n\nCancer Science Institute of Singapore, National University of Singapore, 14 Medical Drive, Singapore, 117599, Singapore\n\nAllison Chan,\u00a0Anand Devaprasath Jeyasekharan\u00a0&\u00a0Wai Leong Tam\n\nDepartment of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore, 117596, Singapore\n\nAnand Devaprasath Jeyasekharan\u00a0&\u00a0Wai Leong Tam\n\nDepartment of Haematology-Oncology, National University Cancer Institute, Singapore, 119074, Singapore\n\nAnand Devaprasath Jeyasekharan\n\nNUS Center for Cancer Research, Yong Loo Lin School of Medicine, National University Singapore, 14 Medical Drive, Singapore, 117599, Singapore\n\nWai Leong Tam\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: W.L.T., P.N. and N.P.; Methodology: Y.Y.L., L.H.V., K.K., M.R., H.M.T., W.L.T., P.N. and N.P.; Formal Analysis: Y.Y.L., L.H.V., K.K., M.R., M.A.G., W.L.T., P.N. and N.P.; Investigation: Y.Y.L., L.H.V., K.K., H.M.T., M.R., J.J.H.L., J.L., A.C., A.D.J., W.L.T., P.N. and N.P.; Writing\u2013Original draft: Y.Y.L., W.L.T., P.N. and N.P.; Writing\u2013Review & Editing: All; Funding Acquisition: P.N., W.L.T. and N.P.; Supervision: A.D.J., P.N., W.L.T., N.P.\n\nCorrespondence to\n                Wai Leong Tam, P\u00e4r Nordlund or Nayana Prabhu.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "P.N. is the inventor of patents related to the CETSA method and is a cofounder and board member of Pelago Biosciences AB. The remaining authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Kasper Fugger, Andre Mateus and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional information",
+            "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",
+            "section_image": []
+        },
+        {
+            "section_name": "Source data",
+            "section_text": "",
+            "section_image": []
+        },
+        {
+            "section_name": "Rights and permissions",
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+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Liang, Y.Y., Khalid, K., Le, H.V. et al. MS CETSA deep functional proteomics uncovers DNA repair programs leading to gemcitabine resistance.\n                    Nat Commun 16, 4234 (2025). https://doi.org/10.1038/s41467-025-59505-8\n\nDownload citation\n\nReceived: 29 July 2024\n\nAccepted: 23 April 2025\n\nPublished: 07 May 2025\n\nVersion of record: 07 May 2025\n\nDOI: https://doi.org/10.1038/s41467-025-59505-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/metadata.json

@@ -0,0 +1,139 @@
+{
+    "title": "Tunable magnons of an antiferromagnetic Mott insulator via interfacial metal-insulator transitions",
+    "pre_title": "Tunable magnons of an antiferromagnetic Mott insulator via interfacial metal-insulator transitions",
+    "journal": "Nature Communications",
+    "published": "15 April 2025",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58922-z/MediaObjects/41467_2025_58922_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Transparent Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58922-z/MediaObjects/41467_2025_58922_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58922-z/MediaObjects/41467_2025_58922_MOESM3_ESM.zip"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-025-58922-z#Sec11"
+    ],
+    "code": [],
+    "subject": [
+        "Magnetic properties and materials",
+        "Phase transitions and critical phenomena",
+        "Surfaces, interfaces and thin films"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4753008/v1.pdf?c=1744801657000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4753008/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-025-58922-z.pdf",
+    "preprint_posted": "25 Jul, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Antiferromagnetic insulators offer an alternative to ferromagnets due to their ultrafast spin dynamics essential for low-energy terahertz spintronic device applications. One way is to utilize magnons, i.e., quantized spin waves, which can carry information through excitations. However, finding external knobs for tuning the magnons has been a significant challenge. Here we report that interfacial metal-insulator transitions can be an effective means for controlling the magnons of a strongly spin-orbit-coupled antiferromagnetic Mott insulator, Sr2IrO4. From resonant inelastic X-ray scattering and Raman spectroscopy, we have observed a pronounced softening of zone-boundary magnon energies in several Sr2IrO4 thin-film systems that are epitaxially contacted with metallic 4d transition-metal oxides (TMOs). Therefore, the magnon dispersion of Sr2IrO4 is tunable by metal-insulator transitions of the 4d TMO crystals. Remarkably, this non-trivial behavior of magnons is a long-range phenomenon coupled with intriguing magnon-phonon interactions. Our experimental finding proposes a new scheme for magnonics.Physical sciences/Physics/Condensed-matter physics/Magnetic properties and materialsPhysical sciences/Materials science/Nanoscale materials/Magnetic properties and materialsPhysical sciences/Nanoscience and technology/Nanoscale materials/Magnetic properties and materials",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplementaryInformation.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Antiferromagnetic insulators present a promising alternative to ferromagnets due to their ultrafast spin dynamics essential for low-energy terahertz spintronic device applications. Magnons, i.e., quantized spin waves capable of transmitting information through excitations, serve as a key functional element in this paradigm. However, identifying external mechanisms to effectively tune magnon properties has remained a major challenge. Here we demonstrate that interfacial metal-insulator transitions offer an effective method for controlling the magnons of Sr2IrO4, a strongly spin-orbit coupled antiferromagnetic Mott insulator. Resonant inelastic x-ray scattering experiments reveal a significant softening of zone-boundary magnon energies in Sr2IrO4 films epitaxially interfaced with metallic 4d transition-metal oxides. Therefore, the magnon dispersion of Sr2IrO4 can be tuned by metal-insulator transitions of the 4d transition-metal oxides. We tentatively attribute this non-trivial behavior to a long-range phenomenon mediated by magnon-acoustic phonon interactions. Our experimental findings introduce a strategy for controlling magnons and underscore the need for further theoretical studies to better understand the underlying microscopic interactions between magnons and phonons.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Magnons, i.e., collective spin wave excitations originating from spin precession in magnetically ordered materials, have the potential to serve as a promising medium for quantum information devices. Since the propagation of magnons does not require the transport of a charge, preventing electrical losses such as Joule heating1, it gives rise to a burgeoning research field known as magnonics2,3. Antiferromagnetic insulators have garnered considerable attention in this emerging field, primarily due to their ultrafast spin dynamics compared to ferromagnetic counterparts, which are essential for device operation in the terahertz range4,5,6. Nevertheless, effectively guiding and coherently manipulating magnons using external stimuli is still a significant challenge.\n\nHeterointerfaces between two different materials can provide model systems for investigating the relation between external stimuli and collective spin waves. Examples are the effects of lattice strain7, interfacial coupling, and charge transfer on magnons8,9 and their spin currents10,11,12. In particular, interfaces between an antiferromagnetic insulator and a metal have been considered for novel spin-charge conversion13,14. Despite some astonishing predictions from magnetic insulator/metal interfaces15, the fundamental understanding of how a metallic interface affects the spin-wave dispersion of an antiferromagnetic insulator remains elusive.\n\nSr2IrO4, a 5d transition-metal oxide, is a quasi-two-dimensional antiferromagnetic insulator with strong spin-orbit interaction resulting in the Jeff\u2009=\u20091/2 pseudospins. The distinctive canted antiferromagnetism and magnetic anisotropy in the Jeff\u2009=\u20091/2 state can be useful for spintronic applications16,17. Notably, Sr2IrO4 hosts spin waves at terahertz frequencies with a significant stress response mediated by strong spin-orbit interactions7. Its similarities to La2CuO4, a parent compound of high Tc superconductors, suggest a potential for superconducting antiferromagnetic magnonics18. Therefore, Sr2IrO4 presents a compelling avenue for studying the influence of metallic interfaces on spin-wave dispersion and its heterostructures offer opportunities to explore intriguing phenomena19,20.\n\nIn this article, we report a systematic investigation of the spin-wave dispersion in Sr2IrO4 thin films epitaxially interfaced with various metallic and insulating single crystals. High-resolution resonant inelastic x-ray scattering (RIXS) measurements reveal a significant softening of single-magnon peaks near the (\u03c0/2, \u03c0/2) zone boundary for Sr2IrO4 thin films interfaced with metallic crystals, without any accompanying broadening of the magnon spectrum. In contrast, the magnon spectrum of Sr2IrO4 thin films remains unaltered when interfaced with insulating crystals. Raman spectroscopy further corroborates these findings, as the two-magnon excitations\u2014predominantly representing zone-boundary magnons\u2014exhibit a consistent trend. We propose that electron-phonon interactions, occurring either at the heterointerface or within the metallic substrate, may modify the magnon dispersion in Sr2IrO4 thin films via long-range magnon-acoustic phonon interactions. Complementary experimental techniques, including resonant elastic x-ray scattering, optical spectroscopy, and transmission electron microscopy, indicate that conventional interfacial mechanisms such as strain, doping, and proximity effects are unlikely to account for these observations. Our findings give an insight into magnonics, leveraging metal-insulator transitions in adjacent crystals as an effective mechanism for manipulating terahertz magnon propagation.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "We constructed epitaxial heterostructures by depositing Sr2IrO4 epitaxial thin films on ruthenates single crystals (Fig.\u00a01) using pulsed laser deposition21,22. To minimize the potential influence of spin-spin interactions, we selected ruthenates with paramagnetic characteristics. Sr2RuO4 single crystals exhibit a tetragonal crystal structure and metallic transport behavior, while Ca3Ru1.98Ti0.02O7 single crystals are orthorhombic and undergo a metal-insulator transition at 55\u2009K, exhibiting metallic behavior above this temperature and insulating behavior below it23,24. This transition is accompanied by minor changes in the b- and c-lattice constants (Supplementary Fig.\u00a01). To systematically investigate the strain effects, we also studied Sr2IrO4 thin films deposited on insulating (LaAlO3)0.3(Sr2TaAlO6)0.7 (LSAT) substrates, which has a similar lattice mismatch with Sr2IrO4 as Sr2RuO4.\n\nSchematic representation of Sr2IrO4 thin films interfaced with various substrates, including ruthenates single crystals and reference LSAT substrates. Sr2RuO4 and LSAT single crystals have comparable in-plane lattice constants but exhibit distinct electronic properties, with Sr2RuO4 being metallic and LSAT insulating. The Ca3Ru1.98Ti0.02O7 single crystal has a temperature-dependent metal-insulator transition, exhibiting metallic behavior above 55\u2009K and becoming insulating below this temperature.\n\nHigh-resolution Z-contrast scanning transmission electron microscopy of the Sr2IrO4/Sr2RuO4 heterostructure (Supplementary Fig.\u00a02) reveals an atomically sharp heterointerface with minimal interfacial diffusion, similar to the sharp Sr2IrO4/Ca3Ru2O7 (ref. 25) and Sr2IrO4/Ca3Ru1.98Ti0.02O7 heterointerfaces (Supplementary Fig.\u00a03). Distinct x-ray (0 0\u2009l)-diffraction peaks are observed for the Sr2IrO4 thin films as well as the Sr2RuO4, Ca3Ru1.98Ti0.02O7, and LSAT substrates, accompanied by interference fringes near the (0 0 12) peak (Supplementary Fig.\u00a04a).\n\nX-ray reciprocal space mapping confirms that the in-plane lattice of Sr2IrO4 thin films is strained to all three substrates without relaxation (Supplementary Fig.\u00a04b, c). Notably, the Sr2IrO4 thin films on both Sr2RuO4 and LSAT experience the same amount of compressive strain of \u22120.51% (Supplementary Table\u00a01). In contrast, the Sr2IrO4 thin film on Ca3Ru1.98Ti0.02O7 undergoes a more complex strain profile: \u22122.22% compressive strain along the a-axis and 0.33% tensile strain along the b-axis above 55\u2009K, which increases to 2% tensile strain along the b-axis below 55\u2009K (Supplementary Table\u00a02).\n\nIn recent years, RIXS has emerged as a powerful tool for collecting momentum-resolved and element-specific insights into collective magnetic excitations, such as magnons and spin-orbit excitons, in transition metal oxides26,27,28,29. Figure\u00a02a presents representative energy loss spectra of our Sr2IrO4/Sr2RuO4 heterostructure, measured with different momentum transfers in the magnetic Brillouin zone. Figure\u00a02b provides an intensity map derived from the energy loss spectra in Fig.\u00a02a, highlighting the key spectral features. The low-energy range (0\u20130.25\u2009eV) shows a dispersive magnetic excitation (magnon), while the higher-energy range (0.30\u20130.90\u2009eV) reveals a dispersive orbital excitation (spin-orbit exciton), both of which reflect the intrinsic properties of the system18,27. The presence of well-defined dispersive magnons and spin-orbit excitons underscores the high crystallinity of the Sr2IrO4/Sr2RuO4 heterostructure, further validating the quality of the epitaxial thin films used in this study.\n\na Energy loss spectra of the Sr2IrO4/Sr2RuO4 heterostructure at 20\u2009K with different momenta in the Brillouin zone. The inset illustrates high symmetry points of both the undistorted tetragonal unit cell and the magnetic unit cell. b Image plot of the energy loss spectra shown in (a), highlighting the dispersive magnon and spin-orbit exciton modes. The detection of well-defined dispersive features confirms the high crystalline quality of the Sr2IrO4 thin film. The orange dashed line in (a, b) serves as a guide to the magnon dispersion. The intensity scale bar is in arbitrary units. c Schematic of the horizontal scattering geometry used during the RIXS measurements.\n\nThe noteworthy feature observed in the RIXS experiments is the pronounced softening of the low-energy magnon dispersion in Sr2IrO4 films, specifically near the (\u03c0/2, \u03c0/2) zone boundary, when their interfaced single-crystal substrates transition from insulating to metallic states. This softening, by ~20\u2009meV, is a striking contrast to the consistent magnon energies observed in other regions of the Brillouin zone. Figure\u00a03a illustrates the low-energy magnon spectra of all Sr2IrO4 thin films along the high-symmetry directions (\u03c0, 0) and (\u03c0/2, \u03c0/2). At (\u03c0, 0), the magnon peak energy remains nearly identical, around 200\u2009meV, across all systems studied: Sr2IrO4/Sr2RuO4, Sr2IrO4/Ca3Ru1.98Ti0.02O7 (both above and below 55\u2009K), Sr2IrO4 /LSAT, and the Sr2IrO4 single crystal. However, at (\u03c0/2, \u03c0/2), a notable difference emerges. Thin films interfaced with metallic substrates\u2014Sr2IrO4/Sr2RuO4 (orange) and Sr2IrO4/Ca3Ru1.98Ti0.02O7 above 55\u2009K (red)\u2014exhibit a significant softening of the magnon peak energy, with a reduction of approximately 20\u2009meV. In contrast, thin films on insulating substrates, such as Sr2IrO4/Ca3Ru1.98Ti0.02O7 below 55\u2009K (blue) and Sr2IrO4/LSAT (cyan), show magnon peak energies at (\u03c0/2, \u03c0/2) that are indistinguishable from that of the Sr2IrO4 single crystal. This shift in magnon energy exceeds the experimental error bar, underscoring the significance of this observation28.\n\na RIXS spectra of Sr2IrO4 thin films on various substrates, compared to single-crystal Sr2IrO4 (light green) at (\u03c0, 0) and (\u03c0/2, \u03c0/2). Substrates include metallic (Sr2IrO4/Sr2RuO4 (orange) and Sr2IrO4/Ca3Ru1.98Ti0.02O7 at T\u2009>\u200955\u2009K (red)) and insulating (Sr2IrO4/LSAT (cyan) and Sr2IrO4/Ca3Ru1.98Ti0.02O7 at T\u2009<\u200955\u2009K (blue)) crystals. Thin films interfaced with metallic substrates show pronounced softening of the single magnon mode at the (\u03c0/2, \u03c0/2) zone boundary. b Magnon dispersion extracted from the RIXS spectra and compared with single-crystal Sr2IrO4 data. The solid orange and cyan lines represent theoretical fits using the model Hamiltonian for Sr2IrO4/Sr2RuO4 and Sr2IrO4/LSAT, respectively.\n\nEssentially, the results can be categorized into two groups (Fig.\u00a03b): Sr2IrO4 heterostructures with insulating substrates retain higher magnon energy near (\u03c0/2, \u03c0/2), comparable to single-crystal Sr2IrO4, while those interfaced with metallic substrates exhibit a pronounced magnon energy softening, reduced by approximately 20\u2009meV. This behavior highlights the critical role of substrate properties\u2014particularly metallicity\u2014in tuning the magnon dispersion of Sr2IrO4 thin films. The softened magnon energy at (\u03c0/2, \u03c0/2) is a highly unusual phenomenon, as it increases the magnon dispersion between the (\u03c0, 0) and (\u03c0/2, \u03c0/2) zone boundaries.\n\nIn typical antiferromagnetic S\u2009=\u20091/2 systems like La2CuO4, the magnon energy difference between these zone boundaries is relatively small, as observed in inelastic neutron scattering experiments30. However, in Sr2IrO4 single crystals (a Jeff\u2009=\u20091/2 pseudospin system), this energy difference is significantly larger, and it increases further in Sr2IrO4 thin films interfaced with metallic substrates. According to linear spin-wave theory18,31, the magnon energy dispersion (\\({\\omega }_{{{\\bf{q}}}}\\)) is described as: \\({\\omega }_{{{\\bf{q}}}}=\\sqrt{{A}_{{{\\bf{q}}}}^{2}-{B}_{{{\\bf{q}}}}^{2}}\\), where: \\({A}_{{{\\bf{q}}}}=2({J}_{1}-{J}_{2}-{J}_{3}+{J}_{2}\\cos {q}_{x}\\cos {q}_{y})+ {J}_{3}(\\cos {2q}_{x}+\\cos 2{q}_{y})\\), \\({B}_{{{\\bf{q}}}}={J}_{1}(\\cos {q}_{x}+\\cos {q}_{y})\\). Here, J1, J2, and J3 represent the in-plane exchange interactions between the nearest, next-nearest, and third-nearest neighbors, respectively. The magnon energies at the (\u03c0, 0) and (\u03c0/2, \u03c0/2) zone boundaries are expressed as: \\({\\omega }_{\\left(\\pi,0\\right)}\\) = \\(2({J}_{1}-{2J}_{2})\\), \\({\\omega }_{\\left(\\frac{\\pi }{2},\\frac{{{\\boldsymbol{\\pi }}}}{{{\\boldsymbol{2}}}}\\right)}\\)\u2009=\u2009\\(2({J}_{1}-{J}_{2}-2{J}_{3})\\). Thus, the experimentally observed magnon softening near the (\u03c0/2, \u03c0/2) zone boundary can be attributed to changes in J2 and J3, specifically \\({2}({-}{J}_{2}+2{J}_{3})\\).\n\nBy fitting the magnon dispersion data (Fig.\u00a03b) using the Heisenberg spin model, we extracted the in-plane exchange interactions up to the fourth-nearest neighbor (J4) for the Jeff\u2009=\u20091/2 pseudospins in Sr2IrO4. The best-fit parameters are summarized in Supplementary Table\u00a03: For the Sr2IrO4 thin films contacted with insulating substrates: J1\u2009=\u200955\u2009meV, J2\u2009=\u2009\u221217 meV, J3\u2009=\u200916\u2009meV, and J4\u2009=\u20097.3\u2009meV. For thin films interfaced with metallic substrates: J1\u2009=\u200955\u2009meV, J2\u2009=\u2009\u221221 meV, J3\u2009=\u200920\u2009meV, and J4\u2009=\u20094.6\u2009meV. Notably, while J1 remains unchanged, the magnitudes of J2 and J3 increase in Sr2IrO4 interfaced with metallic substrates, consistent with the predictions of linear spin-wave theory discussed above.\n\nThe two-magnon peak energies observed in high-resolution Raman spectra corroborate the RIXS results. Figure\u00a04a illustrates the B2g two-magnon modes in Sr2IrO4/Sr2RuO4 and Sr2IrO4/LSAT at 10\u2009K, while Fig.\u00a04b presents the temperature-dependent Raman spectra of B2g two-magnon modes in Sr2IrO4/Ca3Ru1.98Ti0.02O7. The two-magnon peak energies (\u03c92M) were determined by fitting the data to a model function comprising two Lorentz oscillators. Notably, the two-magnon energies of thin films on metallic substrates (Sr2RuO4 and Ca3Ru1.98Ti0.02O7 above 55\u2009K) are lower than those of thin films on insulating substrates (Ca3Ru1.98Ti0.02O7 below 55\u2009K and LSAT). Given that the two-magnon mode primarily reflects zone boundary excitations32, this observation is consistent with the softening of the (\u03c0/2, \u03c0/2) zone boundary magnon. These results establish a strong agreement between the Raman and RIXS measurements, further confirming the substrate-dependent modification of magnon dynamics in Sr2IrO4 thin films.\n\na Raman spectra of B2g two-magnon modes in Sr2IrO4/Sr2RuO4 (orange) and Sr2IrO4/LSAT (cyan) heterostructures measured at 10\u2009K. b Temperature-dependent Raman spectra of B2g two-magnon modes in the Sr2IrO4/Ca3Ru1.98Ti0.02O7 heterostructure. The two-magnon peak energy is lower in Sr2IrO4 thin films interfaced with metallic substrates (Sr2RuO4 and Ca3Ru1.98Ti0.02O7 at T\u2009>\u200955\u2009K) compared to those interfaced with insulating substrates (LSAT and Ca3Ru1.98Ti0.02O7 at T\u2009<\u200955\u2009K). Two-magnon peak positions in both figures were determined using fits to a model comprising two Lorentz oscillators, represented by smooth solid curves for each component. The total fit is shown by the black solid line.\n\nRaman spectroscopy reveals a noticeable hardening of the phonon modes in Sr2IrO4 thin films by ~8\u201311\u2009cm\u22121 (1\u20131.4\u2009meV) when the contacted substrate transitions from insulating to metallic, likely indicating electron-phonon interactions. Figure\u00a05a shows the A1g and B2g phonon modes of the Sr2IrO4 thin film in the Sr2IrO4/LSAT and Sr2IrO4/Sr2RuO4 heterostructures. While the phonon modes in Sr2IrO4/LSAT closely resemble those of Sr2IrO4 single crystals, the Sr2IrO4/Sr2RuO4 heterostructure exhibits a significant upward energy shift of approximately 1.4\u2009meV for the A1g mode and 1\u2009meV for the B2g mode. Similarly, an upward shift of up to 0.7\u2009meV in phonon energy is observed in Sr2IrO4/Ca3Ru1.98Ti0.02O7 heterostructures when the substrate transitions from an insulating to a metallic state at 55\u2009K (Fig.\u00a05b).\n\na A1g and B2g phonon modes of Sr2IrO4/Sr2RuO4 (orange) and Sr2IrO4/LSAT (cyan) heterostructures. The solid black lines represent Lorentzian fits to the data. b Temperature-dependent evolution of the B2g phonon modes in the Sr2IrO4/Ca3Ru1.98Ti0.02O7 heterostructure. Inset: Temperature-dependent peak position of the B2g phonon modes in the same heterostructure, illustrating the phonon hardening behavior as the substrate transitions from an insulating to a metallic state. Error bars represent uncertainties estimated from the peak widths and curve-fits.\n\nIt is important to note that the structural change occurring in the Ca3Ru1.98Ti0.02O7 substrate at 55\u2009K may also contribute to the observed phonon mode shift in Sr2IrO4/Ca3Ru1.98Ti0.02O7 heterostructures. Additionally, we investigated other heterostructures, including Sr2IrO4/Ca2Ru0.91Mn0.09O4 (insulator), Sr2IrO4/Sr2RhO4 (metal), and Sr2IrO4/Ca3Ru2O7 (metal) at 10\u2009K. These heterostructures exhibited a similar trend: the metallic substrates induced hardening of the B2g phonons in the Sr2IrO4 thin films by about 8\u2009cm\u22121 (1\u2009meV) compared to insulating substrates (Supplementary Fig.\u00a05). Overall, a common factor across all results is the metallic state of the substrate, which strongly correlates with the observed phonon mode stiffening in Sr2IrO4 thin films.",
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "Our observations of significant magnon softening and phonon mode hardening in Sr2IrO4 thin films interfaced with metallic substrates, compared to insulating substrates, offer valuable insights into the underlying coupling mechanisms. However, the change in phonon energies (\u2206\u03c9phonon) is an order of magnitude smaller than the change in magnon energies (\u2206\u03c9magnon). Reconciling the relatively small (~1\u2009meV) phonon shifts with the much larger (~20\u2009meV) magnon energy changes is challenging. Instead, these phonon shifts likely originate from structural distortions associated with the metal-insulator transition, as their magnitudes are comparable to shifts induced by thermal effects.\n\nDeveloping a microscopic, quantitative theory for electron-phonon-magnon couplings in such complex heterostructures is beyond the scope of this work. The form of the magnon-phonon (or spin-phonon) Hamiltonian is inherently complex, as lattice vibrations break lattice symmetries, introducing anisotropic magnetic interaction terms even in two-dimensional systems. Consequently, first-principle calculations of all magnon-phonon coupling parameters are practically infeasible.\n\nNevertheless, to explain the observed modifications in magnetic interactions across the tens-of-nanometer-thick Sr2IrO4 thin film, we propose a mechanism involving propagating longitudinal acoustic phonons. These phonons, sensitive to substrate charge carriers due to metallic screening effects, mediate long-range spin interactions through magnetoelastic coupling. This coupling, particularly pronounced in spin-orbit entangled magnets like Sr2IrO4, arises from the direct interaction of lattice vibrations with the unquenched orbital components of the magnetic moments. Magnetoelastic interactions effectively generate spin-spin couplings mediated by dispersive phonon modes, extending beyond nearest neighbors. As metallic screening softens the acoustic phonons, the resulting enhancement in phonon-mediated spin interactions aligns with the increased J2 and J3 values\u2014while J1 remains constant\u2014as deduced from our phenomenological fits (Fig.\u00a03b) and linear spin-wave theory.\n\nSimilar effects have been observed in lightly doped Sr2IrO4, where magnon softening and anisotropic momentum-space behavior were attributed to enhanced longer-range spin couplings mediated by softened phonons33. However, unlike the doped case, our heterostructures exhibit no broadening of magnon peaks or collapse of long-range magnetic order, suggesting that the observed changes are driven by indirect acoustic phonon-mediated interactions rather than direct coupling between Ir magnetic moments and substrate conduction electrons.\n\nAlternative mechanisms, such as lattice strain, interfacial proximity effects, or charge transfer, are insufficient to explain the observed phenomena. The strain effect, for example, fails to account for magnon softening in Sr2IrO4/Sr2RuO4 heterostructures, as similar strain states in Sr2IrO4/LSAT heterostructures do not show comparable effects. Proximity effects at the interface are also unlikely, as our observations are made through bulk-sensitive techniques26. These experimental techniques probe the entire volume of the 20\u201350\u2009nm (30-80 IrO2 layers) thin films, rather than being confined to the interface region. Moreover, Sr2IrO4/Sr2RuO4 heterostructures of varying thicknesses (12, 30, and 50\u2009nm) show consistent magnon softening without a noticeable thickness dependence (Supplementary Fig.\u00a06), indicating that the observed behavior is a long-range effect throughout the thin film.\n\nCharge transfer as a mechanism also fails to explain the results. If charge transfer at the interface involved hole doping, it would typically harden the magnon peak energy at the zone boundary34,35, contrary to our observations. On the other hand, electron doping is known to soften the magnon peak energy but often results in significant broadening of the magnon spectrum and a collapse of long-range magnetic order in Sr2IrO4 (refs. 33,36.). In both Sr2IrO4/Ca3Ru1.98Ti0.02O7 and Sr2IrO4/Sr2RuO4 heterostructures, however, no broadening of magnon peaks (Fig. 3a)\u00a0or collapse of long-range magnetic order was observed (Supplementary Fig.\u00a07).\n\nResonant x-ray scattering near the Ru L2 edge further confirms the absence of Ru ions in the Sr2IrO4 thin film (Supplementary Fig.\u00a08), ruling out intermixing between Ir and Ru ions. Additionally, optical spectroscopy reveals a clear insulating gap of approximately 0.3\u2009eV in the Sr2IrO4/Sr2RuO4 heterostructure\u00a0(Supplementary Fig. 9), similar to that of Sr2IrO4 single crystals, further eliminating charge transfer as a plausible explanation for the observed phenomena.\n\nIn summary, we have observed a pronounced softening of the zone boundary magnon energy when Sr2IrO4 thin films are epitaxially interfaced with metallic 4d TMO single crystals. We propose that electron-phonon coupling, occurring either at the interface or within the metallic substrate, influences the magnon dispersion in Sr2IrO4 via a long-range magnon-acoustic phonon interaction. This mechanism enhances second- and third-nearest-neighbor interactions, while its impact on the nearest-neighbor exchange remains minimal. Our findings underscore the need for further theoretical studies and calculations to develop a deeper understanding of the microscopic interactions between magnons and phonons in such complex systems.\n\nIn addition to the well-established role of acoustic waves in mediating magnon-phonon coupling37, we propose that metal-insulator transitions at heterointerfaces represent an effective mechanism for tuning magnons\u2014an essential requirement for advancing magnonics. Our study also raises several compelling questions and broader perspectives. For instance, is this phenomenon unique to 5d/4d TMO heterostructures, or could it be extended to other magnetic systems, such as Yttrium iron garnets38 or van der Waals heterostructures incorporating two-dimensional magnets like Fe3GeTe2, NiPS3, CrSBr, where strong magnon-exciton coupling39 has already been observed? Future investigations across a broader range of heterostructures, featuring diverse materials and magnetic systems, will be essential for addressing these questions and further unraveling the fundamental interactions governing spin excitations in complex quantum systems.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The 5d/4d epitaxial heterostructures were fabricated by depositing Sr2IrO4 epitaxial thin films on ruthenates single crystals using a custom-built pulsed laser deposition system40. The growth conditions are a laser fluence of 1.2\u2009J/cm2, a substrate temperature of 700\u2009\u00b0C, and an oxygen partial pressure of 10\u2009mTorr. The thicknesses of the Sr2IrO4 thin films were determined to be ~12\u2009nm, 30\u2009nm, 50\u2009nm (Sr2IrO4/Sr2RuO4), 50\u2009nm (Sr2IrO4/LSAT), and 20\u2009nm (Sr2IrO4/Ca3Ru1.98Ti0.02O7).\n\nHigh-resolution x-ray diffraction and RIXS experiments were conducted at the 6-ID-B beamline and the 27-ID beamline of the Advanced Photon Source, Argonne National Laboratory, respectively. For RIXS spectra, a horizontal scattering geometry was employed with incident x-ray photons tuned to the Ir L3 edge (\u045b\u03c9\u2009=\u200911217\u2009eV) and polarized in the \u03c0-direction, as illustrated in Fig.\u00a02c.\n\nCross-sectional specimens for high-resolution scanning transmission electron microscopy were prepared using a Thermofisher Helios focused ion beam with a gallium ion source. These specimens underwent additional cleaning via argon ion milling (Fichicone Nanomill) at a beam energy of 500\u2009eV to eliminate amorphous layers. Transmission electron microscopy images were captured with a Thermofisher Titan operating at 300\u2009kV and employing a collection angle range of 80\u2013300 milliradians.\n\nTemperature-dependent Raman spectra were obtained in back-scattering geometry using a Jobin Yvon LabRAM HR800 spectrometer equipped with a confocal microscope. A 1.96\u2009eV excitation line from a helium-neon laser was used, producing a focused beam spot with a diameter of ~5\u2009\u03bcm.\n\nThe optical conductivity spectrum was measured using a home-built ellipsometer attached to a Bruker 66\u2009V FT-IR spectrometer and an M2000 ellipsometer (Woollam) for the spectral ranges of 0.05\u20131.0\u2009eV and 1.2\u20136\u2009eV, respectively.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All the data used to generate figures are provided with this paper in the Source Data file. Additional data related to this paper may be requested from the corresponding author.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "We acknowledge the support of National Science Foundation Grant No. DMR\u22122104296 for sample synthesis and characterization. This research used resources of the Advanced Photon Source; a U.S. Department of Energy (DOE) Office of Science user facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Electron microscopy was performed at the Center for Electron Microscopy and Analysis at the Ohio State University supported by National Science Foundation Grant No. DMR\u22121847964. G.C. acknowledges NSF support via Grant No. DMR 2204811. We acknowledge support from the European Research Council under Advanced Grant No. 101141844 (SpecTera) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through Project No. 107745057-TRR 80.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Department of Physics and Astronomy, University of Kentucky, Lexington, KY, USA\n\nSujan Shrestha,\u00a0Maryam Souri\u00a0&\u00a0Ambrose Seo\n\nAdvanced Photon Source, Argonne National Laboratory, Argonne, IL, USA\n\nChristopher J. Dietl,\u00a0Jong-Woo Kim\u00a0&\u00a0Jungho Kim\n\nDepartment of Physics, University of Alabama at Birmingham, Birmingham, AL, USA\n\nEkaterina M. P\u00e4rschke\n\nMax-Planck-Institut f\u00fcr Festk\u00f6rperforschung, Stuttgart, Germany\n\nMaximilian Krautloher,\u00a0Matteo Minola,\u00a0Xiatong Shi,\u00a0Alexander V. Boris,\u00a0Giniyat Khaliullin\u00a0&\u00a0Bernhard Keimer\n\nDepartment of Materials Science and Engineering, The Ohio State University, Columbus, OH, USA\n\nGabriel A. Calderon Ortiz\u00a0&\u00a0Jinwoo Hwang\n\nDepartment of Physics, University of Colorado at Boulder, Boulder, CO, USA\n\nGang Cao\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nS.S., M.S., and A.S. fabricated the heterostructures. S.S., C.D., A.S., J.-W.K., and J.K. performed synchrotron x-ray experiments and data analysis. M.K., B.K., and G.C. synthesized ruthenate single crystals. A.S. and M.M. performed Raman spectroscopy and data analysis. G.A.C.O. and J. H. performed transmission electron microscopy. X.S., A.S., and A.V.B. performed infrared spectroscopy. E.M.P., G.K., and B.K. contributed to the theoretical calculations and insights. A.S. designed the project. S.S., M.S., and A.S. wrote the manuscript with all authors\u2019 input.\n\nCorrespondence to\n                Ambrose Seo.",
+            "section_image": []
+        },
+        {
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+            "section_image": []
+        },
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+            "section_text": "Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
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+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions",
+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Shrestha, S., Souri, M., Dietl, C.J. et al. Tunable magnons of an antiferromagnetic Mott insulator via interfacial metal-insulator transitions.\n                    Nat Commun 16, 3592 (2025). https://doi.org/10.1038/s41467-025-58922-z\n\nDownload citation\n\nReceived: 16 July 2024\n\nAccepted: 04 April 2025\n\nPublished: 15 April 2025\n\nVersion of record: 15 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58922-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+    "pre_title": "Learning Superior Energy Management from Electric Vehicle Data",
+    "journal": "Nature Communications",
+    "published": "22 March 2025",
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+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58192-9/MediaObjects/41467_2025_58192_MOESM3_ESM.zip"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-025-58192-9#MOESM1",
+        "/articles/s41467-025-58192-9#Sec18"
+    ],
+    "code": [
+        "/articles/s41467-025-58192-9#ref-CR48",
+        "https://doi.org/10.5281/zenodo.14848553",
+        "https://github.com/wangjail/LearningEMS"
+    ],
+    "subject": [
+        "Batteries",
+        "Electrical and electronic engineering"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4523312/v1.pdf?c=1742727972000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4523312/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-025-58192-9.pdf",
+    "preprint_posted": "02 Jul, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Despite the promising potential of energy management technologies in optimizing electric vehicle (EV) performance and fostering global energy sustainability, the extensive research conducted over the past decade has yet to translate into practical applications. This discrepancy arises primarily from the reliance of existing methodologies on simulation-based development paradigms, leading to a significant disparity between simulated results and real-world efficacy. Herein, we present a pioneering real-world data-driven energy management strategies (EMS) approach that utilizes an innovative offline reinforcement learning (ORL) framework. This paradigm enables EMS to learn from diverse real-world data, obviating the need for explicit rule design or high-fidelity simulators, and allowing for seamless application of the proposed method to any existing EMS. Moreover, it continuously enhances performance even after deployment in actual energy management systems. We evaluate the proposed ORL method on fuel cell EVs, training the ORL agent to optimize energy consumption and system degradation. The EV monitoring and management platform in China provides real-world data for validating our methodology. The results demonstrate that ORL consistently learns superior EMS in various conditions. With increasing data availability, its performance improves significantly, from 88% to 98.6% relative to theoretical optimality after two data updates. After training with more than 60 million kilometers of data, the ORL agent can learn a general EMS that adapts to unseen and corner-case conditions. These results highlight the effectiveness of integrating the data-driven method with established EMS techniques to enhance performance and underline its potential to utilize large-scale data to improve vehicle energy efficiency and longevity.Physical sciences/Engineering/Electrical and electronic engineeringPhysical sciences/Energy science and technology/Energy storage/BatteriesEnergy managementElectric vehicle dataReinforcement learningFuel cell vehiclesData-driven",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Energy management technologies have significant potential to optimize electric vehicle performance and support global energy sustainability. However, despite extensive research, their real-world application remains limited due to reliance on simulations, which often fail to bridge the gap between theory and practice. This study introduces a real-world data-driven energy management framework based on offline reinforcement learning. By leveraging electric vehicle operation data, the proposed approach eliminates the need for manually designed rules or reliance on high-fidelity simulations. It integrates seamlessly into existing frameworks, enhancing performance after deployment. The method is tested on fuel cell electric vehicles, optimizing energy consumption and reducing system degradation. Real-world data from an electric vehicle monitoring system in China validate its effectiveness. The results demonstrate that the proposed method consistently achieves superior performance under diverse conditions. Notably, with increasing data availability, performance improves significantly, from 88% to 98.6% of the theoretical optimum after two updates. Training on over 60 million kilometers of data enables the learning agent to generalize across previously unseen and corner-case scenarios. These findings highlight the potential of data-driven methods to enhance energy efficiency and vehicle longevity through large-scale vehicle data utilization.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "The automotive industry is undergoing a significant transformation, primarily due to the global focus on sustainability and environmental conservation. Electric vehicles (EVs) are leading this shift, playing a key role in mitigating environmental challenges and advancing sustainable transportation solutions1,2. Concurrently, the emergence of hybrid energy systems (HES) within the EV powertrain represents an emerging trend, offering superior solutions over single energy systems3. By integrating multiple energy sources such as batteries, fuel cells, and internal combustion engines, HES improves overall efficiency, sustainability, and reliability, while also providing adaptability to a wide range of driving conditions4. Propelled by rapid technological advancements and supportive policies, EVs equipped with HES, including Hybrid EVs (HEV), Plug-in hybrid EVs (PHEV), and Fuel cell EVs (FCEV), are gaining traction worldwide5. For example, the EV manufacturer BYD achieved sales of 3.02 million EVs in 2023, and PHEVs represent 47.9% of total sales. By 2024, the share of PHEVs increased significantly to 58.2% of total sales, highlighting their rising popularity in the market.\n\nGiven the increasing complexity and capabilities of HESs, effective energy management is crucial for optimizing their overall performance. An energy management strategy (EMS) serves as a vital component in EVs, specifically designed to regulate energy flow allocation among various sources within HES to achieve predefined operational objectives6. An EMS performs several critical functions essential for the optimal operation of EVs: (1) EMSs optimize system efficiency by intelligently allocating energy flow based on driving conditions, reducing overall energy consumption and extending the driving range7; (2) EMSs optimize power delivery based on driver demand, enhancing acceleration and responsiveness while ensuring smooth power delivery for improved driving experience; and (3) By considering the unique characteristics of different power sources, EMSs extend the lifespan of HESs, thereby enhancing system reliability and safety8. Early EMSs primarily relied on various rule-based approaches to achieve energy-saving objectives. These strategies involve the creation of predefined rules and parameters tailored to specific driving conditions and vehicle characteristics, relying heavily on expert knowledge with iterative refinement via testing feedback9. However, while effective, this approach can be labor-intensive and time-consuming, requiring significant manual expertize/effort for rule formulation and extensive experimentation for parameter calibration. Moreover, the static nature of rule-based EMSs limits their adaptability to dynamic driving scenarios, thereby reducing their effectiveness in maximizing energy savings and overall performance.\n\nTo address the complex challenge of energy management, various EMS approaches have been developed over the past decades. Among these, optimal control theory-based methods, such as dynamic programming (DP)10 and model predictive control (MPC)11, are widely adopted. These strategies, often categorized as prediction-based EMSs, excel in achieving near-optimal energy allocation by leveraging systematic modeling and predictive capabilities. However, while DP and MPC provide sophisticated solutions utilizing future driving data, accurately forecasting future driving conditions based on historical speed patterns and dynamic traffic information remains a considerable challenge12. Additionally, prediction models and optimization algorithms often introduce considerable computational complexity, leading to suboptimal real-time performance3. Consequently, the application of prediction-based EMS methods in real-world vehicle scenarios remains limited.\n\nIn response to these challenges, advanced machine learning (ML) techniques have emerged as powerful tools for energy management in HESs, with reinforcement learning (RL) (especially deep reinforcement learning (DRL)) marking a key milestone in this field. The optimal formulation of RL is based on a Markov Decision Process (MDP), comprising an environment and an agent. In this framework, the RL agent interacts with the environment to learn strategies that maximize cumulative rewards over time13. Unlike prediction-based EMS methods, RL determines optimal actions through a trial-and-error process, eliminating the need for prior knowledge of system mathematical modeling or future driving conditions14. DRL algorithms, such as Deep Deterministic Policy Gradient (DDPG)15, Soft Actor-Critic (SAC)16, and Proximal Policy Optimization (PPO)17, have demonstrated impressive results in addressing EMS problems. These approaches, collectively termed simulation-based EMSs, leverage high-fidelity EV simulators to safely train DRL agents in developing near-optimal strategies. A key advantage of DRL lies in its self-learning ability, which allows it to autonomously derive effective and adaptive strategies18. However, applying simulation-based DRL methods to real-world vehicle tasks is constrained by sample inefficiency and safety concerns. DRL adopts an online learning paradigm, where agents typically require extensive interactions with the environment to learn effective policies19. In real-world scenarios, direct interaction with vehicles poses safety risks, as the agent may execute suboptimal or unsafe actions during the learning process. Additionally, while existing studies often assume that simulation models accurately replicate real-world conditions, constructing high-fidelity models that comprehensively capture vehicle dynamics, powertrains, traffic scenarios, and driver behavior remains a considerable challenge20. This limitation can result in the \u201csim-to-real\" problem, where EMS strategies developed in simulators fail to transfer effectively to real vehicles, further complicating the development and deployment of EMS solutions.\n\nRecently, advancements in ML and the growing availability of large datasets have made data-driven methods essential for addressing major challenges in the EV industry21,22. With support from data collection platforms and open-access laboratory data, these data-driven approaches have revolutionized various aspects of battery management systems (BMSs)23. Some applications include the automatic discovery of complex battery aging mechanisms24, prediction of battery safety envelopes25, evaluation of safety conditions26, estimation of battery state of health27, and even enhancing battery lifetime prediction models using unlabeled data28. Notably, innovations in feature extraction and supervised ML techniques tailored for time-series data have significantly enhanced prediction accuracy29. This progress has sparked interest in exploring data-driven methods to sequential decision-making tasks, including improving energy management systems. A common approach for implementing a data-driven EMS involves using supervised learning, where the ML model captures complex and non-linear relationships between input features and corresponding control outputs. In refs. 30,31,32, deep neural networks are trained offline using substantial amounts of training data obtained from the global optimization strategy of DP, yielding a near-optimal EMS that closely approximates the DP. It is essential to distinguish this from prediction tasks in BMS applications, as EMS entails sequential decision-making. Although supervised learning can mimic the EMS policy through imitation learning, its heavy reliance on expert data may result in limited generalization to new and diverse scenarios33,34. In refs. 35,36, the application of data-driven DRL for energy management was explored, with the agent learning the EMS from data generated by online DRL. While this approach showed promise, a challenge emerged when the dataset quality was poor, hindering successful learning. Therefore, it is crucial to investigate alternative methods for learning EMS from non-expert or suboptimal data, a scenario commonly encountered in automotive applications.\n\nIn this study, we propose a data-driven EMS paradigm that learns solely from pre-generated data from existing EMS methods, eliminating the need for explicit rule design or online interaction. Our approach integrates DRL with supervised learning, advancing beyond traditional rule-based, prediction-based, and simulation-based RL approaches to enhance EMS capabilities, as illustrated in Fig.\u00a01(a). Compared to existing data-driven methods, the main feature of our approach is its ability to learn a near-optimal EMS from large-scale, suboptimal data. Even with poor suboptimal data, it can still achieve successful learning, making it highly practical. Notably, this method can be seamlessly integrated with any EMS algorithm and continuously improve as new data is collected. We term this approach the offline reinforcement learning (ORL) agent, which incorporates a blended policy regularization (BPR) that facilitates effective exploration and ensures constraint satisfaction throughout the learning process. The ORL agent is trained using data from an augmented-reality EV platform, which combines real-world operational data from an EV monitoring and management system in China (Fig.\u00a01(b)) with a high-fidelity simulated FCEV powertrain model. We focus on the FCEV as the subject, collecting data for learning power allocation strategies. Note that the proposed method is also applicable to EMS for other types of vehicles with HESs, including HEVs and PHEVs. Overall, the ORL agent introduced here exhibits four key features (Fig.\u00a01(c)):\n\na Comparison of the four EMS paradigms: Traditional rule-based EMS relies on expert knowledge and calibration based on fixed driving cycles. Prediction-based methods, such as DP and MPC, rely on future driving data to operate. Simulation-based EMS requires high-precision models and entails transitioning from simulation analysis to real-world deployment, resulting in a gap between simulated and real-world performance (sim-to-real gap). In contrast, the proposed real-world data-driven EMS learns directly from actual data. b China has established a three-tier EV monitoring and management system involving the state, local governments, and public enterprises. The National Monitoring and Management Platform collects real-time operational data from over 20 million EVs49. c The ORL agent works with an existing EMS and continuously collects EV data to improve the EMS.\n\nPurely data-driven EMS: By autonomously learning and optimizing from collected offline datasets, our approach facilitates the development of advanced EMS without necessitating expert knowledge or the creation of comprehensive high-fidelity simulators incorporating both vehicle models and traffic scenarios. This data-driven process significantly streamlines EMS development workflows.\n\nLearning from non-optimal data: Our research demonstrates that the ORL agent can learn near-optimal EMS from non-optimal data, and even derive superior policies from poor suboptimal EMS data. This approach is less reliant on data quality, allowing for effective learning. Its practicality enables the use of raw data generated by actual vehicles, a common scenario in automotive applications.\n\nEnhancement with increased data: The ORL agent supports continuous learning by collecting new data online and updating its knowledge offline. The performance improves as more training data is incorporated, highlighting its ability to continuously adapt and enhance EMS performance. By training across diverse datasets, it can potentially adapt to new driving conditions and produce favorable results, even in corner-case situations.\n\nCompatibility with existing EMS: Our approach seamlessly integrates with existing rule-based or simulation-based EMS methods, leveraging data from onboard controllers to augment EMS performance. This ensures that baseline performance is maintained while facilitating further improvements via ORL, making it a valuable extension to conventional EMS methodologies.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58192-9/MediaObjects/41467_2025_58192_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "In Fig.\u00a02(a), the framework overview of ORL for EMS is illustrated. We present the three phases of applying the proposed ORL algorithm to the EMS problem: data collection, offline learning, and evaluation. ORL is an subset of RL methods based on data-driven approaches. Unlike traditional simulation-based RL EMS scheduling methods, the data-driven learning process of ORL does not require online interaction with an EV simulation environment. Instead, it learns solely from pre-generated data obtained from existing EMS methods. To efficiently collect large-scale EV datasets and evaluate the performance of the trained ORL agent during the evaluation phase, an augmented-reality EV platform is developed. This platform integrates real-world operational data from an EV monitoring and management system with a high-fidelity simulated FCEV powertrain model. As shown in Fig.\u00a02(b), the platform synchronizes the movements of a real FCEV on a physical test track with its virtual counterpart, enabling the real vehicle to interact with virtual vehicles in a realistic traffic environment. Real driving conditions serve as input to the powertrain system, which iteratively interacts with existing EMS methods to generate high-quality data. Specifically, raw data such as vehicle speed, battery voltage, current, FC system power, and motor power are collected through the EV monitoring and management system. These raw inputs are processed by the simulated FCEV powertrain system to generate refined outputs, such as acceleration, hydrogen and electricity consumption, FC degradation, battery state of charge (SOC), battery degradation, and accurately derived values for FC power, power variations, and motor power.\n\na The framework overview of ORL for EMS, including data collection, offline learning, and evaluation. b The augmented-reality testing platform enhances real-world operational data from an EV monitoring and management system with a high-fidelity simulated FCEV powertrain system, creating an efficient data collection and algorithm-testing environment for EVs. FC: fuel cell, DC/DC: DC-to-DC converter, DC/AC: DC-to-AC converter, DC: direct current, AC: alternating current.\n\nIn the data collection phase, the augmented-reality platform facilitates the efficient creation of comprehensive, large-scale, and high-precision EMS datasets. Metrics like hydrogen consumption and degradation, which are difficult to measure directly and often lack accuracy in real-world tests, are precisely derived through the high-fidelity simulation model. Additionally, the EV monitoring and management system effectively captures real driving conditions and driver behavior, which are otherwise challenging to replicate in purely simulated environments. By combining both real-world data and simulated model, the platform ensures the generation of comprehensive and reliable EMS datasets. In this study, EMS datasets of varying sizes are constructed, with the largest dataset encompassing over 60 million kilometers of driving data. The collected data are further processed into a standardized format, suitable for EMS applications. During the data encoding process, raw data from the augmented-reality platform are converted into a transition dataset, \\({{{\\mathcal{D}}}}={(s,a,{s}^{{\\prime} },r)}_{i}\\), organized in a time-series structure (i). Here, s represents the state, a the action, \\({s}^{{\\prime} }\\) the next state, and r the reward. Detailed descriptions of the states, actions, and reward functions are provided in the Methods section. The encoded state-action-reward sequences are stored in an experience replay buffer, serving as the basis for subsequent policy learning.\n\nIn the offline learning phase, we propose Actor-Critic with BPR (AC-BPR), a novel ORL method addressing limitations of traditional approaches. AC-BPR incorporates BPR, which combines Behavior Cloning (BC)37 and Discriminator-based Regularization (DR)38. BC ensures policy alignment with expert behavior by minimizing divergence, while DR employs an adversarial module to encourage exploration in high Q-value regions. This strategic balance between conservatism and exploration allows AC-BPR to effectively improve performance, even when learning from suboptimal or lower-quality datasets. In each training step, mini-batches of transitions \\((s,r,a,{s}^{{\\prime} })\\) stored in a data buffer are sampled to update the Actor-Critic networks. The Actor network selects actions based on the current policy (\u03c0(s)), which is guided by both expert behavior (via BC) and exploration of high Q-value(through DR). The Critic network evaluates the actions by estimating the Q-values for state-action pairs, providing feedback to the Actor for policy refinement. AC-BPR can be seamlessly integrated with any Actor-Critic algorithm and is practically implemented using the Twin Delayed Deep Deterministic Policy Gradient (TD3)39 framework, featuring a highly efficient design (as detailed in the Methods section). Both empirical results and theoretical analysis demonstrate that AC-BPR effectively mitigates distribution shifts in ORL by introducing blended regularization.\n\nUpon completion of the training phase, the neural network parameters representing the EMS of the ORL agent are saved for future use. Subsequently, the trained ORL agent is evaluated to gauge its effectiveness and performance. Utilizing the FCEV environment established during the data collection phase, we conduct experiments with three standard driving cycles (WTVC: World Transient Vehicle Cycle; CHTC: China Heavy-duty Commercial Vehicle Test Cycle; FTP: Federal Test Procedure 75) to evaluate energy costs. Additionally, various real-world driving scenarios are incorporated to comprehensively assess the trained EMS. Following the evaluation, adjustments to the agent\u2019s hyperparameters or training process may be made to improve its performance. This iterative process of training, evaluation, and refinement continues until the EMS attains the desired level performance. Once satisfactory performance is reached, the trained agent becomes eligible for deployment in real-world scenarios, where it can be utilized to efficiently optimize energy management systems.\n\nWe select the PPO as the expert EMS, as it demonstrates the best performance among online DRL algorithms for our EMS problem. Details regarding the performance of different EMS algorithms are presented in Table\u00a0S1. Using PPO, we generate datasets, denoted as \\({{{{\\mathcal{D}}}}}^{E}\\), comprising 300e3 time steps. Additionally, we employ a random agent that samples actions randomly, generating datasets, denoted as \\({{{{\\mathcal{D}}}}}^{R}\\), which represent poor performance. To create settings with varying levels of data quality in the suboptimal offline dataset, we combine transitions from the expert datasets \\({{{{\\mathcal{D}}}}}^{E}\\) and the random datasets \\({{{{\\mathcal{D}}}}}^{R}\\) in different ratios. Specifically, we consider four different dataset compositions, denoted as D1, D2, D3, and D4, defined as follows: D1 (Data-1): Consists solely of transitions from the expert dataset \\({{{{\\mathcal{D}}}}}^{E}\\), representing the expert policy. D2 (Data-2): Contains two-thirds of transitions from the expert dataset \\({{{{\\mathcal{D}}}}}^{E}\\) and one-third from the random dataset \\({{{{\\mathcal{D}}}}}^{R}\\), representing suboptimal data. D3 (Data-3): Comprises one-third of transitions from the expert dataset \\({{{{\\mathcal{D}}}}}^{E}\\) and two-thirds from the random dataset \\({{{{\\mathcal{D}}}}}^{R}\\), representing another form of suboptimal data. D4 (Data-4): Composed solely of transitions from the random dataset \\({{{{\\mathcal{D}}}}}^{R}\\), representing the random policy.\n\nFigure\u00a03 (a) depicts the action distributions for the four datasets, revealing significant differences among the four EMS policies. The action range for D1 falls within (\u22120.2, 0.5), indicating relatively stable variations in FC power. In contrast, the introduction of random policy data broadens the action ranges for the other datasets, all spanning (\u22121, 1). Notably, D4 exhibits a uniformly distributed action range across (\u22121, 1), indicating that this policy is noisy and represents a poor EMS. Figure\u00a03(b) illustrates the state distributions for the four datasets, where all states have undergone post-processing and scaling to the (0, 1) interval. Comparing the box plots of the four datasets reveals that the SOC of D1 remains within a reasonable range (0.38\u20130.7), adhering to EMS constraints for battery SOC. However, the SOC of the other datasets falls into unreasonable ranges, such as (0.2, 1) for D3. Additionally, with the increase in \\({{{{\\mathcal{D}}}}}^{R}\\) data, the FC power distribution ranges in D3 and D4 become wider. Since the conditions of the four datasets are derived from fixed segments of standard driving cycles, the velocity distribution remains the same across all datasets.\n\na Distribution of encoded actions for the four datasets, with each action normalized to the range [\u22121,1]. D1 represents data generated by the PPO expert policy; D2 and D3 denote suboptimal data generated by a combination of expert and random policies; and D4 comprises entirely random data. b The state distribution of four datasets, including battery SOC expressed as a percentage, fuel cell system output power scaled to the range [0, 1], and velocity also normalized to the range [0, 1].\n\nCreating challenging datasets is practical as generating suboptimal or random data is more cost-effective than collecting expert-level data from real vehicles. Consequently, an effective data-driven EMS method must be able to effectively handle and learn from these suboptimal offline datasets.\n\nWe first examine the performance of the ORL agent with different datasets. To ensure a fair comparison, the algorithm employs uniform experimental settings and network parameters across all four datasets. Figure\u00a04(a) illustrates the average reward during the training process for each dataset. This average is computed as the mean reward over every 1000 training steps and validated across 10 iterations using three standard driving cycles: WTVC, CHTC, and FTP. The training process involves utilizing a buffer comprising 300e3 samples, with the ORL agent randomly selecting 256 data points for each training iteration, totaling one million training epochs. For D1, which comprises exclusively expert data, convergence is observed after approximately 210e3 episodes. However, the ORL agent exhibits slower convergence speed during iterative learning on the D2, D3, D4 datasets, converging at around 330e3, 600e3, and 360e3 steps, respectively. This suggests that the data distribution considerably influences the learning speed. Nevertheless, the ORL agent ultimately succeeds in learning an effective EMS.\n\na Learning curves of the ORL agent for the four different datasets. b The comparison of absolute rewards (original rewards are negative) under three validation conditions. Expert refers to the original D1 dataset generated by the PPO policy, while D1, D2, D3, and D4 correspond to the best rewards achieved by ORL after learning on each respective dataset. Notably, the ORL agent\u2019s performance on various datasets closely approximates or exceeds the expert policy. c The comparison of energy costs between the original EMS and the optimized EMS using ORL demonstrates a significant reduction in energy costs via the data-driven learning process. d The action distributions (FC power slopes) of the optimized EMS using ORL.\n\nFigure\u00a04 (b) presents the reward performance of trained ORL agents across three driving cycles. Notably, the absolute reward value achieved via ORL decreases in D1, from 323.4 to 297.3 in the CHTC cycle, representing an improvement of 8.8%. Surprisingly, even when trained on suboptimal datasets D2 and D3, ORL outperforms the expert strategy, achieving reward increases of 1.8 and 3.4%, respectively. Similarly, under WTVC and FTP conditions, the ORL agent showcases superior performance, learning more effective strategies from the suboptimal datasets D2 and D3. An exception occurs with D3 under the WTVC condition, likely due to the high-speed nature of this cycle, leading to larger reward values for SOC. Despite this, the final energy consumption results remain within reasonable limits. Particularly noteworthy is the exceptional performance of the ORL agent on the random dataset D4, where it closely approaches expert-level results across all three validation conditions, achieving rewards of 402, 325, and 395. Compared to the original average reward of 2637 for the D4 dataset, ORL has reduced the reward by 85.8%. The enhanced performance of the ORL agent on suboptimal or non-expert datasets can be attributed to the proposed AC-BPR algorithm. By employing blended regularization techniques, ORL effectively balances conservative imitation with exploratory learning. This enables the ORL agent to maintain robust performance across diverse data qualities, achieving superior results on D1, D2, and D3, while also effectively exploring and optimizing the random dataset D4.\n\nFigure\u00a04 (c) provides a detailed comparison of the energy costs between the original EMS datasets and the optimized EMS using ORL. The blue dots represent the mean energy costs for the four original EMS datasets, with error bars indicating range between the maximum and minimum costs. In contrast, the red dots depict the energy costs incurred by ORL on the corresponding datasets. Despite the inclusion of random data, which degrades cost performance in the original datasets, ORL consistently achieves lower costs across all data sets. For instance, under the WTVC condition, the energy cost escalates from the initial 90 RMB in dataset D1 to 163 RMB in dataset D4. However, ORL consistently helps maintain costs within the narrow range of 90\u201395 RMB. In particular, the minimum cost values in the original D4 dataset significantly exceed those achieved by the ORL agent, which reduces costs by over 40% across all three conditions. These results underscore the ORL agent\u2019s ability not only to leverage expert EMS for superior outcomes but also to consistently deliver excellent performance from increasingly suboptimal datasets. Remarkably, the ORL agent even attains expert-level EMS performance when trained solely on noisy datasets.\n\nTo elucidate the rationale behind the performance improvements, Fig.\u00a04(d) illustrates the action distributions of the optimized EMS using ORL. As different EMS policies can be reflected by the actions taken, in the context of the FCEV considered here, this pertains to the FC power slope under the same driving cycle. Comparing Figs.\u00a03(a) and 4(d), significant changes are observed in D2, D3, and D4 with respect to Fig.\u00a03(a). In D2, D3, and D4, the action distributions closely resemble those of expert data in D1, concentrating within the range of [-0.3, 0.3], as opposed to the wider range of [-1, 1] seen in Fig.\u00a03(a). This change is particularly pronounced in D4, where the lack of expert data results in slight differences in the action distributions compared to D1, D2, and D3. However, all ORL policies consistently learn FC power variations with smaller ranges, ensuring smoother FC power output while effectively meeting the power demand requirements.\n\nIn conclusion, experimentation across three validation conditions and four datasets, our ORL agent demonstrates robust performance across diverse dataset conditions, including expert, suboptimal, and random datasets. By incorporating the AC-BPR algorithm, which balances BC with discriminator-based regularization, the ORL agent can effectively learn from non-optimal datasets.\n\nTo demonstrate the superior performance of ORL, we contrast it with simulation-based and imitation learning EMS approaches. Since imitation learning and ORL are closely related, both involve learning EMS from data. We first compare the performance of ORL with that of BC. Notably, BC typically employs a supervised learning paradigm, relying solely on expert data, while the ORL agent incorporates RL with exploration mechanisms. This distinctive learning mechanism results in significant performance differences between the two methods.\n\nIn Fig.\u00a05(a), we compare the testing rewards across the WTVC, CHTC, and FTP driving cycles, and calculate the percentage of ORL and BC costs relative to the expert EMS (PPO). In D1, both ORL and BC achieve favorable results, with ORL surpassing the original expert data by a maximum of 8.8%, while BC remains comparable to the expert. In D2, ORL maintains superiority over expert-based EMS, while BC experiences significant cost degradation (ranging from 6 to 70%). In D3 and D4, the ORL agent continues to outperform or closely match the expert, while BC, constrained by data quality, fails to learn an optimal EMS. This underscores ORL\u2019s ability to learn superior EMS from non-expert data, while imitation learning demonstrates poorer performance and struggles to learn favorable EMSs from non-expert data.\n\na The comparison between two data-driven EMS methods: The matrix numbers represent the relative reward rates of ORL and BC compared to the expert EMS (PPO) under the same conditions, emphasizing the minimal influence of data quality on the ORL agent\u2019s performance. b Comprehensive performance of different algorithms under the WTVC condition, with DP representing the globally optimal EMS. c Comprehensive performance under the CHTC condition. d Comprehensive performance under the FTP condition. e The distribution of FC system power across the efficiency curve and degradation regions, where Region 1, Region 2, and Region 3 represent the high-degradation zones, the high-efficiency range, and the overlapping area, respectively. f FC degradation costs under three conditions.\n\nFigure\u00a05 (b\u2013d) presents detailed results of different methods under the WTVC, CHTC, and FTP conditions, with the red lines representing the percentage of cost compared to DP. It is evident that ORL learns an optimal EMS on the D1 dataset, achieving percentages close to 99.9, 99.4, and 97.6% of DP, respectively. In comparison, PPO, as a benchmark expert policy, yields cost results 98.6, 98.0, and 97.6% of DP, respectively. Thus, while BC learns a similar expert EMS in D1, its performance significantly deteriorates on suboptimal D2 data. Another online DRL method, TD3, also demonstrates satisfactory performance; however, its overall costs are higher than those of PPO and ORL.\n\nIn Fig.\u00a05(e), the FC power distribution of the EMS learned by ORL on the D1 dataset is illustrated, mapped against its efficiency curve and degradation-prone regions. Power levels below 20% of the FC\u2019s maximum power (low-power operation) and above 80% of its maximum power (high-power operation) are identified as high-degradation zones (indicated as Region 1). In contrast, power levels corresponding to an efficiency exceeding 45% are classified within the high-efficiency region, representing optimal energy utilization (indicated as Region 2). Region 3 in the figure highlights the overlapping area between the high-degradation and the high-efficiency region. Under all three driving conditions, the power distribution guided by the ORL-based EMS is primarily concentrated in the high-efficiency region, with minimal operation in the high-degradation zones. This underscores the effectiveness of the ORL in learning an optimized EMS from data, ensuring the FC system operates predominantly in the high-efficiency range, thereby reducing both hydrogen consumption and overall system costs. Additionally, a narrower power variation range, as shown in Fig.\u00a05(d), minimizes FC degradation costs. As illustrated in Fig.\u00a05(f), ORL incurs minimal FC degradation costs across the WTVC, CHTC, and FTP conditions, with costs of 2.3, 1.4, and 1.4, respectively. Furthermore, examination of Fig.\u00a05(b\u2013d) indicates that the battery SOC remains within a reasonable range. These findings collectively affirm that the ORL agent has successfully learned a superior EMS.\n\nWe have demonstrated in previous experiments that the ORL can learn optimal EMS strategies from data and outperform other methods. In this section, we further showcase an ORL approach for continuous learning from data. We conduct experiments pertaining to three cases depicted in Fig.\u00a06(a), collecting real-vehicle data in different driving scenarios, including urban roads, highways, and downtown roads for the three cases (Fig.\u00a06(b)). Notably, the training datasets used here differ from those (D1, D2, D3, D4) in the previous sections. The training data for Case 1 and Case 3 consists of EMS data generated by the augmented-reality EV platform, which utilizes real-world operational data as input. In Case 2, the data combines results from a simulation-based RL strategy applied to standard driving cycles, with a subset of real-world driving conditions.\n\na ORL for continuous learning from data in three scenarios. b Driving data collected from the real-world route. ZBDC: Zhengzhou Bus Driving Cycle; HWDC: Highway Driving Cycle; GCDC: Guiyang City Driving Cycle. c Speed trajectories of three ZBDC conditions. d Comparing the total cost under ZBDC-No. 1, which includes hydrogen consumption, battery cost, and cell degradation cost. e Comparing the total cost under ZBDC-No. 2. f Comparing the total cost under ZBDC-No. 3. g FC power distribution cloud chart of baseline EMS. h Cloud chart of FC power distribution following one data update. i FC power distribution cloud chart following two data updates.\n\nIn Case 1, we illustrate the concept by using the example of driving a bus on fixed routes. Real electric bus driving data was collected in Zhengzhou, China, over three consecutive days. Figure\u00a06(c) shows the speed trajectories of the bus for each of the three days, labeled ZBDC-No. 1, ZBDC-No. 2, and ZBDC-No. 3. Noticeable variations in the speed trajectories are observed along the same route over different days. Figure\u00a06(d\u2013f) shows the total cost of different EMS strategies across the three scenarios. These costs include hydrogen consumption, battery costs, and FC degradation. The baseline is the original EMS of the FCEV, which is used as a reference. For the first scenario, we use the baseline data from the ZBDC-No.1 driving cycle to train the ORL agent, yielding the ORL(Z1) strategy. This strategy is then applied to the new condition ZBDC-No. 2. Furthermore, a new ORL EMS, ORL(Z2), is trained using data from both the baseline data from ZBDC-No. 1 and the previously learned ORL(Z1) strategy from ZBDC-No. 2. This new strategy is then validated on the final driving cycle ZBDC-No. 3.\n\nThe baseline EMS demonstrates poor performance on the first day (ZBDC-No. 1), achieving 88.0% of the cost efficiency compared to DP. The corresponding FC power and power slope distributions are depicted in Fig.\u00a06(g). On the second day, as illustrated in Fig.\u00a06(e) and (h), the ORL(Z1) strategy significantly improves by learning from the previous data, achieving 96.4% of DP\u2019s cost efficiency under the ZBDC-No. 2 conditions. By the third day, after continuously learning from additional data, the ORL(Z2) further enhances cost efficiency, achieving 98.6% of DP\u2019s performance on ZBDC-No. 3, as shown in Fig.\u00a06(f) and (i). A comparison of the power distributions across the three scenarios highlights that ORL(Z1) and ORL(Z2) allocate a greater proportion of FC output power to the high-efficiency range (Fig.\u00a06(g), (h), and (i)). This adjustment not only reduces overall energy consumption but also minimizes the power slope, effectively lowering system degradation costs.\n\nIn conclusion, with continuous data updates, new information can be effectively utilized to train the ORL agent, enabling the development of progressively optimized EMS strategies. This demonstrates the capacity of ORL for continuous learning and improvement from historical data. Additionally, our approach integrates seamlessly with established EMS frameworks by leveraging real-time data from onboard controllers to enhance EMS performance. This integration ensures the preservation of baseline performance while facilitating further improvements through ORL, making it a valuable and adaptable extension to conventional EMS methodologies.\n\nSimulation-based EMS offers a low-cost and efficient approach to developing strategies derived from simulated EV models. However, as highlighted in the introduction, deploying these strategies in real-world scenarios often results in performance discrepancies, commonly referred to as the sim-to-real gap. This gap arises due to differences between simulated and real-world conditions, including variations in environmental dynamics, noise, and other uncertainties. Combining ORL with online RL presents a promising solution to address this challenge. In Case 2, we aim to experimentally demonstrate that the proposed ORL method effectively reduces the gap, enhancing the performance of simulation-based EMS in real-world environments. Specifically, we examine an online RL-based EMS method implemented using the PPO algorithm. Initially trained on limited data from a simulated environment, this algorithm is then deployed in a new environment characterized by altered vehicle parameters and unknown driving conditions.\n\nAs shown in Fig.\u00a07(a), during the simulation phase, the PPO algorithm is trained on the standardized driving cycle (WTVC) and three specific driving conditions (ZBDC) (Fig.\u00a06(c)) to derive an ideal EMS, denoted as PPO (Train). Subsequently, the resulting EMS is validated across 12 different local driving conditions, denoted as PPO (Test). To simulate the environmental discrepancies between PPO (Train) and PPO (Test), the vehicle mass is varied\u2014set to 4500\u2009kg during training and increased to 5000\u2009kg during testing\u2014emphasizing the differences in operational scenarios. As depicted in Fig.\u00a07(b), the cost difference between PPO (Train) and DP across the four training conditions is minimal, averaging 3.16% (Fig.\u00a07(b)). However, when tested on the 12 new conditions (DC-1 to DC-12), the average cost difference between PPO (Test) and DP considerably rises to 12.75%. This indicates a significant performance degradation of DRL-based methods when transitioning from simulation to real-world conditions.\n\na Performance of PPO trained on four datasets and tested on 12 testing datasets. b Comprehensive performance comparison of different EMS methods, revealing that ORL can significantly mitigate performance degradation observed in testing phase of PPO. c Performance of ORL across 12 testing datasets.\n\nTo mitigate the sim-to-real problem, our proposed ORL method leverages data from PPO (Test) for further learning. As illustrated in Fig.\u00a07(c), the ORL approach achieves substantially lower costs across the 12 local operating conditions compared to the PPO (Train) strategy. The average cost difference between ORL and DP is reduced to just 1.42% (Fig.\u00a07(b)). This notable cost reduction underscores ORL\u2019s ability to refine the initial EMS strategy derived from simulation-based methods and adapt it effectively to real-world conditions. In summary, our experiments demonstrate that ORL can learn from simulated data to enhance the performance of the original EMS, providing a robust solution to the sim-to-real problem inherent in traditional simulation-based methods.\n\nTo evaluate the generalization performance of the ORL model, the agent is trained on extensive data encompassing over 60 million kilometers and tested on four novel driving conditions. The training data, sourced from the augmented-reality EV platform, primarily comprises real-world driving conditions and a limited number of standard driving cycles, excluding the four test conditions reserved for validation. Four training datasets of varying scales are constructed, containing 4e4 (ORL-4), 20e4 (ORL-20), 100e4 (ORL-100), and 500e4 (ORL-500) samples. Figure\u00a08(a) illustrates the speed distributions for these test conditions. Among these, the CLTC serves as the standard cycle, GCDC is based on data sourced from an EV operating in urban downtown areas, ZBDC represents a new condition recorded in Zhengzhou, and HWDC is derived from a fuel vehicle traveling on a highway. These conditions reflect diverse road, driver, and vehicle types, exhibiting significant variations in average speed Vm. Notably, under the HWDC condition, the FCEV experiences power demands exceeding 100 kW in over 42% of instances, with a peak demand surpassing 250 kW. As depicted in Fig.\u00a08(b), this demonstrates that HWDC represents an extreme condition for the FCEV, as the power demand exceeds the 100 kW maximum output capacity of the FC system.\n\na Speed distribution of four conditions. b Demand power of HWDC, representing an extreme condition. The red dashed line indicates the maximum output power of the FC system. c Overall performance of the four cases as training data increases under the China Light-Duty Vehicle Test Cycle (CLTC). d Performance under the GCDC; e Performance under the ZNDC; f Performance under HWDC, indicating that the ORL agent can effectively learn a reasonable EMS even under extreme driving conditions. g Battery SOC trajectories for different EMS under the HWDC.\n\nThe results for the four validation conditions are depicted in Fig.\u00a08(c\u2013f). It is evident from Fig.\u00a08(c), (d), and (e) that both the reward and cost exhibit a gradual decrease as the training data increases. With more training data, the ORL model consistently enhances its performance. Notably, the rate of performance improvement diminishes after reaching the 100e4 sample mark, with minimal disparity observed between the ORL-100 and ORL-500. At approximately 20e4 samples, the ORL model outperforms the DRL-TD3 algorithm (as indicated by the dashed lines in the figures). Notably, under the extreme HWDC condition, while ORL-20 achieves the lowest cost (Fig.\u00a08(f)), its reward absolute value is not the lowest. This phenomenon occurs because ORL-20 tends to prioritize battery consumption during periods of high power demand, exceeding the maximum output of the FC system. Consequently, the strategy fails to maintain the SOC within the desired range, rendering it ineffective as an EMS. Conversely, ORL-500 demonstrates superior performance, achieving both lower reward and cost while keeping the SOC within a reasonable range, as illustrated in Fig.\u00a08(g). Overall, after training on five million data points (equivalent to over 60 million kilometers), the ORL agent successfully learns a general EMS that can adapt to unseen and even corner-case conditions.\n\nThis result highlights two key advantages of the ORL agent: First, its performance surpasses that of the original policy; second, it demonstrates that with increased data availability, learning performance improves. The ORL model effectively learns a general EMS from large-scale EV data, showcasing its adaptability and capability to enhance EMS performance as more data becomes available.",
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+            ]
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+        {
+            "section_name": "Discussion",
+            "section_text": "In conclusion, we present a data-driven EMS for HESs in EVs. Our approach leverages an innovative ORL agent, which learns directly from EV data. To efficiently collect large-scale EMS datasets, we develop an augmented-reality EV platform that integrates real-world driving data from the EV monitoring and management system with a simulated FCEV powertrain model. Furthermore, we propose the AC-BPR, which incorporates BPR by combining BC with a discriminator. The BPR mechanism strikes a balance between conservatism and exploration, enabling AC-BPR to refine its policy even when learning from suboptimal or low-quality datasets. Experimental results demonstrate that the ORL agent not only learns optimal EMS strategies from expert data but also exhibits the ability to learn superior EMSs from datasets containing a mixture of expert and noisy data. The agent is also capable of achieving near-optimal strategies from entirely noisy datasets. Moreover, our approach demonstrates that, with increasing data availability, performance improves as the agent is trained with more data.\n\nThis approach offers three notable benefits. First, it is simple and data-driven, relying solely on collected data for automatic learning by the agent, unlike the traditional EMS development process, which often requires extensive expert knowledge and repeated measurements. Additionally, the data used in our approach are non-expert data that can be readily obtained from real vehicles. Second, our method ensures stable performance by seamlessly integrating with existing EMS, without altering the original baseline. Through data-driven enhancements, our approach continuously improves upon the baseline EMS, leveraging the strengths of both technologies. For example, to address the performance shortcomings in rule-based EMS, ORL enables incremental learning, allowing for continual enhancement of EMS performance using historical data. Similarly, ORL addresses the sim-to-real gap problem in simulation-based methods by refining pre trained EMS models, ensuring their effectiveness in real-world deployment scenarios. Finally, our approach demonstrates versatility: with sufficient data, it can learn a generalized EMS applicable across various EVs and operating conditions. This aligns with the current trends in artificial intelligence (AI) involving large-scale language models and similar approaches, where a single large model with large-scale data can be trained to perform well across diverse tasks and domains. Overall, we believe that ORL has the potential to serve as a foundational framework for data-driven EMS, with applications extending beyond EVs to include grid EMS, industrial energy management systems, and other vehicle control systems.\n\nA limitation of this work is that the ORL agent requires significantly more data compared to traditional methods. In our experiments, over 60 million kilometers of EV driving data were utilized to develop a superior EMS. However, collecting such an extensive dataset from a single vehicle within a short timeframe is impractical. Leveraging data from large-scale vehicle fleets presents a more feasible solution. In China, for instance, a comprehensive EV monitoring and management system allows automakers to collect vast amounts of data via cloud-based platforms. This data can be utilized to enhance energy efficiency using the proposed ORL-based approach. However, current EV data standards, such as the GB/T 32960 protocol, are insufficient for this purpose and require more comprehensive and preprocessed datasets. Furthermore, ensuring the safety and reliability of AI-driven systems is paramount for real-world applications40,41. Integrating ORL with traditional EMS methods can provide a robust solution, where a baseline EMS ensures safety and a guaranteed minimum performance, while the ORL-based EMS optimizes energy efficiency using the available data. Further research is essential to develop more data-efficient algorithms and hybrid EMS frameworks that ensure safety, robustness, and adaptability for real-world large-scale vehicle applications.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "In this work, we evaluate the EMS performance using the FCEV within a simulation environment. Figure\u00a02(b) illustrates the schematic diagram of the FCEV and its components, which include the FC system, a hydrogen storage tank, an electric motor (EM), and a Lithium-ion battery (LIB) pack. The FC stack serves as the primary power source to meet the energy requirements of the vehicle. The diagram also depicts the energy flow from the hydrogen storage tank to the motor. The FC system converts hydrogen energy into electricity. This electricity then collaborates with the LIB via the high-voltage bus, powering a single electric motor, connected to the driving wheel via a fixed-ratio final gear. The main parameters of the FCEV model are listed in Table\u00a0S4. According to the vehicle driving resistance equation, the driving power demand is determined by the speed and acceleration of the FCEV, and can be expressed as follows:\n\nwhere \u03b7me is the efficiency of the vehicle drivetrain, m represents the vehicle mass, g denotes the gravitational constant, Cf is the rolling resistance coefficient, vt indicates the longitudinal velocity at the time step t, at signifies the acceleration, \u03b4 refers to the rotational mass conversion coefficient, CD represents the air resistance coefficient, A denotes the frontal area, and \u03b8s represents the angle of slope of the road. The power demand is provided by the FC system and the battery pack, with the power balance of the FCEV formulated as:\n\nwhere Pfc and Pbat respectively denote the output power of the FC system and the LIB pack; \u03b7DC/DC,\u00a0\u03b7DC/AC, and \u03b7EM represent the efficiency of the DC/DC converter, DC/AC inverter, and the electric motor, respectively. The battery pack is modeled using an equivalent circuit model, as shown in Equation (3):\n\nwhere SOC denotes the battery state of charge, Voc is the open-circuit voltage, It represents the current at time t, R0 indicates the internal resistance, Pbat refers to the output power in the charge-discharge cycles, Q0 signifies the initial battery capacity, and Q is the nominal battery capacity.\n\nAccording to the battery aging model in16, the degradation rate of battery operation \u03b3bat is influenced by the charge/discharge rate (Crate). The relationship between the battery aging correction factor and Crate can be derived from experiment data:\n\nwhere \u03bc1,\u00a0\u03bc2,\u00a0\u03bc3 are the curve-fitting coefficients. LIB can operate for about 5000 full cycles in a lifetime. The battery degradation cost Cbat,degr can be calculated by:\n\nwhere PRbat is the battery price per kWh that is 1500RMB/kWh.\n\nThe efficiency of the FC system under different power conditions is obtained from experiment data. Thus, the mass flow rate of the hydrogen consumption can be calculated by:\n\nwhere \u03b7fcs is the FC system efficiency; Pfcs denotes the FC system output power; and \\({{\\mbox{LHV}}}_{{H}_{2}}\\) represents the hydrogen low calorific value. The FC hydrogen cost can be calculated by:\n\nwhere \\(P{R}_{{H}_{2}}\\) is the hydrogen price per kilogram(60RMB/kg).\n\nThe FC degrades rapidly under four typical conditions: load changing, start/stop, low power, and high power conditions. We assume that the FC system continues operating until the vehicle power system is shut down, thus the start/stop condition is not considered in the EMS. The degradation rate of the FC voltage, denoted as \u03b3fcs, can be calculated by:\n\nwhere \u03balow is the degradation rate under low power conditions; Tlow denotes the duration of low power conditions; \u03bahigh represents the degradation rate under high power conditions; Thigh indicates the duration of high power conditions; \u03bacha refers to the degradation rate under load-changing conditions; and \u0394Pfcs is the FC power slope.\n\nThe FC is considered to reach the end of its life when it has lost 10% of voltage at rated power. The FC operation degradation cost can be calculated as:\n\nwhere kfcs is the FC life correction factor; Vfcs,end denotes the FC voltage drop at the end-of-life; Pfcs,rate represents the rated power of the FC; and PRfcs is the FC price per kilowatt(4000RMB/kW).\n\nIn this work, the EMS of electric vehicles is modeled as a long-term sequential decision process objective to minimize total energy costs while maintaining battery SOC within reasonable limits. The optimization objective can be formulated as:\n\nwhere T is the total length of the driving cycle; cost(t) denotes the energy cost, including hydrogen consumption, battery costs, and FC degradation; fs(SOC(t)) represents the SOC maintaining function; and \u03b1 refers to the tradeoff between energy cost and SOC.\n\nTo tackle the sequential decision, the energy management system is formulated as an MDP, which provides a framework for learning the optimal EMS through interaction in order to minimize total energy costs. The MDP is defined by a tuple \\(\\left(S,A,P,R,{\\rho }_{0},\\gamma \\right)\\), where \\({{{\\mathcal{S}}}}\\) denotes the state space, A represents the action space, \\(P\\left({s}^{{\\prime} }| s,a\\right)\\) is the transition distribution, \u03c10(s) indicates the initial state distribution, R(s,\u00a0a) refers to the reward function, and \u03b3 \u2208 (0, 1) denotes the discount factor. The goal is to identify a policy \u03c0(a\u2223s) that maximizes the expected cumulative discounted rewards \\(J(\\pi )={E}_{\\pi,P,{\\rho }_{0}}\\left[{\\sum }_{t=0}^{\\infty }{\\gamma }^{t}R\\left({s}_{t},{a}_{t}\\right)\\right]\\). For the FCEV, the state space at time point t is defined as:\n\nwhere vt, at, Pfcs, SOCt are the vehicle speed, acceleration, FC power, and battery SOC, respectively. The action represents the control variable, which involves allocating power to the energy sources of the vehicle. In the context of the FCEV, the action is defined as the FC power slope, denoted as \u0394Pfcs. The continuous action can be described as follows:\n\nThe reward function R represents the reward R(st+1;\u00a0st;\u00a0at) associated with transitioning from state st to state st+1 using action at. The design of the reward function is crucial to the learning process. For the FCEV, multiple objectives are considered, such as hydrogen consumption, FC degradation, and battery-related costs (including electricity consumption and degradation). Additionally, it is essential to maintain the battery SOC. Therefore, the reward function is defined as the sum of energy costs, while ensuring that the battery charge-sustaining constraints are satisfied:\n\nThe battery electricity consumption \\({C}_{{{{\\rm{bat}}}},e{H}_{2}}\\) is calculated according to the battery charge/discharge efficiency and converted into price cost:\n\nwhere \u03b7d/c is the battery discharge/charge efficiency.\n\nRL is a paradigm for learning optimal policies in a sequential decision-making problem. It involves an agent interacting with an environment, taking actions, and receiving feedback in the form of rewards. In this study, we apply the RL paradigm to address the MDP problem described earlier. The objective is to learn a policy \u03c0\u00a0~\u00a0at (\u03c0:\u00a0S\u00a0\u2192\u00a0A) that maximizes the expected sum of discounted rewards J(\u03c0). Each policy \u03c0 has a corresponding state-action value function (also known as the Q function), which denotes the expected return Q(s,\u00a0a) when following the policy \u03c0 after taking an action a in state s.\n\nwhere \\({\\mathbb{E}}()\\) denotes the mathematical expectation. Online RL is an interactive ML paradigm where the agent learns from continuous interactions with the environment. This allows the agent to gradually improve its policy over time. This approach benefits from exploration, where the agent attempts different actions to discover potentially superior policies. However, the main challenge in interactive learning is the need to recollect the dataset every time the policy changes, which can be costly and impractical in real-world scenarios34. To address this, online RL often relies on simulated training to avoid the expenses and risks associated with real-world interactions.\n\nORL is a data-driven extension of traditional RL, leveraging pre-existing datasets to refine policy training without requiring further interaction with the environment. This paradigm is particularly useful in scenarios where real-world interactions are costly, risky, or infeasible. By utilizing datasets collected under a behavioral policy (\u03c0\u03b2), ORL aims to derive an optimized decision policy (\u03c0off) while avoiding direct exploration of the environment. Given a static dataset of transitions \\(D=\\left\\{{\\left({s}_{t},{a}_{t},{s}_{t+1},{r}_{t}\\right)}_{i}\\right\\}\\) where i indexes a transition, the actions are sampled from the behavior policy \\({a}_{t} \\sim {\\pi }_{\\beta }\\left(\\cdot | {s}_{t}\\right)\\), the states are drawn from a distribution induced by the behavior policy \\({s}_{t} \\sim {d}^{{\\pi }_{\\beta }}(\\cdot )\\), the next state is determined by the transition dynamics \\({s}_{t+1} \\sim \\,T\\left(\\cdot | {s}_{t},{a}_{t}\\right)\\), and the reward is a function of state and action \\({r}_{t}=r\\left({s}_{t},{a}_{t}\\right)\\). The objective of ORL remains to identify a policy \u03c0off that maximizes the expected return.\n\nHowever, a critical challenge in ORL is the state-action distribution shift, where the learned policy \u03c0off encounters states or actions that are not adequately represented in the dataset42. In other words, the ORL agent may face unfamiliar state-action regimes that were not covered by the offline EMS dataset, leading to inaccurate Q-value estimation and reduced policy performance. Additionally, offline EMS datasets are often diverse or of a poor-quality, as they contain suboptimal actions or noisy data due to imperfections in the behavior policy \u03c0\u03b2 or inconsistencies during data collection. This further complicates the learning process and makes identifying the optimal policy challenging. To address these issues and ensure sample-efficient learning in ORL, the AC-BPR algorithm is proposed. This algorithm introduces BPR, which integrates BC and a discriminator to balance conservatism with exploratory learning.\n\nBC aims to ensure the learned policy remains aligned with the behavior policy that generated the offline dataset. By minimizing the divergence between the learned policy and the demonstrated actions, BC mitigates distribution shifts37. The BC objective is defined as:\n\nwhere \u03c0(a\u2223s) represents the likelihood of selecting action a in state s under the policy \u03c0. This objective encourages the learned policy to mimic the expert behavior encoded in the dataset. However, in scenarios with non-optimal data, BC alone may lead to overfitting to undesirable actions, limiting policy performance. To address this limitation, BPR introduces a discriminator. The discriminator is trained to distinguish whether a given state-action pair (s,\u00a0a) belongs to the original dataset \\({{{\\mathcal{D}}}}\\), or if it was generated by the learned policy \u03c0\u03b8. In this setup, the policy \u03c0\u03b8 acts as the generator in a Generative Adversarial Network (GAN)43 setup. The discriminator helps guide the policy by encouraging exploration of actions that might not be represented in the dataset but are still plausible according to its judgment38,44.\n\n\\(D\\left({s}_{t},{a}_{t}\\right)\\) evaluates the probability that \\(\\left({s}_{t},{a}_{t}\\right)\\) originates from the offline dataset, and \\(\\pi \\left({s}_{t}\\right)\\) denotes the action suggested by the learned policy. The trained discriminator D(s,\u00a0\u03c0(s)) is integrated into the policy objective as a reward signal. This incentivizes the learned policy to explore diverse, high-quality actions while discouraging over-reliance on suboptimal dataset behaviors.\n\nThe BPR mechanism combines the strengths of BC and DR within the Actor-Critic framework. AC-BPR can be seamlessly integrated into any Actor-Critic algorithm, and in this study, we implement it using the TD3 framework. In this setup, the Actor-network is responsible for selecting actions based on the current policy \u03c0(s), which is influenced by expert behavior through BC and exploration of high Q-value regions via DR. The Critic network estimates the Q-values for state-action pairs, providing feedback to refine the policy. The final optimization objective of AC-BPR balances Q-value maximization, BC regularization, and discriminator-based exploration. This balance between conservatism and exploration allows AC-BPR to learn effectively even with suboptimal datasets. The Actor network is updated by optimizing the BPR objective, which combines these components to guide the policy toward more effective and diverse actions, improving learning efficiency in offline settings.\n\nwhere Q(s,\u00a0\u03c0(s)) represents the expected return for the policy action \u03c0(s) in state s. The term (\u03c0(s)\u2212a)2 penalizes deviations from the dataset actions to ensure conservatism. \\(\\log (D(s,\\pi (s)))\\) rewards exploration in high-reward regions identified by the discriminator. The parameter \u03b2 (range of 0 to 1) adjusts the balance between BC and DR constraints. The \u03bb is a normalization term based on the average absolute value of Q to control the balance between RL and imitation, defined as:\n\nThe parameter \u03b1 is used to control the strength of the regularization, with a larger value of \u03b1 causing the algorithm to lean more toward more RL. The parameter N represents the number of transitions in the dataset and is used to normalize the characteristics of each state within the provided dataset. Let si be the i-th feature of the state s in the dataset, with \u03bci,\u00a0\u03c3i being the mean and standard deviation (\u03b7 is a constant value to avoid division by zero.):\n\nThe Critic network is responsible for estimating the Q-values for state-action pairs, providing feedback to the Actor network during training. It plays a crucial role in evaluating the Actor\u2019s actions and guiding policy improvement. To address the issue of overestimation bias in Q-values, AC-BPR utilizes dual Critic networks, a strategy analogous to that employed in the TD3 algorithm. Each Critic network, \\({Q}_{1}(s,a| {\\theta }^{{Q}_{1}})\\) and \\({Q}_{2}(s,a| {\\theta }^{{Q}_{2}})\\), corresponds to a target network \\({Q}_{1}^{{\\prime} }(s,a| {\\theta }^{{Q}_{1}^{{\\prime} }})\\) and \\({Q}_{2}^{{\\prime} }(s,a| {\\theta }^{{Q}_{2}^{{\\prime} }})\\), respectively. The minimum Q-value among the two Critics is used as the target Q-value during training. The Critic network is updated by minimizing the loss function:\n\nwhere \u03b8Q denotes the weights of the Critic network. The target Q-value y is evaluated by taking the minimum of the estimates from the two Q-functions:\n\nwhere \\({a}_{t+1} \\sim {\\pi }_{{\\phi }^{{\\prime} }}\\left({s}_{t+1}\\right)+\\epsilon\\), \\(\\quad \\epsilon \\sim {{\\mathrm{clip}}}\\,({{{\\mathcal{N}}}}(0,\\tilde{\\sigma }),-c,c)\\) is the exploration noise. This noise helps smooth the value estimates and improves the robustness of the learned Q functions. The term r represents the instantaneous one-step reward, with \u03b3 denoting the discounting factor.\n\nWe use a series of baseline EMS methods for comparatively evaluating the ORL method. The inputs and outputs of all baselines are the same as those of the proposed method.\n\nDynamic Programming (DP)45: In optimization control methods, the EMS problem is formulated as a nonlinearly constrained optimization problem, aiming to minimize the objective function presented in Equation (10). DP is an optimization control method that operates by seeking the shortest path backward in time. Its objective is to derive the minimum cost function for each grid at every stage in reverse chronological order. In our study, DP is used as the benchmark EMS, representing the global optimum and providing upper limits for comparison. It\u2019s important to recognize that DP requires future information as input to achieve the optimization objective.\n\nBehavior Cloning (BC)46: BC, as a fundamental imitation learning approach, seeks to emulate the EMS by directly learning from the provided dataset, which is assumed to be generated by an expert policy or near-expert policy. It employs supervised learning techniques to train a model to map states to actions. Both BC and ORL involve learning from data for EMS applications. In this context, we establish BC as the benchmark and aim to showcase the superior performance of ORL.\n\nProximal Policy Optimization (PPO)47: PPO is a state-of-the-art online DRL algorithm, which has been extensively applied in various applications requiring sophisticated decision-making in dynamic environments. PPO offers a robust and efficient approach to training agents by leveraging on-policy learning, effective use of data through mini-batch updates, stability through policy clipping, and adaptive learning rates. Leveraging the strengths of PPO, we utilize it to generate the dataset necessary for ORL, with its policy serving as an expert (near-optimal) strategy for comparison purposes. We provide it to explore the superiority of ORL compared to the online DRL.\n\nTwin Delayed Deep Deterministic Policy Gradient (TD3)39: TD3 is an advanced online DRL algorithm, stemming from the Actor-Critic framework. It has garnered significant attention due to its effectiveness in overcoming challenges associated with continuous action spaces and high-dimensional state spaces. TD3 employs twin critic networks to estimate the value of actions more accurately. By utilizing two critic networks, TD3 mitigates overestimation bias and enhances the robustness of value function estimation. We also provide it to explore the superiority of ORL compared to the online DRL.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data generated in this study are provided in the\u00a0Supplementary Information/Source Data file. The raw datasets used for modeling the driving conditions are sourced from the EV monitoring and management system in China. Source data are provided in this paper.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "The code for data analysis can be obtained from the Source Data file. The DRL algorithms and EV powertrain model used in this study, LearningEMS48, are publicly available at https://doi.org/10.5281/zenodo.14848553, which links to the GitHub repository: https://github.com/wangjail/LearningEMS. All other codes used in this study are available from the corresponding authors upon request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "H.H. was supported by National Natural Science Foundation of China (Grant No. 52172377) and Beijing-Tianjin-Hebei Basic Cooperation Special Project (Grant No. F2021203118).",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China\n\nYong Wang,\u00a0Jingda Wu,\u00a0Hongwen He,\u00a0Zhongbao Wei\u00a0&\u00a0Fengchun Sun\n\nNational Key Laboratory of Advanced Vehicle Integration and Control, Beijing Institute of Technology, Beijing, China\n\nYong Wang,\u00a0Jingda Wu\u00a0&\u00a0Hongwen He\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.W. designed the study and methodology; Y.W., J.Wu., and H.H. collected and analyzed data; Y.W. generated the figures; Y.W., J.Wu., and W.Z. wrote the manuscript; H.H., W.Z., and F.S. reviewed and edited the manuscript. H.H. and F.S. planned and supervised the project. All authors reviewed and approved the manuscript.\n\nCorrespondence to\n                Hongwen He.",
+            "section_image": []
+        },
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+        },
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+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Emanuele de Santis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
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+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions",
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+            "section_name": "About this article",
+            "section_text": "Wang, Y., Wu, J., He, H. et al. Data-driven energy management for electric vehicles using offline reinforcement learning.\n                    Nat Commun 16, 2835 (2025). https://doi.org/10.1038/s41467-025-58192-9\n\nDownload citation\n\nReceived: 03 June 2024\n\nAccepted: 12 March 2025\n\nPublished: 22 March 2025\n\nVersion of record: 22 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58192-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            ]
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+            "section_text": "Focus\n\nCollection",
+            "section_image": []
+        }
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+}

0b8f1f6be4795d439f43f4c9625e8c3c0efbb434a42fa2851f1539502182ecc0/metadata.json

@@ -0,0 +1,162 @@
+{
+    "title": "Effect of trade on global aquatic food consumption patterns",
+    "pre_title": "Trade is improving global aquatic food consumption patterns",
+    "journal": "Nature Communications",
+    "published": "15 February 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45556-w/MediaObjects/41467_2024_45556_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45556-w/MediaObjects/41467_2024_45556_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45556-w/MediaObjects/41467_2024_45556_MOESM3_ESM.pdf"
+        },
+        {
+            "label": "Supplementary Data 1",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45556-w/MediaObjects/41467_2024_45556_MOESM4_ESM.xlsx"
+        },
+        {
+            "label": "Supplementary Data 2",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45556-w/MediaObjects/41467_2024_45556_MOESM5_ESM.xlsx"
+        },
+        {
+            "label": "Supplementary Data 3",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45556-w/MediaObjects/41467_2024_45556_MOESM6_ESM.xlsx"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45556-w/MediaObjects/41467_2024_45556_MOESM7_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [
+        "https://doi.org/10.6084/m9.figshare.21692186.v3"
+    ],
+    "code": [
+        "https://github.com/zhaokangshun/Effect-of-trade-on-global-aquatic-food-consumption-patterns.git",
+        "https://doi.org/10.5281/zenodo.10129727"
+    ],
+    "subject": [
+        "Agriculture",
+        "Interdisciplinary studies",
+        "Socioeconomic scenarios",
+        "Sustainability"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3085251/v1.pdf?c=1708089069000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3085251/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-45556-w.pdf",
+    "preprint_posted": "29 Jun, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Globalization of fishery products is playing a significant role in shaping the harvesting and use of aquatic foods, but the vigorous debate has focused on whether the trade is a driver of the inequitable distribution of aquatic foods. Here, we develop species-level mass balance and trophic level identification datasets for 174 countries and territories to analyze global aquatic food consumption patterns, trade characteristics, and impacts from 1976 to 2019. We find that per capita consumption of aquatic foods has increased significantly at the global scale, but the human aquatic food trophic level (HATL), i.e., the average trophic level of aquatic food items in the human diet, is declining (from 3.42 to 3.18) because of the considerable increase in low-trophic level aquaculture species output relative to that of capture fisheries since 1976. Moreover, our study finds that trade can improve food security by contributing to increasing the availability and quality of aquatic foods in >60% of the world\u2019s countries. Trade has also reduced geographic differences in the quality of aquatic food consumption among countries over recent decades. We suggest that there are important opportunities to widen the current focus on productivity gains and economic outputs to a more equitable global distribution of aquatic food quantity and quality.Earth and environmental sciences/Environmental social sciences/Socioeconomic scenariosEarth and environmental sciences/Environmental social sciences/SustainabilityScientific community and society/AgricultureScientific community and society/Social sciences/Interdisciplinary studies",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SMdata1Liveweightandconversionfactorsoftradedcommodities.xlsxDataset 1SMdata2Trophiclevelidentificationproductiondata.xlsxDataset 2SMdata3Data2continued.xlsxDataset 3Supplementarymaterial.pdfSupplementary materialNCOMMS2327307rs.pdfReporting Summary",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Globalization of fishery products is playing a significant role in shaping the harvesting and use of aquatic foods, but a vigorous debate has focused on whether the trade is a driver of the inequitable distribution of aquatic foods. Here, we develop species-level mass balance and trophic level identification datasets for 174 countries and territories to analyze global aquatic food consumption patterns, trade characteristics, and impacts from 1976 to 2019. We find that per capita consumption of aquatic foods has increased significantly at the global scale, but the human aquatic food trophic level (HATL), i.e., the average trophic level of aquatic food items in the human diet, is declining (from 3.42 to 3.18) because of the considerable increase in low-trophic level aquaculture species output relative to that of capture fisheries since 1976. Moreover, our study finds that trade has contributed to increasing the availability and trophic level of aquatic foods in >60% of the world\u2019s countries. Trade has also reduced geographic differences in the HATL among countries over recent decades. We suggest that there are important opportunities to widen the current focus on productivity gains and economic outputs to a more equitable global distribution of aquatic foods.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "The Sustainable Development Goals (SDGs) agenda puts food security and ending malnutrition as a global priority1. Aquatic systems have a significant role to play in meeting these objectives. Fisheries, aquaculture, and their trade are critical to the achievement of food security and sustainable economic, social, and environmental development goals2,3. In recent decades, global fisheries and aquaculture production have grown substantially. Aquatic foods are among the most highly traded commodities in the global food system and are becoming increasingly globalized4,5.\n\nAs a highly diverse food group, aquatic foods are now widely recognized in global food systems and can supply critical nutrients and improve overall human health6,7,8,9. However, accelerating climate change, overfishing, industrial pollution, and coastal urbanization challenge the ocean\u2019s ability to meet growing aquatic food demands10,11,12,13. The percentage of fishery stocks at biologically unsustainable levels has increased from 10% in 1974 to 35.4% in 201914. Promisingly, global aquaculture has rapidly developed over the past few decades and is thought to be the only reliable way to meet the growing future demand for aquatic foods15,16. Meanwhile, given the geographic patchiness of wild fish and aquaculture production, trade will be increasingly essential for the redistribution of global aquatic products and food security.\n\nOur understanding of the wide diversity of aquatic species produced and traded worldwide, and the impacts of aquatic food products trade across geographies on food security goals remain ambiguous and the evidence is mixed17,18,19,20. While previous studies have provided essential insights into general aquatic food trade and consumption characteristics and trade impacts in several countries and regions5,17,19,20,21, our collective understanding of the outcomes of aquatic food globalization is still limited by a fundamental gap between detailed production and trade data. Reconciling aquatic food production and trade data has remained a challenge due to mismatches in species- versus product-level reporting and weight losses during processing22,23. Compared with a deeper understanding of the role of trade in land-based food systems (e.g., agricultural and livestock products)5,24,25, insights into global aquatic food consumption patterns and the impact of trade continue to lag far behind26.\n\nThe trophic levels of animal or plant species, representing their relative positions in the aquatic food chains, are a primary metric used in ecological studies for a wide range of applications27. They not only represent a synthetic metric of species\u2019 diets, which is an important indicator of different aspects of the environmental footprint of food production for aquaculture and wild caught aquatic foods28,29, but they are also widely recognized as an appropriate indicator of aquatic food value (i.e., higher trophic level generally corresponding to higher price)30,31,32. Although the trophic level of food items in the human diet (human trophic level, HTL) has been considered a simple composite metric that synthetically reflects global patterns of human diet33, there is currently no quantitative assessment of the human aquatic food trophic level (HATL) and the impacts of trade on it. Nonetheless, we note that some small low-trophic level pelagic and inland fish are also nutrient-rich (e.g., calcium, iron, zinc, long-chain omega-3 polyunsaturated fatty acids)6,8,34, and that wild-capture high-trophic level species are more likely to be contaminated with biomagnifying substances such as persistent organic pollutants (POPs), heavy metals, and microplastics35,36,37. Therefore, the trophic level of aquatic foods can indicate the value of aquatic foods based on price, but it does not predictably reflect the concentration of any nutrients or contaminants status. Here, we first use the FAO national fisheries and aquaculture production and trade data (1979\u20132019) to develop a species-level mass balance dataset and a trophic level identification dataset for 174 countries and territories (hereafter called countries). We then calculate the HATL and per capita consumption across different countries and regions to analyze global aquatic food (i.e., fish, cephalopods, and crustaceans) consumption patterns, trade characteristics, and impacts.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Population, income growth, and associated changes in dietary habits are the main influential drivers of the increase in global fish demand in recent decades38,39. From 1976 to 2019, global per capita consumption of aquatic foods has increased significantly, but the HATL has declined from 3.42 to 3.18 (Fig.\u00a01a), contrary to the global trend of HTL that also includes land based foods33. This declining trend for aquatic foods can be primarily explained through a combination of two factors. First, the global aquaculture trophic level is significantly lower than that of capture fisheries (nearly 0.8 lower than capture fisheries on average) (Fig.\u00a01b). Second, while global capture fisheries production has experienced only a marginal increase in recent decades, aquaculture output has experienced a sustained and very rapid increase over the entire study period, especially in Asia (Fig.\u00a01b, Supplementary Figs.\u00a01 and 2). The proportion of aquatic foods originating from aquaculture production rose from 6% in the 1960s to 56% in 202014. This finding indicates that aquaculture is driving the decreasing trend in global HATL, despite increasing consumption of aquatic food (Fig.\u00a01a, b) and the rapid growth in the production of high-trophic level species driven by globalized trade and favorable economic conditions for large-scale intensive farming40. Asia, and China in particular, play a crucial role not only because of its significant contribution to global aquaculture production, but also because it accounts for the world\u2019s largest quantity of farmed low-trophic level species (Supplementary Figs\u00a01\u20134 and 5b). Nonetheless, it should be noted that aquatic food consumption in this study does not refer to the quantity effectively eaten but to the theoretical maximum live weight available before consumption.\n\na The global change in per capita consumption of aquatic foods (inc. fish, cephalopods, and crustaceans) and HATL. b The global change in production and trophic level of aquaculture and capture fisheries. The line refers to trophic level, and the envelope refers to production. c Trends of per capita consumption of aquatic foods in different continents. d Trends of HATL in different continents. e The mean country-level per capita consumption of aquatic foods. f The median country-level HATL. HATL human aquatic food trophic level. Countries in gray: No data available.\n\nRegional consumption and HATL trends show large variation (Fig.\u00a01c, d). In line with global trends, Asia\u2019s per capita consumption of aquatic foods has increased rapidly, mainly driven by China, whereas the HATL declined at an approximate rate of 0.08 per decade; approximately 1.4 times the global rate (Fig.\u00a01a, c, d and Supplementary Fig.\u00a04c, d). By contrast, the per capita consumption of aquatic foods in Europe and South America rose rapidly for a short time and then fell sharply from the 1990s, coinciding with the increase in their HATLs. North America experienced a slight overall increase in per capita consumption of aquatic foods but a decrease in HATL. Interestingly, Oceania was the only region where both per capita consumption of aquatic foods and HATL increased over the study period, although recent years suggest a decline in per capita consumption (Fig.\u00a01c, d). Currently, Asia has the lowest HATL, Africa has the lowest per capita consumption of aquatic foods and a low HATL, and Europe and Oceania are the regions with the highest per capita consumption of aquatic foods and HATL (Fig.\u00a01c\u2013f). In general, we find regions with more developing countries to have lower per capita consumption and lower trophic level of aquatic foods than regions with more developed countries. Nonetheless, trade seems to mediate these apparent strong imbalances in aquatic food consumption across regions and countries (see next section).\n\nIn the past decades, international trade in aquatic foods has risen across all continents, especially in Asia and Europe, which represent the two major trading regions (Fig.\u00a02a). Historically, aquatic food trade has been dominated by a few countries, such as China, USA, Norway, Thailand, and Japan (Fig.\u00a02c, e and Supplementary Table\u00a01). Since the World Trade Organization (WTO) was founded in 1995, Asia and South America have been the major trade surplus regions (i.e., higher exports than imports), whereas Africa and North America have been the major trade deficit regions (i.e., higher imports than exports) (Fig.\u00a02a). The share of imports in total aquatic food consumption has been rising in developed countries, which have good supply chain infrastructures and more consumers who can afford to buy imported high-value species2,14. Developing countries are becoming increasingly prominent in the supply of aquatic products and becoming increasingly important as supply chain intermediaries, importing raw materials and re-exporting processed or value-added products14. For example, although China is also one of the largest importers and exporters (in terms of live weight), more than two-thirds of these imports are raw materials that are processed and re-exported23.\n\na Trends of aquatic food import and export volume in different continents. b Trends of aquatic food import and export trophic level in different continents. The mean country-level aquatic food import volume (c) and export volume (e). The median country-level aquatic food import trophic level (d) and export trophic level (f). Countries in gray: No data available.\n\nThe trophic level of continental trade from 1976 to 2019 was generally above 3.3 across all continents (Fig.\u00a02b), while the country-level median import and export trophic level was above 3 for most countries (Fig.\u00a02d, f). These figures suggest that most of the exported aquaculture and capture fisheries products consist of high-trophic level species for international markets, particularly from Europe and Oceania. Interestingly, Asia and South America\u2019s aquatic food import trophic levels have surpassed those of exports in recent decades, whereas North America and Europe show the opposite trend (Fig.\u00a02b). Meanwhile, although the trophic level of imports and exports has remained similar in Africa, its import volume is gradually increasing faster than the export volume (Fig.\u00a02a, b). Together, these results indicate that the trade structure and consumption features of aquatic foods in these regions are changing. First, a more robust demand for aquatic species with higher trophic level (i.e., higher value) is apparent in Asia and South America as production and incomes rise, gradually redirecting products once produced mainly for exports toward domestic markets15,38,41,42. Second, the trend in developing countries to export high-value aquatic foods in exchange for low-value aquatic foods from industrial fisheries is being reversed21. While significant quantities of high-trophic level species (e.g., salmonids) are traded and continue to grow, the trade volume of low-trophic level species (e.g., tilapia and shrimp) has also increased drastically14, which has\u00a0helped keep\u00a0the global import trophic level largely stable between 3.5 to 3.6 (Supplementary Fig.\u00a06). Asian and South American countries, especially in East Asia, have been the central supply regions for relatively low-trophic level species in recent decades (Fig.\u00a02a, b, d, f).\n\nDifferences in per capita consumption of aquatic foods and HATL by country between \u2018before trade\u2019 (i.e., consumption stage before trade transactions) and \u2018after trade\u2019 (i.e., apparent consumption patterns after completing trade transactions) reveal the rapidly increasing volume and shifting trade features in various regions that have affected both per capita consumption of aquatic foods and HATL in most parts of the world. Continentally, the per capita consumption of aquatic foods has decreased in Asia and especially South America after trade (Fig.\u00a03a). Meanwhile, the HATL of these two regions remained nearly unchanged (Fig.\u00a03c). After 2000, Asia and South America were two major trade surplus regions (i.e., after-trade aquatic food consumption was lower than before trade), supplying increasing quantities of low-trophic level species and playing an essential role in boosting per capita consumption of aquatic foods in the rest of the world (Figs.\u00a02a, b, and 3a, b). North America has maintained its per capita consumption of aquatic foods over time, despite producing less, through a gradually increased reliance on imports (Figs.\u00a02a and 3a). For example, the import share of total aquatic food consumption in the USA rose from one-third in 1961 to nearly three-quarters in 201914. Conversely, import and export volumes in Europe have grown at similar rates, resulting in only slight differences in per capita consumption of aquatic foods before and after trade in recent years. Countries in both North America and Europe, mostly developed, have experienced mild declines in post-trade HATL over the past decade (Fig.\u00a03c). Meanwhile, although differences in per capita consumption of aquatic foods before and after trade in Oceania are small, Oceania\u2019s HATL has decreased considerably after trade (Fig.\u00a03a, c), because the trophic level of exports is significantly higher than that of imports (Fig.\u00a02b). Finally, the fact that Africa is notably the only region where both post-trade per capita consumption of aquatic foods and HATL have increased underscores the importance of trade in improving aquatic food availability in Africa.\n\na Trends of continental per capita consumption of aquatic foods before and after trade. b The mean country-level change in annual per capita consumption of aquatic foods after trade from 1976 to 2019. c Trends of continental HATL before and after trade. d The mean country-level change in annual HATL after trade from 1976 to 2019. All country-level changes in per capita consumption of aquatic foods and HATL are the post-trade value minus the pre-trade value year by year. Percentage values indicate the proportion of countries affected by trade (positively or negatively) in each region (a, c) and globally (b, d) (for details, see Supplementary Table\u00a02). \u2018Before trade\u2019 represents the maximum available live weight per capita of domestically produced aquatic foods, while \u2018after trade\u2019 refers to the apparent consumption patterns after completing trade transactions. HATL human aquatic food trophic level. Countries in gray: No data available.\n\nAt the national scale, although Asia is a trade surplus region, more than 60% of Asian countries have benefited from trade in their aquatic food consumption (Fig.\u00a03a, c). Except for South America, per capita consumption of aquatic foods increased in most countries of all continents (Fig.\u00a03a). Similarly, trade has also increased HATLs in most countries across continents except Oceania (Fig.\u00a03c). In particular, over 70% of countries in Africa and Europe benefit from trade in both aspects of aquatic food consumption. Indeed, the dominance of certain countries in international fish trade masks the importance of trade for Africa, where aquatic food demand has grown faster than supply, resulting in an increase in the import share of consumption from 16% in 1970 to 39% in 2017 as production from domestic fish capture has either stagnated or been exported18,43. Globally, international trade has increased the availability and trophic level of aquatic foods in most (>60%) countries over the past decades (Fig.\u00a03b, d). Furthermore, the heterogeneity in the\u00a0HATL of countries declines globally after trade, especially in Europe, North America, and Africa, while the mean HATL has increased in most continents (Fig.\u00a04). Overall, our findings suggest that international trade has both reduced geographic differences in HATL and improved the aquatic food consumption in most parts of the world. Trade will therefore be an important part of a transition to sustainable fisheries.\n\nIn all plots, each point shows the mean estimate, and error bar shows 95% reference range (mean\u2009\u00b1\u20091.96\u2009SD) for each country. The shaded blue column (before trade) and green column (after trade) indicate the 95% reference range (mean\u2009\u00b1\u20091.96\u2009SD) for all countries in different continents. The dotted lines correspond to the HATL averages of all countries in different continents. Numbers refer to the number of countries in each region (N\u2009=\u2009174 total). \u2018Before trade\u2019 represents the maximum available live weight per capita of domestically produced aquatic foods, while \u2018after trade\u2019 refers to the apparent consumption patterns after completing trade transactions. HATL human aquatic food trophic level.",
+            "section_image": [
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+        {
+            "section_name": "Discussion",
+            "section_text": "The need to increase aquatic food diversity and supply to help achieve global food and nutrition security goals within environmental boundaries is a global consensus. In recent decades, the rapid growth in aquatic food globalization and consumption has been driven by increased trade liberalization and facilitated by advances in food processing and transportation technologies2,14. Aquatic foods are among the most highly traded commodities4,5, comprising nearly 10% of all food trade by value21. Obviously, international trade in fishery products is playing a significant role in shaping global aquatic food harvesting and consumption4. However, aquatic foods have often been excluded from previous studies on detailed global food trade due to the difficulty in reconciling species-level production and trade data18,22.\n\nBy developing a species-level mass balance dataset and a trophic level identification dataset for 174 countries, we first reveal precise country-level dynamics in aquatic food consumption patterns, identify the features of aquatic food trade, and quantify the effect of trade on aquatic food consumption globally. From 1976 to 2019, global per capita consumption of aquatic foods has increased significantly, but the HATL is declining (from 3.42 to 3.18). The rapid development of aquaculture and its significantly lower average trophic level relative to capture fisheries primarily contributed to this trend (Fig.\u00a01a, b). Meanwhile, international trade has played an important role in harmonizing the global consumption of aquatic products, increasing the availability and trophic level of aquatic foods in most countries (especially for Africa and Europe), and reducing HATL heterogeneity worldwide. In this study, we did not consider mollusks and aquatic plants. Given that these groups have a lower trophic level than most other aquatic foods and considering the recent increase in the consumption of mollusks and aquatic plants2,15,29, our estimate of the current consumption trophic level can be considered conservative (i.e., the inclusion of these groups will likely lower our estimated HATL). Nonetheless, the continuous decrease in the effective trophic level of the majority of farmed species29 coupled with the increasing proportion of low trophic level species in the diet are encouraging for progressing towards reducing dependence on multiple marine ingredients (e.g., fishmeal and oil). Further improvements in resource conversion efficiency on this basis will yield even greater results. Furthermore, aquatic foods not only provide comparatively higher nutrient richness across multiple micronutrients, vitamins, and long-chain polyunsaturated fatty acids relative to terrestrial animal-source foods6, but they also typically have lower environmental footprint compared to other animal-sourced foods44,45. The observed trends of increasing global contributions from aquatic foods suggest a promising future for more sustainable global diets.\n\nThe impacts of the aquatic food trade on food security and well-being continue to be a subject of intense debate3,20. While some claimed that fish trade is beneficial for marginal and vulnerable local communities, others denounced a negative impact of fish exports on food security for these communities20,46. Our study supports the conclusion that global trade can improve food availability by allowing most countries to access larger quantities and higher trophic level aquatic foods that otherwise are domestically unavailable. However, the direct contribution of trade to the food system in vulnerable population groups is limited because the beneficiaries tend to be high-income groups as most exported products consist of high-trophic level species (high-value species) for international markets47. Furthermore, differences in dietary habits, income levels, natural resource conditions, and other aspects among different regions can lead to variation in the cost of aquatic products and consumers\u2019 affordability. These situations highlight the need for fair, transparent, sustainable, and adaptive trade and market policies to ensure that more segments of society benefit from international trade46.\n\nUntil now, the problem of imbalances in the growth of demand and aquatic food supply remains prevalent across regions, countries, and income groups18,48. Many people remain under multiple forms of malnutrition and per capita consumption of aquatic foods is far below the world average6,14. Geography plays a major role in explaining these differences14. As the world may be approaching the constraints of a finite, global, aquatic food production capacity49,50, sourcing trajectories from all countries must be considered together38. Based on our findings, it is evident that trade has played an important role in harmonizing the global aquatic food consumption. Nevertheless, there are still some countries where per capita consumption of aquatic foods has not improved after trade and remains quite low (Figs.\u00a01e and 3b). Heightened attention and concerted efforts for context-specific mitigation should therefore be given in the future to these countries. To this end, attaining globally equitable trade distribution patterns as well as more harmonized trade environment and policies should be a priority. Despite the important progress attained in research on aquatic food production and trade, significant challenges persist in achieving a comprehensive understanding of the outcomes of aquatic food trade5,17,18,19,20,21. Our work adds another piece to this puzzle by identifying the implications of trade for contemporary changes in global aquatic food consumption patterns, highlighting the increased availability and trophic level of consumed aquatic foods in a majority of countries with reduced differences in HATL despite the important remaining inequalities. These results provide an important foundation to guide future research on the globalization of aquatic food systems and the impacts of trade on food security.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "Three original global fisheries statistic datasets (aquaculture, capture, and trade) were taken from the FishStatJ software51. The original trade dataset contained more than 100,000 commodities, each mixed by species/species group, preservation, and preprocessing method, such as \u2018Catfish fillets, frozen\u2019. The original aquaculture and capture datasets also had more than 25,000 items. The earliest coincident year of these three datasets was 1976. We removed all items of negligible importance (i.e., total volume <100t) from 1976 to 2019 in all original datasets to facilitate the definition of the live weight conversion factor for trade commodities and the trophic level of species or species groups in aquaculture and capture datasets according to the fishing area in each country (see next two sections). Removed items accounted for a total of 0.001% of aquaculture production, 0.004% of capture production, and 0.044% of trade volume.\n\nThe term \u2018aquatic foods\u2019 is used throughout this study to denote all freshwater and marine fish, cephalopods, and crustaceans. Brackish fish were identified as freshwater fish or marine fish according to the major fishing area in the aquaculture and capture datasets. Algae, aquatic plants, mollusks (Bivalvia, Gastropod, Barnacle, and Ascidiacea), echinoderm, cnidaria, miscellaneous aquatic animals (such as turtles, frogs, and mammals), and reported inedible species were not considered for this study. Although mollusks and algae accounted for a significant proportion of fisheries output in live weight, especially in aquaculture, they comprised a very small proportion in edible weight15,18,51,52. Further, we were unable to find publicly available preservation factors (i.e., the ratio of edible portions to final product live weight) for the different processing methods of these groups.\n\nIn the trade dataset, we deleted commodities for which no live weight conversion factors were available, such as fish sausage and fish cake. We have limited our focus to edible aquatic foods and have discarded commodities such as fishmeal and fish oil that are unsuitable for direct human consumption. Therefore, to mitigate the impact of specific fish used in the production of fishmeal and fish oil on aquatic food consumption in several countries, we removed the world\u2019s most important forage fish (i.e., Engraulis ringens) from the capture dataset. Engraulis ringens had comparatively a massive production but is rarely used for direct human consumption in Chile and Peru53. Additionally, due to the absence of accurate or even approximate long-term time series data on Engraulis ringens consumption proportions, accurate country-level estimations of the theoretical maximum live weight availability for this species were not possible. While many other small pelagic fish (e.g., herrings and sardines) are also partly reduced to fishmeal and fish oil, their volumes were relatively low and some were used directly for human consumption. Therefore, we retained these fish species for analysis. Following these data processing steps, 174 countries with available aquaculture, capture, and trade data accounting for 95.8% of the world\u2019s fisheries and aquaculture production from 1976 to 2019 were included for subsequent analyses.\n\nTo determine whether the removal of production and trade items with a total volume <100t from 1976 to 2019 would result in highly unequal impacts on small and large countries, we examined the proportion of removed items in production and trade for all 174 countries. We discovered that in 97% of countries, the ratio of excluded items to total production is below 1%. Additionally, in 95% of countries, the proportion of excluded items to total trade volume is less than 3%. Therefore, the removal of items with a total volume <100t would not have significant unequal impact on different countries.\n\nTrade data were reported at the species-level and broader commodity groups (e.g., \u2018Salmons nei, Eels nei, and Tunas nei\u2019) and labeled based on processing (e.g., fresh, frozen, dried, fillets, etc.). In order to back-transform processed product weight into whole-animal live weight equivalents, we first extracted commodity species or species groups, preprocessing and preservation methods from original commodities in trade dataset. We then used conversion factors from ref. 54.,Hortle55, expressed as live weight (kg) of aquatic animals required to make 1\u2009kg of product, calculated as the product of the preprocessing factor and the preservation factor. The preprocessing factor represents the ratio of live weight to edible portions after cleaning (beheading, gutting, etc.). The preservation factor is defined as the ratio of edible portions to final product weight. Aquatic products for which no preservation and/or preprocessing methods were reported were assumed to refer to fresh units and/or whole-animal. All conversion factors are listed in Supplementary Table\u00a04. After conversion, the average live weight ratio of the total imports to the total exports was 0.99\u2009\u00b1\u20090.04:1 over the study period (1976\u20132019), suggesting that the conversion factors were relatively reliable, because in theory the world\u2019s total import volume should equal total export volume. All processing details and results were available in Supplementary Data\u00a01.\n\nItems in the aquaculture and capture production datasets were often not provided at the species-level but were more generically referred to by a species-group name (e.g., \u2018Groupers nei\u2019). In such cases, we searched each species group in Fishbase (www.fishbase.org)56 using the \u2018Common name is\u2019 function to screen out all species identified by that common name and with clear economic value according to the reported main fishing area by country. In a few cases, no species were found. In those rare cases, we used the \u2018Common name ends with\u2019 function instead to identify the species involved. The trophic level of each species group was then calculated as the mean trophic level of all identified species for that group (Supplementary Data\u00a02 and 3). The trophic level of specific species was extracted directly from Fishbase.\n\nFor unidentified \u2018Freshwater fish nei\u2019 and \u2018Marine fish nei\u2019 items in production dataset, the trophic level was obtained by the production-weighted average of trophic levels of identified fish species or species groups according to the fishing area in each country. For those few countries that did not have any identified species or species groups, we weighted and averaged the trophic level of identified \u2018Freshwater fish nei\u2019 and \u2018Marine fish nei\u2019 items from all countries in the same fishing area. The trophic levels of marine \u2018Pelagic fish nei\u2019 and \u2018Demersal fish nei\u2019 were considered the same as \u2018Marine fish nei\u2019. The trophic level of unidentified items was based on the trophic level and total production of identified species or species groups:\n\nwhere TL is the trophic level of unidentified items, TLn is the trophic level of involved species or species group to be weighted, and Wn is the total production of involved species or species group from 1976 to 2019.\n\nIn the import dataset, the trophic level of specific species was also extracted directly from Fishbase. For generic import commodities, such as \u2018Tunas nei\u2019, the trophic level was calculated as the weighted average of trophic levels based on global production data for all involved species in the group. It was assumed that the most productive species were the most likely to enter the trade flow. When several trade commodities included more than three species or species groups (e.g., \u2018Herring, anchovy, sardine, sardinella, brisling/sprat, mackerel, Indian mackerel, seerfish, jack & horse mackerel, jack, crevalle, cobia, silver pomfret, pacific.saury, scad, capelin, etc.\u2019), we kept the first three species or species groups (i.e., \u2018Herring, anchovy, and sardine\u2019) based on the importance ranking assumption. The trophic level of this commodity type was the weighted average of trophic levels of all involved species in these three groups. Moreover, the trophic level of import \u2018marine fish nei\u2019 and \u2018freshwater fish nei\u2019 commodities were obtained as the weighted average of trophic levels of all \u2018marine fish nei\u2019 and \u2018freshwater fish nei\u2019 items in the global production dataset. Since we subtracted exports and reexports from production and imports after identifying the trophic level of items in production and import datasets, the trophic level of exported commodities could be acquired when the species-level mass balance was finished (see \u2018Species-level mass balance from FAO statistics\u2019 below).\n\nIn the production and trade datasets, \u2018Crustacean\u2019 was given a trophic level of 2.5, \u2018Cephalopod\u2019 of 3.0, and \u2018Demersal percomorphs nei\u2019 of 4.0. Although the trophic level of cultured species is related to the feed composition and diverges in effective trophic level from their wild counterparts29,31, we did not consider this due to the lack of sufficient data. Similarly, the trophic level of the same wild capture species was considered constant across different seas and time periods.\n\nIn this study, human aquatic food trophic level (HATL) was considered a composite metric that reflects human aquatic food diet patterns simply and synthetically. We calculated the HATL using trophic level and live weight data of consumed species or species groups57,58:\n\nwhere TLi is the trophic level of species or species group i, and Wij is the live weight of species or species group i in year j. HATL is the quantity-weighted average of trophic levels of species or species groups consumed in a particular year by country. Similarly, the trophic level of aquaculture, capture, imports, and exports were calculated using Eq. (2) as defined above.\n\nWe subtracted export weights from production in four sequential steps and present the details of species-level mass balance from FAO statistics in Supplementary Fig.\u00a07. Once the exports and reexports were subtracted from production and imports separately, the remaining weight was assumed to represent apparent consumption per commodity group in each country.\n\nEach reexported commodity was matched one-to-one with the imported commodity with the same common name. All unmatched commodities were matched with each country\u2019s \u2018Fish nei\u2019 or \u2018Freshwater fish nei\u2019 or \u2018Marine fish nei\u2019 items. Step 1 produced the remaining imports data.\n\nWe combined aquaculture, capture, and remaining imports data (produced in step 1) into one dataset. The same species or species groups in each country were summed, and the trophic levels were simultaneously weighted and averaged.\n\nEach exported commodity was matched one-to-one with the combined data items with the same common name (i.e., aquaculture + capture + remaining imports). All unmatched commodities were matched with each country\u2019s \u2018Fish nei\u2019 or \u2018Freshwater fish nei\u2019 or \u2018Marine fish nei\u2019 items. This step was deemed necessary, because we believed that global reexport volumes were underestimated. For example, Asche, et al23. estimated that 74.9% of China\u2019s seafood imports were reexported, but there were very few records of China\u2019s reexports in the FAO trade dataset, so some reexports in the trade dataset must be only roughly marked for exports or omitted. Thus, we combined aquaculture, capture, and remaining imports data to subtract exports. Step 2 produced the remaining exports 1 (negative value) and consumption volume 1 (positive value).\n\nSpecies-level remaining exports 1 commodity was matched with the generic item (e.g., \u2018Yellow tuna\u2019 can be matched with \u2018Tuna nei), and generic commodity was matched with all contained species (e.g., \u2018Tunas nei\u2019 could be matched with all tuna species) in consumption volume 1. Some detailed matching information between several specific exported commodity groups and production items can be searched in Supplementary Table\u00a03.\n\nAll unmatched remaining exports 1 commodities were matched with the \u2018Fish nei\u2019 or \u2018Freshwater fish nei\u2019 or \u2018Marine fish nei\u2019 items in each country. A few remaining unmatched \u2018Fish nei\u2019 or \u2018Freshwater fish nei\u2019 or \u2018Marine fish nei\u2019 were matched with the three most productive items in each country. Each remaining exports 1 commodity was deducted from consumption volume 1 according to the proportion of the output of all matching items each year. Step 3 produced the remaining exports 2 (negative value) and consumption volume 2 (positive value).\n\nExcept for each country\u2019s \u2018Freshwater fish nei\u2019, \u2018Marine fish nei\u2019, \u2018Fish nei\u2019, \u2018Pelagic fish nei\u2019, and \u2018Demersal fish nei\u2019 were matched with the three most productive items, all remaining export 2 commodities were matched with the \u2018Freshwater fish nei\u2019 or \u2018Marine fish nei\u2019 or \u2018Fish nei\u2019 items in consumption volume 2. Each remaining exports 2 commodities was deducted from consumption volume 2 according to the proportion of the output of all matching items every year. Step 4 produced the remaining unexplained exports (negative value) and final consumption volume (positive value).\n\nTheoretical reexported commodities and volumes can be obtained by comparing the original imports with the subtracted imports. Likewise, the theoretical exports are the difference between the final consumption volume and the original data (i.e., aquaculture + capture + remaining imports).\n\nTheoretical exports plus theoretical reexports were hereafter called exports. After the process described above, the total exports accounted for 95.3\u2009\u00b1\u20090.9% (mean\u2009\u00b1\u2009SD) of the total original exports (including exports and reexports), and the ratio to imports was 0.96\u2009\u00b1\u20090.03:1 from 1976 to 2019 (Supplementary Fig.\u00a06 a). We also calculated the proportion of export volume to original export volume for all countries and regions in each year, with a minimum average of 72.6% from 1979 to 2019, and over 90% of countries and regions exceeding 85%. The remaining unmatched exports might be due to imprecise conversion factors, imperfect matching, and reporting errors23, which only account for 1.1\u2009\u00b1\u20090.4% of total consumption from 1976 to 2019 (Supplementary Fig.\u00a06 b). Due to these inevitable errors mentioned above, it is almost impossible for global import and export trophic levels to be theoretically identical. Similarly, Asche, et al.23.,Kroetz, et al.22 also indicated that it was difficult to reconcile aquatic food production and trade data because of mismatches in species- versus product-level reporting and weight losses during processing. In this study, the average difference between trophic levels of imports and exports is only 0.02 (Supplementary Fig.\u00a06c), and global human aquatic food trophic level trends before and after trade almost coincided (Supplementary Fig.\u00a06d). Thus, theoretical exports could be a good proxy for original exports, and remaining unmatched exports would not affect global human aquatic food consumption patterns.\n\nAlthough we provided more detailed information on aquatic food production, trade, and consumption than the FAO Food Balance Sheets, which contain live weight of broad taxonomic groups, we acknowledge that our study has some limitations due to a lack of sufficient data. First, it is inevitable that production and trade data in some of the countries analyzed are inconsistent or not perfectly processed, leading to over- or under-estimated apparent consumption and HATL. Second, to see the magnitude of the differences between both estimates, we compared our balanced average annual per capita consumption to the average annual per capita supply in the FAO Food Balance Sheets. A total of 66.7% of countries had an average difference magnitude below 20%, and a large majority (73.6%) fell below 30%. These differences can mainly be attributed to the fact that the FAO accounts for non-food uses and variations in stocks, as well as differences in live weight conversion factors. Although we removed the world\u2019s most important forage fish (i.e., Engraulis ringens) from the capture dataset, we couldn\u2019t remove the effect of all other existing fish species used in the production of fishmeal, fish oil, and other non-food uses, as well as variations in stocks on aquatic food consumption in all countries. Third, given the increasing rates of food loss and waste linked to the increasing industrialization and income of countries, it is likely that the effects of trade may have been offset to some extent by supply chain leakages, resulting in some cases in smaller gains than those estimated here or perhaps even reduction in actual consumptions. Therefore, our trade analysis provides a baseline for potential food security gains, but we recognize that other factors not addressed here are also crucial to draw a strong conclusion on how trade affects food security and should be subject of future studies. Fourth, we didn\u2019t account for the difference between the wild trophic level of farmed species versus actual trophic level based on what they were actually being fed29. These patterns were subject to both strong temporal trends and significant spatial variation. Finally, live weight conversion factors could vary over time and geographies due to differences in processing technologies, even for the same species.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The raw data used in this study are publicly available (see Methods). The processed data generated in this study are either included in the Supplementary Information or available in the figshare repository (https://doi.org/10.6084/m9.figshare.21692186.v3).",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "The R codes used to conduct the study are available in the GitHub (https://github.com/zhaokangshun/Effect-of-trade-on-global-aquatic-food-consumption-patterns.git) and archived through Zenodo (https://doi.org/10.5281/zenodo.10129727).",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This research was supported by the National Key R&D Program of China (grant no. 2018YFD0900904).\u00a0K.Z. was funded by the China Scholarship Council. J.X. acknowledges the support received from the International Cooperation Project of the Chinese Academy of Sciences (grant no. 152342KYSB20190025)\u00a0and the National Natural Science Foundations of China (grant no. 31872687).",
+            "section_image": []
+        },
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+            "section_name": "Author information",
+            "section_text": "Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Key Laboratory of Lake and Watershed Science for Water Security, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China\n\nKangshun Zhao\u00a0&\u00a0Jun Xu\n\nBren School of Environmental Science & Management, University of California, Santa Barbara, CA, USA\n\nKangshun Zhao\u00a0&\u00a0Steven D. Gaines\n\nArctic Research Center, Hokkaido University, Sapporo, Japan\n\nJorge Garc\u00eda Molinos\n\nHubei Provincial Engineering Laboratory for Pond Aquaculture, Engineering Research Center of Green Development for Conventional Aquatic Biological Industry in the Yangtze River Economic Belt, College of Fisheries, Huazhong Agricultural University, Wuhan, China\n\nMin Zhang\n\nState Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, China\n\nJun Xu\n\nLaboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China\n\nJun Xu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nK.Z. and J.X. conceived the idea. S.D.G. contributed to the study design. K.Z., M.Z., and J.X. contributed to the acquisition and analysis of data. K.Z., S.D.G., and J.G.M. contributed to the interpretation of results. K.Z., S.D.G., M.Z., and J.G.M. wrote and edited the manuscript.\n\nCorrespondence to\n                Min Zhang or Jun Xu.",
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+            "section_text": "Zhao, K., Gaines, S.D., Garc\u00eda Molinos, J. et al. Effect of trade on global aquatic food consumption patterns.\n                    Nat Commun 15, 1412 (2024). https://doi.org/10.1038/s41467-024-45556-w\n\nDownload citation\n\nReceived: 20 June 2023\n\nAccepted: 28 January 2024\n\nPublished: 15 February 2024\n\nVersion of record: 15 February 2024\n\nDOI: https://doi.org/10.1038/s41467-024-45556-w\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Nature Communications (2025)\n\nAquaculture International (2025)",
+            "section_image": []
+        }
+    ]
+}

0b9fa4ed624e47e0feb3298b20fd61b8f8fc434f97c84e5e73e11eec0df23a0f/metadata.json

@@ -0,0 +1,160 @@
+{
+    "title": "Choosing fit-for-purpose biodiversity impact indicators for agriculture in the Brazilian Cerrado ecoregion",
+    "pre_title": "Choosing fit-for-purpose biodiversity impact indicators in agriculture",
+    "journal": "Nature Communications",
+    "published": "20 February 2025",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57037-9/MediaObjects/41467_2025_57037_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57037-9/MediaObjects/41467_2025_57037_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57037-9/MediaObjects/41467_2025_57037_MOESM3_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [
+        "https://www.iucnredlist.org/",
+        "https://brasil.mapbiomas.org/en/",
+        "https://www.opendem.info/link_dem.html",
+        "https://www.ibge.gov.br/en/geosciences/territorial-organization/territorial-meshes/",
+        "/articles/s41467-025-57037-9#Tab2",
+        "/articles/s41467-025-57037-9#Fig1",
+        "/articles/s41467-025-57037-9#Fig2",
+        "/articles/s41467-025-57037-9#Fig3",
+        "/articles/s41467-025-57037-9#Fig4",
+        "/articles/s41467-025-57037-9#Fig5",
+        "/articles/s41467-025-57037-9#MOESM1",
+        "/articles/s41467-025-57037-9#MOESM1",
+        "https://doi.org/10.5281/zenodo.11352608",
+        "/articles/s41467-025-57037-9#ref-CR44",
+        "/articles/s41467-025-57037-9#MOESM1"
+    ],
+    "code": [
+        "https://doi.org/10.5281/zenodo.11352608",
+        "/articles/s41467-025-57037-9#ref-CR44"
+    ],
+    "subject": [
+        "Agriculture",
+        "Biodiversity",
+        "Sustainability"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4253346/v1.pdf?c=1740144671000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4253346/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-025-57037-9.pdf",
+    "preprint_posted": "06 Jun, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Understanding and acting on biodiversity loss requires robust assessment tools that link biodiversity impacts to land use (LU) change. Here we estimate agriculture\u2019s impact on biodiversity using three approaches \u2014countryside-Species Area Relationship (cSAR), Species Threat Abatement and Restoration (STAR) and Species Habitat Index (SHI)\u2014 for the Brazilian Cerrado, to assess how indicator choice affects impact assessments and resulting decision-support. All indicators show biodiversity has become increasingly under pressure due to agriculture expansion. Results suggest that metrics are complementary, providing distinctly different insight into biodiversity change drivers and impacts. Meaningful applications of biodiversity indicators therefore require compatibility between focal questions and indicator choice, in terms of the temporal, spatial and ecological perspectives on impact and drivers being offered. \u2018Backward-looking\u2019 analyses focused on historical LU transformation and accountability are best served by cSAR and SHI. \u2018Forward-looking\u2019 analyses of impact risk hotspots and mitigation of global extinctions are best served by STARBiological sciences/Ecology/BiodiversityScientific community and society/AgricultureScientific community and society/Scientific community/PolicyEarth and environmental sciences/Ecology/Conservation biology",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplementaryInformation.docx",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Understanding and acting on biodiversity loss requires robust tools linking biodiversity impacts to land use change, the biggest threat to terrestrial biodiversity. Here we estimate agriculture\u2019s impact on the Brazilian Cerrado\u2019s biodiversity using three approaches\u2014countryside\u00a0Species-Area Relationship, Species Threat Abatement and Restoration and Species Habitat Index. By using same input data, we show how indicator scope and design affects impact assessments and resulting decision-support. All indicators show agriculture expansion\u2019s increasing pressure on biodiversity. Results suggest that metrics are complementary, providing distinctly different insight into biodiversity change drivers and impacts. Meaningful applications of biodiversity indicators therefore require compatibility between focal questions and indicator choice regarding temporal, spatial, and ecological perspectives on impact and drivers. Backward-looking analyses focused on historical land use change and accountability are best served by the countryside-Species Area Relationship and the Species Habitat Index. Forward-looking analyses of impact risk hotspots and global extinctions mitigation are best served by the Species Threat Abatement and Restoration.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Transformation of the Earth\u2019s systems by humans has caused biodiversity to steeply decline in recent history, primarily driven by natural habitat loss or deterioration due to land use (LU) change, with agriculture as its most prominent driver1. In order to develop evidence-based actions for achieving the targets of the United Nations\u2019 Kumming-Montreal Global Biodiversity Framework, we need robust tools for assessing LU change impacts on biodiversity that provide decision-support for accountability and effective conservation measures.\n\nBiodiversity indicators are tools to monitor ecosystems current conditions, in order to guide governance for safeguarding biological diversity from the local (e.g., on-the-ground implementation in conservation actions) to the national (e.g., national biodiversity strategies and action plans) or global scales (e.g., the Convention on Biological Diversity (CBD)). They can be used to assess historical contributions to biodiversity decline, its current status, and future trends under agricultural land transformation2,3. The sustainability of a production system can be evaluated according to its interactions with biodiversity, to estimate how much native biodiversity has been, or will be, lost\u2014or alternatively can be conserved or restored\u2014depending on management strategies. In these contexts, indicators are used to assess (biodiversity) impacts linked to different LU change drivers, which includes accountability for historical impacts or identification of current pressures4,5,6. Such biodiversity information is relevant not only to decisions on agricultural LU locally, but also those concerning supply-chain decisions downstream7,8.\n\nWhether used by government agencies, research institutions, non-governmental organisations, businesses or consumers, relating biodiversity indicators to the agricultural LU drivers will be useful if it equips actors with robust information to address trade-offs between production, consumption and conservation. Policy makers may wish for an indicator that is easy to implement and communicate; businesses, for an easy-to-track, target oriented indicator; researchers, for an indicator that captures accurately as many aspects of biodiversity as possible. In practice, however, biodiversity\u2019s multidimensional nature imposes a conceptual challenge that cannot be appropriately reflected by one single \u2018apex\u2019 indicator9 and capturing its dimensions requires alternative metrics. Nonetheless, users might select one main indicator for the sake of simplicity. Given that different indicators emphasise different aspects and, thus, may provide contradictory evidence10, actors may be tempted to choose indicators that best serve their interests instead of the best fit-for-purpose.\n\nDifferent perspectives on biodiversity and its conservation\u2014along with data availability and processing constraints\u2014dictate how indicators are designed and, consequently, influence which indicator is best fit for each case and how to interpret its results11. For instance, a species conservation focus weights rare and endangered species higher whilst placing less emphasis on abundant species, while an ecological resilience focus might emphasise more abundant or key species that are important for ecosystem function10. Similar issues are the baseline choice (i.e. \u201cpristine\u201d or \u201ccutoff\u201d) or the extinction risk scale (i.e. local, regional or global). The perspective favoured will depend on what aspects of the impact of agriculture-linked systems on biodiversity users want to emphasise.\n\nIn recent years, considerable effort has been made to provide end-users with guidance on the suitability of biodiversity impact indicators12,13,14. Previous comparisons between indicators, however, have not used the same input data, making it difficult for users to understand why results differ and increasing the risk of drawing misleading conclusions or applying the wrong indicators. Importantly, the lack of standardised inputs obfuscates the influence of data uncertainties vs indicator choice and design. In contrast, here we use the same input data\u2014taxa coverage, geographic extent, LU configuration\u2014to assess how the scopes of different indicators affect results and in which ways they are useful for providing decision-makers with information on the role of agriculture as a biodiversity loss driver along supply chains. For this, we use three prominent approaches based on species richness\u2014the countryside Species-Area Relationship (cSAR) model15,16, the Species Threat Abatement and Restoration (STAR) metric17, and the Species Habitat Index (SHI)18,19 (Table\u00a01)\u2014to estimate agriculture\u2019s impact on the biodiversity of terrestrial vertebrates in the Brazilian Cerrado.\n\nThe Cerrado is the second largest ecoregion in South America, covering around 2 million km\u00b2, and the world\u2019s most biodiverse savanna, holding 5% of global animal and plant biodiversity, including many endemic species20,21. However, half of its area has been converted to agricultural LU (Supplementary Fig.\u00a01), accounting for 62% of Brazil\u2019s cotton, orange, sugar cane, maize, soybeans, beans, potato, coffee and eucalyptus production and 40% of the country\u2019s heads of cattle22. In comparison to the neighbouring Amazon ecoregion, the Cerrado has weaker habitat protection laws and enforcement, and areas under deforestation alerts have increased by 43% between 2022 and 202323.\n\nTo apply the three approaches, we produced three 5km-resolution rasters of the Area of Habitat (AOH) of 2185 native terrestrial vertebrate species found in the Cerrado, for contemporary (2021) and recent (1985) LU patterns, as well as for pristine conditions (i.e. assuming LU absence). We used spatial information on species distribution ranges and habitat preferences from IUCN, land use and land cover (LULC) maps from Mapbiomas (Collection 7.0) and digital elevation models from Open DEM to produce the rasters. We considered agricultural LU all classes with specific crops, cropland, pasture (or a mix of the latter two), as well as monocultural tree plantations (Supplementary Table\u00a01). LUs related to extractivism or mixed uses within semi-natural land cover were not included in this collection of LULC maps. We assessed global impact\u2014how agriculture in the Cerrado contributes to the species\u2019 global extinction risk\u2014with the STAR approach and the global-weighted applications of cSAR and SHI. Here, impact is weighted by threatened endemic richness, i.e., the species\u2019 threat level following IUCN Red List times the fraction of its range within the region24. The regional impact \u2014how agricultural LU in a specific region contributes to the risk of species disappearing from it\u2014 was assessed at two levels: at the whole Cerrado ecoregion and in 48 mesoregions, i.e. regions in a geographic area with socioeconomic similarities, across the Cerrado\u2019s extent (Supplementary Fig.\u00a01). For this, we used the cSAR and SHI approaches. Finally, we used the cSAR to assess local impacts\u2014risk of biodiversity loss at 5\u2009km pixel-resolution.\n\nIn this work, we present and compare these assessments in terms of: (1) total and taxon-specific biodiversity impacts; (2) geographic distribution of biodiversity impacts; and (3) attribution of biodiversity impacts to LU types. We show that, although all metrics point out the leading role of agricultural LU in the increasing pressure on the Cerrado\u2019s terrestrial biodiversity, they provide distinctly different insights into biodiversity change drivers and impacts. Unlike the consistent results across spatial scales in detecting biodiversity impacts in traditional agricultural areas, local and regional assessments are more effective than global ones in agricultural frontiers. Overall, the cSAR and SHI approaches are best fit for \u2018backward-looking\u2019 analyses with focus on historical LU change and accountability, while the STAR approach best informs \u2018forward-looking\u2019 analyses focused on mitigation of global extinctions or identification of impact risk hotspots. Our standardised setup allows us to discuss how and where there is potential for granular and complementary biodiversity metrics to jointly inform landscape-level attributions of biodiversity risk to agri-production and supply chain activities, and in turn contribute to decision-making processes and appropriate conservation responses.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "All three metrics indicate agricultural LU as the major cause of biodiversity decline in the Cerrado, though the relative importance of agriculture differs between indicators. In cSAR, approximately 98% of the potential species loss by 2021 is associated with agricultural LU at all impact scales assessed. At ecoregion level, the potential regional loss is 287 species, representing around 13% of the species found in the ecoregion (Table\u00a02). The global-weighted impact estimates 14 potential global species losses, varying between 12-14% across taxonomic groups (Table\u00a02).\n\nIn STAR, agricultural threats are expected to cause population decline for 470 species, which corresponds to 3/4 of the species experiencing threats in the Cerrado. In this approach, each species has a global threat abatement (START) score over its total range, according to the threats assigned to it. If all threats would be abated, the START score would be 0. On average, agricultural threats account for 62% of the species\u2019 total START score, though for 81 species, agriculture is the only threat. That the STAR metric includes other threat categories\u2014e.g.\u00a0pollution, or invasive alien species\u2014explains the reduced relative importance of agriculture when compared to the other metrics which focus solely on LU. Agricultural threats have a bigger share in the reptiles\u2019 total START score than in those of the other taxa (Table\u00a02).\n\nIn SHI, 99% of the 1478 species that cannot inhabit agricultural LUs had a decrease in their habitat ecological integrity\u2014i.e.\u00a0habitat area size and connectivity\u2014by 2021. At ecoregion level, the SHI estimates an average loss of 35% in the species\u2019 habitat ecological integrity within the Cerrado compared to pristine conditions. The average loss in the global-weighted SHI remains the same, with small variations across taxa. Amphibians have a smaller loss in both impact scales compared to other taxa (Table\u00a02).\n\nExploring temporal trends on impact can also be relevant. With cSAR and SHI, impacts can be compared across different years as long as there is LU information. For instance, agriculture\u2019s impact at ecoregion level by 1985 was estimated with cSAR as 181 potential regional species losses, or a global-weighted loss of 9 species, indicating that the losses in the Cerrado by 2021 are 58% larger than they were by 1985. Using SHI, the species that cannot inhabit agricultural LUs had a 16% loss in their habitat ecological integrity by 1985, implying that more than half of the loss found by 2021 happened in this 36-years window. In STAR, the score calculation is based on the current threats to species following IUCN\u2019s Threat Classification Scheme, and, thus, such a temporal comparison is not within the metric\u2019s scope.\n\nAt global level, the geographical distribution of impacts assessed with the STAR metric diverges more greatly from the ones assessed with the other two indicators. cSAR and SHI also agree considerably at regional level, with the\u00a0exception of some areas in the northeast Cerrado (Fig.\u00a01).\n\nPotential global (a) and regional (d) loss of native vertebrate species in Cerrado mesoregions by 2021 assessed with the countryside Species-Area Relationship (cSAR). Global (b) and regional (e) loss of habitat integrity in Cerrado mesoregions by 2021 for the native vertebrate species that cannot inhabit agricultural land uses assessed with the Species Habitat Index (SHI). Global threat abatement score (START) disaggregated at mesoregions (c) assessed with the Species Threat Abatement and Restoration metric. Data values were scaled between a range of 0 (lowest) to 1 (highest) with a min-max normalisation for intercomparison between indicators. The Brazilian Cerrado ecoregion is highlighted in red in the map of South America at the bottom right, showing the Brazilian states within its coverage. MA Maranh\u00e3o, TO Tocantins, PI Piau\u00ed, BA Bahia, MT Mato Grosso, GO Goi\u00e1s, MS Mato Grosso do Sul, MG Minas Gerais, SP S\u00e3o Paulo. Source Data is provided in ref. 44.\n\nIn terms of global impacts, the mesoregion with the highest number of potential global species losses, as assessed with both cSAR (1.8 species) and SHI (0.36% of loss in habitat ecological integrity), is South Goi\u00e1s (mesoregion 47, see Supplementary Fig.\u00a01) (Fig.\u00a01a, b). A fraction-of-a-species loss can be interpreted in this context as parts of a species\u2019 population being lost in a region25. In STAR, the mesoregions with the highest START scores, varying from 2706 to 2220, are spread across the central Cerrado, from west to east (Fig.\u00a01c). In contrast to the other two metrics based on lost habitat, the START score will demarcate areas where more threatened and endemic species have more remaining habitat.\n\nWhen it comes to the regional impacts assessed with cSAR, the mesoregions with the highest potential regional species loss, varying from 312 to 256 species, are mostly in southern Cerrado (Fig.\u00a01d). In SHI, the greatest decrease in habitat ecological integrity, varying from 41 to 51% loss, are mostly in southwest Cerrado (Fig.\u00a01e). Interestingly, the biodiversity impact on the MATOPIBA region\u2014an agricultural frontier between the states of Maranh\u00e3o (MA), Tocantins (TO), Piau\u00ed (PI) and Bahia (BA)26\u2014shows in the SHI approach, but not in the cSAR.\n\nFinally, at the local scale, the cSAR metric shows higher potential local species loss in the southern Cerrado, similar to the global and regional assessments (Fig.\u00a01a, d), but it also points out particular locations with high impact that might have been \u2018masked\u2019 at regional or global scales by the considerable size of the surrounding remaining natural habitat (Fig.\u00a02a). This is especially demonstrated by the high scoring pixels in the upper/central east and west parts of the region. Global level assessments may be less sensitive at detecting impacts on areas in current agricultural frontiers because of the mixed attributes of heavily converted local patches and a dense area of species\u2019 natural habitat in the landscape. It is also worth noting that areas with higher potential local species loss in the cSAR are those with lower scores as shown by the global START score disaggregated to pixels (Fig.\u00a02a vs Fig.\u00a02b). This contrast between metrics occurs because the START score is focused on species\u2019 current AOH and does not account threats\u2019 historical impacts. This has the practical implication that areas with intense historical LU change, where species have little or no habitat left, will have low START scores (i.e. there is no current threat in an area where the species\u2019 habitat was already converted), but high impacts when assessed with cSAR and SHI, as is evident in Fig.\u00a02. Here, it is important to bear in mind that, although spatially disaggregated to pixels, the START score reports global impacts.\n\na Potential local loss of terrestrial vertebrate species richness due to agricultural land use in the Cerrado ecoregion by 2021 at 5\u2009km pixel resolution assessed with the countryside\u00a0Species Area-Relationship model. b Global threat abatement score (START) disaggregated at 5\u2009km pixel resolution. Source Data is provided in ref. 44.\n\nFor the attribution to LU types, we first compare the assessments of the global-weighted impact at the level of the whole Cerrado ecoregion. In cSAR, pasture is the LU type with the greatest potential global species loss (7 species), followed by soy (3 species), mosaic of agriculture and pasture (i.e. areas where the remote sensing data could not distinguish between pasture and agriculture, 2 species), tree plantation (0.5 fraction of a species) and sugar cane (0.4 fraction of a species).\n\nIn STAR, allocation is based on the threat categories of the IUCN\u2019s Threat Classification Scheme27. The three categories of agricultural threats\u2014Annual & perennial non-timber crops, Livestock farming and ranching, and Wood & pulp plantation\u2014can be disaggregated to farming scale, e.g. small-holder or agro-industry, but not to individual LU types. Annual & perennial non-timber crops had the highest START score, 8785, followed by Livestock farming & ranching with 5095, and Wood & pulp plantation with 1518.\n\nIt is worth noting that differing criteria for allocations result in different patterns. For instance, in cSAR, more impact is allocated to pasture based on the greater area shares of this LU and the lower species affinities to it, whereas in STAR, more allocation is given to Annual & perennial non-timber crops because more species are classified as currently threatened by it in the IUCN\u2019s Threat Classification Scheme.\n\nIn the global-weighted cSAR at the mesoregion level, pasture is the LU type with highest potential global species loss in 31 mesoregions, followed by soy in 7 mesoregions, such as north Mato Grosso (mesoregion 38) and the MATOPIBA area (mesoregions 9, 12 and 14), which evidences the biodiversity impacts linked to soy production in these new agricultural frontiers (Fig.\u00a03). In STAR, when the global START score is disaggregated to mesoregions, all 48 mesoregions have higher scores for Annual & perennial non-timber crops than for the other two agricultural categories. When the score is disaggregated to pixels, there are patches with higher scores widespread through the Cerrado for Annual & perennial non-timber crops, while there is a concentration of higher scores in southeast regions for Livestock farming & ranching (Fig.\u00a04).\n\nNumber of potential native vertebrate species loss attributed to agricultural land uses on 48 mesoregions of the Cerrado by 2021 assessed by the global-weighted countryside Species-Area Relationship. The loss of a fraction of a species can be interpreted in this context as parts of a species\u2019 population being lost in a mesoregion. Mesoregions 1-15, marked in yellow, are in North and Northeast Brazil; mesoregions 16\u201333, marked in violet, are in Southeast Brazil; and mesoregions 34\u201348, marked in pink, are in Central-West Brazil. Source Data is provided in ref. 44.\n\nGlobal threat abatement score (START) visualized at 5\u2009km pixel resolution and disaggregated by the broad threat categories (a) Annual & perennial non-timber crops, represented by the soybean icon; (b) Livestock farming & ranching, represented by the cow icon; and (c) Wood & pulp plantations, represented by the trees icon. Broad threat categories follow the IUCN threat classification for species. Source Data is provided in ref. 44.\n\nOne may also be interested in how the impact of specific LU types on biodiversity has changed throughout time. Comparing the local impact of pasture and soy by 1985 and by 2021 as assessed with cSAR, shows an overall increase of the biodiversity impacts of both LUs in the Cerrado (Fig.\u00a05). Interestingly, the decrease in the local impacts of pasture in parts of the south can be related to a substitution of pasture by soy cultivation, as there is a reciprocal increase in the impacts caused by soy in most of these areas. This illustrates that cSAR\u2019s attribution to specific LUs is dependent on the LU composition used as a comparison to the pristine baseline, disregarding historical transformation between LUs. This is especially important in approaches for attributing occupational LU impacts, like in standard Life Cycle Assessments (LCAs), as, particularly in areas with large-scale land-dynamics like those in central and southern Cerrado, not accounting for LU substitutions can have potentially large effects on estimated characterization factors. To allocate impacts over LUs that have been present throughout time, the cSAR model could be run year by year with annual LU maps, to calculate a compound score averaged over the years.\n\nChanges in the potential local species loss attributed to (a) pasture land use, represented by the cow icon, and (b) soy land use, represented by the soybean icon, between 1985 and 2021 calculated with the countryside Species-Area Relationship, at 5\u2009km resolution. Changes are shown as the differences between the local impacts by 2021 and the local impacts by 1985. Source Data is provided in ref. 44.\n\nThe standard application of the SHI approach does not attribute loss of habitat ecological integrity to specific LU types. For such attribution to be possible, the changes in each LU type must be tracked in the LULC maps and then proportionally allocated to the losses in habitat area size and connectivity driven by the LU transformation. By building on the original calculations for the area size component of the SHI, we explored a complementary way to use the SHI approach to attribute loss in habitat area in Cerrado mesoregions to specific LUs. This exercise can be found in Supplementary Fig.\u00a02. The shares of the specific LU types in the total loss that resulted are similar to those found with cSAR (Fig.\u00a03).",
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "Different indicators capture different components of the biodiversity-impact process, which\u2014if properly applied by stakeholders, can provide useful information tailored to different policy-relevant goals\u2014or if not, lead to confusion and poorly supported decisions. Although indicators are not fully incongruent, neither in their conceptualization, nor in their operationalization, the comparison presented above can facilitate decisions on which indicators will be more fit-for-purpose to particular applications.\n\nA meaningful application of the indicators has to consider their temporal and spatial foci. The START metric is\u00a0a forward-looking approach, attributing impacts based on current and future threats to biodiversity and focusing on areas of remaining habitat that can potentially be conserved. Thus, within the policy landscape, the STAR metric can be a useful tool for identifying measures to mitigate future biodiversity losses. For instance, only 13% of the Cerrado area is currently under conservation protection28, considerably short of the CBD\u2019s target of protecting 30% of land ecosystems by 2030. The START mapping of biodiversity risk hotspots can facilitate the identification of areas of conservation importance and shows that abating the agricultural expansion of annual and perennial non-timber crops, especially in the eastern Cerrado and in localized regions of its transition zones with the Atlantic and Amazon forests and the Pantanal wetland, would contribute most to avoiding global biodiversity extinctions. Such measures would be particularly important for threatened reptile species, for which agriculture represents over 70% of their threat score.\n\nThe SHI and the cSAR in turn are backward-looking approaches that measure impacts on biodiversity based on LU transformations, emphasizing accountability for what has been lost. Such metrics can be useful tools in biodiversity accounting and disclosure within Corporate Social Responsibility or Environment, Social and Governance reporting29. As a prominently used indicator in LCA, it is important to consider that applying the cSAR only to LULC maps with the current LU configuration can disregard potential LU dynamics and give a skewed picture of which LUs (and hence actors) are accountable for historical extinction risks (e.g., disregarding habitat once cleared for use as pasture that more recently is converted to soy production). There can also be an interaction between baseline and time-lags between LU change and biodiversity losses (i.e., extinction debts) in relation to accountability. In a pristine baseline, the emphasis falls on (long-term) accountability, as some of the losses will be impossible to undo (i.e., global extinctions). However, if assessed over shorter historical periods with a more recent baseline, a backward-looking metric such as the SHI may be relevant for mitigatory measures like rehabilitation or reforestation by identifying areas where reversals of recent LU loss can help avoid extinction debts, in particular through reducing habitat fragmentation.\n\nEffective governance for biodiversity in areas such as the Cerrado, where a multitude of social-ecological systems coexist, needs to draw on assessments that best capture the dynamics of their different biophysical components and socioeconomic histories and should contribute to targeted actions for those regions. As an example, the consistently high impacts on biodiversity captured at all scales by cSAR and SHI in south Goi\u00e1s (mesoregion 47, see Supplementary Fig.\u00a01) is a striking result of historical LU processes in the state, which\u2014due to a mixture of national and international incentives and biophysical characteristics favourable to the technological packages within Brazil\u2019s Green Revolution\u2014already had 50% of its extent converted to agriculture by 198630,31. The localised information supplied by the application of such biodiversity indicators can support, for example, the enforcement of the Brazilian Forest Code through spatial planning for restoration focused on priority areas for biodiversity. The Forest Code establishes areas of natural vegetation under permanent protection and legal reserves in rural properties and for which Goi\u00e1s state had a vegetation deficit of 14,467\u2009km\u00b2 by 201732.\n\nIn contrast to the relative levels of consistency across spatial scales observed from the metrics when applied to traditional agricultural areas throughout Cerrado, our results indicate that assessments of local and regional impacts are more sensitive than global level assessments at detecting effects on biodiversity in areas that have been subject to more recent habitat conversion. Such agricultural frontiers, like the MATOPIBA region and northern Mato Grosso (mesoregion 38), are ecological transition zones between the Caatinga and the Amazon biomes, respectively, and face increasing ecological vulnerability and climate pressure due to rapid agribusiness expansion and intensification together with underlying climate change33,34. In such cases, a combination of approaches to detect where local biodiversity has been significantly impacted and where threat abatement efforts would be best enacted to avoid global extinctions, as shown in Fig.\u00a02, would be an informative way for a biodiversity-inclusive spatial planning to mitigate further local deterioration in ecological conditions.\n\nInformation on biodiversity impacts is not only required to monitor the proximate drivers of LU change, but it is also essential to identify key stakeholders, link impact to supply-chain decisions downstream and inform both regulatory and voluntary schemes, as well as evaluate strengths and weaknesses of interventions. For instance, according to data from the Trase platform35, municipalities in east Tocantins (mesoregion 4) traded soy to 16 nations plus the European Union economic bloc in 2020, through 48 trading companies, with China, the European Union and Turkey as the three biggest partners in traded volume and Bunge, Vietnam Agribusiness Limited and LDC Tianjin International Business CO LTD as the three biggest trading companies. This mesoregion has the highest START score in the Cerrado for the broad LU category Annual & perennial non-timber crops and meaningful efforts to abate this threat should involve dialogue with countries and trading actors downstream in supply chains for planning and, potentially, resourcing, the mitigation of biodiversity risks related to the soy production system, the main cultivated crop in the region.\n\nAttributing impact to specific LU products with measures such as cSAR allows additional finer footprinting by linking the conversion of natural ecosystems to productive outputs and then linking these productive outputs to consumptive demand. Here, calculating a compound score\u2014different versions of a footprint with different attributions to LU systems that varied over time\u2014is particularly important for regions with a long history of agricultural use, such as southeast Mato Grosso do Sul (mesoregion 37). There, demand-side actors related to pasture systems would have an increased final footprint in comparison to the footprint calculated only based on the current LU configuration, which gives more weight to consumptive demand linked to soy (Fig.\u00a05). Undertaking this sort of exercise can be valuable for understanding the sectors ultimately responsible over varying time periods and, thus, providing more accurate information to interact with those stakeholders.\n\nThe analysis conducted here demonstrates the potential for granular and complementary biodiversity metrics to jointly inform landscape-level attributions of biodiversity risk to agri-production and supply chain activities, and in turn their role in informing appropriate conservation responses. The advanced LULC mapping data provided by Mapbiomas for the Cerrado considerably reduces data gaps which would otherwise act as a considerable source of uncertainty for the analysis presented in this paper and\u2014unless there are systematic biases in errors of omission and commission for given LUs or across the landscape\u2014misclassifications are not likely to affect the quantitative results to any great extent. However, if a goal of applying biodiversity metrics of this kind is to attribute biodiversity loss to a suite of commodities (such as might be warranted via LCA activities), uncertainties associated with this type of analysis are likely to get much larger as spatial data on cultivation is only available for selected crops. Additionally, for other parts of the world where ongoing conversion of forests and other natural ecosystems is happening data availability poses a serious issue36 and is likely to affect the accuracy of biodiversity indicator assessments.\n\nAlthough some of the differences found across indicators may stem from the biogeographic specifics of the Cerrado ecoregion, most distinctions relate to their conceptual differences and the conclusions from their comparison revealed here are transferable to LUs other than agriculture, as well as to other regions experiencing rapid LU change. To ensure the best fit between the needs of decision-makers, practitioners, and the public and what indicators can and cannot assess, there must be more awareness when establishing the criteria for judging an indicator\u2019s suitability, without assuming that a single indicator will give information that is fit for all cases. The cSAR, STAR and SHI approaches are complementary, although with caveats. Good practice towards a more fit-for-purpose use of indicators requires that the focal questions intended to be answered by the assessments and the choice of indicator are matched in terms of the relevant temporal, spatial, and ecological perspectives relevant to understanding impact on biodiversity.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "This research did not undertake in loco data collection, but has a local researcher in the authors\u2019 team. We have also taken local and regional research relevant to our study into account in citations.\n\nSpecies range maps, extinction risk categories, threat classification, and habitat affinity data were obtained for all amphibians, reptiles, birds and mammals in the Cerrado from IUCN Red List (version 2022-2)27. We used 30\u2009m-resolution LULC maps from 2021 from the Mapbiomas platform (Collection 7.0), overlaid with digital elevation maps and IUCN species\u2019 range maps to produce 5-km-resolution rasters of each species\u2019 contemporary Area of Habitat (AOH) within the Cerrado37,38, excluding parts of the species\u2019 range maps where presence was coded as extinct or possibly extinct. We constrained the species range maps to areas within compatible elevation range and habitat type according to IUCN Red List assessments. We excluded exclusively aquatic species and aquatic LULC types from the analyses.\n\nWe aggregated the 22 terrestrial LULC types in Mapbiomas into twelve categories to match the IUCN species\u2019 habitat classes: Arable Land (including soybeans, rice, other temporary crops, and mosaic of agriculture and pasture); Plantation (including tree plantation, coffee, citrus, other perennial crops, sugar cane, and cotton - complying with IUCN habitat classification description for the plantation category); Pasture; Forest; Mangrove; Savanna; Grassland; Wetland; Natural Non-Vegetated Areas (including rocky outcrop, beach and dune, and salt flat); Urban; and Other Non-Vegetated Areas (including mining, and other non-vegetated areas, like rural infrastructure). Pasture are areas predominantly planted with grasses linked to livestock rearing activity. Natural grasslands used for livestock grazing are predominantly classified as Grassland, which may or may not be grazed. Tree plantations are areas where the natural land cover was converted to areas with tree species planted for commercial purposes (e.g., pine, eucalyptus, araucaria), usually in monoculture.\n\nTo determine the species\u2019 AOH in the recent past, we followed the same steps described above using the Mapbiomas LULC maps from 1985, this time also including parts where presence was coded as extinct or possibly extinct. To determine the species AOH in the distant (pre-large-scale human activity) past we considered the whole extension of the species\u2019 range maps, constrained to areas within compatible elevation ranges. Species past ranges were not extended beyond IUCN\u2019s current species distributions range maps due to lack of historical distribution range maps.\n\nThe cSAR uses information on the area occupied by different LULC types, baseline richness, and the species affinity to each LULC type to estimate the potential species loss to LU change in a given region, and attribute this to the different LULC types that have replaced natural vegetation. The potential species loss evaluates the number of species committed to extinction due to the LU configuration found in an area in relation to the potential species richness that could be found in the same area in a natural habitat condition. The cSAR approach has mostly been used at large scale resolutions, i.e. biogeographical regions5,39, but can be applied at finer pixel resolutions, i.e., 10\u2009km16.\n\nThe countryside Species-Area Relationship model16 estimates the number of potential species loss for each taxonomic group g in location j (\\({{Sloss}}_{g,j}\\)) as:\n\nwhere \\({{Spot}}_{g,j}\\) is the potential species richness in original natural habitat conditions; \\({{Apot}}_{j}\\) is the potential area of natural habitat in location j, i.e., the land area in the respective location; \\({h}_{g,b,j}\\) is the affinity parameter of taxon g to each broad LULC type b in location j; \\({A}_{b,{j}}\\) is the area occupied by each broad LULC type b in location j; and z is the SAR exponent for non-forest ecoregions, obtained from ref. 40 and equivalent to 0.23. The SAR exponent indicates how rapidly species are lost in an ecosystem as it loses natural habitat.\n\nThe potential species richness in location j (\\({{Spot}}_{g,j}\\)) is given by the number of species that have their pristine AOH overlapping the location. The potential species richness was then assessed by creating a count of all species present in the area. The potential area of natural habitat (\\({{Apot}}_{j}\\)) is the entire terrestrial area of location j.\n\nThe species affiliation to each broad LULC type (natural habitat, arable land, plantation, pasture, urban and other non-vegetated areas) was calculated based on the IUCN Red List Habitat Classification Scheme, which provides species-specific information on habitat preferences. The affinity of taxon g to the broad LULC type b in location j (\\({h}_{g,b,j}\\)) was given by the number of species affiliated with the broad LULC type b in location j \\(({S}_{g,b,j})\\) divided by the number of species expected in the location under natural habitat cover \\(({S}_{g,j})\\), raised to the power of \\(\\frac{1}{z}\\) (ref. 41):\n\nFor natural habitat land cover, affinity equals 1 as \\({S}_{g,b,j}\\)\u2009=\u2009\\({S}_{g,j}\\). The area of natural habitat land cover currently found in the location was given by the sum of the areas classified as Forest, Savanna, Grassland, Wetland, Mangrove, Beach and Dune, Salt Flat and Rocky Outcrop in Mapbiomas.\n\nThe total potential species loss can be allocated to each individual LU category based on their area share and the taxon affinity to them. Following ref. 39, the allocation factor of LULC type b in location j (\\({a}_{b,j}\\)) is:\n\nwhere N is the number of LULC types in location j. The allocation factor is then:\n\nwhere 0 < \\({a}_{b,j}\\,\\)<\u20091, and \\({\\sum }_{b=1}^{N}{a}_{b,j}=1\\).\n\nFinally, to assess global impact, the impact is weighted by the threatened endemic richness24. For this, we first calculate the endemic richness (\\({{ER}}_{g,j}\\)) of each taxon g in location j as:\n\nwhere m is the total number of species of taxa g found within location j, \\({{GR}}_{s,g,j}\\) is the area of species s habitat range within location j, and \\({{GR}}_{s,g}\\) is the total (global) area of species s habitat range. The range fraction of each species s in location j is then multiplied by its threat level (\\({TL}\\)) according to IUCN Red List to calculate the threatened endemic richness (TER) per taxa g in location j:\n\n\\({TL}\\) is a linear rescaling of the categories defined in IUCN Red List from 0.2 to 1 (least concern, 0.2; near threatened, 0.4; vulnerable, 0.6; endangered, 0.8; and critically endangered, 1).\n\nTo calculate the global weighted impact, the potential species richness (\\({{Spot}}_{g,j}\\)) in Eq.\u00a01 can be then substituted by the threatened endemic richness (\\({{TER}}_{g,j}\\)):\n\nIn\u00a0the STAR metric, the\u00a0STAR threat abatement (START) score uses information on species\u2019 extinction risks and on threats that can cause population decline to estimate the proportional effect that abating a threat in the species\u2019 remaining habitats represents in relation to the global extinction risk imposed by all the threats to this species. All risk categories were included in the analysis, as threats are currently coded for the majority of species on the IUCN Red List. Data Deficient species were excluded. Description of threats include timing (past, ongoing, future); scope (the percentage of the population affected by the threat); and severity (the rate of population decline caused by the threat within its scope). Threats with past, unlikely to return timing were excluded. Threats with a combination of scope and severity that is not expected to lead to population decline were also excluded (including severity coded as no decline and a combination of severity coded as negligible decline and scope coded as affecting either the minority or majority of the species\u2019 distribution, see ref. 17). By doing so, any species assigned to threats that were not expected to result in population decline were not considered in the analysis. Although scope and severity data are mostly complete for birds, this information is still lacking for some amphibian, reptile and mammal species. Ref. 17 explored approaches to deal with missing scope and severity data and concluded that using the intermediate classification of the possible values of scope and severity to replace unknown or missing data was a suitable approach (the intermediate classification for scope is Majority (50-90%), and the intermediate classification for severity is Slow, Significant Declines).\n\nA global START score was calculated for 635 vertebrate species that are threatened with population decline within the Cerrado ecoregion (147 amphibians, 143 reptiles, 210 birds and 135 mammals), representing 29% of the total species.\n\nThe START score for pixel n and threat t (\\({T}_{t,n}\\)) is:\n\nwhere \\({P}_{s,n}\\) is the percentage of the total current AOH of species s within pixel n; \\({W}_{s}\\) is a factor weighted by the risk category of species s according to IUCN Red List assessment (Least Concern = 1; Near Threatened = 2; Vulnerable = 3; Endangered = 4; Critically Endangered = 5; see ref. 17); \\({C}_{s,t}\\) is the relative contribution of threat t to the extinction risk of species s; and \\({N}_{s}\\) is the total number of species in pixel n.\n\nThe relative contribution of a threat to the total extinction risk of a species is the percentage of population decline expected to be caused by that threat, reproduced from ref. 17 (see Supplementary Table\u00a02), divided by the sum of the percentage population declines of all threats affecting this species. For instance, if a species has three threats, T1, T2 and T3, expected to cause a population decline of 18%, 9% and 5%, respectively, the relative contribution of threat T1 to the total extinction risk of the species will be 18 / (18\u2009+\u20099\u2009+\u20095)\u2009=\u20090.56. The effect of the weighting factor for the species\u2019 threat classification \\(({W}_{s})\\) in the final scores was tested in a sensitivity analysis (Supplementary Fig.\u00a03).\n\nWe used one level of aggregation of agricultural threats for the calculation: Annual & Perennial Non-Timber Crops includes shifting agriculture, small-holder farming, and agro-industry farming; Wood & Pulp Plantations includes small-holder plantations, and agro-industry plantations; and Livestock Farming & Ranching includes nomadic grazing, small-holder grazing, ranching or farming, and agro-industry grazing, ranching or farming.\n\nThe SHI uses information on the size and connectivity of the species\u2019 AOH to estimate alterations in the ecological integrity of the species habitat in an area. It is a two-step approach obtained by calculating two Species Habitat Scores (SHS), the Area Size Score and the Connectivity Score, for each species in a region and then aggregating them to derive the SHI for the region18,19.\n\nThe size of suitable habitat area in region j for species s\\(\\,({A}_{s,j})\\) is given by the sum of the pixel-level suitability in region j:\n\nwhere \\(a\\) is the pixel area and \\({S}_{n,s}\\) is the percentage of suitable habitat in pixel n for species s.\n\nThe connectivity of suitable habitat area in region j for species s (\\({{CS}}_{s,j}\\)) is calculated based on pixel-level presence-absence binary maps for the species in the region. A pixel with less than 1% of its area covered with habitats that are suitable for a species was coded as non-suitable (absent) in the binary map of this respective species, to reduce the influence of spurious, isolated patches29. For each suitable pixel, the Euclidean distance (i.e., as the crow flies) to the nearest non-suitable cell is estimated. The connectivity is then given by the average Euclidean distance of all suitable pixels from the nearest edge (i.e., GISFrag metric, ref. 42).\n\nThe Area Size Score, \\({{AS}}_{s,j,k},\\) and the Connectivity Score, \\({{CS}}_{s,j,k}\\), for species s, in region j for a particular year k (here 2021), in relation to the baseline 1 are then:\n\nThe mean of the Area Score and the Connectivity Score is the SHS for species s in region j and year k:\n\nThe Species Habitat Index of region j in year k \\(({{SHI}}_{j,k})\\) is then the average of the SHS for all N species in the region:\n\nTo calculate the global weighted SHI, each species\u2019 SHS was multiplied by its range fraction in location j (Eq.\u00a05) and threat level (\\({TL}\\)) as a weight for threatened endemism (Eq.\u00a06). The global weighted SHI for region j in year k is then:\n\nWe applied the indicators to assess three different scales of impact on biodiversity: local impact (with the cSAR), regional impact at two levels (1) ecoregion and (2) geographical mesoregions (with the cSAR and the SHI) and global impact (with the cSAR, the SHI and the STAR). When assessing local impact, location j was equivalent to each 5-km-resolution pixel within the Cerrado. When assessing regional impact at the level of ecoregion and geographical mesoregions, location j was equivalent to the total extent of the Cerrado ecoregion or to the area of each mesoregion within the Cerrado\u2019s extent, respectively (see Supplementary Fig.\u00a01). Calculating local impact with SHI is also possible. However, for a large area like the Cerrado ecoregion, such calculation is very computationally intensive and was not performed in this study.\n\nAll analyses and maps were done with R Studio software (R Core Team version 4.2.2 2021)43, with packages terra (version 1.7-71), raster (version 3.6-26), data. table (version 1.15.4), exactextractr (version 0.10.0) and caret (version 6.0-94).\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data on species (i.e., range distribution maps, habitat preferences, threats, etc.), land use and land cover, elevation digital models, and administrative borders supporting the findings of this study are publicly available on IUCN Red List of Threatened Species (https://www.iucnredlist.org/), Mapbiomas platform (https://brasil.mapbiomas.org/en/), Open DEM and the Brazilian Institute for Geography and Statistics\u2019 website (https://www.ibge.gov.br/en/geosciences/territorial-organization/territorial-meshes/). All data produced in the study, as well as Source Data for Table\u00a02, Figs.\u00a01, 2, 3, 4 and 5, and Supplementary Figs.\u00a02 and 3 are readily available on Zenodo repository (https://doi.org/10.5281/zenodo.11352608)44. Source Data for generating Supplementary Fig.\u00a01 is publicly available on Mapbiomas platform and the Brazilian Institute for Geography and Statistics\u2019 website.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "All coding used in this study is available on Zenodo repository (https://doi.org/10.5281/zenodo.11352608)44.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "G.R., T.K., M.P., and C.W. acknowledge funding from the German Federal Ministry for Economic Cooperation and Development (GRADED project, grant number GS22 E1070-0060/029). T.K. and M.P. acknowledge funding from the Belmont Forum SSCP BEDROCK project, through the German Research Foundation (DFG, grant KA 4815/2-1) and Formas (grant 2022-02563). CW acknowledges funding from the UK Research and Innovation\u2019s Global Challenges Research Fund (UKRI GCRF) Trade, Development and the Environment Hub project (grant number ES/S008160/1). G.R. and T.K. acknowledge funding from the German Federal Ministry of Education and Research (TransRegBio project, grant 031B0901A).",
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+        },
+        {
+            "section_name": "Funding",
+            "section_text": "Open access funding provided by Chalmers University of Technology.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany\n\nGabriela Rabeschini\u00a0&\u00a0Thomas Kastner\n\nFaculty of Biological Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany\n\nGabriela Rabeschini\n\nDepartment of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden\n\nU. Martin Persson\n\nStockholm Environment Institute York, University of York, York, UK\n\nChris West\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nG.R. and T.K. conceived the study. G.R. performed the analyses and developed the figures, with inputs from T.K., M.P., and C.W.; G.R. led the writing of the manuscript with inputs from T.K., M.P., and C.W.; All authors contributed to discussion of content and review.\n\nCorrespondence to\n                Gabriela Rabeschini or U. Martin Persson.",
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+            "section_text": "Rabeschini, G., Persson, U.M., West, C. et al. Choosing fit-for-purpose biodiversity impact indicators for agriculture in the Brazilian Cerrado ecoregion.\n                    Nat Commun 16, 1799 (2025). https://doi.org/10.1038/s41467-025-57037-9\n\nDownload citation\n\nReceived: 06 May 2024\n\nAccepted: 10 February 2025\n\nPublished: 20 February 2025\n\nVersion of record: 20 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-57037-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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@@ -0,0 +1,176 @@
+{
+    "title": "Seasonal antigenic prediction of influenza A H3N2 using machine learning",
+    "pre_title": "Seasonal antigenic prediction of influenza A H3N2 using machine learning",
+    "journal": "Nature Communications",
+    "published": "07 May 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47862-9/MediaObjects/41467_2024_47862_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47862-9/MediaObjects/41467_2024_47862_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47862-9/MediaObjects/41467_2024_47862_MOESM3_ESM.docx"
+        },
+        {
+            "label": "Supplementary Data 1",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47862-9/MediaObjects/41467_2024_47862_MOESM4_ESM.xlsx"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47862-9/MediaObjects/41467_2024_47862_MOESM5_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-47862-9/MediaObjects/41467_2024_47862_MOESM6_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-024-47862-9#ref-CR12",
+        "/articles/s41467-024-47862-9#ref-CR35",
+        "/articles/s41467-024-47862-9#ref-CR13",
+        "/articles/s41467-024-47862-9#ref-CR14",
+        "/articles/s41467-024-47862-9#MOESM4",
+        "/articles/s41467-024-47862-9#ref-CR12",
+        "/articles/s41467-024-47862-9#ref-CR13",
+        "/articles/s41467-024-47862-9#ref-CR14",
+        "https://www.rcsb.org",
+        "/articles/s41467-024-47862-9#ref-CR19",
+        "/articles/s41467-024-47862-9#Sec23"
+    ],
+    "code": [
+        "https://github.com/saws-lab/SAP_H3N2_ML",
+        "/articles/s41467-024-47862-9#ref-CR57",
+        "https://huggingface.co/spaces/sawshah/SAP_H3N2"
+    ],
+    "subject": [
+        "Computational models",
+        "Influenza virus",
+        "Machine learning",
+        "Policy and public health in microbiology",
+        "Viral evolution"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-2924528/v1.pdf?c=1715166646000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-2924528/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-47862-9.pdf",
+    "preprint_posted": "23 May, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.Biological sciences/Computational biology and bioinformatics/Computational modelsHealth sciences/Diseases/Infectious diseases/Influenza virusBiological sciences/Microbiology/Policy and public health in microbiologyBiological sciences/Microbiology/Virology/Viral evolutionBiological sciences/Computational biology and bioinformatics/Machine learning",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SuppTable1.csvSupp. Table 1",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Genetic changes accumulated in the influenza virus population may alter their antigenic properties, resulting in antigenic drift1. Antigenically drifted influenza strains may escape immunity induced by previous infection or vaccination2, leading to an increase in morbidity and mortality1. To counter antigenic drift, influenza virus strains included in the human influenza vaccine are regularly updated. The World Health Organization (WHO) holds vaccine composition meetings (VCMs) twice each year to recommend vaccine strains for the upcoming northern hemisphere (NH) and southern hemisphere (SH) influenza seasons3. Genetic and antigenic characteristics of circulating isolates are considered when recommending vaccine strains at each meeting3.\n\nAntigenic characteristics of circulating isolates are primarily determined through hemagglutination inhibition (HI) assays utilizing ferret post-infection antisera, although assessments using both human pre- and post-vaccination antisera are also conducted3. The HI assay measures the cross-reactivity of a test virus isolate to an antiserum raised against a reference virus isolate in ferrets or against the vaccine viruses in humans. Ferret antisera are produced in na\u00efve animals and hence have high specificity compared to human antisera which generally have extensive cross-reactive antibodies due to encountering multiple infections or vaccinations against influenza. Large-scale antigenic characterization of circulating isolates using HI assays incurs high costs and is time and labor-intensive4,5.\n\nComputational methods that predict ferret HI titers of influenza viruses using genetic sequence data may help to reduce these burdens1,6. Accurate sequence-based models could enable more comprehensive antigenic surveillance of circulating virus isolates without the need for increased experimental resources1. The efficiency of evolutionary monitoring and vaccine selection procedures may be improved by providing targeted sets of isolates for experimental evaluation. Furthermore, by learning the complex map from genetic to antigenic changes, accurate prediction methods could yield new insights into influenza evolution and the processes underpinning antigenic drift2,7.\n\nHere we develop a machine learning (ML) model that predicts antigenic properties of influenza A virus (IAV) H3N2 isolates circulating in a season using their HA1 sequences and associated metadata while being trained on data from past seasons only. The model is designed and evaluated for predicting, on a season-by-season basis, HI titers of virus-antiserum pairs involving viruses sequenced globally as part of WHO\u2019s seasonal influenza surveillance. This approach is distinct from previous sequence-based HI titer prediction methods4,7,8,9,10,11, which in many cases have considered the problem of predicting HI titers of virus-antiserum pairs randomly selected over time. For training and testing our model, we use the IAV H3N2 antigenic data of influenza seasons from 2003 \u2013 2021 reported by the Worldwide Influenza Center at the Francis Crick Institute12, genetic data available at influenza sequence databases13,14, and their associated metadata. The model predicts HI titers of virus-antiserum pairs with a mean absolute error (MAE) of 0.702 antigenic units (where 1 antigenic unit \u2248 2-fold change in HI titer) per season and exhibits a strong discriminatory ability in distinguishing antigenic variants across seasons. The developed data-driven ML model captures from data the nonlinear effects in the relation between IAV H3N2 antigenic and genetic changes, which has been suggested by recent experimental studies15,16. We show that the model\u2019s predictive power is robust to limiting training data per season. Moreover, incorporating a small amount of antigenic data from circulating isolates in model training significantly enhances its accuracy, particularly for seasons associated with strong antigenic drift. The model identifies key sites with the strongest impact on IAV antigenic change, most of which are located in HA1 epitopes, and reveals how they vary across different seasons. Overall, accurate prediction of HI titers by the developed model across seasons shows its viability for seasonal antigenic characterization of IAV H3N2.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Our ML model for seasonal antigenic characterization of IAV H3N2 was designed under a seasonal framework (Fig.\u00a01a) that mimics the WHO VCM protocols12 (Supplementary Fig.\u00a01). The NH VCM is held each February and considers antigenic data for circulating isolates from the preceding September to January, while the SH VCM is held each September and considers isolates from the preceding February to August. Each of these periods constitutes an influenza season. Under the seasonal framework, for any given season, our model is trained using genetic, antigenic, and metadata information available prior to that season. The trained model predicts antigenic data for the current season based on genetic data of isolates circulating in that season, along with metadata (Fig.\u00a01a).\n\na Seasonal division of data into training and test datasets respectively for training and evaluation of computational methods in a time-series fashion. Under this framework, historical genetic, antigenic, and metadata information of virus isolates from past seasons is included in the training dataset, while genetic and metadata information of virus isolates from the current season form the test dataset. b The trained AdaBoost model was used to predict NHTs using only encoded genetic difference and metadata information of virus-antiserum pairs. c Details of the encoding performed at the input of the AdaBoost model. The HA1 sequences of isolates in each virus-antiserum pair were encoded using the amino acid mutation matrix available in the AAindex2 database. One-hot encoding was used to represent the metadata information, which includes virus avidity, antiserum potency, and passage category of isolates. The encoded genetic difference and metadata of each virus-antiserum pair were used as input features of the AdaBoost model. Each training virus-antiserum pair was labeled by NHT-based antigenic difference (see \u201cMethods\u201d).\n\nThe model employs an adaptive boosting method (AdaBoost)17,18 consisting of an ensemble of decision trees (Fig.\u00a01b). The model is trained in a supervised fashion and learns a nonlinear mapping from genetic difference to antigenic difference (defined as normalized HI titers (NHT); \u201cMethods\u201d) between virus-antiserum pairs of past isolates. Pairwise genetic difference is based on the HA1 gene of isolates in a virus-antiserum pair and is encoded using the GIAG010101 mutation matrix from the amino acid index 2 (AAindex2) database19 (\u201cMethods\u201d, Fig.\u00a01c). The model also utilizes metadata information including virus avidity7, antiserum potency7, and passage category (egg or cell) of virus isolates and antisera, which is represented using one-hot encoding (\u201cMethods\u201d, Fig.\u00a01c). The trained model predicts the antigenic differences (in terms of NHTs) of circulating virus isolates using only their HA1 genetic sequences and metadata information (Fig.\u00a01b, c).\n\nFor model training, optimization, and evaluation, we compiled HI titers data of IAV H3N2 from reports published by the Worldwide Influenza Center (WIC) at the Francis Crick Institute, London,12 and genetic data from influenza sequence databases, GISAID13 and IVR14. The processed dataset included NHTs of 36,709 virus-antiserum pairs with corresponding metadata, spanning 37 influenza seasons from 2003NH to 2021SH (\u201cMethods\u201d). Data availability was limited in the early seasons and increased progressively over time (Supplementary Fig.\u00a02a). Preliminary assessment using a baseline model (\u201cMethods\u201d) revealed sufficient data for reliable predictive performance from the 2012NH season onwards (Supplementary Fig.\u00a02b). The four seasons 2012NH to 2013SH were selected as validation seasons to perform feature selection and model optimization.\n\nUsing the validation seasons, it was found that incorporating all four metadata features provided optimal performance (MAE of 1.091) (Supplementary Fig.\u00a03a) and substantially outperformed the baseline model trained with no metadata (MAE of 1.641). The metadata captures distinct information: virus avidity and antiserum potency account for experimental variations among HI assays7, while the passage category informs about antigenicity-altering mutations incurred during in vitro propagation of virus isolates using cell or egg lines1,20. Optimization of the model hyperparameters significantly improved performance (from MAE of 1.091 to 0.759) over validation seasons (Supplementary Fig.\u00a03b). Selecting the optimal amino acid mutation matrix for genetic data encoding (Supplementary Fig.\u00a03c; for details see \u201cMethods\u201d) further slightly improved performance (MAE of 0.75).\n\nThe performance of the optimized model in predicting NHTs was evaluated for each of the 14 test seasons (2014NH to 2020SH). This yielded a MAE, averaged across seasons, of 0.702 antigenic units (Fig.\u00a02a). Predictions were generally more accurate in more recent influenza seasons, likely due to the increased availability of data over time (Supplementary Fig.\u00a02a). Further experiments assessed the robustness of our model to variations in the training data. Prediction accuracy was retained even under conditions where there is substantially less antigenic data for training (Supplementary Fig.\u00a04a, b). Minimal effects on performance (compromised performance for a single season only) were observed when omitting HI titers data from an entire season (Supplementary Fig.\u00a04c).\n\na, b Model prediction and classification performance is shown in terms of (a) MAE and (b) AUROC respectively over 14 test seasons from 2014NH to 2020SH. The optimized model consisted of encoded genetic difference using the best-performing amino acid mutation matrix GIAG010101, optimized hyperparameters (see \u201cMethods\u201d), and all features in the metadata information (virus avidity, antiserum potency, and passage category (egg or cell) of virus isolates and antisera) (Supplementary Fig.\u00a03). The classification score AUROC was obtained by converting the measured and predicted NHTs to binary labels such that if NHT was greater than 2 units it was assigned a binary label 1, otherwise 0. The \u2018Average\u2019 cell in (a, b) indicates the score averaged over 14 test seasons from 2014NH to 2020SH. The darker color cells indicate better performance. Source data are provided as a Source Data file.\n\nThe ability of our model to detect antigenic variants was also examined. An influenza virus is considered antigenically distinct from the virus used to generate the antiserum if a more than 4-fold reduction in HI titers is observed against the antiserum4,21. Our model classified antigenic variants and non-variants with an average area under the receiver operating characteristic (AUROC) of 92% across the 14 test seasons (Fig.\u00a02b). Additional metrics (e.g., sensitivity and specificity) further demonstrated classification accuracy (Supplementary Fig.\u00a05a). We have incorporated the model into a web application (https://huggingface.co/spaces/sawshah/SAP_H3N2) that reports predicted NHTs for user-specified H3N2 virus-antiserum pairs (see \u201cMethods\u201d).\n\nTo further calibrate model performance, we assessed alternative approaches. These included a linear method (NextFlu substitution model7), ML methods (random forest (RF)22 and extreme gradient boosting (XGBoost)23), and neural network methods (multi-layer perceptron (MLP)24 and residual neural network (ResNet)24). These models, along with their implementation details, are described in \u201cMethods\u201d. Among these alternative models, NextFlu is the most widely used model for antigenic prediction. It has been employed to predict NHTs under a non-seasonal framework, where the model was trained on data spanning all time periods and the predictions were made for randomly selected historical NHTs. When evaluated under the seasonal prediction framework (Fig.\u00a01a) over 14 test seasons (2014NH to 2020SH), the AdaBoost model achieved the best performance (MAE of 0.702), followed by the ML methods (MAE of 0.72 and 0.738 respectively for XGBoost and RF) and the NextFlu model (MAE of 0.819). The neural network methods achieved the worst performance (MAE of 0.964 and 0.986 respectively for ResNet and MLP) (Supplementary Fig.\u00a06).\n\nDisregarding the initial season of 2014, the MAE of our optimized model was well below average in two seasons: 2016NH and 2019NH (Fig.\u00a02a). This appears to be attributed to a larger antigenic drift observed in these seasons, which is evident from the presence of circulating isolates (red circles) that are widely dispersed from isolates of past seasons (gray points) (Fig.\u00a03a). Importantly, performance was recuperated in subsequent seasons and degradation was not carried forward (Fig.\u00a02a).\n\na Antigenic maps56 to visualize the antigenic drift in circulating isolates compared to isolates from the previous two recent seasons (see \u201cMethods\u201d). The maps on the left show two instances of large antigenic drift in the 2016NH (top-left) and 2019NH (bottom-left) seasons, while the maps on the right show two instances of small antigenic drift in the 2017SH (top-right) and 2020SH (bottom-right) seasons. Each square in a grid indicates the antigenic difference of two units, corresponding to a four-fold dilution of the antibody in the HI assay. Large antigenic drift is indicated by the presence of circulating isolates (red circles) dispersed far from past isolates (gray points). b The MAE performance of the model was evaluated over 14 test seasons, ranging from 2014NH to 2020SH. The top panel displays the MAE performance of the model trained on data from 2003NH up to the corresponding test season. The bottom panel shows the MAE performance of the model when data of randomly selected 10% circulating isolates was included in model training. For each test season, average scores of 50 Monte Carlo runs are reported. Source data are provided as a Source Data file.\n\nWhile significant antigenic drift makes prediction more challenging, access to partial antigenic data for circulating virus isolates in a season may help overcome this challenge. Further analysis confirmed this hypothesis. For each test season, including as little as 10% of the antigenic data for circulating isolates in the model training improved performance uniformly, with the most significant gains observed in those seasons with large antigenic drift (Fig.\u00a03b and Supplementary Fig.\u00a07). Access to a small amount of antigenic data can therefore help ensure high prediction accuracy irrespective of the level of drift experienced by IAV H3N2.\n\nAnalysis of historical data has demonstrated that the antigenic evolution of influenza is strongly influenced by mutations at a subset of sites within HA17,25,26. Our model enables the identification of the specific HA1 sites that have the greatest effect on antigenic changes during a given season, providing insights into the seasonal dynamics of these sites. Such sites can be predicted based on their feature importance22 scores from the model (\u201cMethods\u201d). Aggregating the top 20 sites identified for seasons 2014NH to 2020SH revealed 30 important sites in total (Fig.\u00a04a). Of these, 25 were located within established HA1 epitopes25,27,28 (A, B, C, D, and E). Substitutions in these epitope regions are known to have a dominant effect on the antigenic evolution of IAV H3N22,25. Epitopes A and B were statistically significantly enriched among the identified 30 sites (\\({{{{{\\rm{P}}}}}} \\, < \\, 0.05\\)), which supports previous findings that epitopes A and B are the most immunodominant26,28.\n\na Majority of the 30 important sites identified by the model based on the feature importance scores lie in known IAV H3N2 epitopes. The sites are color-coded according to epitopes. The sites that do not lie in any known epitope are referred to as unknown. P value indicates the one-sided statistical significance of epitope enrichment within the identified important sites (see \u201cMethods\u201d). b The AdaBoost-based feature importance scores for the 30 important sites are analyzed across subsets of training data from 2003NH to x (x ranges from 2014NH to 2020SH), with the top 20 sites based on the feature importance scores listed for each subset. The darker color cells indicate a higher importance score of a site. c Change in the set of important sites, color-coded by epitopes, across two seasons (2014NH and 2020SH) is displayed over the HA structure (Protein Data Bank ID: [6AOU]; A/Brisbane/10/2007). The sites in epitopes A and D are labeled in the top-view (left panel) while the sites in epitopes C, E, and the unknown region are labeled in the front-view (right panel). For epitope B, sites 158, 159, 189, and 196 are labeled in the top-view (left panel) and sites 186, 193, 194, and 197 are labeled in the front-view (right panel). HA1 subunit is shown in white and the HA2 subunit is colored gray. Source data and exact P-values are provided as a Source Data file.\n\nSeasonal analysis (Fig.\u00a04b) revealed nine sites within epitopes that were consistently ranked in the top 20 sites over all 14 seasons from 2014NH to 2020SH. These comprised three sites in epitope A (140, 144, 145), three in B (158, 186, 189), and three in D (173, 208, 213). The relative importance of epitopes A and B persisted across all seasons, though epitope A appears to have become more important more recently (Fig.\u00a04b, c). Outside epitopes A and B, the importance of epitope D was also reasonably stable across all seasons and this epitope was relatively more important than epitopes C and E (Fig.\u00a04b, c). In earlier seasons (2014NH and 2014SH), site 189 of epitope B was predicted to be the most important antigenic site by our model. This site was previously identified experimentally to be responsible for two antigenic cluster transitions26 (EN72 to VI75 and BK79 to SI87). This site was also predicted to be the most important antigenic site by NextFlu7 on a dataset from 2005\u20132016. In recent seasons (2019SH to 2020SH), our model predicts site 159 of epitope B to be the most important antigenic site. The genetic analysis by Crick WIC12 showed that most of the viruses circulating in these seasons belong to clade 3C.2a1b.2a.2, where one of its characteristic substitutions includes Y159N resulting in loss of glycosylation that affects recognition of epitopes by antibodies29.\n\nOf the 30 sites identified across seasons as being most important, five did not belong to any known epitope (Fig.\u00a04a). Among these, four sites are in close proximity (with distance between carbon-alpha atoms <8\u2009\u00c5) to the known epitopes: sites 223 and 241 are located close to epitope D, site 269 is located close to epitope E, while site 225 is close to both epitopes A and D (Fig.\u00a04c). Two of these sites, 183 and 225, are part of the functionally important receptor binding sites (RBS)28. Site 225 was consistently ranked in the top 20 important sites across all seasons considered (Fig.\u00a04b). Mutations at site 225 can alter the fitness landscape of epitope B30, and a mutation at this site was linked to egg-passaging adaptation in isolates circulating from 2019 to 202131.\n\nOverall, our model identified HA1 sites (predominantly within known epitopes but also some outside) that contributed significantly to the antigenic evolution of IAV H3N2 in the last decade and characterized the dynamics of these antigenic \u201cdrivers\u201d over time.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "We have presented a machine learning model (Fig.\u00a01b, c) that can accurately predict antigenic properties (in terms of NHTs) of IAV H3N2 isolates circulating in an influenza season using only their genetic sequence data and associated metadata. The model was trained and tested under a seasonal framework (Fig.\u00a01a), mimicking the periodic influenza surveillance process followed by WHO for annual vaccine strain selection (Supplementary Fig.\u00a01). The model remained robust under data-limited scenarios.\n\nComputational methods have been developed previously for antigenic characterization of IAV. These include the well-known antigenic cartography2 method, a multi-dimensional scaling approach that is helpful to visualize and study the relationship among virus isolates and antisera in two dimensions. Other sequence-based models have also been developed1,6, most of which considered a non-seasonal framework4,7,8,9,10,11, distinct from the seasonal framework (Fig.\u00a01a) adopted in this work. The non-seasonal framework disregards season/time information and randomly distributes HI titers (or virus isolates) in the multi-seasonal HI data among training and test datasets. Under this framework, the testing data may comprise isolates having antigenic changes that the model has already learned during training, which can lead to overfitting and inflate model performance. In addition to sequence data, information such as structural and physicochemical properties of HA have also been used for IAV antigenic prediction10,11.\n\nAntigenic changes in influenza HA have been shown to be nonlinearly related to genetic changes in recent experimental studies15,16. These studies demonstrated that epistatic interactions or specific HA backgrounds can affect the antigenicity of HA substitutions. Thus, linear or additive models that assume independent effects of HA substitutions on antigenicity might be suboptimal for capturing the genetic-to-antigenic relation for HA. By adopting a data-driven ML approach, tree-based models (including AdaBoost, as well as XGBoost and RF) capture nonlinearities in the mapping between genetic and antigenic changes. This is shown to yield improved performance when compared to a linear prediction model7 (Supplementary Fig.\u00a06). Moreover, improved performance is still observed even when the AdaBoost model parameters are matched to those of the linear model (Supplementary Fig.\u00a06). Capturing nonlinearities is however not the only factor which determines the performance, as highlighted by the inferior performance of the nonlinear NN models (MLP and ResNet). This discrepancy in the performance of NN models could be attributed to the tabular structure of the dataset used. Similar findings have been reported in the literature, indicating diminished performance of NN models when applied to tabular datasets32.\n\nPrevious studies7,11,33 have demonstrated the value of incorporating virus avidity and antiserum potency in the computational antigenic characterization of IAV H3N2. Our findings highlight the importance of using passage history categories of virus isolates and antisera (e.g., if ferret antisera were raised to cell-propagated or egg-propagated virus isolates), as additional metadata features in model development. Using passage categories alone leads to performance improvement similar to that of using virus avidity and antiserum potency, and we show that incorporating all of these features together leads to significantly improved model performance (Supplementary Fig.\u00a03a).\n\nThe model\u2019s predictive power is robust to variations in the training data (Supplementary Fig.\u00a04a, b). Omitting data from a complete season degrades model performance in the following two seasons, but not beyond that (Supplementary Fig.\u00a04c). This indicates that errors due to a lack of data in specific seasons are not retained in later seasons, and only affect the model\u2019s accuracy for a maximum of one or two test seasons. Additional tests showed that training with data from only the two most recent seasons performed similarly to training based on all historical seasons (Supplementary Fig.\u00a08). This is in line with the observed rate of antigenic drift of 1.2 units per year2 (equivalent to two seasons) for IAV H3N2, which infers that the antigenicity of H3N2 isolates would differ substantially beyond two seasons and thereby the corresponding data would likely contribute less to predicting antigenicity of the isolates in the current season.\n\nSome clades of H3N2, e.g., 3C.2a, failed to react in HI assays in the past as they had lost the ability to agglutinate red blood cells (RBC)34. To avoid such issues, HI assays are complemented with virus neutralization assays3. In comparison to HI assay data, neutralization assay-based antigenic data has been rarely used5 for developing computational models. This is because the HI assay is still considered the gold standard for characterizing IAV antigenicity, given its well-established protocols and high level of reproducibility and reliability, and in the last few years very few H3N2 viruses do not bind avian or mammalian RBC. Nonetheless, our model can be adapted to predict neutralization titers, a worthwhile problem to pursue in a future study.\n\nTo predict NHTs, we used genetic information from the HA1 subunit of the HA protein since it contains the key antibody binding sites (epitopes)1,2. Recent research has shown high rates of amino acid substitutions outside the HA1 epitope region as well as in the other influenza surface protein (neuraminidase, NA), possibly indicating positive selection by host immunity1. The proposed model could be augmented with genetic information of the HA2 subunit and NA protein for potentially improving prediction accuracy and for examining the role (and temporal dynamics) of HA2 and NA in driving further antigenic changes in IAV H3N2. We focused on IAV H3N2 due to the availability of rich HI titers data for this subtype, as compared to other human influenza viruses1,21. We have also adapted the proposed model for IAV H1N1 using a dataset33,35 spanning 18 influenza seasons from 2001NH to 2009SH. This data set lacked comprehensive passage information, and hence, passage metadata was excluded. We found that the H1N1-adapted model performed seasonal antigenic characterization with an average MAE of 0.747 over these 18 influenza seasons (Supplementary Fig.\u00a09). Our findings motivate the development of methods to track the antigenic evolution of other influenza subtypes, such as IAV (H1N1) pdm09 or influenza B viruses, provided sufficient antigenic data is available.\n\nSerological data is useful not only for guiding IAV vaccine strain selection during VCMs, but also for building computational models addressing general questions related to influenza evolution. These include models for identifying antigenic clusters2,36,37, and predicting relative growth of viral clades (genetically related isolates stemming from a common ancestor) and forecasting the clades that will likely proliferate in the next season5,38,39,40,41. The predictions produced by our model can augment experimentally available serological data and can, in turn, be incorporated into models of influenza evolution that use antigenic data.\n\nThe AdaBoost model designed for seasonal antigenic characterization has several limitations. First, like any ML model, its performance is contingent on the availability of training data (Supplementary Fig.\u00a02b). Given the limited training data available before 2012 (Supplementary Fig.\u00a02a), we evaluated the model\u2019s performance for seasons after 2012 only. Second, regardless of the amount of training data, the model\u2019s performance is reduced in seasons with large antigenic drift (Fig.\u00a03a). A small amount of antigenic information of circulating virus isolates (e.g., as low as 10% of the available data) for model training can help to largely overcome this issue (Fig.\u00a03b). Third, since the model uses amino acid sites as features, it can determine the importance of individual sites (Fig.\u00a04), but not specific amino acid substitutions. This may be addressed in future work by using amino acid substitutions as features for the AdaBoost model. Lastly, while the AdaBoost model can learn a nonlinear genotype-to-phenotype mapping and identify important sites individually, it cannot explicitly identify the collective effects of sites (i.e., epistasis) on antigenicity. Interpretable artificial intelligence techniques, such as Shapley Additive exPlanations (SHAP)42, may potentially be explored to study the effect of interactions between sites.\n\nSeasonal influenza poses a significant threat to global public health, with high mortality and morbidity rates. The virus\u2019s ability to evolve and evade population-level immunity developed from past infections and vaccinations underscores the importance of continued antigenic surveillance for controlling future influenza outbreaks. Noting that only a subset of circulating viruses is tested with HI assays due to practical constraints (e.g., animal availability, resources, cost), our approach could be used to provide normalized HI titers estimates for all sequenced circulating viruses in a given season. This would provide a more comprehensive picture of the antigenic landscape of viruses circulating in each season and could provide complementary input when making vaccine strain selection decisions. Furthermore, our approach can be applied to make rapid sequence-based predictions that suggest which subset of circulating viruses should be tested experimentally with HI assays in a given season. ML-based models, like the one proposed in this work, offer powerful tools for complementing existing antigenic characterization efforts, enabling comprehensive global influenza antigenicity monitoring, improved vaccine strain selection, and effective public health management.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "We obtained the antigenic HI titers data for IAV H3N2 from 35 biannual reports published during 2003 \u2013 2021 by the Worldwide Influenza Center (WIC) at the Francis Crick Institute, London12 (Supplementary data\u00a01). A total of 82,776 HI titers values against virus-antiserum pairs were extracted from these reports, where in each pair the virus represents the circulating/test virus isolate and the antiserum represents the reference virus isolate against which the post-infection ferret antiserum was raised. From these reports, we also extracted the metadata information of virus isolates including their names, passages, and collection dates. Based on the passage information, we labeled each virus isolate with either a cell or egg passage category. We used both the name and passage to represent a unique virus isolate. Invalid HI titers43 and HI titers of virus-antiserum pairs with passage categories other than egg or cell were removed. Following standard practices of the WHO2,7, we computed NHT-based antigenic differences for each virus-antiserum pair from the compiled HI titers values. NHT is defined as the difference of the 2-fold dilutions of the homologous and heterologous titers values as follows2,7\n\nwhere the homologous titers \\({T}_{b\\beta }\\) and the heterologous titers \\({T}_{a\\beta }\\) represent the reciprocal of the maximum dilution of antiserum \\(\\beta\\) that is required to inhibit cell agglutination by the reference virus isolate \\(b\\) and the test virus isolate \\(a\\), respectively. In case the homologous titers were unavailable, we used the maximum titers value available for that antiserum2. We removed the virus-antiserum pairs against which sequences were not found in the influenza genetic databases and used the remaining antigenic data for seasonal antigenic characterization. This included a total of 36,709 NHTs corresponding to 3737 virus isolates paired with 268 antisera.\n\nIn addition to NHT, Archetti Horsfall Titers (AHT)9,44 is also used to characterize antigenic differences between virus isolates. AHT measurement is a two-way analysis45 that requires four HI assays, and antiserum must be raised against each virus isolate in a pair. AHT is not used by WHO46 and thus was not considered in this work. We also note that HI assays are dependent on the agglutination of red blood cells. The source of these red blood cells has varied from chicken to turkey and then to guinea pig over the course of time, due to changes in receptor binding sites47 of IAV H3N2. While these variations are present in the dataset that we consider, the insensitivity of the model to these variations shows that they are likely taken care of by the model parameters of virus avidities and antiserum potencies7.\n\nFor the virus isolates and antisera in this data, we downloaded the corresponding HA protein sequences from the GISAID13 and the IVR14 databases. We aligned the HA protein sequences using MAFFT48 with the full-length HA protein (566 amino acids) of A/Beijing/32/1992 (isolate ID: AAA87553)7 as a reference. We restricted our model to the HA1 subunit (amino acid sites 17\u2013345) of the HA protein, as this subunit forms the globular head of the HA protein containing key epitopes known to be important for antigenicity of IAV H3N21,2.\n\nTo provide inputs in numeric form to the AdaBoost17 model, we encoded the genetic sequences of virus isolates using the amino acid mutation matrices in the AAindex2 database19 (Fig.\u00a01c). As alternatives, we also explored binary and one-hot encoding methods for encoding the genetic sequences of virus isolates (see below for details). The metadata information of virus isolates was represented using the standard one-hot encoding (Fig.\u00a01c). The encoded genetic and metadata information was used as input features of the AdaBoost model.\n\nThe AAindex2 database contains 94 20\u2009\u00d7\u200920 amino acid mutation matrices, where each numeric entry of a matrix describes the rate at which an amino acid in a protein sequence is replaced by another amino acid over evolutionary time. These numerical values are based on the physiochemical and biochemical properties of pairs of amino acids. Of these 94 matrices, two matrices (MEHP950101 and MEHP950103) were discarded for being incomplete as they included gaps in their entries. Thus, the remaining 92 matrices were investigated for encoding the genetic information of isolates. Specifically, for each virus-antiserum pair, we computed genetic difference from a reference (antiserum) to test virus isolate by encoding the amino acid mutations at each site of their HA1 protein sequences using the numeric entry of the corresponding amino acid pairs in mutation matrices as described in ref. 44. Briefly, for a specific mutation matrix \\({{{{{\\rm{M}}}}}}\\), the encoded genetic difference \\(g\\) between the sequences of a virus \\(v\\) and an antiserum \\(a\\) at HA1 position \\(i\\) is given by:\n\nwhere \\({v}_{i}\\) and \\({a}_{i}\\) are respectively the amino acids at position \\(i\\) in the virus and antiserum sequence, and \\({m}_{j,k}\\) is the entry of the matrix \\({{{{{\\rm{M}}}}}}\\) corresponding to amino acids \\(j\\) and \\(k\\).\n\nIn the binary encoding scheme, for each virus-antiserum pair, the amino acid differences at each HA1 site were encoded as \u20181\u2019 and otherwise \u20180\u2019. Any ambiguous amino acid or gap in the protein sequences was also encoded as zero to avoid mapping ambiguous genetic information to antigenicity. For each virus-antiserum pair, the binary encoded genetic difference was represented by a binary vector of length 329 corresponding to the length of the HA1 protein sequence.\n\nIn the one-hot encoding scheme, for each virus-antiserum pair, at each HA1 site, the amino acids in the two sequences were initially represented as binary vectors of length 20 (corresponding to 20 valid amino acids), as per standard one-hot encoding. Subsequently, a logical OR operation was applied between these two vectors to encode the amino acid differences. Consequently, at each HA1 site, distinct amino acids in a virus-antiserum pair are encoded as a binary vector of length 20 with a pair of ones, each representing a one-hot encoded amino acid. For the alternative case in which the amino acids are the same at a given site, these are encoded into a binary vector of length 20 with a single one, preserving the amino acid information. With this one-hot encoding strategy together with logical OR combining, the genetic difference for each virus-antiserum pair produces an encoded binary vector of length 20\u2009\u00d7\u2009329.\n\nEach metadata information of isolates\u2014including their virus avidities, antiserum potencies, and passage categories\u2014was considered as categorical data and converted to numeric data using one-hot encoding scheme in Scikit-learn49. The encoded vector corresponding to each virus-antiserum pair represents virus avidity, antiserum potency, and passage categories of virus and antiserum. The virus avidity of an isolate is represented by a binary sparse vector of length equal to the number of unique virus isolates in the training dataset, wherein all entries are \u20180\u2019 except a \u20181\u2019 at the position of that isolate in an array of all the virus isolates sorted by their collection dates, names, and then passages. A similar procedure was followed to represent the antiserum potencies corresponding to antisera. For instance, if the training dataset contains 100 unique virus isolates and 10 unique antisera and considering the two passage categories (cell/egg) for isolates corresponding to both virus and antiserum, the one-hot encoding corresponding to each virus-antiserum pair will result in a binary vector of length 100\u2009+\u200910\u2009+\u20092\u2009+\u20092\u2009=\u2009114. Hence, the one-hot encoding scheme resulted in a sparse binary vector of length equal to the number of categories in each metadata information for the corresponding virus-antiserum pairs in the training dataset. It is worth noting that when predicting the antigenicity of a circulating virus isolate against an antiserum, the virus avidity is represented by a zero vector. This is because the virus itself is not available during the model\u2019s training process under the seasonal framework.\n\nThe compiled dataset consisted of 37 influenza seasons from 2003NH to 2021NH. For each test season \\(s\\), the training dataset includes the NHTs corresponding to past virus-antiserum pairs starting from the earliest season 2003NH to the most recent season \\(s-1\\), while the test dataset includes NHTs of the isolates circulating in the test season \\(s\\) paired with the past antisera. The training and test data for each season are described in Supplementary Fig.\u00a02a.\n\nWe selected four seasons from 2012NH to 2013SH as validation seasons, which were used for model optimization. The next 14 seasons from 2014NH to 2020SH were selected as test seasons for model evaluation. This selection was based on the stable performance of a baseline model (explained in the next section) over these seasons. Note that virus-antiserum pairs available for the 2021NH season were very limited (Supplementary Fig.\u00a02a). Thus, this season was excluded from the analysis to allow reliable model evaluation. Unlike prior works21,46,50,51 that used the entire dataset (including test seasons) for model optimization, our model was optimized solely using the data of past seasons to prevent data leakage issues52 that could inflate model performance46.\n\nIn the designed AdaBoost17,18 model, the encoded genetic difference at each site of the HA1 protein sequences was treated as an input feature that nonlinearly contributes toward the computation of the NHT. The remaining features of the AdaBoost model consist of binary identifiers for the virus and antiserum related metadata information, including virus avidity, antiserum potency, and their passage categories (Fig.\u00a01c). The designed AdaBoost model is an ensemble of sequentially trained decision trees employing a boosting technique, where each subsequent decision tree seeks to rectify errors present in the preceding decision tree by assigning more weight to training data samples with large errors. At each splitting node of a decision tree, the candidate set of features is a random subset of the features (including encoded genetic differences at each site of the HA1 protein sequences and one-hot encoded metadata information). The predicted NHT by the AdaBoost model is the sum of the weighted predicted NHTs by an ensemble of decision trees.\n\nThe baseline model was based on the AdaBoost model with default hyperparameters within the module AdaBoostRegressor in Scikit-learn49, which consists of the following hyperparameters: n_estimators\u2009=\u200950, learning_rate\u2009=\u20091.0, loss\u2009=\u2009linear, base_estimator\u2009=\u2009decision tree with a hyperparameter of max_depth\u2009=\u20093. We used a random seed equal to 100 for reproducibility. No metadata information was provided to this model and a binary encoding scheme was used. Hence, in this case, NHTs were predicted based on only the binary encoded genetic difference at each site of the HA1 protein sequences of the virus-antiserum pair.\n\nTo optimize the AdaBoost model, we performed hyperparameter optimization independently for each of the 92 amino acid mutation matrices as well as for binary and one-hot encoding. We considered two hyperparameters in the module AdaBoostRegressor in Scikit-learn49 with each hyperparameter optimized over a search space defined as follows: n_estimators49\u2014ranging from 10 to 1000 in steps of 10; and learning_rate49 ranging from 0.1 to 1.5. We set the estimator hyperparameter of the AdaBoostRegressor to DecisionTreeRegressor with its two hyperparameters optimized over a search space defined as follows: max_depth49\u2014ranging from 1 to 10000 in steps of 10; and max_features49\u2014 ranging from 0.1 to 1. The values of hyperparameter learning_rate49 and max_features49 were sampled from a uniform distribution, while the rest of the hyperparameters were sampled from a quantized uniform distribution53. Bayesian optimization procedure termed as the Tree of Parzen Estimator (TPE)53 under module hyperopt53 was used to automate the process of hyperparameter optimization over 100 runs on the defined search space. The hyperparameter optimization of the AdaBoost model (with binary encoded genetic data and including all metadata features) significantly improved its performance from MAE of 1.091 to 0.759 (Supplementary Fig.\u00a03b). Further, depending on the choice of the mutation matrix the MAE varied between 0.835 to 0.750 (Supplementary Fig.\u00a03c). This performance variation occurs because each mutation matrix incorporates specific amino acid attributes. The optimized AdaBoost model consisted of genetic difference encoded using the best-performing amino acid mutation matrix, GIAG01010119, and the hyperparameters were set as follows: n_estimators\u2009=\u2009230, learning_rate\u2009=\u20091.393, max_depth\u2009=\u20091860, and max_features\u2009=\u20090.394. To ensure reproducibility, we maintained a fixed random state of 100 for each Python package across all simulations.\n\nTo assess the performance of the developed model in a particular season, we computed the MAE between the measured \\(d\\) and predicted \\(\\hat{d}\\) NHTs as\n\nHere, \\(S\\) denotes the set of virus-antiserum pairs \\((i,j)\\) in a season and \\(\\#(S)\\) represents the cardinality of the set \\(S\\).\n\nTo compute the average performance of the model over \\(N\\) test seasons, we used the weighted average of the \\({{{{{\\rm{MA}}}}}}{{{{{{\\rm{E}}}}}}}_{{S}_{n}}\\) obtained for the season \\(n\\), with the weights equal to the cardinality of the dataset in the season \\(n\\). This is given by\n\n\\({{{\\mbox{Average}}}}\\,{{{\\mbox{MAE}}}}=\\frac{{\\sum }_{n=1}^{N}\\#({S}_{n}){{{\\mbox{MA}}}}{{{\\mbox{E}}}}_{{S}_{n}}}{{\\sum }_{n=1}^{N}\\#{ (S_n)}}\\)\n\nTo compute the classification scores, the NHTs were converted to binary labels using a threshold of 2 antigenic units4,21 (equivalent to 4-folds change in HI titers). Thus, a virus-antiserum pair was classified as either antigenic variant (NHT\u2009>\u20092) and assigned a binary label \u20181\u2019 or antigenically similar (NHT\u2009\u2264\u20092) and assigned a binary label \u20180\u2019. The ability of the model to classify antigenic variants was then determined using standard classification metrics including accuracy, sensitivity, specificity, MCC, and AUROC. Similar to MAE, the classification performance of the model across seasons was computed using a weighted average. Note that the classification threshold can be chosen to improve the classification performance for either antigenic variants or antigenically similar virus-antiserum pairs, considering the target problem. For example, in the scenarios when both sensitivity and specificity are equally important, the threshold can be optimized to maximize Youden\u2019s index (sensitivity\u2009+\u2009specificity\u2009\u2013\u20091) averaged over the most recent three seasons for a given test season (Supplementary Fig.\u00a05b).\n\nTo benchmark the performance of the AdaBoost model for H3N2 antigenic characterization, we compared it with alternate methods. These included a linear method (NextFlu substitution model7), two ML methods (RF22 and XGBoost23), and two neural network methods (MLP24 and ResNet24). The implementation details of these methods are provided below.\n\nLinear prediction model (NextFlu): The NextFlu substitution model, a well-known linear model for antigenic prediction, was employed to benchmark our model\u2019s ability to capture nonlinearities in the genetic-to-antigenic mapping. In the original work7, this model was evaluated under a non-seasonal framework. We adapted its implementation (available at https://github.com/nextstrain/augur/blob/master/augur/titer_model.py) to fit our seasonal framework (Fig.\u00a01a) and input data format. Our adapted version, like the original, did not incorporate passage information, and modeled normalized HI titers (NHT) as a linear combination of genetic difference, virus avidity, and antiserum potency.\n\nMachine learning and neural network models: For the RF model, we used the module RandomForestRegressor in Scikit-learn49. For the XGBoost model, we used module XGBRegressor in XGBoost23. For the MLP and ResNet models, we used TensorFlow54 to implement the architectures used in ref. 24., where the MLP architecture is defined as\n\nand the ResNet architecture is defined as\n\nwhere, \\({{{{{\\bf{x}}}}}}=[{x}_{1}\\ldots {x}_{j}]\\) is a feature vector of \\(j\\) features corresponding to a single data sample. Here, \u2018Linear\u2019 indicates a fully connected neural network layer, \u2018Dropout\u2019 layer is used for regularization to reduce overfitting that makes the input equal to zero of a few nodes/neurons in a layer, \u2018BatchNorm\u2019 layer normalizes the outputs such that the mean is close to zero and the standard deviation is close to one, and rectified linear unit \u2018ReLU\u2019 and \u2018LeakyReLU\u2019 indicate nonlinear activation functions defined as\ufeff\n\nFor a fair performance comparison against the AdaBoost model, we optimized the hyperparameters of these models to minimize the average MAE over four validation seasons (2012NH to 2013SH). For optimization of the RF model, we used 92 mutation matrices in the AAindex219 database as well as binary encoding. We found that the selection of mutation matrices had a relatively minor effect on the performance of the model (Supplementary Figs.\u00a03c, 10). Based on this observation, we used nine mutation matrices (obtained by combining the set of top five mutation matrices for AdaBoost (Supplementary Fig.\u00a03c) and RF models (Supplementary Fig.\u00a010) for optimizing the remaining models (XGBoost, MLP, and ResNet). As for the AdaBoost model, a Tree of Parzen Estimator (TPE)-based Bayesian optimization procedure53 under module hyperopt53 was used to automate the process of hyperparameter optimization over 100 runs for the RF and XGBoost models, and the same procedure under module optuna55 was used over 50 runs for the MLP and ResNet models on their defined search space of hyperparameters (Supplementary Table\u00a01). The optimized models include optimal values of their hyperparameters (Supplementary Table\u00a01) for the top-performing mutation matrix (AZAE970101 for RF, GIAG010101 for XGBoost, WEIL970102 for MLP, and MUET010101 for ResNet) and their performance was then evaluated over 14 test seasons (Supplementary Fig.\u00a06).\n\nTo observe antigenic drift of IAV H3N2 isolates across seasons (Fig.\u00a03a), we performed antigenic cartography of these isolates using R\u2019s (version 4.2.0) Racmacs package56 (version 1.1.35). Racmacs uses the multidimensional scaling procedure, proposed in ref. 2., to position virus isolates and antisera on a lower-dimensional space (2D in our case) based on their HI titers. The 2D coordinates of virus isolates were obtained using default settings of Racmacs with 1000 optimizations and setting parameter minimum_column_basis to \u2018none\u2019.\n\nIn the AdaBoost model, the feature importance scores depend on the base estimator, which is a decision tree in the proposed model. First, the importance of a feature in each decision tree is determined by how much that feature contributes to increasing leaf purity through variance reduction22. The importance scores from each tree are subjected to a weighted average calculation and normalized to a sum of one. The relatively high scores indicate more important features. We computed feature importance scores for all HA1 sites in the proposed AdaBoost model using the built-in function feature_importances_ in Scikit-learn49. To compute these scores, the AdaBoost model was trained on subsets of training data from 2003NH to \\(x\\) (\\(x\\) ranges from 2014NH to 2020SH). For each subset, out of the 329 HA1 sites, we selected the top 20 sites corresponding to the highest feature importance scores.\n\nEpitope enrichment in the 30 important sites, identified using feature importance scores across seasons (Fig.\u00a04), was calculated using a \\({{{{{\\rm{P}}}}}}\\) value. It represents the probability of observing at least \\(i\\) sites out of \\(j\\) epitope sites in the set of important sites, where the set of important sites comprises 30 sites out of a total of 329 HA1 sites. Mathematically, this can be written as\n\nThe null hypothesis that \\(i\\) epitope sites were observed in the 30 important sites by a random chance was rejected if \\({{{{{\\rm{P}}}}}} \\, < \\, 0.05\\).\n\nWe used Pymol (www.pymol.org) for representing the identified important sites over the three-dimensional HA structure of IAV H3N2 A/Brisbane/10/2007 (available in the Protein Data Bank; PDB ID: [6AOU]). To calculate the distance between an epitope and an identified important site that did not lie in any known epitope, we measured the 3D distance between the carbon-alpha of each epitope site and that of the identified site. The identified site was considered close to the epitope if the calculated distance was less than eight Angstroms for at least one of the epitope\u2019s sites.\n\nUsing streamlit (https://streamlit.io), we have developed a web application that provides an easy-to-use GUI for applying our model to perform seasonal antigenic prediction for IAV H3N2. With this web application, users can directly input full-length (329 amino acids) HA1 sequences of test virus-antiserum pairs and corresponding (optional) metadata information, or they may choose to upload the same data for multiple virus-antiserum pairs using a CSV file. Based on the season of the virus isolates being tested, the web application allows the user to select the appropriate model trained up to (but excluding) the test season.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The antigenic HI titers data for IAV H3N2 were obtained from biannual reports published by the Worldwide Influenza Center at the Francis Crick Institute, London12. The antigenic HI titers data for IAV H1N1 were obtained from the published dataset35. The corresponding HA protein sequences for IAV H3N2 and H1N1 were downloaded from the GISAID13 and the IVR14 databases. Supplementary Data\u00a01 provides information on the virus-antiserum pairs of IAV H3N2 used in this analysis. It identifies the specific HI data from the Crick WIC reports12 and the HA protein sequence data from the GISAID13 and the IVR14 databases. The three-dimensional HA structure of IAV H3N2 A/Brisbane/10/2007 (PDB ID: [6AOU]) used in this analysis was obtained from the Protein Data Bank (https://www.rcsb.org). The amino acid mutation matrices were obtained from AAindex19 database. All data used in this work is publicly available as of the date of publication. Source data are provided in this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "Source codes implementing the proposed AdaBoost model, and the results presented in this paper can be accessed from GitHub (https://github.com/saws-lab/SAP_H3N2_ML)57. The web server running the web application for seasonal antigenic prediction of IAV H3N2 using our proposed AdaBoost model can be accessed from Hugging Face Spaces (https://huggingface.co/spaces/sawshah/SAP_H3N2). All statistical analyses in this work were performed using Python 3.8.12.",
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+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "The authors thank all of the World Health Organization National Influenza Centers, comprising the WHO Global Influenza Surveillance and Response System (GISRS), for their continuous monitoring of influenza strains around the world. We especially acknowledge the Worldwide Influenza Center at the Francis Crick Institute, London, for sharing influenza antigenic data through reports on their webpage12, without which this research would not have been possible. We acknowledge all researchers at the originating and submitting laboratories that sequenced influenza viruses and made them available in GISAID and IVR databases. S.A.W.S and D.P.P. were supported by the General Research Fund (16201620) of the Hong Kong Research Grants Council. L.L.M.P. was supported by the Theme-Based Research Scheme (T11-712/19-N) of the Hong Kong Research Grants Council. A.A.Q. and M.R.M. were supported by the Australian Research Council through the Discovery Project (DP230102850). M.R.M. is the recipient of an Australian Research Council Future Fellowship (FT200100928) funded by the Australian Government.",
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+        },
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+            "section_name": "Author information",
+            "section_text": "Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China\n\nSyed Awais W. Shah,\u00a0Daniel P. Palomar\u00a0&\u00a0Ahmed Abdul Quadeer\n\nDepartment of Industrial Engineering & Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China\n\nDaniel P. Palomar\n\nWHO Collaborating Centre for Reference and Research on Influenza, Melbourne, Victoria, Australia\n\nIan Barr\n\nDepartment of Microbiology and Immunology, University of Melbourne, at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia\n\nIan Barr\u00a0&\u00a0Matthew R. McKay\n\nSchool of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China\n\nLeo L. M. Poon\n\nCentre for Immunology & Infection, Hong Kong SAR, China\n\nLeo L. M. Poon\n\nDepartment of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia\n\nAhmed Abdul Quadeer\u00a0&\u00a0Matthew R. McKay\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA.A.Q. and M.R.M. conceived and designed the research. S.A.W.S. curated the datasets and developed the models. All authors contributed to data analysis and data visualization. L.L.M.P. and I.B. provided ideas related to influenza evolution, and D.P.P. provided insights on modeling. A.A.Q. and M.R.M. were responsible for project supervision. S.A.W.S. wrote the original manuscript, and D.P.P., I.B., L.L.M.P., A.A.Q., and M.R.M. reviewed and edited it. All authors discussed and approved the manuscript.\n\nCorrespondence to\n                Ahmed Abdul Quadeer or Matthew R. McKay.",
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+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks the anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
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+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions",
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+            "section_name": "About this article",
+            "section_text": "Shah, S.A.W., Palomar, D.P., Barr, I. et al. Seasonal antigenic prediction of influenza A H3N2 using machine learning.\n                    Nat Commun 15, 3833 (2024). https://doi.org/10.1038/s41467-024-47862-9\n\nDownload citation\n\nReceived: 22 May 2023\n\nAccepted: 10 April 2024\n\nPublished: 07 May 2024\n\nVersion of record: 07 May 2024\n\nDOI: https://doi.org/10.1038/s41467-024-47862-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "BMC Infectious Diseases (2025)\n\nnpj Viruses (2024)",
+            "section_image": []
+        }
+    ]
+}

0c8e96cefef7de3e586d6f95c286e441bad381dad34d27b920d415c13c90e794/metadata.json

@@ -0,0 +1,146 @@
+{
+    "title": "Rare genetic associations with human lifespan in UK Biobank are enriched for oncogenic genes",
+    "pre_title": "Genetic associations with human longevity are enriched for oncogenic genes",
+    "journal": "Nature Communications",
+    "published": "28 February 2025",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57315-6/MediaObjects/41467_2025_57315_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57315-6/MediaObjects/41467_2025_57315_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Supplementary Data 1",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57315-6/MediaObjects/41467_2025_57315_MOESM3_ESM.xlsx"
+        },
+        {
+            "label": "Supplementary Data 2",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57315-6/MediaObjects/41467_2025_57315_MOESM4_ESM.xlsx"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57315-6/MediaObjects/41467_2025_57315_MOESM5_ESM.pdf"
+        },
+        {
+            "label": "Transparent Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-57315-6/MediaObjects/41467_2025_57315_MOESM6_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [
+        "https://www.ukbiobank.ac.uk/enable-your-research/register"
+    ],
+    "code": [
+        "https://github.com/Junkkkk/Lifespan-studies",
+        "https://github.com/MRCIEU/PHESANT"
+    ],
+    "subject": [
+        "Ageing",
+        "Genome-wide association studies",
+        "Oncogenes"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4837717/v1.pdf?c=1740834342000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4837717/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-025-57315-6.pdf",
+    "preprint_posted": "27 Aug, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Human lifespan is shaped by both genetic and environmental exposures and their interaction. To enable precision health, it is essential to understand how genetic variants contribute to earlier death or prolonged survival. In this study, we tested the association of common genetic variants and the burden of rare non-synonymous variants in a survival analysis, using age-at-death (N = 35,551, median [min, max] = 72.4 [40.9, 85.2]), and last-known-age (N = 358,282, median [min, max] = 71.9 [52.6, 88.7]), in European ancestry participants of the UK Biobank. The associations we identified seemed predominantly driven by cancer, likely due to the age range of the cohort. Common variant analysis highlighted three longevity-associated loci: APOE, ZSCAN23, and MUC5B. We identified six genes whose burden of loss-of-function variants is significantly associated with reduced lifespan: TET2, ATM, BRCA2, CKMT1B, BRCA1 and ASXL1. Additionally, in eight genes, the burden of pathogenic missense variants was associated with reduced lifespan: DNMT3A, SF3B1, CHL1, TET2, PTEN, SOX21, TP53 and SRSF2. Most of these genes have previously been linked to oncogenic-related pathways and some are linked to and are known to harbor somatic variants that predispose to clonal hematopoiesis. A direction-agnostic (SKAT-O) approach additionally identified significant associations with C1orf52, TERT, IDH2, and RLIM, highlighting a link between telomerase function and longevity as well as identifying additional oncogenic genes.\r\nOur results emphasize the importance of understanding genetic factors driving the most prevalent causes of mortality at a population level, highlighting the potential of early genetic testing to identify germline and somatic variants increasing one\u2019s susceptibility to cancer and/or early death.Biological sciences/Genetics/Genetic association study/Genome-wide association studiesHealth sciences/Diseases/Cancer",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Human lifespan is shaped by genetic and environmental factors. To enable precision health, understanding how genetic variants influence mortality is essential. We conducted a survival analysis in European ancestry participants of the UK Biobank, using age-at-death (N=35,551) and last-known-age (N=358,282). The associations identified were predominantly driven by cancer. We found lifespan-associated loci (APOE, ZSCAN23) for common variants and six genes where burden of loss-of-function variants were linked to reduced lifespan (TET2, ATM, BRCA2, CKMT1B, BRCA1, ASXL1). Additionally, eight genes with pathogenic missense variants were associated with reduced lifespan (DNMT3A, SF3B1, TET2, PTEN, SOX21, TP53, SRSF2, RLIM). Many of these genes are involved in oncogenic pathways and clonal hematopoiesis. Our findings highlight the importance of understanding genetic factors driving the most prevalent causes of mortality at a population level, highlighting the potential of early genetic testing to identify germline and somatic variants increasing one\u2019s susceptibility to cancer and/or early death.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Human lifespan is a complex trait influenced by both genetic and environmental factors and their interactions1. According to previous studies, genetics accounts for less than 10%2 or up to 25% of the heritability of longevity3. Identifying the genetic variants that contribute to earlier death or prolonged survival can highlight key biological pathways linked to lifespan and inform genetic testing for general health and screening and enabling precision health. Previous genome-wide association studies (GWAS) have identified over 20 associated loci including APOE4,5, CHRNA3/56, HLA-DQA1 and LPA7. Recently, a burden analysis of protein-truncating variants from whole-exome sequencing (WES) data identified four additional genes (BRCA2, BRCA1, ATM, and TET2) linked to reduced lifespan8. However, most previous research on lifespan genetics has predominantly used proxy data, such as parents\u2019 age at death, due to a lack of proband lifespan data. While proxy-based GWAS have been useful to gain genomic insights into age-related diseases in cohorts primarily composed of middle-aged individuals, and show some consistency with associations related to lifespan8, they may fail to fully capture the genetic influences that directly impact individual lifespan, particularly CHIP-related somatic variants9. On the other hand, some studies have employed logistic regression models on cases of extreme longevity and younger controls10,11,12. This approach may offer new insights by focusing on exceptionally long-lived individuals, yet they can be limited and costly. Moreover, replication of borderline significant variants remains an issue due to varying case definitions across studies, with some defining cases as individuals who survive to ages beyond 90 or 100 years or using the 90th or 99th survival percentiles as the age cutoff.\n\nIn this study, we carried out a genetic analysis of direct mortality data in the UK Biobank (UKB), the genetic database with the largest number of reported deaths (35,551 subjects) and aged individuals (344,237 subjects over 60 years old). To assess the association of genetic variants with lifespan in a survival analysis, we performed GWAS of common variants imputed from microarray data as well as burden/sequence kernel association test-optimized (SKAT-O) association of rare non-synonymous variants from WES data.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Our GWAS assessed 10,104,569 common variants (minor allele frequency (MAF) \\(\\ge\\) 0.1%) using Martingale residuals on 393,833 individuals including 35,551 deceased subjects (mean age at death: 71.2 years) and 358,282 living subjects (mean current age: 70.7) from UKB (Supplementary Table\u00a01)13. Two loci reached genome-wide significance (GWS) (\\(p\\)\u2009<\u2009\\(5.0\\times {10}^{-8}\\)) on chromosomes 19 and 6 (Fig.\u00a01A). On chromosome 19, rs429358 was the lead variant at the APOE locus (\u03b2\u2009=\u20090.013, p\u2009=\u20093.9\u2009\u00d7\u200910\u221245), MAF\u2009=\u200915.6%). We tested whether the presence of APOE-\u03b54 was enriched in certain primary causes of death. Among the top four causes of death, each representing over 5% of total deaths (Fig.\u00a01B), only those due to \u201cDiseases of the circulatory system\u201d (Chi-square \\(p\\)\u2009=\u20091.6\u2009\u00d7\u200910\u221216) and \u201cDiseases of the nervous system\u201d (\\(p\\)\u2009=\u20091.1\u2009\u00d7\u200910\u221271) showed a significant enrichment in the proportion of \u03b54 carriers compared to the prevalence of \u03b54 carriers among all subjects (Fig.\u00a01C). On chromosome 6 locus overlapping ZSCAN23, the top genome-wide significant variant was rs6902687, located 2.2\u2009kb upstream of the transcription start site (TSS) (rs6902687_C: \u03b2\u2009=\u20090.004, p\u2009=\u20092.7\u2009\u00d7\u200910\u207b\u2078, MAF\u2009=\u200936.6%). This variant is in almost perfect linkage disequilibrium (R\u00b2\u2009>\u20090.99) with three other significant variants in this region, including rs13215804_G (located 4.2\u2009kb upstream of the TSS), rs111859903_G (located in an intron) and rs13190937_A (situated in the 5\u2019 untranslated region) (Fig.\u00a01D).\n\nA Manhattan plot. B The proportion of cause of death for the top 4 categories, each accounting for more than 5% of total deaths. C Association of causes of death with APOE-\u03b54 genotype. D LocusZoom and E colocalization plots at the ZSCAN23 locus, colocalized with ZSCAN23 eQTL in pancreatic tissue in GTEx. PP4 posterior probability of colocalization.\n\nTo explore a potential regulatory function for variants at the ZSCAN23 locus, we investigated whether the lead SNPs were expression quantitative trait loci (eQTLs) in the Genotype-Tissue Expression Project (GTEx) v8 database. rs13190937 was significantly associated with increased ZSCAN23 expression in pancreatic tissue and the GWAS on Martingale residuals signal colocalized with the ZSCAN23 expression quantitative trait loci (eQTL) (posterior probability of colocalization (PP4)\u2009=\u20090.934; Fig.\u00a01E). Phenome-wide association study analysis (PheWAS) using PheWeb14 based on UKB Neale v1 dataset shows that the main associations of rs13190937 are with celiac disease and intestinal malabsorption (\\(p\\)\u2009=\u20091.8\\(\\times {10}^{-57}\\), OR\u2009=\u20091.003) (Supplementary Fig.\u00a01).\n\nIn sex-stratified GWAS (180,970 males and 212,863 females), the APOE locus was again linked to reduced lifespan in both males and females (Supplementary Table\u00a01 and Supplementary Fig.\u00a02A, B). In males, a significant association with reduced lifespan was observed for rs577106756_A located in intron of PRKD3 on chromosome 2 (\u03b2\u2009=\u20090.09, p\u2009=\u20093.2\\(\\times {10}^{-8}\\), MAF\u2009=\u20090.1%). PheWAS analysis, based on the UKB Neale v1 dataset, revealed that rs577106756_A was associated with ICD10 code C10.9, Malignant neoplasm of oropharynx, unspecified, as the primary cause of death as the primary cause of death (\\(p\\)\u2009=\u20094.5\\(\\times {10}^{-8}\\), OR\u2009=\u20091.04), and self-reported \u201cStomach Cancer\u201d (\\(p\\)\u2009=\u20096.1\\(\\times {10}^{-7}\\), OR\u2009=\u20091.003) (Supplementary Fig.\u00a02C). Additionally, a borderline significant association with reduced lifespan was observed at rs35705950_T, located between MUC5AC and MUC5B on chromosome 11 (\u03b2\u2009=\u20090.01, p\u2009=\u20096.6\\(\\times {10}^{-8}\\), MAF\u2009=\u200911.2%) (Supplementary Fig.\u00a02D). This variant was notably linked to increased MUC5B expression in lung tissue with the GWAS on Martingale residuals signal colocalizing with a MUC5B eQTL (PP4\u2009=\u20090.99; Supplementary Fig.\u00a02E). PheWAS analysis showed associations with a diagnosis of pulmonary fibrosis (\\(p\\)\u2009=\u20094.4\u2009\u00d7\u200910\u221213), OR\u2009=\u20091.002), \u201cOther interstitial pulmonary diseases with fibrosis\u201d as the primary cause of death (\\(p\\)\u2009=\u20091.7\\(\\times {10}^{-5}\\), OR\u2009=\u20091.001), and paternal history of lung cancer (\\(p\\)\u2009=\u20092.1\\(\\times {10}^{-4}\\), OR\u2009=\u20091.004), but no association with maternal history of lung cancer (\\(p\\)\u2009=\u20090.07) (Supplementary Fig.\u00a02F). In female, a significant association with reduced lifespan was observed for rs547541271_T, located in the intron of CELF2 on chromosome 10 (\u03b2\u2009=\u20090.04, p\u2009=\u20093.1\\(\\times {10}^{-8}\\), MAF\u2009=\u20090.3%). PheWAS analysis indicated rs547541271_T was associated with a diagnosis of \u201cMyositis\u201d (\\(p\\)\u2009=\u20091.0\\(\\times {10}^{-6}\\), OR\u2009=\u20091.002), and self-reported Polycystic Ovaries/Polycystic Ovarian Syndrome (\\(p\\)\u2009=\u20092.5\\(\\times {10}^{-5}\\), OR\u2009=\u20091.003) (Supplementary Fig.\u00a02G).\n\nFurther validation of these significant variants was carried out using data from the FinnGen and LifeGen cohorts. Specifically, for common variants, we queried FinnGen (https://r11.finngen.fi/) and obtained summary statistics15 from the LifeGen consortium via the GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics) to assess their association in independent datasets. In FinnGen, rs13190937 was not significantly associated with the \u201cDeath\u201d phenotype (p\u2009=\u20090.2), while it was significantly associated with a decrease in \u201cParental age at death\u201d in the UKB and LifeGen consortium (\\(p\\)\u2009=\u20091.4\\(\\times {10}^{-4}\\), \u03b2\u2009=\u2009\u22120.015). Similarly, rs35705950 showed a significant association with increased death in FinnGen (\\(p\\)\u2009=\u20096.0\\(\\times {10}^{-3}\\), \u03b2\u2009=\u20090.034) and with decrease in \u201cParental age at death\u201d in the UKB and LifeGen consortium (\\(p\\)\u2009=\u20096.6\\(\\times {10}^{-3}\\), \u03b2\u2009=\u2009\u22120.023). However, neither rs577106756 nor rs547541271 showed a significant association with the \u201cDeath\u201d phenotype in FinnGen (p\u2009=\u20090.55 and 0.93, respectively) (Supplementary Table\u00a02).\n\nAmong 26,230,624 variants with MAF\u2009<\u20091%, a total of 1,830,070 variants (17,174 genes) were annotated as loss-of-function (LoF) or missense variants. We excluded 199 genes with fewer than 10 total variant carriers, resulting in 476,447 predicted LoF variants (15,908 genes with a median of 23 variants per gene), 751,523 missense variants predicted as damaging by AlphaMissense (15,212 genes with a median of 37 variants per gene), and 262,866 missense variants predicted as damaging by rare exome variant ensemble learner (REVEL) (9231 genes with a median of 15 variants per gene). Of variants classified by each, 23.4% of AlphaMissense and 66.8% of REVEL variants were also pathogenic by the other classifier. A list of SNPs list used for the gene-based analyses is provided in Supplementary Data\u00a01.\n\nWe identified six genes whose burden of LoF variants is significantly associated with reduced lifespan: TET2 (\\(p\\)\u2009=\u20092.6\\(\\times {10}^{-34}\\)), ATM (\\(p\\)\u2009=\u20096.4\\(\\times {10}^{-10}\\)), BRCA2 (\\(p\\)\u2009=\u20091.2\\(\\times {10}^{-33}\\)), CKMT1B (\\(p\\)\u2009=\u20094.3\\(\\times {10}^{-7}\\)), BRCA1 (\\(p\\)\u2009=\u20095.6\\(\\times {10}^{-12}\\)) and ASXL1 \\(\\left(\\right.p\\)\u2009=\u20091.3\u2009\u00d7\u200910\u221251) (Fig.\u00a02A and Table\u00a01). All of these but CKMT1B also showed gene-wide significance in a direction-agnostic (SKAT-O) approach (Supplementary Fig.\u00a03A). Additionally, in eight genes, the burden of missense variants predicted as pathogenic by AlphaMissense was associated with reduced lifespan: DNMT3A (\\(p\\)\u2009=\u20096.9\\(\\times {10}^{-12}\\)), SF3B1 (\\(p\\)\u2009=\u20091.9\\(\\times {10}^{-13}\\)), TET2 (\\(p\\)\u2009=\u20099.2\\(\\times {10}^{-8}\\)), PTEN (\\(p\\)\u2009=\u20091.6\\(\\times {10}^{-8}\\)), SOX21 (\\(p\\)\u2009=\u20092.2\\(\\times {10}^{-8}\\)), TP53 (p\u2009=\u20098.6\u2009\u00d7\u200910\u201317), SRSF2 (\\(p\\)\u2009=\u20091.8\u2009\u00d7\u200910\u221294) and RLIM (\\(p\\)\u2009=\u20096.0\\(\\times {10}^{-7}\\)) (Fig.\u00a02B). Lastly, three genes showed gene-wide significance for burden of missense variants predicted by REVEL: DNMT3A (\\(p\\)\u2009=\u20095.2\u2009\u00d7\u200910\u221211), PTEN (\\(p\\)\u2009=\u20091.2\\(\\times {10}^{-7}\\)), and TP53 (\\(p\\)\u2009=\u20092.2\u2009\u00d7\u200910\u22129) (Supplementary Fig.\u00a04 and Supplementary Table\u00a03). SKAT-O identified additional associations with pathogenic missense variants predicted by AlphaMissense in C1orf52 (\\(p\\)\u2009=\u20097.2\\(\\times {10}^{-8}\\)) and IDH2 (p\u2009=\u20095.4\u2009\u00d7\u200910\u221242) (Supplementary Fig.\u00a03B), and by REVEL in NMNAT2 (\\(p\\)\u2009=\u20096.7\\(\\times {10}^{-7}\\)) and TERT (\\(p\\)\u2009=\u20093.3\\(\\times {10}^{-10}\\)) (Supplementary Fig.\u00a03C and Supplementary Table\u00a03).\n\nRare variant burden association with lifespan, considering loss-of-functions (A) and AlphaMissense pathogenic variants (B). Genes highlighted in red represent those not previously identified as significant in ref. 8. A gene-wide significance threshold of \\(p\\)\u2009=\u20097.4\\(\\times {10}^{-7}\\) was applied.\n\nIn addition, we validated these findings within the UKB dataset using two approaches: an independent sample separate from our discovery data and a five-fold cross-validation (CV) within the discovery cohort. This independent validation included 73,281 subjects who were not categorized as having European ancestry based on genetic ethnic grouping. These participants were classified into five groups based on their self-reported ethnicity (Field: 21000): White (66.3%), Asian (14.4%), Black (9.9%), Other (5.7%), and Mixed (3.7%). Among the 21 novel genes identified in the discovery, four achieved significance under the Bonferroni correction threshold 1.1\\(\\times {10}^{-3}\\) (0.05/42) in this validation cohort: BRCA2 (\\(p\\)\u2009=\u20091.1\\(\\times {10}^{-3}\\), burden), ASXL1 (\\(p\\)\u2009=\u20091.2\\(\\times {10}^{-5}\\), burden; \\(p\\)\u2009=\u20096.7\\(\\times {10}^{-6}\\), SKAT-O) with LoF variants and (IDH2 \\(p\\)\u2009=\u20092.0\\(\\times {10}^{-7}\\), SKAT-O) and SRSF2 (\\(p\\)\u2009=\u20099.2\\(\\times {10}^{-9}\\), burden; \\(p\\)\u2009=\u20092.2\u2009\u00d7\u200910\u221210), SKAT-O) with pathogenic missense variants predicted by AlphaMissense (Supplementary Table\u00a04). To further validate our findings, we performed five-fold CV within the discovery dataset of 393,833 individuals, dividing it into five folds. For each fold, 80% of the data (315,066 individuals) was used for analysis. The results across folds were highly consistent. For example, TET2, BRCA2, BRCA1, ASXL1 (LoF), SF3B1, DNMT3A, IDH2, TP53, SRSF2 (AlphaMissense) and DNMT3A (REVEL) achieved gene-wide significance across all five folds. Except for CKMT1B (LoF), C1orf52, TET2, RLIM (AlphaMissense) and NMNAT2 (REVEL), all other genes that showed significance in the main analysis were significant in at least 3 out of 5 folds. This consistency across folds confirms the robustness of the associations identified in our study. Fold-specific results for each gene and variant category are provided in Supplementary Data\u00a02.\n\nFor sex-specific gene-based analysis, an additional four genes not identified in the whole-cohort analysis showed gene-wide significance in males by either burden or SKAT-O: CDKN1A and PTPRK (LoF); COA7 and TG (AlphaMissense) (Supplementary Figs.\u00a05A, 6A and Supplementary Table\u00a05). In females, we identified five additional genes associated with reduced lifespan: PORCN (AlphaMissense); UGT1A8, CBX3, IFITM10, and OLIG1 (REVEL) (Supplementary Figs.\u00a05B, 6B and Supplementary Table\u00a06).\n\nFor the 14 gene-wide significant genes in the burden analyses, we assessed the association of variant carrier status with lifespan using Cox proportional hazards regression. Carriers of LoF variants in six genes were associated with decreased survival compared to non-carriers: CKMT1B (HR\u2009=\u20093.9, \\(p\\)\u2009=\u20092.6\\(\\times {10}^{-6}\\)), ASXL1 (HR\u2009=\u20092.5, \\(p\\)\u2009=\u20096.2\u2009\u00d7\u200910\u221233) (Fig.\u00a03A), TET2 (HR\u2009=\u20092.3, \\(p\\)\u2009=\u20091.9\u2009\u00d7\u200910\u221222), ATM (HR\u2009=\u20091.7, \\(p\\)\u2009=\u20093.0\\(\\times {10}^{-10}\\)), BRCA2 (HR\u2009=\u20092.4, \\(p\\)\u2009=\u20099.7\u2009\u00d7\u200910\u221239), and BRCA1 (HR\u2009=\u20092.2, \\(p\\)\u2009=\u20091.3 \\(\\times {10}^{-12}\\)) (Supplementary Fig.\u00a07A). Similarly, carriers of AlphaMissense-predicted pathogenic variants exhibited significantly earlier mortality compared to non-carriers on the following genes: DNMT3A (HR\u2009=\u20091.5, \\(p\\)\u2009=\u20091.4\\(\\times {10}^{-9}\\)), SF3B1 (HR\u2009=\u20092.3, \\(p\\)\u2009=\u20094.4\\(\\times {10}^{-10}\\)), PTEN (HR\u2009=\u20094.0, \\(p\\)\u2009=\u20091.1\\(\\times {10}^{-9}\\)), SOX21 (HR\u2009=\u20091.9, \\(p\\)\u2009=\u20093.3\\(\\times {10}^{-8}\\)), TP53 (HR\u2009=\u20093.9, \\(p\\)\u2009=\u20091.9\\(\\times {10}^{-14}\\)), SRSF2 (HR\u2009=\u20095.8, \\(p\\)\u2009=\u20093.3\u2009\u00d7\u200910\u221261), RLIM (HR\u2009=\u20093.1, \\(p\\)\u2009=\u20092.9\\(\\times {10}^{-4}\\)) (Fig.\u00a03B) and TET2 (HR\u2009=\u20091.5, \\(p\\)\u2009=\u20098.4\\(\\times {10}^{-7}\\)) (Supplementary Fig.\u00a07B). Carriers of pathogenic variants predicted by REVEL showed similar trends: DNMT3A (HR\u2009=\u20091.6, \\(p\\)\u2009=\u20092.4\\(\\times {10}^{-8}\\)), PTEN (HR\u2009=\u20094.8, \\(p\\)\u2009=\u20091.0\\(\\times {10}^{-9}\\)), and TP53 (HR\u2009=\u20092.5, \\(p\\)\u2009=\u20091.5\\(\\times {10}^{-8}\\)) (Supplementary Fig.\u00a07C).\n\nSurvival curves comparing carriers and non-carriers of variants on genes with a significant burden of loss-of-function (A) and AlphaMissense pathogenic (B) variants. For each gene, the survival curve includes Cox regression hazard ratio (HR), p value, the number of carriers, and their proportion within the total sample. A gene-wide significance threshold of \\(p\\)\u2009=\u20097.4\\(\\times {10}^{-7}\\) was applied.\n\nTo explore the contribution of individual rare variants to mortality in each gene-wide significant gene in the burden and SKAT-O tests, we conducted Cox proportional hazards regression for each variant with a minor allele count (MAC) of three or more (Table\u00a02). In total, 587 variants including LoF, AlphaMissense, and REVEL variants were examined. After applying a Bonferroni correction for multiple testing, setting the significance threshold at 8.3\\(\\times {10}^{-5}\\) (0.05/599), we identified significant associations with reduced lifespan for four LoF variants: rs370735654 in TET2 (MAC\u2009=\u200917, HR\u2009=\u20097.9, \\(p\\)\u2009=\u20096.1\\(\\times {10}^{-10}\\)), rs587779834 in ATM (MAC\u2009=\u2009113, HR\u2009=\u20092.5, \\(p\\)\u2009=\u20093.1\\(\\times {10}^{-5}\\)), rs80359705 in BRCA2 (MAC\u2009=\u200913, HR\u2009=\u200911.4, \\(p\\)\u2009=\u20092.5\\(\\times {10}^{-9}\\)), and rs750318549 in ASXL1 (MAC\u2009=\u2009201, HR\u2009=\u20092.8, \\(p\\)\u2009=\u20093.5\\(\\times {10}^{-19}\\)). Additionally, significant associations with AlphaMissense variants were noted in seven genes, impacting lifespan: rs769009649 in C1orf52 (MAC\u2009=\u200962, HR\u2009=\u20093.3, \\(p\\)\u2009=\u20093.4\\(\\times {10}^{-7}\\)), rs147001633 in DNMT3A (MAC\u2009=\u2009269, HR\u2009=\u20091.8, \\(p\\)\u2009=\u20098.8\\(\\times {10}^{-6}\\)), rs377023736 in SF3B1 (MAC\u2009=\u200912, HR\u2009=\u20097.3, \\(p\\)\u2009=\u20092.5 \\(\\times {10}^{-9}\\)), rs121913502 in IDH2 (MAC\u2009=\u200945, HR\u2009=\u20096.8, \\(p\\)\u2009=\u20099.2\\(\\times {10}^{-25}\\)), rs11540652 in TP53 (MAC\u2009=\u20095, HR\u2009=\u200911.5, \\(p\\)\u2009=\u20092.3\\(\\times {10}^{-5}\\)), rs751713049 in SRSF2 (MAC\u2009=\u200951, HR\u2009=\u20096.7, \\(p\\)\u2009=\u20099.3\\(\\times {10}^{-31}\\)) and rs75871009 in RLIM (MAC\u2009=\u20096, HR\u2009=\u20096.2, \\(p\\)\u2009=\u20096.0\\(\\times {10}^{-7}\\)). For missense variants predicted by REVEL, rs201746612 in NMNAT2 (MAC\u2009=\u20095, HR\u2009=\u200911.0, \\(p\\)\u2009=\u20091.7\\(\\times {10}^{-6}\\)), rs1043358053 in TERT (MAC\u2009=\u20095, HR\u2009=\u200916.8, \\(p\\)\u2009=\u20091.7\\(\\times {10}^{-8}\\)), and rs11540652 in TP53 (MAC\u2009=\u20095, HR\u2009=\u200911.5, \\(p\\)\u2009=\u20092.3\\(\\times {10}^{-5}\\)) were significantly linked to reduced lifespan (Supplementary Table\u00a07).\n\nFor the nine novel genes identified in the burden test (CKMT1B, ASXL1, DNMT3A, SF3B1, PTEN, SOX21, TP53, SRSF2 and RLIM), we examined the burden of LoF or pathogenic missense variants through PheWASs across 1670 UKB phenotypes including disease occurrences derived from electronic health record, self-reported family history, and physical measures (Supplementary Fig.\u00a08). The burden of LoF variants in ASXL1 and AlphaMissense variants in DNMT3A, SF3B1, PTEN, TP53 and SRSF2 were strongly linked to an increased risk of leukemia: acute myeloid leukemia (ASXL1: Odds Ratio (OR)\u2009=\u20091.05; \\(p\\)\u2009=\u20098.6\\(\\times {10}^{-170}\\); DNMT3A: OR\u2009=\u20091.03, \\(p\\)\u2009=\u20092.1\\(\\times {10}^{-150}\\); SRSF2: OR\u2009=\u20091.3, \\(p\\)\u2009=\u20091.2\\(\\times {10}^{-195}\\); TP53: OR\u2009=\u20091.05, \\(p\\)\u2009=\u20094.7\\(\\times {10}^{-35}\\)), monocytic leukemia (DNMT3A: OR\u2009=\u20091.01, \\(p\\)\u2009=\u20092.5\u2009\u00d7\u200910\u22129), chronic lymphoid leukemia (SF3B1: OR\u2009=\u20091.07, \\(p\\)\u2009=\u20094.1\\(\\times {10}^{-68}\\)) and acute lymphoid leukemia (PTEN: OR\u2009=\u20091.01, \\(p\\)\u2009=\u20092.1\\(\\times {10}^{-14}\\)). Additionally, the burden of LoF in CKMT1B was associated with hypopharynx cancer (OR\u2009=\u20091.03, \\(p\\)\u2009=\u20093.9\\(\\times {10}^{-26}\\)), vertiginous syndromes (OR\u2009=\u20091.03, \\(p\\)\u2009=\u20093.0\\(\\times {10}^{-17}\\)) and salivary glands cancer (OR\u2009=\u20091.03; \\(p\\)\u2009=\u20093.2\\(\\times {10}^{-12}\\)). SOX21 burden was associated with increased acne (OR\u2009=\u20091.01, \\(p\\)\u2009=\u20096.9\\(\\times {10}^{-7}\\)) and spinocerebellar disease (OR\u2009=\u20091.01, \\(p\\)\u2009=\u20092.3\\(\\times {10}^{-6}\\)). Lastly, the burden of AlphaMissense variants in RLIM were associated with chromosomal anomalies and genetic disorders (OR\u2009=\u20091.02, \\(p\\)\u2009=\u20094.2\\(\\times {10}^{-16}\\)), other and unspecified congenital anomalies (OR\u2009=\u20091.02, \\(p\\)\u2009=\u20098.2\\(\\times {10}^{-11}\\)), malignant neoplasm of small intestine, including duodenum (OR\u2009=\u20091.02, \\(p\\)\u2009=\u20093.5\\(\\times {10}^{-6}\\)) and cancer of oropharynx (OR\u2009=\u20091.02, \\(p\\)\u2009=\u20091.3\\(\\times {10}^{-5}\\)).\n\nWe computed the variant allelic fraction (VAF) per carrier for each variant included in the analysis. Generally, germline variants have a mean VAF close to 50%, while somatic variants\u2019 mean VAF will be lower16. Thus, when an association is linked to clonal hematopoiesis of indeterminate potential (CHIP), we expect the distribution of VAF to be left-shifted compared to a normal distribution centered at VAF\u2009=\u200950%. Considering LoF variants, TET2 (mean VAF across variants [95% bootstrap confidence interval for the mean VAF]\u2009=\u20090.33 [0.31,0.34]) and ASXL1 (mean VAF\u2009=\u20090.32 [0.31,0.33]) burden test associations are supported by variants with a left-shifted VAF distribution (Supplementary Table\u00a06 and Supplementary Fig.\u00a09A). Similarly, considering pathogenic AlphaMissense variants, in DNMT3A (mean VAF\u2009=\u20090.24 [0.23\u20130.24]), TET2 (mean VAF\u2009=\u20090.36 [0.34,0.38]), TP53 (mean VAF\u2009=\u20090.28 [0.24,0.34]), SRSF2 (mean VAF\u2009=\u20090.30 [0.28,0.31]) and SF3B1 (mean VAF\u2009=\u20090.31 [0.26,0.37]) are also left-shifted and the observed associations may be linked to CHIP (Supplementary Table\u00a08 and Supplementary Fig.\u00a09B).",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "In this study, we report several known and novel findings related to genetic risks associated with lifespan analyzing 393,833 European participants from the UKB. In the common variant GWAS, five independent loci associated with increased mortality risk were identified. In the gene-based analysis of rare non-synonymous variants, 16 genes were associated with lifespan via burden or SKAT-O tests.\n\nConsistent with previous reports, rs429358, determining the APOE-\u03b54 allele dosage, was associated with decreased lifespan across both sexes. APOE-\u03b54 is well known for its associations with Alzheimer\u2019s Disease17 and cardiovascular disease18. In our dataset, the proportion of \u03b54 carriers was significantly higher for deaths caused by \u201cDisease of the circulatory system\u201d and \u201cDiseases of the nervous system\u201d compared to the general prevalence of \u03b54 carriers, which could explain the effect of \u03b54 on lifespan. Examining the subcategories of these ICD-10 chapters, \u201cDisease of the circulatory system\u201d includes cardiovascular disease (I51.6), while \u201cDiseases of the nervous system\u201d covers Alzheimer\u2019s disease (G30). We also identified a genome-wide association at the ZSCAN23 locus, which, while not previously reported in human lifespan studies, has been associated with rheumatoid arthritis19,20, multiple sclerosis19, and COVID-1920 in other studies. Our colocalization analysis revealed that the lifespan-associated signal colocalizes with a ZSCAN23 eQTL in pancreatic tissue with increased expression observed in minor allele carriers. Although the role of ZSCAN23 remains unclear, recent studies have linked its expression to pancreatic tumors, supporting our colocalization findings21. Sex-specific GWAS in males identified a significant association in PRKD3, which PheWAS linked to neoplasms and stomach cancer. PRKD3 has been highlighted as a potential oncogene in various cancer types22,23. Additionally, a borderline significant association was found in males between MUC5AC and MUC5B, which highly colocalizes with a MUC5B eQTL in lung tissue, and several studies have linked this variant to pulmonary disease like idiopathic pulmonary fibrosis24,25 and COVID-1926,27. In females, sex-stratified GWAS identified a variant in CELF2 associated with reduced lifespan. CELF2, an RNA-binding protein, is a candidate gene for certain neurological disorders28,29,30, and its activity has also been implicated in the development of ovarian and breast cancers31,32. Previously reported SNP associations with lifespan were concordant in our dataset but none of these passed the GWAS suggestive threshold (\\(p\\)\u2009=\u20091.0\\(\\times {10}^{-5}\\)) except for those at the APOE locus (Supplementary Table\u00a09). This phenomenon likely resulted from previous studies relying on proxy data such as parental age at death, which may capture a different set of genetic factors than direct proband mortality data.\n\nIn our gene-based rare variant analysis, 16 genes achieved gene-wide significance (\\(p\\)\u2009<\u20097.4\\(\\times {10}^{-7}\\)) in either the burden or SKAT-O test. Four of these, TET2, ATM, BRCA2, and BRCA1, were reported in a previous rare-variant analysis of lifespan in UKB8. We identified 13 novel genes associated with lifespan\u2014CKMT1B, ASXL1, C1orf52, DNMT3A, SF3B1, PTEN, SOX21, IDH2, TP53, SRSF2, RLIM, NMNAT2 and TERT\u2014when assessing variants causing genetic LoF or missense variants classified as pathogenic by REVEL or AlphaMissense. ASXL1 was missed by a previous study considering protein truncating variants8, as they excluded variants annotated as end-truncation, notably the main ASXL1 variant driving CHIP (rs750318549 in Table\u00a02). Of note, LoF and missense variant analyses identified mostly separate genes with only one overlap (TET2). This supports the use of both categories in rare variant analyses and may indicate that missense variants as classified by AlphaMissense capture a wider range of variation missed when only assessing LoF variants, which are generally interpreted as resulting in haploinsufficiency. Importantly, missense variants may lead to increased or decreased protein function. In our analyses, IDH2 was not gene-wide significant with the burden test (\\(p\\)\u2009=\u20091.4\\(\\times {10}^{-4}\\), Table\u00a01) but was highly significant with SKAT-O (\\(p\\)\u2009=\u20095.4\\(\\times {10}^{-42}\\), Table\u00a01). Since SKAT-O does not lose power when variants have differing directions of effect, this suggests that different mutations in IDH2 can lead to either increased or decreased lifespan. These results underline the gain in information achieved when studying rare missense variants as well as LoF using appropriate statistical techniques.\n\nStrikingly, most of the genes we identified carrying lifespan-associated rare variants have been previously linked to cancer. TET2, ASXL1, DNMT3A, and SF3B1 are all known to harbor causal leukemia variants33,34,35,36, and somatic variants in SRSF2 have been described in myelodysplastic syndrome37. ATM, BRCA2, and BRCA1 mutations have been well characterized in breast, ovarian, and other cancers38,39,40. RLIM appears to be a regulator of estrogen-dependent transcription, an important pathway in breast cancer41, and has been recently described as a potential tumor suppressor42. PTEN and TP53 are well studied due to their critical role in genomic stability and are the two most mutated genes in human cancer43. IDH2 is also frequently mutated in many kinds of cancer44. The antisense long noncoding RNA SOX21-AS1, but not SOX21, has been linked to oral, cervical, and breast cancer45,46,47. A recent study found potential for CKMT1B expression as a prognostic biomarker in glioma48. NMNAT2 expression has been found to be upregulated in colorectal cancer49. Finally, variation in both the coding and promoter sequences of TERT has been associated with a variety of cancer types50,51.\n\nOur PheWAS results also suggest that most of these genes are associated with cancer, specifically blood-based tumors such as myeloid leukemia. Combined with the common ZSCAN23 locus we identified, associated with pancreatic tumors, this points to cancer being the major genetic factor currently affecting lifespan in UKB. This is consistent with a previous study of health span that found cancer to be the first emerging disease in over half of disease cases in UKB52. These results likely reflect the characteristics of the cohort, comprised of predominantly middle-aged individuals, with age-at-death ranging from 40.9 to 85.2 years and last-known ages between 52.6 and 88.7 years.\n\nFor sex-specific rare variant analyses, we identified four novel genes (CDKN1A, PTPRK, COA7, TG) in males and five genes (PORCN, UGT1A8, CBX3, IFITM10 and OLIG1) in females. Some of these genes have been found to be associated with sex-specific diseases. In one study, advanced prostate cancer patients had a higher frequency of a variant on the 3\u2019UTR of CDKN1A53 and the gene has received attention as a potential therapeutic target for prostate cancer54. PORCN is located on the X chromosome and mutations on it can cause Goltz-Gorlin Syndrome55, but it has also been found to regulate a signaling pathway that controls cancer cell growth56. UGT1A8 expression is altered in endometrial cancer57 and amino acid substitutions in it may modulate estradiol metabolism leading to an increased risk of breast and endometrial cancer58.\n\nSince UKB collected DNA from peripheral blood mononuclear cell samples, we explored whether the variants were potentially of somatic origin, picked up by WES genotyping due to CHIP. The VAF distribution of variants included in our analysis emphasizes that several associations are likely linked to CHIP and notably include the well-established CHIP-related genes TET2, ASLX1, DNMT3A, SF3B1, TP53 and SRSF2. While WES heterozygote genotypes for these variants will not include all variants with some degree of CHIP within these genes, it does capture CHIP-related somatic variants sufficiently to establish robust associations with lifespan. In UKB the mean duration between the primary visit (blood draw date) and death is currently 9.2 years (\\(\\pm\\) 3.8) and suggests that WES screening for CHIP variants may be used as a precision health tool to contribute to earlier cancer detection by assessing individuals with higher susceptibility risks59. In addition to known cancer variants, such as breast cancer-related BRCA1/BRCA2, our study highlights novel associations that should be considered in cancer susceptibility screenings.\n\nWhile our study lacks an independent external replication and only a small number of genes formally replicated in the independent test set within UKB, the stability of the burden test association to five-fold CV suggests that these results are not due to outliers and are robust within the UKB.\n\nBy combining large-scale GWAS with rare variant analysis, this study enhances our understanding of the genetic basis of human lifespan. Our results emphasize the importance of understanding the genetic factors driving the most prevalent causes of mortality on a population level, highlighting the potential for early genetic testing to identify germline and somatic variants that place some individuals at risk of early death. Understanding the biological pathways through which these genes influence cancer and aging, as well as the environmental factors interacting with these pathways, will be essential for developing therapeutic targets aimed at extending a healthy lifespan. Our study\u2019s implications thus extend beyond genetics, as they touch on the broader aspects of health care, public health policy, and preventive strategies against age-related diseases.\n\nIn conclusion, this study enhances our understanding of the genetic basis of human lifespan by combining large-scale GWAS with detailed rare variant analysis. The novel loci identified warrant further exploration to understand their biological roles and interactions with environmental factors, which will be crucial for unraveling the complex nature of aging and developing strategies to mitigate its adverse effects.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The UKB is a large population-based longitudinal cohort study with recruitment from 2006 to 2010 in the United Kingdom60. In total, 502,664 participants aged 40\u201369 years were recruited and underwent extensive phenotyping including health and demographic questionnaires, clinic measurements, and blood draw at one of 22 assessment centers, of whom 468,541 subjects have been genotyped by both SNP array and WES.\n\nWe restricted our analysis to 393,833 individuals who self-reported their ethnic background as \u201cwhite British\u201d and were categorized as European ancestry based on genetic ethnic grouping (Field: 21000). Among them, 35,551 subjects were reported deceased, and their ages at death were recorded from the UK Death Registry (Field: 40007). For the other 358,282 subjects without death records, we assumed they were still alive by the latest censoring date (November 30, 2022). We determined their last known ages by subtracting their year and month of birth (Field: 33) from the censoring date.\n\nAll participants provided written informed consent, as outlined in the UK Biobank ethics framework (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/governance/ethics-advisory-committee).\n\nA total of 488,000 UKB participants were genotyped using one of two closely related Affymetrix microarrays (UKB Axiom Array or UK BiLEVE Axiom Array) for approximately 850,000 variants. The genotyped dataset was phased using SHAPEIT3 and imputed with IMPUTE4, leveraging reference panels from UK10K, 1000 Genomes Project phase 3, and Haplotype Reference Consortium reference panels, resulting in approximately 97 million variants61. Additionally, we removed SNPs with imputation quality score <0.3, genotype missing rate >0.05, minor allele frequency (MAF)\u2009<\u20090.1%, and Hardy-Weinberg equilibrium \\(p\\)\u2009<\u2009\\(1.0\\times {10}^{-6}\\).\n\nWe performed linear regression analyses using BOLT-LMM (v.2.3.4)62, which employs a linear mixed-effects model to test the association of common variants (MAF \\(\\ge\\) 0.1%) with lifespan for the entire cohort, as well as stratified by sex. For all three analyses, we used Martingale residuals calculated using the Cox proportional hazards model as the outcome variable. The procedure for calculating Martingale residuals was as follows. First, a Cox proportional hazards model63 was fitted without genotype:\n\nwhere \\({H}_{0}(t)\\) is the baseline hazard function at time point \\(t\\) given the age at enrollment (Field: 54), last-known age and dead/alive status, \\({{{\\rm{Z}}}}=[{{{{\\rm{Z}}}}}_{1},\\,\\ldots,{{{{\\rm{Z}}}}}_{k}]\\) is a covariate matrix, and \\({{{\\rm{\\beta }}}}=[{{{{\\rm{\\beta }}}}}_{1},\\,\\ldots,{{{{\\rm{\\beta }}}}}_{k}]\\) is a coefficient matrix for Z. Here, we included sex and the first 40 principal components (PC) and geographic covariates (Field: 20118) under the Geographical and Location category (100113) as covariates, but for sex-specific analyses, sex was excluded. Then, Martingale residuals were calculated as:\n\nwhere \\({\\delta }_{i}\\) is the dead/alive status (0\u2009=\u2009alive, 1\u2009=\u2009dead) of the \\(i\\) th subject and \\(\\hat{{{{\\rm{\\beta }}}}}\\) is the estimated coefficient matrix. We adapted the coxph function from the survival (v.3.2.13)64 in R (v.4.1.3) package to compute the Martingale residuals. Genome-wide significance threshold was set at the standard GWAS level (\\(p\\)\u2009=\u2009\\(5.0\\times {10}^{-8}\\)). We used LocusZoom65 to generate regional plots and Python v.3.7 to create Manhattan plots.\n\nTo evaluate the effect of the significant loci identified in our GWAS, we examined expression quantitative trait loci (eQTLs) across 49 tissues having at least 73 samples from the Genotype-Tissue Expression Project (GTEx) version 866. Bayesian colocalization analysis was employed using the COLOC package (v.5.2.3)67 in R and the posterior probability of colocalization (PP4) was calculated between GWAS findings and eQTL associations within a 1 megabase (Mb) window. Additionally, colocalization was visualized using the locuscompareR package68.\n\nWhole-exome sequencing (WES) data was available for 469,835 UKB participants. The dataset was generated by the Regeneron Genetics Center69. Details about the production and QC for the WES data was previously described69. We restricted the WES analysis to rare variants (MAF\u2009<\u20091%).\n\nRare variants in WES data were annotated using Variant Effect Predictor (v. 112) provided by Ensembl70. We defined LoF variants as those with predicted consequences: splice acceptor, splice donor, stop gained, frameshift, start loss, stop loss, transcript ablation, feature elongation, or feature truncation. Missense variants were annotated using AlphaMissense71 and REVEL72 plugins and included if they had an AlphaMissense score \\(\\ge\\) 0.7 or REVEL score \\(\\ge\\) 0.75. All annotation was conducted based on GRCh38 genome coordinates.\n\nFor testing groups of rare variants, genotype matrices were first transformed into a binary variable describing whether samples carry a variant of a given class as follows:\n\nWhere \\({g}_{{ij}}\\) is the minor allele count observed for subject \\(i\\) at variant \\(j\\) in the gene and \\(k\\) is the number of variants in the gene.\n\nTo account for relatedness and population structure, Martingale residuals were first adjusted using a linear mixed model approach implemented in fastGWA (--save-fastGWA-mlm-residual)73. The adjusted residuals were then used as the phenotype for the rare variant analyses, ensuring the robustness of the results in the presence of related individuals and population stratification.\n\nWe carried out two gene-based tests: the burden test and sequence kernel association test-optimized (SKAT-O)74. The burden test is a mean-based test that assumes the same direction of effects for all variants within a gene. On the other hand, SKAT-O employs a weighted average of the burden test and SKAT75, the latter a variance-based test that does not lose power when variants have opposing directions of effect.\n\nAssociation tests were performed for each gene and rare variant class separately, including LoF variants, missense variants with an AlphaMissense score \\(\\ge\\) 0.7, and missense variants with a REVEL score \\(\\ge\\) 0.75, using Martingale residuals as the phenotype as in the common variant analyses. We excluded genes with fewer than 10 variant carriers to ensure the reliability of our analyses. A gene-wide significance threshold was established at \\(p\\)\u2009=\u20097.4\\(\\times {10}^{-7}\\) based on the Bonferroni method accounting for the number of genes, variant classes, and statistical methods. Gene-based analyses were carried out using the SKAT package (v.2.2.5) in R.\n\nTo characterize the impacts of gene burden in significant genes, we compared lifespan survival depending on gene burden using Kaplan\u2013Meier survival curves, and Cox proportional hazard regression analyses. Additionally, we performed Cox proportional hazards regression to assess the effect of each rare variant in a gene. The survival (v.3.2.13) package in R (v.4.1.3) was utilized for the survival analysis and the lifelines package (v.0.28.0) in Python v.3.7 was used for generating Kaplan\u2013Meier survival curves.\n\nFor gene-wide significant genes, we conducted phenome-wide association studies (PheWAS) of variant carrier status across 1670 phenotypes in the UKB derived from binary, categorical, and continuous traits using the PHEnome Scan ANalysis Tool76. Phenotypes included the International Classification of Disease 10 (ICD-10) codes, family history (e.g., father\u2019s illness, father\u2019s age at death), blood count (e.g., white blood cell count), blood biochemistry (e.g., Glucose levels), infectious diseases (e.g., pp 52 antigen for Human Cytomegalovirus), physical measures (e.g., BMI), cognitive test (e.g., pairs matching) and brain measurements (e.g., subcortical volume of hippocampus). For ICD-10 codes, we excluded phenotypes from the following ICD-10 chapters: \u201cInjuries, poisonings, and certain other consequences of external causes\u201d (Chapter XIX), \u201cExternal causes of morbidity and mortality\u201d (Chapter XX), \u201cFactors influencing health status and contacts with health services\u201d (Chapter XXI), and \u201cCodes for special purposes\u201d (Chapter XXII). The ICD-10 codes were then converted into Phecodes (v.1.2)77 which combine correlated ICD codes into a distinct code and improve alignment with diseases commonly used in clinical practice.\n\nFor binary traits, we removed phenotypes with fewer than 100 cases, and for continuous traits, those with fewer than 100 participants were excluded. Depending on the phenotype, we employed various regression models including binary logistic regression, ordinal logistic regression, multinomial logistic regression, and linear regression. All analyses included age and sex as covariates. Phenome-wide significance threshold was set at \\(p\\)\u2009=\u2009\\(2.9\\times {10}^{-5}\\) based on the number of phenotypes.\n\nTo investigate whether some gene-level associations are enriched for somatic variants, we computed the variant allele frequency (VAF) for each heterozygous sample, reporting the mean VAF and VAF distribution per gene per variant class. VAF is defined as the number of reads with an alternate allele divided by the read depth at a given variant position. We also calculated the confidence interval for the mean VAF per gene using 10,000 bootstrap samples to ensure robust statistical analysis.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "GWAS summary statistics and rare variant results from SKAT-O and burden tests are available in the GWAS Catalog\u00a0database under accession codes GCST90551884\u2013GCST90551889. All phenotypic and genotypic data supporting the findings of this study are available from the UK Biobank (https://www.ukbiobank.ac.uk/enable-your-research/register). Access to these data is available from the authors with UKB permission.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "The codes used for analyses in the present study are available at the following link: https://github.com/Junkkkk/Lifespan-studies. The code for PheWAS analysis was utilized from https://github.com/MRCIEU/PHESANT.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This research has been conducted using the UK Biobank Resource under application number 45420. We thank all the participants and researchers of UK Biobank for making these data open and accessible to the research community. This research was supported by the Dean\u2019s Postdoctoral Fellowship at the School of Medicine, Stanford University. Additionally, this research was partially supported by the Biostatistics Shared Resource (B-SR) of the NCI-sponsored Stanford Cancer Institute: P30CA124435 and by the following NIH funding source of Stanford\u2019s Center for Clinical and Translational Education and Research award, under the Biostatistics, Epidemiology and Research Design (BERD) Program: 1UM1TR004921-01.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Michael D. Greicius, Yann Le Guen.\n\nDepartment of Neurology and Neurological Sciences, Stanford University, Stanford, CA, 94305, USA\n\nJunyoung Park,\u00a0Andr\u00e9s Pe\u00f1a-Tauber,\u00a0Lia Talozzi\u00a0&\u00a0Michael D. Greicius\n\nQuantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, 94304, USA\n\nYann Le Guen\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.P. conducted all analyses, prepared all figures and drafted the manuscript. A.P.T. contributed to refining the manuscript. L.T. provided critical comment on the manuscript. M.D.G. and Y.L.G. planned, organized and supervised the entire study and revised the manuscript. All authors have approved the submitted version.\n\nCorrespondence to\n                Junyoung Park.",
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+            "section_text": "Park, J., Pe\u00f1a-Tauber, A., Talozzi, L. et al. Rare genetic associations with human lifespan in UK Biobank are enriched for oncogenic genes.\n                    Nat Commun 16, 2064 (2025). https://doi.org/10.1038/s41467-025-57315-6\n\nDownload citation\n\nReceived: 16 August 2024\n\nAccepted: 18 February 2025\n\nPublished: 28 February 2025\n\nVersion of record: 28 February 2025\n\nDOI: https://doi.org/10.1038/s41467-025-57315-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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0e6380fd5cbe3c0c7e8a39f2b23083f37592c615a5adfd5421239921c07e585d/metadata.json

@@ -0,0 +1,155 @@
+{
+    "title": "Midlatitude mesoscale thermal Air-sea interaction enhanced by greenhouse warming",
+    "pre_title": "Midlatitude Mesoscale Thermal Air-sea Interaction Enhanced by Greenhouse Warming",
+    "journal": "Nature Communications",
+    "published": "04 September 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52077-z/MediaObjects/41467_2024_52077_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52077-z/MediaObjects/41467_2024_52077_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-52077-z/MediaObjects/41467_2024_52077_MOESM3_ESM.zip"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "https://data.marine.copernicus.eu/products",
+        "https://www.ncei.noaa.gov/products/optimum-interpolation-sst",
+        "https://www.j-ofuro.com/en/",
+        "https://doi.org/10.24381/cds.bd0915c6",
+        "/articles/s41467-024-52077-z#ref-CR33",
+        "https://ihesp.github.io/archive/products/ds_archive/Sunway_Runs.html",
+        "https://pcmdi.llnl.gov/CMIP6/",
+        "/articles/s41467-024-52077-z#Sec13"
+    ],
+    "code": [
+        "https://zenodo.org/records/10610386"
+    ],
+    "subject": [
+        "Climate change",
+        "Physical oceanography"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3932615/v1.pdf?c=1725534392000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3932615/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-52077-z.pdf",
+    "preprint_posted": "18 Feb, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "The influence of greenhouse warming on mesoscale air-sea interactions, crucial for modulating ocean circulation and climate variability, remains largely unexplored due to the limited resolution of current climate models. This study addresses this gap by analyzing eddy-resolving high-resolution climate simulations and observations, focusing on the coupling between mesoscale sea surface temperature (SST) and latent heat flux (LHF) in winter. Our findings reveal a consistent increase in mesoscale SST-LHF coupling in the major western boundary current regions under warming, characterized by a heightened nonlinearity between warm and cold eddies and a more pronounced enhancement in the northern hemisphere. To understand the dynamics, we develop a theoretical framework that links mesoscale thermal coupling changes to large-scale factors, which indicates that the projected changes are collectively determined by historical background wind, SST, and the rate of SST warming. Among these factors, the large-scale SST and its warming rate are the primary drivers of hemispheric asymmetry in mesoscale coupling intensification. This study introduces a simplified approach for assessing the projected mesoscale thermal coupling changes and implies a growing significance of mesoscale air-sea interaction in shaping weather and climate patterns in a warming world.Earth and environmental sciences/Climate sciences/Climate changeEarth and environmental sciences/Climate sciences/Ocean sciences/Physical oceanography",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SI.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "The influence of greenhouse warming on mesoscale air-sea interactions, crucial for modulating ocean circulation and climate variability, remains largely unexplored due to the limited resolution of current climate models. Additionally, there is a lack of theoretical frameworks for assessing changes in mesoscale coupling due to warming. Here, we address these gaps by analyzing eddy-resolving high-resolution climate simulations and observations, focusing on the mesoscale thermal interaction dominated by mesoscale sea surface temperature (SST) and latent heat flux (LHF) coupling in winter. Our findings reveal a consistent increase in mesoscale SST-LHF coupling in the major western boundary current regions under warming, characterized by a heightened nonlinearity between warm and cold eddies and a more pronounced enhancement in the northern hemisphere. To understand the dynamics, we develop a theoretical framework that links mesoscale thermal coupling changes to large-scale factors, which indicates that the projected changes are collectively determined by historical background wind, SST, and the rate of SST warming. Among these factors, the large-scale SST and its warming rate are the primary drivers of hemispheric asymmetry in mesoscale coupling intensification. This study introduces a simplified approach for assessing the projected mesoscale thermal coupling changes in a warming world.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Mesoscale air-sea interactions, predominantly active in the Western Boundary Current (WBC) regions in the midlatitudes, play a critical role in modulating extratropical weather and climate systems1,2,3,4,5. The interaction between mesoscale oceanic eddies and the atmosphere actively impacts precipitation, storms, large-scale atmospheric circulations, and provides feedback to the ocean, influencing oceanic circulations and climate variability6,7,8,9,10,11,12,13,14,15. A key aspect of this interaction is the coupling between sea surface temperature and turbulent heat flux (SST-THF), hereafter denoted as thermal coupling. In terms of the atmosphere, thermal coupling acts as an energy source, which is crucial for the genesis and development of weather systems and deep troposphere response16,17,18,19,20,21. In terms of the ocean, thermal coupling serves as an energy sink, which is the key to dissipating oceanic eddy energy and driving oceanic circulation response11,22.\n\nHow greenhouse warming will impact mesoscale oceanic eddies remains uncertain, let alone mesoscale air-sea coupling. Satellite observations reveal a rise in eddy activity in the WBC regions during recent decades, while climate models project heterogeneous eddy variations in different WBC regions under warming23,24. Additionally, the theoretical framework for predicting the mesoscale thermal coupling change in response to greenhouse warming is currently absent. Thermodynamically, the SST warming and the associated water vapor increase governed by the Clausius\u2013Clapeyron (C-C) relation, appear to strengthen the thermal air-sea coupling (primarily through the enhancement of latent heat flux) in a warming climate. Dynamically, the non-uniform warming between the upper and lower troposphere under anthropogenic forcing tends to increase atmospheric stability, inhibiting the vertical momentum transfer and thereby suppressing the surface wind and thermal air-sea coupling. This is further complicated by significant uncertainties in atmospheric circulation responses under climate change, introducing additional perturbations to SST-THF coupling. It is noteworthy that the aforementioned oceanic and atmospheric changes discussed within a conventional large-scale framework may not necessarily manifest at mesoscales. Physical processes governing the response of mesoscale thermal coupling to greenhouse warming are multifaceted, making it challenging to pinpoint the ultimate dominant factor.\n\nUtilizing an unprecedented set of high-resolution Community Earth System Model (referred to as CESM-HR, Methods) with ~0.25\u00b0 atmosphere and ~0.1\u00b0 ocean components that can explicitly resolve the mesoscale oceanic eddies and their coupling with the atmosphere, we investigated the impact of greenhouse warming on mesoscale thermal coupling in eddy-rich WBC regions. We then constructed a theoretical framework for estimating the mesoscale thermal coupling change in response to greenhouse warming through the decomposition of contributing factors. We further assessed the robustness of the findings by extending the analyses to High-Resolution Model Intercomparison Project (HighResMIP, Methods) models.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "We first evaluated the model\u2019s capability in representing the mesoscale thermal coupling in the CESM-HR simulations against observational data (Methods), in four major WBC regions, i.e., the Kuroshio Extension (KE), the Gulf Stream Extension (GS), the Agulhas Return Current (ARC) and the Brazil-Malvinas Confluence Region (BMC), by detecting eddies using sea surface height anomalies and constructing composite analyses with a reference frame centered on the eddy (see details in Methods). The model shows high fidelity in representing the statistical characteristics of eddies, such as the averaged number, amplitude and size, as detailed in Tab. S1. It also successfully reproduces the eddy-induced turbulent heat fluxes (including both sensible and latent components) along with their seasonality, for both anticyclonic warm and cyclonic cold eddies (Fig. S1). It is noted that the coupling strength peaks during the winter month. Furthermore, the eddy-induced sensible heat flux (SHF) is approximately half that of latent heat flux (LHF) and the differences between historical and future simulations are subtle (Fig. S2). Thus, the mesoscale thermal coupling in the WBC regions and its response to global warming is predominantly influenced by LHF. Consequently, our subsequent analyses will focus on the mesoscale SST-LHF coupling in the hemispheric winter season \u2014 DJF for the Northern Hemisphere (NH) and JJA for the Southern Hemisphere (SH).\n\nTo assess the potential impact of anthropogenic warming on mesoscale thermal coupling, we analyzed the decadal trend of mesoscale SST-LHF coupling in the observational periods based on high-pass spatial filtering fields (Methods). A significant intensification of mesoscale SST-LHF coupling at an approximate rate of 1.5\u2009W/m2/\u00b0C per decade is detected in the WBCs over the past four decades, with the most pronounced increase observed in the GS and KE regions of the NH (Fig.\u00a01a). The CESM-HR successfully simulates the enhanced mesoscale thermal coupling in the WBCs with a magnitude slightly higher than that recorded in the observations (Fig.\u00a01b). The model also captures the north-south hemispheric asymmetry of the changes. The alignment between model simulated and observational trends in the past lends credence to the use of CESM-HR for investigating the mesoscale coupling response under climate change.\n\nGlobal distribution of the decadal trends of mesoscale SST-LHF coupling strength as derived from the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) (a) and high-resolution Community Earth System Model (CESM-HR) (b) during 1979\u20132022. The coupling strength is computed using the linear regression coefficient between high-pass filtered monthly SST and LHF (Methods) with trends significant at a 99% confidence level indicated by black dots. c Composites of SST (color shading,\u00a0\u00b0C) and LHF (contours,\u00a0W/m2) anomalies associated with mesoscale oceanic eddies during historical periods (1956-2005) in CESM-HR. Shown are winter season mean anomalies in anticyclonic warm minus cyclonic cold eddy composites within four western boundary current (WBC) regions (outlined by red boxes in a, b). The white circle represents one eddy radius and the white dot marks the eddy center. d Mesoscale SST-LHF coupling strength\u00a0(W/m2/\u00b0C) during historical (HIS, 1956\u20132005) and future (RCP, 2063-2100) periods in CESM-HR for warm (red) and cold (blue) eddies averaged across four WBC regions. The coupling strength is computed using the linear regression coefficient between SST and LHF anomalies within twice the radius of eddy composites. e Differences in mesoscale SST-LHF coupling strength between future and historical periods in CESM-HR for warm (red bars) and cold (blue bars) eddies in the Kuroshio Extension (KE), the Gulf Stream (GS), the Agulhas Return Current (ARC) and the Brazil-Malvinas Confluence Region (BMC) regions, with fractional differences labeled atop the respective bars. Source data are provided as a Source Data file.\n\nA linear regression between mesoscale SST and LHF based on eddy composites averaged across four WBC regions, yields an estimated coupling coefficient of approximately 29\u2009W/m2/\u00b0C for both anticyclonic warm and cyclonic cold eddies in historical simulations (1956\u20132005, Fig.\u00a01c, d). The nonlinearity between warm and cold eddies appears to be minimal during the historical period. Under the high greenhouse forcing scenario of Representative Concentration Pathway 8.5, future simulations (2063-2100) project an approximate 14% enhancement in mesoscale SST-LHF coupling to 33\u2009W/m2/\u00b0C (Fig.\u00a01d). A detailed examination of the four WBCs reveals a consistent increase in coupling strength by 6% to 18% in future simulations compared to historical simulations (Fig.\u00a01e). Importantly, future simulations project a greater increase of mesoscale SST-LHF coupling strength for warm eddies than for cold eddies, indicating heightened nonlinearity in a warming climate. The increase in nonlinearity is expected to amplify net heat flux from mesoscale oceanic eddies13,25, thereby impacting future weather and climate systems more significantly. Additionally, the projected increase in mesoscale SST-LHF coupling in the GS and KE is nearly twice that of the ARC and BMC, pointing to a more substantial intensification in the NH than in the SH. Combined with the findings in the observational periods, the results indicate that the hemisphere discrepancy in mesoscale SST-LHF coupling is likely to not only persist but also to become more pronounced with ongoing climate warming.\n\nThe LHF is proportional to both the surface wind and the humidity difference at the air-sea interface according to the Bulk formula26. To estimate the mesoscale SST-LHF coupling associated with oceanic eddies, we decompose the relevant fields into large-scale background and mesoscale components in line with previous studies27,28. The mesoscale SST-LHF coupling coefficient can be quantified using the following relationship (see detailed derivation in Methods):\n\nHere, the prime (\u2032) represents mesoscale anomalies defined by the high-pass spatial filtering, and the overbar (\u2006\u203e\u2006) denotes large-scale background excluding the mesoscale signal.\n\nThe mesoscale SST-LHF coupling coefficient \\(\\left(\\frac{{{{\\rm{d}}}}{{{{\\rm{Q}}}}}_{{{{\\rm{L}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}}^{{\\prime} }}\\right)\\) is determined by two components: the thermodynamic adjustment to mesoscale SST multiplied by the largescale wind (\\({\\bar{{{{\\rm{U}}}}}}_{10}\\left(\\frac{{{{\\rm{d}}}}{{{{\\rm{q}}}}}_{{{{\\rm{s}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}}^{{\\prime} }}-\\frac{{{{\\rm{d}}}}{{{{\\rm{q}}}}}_{{{{\\rm{a}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}}^{{\\prime} }}\\right)\\), hereafter denoted as mesoscale thermodynamic adjustment term), and the dynamic adjustment to mesoscale SST multiplied by the large-scale humidity difference (\\(\\frac{{{{\\rm{d}}}}{{{{\\rm{U}}}}}_{10}^{{\\prime} }}{{{{\\rm{d}}}}{{{{\\rm{SST}}}}}^{{\\prime} }}({\\bar{{{{\\rm{q}}}}}}_{{{{\\rm{S}}}}}-{\\bar{{{{\\rm{q}}}}}}_{{{{\\rm{a}}}}})\\), hereafter denoted as mesoscale dynamic adjustment term). Evaluation of historical and future simulations in the WBC regions reveals an increase in both terms due to greenhouse forcing (bars with black borders in Fig.\u00a02a\u2013d). Particularly, the enhancement of the thermodynamic adjustment term is about 3 to 5 times that of the dynamic adjustment term across all four WBCs (Fig.\u00a02a\u2013d), indicating the dominant contribution of the thermodynamic adjustment term to mesoscale SST-LHF coupling modulation.\n\na Differences in thermodynamic and dynamic adjustments between future (RCP) and historical (HIS) periods in high-resolution Community Earth System Model (CESM-HR) in the Kuroshio Extension (KE) region. From left to right, the terms plotted are thermodynamic adjustment (TA), \\({\\bar{{{{\\rm{U}}}}}}_{10}\\left(\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{s}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}-\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{a}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}\\right)\\), dynamic adjustment (DA), \\(\\frac{{{{{\\rm{dU}}}}}_{10}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}({\\bar{{{{\\rm{q}}}}}}_{{{{\\rm{S}}}}}-{\\bar{{{{\\rm{q}}}}}}_{{{{\\rm{a}}}}})\\), large-scale surface wind (\\({\\bar{{{{\\rm{U}}}}}}_{10}\\)) and moisture adjustment (MA), \\(\\left(\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{s}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}-\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{a}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}\\right)\\). (b\u2013d), as for (a), but for the Gulf Stream (GS), the Agulhas Return Current (ARC) and the Brazil-Malvinas Confluence Region (BMC) regions, respectively. Source data are provided as a Source Data file.\n\nAlthough the dynamic adjustment term is substantially weaker than the thermodynamic adjustment, it is notably stronger in the NH compared to the SH. A decomposition of the dynamic adjustment term shows that the response of large-scale air-sea humidity difference under warming generally surpasses the mesoscale wind response to oceanic eddies across the four WBCs (Fig. S3). However, the mesoscale wind response to eddies is more pronounced in the KE and GS regions and minimal in the ARC and BMC regions, contributing to the stronger mesoscale dynamic adjustment in the NH. The intensified mesoscale wind response to eddies in the KE and GS may be associated with the vigorous western boundary currents, stronger oceanic eddy activities8, and the frequent passage of synoptic weather systems that collocate with the KE and GS. These factors lead to an unstable planetary boundary layer and enhanced downward momentum transfer as discussed in previous studies2,29.\n\nA further decomposition of the thermodynamic adjustment term reveals that the large-scale wind change between historical and future simulations is negligible (bars with magenta borders in Fig.\u00a02a\u2013d), while the predominant influence arises from the mesoscale moisture response to oceanic eddies, especially the specific humidity change at the ocean surface. Specifically, \\(\\frac{{{{\\rm{d}}}}{{{{\\rm{q}}}}}_{{{{\\rm{s}}}}}^{{\\prime} }}{{{{\\rm{d}}}}{{{{\\rm{SST}}}}}^{{\\prime} }}\\) is an order of magnitude greater than \\(\\frac{{{{\\rm{d}}}}{{{{\\rm{q}}}}}_{{{{\\rm{a}}}}}^{{\\prime} }}{{{{\\rm{d}}}}{{{{\\rm{SST}}}}}^{{\\prime} }}\\), which may be attributed to the atmospheric boundary layer thermal adjustment that results in a reduced surface air temperature anomaly relative to the mesoscale SST anomaly as indicated by Moreton et al. (2021)30 and Hausmann et al.31. Collectively, the results suggest that the amplification in mesoscale moisture response is the principal driver for the strengthened mesoscale SST-LHF coupling under climate change.\n\nThe above analyses indicate that changes in mesoscale SST-LHF coupling due to warming can be effectively estimated via the mesoscale moisture adjustment process. Nonetheless, this estimation still relies on the availability of both mesoscale and large-scale fields from historical and future simulations. To circumvent this, we apply a Taylor series expansion to the mesoscale moisture derivative (see detailed derivation in Methods). The resultant expression provides a simplified approach to assess mesoscale SST-LHF coupling change using large-scale fields:\n\nHere, \u2018F\u2019 represents future values and \u2018P\u2019 represents historical values in the past. It is evident that changes in mesoscale SST-LHF coupling are collectively affected by the large-scale wind and the curvature of the C-C scaling from the historical simulations, along with the projected warming of large-scale SST. Note that the curvature of the C-C scaling is inherently linked with the background SST, with higher temperature corresponding to a more pronounced moisture-temperature sensitivity. The relationship suggests that projections of future mesoscale SST-LHF coupling are significantly influenced by the historical large-scale oceanic and atmospheric conditions. Given that the large-scale wind and the curvature of the C-C scaling stay positive, it can be inferred that the direction of mesoscale SST-LHF coupling is exclusively determined by the sign of projected SST changes, leading to consistent intensification in line with the warming of underlying SST.\n\nWe then assessed the regional variations in mesoscale SST-LHF coupling responses among the four WBC regions using Eq. (2), with emphasis on the north-south hemisphere asymmetry. The simplified relationship efficiently captures the more pronounced increase in coupling strength within the KE and GS regions compared to the ARC and BMC regions (Fig.\u00a03a), consistent with the projected intensification of mesoscale SST-LHF coupling between future and historical simulations. Detailed examination of contributing factors in CESM-HR demonstrates consistently higher values for large-scale wind and SST in the KE and GS regions in the NH from historical simulations, alongside a more rapid SST warming rate (Fig.\u00a03c), all of which jointly contribute to the enhanced augmentation in mesoscale coupling strength in the NH under climate change. The historical large-scale wind in the KE is approximately 30% stronger than that in the ARC and BMC, and the background SST in the GS is 7\u2009\u00b0C higher than in the BMC, corroborated by observations (Fig.\u00a03d). Additionally, the accelerated SST warming trend in the NH than in the SH is also confirmed by the observational data (Fig.\u00a03d), underscoring the robustness of the hemisphere asymmetry under warming. Further decomposition of the three factors indicates their respective contribution to the overall hemispheric discrepancy (Fig.\u00a03b), with the large-scale SST warming rate being the dominant factor, followed by the background SST.\n\na Estimated changes in mesoscale sea surface temperature-latent heat flux (SST-LHF) coupling between future and historical periods in high-resolution Community Earth System Model (CESM-HR) according to Eq. (2) in the Kuroshio Extension (KE), the Gulf Stream (GS), the Agulhas Return Current (ARC) and the Brazil-Malvinas Confluence Region (BMC) regions. The large-scale SST averaged in the western boundary current (WBC) is displayed on the bottom axis for each respective region. b Contributions of \\({\\bar{{{{\\rm{U}}}}}}_{10}\\) (A), \\(\\frac{{{{{\\rm{d}}}}}^{2}{{{{\\rm{q}}}}}_{{{{\\rm{s}}}}}({{{\\rm{T}}}})}{{{{{\\rm{dT}}}}}^{2}}\\) (B) and \\(\\Delta \\overline{{{{\\rm{SST}}}}}\\) (C) to regional variations in mesoscale SST-LHF coupling response to global warming in the four WBCs. Taking the KE region as an example, the deviation of \\(\\frac{{{{{\\rm{dQ}}}}}_{{{{\\rm{L}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}\\) response from the four-WBC mean can be represented as: \\(\\left(\\frac{{{{{\\rm{dQ}}}}}_{{{{\\rm{L}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}\\right){|}_{({{{\\rm{KE}}}}-{{{\\rm{WBC}}}}{{{\\rm{mean}}}})}={{{\\rm{A}}}}^{\\prime} \\bar{{{{\\rm{B}}}}}\\bar{{{{\\rm{C}}}}}+{{{\\rm{B}}}}^{\\prime} \\bar{{{{\\rm{A}}}}}\\bar{{{{\\rm{C}}}}}+{{{\\rm{C}}}}^{\\prime} \\bar{{{{\\rm{A}}}}}\\bar{{{{\\rm{B}}}}}\\). Here, A, B, and C denotes the respective three terms; the overbar denotes the average values across the four WBCs; the prime denotes the deviation from the WBC mean. For instance, \\({{{\\rm{A}}}}^{\\prime} \\bar{{{{\\rm{B}}}}}\\bar{{{{\\rm{C}}}}}\\) calculates the A\u2032(\\({\\bar{{{{\\rm{U}}}}}}_{10}\\) anomaly) contribution to the total anomaly, assuming that B and C are at their mean levels, as represented by the green bar in (b). c The historical large-scale surface wind, SST and the projected SST warming\u00a0(future minus historical periods) within the four WBC regions in CESM-HR. (d) as for (c), but for observational data. Surface wind is derived from the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) and SST is derived from the National Oceanic and Atmospheric Administration daily Optimum Interpolation SST (NOAA-OISST). \\(\\Delta \\overline{{{{\\rm{SST}}}}}\\) (\u00b0C per century)\u00a0is computed based on the warming trend observed over the last 40 years. The error bars in a,c and d represent the interannual standard deviation of corresponding variables for each region. Source data are provided as a Source Data file.\n\nWe extend the analyses to HighResMIP simulations (Methods) and to different warming periods (2030\u20132050), aiming to verify the robustness of the findings. All models examined show an increase in mesoscale SST-LHF coupling in response to greenhouse warming across four WBCs by the year 2050 (Tab. 1). A more pronounced intensification of this coupling is projected in the NH compared to the SH, in line with CESM-HR results (Fig.\u00a01e), albeit with a generally lower magnitude of change. The magnitude discrepancy is due to the HighResMIP projections here terminating in 2050, whereas CESM-HR projections shown in Fig.\u00a01 extend to a later period of the century (2100).\n\nThe effectiveness of the proposed theoretical framework for estimating changes in mesoscale SST-LHF coupling due to greenhouse warming was also evaluated across different models. A comparison between the coupling strength changes estimated by the theoretical framework and those projected by CESM-HR reveals a significant linear relationship across four WBCs (Fig. S4). The estimated and actual projected changes in mesoscale SST-LHF coupling are highly correlated, with a correlation coefficient of around 0.7 in the KE and GS, and 0.5 in the ARC and BMC (Tab. 1). Similar linear correlations, ranging generally from 0.5 to 0.8 (Tab. 1), are found between estimated and projected mesoscale coupling strength changes in HighResMIP models, confirming the broad applicability of the simplified framework across diverse climate models.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "How greenhouse warming will influence mesoscale air-sea interactions remains an open question. Utilizing eddy-resolving high-resolution CESM simulations, supported by observational and HighResMIP data, we found a ubiquitous intensification of mesoscale thermal coupling in WBC regions by the end of the 21st century under the RCP8.5 warming scenario. The intensification is primarily driven by mesoscale SST-LHF coupling and is characterized by an increased nonlinearity between warm and cold eddies and a more pronounced enhancement in the NH. Considering the recognized importance of mesoscale oceanic processes in influencing weather and climate systems, as highlighted in previous studies4,5,11, our results underscore the critical need for climate models to incorporate mesoscale air-sea interaction for more reliable climate projections.\n\nWe found that the mesoscale moisture response is the key factor driving the strengthened mesoscale SST-LHF coupling under climate change. To further understand the dynamics, we developed a theoretical framework to estimate the mesoscale moisture change. The framework builds a linkage between mesoscale coupling changes and large-scale fields, revealing that the projected mesoscale moisture changes can be estimated by the interplay among historical background wind, SST, and projected SST warming. The direction of mesoscale SST-LHF coupling changes is exclusively determined by the sign of projected SST changes. Given a scenario of SST warming, the mesoscale SST-LHF coupling will invariably exhibit intensification. The relationship highlights the importance of C-C scaling in determining changes in mesoscale SST-LHF coupling, suggesting that higher large-scale SST, determined either by historical baselines or future warming rates, are associated with greater rates of moisture increase and thereby enhanced augmentation of mesoscale SST-LHF coupling.\n\nThe effectiveness of the proposed theoretical framework was evaluated across CESM-HR and HighResMIP models. The analysis reveals a robust linear correlation between estimated and projected changes in mesoscale SST-LHF, suggesting the broad applicability of the theoretical framework within major WBC regions. However, it is important to acknowledge that the prerequisite conditions for the theoretical framework to work are the mesoscale moisture adjustment significantly surpasses the mesoscale wind adjustment, the mesoscale moisture adjustment at the ocean surface significantly exceeds that at the atmospheric surface, and the large-scale wind changes due to warming is minimal. These conditions may not hold outside the WBCs, potentially undermining the framework\u2019s applicability.\n\nIt is important to recognize that an increase in mesoscale thermal coupling strength does not necessarily correspond to higher heat fluxes. The eddy composite analysis reveals a general increase in LHF across the WBCs, yet the accompanying eddies and SST anomalies are weakening (Fig. S5). This reduced eddy activity is possibly linked to enhanced mesoscale thermal coupling, which dampens eddy potential energy11, and to increased oceanic stratification, which inhibits eddy formation. Further in-depth investigation is required to fully understand the changes in eddy dynamics under global warming.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "We utilized the high-resolution Community Earth System Model (CESM-HR) simulations with ~0.25\u00b0 atmosphere and ~0.1\u00b0 ocean components developed by the National Center for Atmosphere Research (NCAR)32. The simulations include a 500-year preindustrial control simulation and a 250-year historical and future simulation from 1850 to 2100. Historical radiative forcing is applied from 1850 to 2005 while the Representative Concentration Pathway 8.5 (RCP8.5, a high greenhouse gas emission) warming forcing is switched from 2006 onwards. The longest available periods with high-frequency (daily) output were chosen to assess the mesoscale thermal coupling response to greenhouse warming in CESM-HR: a historical period from 1956 to 2005 (referred to as HIS) and a future period from 2063 to 2100 (referred to as RCP).\n\nFive HighResMIP simulations with relatively high oceanic resolution were selected: CMCC-CM2-VHR4 (0.25\u00b0 atmosphere and 0.25\u00b0 ocean), HadGEM3-GC31-HH (0.5\u00b0 atmosphere and 0.08\u00b0 ocean), EC-Earth3P-HR (0.5\u00b0 atmosphere and 0.25\u00b0 ocean), CNRM-CM6-1-HR (1\u00b0 atmosphere and 0.25\u00b0 ocean), MPI-ESM1-2-XR (0.5\u00b0 atmosphere and 0.5\u00b0 ocean). We note that only one model (HadGEM3-GC31-HH) has comparable oceanic resolutions with CESM-HR, yet its atmospheric resolution is coarser. All these selected models include a 100-year historical and future (under RCP8.5 scenario) simulation from 1950 to 2050. For a consistent comparison between CESM-HR and HighResMIP simulations, 1950-1969 for historical and 2031-2050 for future projections were selected for the corroborative analysis shown in Table 1.\n\nDaily sea surface height (SSH) derived from Copernicus Marine Environment Monitoring Service (CMEMS) during 2003\u20132007 was used to identify eddies in observations (Tab. S1). Concurrently, SST obtained from the National Oceanic and Atmospheric Administration daily Optimum Interpolation SST (NOAA-OISST) and heat fluxes derived from Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations version3 (J-OFURO3) during the same period were employed to construct observational eddy composites (Fig. S1). ERA5 reanalysis (the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis33) with an extended temporal span from 1979 to 2022 was utilized to examine the decadal trend of mesoscale coupling strength (Fig.\u00a01a).\n\nIn both CMEMS observations and CESM-HR simulations, mesoscale eddies were detected using daily SSH anomalies derived by applying high-pass spatial filtering (20\u00b0 longitude x 10\u00b0 latitude) to remove the large-scale signal, following previous studies34,35. Cyclonic (anticyclonic) eddies are classified by closed contours of SSH anomalies that include a single minimum (maximum), with an SSH anomaly increment (decrement) of 0.05\u2009cm between successive contours. The edge of an eddy is delineated by the outmost closed contour of SSH anomalies. The radius of an eddy corresponds to the radius of a circle with an equivalent area to that enclosed by the outmost contour. The amplitude of an eddy is defined by the SSH anomaly difference between the eddy\u2019s peak and its defined edge. Only eddies with a radius ranging from 70 to 200\u2009km and an amplitude exceeding 3\u2009cm are included in the analysis.\n\nEddy composites were constructed by aligning associated variables to the reference coordinate of the eddy core. The variables were normalized by the individual eddy radius and oriented to the prevailing direction of the large-scale background surface wind, in line with previous research9. Variables within twice the eddy radius were included for composite analysis.\n\nIn addition to eddy composite analysis, we also applied a high-pass Loess Filter with a cutoff wavelength of 30\u00b0 longitude x 10\u00b0 latitude (similar to a 5\u00b0\u2009x\u20095\u00b0 box car average3,11) in ERA5, CESM-HR and HighResMIP, to isolate mesoscale SST and LHF and examined the spatial distribution of their coupling strength (Fig.\u00a01a, b and Fig S4). The coupling strength at each grid point was computed using the linear regression coefficient between high-pass filtered monthly SST and LHF over a spatial domain of 4\u00b0\u2009x\u20094\u00b0. It was noted that applying a high-pass filter directly to monthly data yields a coupling coefficient comparable to that obtained when the filter is first applied to daily data, which is then aggregated into a monthly mean before calculating the coefficient. The former method was selected for its computational efficiency. To highlight regions with pronounced mesoscale SST-LHF coupling, mesoscale signals where the SST anomaly fell below 0.4\u2009\u00b0C in ERA5, below 0.6\u2009\u00b0C in CESM-HR, and coupling strength below 20\u2009W/m2/\u00b0C in CESM-HR were excluded when computing the decadal trend in coupling strength (Fig.\u00a01a, b).\n\nAccording to Bulk formula26, the latent heat flux QL is determined by the equation:\n\nHere, \u03c1a is the surface air density, \u039bv is the latent heat of vaporization, and Ce is the transfer coefficients for evaporation. U10 is the 10\u2009m wind speed, qs is the saturated specific humidity at the ocean surface, and qa is air specific humidity at 2\u2009m.\n\nFollowing previous studies27,28, Eq. (3) can be decomposed into large-scale background and mesoscale components. The mesoscale component of latent heat flux is estimated as follows:\n\nHere, the prime (\u2032) represents mesoscale anomalies defined by the high-pass spatial filtering, and the overbar (\u2006\u203e\u2006) denotes large-scale background excluding the mesoscale signal.\n\nBy differentiating with respect to SST, the mesoscale SST-LHF coupling is expressed as:\n\nBased on calculations, the second term \\(\\frac{{{{{\\rm{dU}}}}}_{10}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}({\\bar{{{{\\rm{q}}}}}}_{{{{\\rm{S}}}}}-{\\bar{{{{\\rm{q}}}}}}_{{{{\\rm{a}}}}})\\) on the right-hand side of Eq. (5) is an order of magnitude smaller than the first term \\({\\bar{{{{\\rm{U}}}}}}_{10}\\left(\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{s}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}-\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{a}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}\\right)\\). Furthermore, \\(\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{a}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}\\) is an order of magnitude smaller than \\(\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{s}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}\\). Disregarding the relatively smaller terms and assuming the changes in \\({\\bar{{{{\\rm{\\rho }}}}}}_{{{{\\rm{a}}}}}{\\bar{\\Lambda }}_{{{{\\rm{v}}}}}{\\bar{{{{\\rm{C}}}}}}_{{{{\\rm{e}}}}}\\) is minimal under global warming (with a relative change of approximately 2%, which is negligible compared to the 17% mesoscale moisture adjustment), the mesoscale SST-LHF coupling changes is predominately influenced by \\({\\bar{{{{\\rm{U}}}}}}_{10}\\frac{{{{{\\rm{dq}}}}}_{{{{\\rm{s}}}}}^{{\\prime} }}{{{{{\\rm{dSST}}}}^{\\prime} }}\\), which can be represented as:\n\nWhere \u2018F\u2019 represents future values and \u2018P\u2019 represents historical values in the past. As the large-scale wind, \\({\\bar{{{{\\rm{U}}}}}}_{10({{{\\rm{F}}}})}\\) and \\({\\bar{{{{\\rm{U}}}}}}_{10({{{\\rm{C}}}})}\\), experience minimal changes (ranging between -0.4% and -3.5% as shown in Fig.\u00a02) in the WBCs, Eq. (6) can be further simplified as:\n\nApplying a Taylor expansion, the right-hand side term can be approximated as:\n\nSubstituting (8) into (7) yields:\n\nWhere \\(\\frac{{{{{\\rm{d}}}}}^{2}{{{{\\rm{q}}}}}_{{{{\\rm{s}}}}}({{{\\rm{T}}}})}{{{{{\\rm{dT}}}}}^{2}}\\) is determined by C-C scaling and exhibits an exponential increase with temperature. Note that the composite coefficient (\\({\\bar{{{{\\rm{\\rho }}}}}}_{{{{\\rm{a}}}}}{\\bar{\\Lambda }}_{{{{\\rm{v}}}}}{\\bar{{{{\\rm{C}}}}}}_{{{{\\rm{e}}}}}\\)) is retained to align the estimated coupling strength changes with magnitude analogous to the actual projections.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "CMEMS data can be obtained through https://data.marine.copernicus.eu/products. NOAA-OISST can be accessed through https://www.ncei.noaa.gov/products/optimum-interpolation-sst. J-OFURO3 can be achieved through https://www.j-ofuro.com/en/. ERA5 reanalysis can be downloaded from https://doi.org/10.24381/cds.bd0915c633. The CESM simulations can be achieved through https://ihesp.github.io/archive/products/ds_archive/Sunway_Runs.html. The HighResMIP data can be downloaded from https://pcmdi.llnl.gov/CMIP6/.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "MATLAB codes to reproduce the analyses are available upon request from the corresponding author or can be accessed through the link https://zenodo.org/records/10610386.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This research is supported by the National Natural Science Foundation of China (42376025 to X.M., 42206016 to X.Z.), Science and Technology Innovation Program of Laoshan Laboratory (LSKJ202300302, LSKJ202202503 to X.M.), Shandong Provincial Natural Science Foundation (ZR2022YQ29 to X.M.), Taishan Scholar Funds (tsqn202103028 to X.M.). We thank Sunway TaihuLight High-Performance Computer (Wuxi), Laoshan Laboratory in Qingdao and the National Supercomputing center in Jinan for providing the high resolution CESM simulations and high-performance computing resources that contributed to the research results reported in this paper.",
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+            "section_name": "Author information",
+            "section_text": "Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China\n\nXiaohui Ma,\u00a0Lixin Wu,\u00a0Zhili Tang,\u00a0Fengfei Song,\u00a0Zhao Jing,\u00a0Hui Chen,\u00a0Yushan Qu,\u00a0Man Yuan,\u00a0Zhaohui Chen\u00a0&\u00a0Bolan Gan\n\nLaoshan Laboratory, Qingdao, China\n\nXiaohui Ma,\u00a0Xingzhi Zhang,\u00a0Lixin Wu,\u00a0Peiran Yang,\u00a0Fengfei Song,\u00a0Zhao Jing,\u00a0Zhaohui Chen\u00a0&\u00a0Bolan Gan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.M. and X.Z conceived the study. X.M. instructed the investigation and wrote the manuscript. X.Z. performed the analyses and produced all figures. L.W. supervised the project. Z.T. contributed to the preprocessing of model data. P.Y., F.S., and M.Y. contributed to the discussion of mesoscale thermal coupling decomposition. Y.Q and H.C. offered insights into eddy detection. Z.J., Z.C., and B.G. contributed to interpreting the results and improving the manuscript.\n\nCorrespondence to\n                Xingzhi Zhang.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Walter Robinson and the other, anonymous, reviewer for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
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+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions",
+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Ma, X., Zhang, X., Wu, L. et al. Midlatitude mesoscale thermal Air-sea interaction enhanced by greenhouse warming.\n                    Nat Commun 15, 7699 (2024). https://doi.org/10.1038/s41467-024-52077-z\n\nDownload citation\n\nReceived: 12 February 2024\n\nAccepted: 26 August 2024\n\nPublished: 04 September 2024\n\nVersion of record: 04 September 2024\n\nDOI: https://doi.org/10.1038/s41467-024-52077-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+        {
+            "section_name": "Abstract",
+            "section_text": "Exploiting solid electrolyte (SE) materials with high ionic conductivity, good interfacial compatibility, and ultraconformal contact with electrode are essential for solid-state sodium metal batteries (SSBs). Here we report a crystalline Na5SmSi4O12 SE which features high room-temperature ionic conductivity of 2.90\u00d710-3 S cm-1 and a low activation energy of 0.15 eV. All-solid-state symmetric cell with Na5SmSi4O12 delivers excellent cycling life over 800 h at 0.15 mA h cm-2 and high critical current density of 1.4 mA cm-2. Such excellent electrochemical performance is attributed to an electrochemically induced in-situ crystalline-to-amorphous (CTA) transformation propagating from the interface to the bulk during repeated deposition and stripping of sodium, which lead to faster ionic transport and superior interfacial properties. Impressively, the Na3V2(PO4)3||Na5SmSi4O12||Na SSBs achieves a remarkable cycling performance over 4000 cycles (6 months) with no capacity loss. These results not only identify Na5SmSi4O12 as a promising SE, but also emphasize the potential of the CTA transition as a promising mechanism towards long-lasting SSBs.Physical sciences/Materials science/Materials for energy and catalysis/Electrochemistry/BatteriesPhysical sciences/Chemistry/Energy",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "supplementarymaterials.pdfSupplementary Information",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Exploiting solid electrolyte (SE) materials with high ionic conductivity, good interfacial compatibility, and conformal contact with electrodes is essential for solid-state sodium metal batteries (SSBs). Here we report a crystalline Na5SmSi4O12 SE which features high room-temperature ionic conductivity of 2.9\u2009\u00d7\u200910\u22123\u2009S\u2009cm\u22121 and a low activation energy of 0.15\u2009eV. All-solid-state symmetric cell with Na5SmSi4O12 delivers excellent cycling life over 800\u2009h at 0.15\u2009mA\u2009h\u2009cm\u22122 and a high critical current density of 1.4\u2009mA\u2009cm\u22122. Such excellent electrochemical performance is attributed to an electrochemically induced in-situ crystalline-to-amorphous (CTA) transformation propagating from the interface to the bulk during repeated deposition and stripping of sodium, which leads to faster ionic transport and superior interfacial properties. Impressively, the Na|Na5SmSi4O12|Na3V2(PO4)3 sodium metal batteries achieve a remarkable cycling performance over 4000 cycles (6 months) with no capacity loss. These results not only identify Na5SmSi4O12 as a promising SE but also emphasize the potential of the CTA transition as a promising mechanism towards long-lasting SSBs.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Solid-state batteries (SSBs) are expected to provide key improvements over today\u2019s rechargeable batteries owing to the inherent merits of solid electrolytes (SEs) such as high safety, long-lasting life, and high energy density1,2. Among them, sodium-based SSBs are drawing ever-increasing interest because of the abundant resource in nature, beneficial to cost saving and sustainability3,4. Despite the progress of sodium-based SSBs in the past few years, it is still a significant challenge to exploit low-cost and facile synthesized SEs with high ionic conductivity, excellent mechanical and chemical stability. Moreover, the poor wetting of the solid-solid interface with sluggish interfacial kinetics is a big hurdle to future Na-SSBs development5,6.\n\nNa-based inorganic SEs can be divided into two categories, e.g., sulfides like Na3SbS4, Na11Sn2PS12, Na7P3S11 and oxides including Na-\u03b2\u201c-Al2O3, NASICON type7. Sulfide electrolytes feature higher ionic conductivity and better ductility than the oxides SEs8,9. However, their chemical instability against air and narrow electrochemical windows are likely to induce complex side reactions, leading to shortened cycling lives10,11. In contrast, NASICON-type oxides could deliver high thermal and chemical stability, and low thermal expansion12,13. Nevertheless, large grain boundary resistance and harsh synthesis conditions are critically challenging for their application14,15. In 1978, Shannon et al. first reported the synthesis of a new inorganic material Na5MSi4O12 and found that the ionic conductivity gradually increases with the increase of M3+ ionic radius with ionic conductivity of 10\u22121\u2009S\u2009cm\u22121 at 200\u2009\u00b0C16. Recently, our group achieved a room-temperature ionic conductivity of 1.59\u2009\u00d7\u200910\u22123\u2009S\u2009cm\u22121 for Na5YSi4O12 via optimization of the synthesis condition17. In comparison with the NASICON-type materials, Na5YSi4O12 can be synthesized at a lower temperature which is beneficial to cost- and energy-saving. More impressively, the stable structure provides sufficient freedom of materials optimization and design by substituting the Y sites by for instance, In, Sc, and the rare earth Lu-Sm, to further enhance the ionic conductivity and understand the ion-conducting behavior of this class of materials18.\n\nBesides the difficulty in the SEs exploration, another challenge of developing Na-SSBs lies in the electrode\u2013electrolyte interfaces, in the mechanics throughout the cell, and in processing at scale19. To begin with, the poor ductility and crystallographic orientation-dependent ionic transport properties of oxide SEs likely induce a large interfacial resistance and sluggish kinetics20,21. In addition, during the continuous dissolution of sodium, the formation of pores at the sodium metal anode interface will further worsen the interfacial physical contact and generate unavoidable surface defects that could disturb sodium-ion flux and work as the nucleation center, leading to rapid dendrite nucleation and growth22,23. Moreover, most SEs are intrinsically thermodynamically unstable against sodium metal, which induces degradation and forms mixed conducting interphases, further accelerating the dendrite propagation24,25. To address these issues, many approaches are employed to fabricate an artificial layer on the surface of sodium metal to improve sodium wetting, chemical stability, and thus reduce interfacial impedance, such as TiO226, SnS227, AlF328, and polymer with intrinsic nanoporosity (PIN)29. However, they face practical limitations, such as complex synthesis procedures and difficulty in controlling the thickness and achieving acceptable adhesion. Even worse, the artificial interface layer is likely to introduce additional interfacial issues with bulk SEs, such as unexpected phase transition, uneven ion flux distribution, and electrostatic potential drop and formation of \u201cspace-charge layer,\u201d seriously limiting the ion transport and reducing the cycle life of SSBs30.\n\nHerein, we report the synthesis and electrochemical properties of Na5SmSi4O12, which has the highest room-temperature ionic conductivity of 2.9\u2009\u00d7\u200910\u22123\u2009S\u2009cm\u22121 among the Na5MSi4O12 family that has been reported. Interestingly, we observe an electrochemically induced crystalline-to-amorphous (CTA) transformation of Na5SmSi4O12 SE during repeated deposition and stripping of Na. This CTA transition is attributed to the lattice stress generated upon Na+ transportation rather than phase transformation due to chemical instability. When applied in a Li symmetric cell with the same cell configuration (Li|Na5SmSi4O12|Li), this CTA process is speeded up because of the mismatch between Li+ and Na+ ionic radius, which further improves the selectivity of the Li SSBs. Beneficial from the enhanced mechanical properties, decreased ion mobility activation energy, and lower interfacial energy of amorphous material and interface than crystalline Na5SmSi4O12, symmetric Na cells deliver a low overpotential of ~26\u2009mV and stable cycling performance over 800\u2009h at 0.15\u2009mA\u2009cm\u22122. Moreover, the amorphous Na5SmSi4O12 facilitates intimate contact of SE with Na metal and brings essentially improved critical current density (CCD) of 1.4\u2009mA\u2009cm\u22122 in comparison with the initial crystalline stage (0.6\u2009mA\u2009cm\u22122). By virtue of the decreased resistance of sodium metal anode, the assembled quasi-solid-state Na|Na5SmSi4O12|Na3V2(PO4)3 cell demonstrates an ultra-long cycle lives over 4000 cycles with ~100% Coulombic efficiency and capacity retention, indicative of the promising application of Na5SmSi4O12 SE in future large-scale energy storage.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Rhombohedral-prismatic crystalline Na5SmSi4O12 was successfully synthesized via two-step solid-state reaction. Detailed synthesis processes are given in the experimental sections (Supplementary Figs.\u00a01 and 2). Note that the sintering temperature of Na5SmSi4O12 is lower than other oxide SEs (Supplementary Table\u00a01), such as Na5YSi4O12, Na-\u03b2\u201c-Al2O3, Na3Zr2Si2PO12 and its derivatives, etc., good for the cost- and energy-saving. As shown in Fig.\u00a01a, X-ray diffraction (XRD) was taken to identify the structural property of Na5SmSi4O12, and the Rietveld refined parameters are listed in Supplementary Table\u00a02. All the diffraction peaks can be indexed into a rhombohedral system with space group R\u22123c, and the lattice parameters are calculated as a\u2009=\u2009b\u2009=\u200922.1461\u2009\u00c5, c\u2009=\u200912.6886\u2009\u00c5. Energy dispersive X-ray spectroscopy (Supplementary Fig.\u00a03) suggests all the elements of Na, Sm, Si, and O are uniformly dispersed in the as-prepared Na5SmSi4O12.\n\na Rietveld refinement based on the powder XRD. b Minimum potential energy path along Na+ diffusion route in crystalline Na5SmSi4O12. c Solid-state 23Na NMR spectrum and its simulation for the crystalline Na5SmSi4O12. The gray line is experimental data and the green-dashed line is the sum of simulation. d Saturation recovery fitting curve for the data obtained at room temperature. e Temperature dependence of 23Na NMR relaxation rate as a function of temperature in K\u22121. The solid line is the fit according to Eq. (1). The derivation of the data is not used for the fit.\n\na Cycling performance of Na|Na5SmSi4O12|Na at room temperature. b Nyquist plots of the SE-based symmetric cell after cycling for different times. Cross-sectional SEM images of the Na metal/Na5SmSi4O12 interfaces: c pristine and d 100\u2009h cycling. e XRD profiles of Na5SmSi4O12 after cycling for different times. f HRTEM and g SAED patterns of Na5SmSi4O12 after cycling.\n\nThe ionic conductivity of Na5SmSi4O12 was then studied by the alternating current (AC) impedance spectra and the Nyquist plot at room temperature is as presented in Supplementary Fig.\u00a04a. Via fitting the bulk and grain boundary resistance, the total ionic conductivity of Na5SmSi4O12 is calculated as 2.9\u2009\u00d7\u200910\u22123\u2009S\u2009cm\u22121, among the highest values in the existing ceramic electrolytes (Supplementary Table\u00a01). Activation energy (Ea) of Na5SmSi4O12 was further evaluated by fitting the Arrhenius plot (Supplementary Fig.\u00a04b). Ea is calculated as small as 0.15\u2009eV, which is smaller than that of Na5YSi4O12, indicative of a fast ionic transportation ability. In addition, the electronic conductivity of Na5SmSi4O12 was measured as about 5.8\u2009\u00d7\u200910\u221210\u2009S\u2009cm\u22121 via a direct current (DC) polarization measurement (Supplementary Fig.\u00a05). The intrinsic electronic insulation can effectively reduce the self-discharge of batteries and suppress dendrite growth, enabling Na5SmSi4O12 as a good candidate for sodium-based SSBs31. Besides the merits of high ionic and low electronic conductivities, Na5SmSi4O12 demonstrates superior moisture stability, whose XRD pattern shows no change after soaking in deionized water for 48\u2009h (Supplementary Fig.\u00a06) or exposed to air for 45 days (Supplementary Fig.\u00a07), showing great potential in future industrial application.\n\nNa5SmSi4O12 demonstrates similar ionic transportation processes as Na5YSi4O1217 and detailed calculation results are listed in Supplementary Fig.\u00a08a\u2013d. To be specific, the conductivity is contributed by a percolating conduction pathway in the mobile region, whereas the rest of Na ions serve as a pillar to hold the structure together. Interestingly, along the ion conduction pathway, three distinctive sites are revealed where Na ions can stably sit, denoted as sites A, B, and C. These sites are connected to each other via the zigzag-like channel as observed from the molecular dynamics simulation trajectory. Furthermore, the diffusion barrier of Na ions between these sites is found to be ~0.3\u2009eV, which is relatively low in comparison with other reported solid ion conductors32,33. However, such a value is larger than the activation energy from experiment. We assign such a deviation to the concerted Na hopping behavior. The ~0.3\u2009eV barrier was calculated by assuming a vacancy-mediated uncorrelated conduction mechanism, while the Na concentration is relatively high and concerted motion is favored. The case of concerted motion is further estimated by assuming a two-ion correlated hopping mechanism, as shown in Fig.\u00a01b. According to such a mechanism, the diffusion barrier along the same path dropped to ~0.19\u2009eV, close to the experimental value.\n\nFurthermore, solid-state 23Na nuclear magnetic resonance (NMR) measurements were carried out to reveal the atomic local structure and dynamics mobility34,35,36. As exhibited in Fig.\u00a01c, the pristine Na5SmSi4O12 conductor shows multiple 23Na resonances, which can be further deconvoluted into six types of signals. Via the aid of crystal structure and the proportion of sodium, the peak at 8.8 ppm is assigned to the mobile sodium at Na5 site, the resonance at 4.5 and 1.6 ppm are attributed to Na1 and Na3, the signal at \u221216.8 ppm is from Na4, and the rest peaks at \u221223.4 and \u221228.6 ppm are assigned to sodium at Na2 and Na6 sites, respectively. The simulated details are listed in Supplementary Table\u00a03, from which the proportion of each Na atom agrees with the theoretical results. To obtain more information about Na+ dynamics properties, NMR spectra at different temperatures were performed. As displayed in Supplementary Fig.\u00a09, the Na5SmSi4O12 show similar spectra upon increasing temperature from 213 to 367\u2009K, except a decrease in signal due to Boltzmann distribution. No obvious change is observed for spectral configuration, possibly attributed to the stable structure caused by the non-mobile Na ions at Na1, Na2, and Na3 sites. Therefore, we turn to a more temperature-sensitive parameter, spin-lattice relaxation (SLR) time (T1). Since the strong quadrupole interaction is observed for 23Na NMR spectrum in our system, the saturation recovery technique is employed to determine T1 values37. Via fitting the spectral intensity vs. saturation recovery period measured at room temperature (Fig.\u00a01d), two relaxation time values of 0.5 and 20\u2009ms are obtained for the non-mobile and mobile Na ions, respectively38. Here, the data analysis is simplified to non-mobile and mobile for convenience. According to Eq. (1), the fitting of the relaxation times as a function of temperature yields an activation energy Ea\u2009\u2248\u20090.13\u2009eV for the mobile Na+, which is in good agreement with the results from AC impedance (0.15\u2009eV).\n\nWhere R1 is NMR spin-lattice relaxation rate, the reciprocal of T1, \u03c90 is resonance frequency, \u03b2 is modified exponent, Ea is activation energy, k is Boltzmann constant, T is the absolute temperature in K. Note that the derivate data points marked by hollow circle, as displayed in Fig.\u00a01e, were excluded from the fits since the R1 rates (1/T1) recorded at low temperature range are mainly governed by non-diffusive background effects, such as lattice vibrations or coupling by paramagnetic impurities39,40,41.\n\nTo further evaluate the chemical and electrochemical stability of SE against Na metal, a symmetric all-solid-state Na cell using Na5SmSi4O12 as the electrolyte was fabricated and tested by the repeatedly galvanostatic stripping and plating at different current densities. The charge/discharge profiles of Na|Na5SmSi4O12|Na without any interfacial modification were recorded at a current density ranging from 0.05 to 0.15\u2009mA\u2009cm\u22122 with 120\u2009min per cycle (Fig.\u00a02a). The cell displays stable and long cyclic performance which maintains an overvoltage of ~26\u2009mV at 0.15\u2009mA\u2009cm\u22122 with negligible fluctuations over 800\u2009h, indicating sodium dendrite-free plating/stripping and excellent kinetic stability of the Na5SmSi4O12 against Na metal. Afterwards, electrochemical impedance was collected from the symmetric cell after cycling 150, 200 and 300\u2009h to reveal the changes in the internal resistance, as illustrated in Fig.\u00a02b and Supplementary Table\u00a04. The resistance of SE (both Rb and RGB) remains nearly unchanged as the sodium plating/stripping proceeds. In contrast, the interfacial resistance (Rint) between sodium metal and SE decreases at the initial few cycles and then stabilizes at a relatively low value, demonstrating excellent compatibility between Na5SmSi4O12 and sodium metal. This phenomenon is quite different from most of the reported SEs without surface modification, whose Rint increases gradually with increasing cycling times as the result of the excessive internal resistance and failure of the cells due to interfacial side reactions or the formation of pores25,28,42.\n\nScanning electron microscopy (SEM) images of the electrodes at different plating/stripping stages are shown in Fig. 2c, d, which demonstrates an interesting tendency of gradually vanishing gap at the interface between Na and Na5SmSi4O12 after cycling and a compact interfacial contact was created. XRD pattern after cycling 200\u2009h suggests a transformation from the crystalline into the amorphous state on the surface of Na5SmSi4O12 SE (Fig.\u00a02e). To further assess the amorphous depth of Na5SmSi4O12, XRD was utilized to monitor the structural changes at various polishing depths, as depicted in Supplementary Fig.\u00a010. The analysis reveals that the SE pellet, after undergoing 100\u2009h of cycling at 0.15\u2009mA\u2009cm\u22122, exhibits no reflections, indicating complete amorphization near the surface of the Na5SmSi4O12 SE. As the polishing depths increased, a limited number of weak reflections of Na5SmSi4O12 emerge, which suggests a gradual amorphization process from the surface to the bulk. Upon polishing to a depth of 0.15\u2009mm, sharp reflections appear which means the crystalline SE may constitute the majority of the volumetric ratio within the entire Na5SmSi4O12 chip. To quantify the weight fraction of amorphization in an SE sample and by what kinetics, the internal standard method was employed to further assess the CTA transformation process, by mixing CeO2 with the Na5SmSi4O12 SE at different plating/stripping stages. The Rietveld refinements and the corresponding results are summarized in Supplementary Figs.\u00a011 and\u00a012, respectively, which clearly reveal the weight fraction of amorphization is strongly related to both the cycling time and current density. With increasing the cycling time, a higher weight fraction of the amorphous phase can be realized and a higher applied current density can accelerate the amorphization process. Additionally, the\u00a0high resolution transmission electron microscope\u00a0(HRTEM) and\u00a0selected area electron diffraction (SAED) measurements were undertaken to further confirm the CTA transition before (Supplementary Fig.\u00a013) and after cycling (Fig.\u00a02f, g). There are no observed lattice fringe and diffraction spot for the cycled SE sample, confirming the electrochemistry-induced CTA transition. Because of the amorphous state, it is difficult to determine the possible local coordination based on the XRD measurement. Therefore, Raman spectra were then used to examine the short-range vibration changes. As displayed in Supplementary Fig.\u00a014, crystalline Na5SmSi4O12 exhibits seven vibration peaks43: the bands in the 900\u20131100\u2009cm\u22121 region are assigned to Si-O stretching vibrations, the symmetrical band at ~624\u2009cm\u22121 is corresponding to O-Si-O bending mode. The low-frequency bands (<550\u2009cm\u22121) are attributed to Sm-O and Na-O bond vibrations in their polyhedral. Though the cycled Na5SmSi4O12 SE loses its long-term ordering (Fig.\u00a02e\u2013g), it maintains nearly all the vibrational peaks that confirm no chemical reaction between the interface of SE and Na metal. The disappearance of the peak at 1041\u2009cm\u22121 might be related to the damage of the fracture of Si-O bond by the amorphous transition. Furthermore, X-ray photoelectron spectroscopy (XPS) suggests that there are no changes in the Sm 3d, Na 1s, and Si 2p XPS spectra after cycling, indicative of no redox reaction occurred between sodium metal and Na5SmSi4O12 (Supplementary Fig.\u00a015). Thus, it can be concluded that Na5SmSi4O12 SE demonstrates an interesting CTA transition with excellent electrochemical stability that enables it as the ideal SE for sodium-based SSBs.\n\nCritical current density (CCD) and long-term cycling performance of Na|Na5SmSi4O12|Na with a long single deposition time were further measured to evaluate the capability of amorphous materials and interface in suppressing dendrite growth. As displayed in Fig.\u00a03a and Supplementary Fig.\u00a016, the amorphous Na5SmSi4O12 facilitates intimate contact of SE with Na metal and brings essentially improved CCD of 1.4\u2009mA\u2009cm\u22122 in comparison with the initial crystalline stage (0.4\u2009mA\u2009cm\u22122). This behavior is mainly because the uneven metal deposition leads to rapid growth of sodium dendrites along the crystalline Na5SmSi4O12 grain boundary with a large deposition current and hence results in the short circuit rapidly. The result is also much higher than most reported values based on oxide SEs without and with SE/Na metal anode interfacial modification (Supplementary Table\u00a06), indicating the superiority of amorphous SE interfaces. In addition, as shown in Supplementary Fig.\u00a017a, under a large area capacity, the overpotential increases during the cycling and drops suddenly less than 25\u2009h, which suggests that crystalline Na5SmSi4O12 can be easily penetrated by sodium dendrites. By contrast, if a low area capacity of 0.05\u2009mA\u2009h cm\u22122 is applied in advance to make the electrolyte transition to an amorphous state, the Na|amorphous Na5SmSi4O12|Na cell displays a stable and long cycling performance which maintains the overvoltage at around 20\u2009mV over 500\u2009h (Supplementary Fig.\u00a017b). All these results indicate the strong capability of amorphous Na5SmSi4O12 in suppressing Na dendrite formation.\n\na Potential response of Na|Na5SmSi4O12|Na cell during the CCD measurement with a low area capacity of 0.05\u2009mA\u2009h\u2009cm\u22122 is applied for 150\u2009h in advance. b Schematic illustration of Na|Na5SmSi4O12|Na3V2(PO4)3. c CV curve of SS|Na5SmSi4O12|Na cell at a scanning speed of 5\u2009mV\u2009s\u22121. d Cycling performance at 0.5 C-rate (1\u2009C corresponds to 118\u2009mA\u2009g\u22121). e Rate capability at 0.2, 0.5, 0.75, and 1 C-rate. f Long-term cycle life at 2 C-rate.\n\nTo further emphasize the superiority of amorphous interface and bulk materials, a Na|Na5SmSi4O12|Na3V2(PO4)3 (NVP) SSB was constructed and evaluated at room temperature, as illustrated in the schematic figure (Fig.\u00a03b). The cyclic voltammetry (CV) curve of stainless steel (SS)|Na5SmSi4O12|Na cell in Fig.\u00a03c shows that Na5SmSi4O12 possesses a wide electrochemical stability window more than 5\u2009V, which is high enough to ensure that the electrolyte does not undergo phase transition within the working voltage range. As shown in Supplementary Fig.\u00a018a, quite flat charge\u2013discharge voltage profiles at 2.3\u20133.9\u2009V with an initial discharge capacity of 112\u2009mA\u2009h\u2009g\u22121 were observed, which matches well with the characteristic NVP redox plateaus in liquid electrolyte (Supplementary Fig.\u00a018b). In addition, the high initial Coulombic efficiency of 99% indicates that there is no irreversible side reaction between Na5SmSi4O12 and Na anode or NVP cathode. Meanwhile, an excellent cycling performance is achieved with high-capacity retention of 95% after 100 cycles (Fig.\u00a03d). Furthermore, specific capacities of 102, 98, and 93\u2009mA\u2009h\u2009g\u22121 can be obtained at 0.5, 0.75, and 1 C-rates, respectively, suggestive of a superior rate capability (Fig.\u00a03e). Finally, the long-term cycling stability was estimated at a current rate of 2\u2009C (Fig.\u00a03f). Impressively, there is no obvious capacity loss during the repeated 4000 cycles (6 months), demonstrating superiority in the state-of-the-art solid-state full cell in Supplementary Table\u00a07. All these features verify the unique interface properties between Na5SmSi4O12 and sodium, which can not only enable sufficient contact with sodium metal, but also inhibit the side reaction and the dendrite growth during cycle process.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "According to the above-mentioned results, the electrochemical-induced CTA transition plays a key role in stabilizing the interfacial properties, suppressing the dendrite formation, and thus increasing the long-term stability and high-rate capability. This superiority of the amorphous stage from the interface to the bulk SE material can be understood in terms of the following two aspects. Firstly, the ionic conductivity of amorphous bulk material was improved. The pathway and the ion transport properties of amorphous Na5SmSi4O12 were investigated in detail by solid-state NMR. Figure\u00a04a shows the 23Na NMR spectra before and after metallic Na cycling by using crystalline Na5SmSi4O12 as an electrolyte. The 23Na NMR spectrum did not change significantly before and after cycling, except changes in the mobile ions (Na4, Na5, and Na6), indicative of their slight redistribution upon cycling. Nevertheless, NMR results demonstrate the non-mobile and mobile segments for Na migration within a stable structure. In addition, spin-lattice relaxation time T1 of 23Na was measured at different temperatures for amorphous Na5SmSi4O12, as shown in Fig.\u00a04b and Supplementary Fig.\u00a019. The activation energy of the mobile Na+ of amorphous Na5SmSi4O12 is calculated as 0.07\u2009eV lower than that of the pristine crystalline state (0.13\u2009eV in Fig.\u00a01k). The decrease in the active energy strongly suggests an enhanced Na+ hopping ability for the amorphous Na5SmSi4O12 with higher ionic conductivity. Secondly, the interfacial issues, such as high interfacial resistance and metal dendrite growth, are strongly alleviated. The schematic illustration of sodium deposition is provided in Fig.\u00a04d. A locally solid\u2013solid contact induces a heterogeneous sodium-ion flux, whereas a tight interfacial contact of amorphous Na5SmSi4O12 lead to uniform deposition of sodium. As mentioned above in Fig.\u00a02b, there is obvious decrease in the Rint upon cycling at different plating/stripping stages. The interesting phenomenon can be attributed to compact interfacial contact between Na metal and electrolyte due to the increased contact area (Fig.\u00a02c, d). Furthermore, the isotropic surface of amorphous material could enhance the wettability with significantly reduced interface resistance and eliminates the influence of crystallographic orientation-dependent ionic transport since the interfacial energy of amorphous Na5SmSi4O12 is calculated as 0.33\u2009J\u2009m\u22122 with sodium, lower than the crystalline Na5SmSi4O12 (0.56\u2009J\u2009m\u22122) (Supplementary Fig.\u00a020). In addition, the amorphous-Na5SmSi4O12 deliver the high mechanical strength, beneficial to inhibiting the dendrite growth. As shown in Fig.\u00a04c, nanoindentation technique was employed to evaluate the Young\u2019s modulus E and hardness H44,45. As for the amorphous sample, the E and H are calculated to be ~79.9 and ~3.8\u2009GPa, respectively, higher than the crystalline Na5SmSi4O12 (~72.6 and ~2.8\u2009GPa). In summary, the intrinsic microstructural and compositional homogeneity, as well as low electronic conductivity, alleviate the potential fluctuations at local positions in the SE and suppress sodium propagation and penetration into amorphous Na5SmSi4O12.\n\na Solid-state 23Na NMR of the pristine crystalline and the cycled amorphous Na5SmSi4O12. b 23Na NMR relaxation rate of Na cycled Na5SmSi4O12 as a function of temperature in K\u22121. c Nanoindentation load\u2013displacement curves of crystalline Na5SmSi4O12 and amorphous Na5SmSi4O12. d Schematic of interface morphology evolution during sodium plating/stripping.\n\nElectrochemically-induced solid-state amorphization (SSA) transformations are popular in the alloying type anode, like the transition from nano Si into amorphous Li-Si phases after electrochemical lithiation46,47. Nevertheless, this kind of SSA process usually undergoes a chemical phase transition, which is disastrous for SEs. To the best of our knowledge, it is the first observation about the electrochemically induced CTA transformation in oxide SEs during alkali metal plating/stripping without chemical phase transition. Generally, the SSA transformation depends on the thermodynamic driving force (pressure, defects, internal stress, etc.), and the existence of kinetic constraints (mainly because of the low experimental temperature) that prevent the formation of full equilibrium crystalline phases48,49,50. For example, when ion conduction is non-homogeneous, the local variance may lead to large local stress, which could in turn lead to the collapse of the structure. Through cycling, SSA transformation may occur. As presented in the schematic crystal structure of Na5SmSi4O12 (Supplementary Fig.\u00a021a), vacancies are observed at Na4, Na5 and Na6 sites, which may accommodate additional Na ions\u2019 insertion and thus induce local variance. This possible insertion can be confirmed by the presence of the initial discharge capacity of Na5SmSi4O12 (Supplementary Fig.\u00a021b) and an increase in the Na content after cycling (energy dispersive spectroscopy mapping, Supplementary Table\u00a08). As the local stress accumulates, the internal strain field leads to the CTA transformation. In this case, we calculate the formation energies of the crystalline and the amorphous phases sampled from quenched melts. As shown in Supplementary Fig.\u00a022, the crystalline phases are thermodynamically favored with an energy ~60\u2009meV\u2009atom\u22121 lower than the amorphous structures. This indicates that, besides the thermodynamic force, kinetics may also play a critical role in the SSA. One way to magnify such kinetic driving force is to apply large lattice strain on the system. In this context, we computationally substitute the Na ions to Li ions in the structures and further compute the energy difference between the crystalline and the amorphous phases. Interestingly, when the Li concentration reaches ~2/5, the energy difference significantly drops to <10\u2009meV\u2009atom\u22121. This further supports our assumption that the driving force led by the large lattice strain could be the origin of the SSA. Following the above results, a Li-Na exchange process is captured by atomistic simulations, since the ionic radius of Li+ (76\u2009pm) is smaller than Na+ (102\u2009pm) that could induce much larger lattice strain. Large-scale molecular dynamics is run on the hypothetical Na5SmSi4O12/Li5SmSi4O12 using a machine-learned forcefield, as shown in Fig.\u00a05a. Interestingly, the Li ions first exchanges with the mobile Na ions at the reaction front followed by mixing with the non-mobile ones, see Fig.\u00a05b\u2013e. When the pillar Na ions are replaced by smaller Li ions, the structure start to collapse. During the initial exchange process, crystalline Na5SmSi4O12 and Na5-xLixSmSi4O12 could co-exist. We computationally evaluated how Li substitution affects the ion diffusion. As shown in Fig.\u00a05f, when all mobile Na ions are replaced by Li, Li diffusion barrier along the original zig-zag route dropped from ~0.3 to ~0.25\u2009eV, indicating a faster diffusion kinetics, which may in turn, result in faster SSA during Li exchange. In conclusion, lattice strain induced by ion intercalation is the main driving force of amorphous transformation. Once the thermodynamic driving force is present, the kinetic hindrance at room-temperature prevents the transition back to amorphous Na5SmSi4O12.\n\na Structure of the Na5SmSi4O12|Li5SmSi4O12 interface model. b Molecular dynamics trajectories of the interface model. Molecular dynamics simulation trajectories of crystalline c Li5SmSi4O12 d Na5SmSi4O12, and e Na3Li2SmSi4O12. f Minimum potential energy path along Li diffusion route in crystalline Na3Li2SmSi4O12. g XRD profiles of Na5-xLixSmSi4O12 after cycling for different times. Solid-state h 23Na and i 7Li NMR of the Na5SmSi4O12 with different cycling times.\n\nGuided by the computational results, a hybrid symmetric cell of Li|Na5SmSi4O12|Li was fabricated to accelerate this SSA transition. As shown in Supplementary Fig.\u00a023, the cell reveals uniform plating and stripping overpotential profiles with an increased current density. This phenomenon suggests that hybrid movement of Li+/Na+ within the bulk of Na5SmSi4O12 and an effective plating/stripping of Na+ at Li anode. As expected, Na5SmSi4O12 exhibits a much shorter amorphous time within 100\u2009h (Fig.\u00a05g and Supplementary Fig.\u00a024), confirming that the CTA transition of Na5SmSi4O12 is mainly triggered by the lattice strain and speeded up because of the mismatch between Li+ and Na+ ionic radius. Furthermore, an obvious reflections shift and weakening can be observed in the XRD patterns after experiencing different cycling time (Supplementary Fig.\u00a025). This shift indicates the existence of microscopic strain in the lattice, and the gradual accumulation of stress leads to the break of more bonds. Thus, with the increase of cycling time, the crystallinity of Na5SmSi4O12 weakens, and the intensity of XRD peaks decreases gradually. To assess the cationic electrochemical exchange mechanism, XPS analysis of SE operating in the hybrid cell was carried out after cycling. As shown in Supplementary Fig.\u00a026, the decrease in Na 1s peak and appearance of Li 1s peak prove that Li+ can successfully replace part of Na+ ions and the strong Li+ mobility within rhombohedral-prismatic Na5SmSi4O12. In addition, there is no observed new Raman peaks after cycling in the hybrid symmetric cell (Supplementary Fig.\u00a027), which indicates that Na5SmSi4O12 SE demonstrates an excellent thermodynamic stability with Li metal.\n\nFigure\u00a05h shows the 23Na spectra of Na5SmSi4O12 cycled with Li metal under different times, from which the stripped Li will exchange with Na on its way when across the electrolyte. The signals at \u221216.8 ppm and \u221223.4 ppm, being assigned to sodium at Na4 and Na6 sites, significantly weakened, reflecting the transport activity of Na4 and Na6 sodium sites during polarization. In addition, the signal at 8.8 ppm, which is assigned to sodium at Na5 site, is weakened but fluctuates in intensity at different cycle times, indicating it likely serves as \u2018bridge\u2019 for transporting Na/Li ions during polarization. These results further conclude that there is a possible 3D pathway of Na5SmSi4O12 between Na4-Na5-Na6. Figure\u00a05i and Supplementary Fig.\u00a028 display the 7Li NMR spectrum of Na5-xLixSmSi4O12 after Li cycled different times (100 and 200\u2009h). The 7Li NMR spectrum of the electrolyte cycled for 200\u2009h is broader than that for 100\u2009h, indicating the growth of amorphous phase. 6Li NMR spectrum after Li cycling is shown in Supplementary Fig.\u00a029. Both 7Li and 6Li NMR spectra present two components, corresponding to the different mobile Na sites, such as Na4 and Na6.\n\nIn summary, dense crystalline Na5SmSi4O12 was prepared and exhibits a high room-temperature conductivity of 2.9\u00d710-3\u2009S\u2009cm\u22121. Driven by microscopic strain in the lattice, Na5SmSi4O12 undergoes an amorphous transformation during the cycling in both symmetric Na|Na5SmSi4O12|Na and Li|Na5SmSi4O12|Li cells. On the one hand, the increased contact area greatly reduces the interfacial resistance between sodium metal and electrolyte and promotes the homogeneous deposition of sodium. On the other hand, the amorphous Na5SmSi4O12 exhibits isotropic ionic transport characteristics, which effectively eliminate ion-blocking crystallographic orientations. This property promotes a uniform distribution of current and homogeneous metal nucleation at the anode interface, further enhancing the overall performance of the solid-state sodium metal battery. Thus, the sodium symmetrical cells manifest stable cycling performance for 800\u2009h at 0.15\u2009mA\u2009cm\u22122@1\u2009h and 500\u2009h at 0.05\u2009mA\u2009cm\u22122@5\u2009h (25\u2009\u00b0C). Furthermore, the successful operation of Na|Na5SmSi4O12|Na3V2(PO4)3 quasi-solid-state sodium batteries with excellent electrochemical performance further implies the superiority of Na5SmSi4O12 electrolyte.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The Na5SmSi4O12 pellets were synthesized by a solid-state sintering method using Na2CO3, Sm2O3 and SiO2 as the starting materials. First, the raw materials of analytical grade were mixed by ball-milling at a milling speed constant of 600\u2009rpm for 15\u2009h. The mixture was dried at 80\u2009\u00b0C for 12\u2009h and calcined at 800\u2009\u00b0C for 8\u2009h. Then the powder was put into a cylindrical pressing mold with diameter of 15\u2009mm and pressed under a pressure of 300\u2009MPa. The pressed pellets were then sintered at 950\u2009\u00b0C for 20\u2009h. Finally, buff pellets were obtained after sintering.\n\nThe Na3V2(PO4)3 (NVP) cathode material was prepared by the sol-gel method according to our previous work (Supplementary Fig.\u00a030)51. Firstly, the stoichiometric amount of Na2CO3, NH4VO3 and NH4H2PO4 with a molar ratio of 3:4:6 was dissolved in deionized water. Secondly, 0.02\u2009M aqueous citric acid [HOC(COOH)(CH2COOH)2] solution was added dropwise into the solution until the ratio of vanadium: citric acid equals to 2:1. Then the gel could be acquired by drying the precursor in an oven at 120\u2009\u00b0C for 12\u2009h. Finally, the NVP/C powder can be acquired after heat treatments in two steps, first at 350\u2009\u00b0C for 5\u2009h and then at 750\u2009\u00b0C for 12\u2009h under a nitrogen atmosphere.\n\nXRD patterns were recorded by a Bruker D8 Advance diffractometer with Cu K\u03b1 radiation and RigaKu D/max-2550 diffractometer (1.6\u2009kW, Cu K\u03b1 radiation, \u03bb\u2009=\u20091.5406\u2009\u00c5), followed by Rietveld refinement using Fullprof software for the crystal structure analysis. The microscopy characteristics of the samples were investigated by Hitachi Regulus8100 FESEM, high resolution transmission electron microscope (HRTEM, talos F200X) and selected area electron diffraction (SAED). The elemental mapping was used to analyze the element distribution of the samples. XPS was carried out on a thermo scientific NEXSA spectrometer. Raman spectra were examined using a Renishaw Raman microscope (model 2000) with Ar-ion laser excitation. Prior to analysis the interface properties between Na/Li metal and Na5SmSi4O12 electrolyte after cycling, emery paper was employed to remove any residual metal on the surface. All 6Li, 7Li, and 23Na magic angle spinning (MAS) NMR experiments were acquired on Bruker 400\u2009MHz (9.4\u2009T) magnets with AVANCE NEO consoles using Bruker 3.2\u2009mm HXY MAS probe. The samples were filled into rotors inside Argon glove box. The Larmor frequencies for 6Li, 7Li, and 23Na were 58.89, 155.53 and 105.86\u2009MHz, respectively. All spectra were acquired by using one-pulse program and were referenced to 1\u2009M LiCl (6Li and 7Li) and 1\u2009M NaCl (23Na) solutions with chemical shifts at 0 ppm. The spinning rate \u03bdrot was set to 14\u2009kHz. 23Na spin-lattice relaxation times (T1) were recorded by using the saturation recovery pulse sequence. The varying temperature experiments were protected by N2 atmosphere. Nanoindentation measurement was taken on a nanoindentation tester (Agilent Nano Indenter G200) equipped with a three-sided pyramidal Berkovich diamond indenter. The applied standard loading, holding, and unloading times were 10, 5, and 10\u2009s, respectively. During the testing, the load-displacement curves up to pellet cracking were recorded and utilized to calculate the Young\u2019s modulus E and hardness H using the Oliver\u2013Pharr method. Indentations with maximum indentation load of 1\u2009mN are conducted on the surface of SE pellets. The reduced modulus Er was determined by the unloading stiffness and projected contact area. By assuming a Poisson\u2019s ratio of 0.3 for samples and 0.07 for single crystalline diamond, their Young\u2019s modulus were estimated.\n\nFor the conductivity measurement, silver was spread on both sides of the ceramic pellets as blocking electrodes. AC impedance spectra were recorded using a Solartron 1260 impedance analyzer over a frequency range of 5\u2009MHz to 1\u2009Hz, with an applied root mean square AC voltage of 30\u2009mV. The temperature dependence of the conductivity was measured in the same way at several specific temperatures ranging from 25 to 175\u2009\u00b0C. For conductivity test at each temperature, the samples were allowed to equilibrate for 2\u2009h prior to measurements. The resistances of the Na|Na5SmSi4O12|Na symmetric cells were tested under the same conditions.\n\nTo obtain NVP cathode, NVP active material (70 wt.%), Super P conductive additive (20 wt.%) and carboxymethyl cellulose (CMC) binder (10 wt.%) were dissolved in water to form a homogeneous slurry, and then uniformly coated onto an aluminum foil current collector. After drying for 12\u2009h at 80\u2009\u00b0C, the electrode was punched into 1\u2009cm diameter wafers for use with the loading mass of 1.0\u20131.5\u2009mg\u2009cm\u22122. Sodium foil was employed as anode. The Na5SmSi4O12 pellet was used as both separator and electrolyte. 20\u2009\u03bcL 1\u2009M NaClO4 in ethylene carbonate (EC) and propylene carbonate (PC) (1:1\u2009v/v) with the addition of 5 vol.% fluoroethylene carbonate (FEC) was added as the interfacial wetting agent at the cathode side. The 2032-type coin cells were assembled in an argon-filled glovebox. Galvanostatic charge-discharge tests were performed in a cutoff potential window of 2.3\u20133.9\u2009V by using Land-2100 automatic battery tester. All-solid-state Na|Na5SmSi4O12|Na and Li|Na5SmSi4O12|Li symmetric cells were assembled to test the sodium/lithium metal stripping/platting at 25 and 50\u2009\u00b0C, respectively. In addition, electrochemical stable window of electrolytes was examined by cyclic voltammetry (CV) measurement with the stainless steel (SS)|Na5SmSi4O12|Na cell in the voltage range of \u22121 to 8\u2009V at a scanning rate of 5\u2009mV\u2009s\u22121. The direct current (DC) polarization measurement was performed on Ag|Na5SmSi4O12|Ag cell with a 300\u2009mV potential and the current response was measured for 100\u2009min at ambient temperature. The CV measurement and the DC measurement were performed using a Bio-Logic electrochemical workstation.\n\nDensity functional theory calculations were carried out using the VASP6.352,53 package following the setup we used in previous work54,55,56. Briefly, the PBE exchange\u2013correlation functional was adopted with a planewave basis and a cutoff energy of 520\u2009eV. The reciprocal space was sampled using Monkhorst\u2013Pack grids with a spacing of 0.04\u2009\u00c5\u22121. The convergence for electron self-consistent computations and structural optimizations are set to 10-4\u2009eV\u2009atom\u22121 and 10-3\u2009eV\u2009atom\u22121, respectively. Due to the need of large-scale molecular dynamics simulations, we trained a machine learning forcefield based on ab initio molecular dynamics simulations (AIMD)54,55. Trajectories from smaller systems with 5 compositions sampled evenly between Na5SmSi4O12 and Li5SmSi4O12 were collected at high temperatures together with their relaxation trajectories. The energy and forces were used as the label to train the model. The production MD simulations were carried out using such a forcefield. The systems were equilibrated at 600\u2009K for 100\u2009ps and gradually dropped to 300\u2009K with a period of another 100\u2009ps under an NPT thermostat at ambient pressure. Then an NVT production run was carried out at 300\u2009K for 200\u2009ps. The time step was 1\u2009fs. For the interface model, we adopted a universal machine learning model which can capture the ground state structures and their energy. Interface models were built to mimic the interaction between the Li electrode and the crystalline and amorphous SE. The models were first equilibrated at 500\u2009K for 1000\u2009fs followed by energy minimization. The interfacial energy was calculated by subtracting the energy of the interfaces from the sum of energies of independent bulk phases.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The authors declare that the data generated in this study have been deposited in the Figshare database\u00a0https://doi.org/10.6084/m9.figshare.24188418. Should any raw data files be needed in another format, they are available from the corresponding author upon reasonable request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "F.D. acknowledges the support from the National Natural Science Foundation of China with Grant No. 12274176. M.T. acknowledges the support from the National Natural Science Foundation of China with Grant No. 21974007. F.D. also would like to thank the support from the Fundamental Research Funds for the Center Universities and Department of Science and Technology of Jilin Province with Grant Nos. 20220201118GX and 20210301021GX.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Ge Sun, Chenjie Lou.\n\nKey Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), State Key Laboratory of Superhard Materials, College of Physics, Jilin University, 130012, Changchun, China\n\nGe Sun,\u00a0Boqian Yi,\u00a0Wanqing Jia,\u00a0Zhixuan Wei,\u00a0Shiyu Yao,\u00a0Gang Chen,\u00a0Zexiang Shen\u00a0&\u00a0Fei Du\n\nCenter for High Pressure Science and Technology Advanced Research (HPSTAR), 100193, Beijing, China\n\nChenjie Lou\u00a0&\u00a0Mingxue Tang\n\nDepartment of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge, CB3 0FS, UK\n\nZiheng Lu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nF.D., M.T., and Z.L. designed and supervised the project. S.Y., B.Y., and W.J. designed the experiments to response the comments from the reviewers. G.S. performed materials synthesis, electrochemical tests, and wrote the manuscript. C.L. and M.T. performed NMR experiments and analyses. Z.L. carried out atomistic simulations and relevant analysis. Z.W., S.Y., G.C., and Z.S. revised the manuscript. All the authors participated in the discussion and provided constructive advice for the experimental design.\n\nCorrespondence to\n                Shiyu Yao, Ziheng Lu, Mingxue Tang or Fei Du.",
+            "section_image": []
+        },
+        {
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+            "section_text": "The authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Toshinori Okura and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.",
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+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Sun, G., Lou, C., Yi, B. et al. Electrochemically induced crystalline-to-amorphization transformation in sodium samarium silicate solid electrolyte for long-lasting sodium metal batteries.\n                    Nat Commun 14, 6501 (2023). https://doi.org/10.1038/s41467-023-42308-0\n\nDownload citation\n\nReceived: 24 February 2023\n\nAccepted: 06 October 2023\n\nPublished: 16 October 2023\n\nVersion of record: 16 October 2023\n\nDOI: https://doi.org/10.1038/s41467-023-42308-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+        {
+            "section_name": "Abstract",
+            "section_text": "Aging is the major risk factor for chronic pancreatitis and severity determinant for its acute attack, yet the underlying cause is unclear. Here, we demonstrate that senescent \u03b2-cells of endocrine pancreas decide the onset and severity of chronic and acute pancreatitis. During physiological aging, senescent \u03b2-cells increase the expression of miR-503-322 which is secreted as nano-vesicles to enter exocrine acinar cells, driving a causal and reversible role on aging-associated pancreatitis. Mechanistically, miR-503-322 represses MKNK1 to inhibit acinar-cell secretion and proliferation, thereby causing autodigestion and repairing damage of exocrine pancreas. In the elderly population, serum miR-503 concentration is negatively correlated with amylase, prone to chronic pancreatitis due to increased miR-503 and decreased MKNK1 in the elderly pancreas. Our findings highlight the miR-503-322\u2013MKNK1 axis mediating the endocrine-exocrine regulatory pathway specifically in aged mice and humans. Modulating this axis may provide potential preventive and therapeutic strategies for aging-associated pancreatitis.Health sciences/Gastroenterology/Gastrointestinal diseases/Pancreatic disease/PancreatitisHealth sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplementaryMaterials.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Aging is the risk factor for chronic pancreatitis and severity determinant for its acute attack, yet the underlying cause is unclear. Here, we demonstrate that senescent \u03b2-cells of endocrine pancreas decide the onset and severity of chronic and acute pancreatitis. During physiological aging, senescent \u03b2-cells increase the expression of miR-503-322 which is secreted as small extracellular vesicles to enter exocrine acinar cells, driving a causal and reversible role on aging-associated pancreatitis. Mechanistically, miR-503-322 targets MKNK1 to inhibit acinar-cell secretion leading to autodigestion and repress proliferation causing repair damage of exocrine pancreas. In the elderly population, serum miR-503 concentration is negatively correlated with amylase, prone to chronic pancreatitis due to increased miR-503 and decreased MKNK1 in the elderly pancreas. Our findings highlight the miR-503-322\u2013MKNK1 axis mediating the endocrine-exocrine regulatory pathway specifically in aged mice and humans. Modulating this axis may provide potential preventive and therapeutic strategies for aging-associated pancreatitis.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Pancreatitis is one of the most common causes of hospitalization worldwide and represents higher prevalence in the elderly1,2,3. Chronic inflammation accumulates during natural aging and has been identified as responsible for the onset of many diseases, including pancreatitis and type 2 diabetes mellitus (T2DM)4. Recent clinical data showed that the incidence of pancreatitis increases in patients with T2DM5,6,7, indicating the endocrine part of the pancreas participants in pancreatitis formation. However, the underlined mechanisms remain elusive.\n\nThe endocrine pancreatic islets have a well-recognized anatomical and physiological integration with the exocrine pancreas and regulate its function8. Involvement of the islet-acinar axis (IAA) has been suggested in the islet-acinar portal system for the physiological regulation of acinar cell function by islet peptides9,10. A recent study found that islet \u03b2-cell-derived cholecystokinin (CCK) acts on acinar cells via the IAA to promote the progression of pancreatic ductal adenocarcinoma (PDAC)11, suggesting that endocrine islet \u03b2-cells can crosstalk with acinar cells. In addition, \u03b2-cell inflammation exacerbates pancreatitis through chemokine signaling12,13. These findings suggest that factors secreted abnormally by pancreatic \u03b2-cells play a key role in the development of pancreatitis. One possibility is that abnormal secretion of microRNAs (miRNAs) may be involved.\n\nPancreatic \u03b2-cells are known to mediate intercellular communication through the secretion of extracellular vesicles (EVs) rich in miRNAs, resulting in reduced insulin sensitivity and secretion capacity in a paracrine or distal manner and elevated blood glucose levels14. However, a regulatory role for miRNAs carried by EVs derived from \u03b2-cells has not been established for pancreatitis. We have previously demonstrated that senescent \u03b2-cells released miR-503-322 as small EVs (~45\u2009nm) which were transported into peripheral target organs to cause insulin resistance, thereby leading to the onset of T2DM15. Serendipitously, overexpression of miR-503 in \u03b2 cells caused pancreatitis-like changes with age, suggesting that miR-503 secreted by endocrine \u03b2-cells may be important in regulating exocrine functions including pancreatitis.\n\nThe X-linked miR-503, clustered with miR-322 has been investigated and shown to play an important role in modulating cell proliferation, cell differentiation, and tissue remodeling16. In the present study, we found that during natural aging, primary miR-503-322 (Pri-miR-503) was transcribed in the endocrine islets while mature miR-503 and miR-322 could be detected in both endocrine and exocrine pancreas. Increased levels of miR-503-322 in senescent acinar cells were derived from \u03b2-cells and intra-acinar miR-503-322 promoted pancreatitis by targeting MAP kinase-interacting kinases (MKNK1). The regulation mode was also conserved in aged population, adding further evidence for endocrine-exocrine crosstalk in regulating pancreatitis and providing therapeutic targets for the prevention and treatment of aging-associated pancreatitis.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Our previous study showed that \u03b2-cell-specific miR-503 transgenic (\u03b2TG) mice suffered from insulin resistance and \u03b2-cell dysfunction, leading to T2DM15. Coincidentally, we noted that the \u03b2TG mice also showed chronic pancreatitis (CP)-like changes with advanced age, including diffuse expansion of the interlobar septae, fat accumulation, and fibrosis (Fig.\u00a0S1A, B). Adult \u03b2TG mice also showed significant exacerbation of caerulein-induced AP attack, as evidenced by pancreatic edema, macrophage infiltration, and more severe histologic scorings compared with the WT mice (Fig.\u00a0S1C\u2013E).\n\nTo understand the role of \u03b2-cell miR-503 on the development of pancreatitis, the expressing distribution of miR-503 in \u03b2TG mice was detected. We found that Pri-miR-503 was significantly increased in islets but not in acini, while the mature miR-503 was increased in both islets and acini (Fig.\u00a01A, B), suggesting \u03b2-cell miR-503 entering acinar cells. The same expression profiles of Pri-miR-503 and mature miR-503 and miR-322 were also observed in aged mice (Fig.\u00a01C, D). We previously reported that senescent \u03b2-cells secrete miR-503-322 within EVs15. To validate our findings, we measured miR-503 and miR-322 (namely miR-424 in humans) levels in EVs from a 69-year-old human islet donor after senescent cell removal using senolytics ABT263 (Fig.\u00a0S2A). Previous studies have shown that senescent \u03b2-cells can be specifically killed in vitro with ABT26317. A 48-hour treatment with ABT263 reduced \u03b2-galactosidase-positive cells and p16INK4a fluorescence in insulin-positive \u03b2-cells, and consequently, the secretion of miR-503-424 in EVs was diminished (Fig.\u00a0S2B\u2013D). These results make us think about the contribution of \u03b2-cell miR-503-322 to pancreatitis in older age. Consistent with our hypothesis, aged mice showed a more severe form of caerulein-induced AP compared to younger mice (Fig.\u00a0S2E\u2013I), which could be significantly improved by blocking \u03b2-cell miR-503-322 levels. An insulin 2 promoter-driven sponge-AAV (SP-AAV) specifically expressed in \u03b2 cells resulted in decreased expression levels of miR-503-322 in pancreas (Fig.\u00a01E\u2013G). Meanwhile, caerulein-induced AP measured by serum amylase and lipase levels, pancreatic edema, histologic scorings were significantly ameliorated in aged mice infected with SP-AAV (Fig.\u00a01H\u2013L). These findings indicate that increased levels of miR-503-322 in senescent \u03b2 cells contribute pancreatitis severity associated with older age.\n\nA qPCR analysis of Pri-miR-503 expression in islets and acini of 20-week-old control (WT) and \u03b2-cell-specific miR-503 transgenic (\u03b2TG) male mice. n\u2009=\u20094. B qPCR analysis of miR-503 expression in acini of WT and \u03b2TG mice. n\u2009=\u20093. C qPCR analysis of Pri-miR-503 expression in pancreas, islets and acini of 12 weeks and 1.5 years old male mice. n\u2009=\u20095. D qPCR analysis of miR-503 and miR-322 expression in islets and acini of 12 weeks and 1.5 years old male mice, respectively. n\u2009=\u20095. E Schematic flow diagram of sponge \u03b2-cell miR-503-322 and induced pancreatitis in aged male mice. The 1.4-years C57BL/6\u2009J male mice were randomly divided into two groups. The control and the experimental group were respectively injected with ctr-AAV and miR-503\u2013322 sponge-AAV through pancreatic ductal Infusion. Two months later, AP was induced by intraperitoneal injection (i.p.) of caerulein (50\u2009\u03bcg/kg, hourly for six consecutive times), and pancreatitis parameters were detected 2\u2009h after the last injection. F Representative sections of ZsGreen (green), insulin (red) and nucleus (blue) immunofluorescence co-staining in pancreas of mice 1 month after AAV injection. n\u2009=\u20093 mice. G qPCR analysis of pancreatic miR-503 and miR-322 in the Ctr and SP groups 2 months after AAV injection. Ctr, n\u2009=\u20093 and SP, n\u2009=\u20095. H\u2013L Pancreatic weights after calibration with body weight (H), serum amylase (I), and lipase (J), representative histologic sections of H&E of pancreas (K), pancreatic histological scores (L) in the Ctr and SP groups after caerulein (50\u2009\u03bcg/kg) induced. Ctr, n\u2009=\u20093 and SP, n\u2009=\u20095. Data are means\u2009\u00b1\u2009SEM. Unpaired Student\u2019s t tests were used to evaluate statistical significance. Source data are provided as a Source Data file.\n\nWe previously verified that islet-derived EVs were secreted from insulin granules and were trafficked into liver and adipose tissues via circulation15. Whether those EVs entered acinar cells was unknown. Here, we show that acinar cells indiscriminately engulfed EVs in vitro models (Fig.\u00a0S3A), and acinar cells that received EVs purified from \u03b2TG islets had significantly greater levels of miR-503 than acinar cells that received EVs from wildtype (WT) islets (Fig.\u00a0S3B). To validate the specificity of \u03b2-cells, we used the cell-permeable zinc-selective dye FluoZinTM-3, which selectively sorts pancreatic \u03b2-cells without compromising their viability or function18, enabling enrichment of \u03b2-cell-derived EVs (\u03b2EVs) (Fig.\u00a0S3C\u2013E). Transmission electron microscopy (TEM) revealed \u03b2EVs with a diameter of about 45\u2009nm (Fig.\u00a02A), and nanoparticle tracking analysis (NTA) confirmed size of 42\u2009nm (Fig.\u00a02B). Western blotting confirmed high expression of EV markers (ALIX, TSG101, and CD63), but not GAPDH which was not included in EVs (Fig.\u00a02C). Although the concentration was not different, \u03b2EVs released from \u03b2TG were found to package more miR-503 than those from WT \u03b2-cells (Fig.\u00a02D, E). The in vitro uptake of \u03b2EVs by acinar cells and flow cytometry data showed that acinar cells can internalize \u03b2EVs with no significant difference in uptake efficiency (Figs.\u00a02F and \u00a0S2F\u2013H). Whereas the level of miR-503 in acinar cells receiving \u03b2TG-\u03b2EVs was significantly higher than those receiving WT-\u03b2EVs (Fig.\u00a02G). We also injected labeled islet-derived EVs into mice via pancreatic ductal infusion and observed that acinar cells indiscriminately engulfed EVs in vivo (Fig.\u00a02H, I).\n\nA\u2013C Identification of EVs collected from 20-week-old WT and \u03b2TG mouse islet \u03b2-cells: TEM images showing the morphology of \u03b2EVs collected (A), NTA analysis of nanoparticle size distribution (B), and molecular marker identification of \u03b2EVs (C). n\u2009=\u20092 independent experiments. As there was no significant difference between WT and \u03b2TG, the results are presented in the figure for \u03b2EVs from WT \u03b2-cells. D NTA analysis of nanoparticle concentration of \u03b2EVs collected from 20-week-old WT and \u03b2TG mouse islet \u03b2-cells. n\u2009=\u20093. E qPCR analysis of miR-503 expression in \u03b2EVs of WT and TG. n\u2009=\u20093. F PKH67-labeled WT-\u03b2EVs or \u03b2TG-\u03b2EVs were co-incubated with fresh acini for 8\u2009h. Representative confocal images of PKH67 (green), phalloidin (red), and nuclei (blue) in primary acini and quantification of PKH67 fluorescence intensity. n\u2009=\u20093 replicates, with each data point representing a count of 3 acini. G qPCR analysis of miR-503 expression in the acini of received \u03b2EVs. n\u2009=\u20093 independent experiments. H, I Experimental scheme: PKH67-labeled WT-EVs or \u03b2TG-EVs were infusion into the C57BL/6\u2009J male mouse pancreas via pancreatic ductal. The pancreas was harvested after 12 h, stained with frozen sections for amylase, and then visualized. Representative confocal images of PKH67 (green), amylase (red) and nuclei (blue) of pancreas and quantitation of relative PKH67 fluorescence intensity. n\u2009=\u20093 mice. J\u2013L Pancreatitis parameters assay after initial caerulein (50\u2009\u03bcg/kg) injection 7\u2009h in 12-week-old WT and \u03b2-cell-specific miR-503-322 knock-in (\u03b2KI) female mice. H&E and F4/80 immunohistochemistry (IHC) of pancreatic sections, quantitation of the number of F4/80 positive cells in pancreatic sections under \u00d7200 microscopic view (J), pancreatic histological scores (K) and level of serum amylase (L). Arrows indicate the macrophages. n\u2009=\u20095 mice. Data are means\u2009\u00b1\u2009SEM. Unpaired Student\u2019s t tests were used to evaluate statistical significance. Source data are provided as a Source Data file.\n\nTo avoid the influence of insulin resistance and hyperglycemia in \u03b2TG mice15, we constructed RIP2-cre;miR-503-322 KI (\u03b2KI) mice which were not overtly diabetic (Fig.\u00a0S3I\u2013L). \u03b2KI mice also exhibited an exacerbation of caerulein-induced AP compared to littermate controls (Fig.\u00a02J\u2013L). Thus, we concluded that \u03b2-cell-derived small EVs enter acinar cells and drive pancreatitis at a miR-503-322\u2013dependent manner in mice.\n\nNext, we sought to investigate the effects of miR-503-322 under inducible global elevation conditions by using CAG-creER;miR-503-322 KI (CKI). After tamoxifen induction three times, Pri-miR-503 expression levels were significantly elevated in the pancreas, skeletal muscle and other metabolic tissues (Fig.\u00a0S4A, B). Surprisingly, CKI mice started to lose weight and activity, culminating in death due to severe AP within 6 days of the first induction, as observed by significantly increased serum amylase and lipase levels, abdominal infiltration of neutrophils and macrophages, and pancreatic saponification, necrosis and histological analysis (Fig.\u00a0S4C\u2013I). However, no concomitant histological changes were observed in other major abdominal organs (Fig.\u00a0S4J). Severe AP-induced systemic inflammatory responses were shown by inverted serum ratios of neutrophils and lymphocytes, and elevated serum levels of C-reactive protein (Fig.\u00a0S4K\u2013M). These results validate that the global overexpression of miR-503-322 promotes severe AP, indicating the specificity of the miR-503-322 for pancreas damage.\n\nTo rule out the contribution of other tissues, Pdx1-cre;miR-503-322 KI (heterozygous PKI/WT and homozygous PKI/KI) mice were used to yield high pancreatic-specific expression of miR-503-322. The pancreatic Pri-miR-503 expression was increased in the heterozygous PKI mice (PKI/WT) compared to WT controls and was further increased in the homozygous mice (PKI/KI) (Fig.\u00a03A). The PKI/KI mice showed an unexpected weight loss at ~6 weeks of age, while the PKI/WT mice showed no change during natural growth (Figs.\u00a03B and\u00a0S5A). The most prominent features of CP, including pancreatic atrophy, fibrosis, tubular complexes, and inflammatory infiltration were observed in PKI/WT mice, with more severe CP and gross changes in the homozygous PKI/KI mice (Figs.\u00a03C\u2013E and\u00a0S5B\u2013D). Accordingly, PKI/KI mice could not survive for 12 weeks (Fig.\u00a03F).\n\nA qPCR analysis for pancreatic Pri-miR-503 expression in 8-week-old control (WT), PKI heterozygous (PKI/WT) and PKI homozygous (PKI/KI) male mice. n\u2009=\u20095. B Weight monitoring in WT, PKI/WT and PKI/KI male mice. n\u2009=\u20094. C, D Photograph of the pancreas (C), representative sections of of H&E, Masson, F4/80 and CK19 immunofluorescence staining in pancreas of 8-week-old PKI male mice (D). n\u2009=\u20093 mice. E Pancreatic weights after calibration with body weight in 8-week-old PKI male mice. WT, n\u2009=\u20096; PKI/WT, n\u2009=\u20095; PKI/KI, n\u2009=\u20093. F Survival curves for WT, PKI/WT and PKI/KI male mice. G Schematic of acinar cell-specific miR-503-322 knock-in (EKI) mice: 6\u20138 weeks WT and EKI male or female mice were injected intraperitoneally (ip.) with tamoxifen solution, 100\u2009mg/kg, in corn oil, for three consecutive days and sacrificed 3 days after the last tamoxifen injection. H qPCR analysis of Pri-miR-503 in acinar cells of WT and EKI male mice after three times tamoxifen-induced. n\u2009=\u20094. I Representative sections of pancreas of H&E, receptor-interacting serine-threonine kinase 3 (RIPK3) immunohistochemistry and immunofluorescence staining of F4/80 after first tamoxifen injection 5 days in WT and EKI male mice. Arrows indicate the macrophages. n\u2009=\u20094 mice. J Pancreatic histological scores, quantitation of average optical density of RIPK3 and the number of F4/80 positive cells in pancreatic sections under \u00d7600 microscopic view for I. n\u2009=\u20095 mice, and at least 10 photographs were taken for statistical analysis. K Survival curves for WT and EKI male and female mice. L Pancreatic H&E of WT and EKI female mice after first tamoxifen injection 28 days. M Representative sections of pancreatic Masson dyeing from EKI male mice after first tamoxifen injection 28 days. ADM, Acinar-to-ductal metaplasia. n\u2009=\u20094 mice. Data are means\u2009\u00b1\u2009SEM. Data were analyzed using one-way ANOVA (A, E), two-way ANOVA with Tukey test (B), unpaired Student\u2019s t tests (H, J) or Survival cure analyses (F, K). Source data are provided as a Source Data file.\n\nPDX1 is a master regulator in pancreas organogenesis while the maturation and identity preservation of islet \u03b2-cells and \u03b4-cells19,20. To avoid development defect, inducible acini-specific miR-503-322 (Elastase-CreER;miR-503-322 KI, EKI) mice were also constructed, and overexpression verified post-induction for 3 days (Fig.\u00a03G, H). After tamoxifen injection, the EKI mice showed significantly increased indicators of AP, including macrophage infiltration, tissue damage, and necrosis (Figs.\u00a03J and\u00a0S5E, F), and had a 50% mortality rate (Fig.\u00a03K). Those mice that survived developed histology of CP one month post-induction, manifested as pancreatic atrophy (Fig.\u00a0S5G), fibrosis, fat replacement, and acinar-to-ductal metaplasia (ADM, Fig.\u00a03L, M), while a return to normal levels of serum amylase and lipase (Fig.\u00a0S5E, F). As shown in Fig.\u00a0S5H\u2013J, EKI female mice presented an AP phenotype similar to that of male mice 5 days after tamoxifen induction.\n\nThe above findings demonstrate that global, pancreatic, and acinar cell-specific overexpression of miR-503-322 can directly trigger (severe) acute and CP in a dose- and tissue-dependent manner.\n\nThe possibility that ablation of miR-503-322 could alleviate AP was investigated by the global deletion of miR-503-322 (KO) (Fig.\u00a0S6A). The KO mice were viable and fertile, with normal body weight (Fig.\u00a0S6B). Histology of the pancreas revealed normal pancreatic morphology (Fig.\u00a0S6C, D). Challenging the KO and WT mice with caerulein or PBS and assessing for AP severity revealed markedly lower pancreatic edema and amylase and lipase levels in the KO group (Fig.\u00a04A\u2013D). Histological examination revealed reduced pancreatic acinar cell damage, less interstitial expansion (indication of edema), and diminished macrophage infiltration in KO mice during the acute AP phase (Fig.\u00a04E\u2013G). To highlight the influence of aging on pancreatitis, 1-year-old KO mice were treated with caerulein. The findings revealed that KO mice experienced a significant alleviation of caerulein-induced AP compared to control mice (Fig.\u00a0S6E\u2013I). Together, these data demonstrate that the deletion of miR-503-322 can significantly alleviate caerulein-induced AP.\n\nA Schematic of caerulien-induced AP on 12-week WT and KO male mice. B\u2013E Pancreatic weights after calibration with body weight (B), serum amylase (C) and lipase (D) levels, histological score of the pancreas (E) after PBS or caerulein treatment groups. n\u2009=\u20095. F, G Representative sections of pancreatic H&E (F) and immunofluorescence staining of F4/80 (green) after PBS or caerulein treatment 7\u2009h in WT and KO male mice. Quantitation of the number of F4/80 positive cells in pancreatic sections under \u00d7600 microscopic view (G). n\u2009=\u20095 mice, and a total of 30 photographs were taken for statistical analysis. Arrows indicate the macrophages. Data are means\u2009\u00b1\u2009SEM. Unpaired Student\u2019s t tests were used to evaluate statistical significance. Source data are provided as a Source Data file.\n\nNext, we sought to identify the mechanisms by which miR-503-322 promotes the development of pancreatitis. TEM images from the pancreas of the PKI/WT mice revealed an increased number of zymogen granules (Fig.\u00a0S7A). However, the significantly lower transcript levels of pancreatic enzyme-related genes implied that this did not represent an increased production of zymogen in the acinar cells (Fig.\u00a0S7B) but was possibly an indication of a secretion defect.\n\nTherefore, we isolated acini and assessed their secretory ability in response to caerulein. The amylase release was significantly lower from the PKI cells than from the WT cells (Fig.\u00a05A). The acinar cells from aged mice showed a similar response to that of the PKI cells, with a reduced secretion of pancreatic enzymes (Fig.\u00a05B), in agreement with the results of previous studies21. By contrast, the primary acinar cells from the KO mice showed enhanced amylase secretion (Fig.\u00a05C). The defect of enzyme secretion was attributed to the loss of cytoskeleton modulation from tip to basolateral membranes of acinar cells responding to caerulein (Fig.\u00a05D).\n\nA Extraction of fresh acinar cells from 8-week-old WT and PKI/WT male mice, in vitro stimulation with caerulein for 30\u2009min and determination of amylase content in the supernatant. n\u2009=\u20093. B, C Amylase levels after calibration of total content release from acinar cells of 12-week WT and KO male mice (B) and 12-week and 1.5-year C57BL/6\u2009J male mice (C) after 30\u2009min of stimulation with caerulein. n\u2009=\u20093. D Pancreatic acini from WT or EKI mice, after 48\u2009h tamoxifen induction, were incubated with or without caerulein (0.01\u2009\u03bcM) for 30\u2009min. Cells were then harvested, stained for F-actin (red) and nuclei (blue), and imaged by laser confocal microscopy. Micrographs of untreated and caerulein-pretreated acini are shown. n\u2009=\u20093 independent experiments. E Representative confocal images of WT and PKI/WT male mice acini after incubation with BziPAR for 30\u2009min at 37\u2009\u00b0C. n\u2009=\u20093 independent experiments. F Detection of serum trypsin activity levels in 16-week WT and PKI/WT male mice and EKI after Tamoxifen injection 5 days. n\u2009=\u20095. G Quantitation of the number of proliferating acinar cells of 8-week WT and PKI/WT male mice; 28 days after tamoxifen induction in WT and EKI male mice; 4 days after caerulein-induced AP in 12-week WT and KO male mice and 12-week and 1.5-year-old male mice. n\u2009=\u20093\u20135 mice per group. n\u2009=\u20095 mice. H\u2013J Representative sections of immunofluorescence staining of amylase (red) and proliferating cell nuclear antigen (PCNA) (green) in pancreatic sections from 8-week WT and PKI/WT male mice (H), 28 days after tamoxifen induction in WT and EKI male mice (I), 4 days after caerulein-induced AP in 12-week WT and KO male mice (J). Arrows indicate proliferating acinar cells; asterisks are proliferating interstitial cells. n\u2009=\u20095 mice. K Representative sections of immunofluorescence staining of PCNA (green) in pancreatic sections from 12-week and 1.5-year-old male mice. n\u2009=\u20095 mice. Arrows indicate proliferating acinar cells. Data are means\u2009\u00b1\u2009SEM. Unpaired Student\u2019s t tests were used to evaluate statistical significance. Source data are provided as a Source Data file.\n\nEnzyme secretion defects may cause trypsinogen activation. We observed that trypsinogen activation in acini was visualized by using rhodamine 110 (BZiPAR) which revealed a clear enrichment of green fluorescence in PKI cells (Fig.\u00a05E), and serum trypsin activity was enhanced in the PKI mice (Fig.\u00a05F). These findings indicate that miR-503-322 inhibits pancreatic enzyme secretion and promotes the intracellular accumulation of zymogen. Subsequent zymogen activation in situ may promote pancreas damage of miR-503-322 elevated mice.\n\nActivation of trypsinogen by lysosomal enzymes after fusion of the lysosome is the classical mode of pancreatic enzyme activation during AP22,23. TEM images of the pancreas from PKI mice show morphological signs of this activation, including numerous autophagy vacuoles in the cytoplasm and abundant zymogen granules varying in size and electron density and sometimes fused together to form irregular \u201clakes\u201d (Fig.\u00a0S7C). These phenomena suggest a classical activation of intracellular zymogen in the lysosomes of acinar cells that highly express miR-503-322. We verified this by inducing pancreatitis in WT and EKI mice by administration of chloroquine, which destroys the acidic environment in autophagic lysosomes (Fig.\u00a0S7D). The AP phenotype was alleviated in EKI mice treated with chloroquine, as evidenced by a smaller weight loss, reduced serum amylase and lipase levels, and less tissue damage compared to saline-treated control mice, despite a similar pancreas weight (Fig.\u00a0S7E\u2013J).\n\nAP significantly stimulates the proliferation of acinar cells almost immediately at the point of injury24. Not surprisingly, immunofluorescence staining for PCNA revealed a reduction in the numbers of proliferating acinar cells in the mice expressing high levels of the miR-503-322, and an increased proliferation of mesenchymal cells (Fig.\u00a05G\u2013I). Conversely, ablation of miR-503-322 enhanced acinar cell proliferation during the repair phase of caerulein-induced AP (Fig.\u00a05G, J). We also conducted a similar test in aged mice and again observed a significant decrease in acinar cell proliferation similar to that seen in the high miR-503-322 expression model mice (Fig.\u00a05G, K).\n\nTaken together, these data suggest that miR-503-322 suppresses zymogen secretion to initiate acute pancreatitis. Meanwhile, miR-503-322 also inhibits the regenerative proliferation of acinar cells to promote the formation of CP.\n\nWe previously used unbiased proteomics to identify target genes of miR-503 in regulating peripheral insulin resistance and \u03b2-cell dysfunction15. By analyzing the same proteomics data combined with Targetscan software analysis, five genes (MKNK1, CCNE1, IGF1R, PI3KR1 and INSR) were potential targets (Fig.\u00a0S8A). After extensively searching and reading literature, we found that the MKNK1, mostly expressed in the exocrine pancreas might contribute to miR-503-322\u2013caused pancreatitis. MKNK1 plays an indispensable role in physiological exocrine secretory response25. Consistent with published data, phosphorylation of MKNK1 and its downstream eIF4E was increased 4\u2009hr after the first caerulein injection and gradually recovered (Fig.\u00a0S8B\u2013E). MKNK1 was redistributed to the basolateral region after caerulein administration, assisting acinar cell secretion (Fig.\u00a0S8F). Previous studies showed that ablation of MKNK1 results in exacerbation of pancreatitis caused by caerulein due to defects of zymogen secretion and acinar cell proliferative in mice25, making us pursue the role of MKNK1 as a target gene of miR-503-322.\n\nOur proteomics analysis showed a decrease in MKNK1 after miR-503 elevation. Dual-luciferase assay confirmed the regulatory role of miR-503-322 on the 3\u2019UTR of Mknk1 gene (Figs.\u00a06A and S9A, B). Next, immunohistochemistry staining of pancreas sections revealed clear suppression of MKNK1 protein amount in the three miR-503-322 overexpressing mouse model. (Fig.\u00a0S9C), while upregulation of MKNK1 was induced by caerulein in KO mice (Fig.\u00a0S9D). The protein levels of MKNK1 and its associated P-MKNK1/P-eIF4E signaling were significantly reduced in the pancreas of miR-503-322 overexpressing model mice and aged mice, and by contrast increased in pancreas of miR-503-322 knockout mice and aged mice with \u03b2-cell-specific blocking miR-503-322 (Figs.\u00a06B and \u00a0S9E\u2013G). Taken together, these findings suggest that miR-503-322 targets MKNK1-eIF4E pathway to inhibit zymogen secretion and acinar cell proliferation, thereby leading to acute and CP.\n\nA The MKNK1 network was predicted based on the common signature from the Ingenuity database overlaid with microarray data from miR-503-overexpressing mouse pancreatic \u03b2 cell line MIN6 cells with a 1.5-fold change cutoff compared with negative control cells. B WT and EKI male mice at 5 days after tamoxifen induction; Male C57BL/6\u2009J at 12-week and 1.5-year; male WT and KO at 12-week after AP induced and \u03b2-cell-specific sponge of miR-503-322 in control and experimental mice pancreatic protein western blotting. n\u2009=\u20093\u20135. C Experimental scheme: 8-week-old WT male mice were injected intraperitoneally (i.p.) with control AAV and EKI male mice were injected with control (Ctr-AAV) and MKNK1-AAV, respectively, one-month later tamoxifen was induced for 3 consecutive days and tested at day 7. n\u2009=\u20095. D Immunofluorescence staining of Flag (red) and MKNK1 (green) of pancreas sections from each group of mice at 13 weeks. n\u2009=\u20095 mice. E Western blotting of pancreatic proteins from each group of mice at 13 weeks. n\u2009=\u20092. F Gain of body weight, serum amylase and lipase level were monitored during tamoxifen induction. n\u2009=\u20095. G Representative images of H&E and F4/80 immunohistochemistry of the pancreas in each group. Arrows indicate the macrophages. H Quantitation of the number of F4/80 positive cells in each group and the pancreatic histological score. n\u2009=\u20095 mice, three sections per mouse (50\u2009\u00b5m apart), and at least 10 microscopic fields per section. Data are means\u2009\u00b1\u2009SEM. Data were analyzed using two-way ANOVA (F) and one-way ANOVA (H) with the Tukey test. Source data are provided as a Source Data file.\n\nNext, we tested whether reconstitution of MKNK1 in pancreas could reverse pancreatitis of EKI mice following the schematic diagram (Fig.\u00a06C). We generated an AAV, serotype pancreas (MKNK1-AAV) that directs specific MKNK1 overexpression in the exocrine pancreas. As shown in Fig.\u00a0S9H, MKNK1 was highly expressed in the acini, but not in the islets of MKNK1-AAV mice. Restoration of MKNK1 also rescued the miR-503-322-suppressive protein levels of phos-MKNK1 and phos-eIF4E in the EKI pancreas (Fig.\u00a06D, E). Consequently, MKNK1-AAV-infected EKI mice showed lessened AP phenotypes compared to Ctr-AAV-infected EKI mice. In detail, the loss of body weight, increased serum levels of amylase and lipase, increased number of macrophage infiltration, and tissue damage in Ctr-AAV infected EKI mice were largely reduced in MKNK1-AAV infected EKI mice (Fig.\u00a06F\u2013H).\n\nOn the other hand, inhibition of MKNK1 by a verified inhibitor, CGP 57380 further exacerbated caerulein-caused AP phenotypes, and totally erased miR-503-322 knockout driven protective effects (Fig.\u00a0S10A\u2013G). These results from acinar cell MKNK1 reconstitution and specific MKNK1 inhibitor support our view that the deficiency of MKNK1 in acini is primarily responsible for the pancreatitis observed in miR-503-322 elevated mice.\n\nAs the expression of miR-503 is specifically increased in senescent \u03b2 cells in mice, we also considered its change in humans. Pancreas sections from elderly adults (EA) showed CP-like changes, including atrophy of the acinar cells, interstitial expansion, and a marked increase in fibrosis (Fig.\u00a07A), as well as a significant reduction in the proportion of proliferating acinar cells (Fig.\u00a07B), compared to that from young adults (YA). Intriguingly, miRNA in situ hybridization showed greater expression of miR-503 in islets than in acini in pancreatic sections from EA group (Fig.\u00a07C), whereas expression of miR-503 was almost undetectable in YA group (Fig.\u00a07C). The expression of MKNK1 was significantly downregulated in the acini from the EA pancreas compared to that from the YA pancreas (Fig.\u00a07D). Moreover, the co-localization of MKNK1 and AMY1 in the young acini was dislocated in the elderly acini (Fig.\u00a07D), indicating activation of MKNK1 in the EA. Thus, the increased level of miR-503 in the acini may come from the islet \u03b2 cells and contribute to the decreased but activated MKNK1 protein in the elderly Chinese population.\n\nA Representative images of H&E and Masson staining of pancreatic sections from the young adult (YA) and the elderly adult (EA); quantitation of collagen volume fraction. The dashed area indicates acini. n\u2009=\u200910 and a total of 30 photographs were taken for statistical analysis. B Representative images of immunofluorescence staining of PCNA (green) in pancreatic sections and counted the number of PCNA-positive cells. n\u2009=\u200910. Arrows indicate proliferating acinar cells. n\u2009=\u200910 and a total of 30 photographs were taken for statistical analysis. C In situ hybridization of miR-503 (40\u2009nM) in young and elderly pancreatic sections. Scramble-RNA was negative reference (40\u2009nM), and U6 was positive reference (0.1\u2009nM). The dotted line indicates islet, and the solid line is exocrine. n\u2009=\u20093 independent experiments. D Representative images of immunofluorescence staining of MKNK1 (green) and amylase (red) in pancreatic sections from young and elderly people and quantitation of amylase and MKNK1 mean fluorescence intensity. n\u2009=\u200910. E Serum amylase assay of the young adult (YA), the elderly adult (EA) and the elderly adult with diabetes (EA\u2009+\u2009DM). YA group, n\u2009=\u200965. EA group, n\u2009=\u200965. EA\u2009+\u2009DM, n\u2009=\u200930. F MiR-503 concentration in human serum of YA, EA and EA\u2009+\u2009DM. YA group, n\u2009=\u200945. EA group, n\u2009=\u200945. EA\u2009+\u2009DM, n\u2009=\u200930. G Correlation analysis of amylase levels of human serum and age. Each point represents one people (n\u2009=\u2009160). Correlation coefficient (R) and p value from simple linear regression are shown. H Correlation analysis of miR-503 concentration in human serum and serum amylase levels. Each point represents one people (n\u2009=\u2009120). Correlation coefficient (R) and P value from simple linear regression are shown. Box plots with centerline\u2009=\u2009median, box\u2009=\u200925th\u201375th percentile, and whiskers\u2009=\u20095th\u201395th percentile, outliers\u2009=\u2009open circles (A, B, D\u2013F). Data are means\u2009\u00b1\u2009SEM. Data were analyzed using unpaired Student\u2019s t tests (A, B, D), one-way ANOVA with Tukey test (E, F) or Correlation analysis (G, H). Source data are provided as a Source Data file.\n\nNumerous studies have reported that exocrine pancreas function is impaired in both healthy and diabetic older adults independent of gastrointestinal disease, judged by serum levels of amylase and maximum bicarbonate concentration26,27. Consistently, we observed a significantly decreased level of serum amylase in the EA with T2DM (EA\u2009+\u2009DM) compared to that in YA, moreover, the EAs also showed a decreased amylase level (Fig.\u00a07E). Further analysis showed that serum concentration of miR-503 in EVs was elevated in the elderly compared to that in the YAs and was further elevated in the EA\u2009+\u2009DM (Fig.\u00a07F). The human subjects displayed negative associations of serum amylase levels with both age and serum concentrations of miR-503 in EVs (Fig.\u00a07G, H). These results support the pancreatic exocrine insufficiency in elderly and diabetic patients and point out serum concentrations of miR-503 in EVs as a molecular marker of ageing-associated pancreatitis in the Chinese population.",
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "In this study, we demonstrated that miR-503-322 derived from endocrine \u03b2-cells promotes aging-associated pancreatitis by targeting MKNK1 in exocrine acinar cells. miR-503-322, which is produced by senescent \u03b2-cells, had an in situ effect in acinar cells that inhibits zymogen secretion and regenerative proliferation. Thus, the miR-503-322\u2013MKNK1 axis caused pancreas autodigestion and repairing damage, leading to the onset of acute and CP in mice. This discovery provides an epigenetic mechanism for pancreatitis and adds to the existing evidence of crosstalk between pancreatic endocrine and exocrine.\n\nGallstones and excessive alcohol use are known to be the major causes of AP in the clinic. Our study identified miR-503-322 derived from senescent \u03b2-cells as a factor that complements traditional etiologies of pancreatitis. Evidence suggests that insulin resistance and diabetes also play roles in pancreatitis28,29. MiR-503-322 secreted by senescent \u03b2 cells contributes to pancreatitis, independent of insulin resistance and diabetes, as shown by increased severity of caerulein-induced AP in \u03b2KI mice prior to hyperglycemia and insulin resistance. However, in conditions of insulin resistance and diabetes, miR-503-322 exacerbates the severity of pancreatitis, as evidenced by the pancreas-specific knock-in heterozygous and homozygous mice with concomitant exacerbation of diabetes and a more severe pancreatitis phenotype. Previous studies have noted that patients with diabetes develop exocrine dysfunction without obvious symptoms or abnormalities of the pancreatic ducts, termed diabetic pancreatic exocrine disease (DEP)30,31. Several hypotheses have been proposed to explain the features of DEP32,33,34,35. However, none of these concepts are sufficient to explain all the pathological findings. Our previous results showed a significant upregulation of islet miR-503 expression in patients with T2DM15. Suggested by our current investigation, the expressed miR-503 can then enter and accumulate in the exocrine acini, where it triggers damage to some of the acinar cells and causes CP-like changes in the exocrine pancreas due to repeated pancreas damage.\n\nMost studies report higher overall morbidity and mortality from pancreatitis in the elderly1, and several explanations for this phenomenon have been put forward36,37. The present results confirmed that \u03b2-cell-derived miR-503-322 promotes both acute and chronic damage in the exocrine pancreas and increases mouse mortality with acute and high miR-503-322 expression (CKI and PKI/KI mice). Therefore, miR-503-322 may be a common pathogenic factor that can explain the higher morbidity and mortality from pancreatitis in the elderly. Histologically, focal fibrosis also appears to be common in the pancreas of the elderly38,39. This is consistent with the observations in human pancreatic sections in the present study. Clinical studies have indicated that pancreatic exocrine function is impaired in healthy older individuals without any gastrointestinal disease40. The human subjects displayed negative associations of serum amylase levels with both age and serum concentrations of miR-503. These results support pancreatic exocrine insufficiency in the elderly and diabetic patients. In our study, we found that miR-503 inhibits pancreatic enzyme secretion in acinar cells by targeting MKNK1. Decreased MKNK1 expression resulted in dysregulated cytoskeletal remodeling, thus defective movement of zymogen granules from tip to basolateral membrane, which ended up with enzyme secretion defect. Secretion of pancreatic enzyme inhibition with upregulation of miR-503 expression in normal elderly individuals manifests as lower serum amylase levels. However, it is crucial to note that this secretory blockage can precipitate the accumulation of digestive enzymes in acinar cells, which ultimately results in autodigestion, causing cellular damage and acute attack. In such cases, a transient yet significant release of large quantities of amylase into the serum resulted in a temporary elevation of serum amylase levels. We propose that serum amylase levels are elevated in elderly patients during acute attacks, despite the upregulation of miR-503. Although the correlations of tissue and serum miR-503-322 and MKNK1 expression in the aging human population may not necessarily validate the proposed mechanisms and functions observed in transgenic mice, these findings provide valuable clues for further studies of aging-associated pancreatitis.\n\nIn humans, AP and CP are diagnosed based on well-defined criteria41,42. In mouse models of pancreatitis, while classic symptoms like upper epigastric pain and vomiting are absent, affected mice may show reduced activity and weight loss. Diagnosis relies on increased plasma pancreatic enzyme levels, particularly amylase and lipase, and the pathological features of pancreatic tissue. In our study, global miR-503-322 overexpressing mice showed increased serum amylase and lipase on day two, with amylase normalizing by day five and lipase remaining elevated. Acinar cell-specific miR-503-322 knock-in mice exhibited significant enzyme upregulation on day three, with no further changes or decreases. Pancreatic pathology, quantified using a scoring system described by Schmidt et al. is essential for diagnosis43. Human pancreatitis follows a course of acute attacks, interventions, and recurrent attacks, ultimately leading to CP42. In mouse models, some mice die without prompt treatment, whereas survivors develop CP. The timescale of pancreatitis was more distinct in the inducible acini-specific miR-503-322 knock-in mouse model. AP was significantly detected two days after tamoxifen injection, with a 50% mortality rate. Mice that survived developed histology of CP one-month post-induction, manifested as pancreatic atrophy, fibrosis, fat replacement, and acinar-to-ductal metaplasia, while returning to normal levels of serum amylase and lipase. Despite the morphology of a human pancreas and timescale of developing pancreatitis making it difficult to discern acute and CP in a mouse model, we differentiated AP and CP in mice through serum enzymes and pancreatic pathology.\n\nIn healthy adults, miR-503-322 is expressed mainly in lung, heart, and skeletal muscle progenitor cells44. Upregulation of miR-503-322 occurs in aging acinar cells and is likely to arise from pancreatic \u03b2-cells, based on our present observations. Our evidence for this is that blocking miR-503-322 in islet \u03b2-cells of aging mice alleviated caerulein-induced pancreatitis. Our previous findings revealed that miR-503 from pancreatic islet \u03b2-cells reaches the liver and adipose tissue in the form of exosomes, which are known to transport biologically active proteins and miRNAs in their active forms to neighboring cells or distant organs45,46,47. Thus, the involvement of EVs in inter-organ and intra-organ crosstalk has been increasingly studied48,49. EVs derived from mesenchymal stem cells have been reported as a treatment for AP by delivering mitochondria and anti-inflammatory factors50,51. In addition, senescent \u03b2 cells have been reported to secrete senescence-associated secretory phenotypes that are rich in EVs and cause dysfunction of adjacent cells through paracrine effects52,53. The reported anatomical characteristics of an IAA permit the access of high concentrations of islet-derived miR-503-322 to exocrine cells. Indeed, a recent study has determined that islet CCK can promote Kras-driven PDAC development of an endocrine exchange signal other than insulin11, supporting the existence of endocrine-exocrine crosstalk via IAA. Therefore, we hypothesize that islet-derived miR-503-322 is transferred via EVs and the IAA into acinar cells.\n\nOur results support MKNK1 as a miR-503-322 target gene for the development of pancreatitis. However, MKNK1-knockout mice showed normal pancreatic histology25, which was inconsistent with the phenotype of AP induced by miR-503-322. This normal histologic may reflect the presence of other compensatory pathways in MKNK1-knockout mice as the use of a global mouse model. Indeed, the knockout of MKNK1 adds to the growing list of proteins that have a protective role during AP54, whereas the acute induction of miR-503-322 lacks an effective compensatory mechanism. Alternatively, other target genes of miR-503-322 co-regulating the development of pancreatitis may exist. Moreover, EVs carrying miR-503-322 may function through inter-organ crosstalk to regulate the severity of AP as shown in CKI mice. In addition, tamoxifen administration occasionally causes pancreatitis also reminded us that the effect of tamoxifen itself cannot be ignored, although it was added to the control group55. The mechanisms involved in these possibilities need to be unraveled in further studies.\n\nIn conclusion, we demonstrate the role and mechanism of action for pancreatic endocrine-derived miR-503-322 in promoting pancreatitis in the elderly. Blocking miR-503-322 in \u03b2-cells of aged mice showed good inhibitory effects on pancreatitis, revealing miR-503-322 as a potential therapeutic target for elderly patients with pancreatitis.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "For human pancreas sections and islets study, conducted in Organ Transplant Center, Tianjin First Central Hospital, Nankai University, Tianjin, China. A total of 20 healthy individuals were recruited, of these, 10 were YA (18\u201325 years old) and 10 were the EA (60\u201385 years old) for human pancreas sections. Human islet donor from an elderly person. Islets were digested into single cells with 0.25% trypsin-EDTA (Gibco, USA) and treated with ABT263 (5\u2009\u03bcM) (MCE, China) for 48\u2009h, followed by a change to the fresh culture medium. After 24\u2009h, EVs were enriched in the supernatant, and the cells were stained with \u03b2-galactosidase (Beyotime, China). Quantification was performed with three replicates per group, at least 15 microscopic fields per well, and a minimum count of 1000 cells. The detailed information of donors was listed in Table\u00a0S1. Informed consent was obtained from all patients, and the research protocol was reviewed and approved by the research ethics committee of Tianjin First Central Hospital (No. 2018N112KY).\n\nFor blood sample collection, conducted in the Department of Endocrinology, Geriatric Hospital of Nanjing Medical University, Nanjing, China, 160 individuals were recruited, including 65 YA (18-55 years old), 65 EA (60-85 years old), and 30 EA with T2DM (EA\u2009+\u2009DM). Fasting blood samples, collected from all participants, were centrifuged at 850\u2009\u00d7\u2009g for 20\u2009min to separate sera and blood cells, the sera were used for miR-503 concentration analysis. Detailed information about donors, including age range, fasting blood glucose levels, and history of prior diseases, was listed in Table\u00a0S2. The study was approved by the research ethics committee of Nanjing Medical University (2022006), and all the volunteers gave written informed consent.\n\nAnimal studies were approved by the Research Animal Care Committee of Nanjing Medical University (IACUC-1707023 and IACUC-2004040). Generation of the mouse miR-503-322 knock-in mouse (H11-CAG-LSL-miR-503-322 Cas9-KI) by CRISPR/Cas9 was outsourced to GemPharmatech Co, Ltd. The mice were created on the C57BL/6\u2009J genetic background. The gRNA (5\u2032- CTGAGCCAACAGTGGTAGTA -3\u2032) to the Hipp11 (H11) locus, the donor vector containing the \u201cCAG-loxP-Stop-loxP-mouse miR-503-322-polyA\u201d cassette, and Cas9 mRNA were co-injected into fertilized mouse eggs to generate targeted conditional knock-in offspring. Rat insulin 2 promoter (RIP2)-Cre (JAX:003573), CAG-CreER (JAX:004453) and PDX1-Cre (JAX:014647) mice were obtained from the Jackson Laboratory. Elastase (ELA)-CreER mice were obtained from Dr. Xianghui Fu (Professor of the West China Hospital, Sichuan University). We then crossed KI mice with CAG-CreER, PDX1-Cre, ELA-CreER, and RIP2-Cre mice, respectively, to obtain global inducible (CKI), pancreas-specific (PKI), acinar cell-specific inducible (EKI), and islet \u03b2-cell-specific (\u03b2KI) overexpressing miR-503-322 mice. Details on each animal strain are listed in Table\u00a0S3. EKI or CKI and their litter control mice were injected intraperitoneally with tamoxifen solution, 100\u2009mg/kg, in corn oil, for three consecutive days to induce miR-503-322 overexpression in acinar cells or the whole body, respectively. The control groups used their respective littermates and were genotyped as KI-positive and Cre-negative. All experimental mice were heterozygous except for PKI mice, which included both homozygotes and heterozygotes. MiR-503 transgenic mice (\u03b2TG) and miR-503-322 global deletion mice (KO) were also generated by GemPharmatech Co, Ltd. Refer to our previous findings for the exact construction workflow15. Aged C57BL6/J mice were purchased from GemPharmatech Co, Ltd.\n\nThe animals were randomly allocated to experimental groups, at least four per group, not according to genotype to minimize potential confounding factors. Male mice were mostly used in this study, and female mice were also involved to rule out the sex bias, as described in the figure legends. Mice were housed in a temperature- and humidity-controlled environment (23\u201325\u2009\u00b0C, 12-h light/dark cycle, 60\u201370% humidity) in a specific pathogen-free facility at Nanjing Medical University and provided with free access to commercial rodent chow (Research Diets, D12450J) and tap water. Health was monitored at least weekly by weight, food and water intake, and general assessment of animal activity, panting, and fur condition. Mice were euthanized by CO2 asphyxiation when met euthanasia criteria.\n\nAdult animals of both genders were used in tamoxifen induction studies. Collected blood serum was used to measure amylase and lipase. The pancreatic tissue was collected and immediately embedded in an optimum cutting temperature compound for hematoxylin and eosin staining, evaluation of necrosis, and immunohistochemistry.\n\nMouse pancreatic acini were isolated using the standard collagenase digestion protocol56. Acini were isolated and left to recover for 30\u2009min at 37\u2009\u00b0C before stimulation with the indicated concentrations of caerulein (MCE, Shanghai, China) to assess the secretory capacity. The supernatant for amylase activity was analyzed with a commercial kit (JianCheng Bioengineering Institute, Nanjing, China) and the percentage of amylase secretion was calculated. To visualize trypsinogen activation in acinar cells, freshly prepared acini were loaded with active trypsin enzyme substrate BZiPAR (10\u2009\u03bcM) (Invitrogen, America) and incubated for 30\u2009min. For cytoskeletal analysis, pancreatic acini were isolated and incubated with or without caerulein (0.01\u2009\u03bcM) for 30\u2009min. At the indicated times, the cells were harvested and stained for F-actin with phalloidin and nuclei. Images were captured and analyzed by a confocal laser scanning microscope (Olympus FV1200). The image fluorescence intensity was analyzed with ImageJ software. For flow cytometry analysis, EVs labeled with PKH67 were co-incubated with freshly isolated acini for 8\u2009h. Following this, the samples were digested into single cells and subsequently analyzed to determine the percentage of FITC-positive cells.\n\nCaerulein was solubilized in phosphate-buffered saline at a final concentration of 15\u2009mg/mL. Experimental mice were challenged with caerulein (50\u2009mg/kg, intraperitoneal injection, once an hour, six times) to induce AP. Control animals received an equal amount of saline. The parameters of AP were assessed 2\u2009h after the last caerulein treatment. Edema, serum lipase (ElabScience, Wuhan, China), amylase and trypsin activity (JianCheng Bioengineering Institute, Nanjing, China) were analyzed as parameters of pancreatitis. Necrosis and acinar cell damage quantified by morphometry57. Tissue damage was quantified using scoring system as describe by Schmidt et al.43.\n\nMice were euthanized by CO2 asphyxiation and tissue was dissected, rinsed in PBS and fixed overnight in 4% paraformaldehyde (Servicebio). Paraffin embedding, serial sectioning, H&E and Masson staining of all samples were commissioned from Servicebio Technologies. After dewaxing and antigen retrieval, the pancreatic paraffinic sections were incubated with primary antibodies overnight at 4\u2009\u00b0C. According fluorescent-conjugated secondary antibodies (Proteintech) were used for multiple labeling, and the nuclei were stained with Hoechst 33342 (5\u2009\u03bcg/mL) (Sigma-Aldrich). Fluorescent images were visualized by a confocal laser scanning microscope (Olympus FV1200). Immunohistochemistry staining was labeled with a DAB substrate system (BCA Kit) (Gene Tech), and positively labeled cells were captured by a light microscope (Leica, Germany). Quantification was done with at least three mice per group, three sections per mouse (50\u2009\u00b5m apart), and at least 10 microscopic fields per section. The antibodies are listed in Table\u00a0S4.\n\nPancreatic ductal infusion was performed following the standard surgical protocol58. Serotype 2/8 under insulin 2 promoter of HBAAV2/8-insulin 2-scrambled sequence-zsGreen (Ctr) and HBAAV2/8-insulin 2-mmu-miR-503/322-5p-sponge-zsGreen (SP) were provided by the company of Hanheng Biotechnology Co, Ltd. AAV titer of 1011/mL in PBS, 100\u2009\u03bcL total volume in 20\u2009g body weight mice was infused at a rate of 6\u2009\u03bcL/min. After infusion and suture, surgical mice were placed on a heated pad (37\u2009\u00b0C) until full recovery. Ketoprofen (Sigma, k1751) at a dose of 5\u2009mg/kg once per day was given continuously for 3\u2009d for post-surgery analgesia. Serotype pancreas of PAAV-CMV-MCS-EF1-mNeonGreen-WPRE (Ctr-AAV) and PAAV-CMV-MKNK1-flag-EF1-mNeonGreen-WPRE (MKNK1-AAV) were provided by the company of OBIO Technology Co, Ltd and were administered to mice via intraperitoneal injection. AAV titer of 1011/mL in PBS, 100\u2009\u03bcL total volume in 20\u2009g body weight mice.\n\nLocked nucleic acid-based in situ assay was introduced to detect miR-503 in human pancreas sections. Double-labeled with carboxyfluorescein (FAM), LNA-enhanced probes including U6 snRNA control probe, negative scramble-miR control and has-miR-503 were constructed by QIAGEN. The assay was performed according to the manufacturer\u2019s protocol59. In short, sample slides were deparaffinized in xylene and ethanol solutions at room temperature (15\u201325\u2009\u00b0C) and digested with Proteinase K reagent for 10\u2009min at 37\u2009\u00b0C. After washing, each sample was reacted with 50\u2009\u00b5L of hybridization mix (1\u2009nM LNA U6 snRNA probe, 40\u2009nM double-FAM LNA miR-503 probe and scramble-miR) in a programmed hybridizer for 1\u2009h. After strictly washing and blocking, the samples were incubated with anti-FAM reagent for 1\u2009h and labeled with alkaline phosphatase substrates for 2\u2009h. The nuclei were labeled with Nuclear Fast Red. All sample slices were visualized by light microscopy.\n\nFreshly isolated islets were cultured in serum-EVs-free medium (11.1\u2009mM glucose) for 7 days, with the medium replaced and collected every 24\u2009h. Mouse islets were digested into single cells and treated with FluoZinTM-3 (5\u2009\u03bcM) (Thermo Fisher Scientific) for 30\u2009min, followed by fluorescence-activated cell sorting to obtain purified \u03b2-cells. \u03b2-cells were cultured in 0.1\u2009mg/mL poly-D-lysine (Beyotime, China)-coated well plates and EVs-free medium for 3 days and supernatants were collected for enrichment. The culture medium was centrifuged at 300\u2009\u00d7\u2009g for 5\u2009min and then at 3000\u2009\u00d7\u2009g for 20\u2009min to remove cells and other debris, followed by centrifugation at 10,000\u2009\u00d7\u2009g for 30\u2009min to remove large vesicles. Then, the supernatant was centrifuged at 110,000\u2009\u00d7\u2009g for 2\u2009h. EVs were collected from the pellets and resuspended in an FBS-free medium or PBS. All centrifugation steps were performed at 4\u2009\u00b0C. For the identification of EVs, TEM, NTA, and Western blot analyses were performed. For TEM, EVs were fixed overnight at 4\u2009\u00b0C in a droplet of 2.5% glutaraldehyde in PBS (pH 7.2). The samples were then washed with PBS three times (10\u2009min each) and post-fixed in 1% osmium tetroxide for 60\u2009min at room temperature. Samples were then embedded in 10% gelatin, fixed in glutaraldehyde at 4\u2009\u00b0C, cut into several blocks (<1\u2009mm3 in volume), and dehydrated for 10-min dehydration steps in alcohol at increasing concentrations (30%, 50%, 70%, 90%, 95%, and 100%\u2009\u00d7\u20093). Pure alcohol was then replaced with propylene oxide, and the samples were infiltrated with Quetol 812 epoxy resin at increasing concentrations (25%, 50%, 75%, and 100%) with propylene oxide for a minimum of 3\u2009h per step. Next, the samples were embedded in 100% fresh Quetol 812, polymerized at 35\u2009\u00b0C for 12\u2009h, and then at 60\u2009\u00b0C for 24\u2009h. Ultrathin sections (100\u2009nm) were obtained from the prepared samples using a Leica UC6 ultramicrotome. Finally, the samples were post-stained with uranyl acetate for 10\u2009min and lead citrate for 5\u2009min at room temperature and observed using an FEI Tecnai T20 TEM operated at 120\u2009kV. NTA was performed using a NanoSight NS300 system (NanoSight), which focuses a laser beam through a suspension of the particle of interest. The results were visualized by light scattering. For western blotting, ALIX, TSG101, and CD63 were used as markers for nano-vesicles and GAPDH was used as a negative control. For cell imaging, EVs were labeled with PKH67 (Sigma-Aldrich) for 1\u2009h and then washed three times with PBS. PKH67-labeled EVs (6e\u2009+\u20095 particles/35\u2009mm culture dish) were resuspended in PBS and then incubated with freshly isolated acini for 8\u2009h. The acini were then stained with phalloidin (MCE) for 15\u2009min and Hoechst 33342 for 8\u2009min. Images were taken and analyzed by a confocal laser scanning microscope (Olympus FV1200).\n\nThe WT and mutant 3\u2019 UTR-luciferase constructs containing miR-503-322 binding site of mouse Mknk1 were generated by annealing and cloning the short sequences into pMIR-REPORT Luciferase miRNA Expression Reporter Vector (Ambion) between the SpeI and HindIII sites. Primer sequences are listed in Table\u00a0S5. Luciferase activities were measured using the Dual-Glo Luciferase Assay System (Promega, America) on a TD-20/20 Luminometer (Turner BioSystems, America) according to the manufacturer\u2019s protocols.\n\nTotal RNA was extracted from cells and tissues using Trizol reagent (Invitrogen). cDNA was synthesized from total RNA using a ReverTra Ace Kit (TOYOBO, Japan). qPCR of Pri-miRNA and miRNA were performed using the THUNDERBIRD probe qPCR Mix (TOYOBO, Japan), and SYBR Green qPCR Master Mix (Vazyme, China) for mRNA on Roche LightCycle480 II Sequence Detection System (Roche, Switzerland). Primers of qPCR for pri-miRNA and miRNA were purchased from Thermofisher Co., Ltd, other primer sequences were available in Table\u00a0S5.\n\nCells or tissues were lysed with ice-cold RIPA buffer (Thermo Fisher Scientific), supplemented with 0.5\u2009mM EDTA and Halt protease/phosphatase inhibitor cocktail (Thermo Fisher Scientific), rotated at 4\u2009\u00b0C for 15\u201330\u2009min to mix, and centrifuged at maximum speed for 15\u2009min to collect whole cell lysates. Protein concentration was measured with the BCA protein assay (Takara). Thirty mg of total protein per sample was loaded into 4\u201312% NuPAGE Tris-Bis (Thermo Fisher Scientific) gradient gels and separated by SDS-PAGE. Proteins were transferred to PVDF membranes (Millipore Billerica) and blocked with 5% milk. Beta-actin and \u03b1-tubulin were used as loading controls. Primary antibodies were detected with HRP-conjugated (Sigma-Aldrich) secondary antibodies for chemiluminescent detection (Perkin Elmer ECL). Protein quantification was performed by ImageJ (NIH Image). Key reagents and antibodies are listed in Table\u00a0S6.\n\nAll results were expressed as mean\u2009\u00b1\u2009SEM. Results were analyzed with GraphPad Prism software (version 8.3.0, San Diego, CA, USA). Two-tailed unpaired Student\u2019s t test was used for the comparison of two sets. Differences in means between multiple groups were analyzed by ordinary one-way analysis of variance followed by Tukey\u2019s multiple comparisons. Two-way ANOVA followed by Tukey\u2019s multiple comparisons was used for two-way analysis. Linear regression analysis was performed using GraphPad Prism software. Pearson correlation analysis was used to test for correlations. In all analyses, P\u2009<\u20090.05 was considered statistically significant.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The source data generated in this study are provided in the Source Data file. This paper does not report the original code. Any additional information is available upon request to the corresponding author (Yunxia Zhu, zhuyx@njmu.edu.cn).\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "We are indebted to Scribendi Inc. (Chatham, ON, Canada) for proofreading the manuscript. This work was supported by grants from the National Natural Science Foundation of China (82330027 to X.H.; 82470840, 82270844, and 82070843 to Y.-X.Z.; 82401002 to K.-R.L.) and Wuxi Science and Technology Development Fund (K20231061 to K.-R.L.).",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Key Laboratory of Human Functional Genomics of Jiangsu Province, Biochemistry and Molecular Biology, Nanjing Medical University, Nanjing, Jiangsu, China\n\nKerong Liu,\u00a0Tingting Lv,\u00a0Lu He,\u00a0Xiao Xiao,\u00a0Yating Li,\u00a0Xiaoai Chang,\u00a0Xiao Han\u00a0&\u00a0Yunxia Zhu\n\nDepartment of Endocrinology, Affiliated Children\u2019s Hospital of Jiangnan University, Wuxi Children\u2019s Hospital, Wuxi, Jiangsu, China\n\nKerong Liu\n\nDepartment of Endocrinology, Geriatric Hospital of Nanjing Medical University, Nanjing, Jiangsu, China\n\nWei Tang\n\nChildren\u2019s Hospital of Nanjing Medical University, Nanjing, Jiangsu, China\n\nYan Zhang\n\nOrgan Transplant Center, Tianjin First Central Hospital, Nankai University, Tianjin, China\n\nShusen Wang\n\nDivision of Gastroenterology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA\n\nStephen J. Pandol\n\nDepartment of Endocrinology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China\n\nLing Li\n\nDepartment of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China\n\nXiao Han\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nConceptualization: Y.-X.Z., X.H. and L.L.; methodology: K.-R.L., T.-T.L., L.H., Y.Z. and X.X; investigation: Y.Z., L.H., W.T. and S.-S.W.; visualization: K.-R.L. and Y.-X.Z.; supervision: Y.Z., Y.-T.L., and X.-A.C.; writing\u2014original draft: K.-R.L. and Y.-X.Z.; writing\u2014review & editing: K.-R.L., X.H., S.-J.P. and Y.-X.Z.; funding acquisition: X.H., Y.-X.Z. and K.-R.L. All authors discussed the results and commented on the manuscript.\n\nCorrespondence to\n                Stephen J. Pandol, Ling Li, Xiao Han or Yunxia Zhu.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Naziruddin Bashoo and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional information",
+            "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",
+            "section_image": []
+        },
+        {
+            "section_name": "Source data",
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+            "section_image": []
+        },
+        {
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+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions",
+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Liu, K., Lv, T., He, L. et al. Endocrine-exocrine miR-503-322 drives aging-associated pancreatitis via targeting MKNK1 in acinar cells.\n                    Nat Commun 16, 2613 (2025). https://doi.org/10.1038/s41467-025-57615-x\n\nDownload citation\n\nReceived: 03 June 2024\n\nAccepted: 23 February 2025\n\nPublished: 17 March 2025\n\nVersion of record: 17 March 2025\n\nDOI: https://doi.org/10.1038/s41467-025-57615-x\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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1024b305173c41b8401ff5ab5cc9c8082f65a9cfffd5cbcad6232e5357b19148/metadata.json

@@ -0,0 +1,160 @@
+{
+    "title": "Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint",
+    "pre_title": "Unsupervised classification of brain-wide axons reveals neuronal projection blueprint",
+    "journal": "Nature Communications",
+    "published": "20 February 2024",
+    "supplementary_0": [
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45741-x/MediaObjects/41467_2024_45741_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45741-x/MediaObjects/41467_2024_45741_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45741-x/MediaObjects/41467_2024_45741_MOESM3_ESM.zip"
+        },
+        {
+            "label": "Source Data Supplement",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-45741-x/MediaObjects/41467_2024_45741_MOESM4_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-024-45741-x#Fig1",
+        "/articles/s41467-024-45741-x#Fig3",
+        "/articles/s41467-024-45741-x#Fig4",
+        "/articles/s41467-024-45741-x#Fig6",
+        "/articles/s41467-024-45741-x#Fig7",
+        "/articles/s41467-024-45741-x#Fig3",
+        "/articles/s41467-024-45741-x#Fig4",
+        "/articles/s41467-024-45741-x#Sec22"
+    ],
+    "code": [
+        "https://github.com/Projectomics"
+    ],
+    "subject": [
+        "Classification and taxonomy",
+        "Neural circuits"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3044664/v1.pdf?c=1708521191000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3044664/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-45741-x.pdf",
+    "preprint_posted": "03 Jul, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Long-range axonal projections are quintessential determinants of network connectivity, linking cellular organization and circuit architecture. Here we introduce a quantitative strategy to identify, from a given source region, all \u201cprojection neuron types\u201d with statistically different patterns of anatomical targeting. We first validate the proposed technique with well-characterized data from layer 6 of the mouse primary motor cortex. The results yield two clusters, consistent with previously discovered cortico-thalamic and intra-telencephalic neuron classes. We next analyze neurons from the presubiculum, a less-explored region. Extending sparse knowledge from earlier retrograde tracing studies, we identify five classes of presubicular projecting neurons, revealing unique patterns of divergence, convergence, and specificity. We thus report several findings: (1) individual classes target multiple subregions along defined functions, such as spatial representation vs. sensory integration and visual vs. auditory input; (2) all hypothalamic regions are exclusively targeted by the same class also invading midbrain, a sharp subset of thalamic nuclei, and agranular retrosplenial cortex; (3) Cornu Ammonis, in contrast, receives input from the same presubicular axons projecting to granular retrosplenial cortex, also the purview of a single class; (4) path distances from the presubiculum to the same targets differ significantly between classes, as do the path distances to distinct targets within most classes, suggesting fine temporal coordination in activating distant areas; (5) the identified classes have highly non-uniform abundances, with substantially more neurons projecting to midbrain and hypothalamus than to medial and lateral entorhinal cortex; (6) lastly, presubicular soma locations are segregated among classes, indicating topographic organization of projections. This study thus demonstrates that classifying neurons based on statistically distinct axonal projection patterns sheds light on the functional organizational of their circuit.Biological sciences/Neuroscience/Neural circuitsBiological sciences/Computational biology and bioinformatics/Classification and taxonomy",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.\nSupplemental Tables are not available with this version.",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "We present a quantitative strategy to identify all projection neuron types from a given region with statistically different patterns of anatomical targeting. We first validate the technique with mouse primary motor cortex layer 6 data, yielding two clusters consistent with cortico-thalamic and intra-telencephalic neurons. We next analyze the presubiculum, a less-explored region, identifying five classes of projecting neurons with unique patterns of divergence, convergence, and specificity. We report several findings: individual classes target multiple subregions along defined functions; all hypothalamic regions are exclusively targeted by the same class also invading midbrain and agranular retrosplenial cortex; Cornu Ammonis receives input from a single class of presubicular axons also projecting to granular retrosplenial cortex; path distances from the presubiculum to the same targets differ significantly between classes, as do the path distances to distinct targets within most classes; the identified classes have highly non-uniform abundances; and presubicular somata are topographically segregated among classes. This study thus demonstrates that statistically distinct projections shed light on the functional organization of their circuit.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "The classification of neurons in the mammalian nervous system has long been a focus of intensive investigation. While local features from slice preparations in vitro may suffice to infer the circuit roles of GABAergic interneurons1,2,3, long-range projecting axons are crucial architectural elements of neural organization4,5 constituting the conceptual and physical nexus between brain-wide circuits and synaptic communication6. Thus, projection axons have long been digitally traced from serial sections after in vivo labeling and light microscopy imaging7,8,9,10. At the same time, their macroscopic extent (~1\u2009cm span; ~1\u2009m cable length) and microscopic caliber (~100\u2009nm branch thickness) combine into a formidable technological challenge for large-scale collection11,12. As a result, the number of completely reconstructed projection axons in any mammalian neural system has until recently remained into the low tens.\n\nA source brain region projecting to N targets (where N typically ranges between 10 and 50 in the mouse cortex) could contain any combination of 2N\u22121 distinct axonal projection types. Such a combinatorics challenge requires a large-scale data collection for proper classification. Projects based on fluorescent Micro-Optical Sectioning Tomography (fMOST) technology13,14,15 or the Janelia MouseLight platform16, launched in recent years to address this need, produced nearly 10,000 mouse whole-brain single neuron reconstructions registered to a 3D Common Coordinate Framework (CCF)17 with consensus anatomical labeling18. However, these newly available data do not themselves generate novel scientific insights, explain brain circuitry, or even disprove that axons might simply invade a random subset of the regional target areas19. Rigorous methods are needed to test the hypothesis that specific projection types exist, to characterize their identities, and to quantify their population sizes20.\n\nThis study introduces an original technique to objectively identify projection-based neuronal classes. To ascertain whether a collection of axonal projections might result from essentially random variation within the constraints of regional connectivity or likely reflects distinct neuron types, we begin from the foundational criterion for classification: if a set of items belongs to segregated classes, their pairwise inter-individual differences must be on average larger between than within classes. In other words, two items from the same class should tend to be more similar to each other than two items from separate classes. To implement this logic into a classification framework, we couple rigorous statistical testing with unsupervised hierarchical clustering. A unique strength of this approach is its entirely data-driven granularity: the continuous accumulation of new tracings will progressively refine the classification details with increasing statistical power. We can then characterize the identified projection classes by quantifying their population size, topographic soma distributions, and convergence and divergence patterns.\n\nIn the remainder of this article, we first propose a formal definition of and a quantitative solution for the classification problem. We validate our approach by applying it to layer 6 of the primary motor cortex, and then utilize it to study the presubiculum, a rather under-investigated region of the mouse brain. We next quantify the neuronal population sizes of the presubicular projection classes and characterize the spatial distribution of their somata. Finally, we analyze the patterns of divergence and convergence of presubicular projection classes. We conclude by discussing the biological interpretations of these results.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "The axonal projections of each neuron in a source region can be represented as k-dimensional vectors, where k is the number of target regions invaded by the source region. Each of the k components of the vector quantifies the number of axonal points within the corresponding region (Fig.\u00a01; see \u201cChoice of metric to quantify axonal extent\u201d in \u201cMethods\u201d). We explore the null hypothesis, H0, that all neurons from a source region belong to a single projection class (Fig.\u00a02a), as opposed to the alternative hypothesis, HA, that distinct projection classes exist from that source region (Fig.\u00a02b). If two hypothetical classes exist, the projections will be more similar between neurons within a class and more different across classes (Fig.\u00a02c). In such a two-class scenario, the combined within- and across-class distances would thus form a wider distribution than the distribution generated if all neurons belong to just a single class (Fig.\u00a02d). To formally test HA, we measure all pairwise differences between neurons (as arccosine vector distances, see \u201cMethods\u201d). We then generate the distribution of distances for H0 by randomizing the projection patterns while preserving total axonal extent both by neuron and target region. We achieve this single-class continuum by iterative stochastic swapping of axonal points between neurons across two target regions (see Fig.\u00a02e and \u201cMethods\u201d). We can then apply Levene\u2019s one-tail statistical test to ascertain whether the original distribution of pairwise distances has significantly larger variance than the randomized distribution. If the answer is positive, we must discard H0 and accept HA. Starting from the top node in an unsupervised hierarchical clustering tree, we can thus repeat Levene\u2019s test on the neurons of each of the two subtrees, continuing the process until none of the variance differences are statistically significant (Fig.\u00a02f). When Levene\u2019s test fails (i.e., it provides a negative answer), the precise cutoff is determined independently of the other points of failure. Therefore, all neurons within a cluster (i.e., under the same Levene failure point) are statistically equivalent with respect to the axonal projection patterns across the target regions, but each cluster is independent of the other clusters. Moreover, there is no correspondence between the cutoff levels and the resulting number of neurons in each cluster.\n\nCCF-registered reconstruction of two presubicular neurons (brain depiction and neurons AA1090 in black and AA1058 in blue from the Janelia MouseLight project) invading 9 regions out of 40 potential targets along with the numbers of axonal points of the neurons in each highlighted region (posterior view of brain). Source data are provided as a Source Data file. CCF common coordinate framework, AM anteromedial nucleus, AV anteroventral nucleus, cc corpus callosum, dhc dorsal hippocampal commissure, fx fornix, LHA lateral hypothalamic area, LM lateral mammillary nucleus, MM medial mammillary nucleus, TH other thalamic nuclei.\n\na In a single-class scenario, the distribution of differences between neurons can be calculated for all neuron pairs (pink double-arrows). b If two distinct classes exist, neurons (represented here as black dots) will tend to have more similar projections within their class (red double-arrows) and more different ones across classes (blue double arrow). c The differences within the classes (red distribution) will be smaller than those between classes (blue distribution). d The distribution of the combined frequency of differences, in a multi-class scenario (red-blue stacked areas; green half-height width), will be wider than that of a single-class distribution (pink curve; orange half-height width). e Diagram showing the randomization of projection patterns through the repeated pairwise swapping of axonal point counts between two neurons across two of their potential target regions, which preserves the column (for a given region) and row (for a given neuron) sums of the matrix. This swapping results in a projection pattern continuum that matches with the overall distribution representing the 1-class null hypothesis. f Unsupervised hierarchical clustering groups a set of neurons into classes based on their relative pairwise differences or similarities, as modeled by a binary dendrogram. The top (root) of the dendrogram represents all neurons lumped into the same class, while the bottom (leaves) shows every neurons split into separate classes. The p value that determines whether to keep splitting is derived from Levene\u2019s test based on a one-way ANOVA of the absolute data values and the group means to which the data values belong.\n\nTo validate the above research design, we first analyzed 52 MouseLight layer 6 neurons from the primary motor cortex21 (Source data are provided as a Source Data file). This anatomical area is known to contain two distinct projection classes with well-defined subdivisions: cortico-thalamic (CT) and cortico-cortical or intra-telencephalic (IT) neurons22. The variance of the distribution of pairwise axonal projection differences of these neurons was significantly larger than that of the randomized projections (p\u2009=\u20096.46\u2009\u00d7\u200910\u221251; variance of real data\u2009=\u2009373.4; variance of randomized data\u2009=\u2009195.7), indicating the existence of distinct clusters. However, both subtrees after the first split of unsupervised hierarchical clustering returned a non-significant Levene\u2019s test (IT: p\u2009=\u2009N/A; variance of real data\u2009=\u2009219.9; variance of randomized data\u2009=\u2009240.0; CT: p\u2009=\u20090.24; variance of real data\u2009=\u2009295.1; variance of randomized data\u2009=\u2009264.0), revealing exactly two clusters (Fig.\u00a03a). The first cluster, consisting of 21 neurons, projected almost exclusively to motor cortical targets; the second cluster of 31 neurons projected primarily to thalamic targets (Fig.\u00a03b\u2013d). These patterns were fully consistent with the axonal pathways of the IT and CT neuronal classes, respectively. This finding thus corroborates the validity of employing Levene\u2019s test of variance on pairwise difference distributions to identify statistically distinct classes in unsupervised hierarchical clustering.\n\na Representation of the two clusters produced by Levene\u2019s one-tailed test for the equality of variances and unsupervised hierarchical clustering, using MouseLight neurons from the primary motor cortex, layer 6 (n\u2009=\u200952). b Colormap of the axonal distributions of neurons (columns) across anatomical regions (rows), with darker shades representing more axonal projections. The axonal points for the thalamic targets are more numerous than those for the motor cortical targets by a factor of two. Source data are provided as a Source Data file. c CCF-registered reconstructions of the axonal pathways of representative IT (intra-telencephalic, red, AA0876) and CT (corticothalamic, blue, AA0398) neurons with semitransparent surfaces of primary motor cortex layer 6 (green) and selected thalamic nuclei (pink). The two black dots indicate the cell body locations of the two representative cells from each class. d CCF-registered reconstructions of the axonal pathways of all IT and CT neurons (see Source Data for a full listing of the 52 neurons depicted) in the MouseLight sample (same color coding). The neurons and brain depictions in panels (c) and (d) are from the Janelia MouseLight project.\n\nWe then applied our analytic technique to a lesser-explored source region of the mouse brain: the presubiculum. Unsupervised clustering and the test of variance demonstrated that the 93 MouseLight neurons from the presubiculum form five distinct projection classes (Fig.\u00a04a\u2013c). We designate each class by a letter (A-E) followed by the number of neurons in the class (Fig.\u00a04c). The first class, A38, primarily targets the lateral entorhinal cortex (LEC), accounting for 82% of axonal extent outside of the presubiculum. This class also invades the dorsoventral (granular) retrosplenial cortex as well as the hippocampal formation (dentate gyrus, CA3, CA2, CA1, and subiculum). The second class, B27, mainly targets the dorsal portion of the medial entorhinal cortex (dMEC), accounting for 92.5% of extra-presubicular axonal extent, as well as retrohippocampal zone and parasubiculum. Class C3 neurons mostly target the contralateral dMEC (42%) and LEC (40%), subiculum (14%), and parasubiculum (4%) through extensive callosal and commissural fibers. Class D19 has the most complex (and unreported) pattern of innervation: in addition to major projections to the subiculum (40.8%) and dentate gyrus (16.3%), it is the sole source of projections to the lateral (agranular) retrosplenial cortex, to the hypothalamus (including the lateral mammillary nucleus and 18 additional nuclei), and to the superior and inferior colliculi in the midbrain. This neuronal class also projects to a subset of 8 thalamic nuclei, including the medial part of the anterior thalamic nucleus (ATN) and the lateral geniculate nucleus. Lastly, class E6 projects to a complementary set of 14 other thalamic nuclei, including the ventral, dorsal, anterior, and lateral parts of the ATN and the medial geniculate nucleus. Neurons from all five projection classes also have substantial collaterals within the presubiculum. Examples of projection neurons from each of the presubicular projection classes are depicted in Fig.\u00a04d\u2013e.\n\na Representation of 5 axonal clusters produced by Levene\u2019s test and unsupervised hierarchical clustering of neurons from the presubiculum (n\u2009=\u200993). b Colormap of the axonal distributions of neurons (columns) across anatomical regions (rows), with darker shades representing more axonal projections. Parcel names highlighted in pink are hypothalamus related. Parcel names highlighted in yellow and light blue are thalamus related. Source data are provided as a Source Data file. c Neuron-to-target assignments for the identified axonal projection classes and corresponding anatomical regions (dotted line: contralateral). d Anterior view of the mouse brain with a CCF-registered reconstruction of one neuron from each class (cluster A, blue, AA0021; cluster B, red, AA0724; cluster C, cyan, AA0168; cluster D, brown, AA0031; cluster E, green, AA0244). Color coding of neurons and semitransparent anatomical areas shown in (a, b, and c). e Posterior view of the brain with CCF-registered reconstructions of all 93 MouseLight presubicular neurons (see Source Data for a full listing of the neurons depicted). The highlighted parcels are the same as those depicted in panel (c), with the same color coding. The neurons and brain depictions in panels (d) and (e) are from the Janelia MouseLight project. CA3\u2009+\u2009CA1 Cornu Ammonis areas 3 and 1, DG dentate gyrus, Sub subiculum, LEC lateral entorhinal cortex, dMEC dorsal portion of the medial entorhinal cortex, ParaS parasubiculum, PostS postsubiculum, Retrohipp retrohippocampal region, DV(gr.)RtSpl dorsal and ventral (granular) retrosplenial cortex, L(ag.)RtSpl lateral (agranular) retrosplenial cortex, MidB midbrain, Hyp hypothalamus, PMdv\u2009+\u2009TU dorsal and ventral premammillary nucleus and tuberal nucleus, MM\u2009+\u2009LZ: medial mammillary nucleus and hypothalamic lateral zone, MBO\u2009+\u2009LM mammillary body and lateral mammillary nucleus, mATN\u2009+\u2009PT medial anterior thalamic nucleus and parataenial nucleus, TH\u2009+\u2009LGN thalamus and lateral geniculate nucleus, dvATN\u2009+\u2009MGN dorsal and ventral anterior thalamic nucleus and medial geniculate nucleus, IAD\u2009+\u2009IAM interanterodorsal and interanteromedial nucleus of the thalamus, LD\u2009+\u2009AD lateral dorsal and anterodorsal nucleus of thalamus.\n\nNext, we quantified the proportion of neurons in the mouse presubiculum that belong to each projection class. To this aim, we extracted the anterograde tract tracing density distributions from the Allen Institute regional connectivity atlas and matched the fractions of neurons in every class based on their axonal patterns by numerical optimization (see Non-Negative Least Squares in Methods; Source data are provided as a Source Data file). The results converged with very small residual error (<0.0006%) indicating a near-exact correspondence between single-neuron and regional projections. Fully sampling neurons from across the presubiculum, Class D19, reaching the midbrain, hypothalamus, lateral (agranular) retrosplenial, and the lateral geniculate (visual thalamus) accounted for the greatest portion (38.1%) of neurons. Class A38, targeting the hippocampus, subiculum, dorsoventral (granular) retrosplenial cortex, and lateral entorhinal cortex (what pathway), accounted for the second largest share (30.6%) of neurons. Class B27, projecting to the parasubiculum and medial entorhinal cortex (where pathway) consisted of 16.3% of projection neurons. Class E6, focused on other thalamic nuclei including medial geniculate (auditory), was responsible for 13.7% of presubicular neurons. The diffuse contralateral projections of class C3 comprised the remaining 1.3%.\n\nWhen accounting for these relative proportions together with the MouseLight axonal projections, we can estimate the contribution of each class to the presubicular projections in each collection of target regions. In particular, the dentate gyrus receives 21% of its presubicular afferents from class A38 and 79% from class D19. The subiculum receives 69% of its presubicular afferents from class D19, 30% from class A38, and 1% from class C3. The lateral entorhinal cortex receives 99% of presubicular afferents from class A38 and 1% from class C3. The dorsal medial entorhinal cortex and parasubiculum receive 99% of presubicular afferents from class B27 and 1% from class C3. All other regions are targeted by individual classes: CA3, CA1, and the dorsoventral (granular) retrosplenial cortex by A38; the midbrain, hypothalamus, lateral (agranular) retrosplenial cortex, and part of the thalamic nuclei including medial ATN and lateral geniculate nucleus by D19; and the rest of the thalamic nuclei including dorsoventral ATN and medial geniculate nucleus by E6.\n\nComputational geometry analysis of soma locations within the presubiculum demonstrated a clear spatial separation among the four main projection classes: A38, B27, D19, and E6 (the smallest class, C3, is largely contralateral projecting). Specifically, the convex hull volume of each neuron class overlapped only minimally (~5\u201320%) with that of other neuron classes (Fig.\u00a05a\u2013c). In particular, class A38 was positioned more rostrally and dorsally relative to the caudal-ventral position of class B27, with approximately 14% of overlap (Fig.\u00a05a). The overlap of A38 was maximal with D19 (21%); however, while most A38 neurons had a selective somatic concentration in layer 2 (34/38: 89.5%), D19 had a somatic distribution across all 3 presubicular layers: 21% in layer 1 and 26% in layer 3 (Fig.\u00a05b). Class E6 had the most lateral positioning resulting in almost complete segregation from the other projection classes: there were so few overlapping somata that a proper convex hull volume of the overlap could not be calculated (Fig.\u00a05c, d).\n\nConvex hulls of neurons (spheres) from classes A38 (blue), B27 (red), D19 (brown), and E6 (green), and semitransparent presubiculum (green). a Left sagittal view of A38 and B27. b Layer 1 (green), layer 2 (purple), and layer 3 (orange) of the presubiculum are highlighted in an anterior coronal view, with somata from A38 in blue and D19 in brown. Most of the A38 somata are concentrated in layer 2, while the D19 somata tend to be more concentrated in layers 1 and 3. Somata that do not follow this pattern are indicated with a white dot inside of the circle. c Left sagittal view of D19 and E6. d Posterior coronal view of B27 and E6. The brain depictions in all panels are from the Janelia MouseLight project, and the spheres were generated with MATLAB.\n\nWe tested whether the path distances from presubicular neurons of a given projection class differed across their divergent target regions (Fig.\u00a06). In these analyses of divergence, ipsilateral and contralateral targets were considered separately, as the latter are systematically farther than the former. For class A38 neurons, projection distances to the ipsilateral lateral entorhinal cortex, subiculum, and dentate gyrus are significantly shorter than those to the ipsilateral hippocampus; moreover, projection distances to the ipsilateral lateral entorhinal cortex are significantly longer than those to the ipsilateral subiculum and dentate gyrus. Similarly, projection distances to the contralateral subiculum and lateral entorhinal cortex are significantly shorter than those to the contralateral hippocampus. Thus, presubicular efferent path distances differ less between ipsilateral and contralateral hippocampus than between other targets across brain hemispheres (Fig.\u00a06a). For class B27, projections to the ipsilateral parasubiculum have significantly shorter paths than those to medial entorhinal cortex, dorsal zone, but the distances are comparable in the contralateral case (Fig.\u00a06b). Finally, for class D19, projections both to the ipsilateral medial anterior thalamic nucleus and lateral geniculate nucleus, and to the ipsilateral hypothalamus and lateral mammillary nucleus combined have significantly longer paths than those to the ipsilateral midbrain (Fig.\u00a06c).\n\na Box and whisker plot depicting the range of the path distances from class A38 to its major ipsilateral (I) and contralateral (C) targets ((I)Sub: n\u2009=\u20092865 independent axonal path lengths; (I)DG: n\u2009=\u2009771; (I)LEC: n\u2009=\u200918,720; (I)CA: n\u2009=\u2009957; (C)CA: n\u2009=\u2009304; (C)LEC: n\u2009=\u200917,142; (C)Sub: n\u2009=\u2009273). Based on a CCF-registered reconstruction, the axonal path distance of an archetype neuron (AA0159) from class A38 (light blue), from its soma (black) in the ipsilateral presubiculum (green) to the subiculum (purple), is significantly shorter than that (dark blue) to the lateral entorhinal cortex (orange). b Box and whisker plot depicting the distributions of path distances from class B27 to its major ipsilateral and contralateral targets ((I)ParaS: n\u2009=\u20091622 independent axonal path lengths; (I)dMEC: n\u2009=\u200925,005; (C)dMEC: n\u2009=\u200915,800; (C)ParaS: n\u2009=\u20091666). Based on a CCF-registered reconstruction, the axonal path distance of an archetype neuron (AA0374) from class B27 (light red), from its soma (black) in the ipsilateral presubiculum (green) to the parasubiculum (brown), is significantly shorter than that (dark red) to the medial entorhinal cortex, dorsal zone (cyan). c Box and whisker plot depicting the path distances from class D19 to its major ipsilateral targets ((I)MidB: n\u2009=\u2009405\u00a0independent axonal path lengths; (I)mATN+LGN: n\u2009=\u2009349; (I)Hyp+LMN: n\u2009=\u2009469). Based on a CCF-registered reconstruction, the axonal path distance of an archetype neuron (AA0031) from class D19 (light brown), from its soma (black) in the ipsilateral presubiculum (green) to the midbrain (magenta), is significantly shorter than that (dark brown) to the hypothalamus and lateral mammillary nucleus (red). See Fig.\u00a04 for abbreviation definitions. The red horizontal lines in the box and whisker plots depict the medians. The first quartiles (Q1) and the third quartiles (Q3) are represented, respectively, by the lower and upper bounds of the boxes. Error bars represent the data range, where the lower line is Q1\u2009\u2212\u20091.5\u2009\u00d7\u2009(Q3\u2009\u2212\u2009Q1) and the upper line is Q3\u2009+\u20091.5\u2009\u00d7\u2009(Q3\u2009\u2212\u2009Q1). Red pluses are outlier data points that are greater than Q3\u2009+\u20091.5\u2009\u00d7\u2009(Q3\u2009\u2212\u2009Q1) or less than Q1\u2009\u2212\u20091.5\u2009\u00d7\u2009(Q3\u2009\u2212\u2009Q1). In all depicted comparisons, significant differences in distances were calculated using a two-sided Wilcoxon Signed Rank Test performed on neuronal path distances and multiple testing was corrected for by False Discovery Rate to determine the significance of the resultant p-values. A * indicates that the path differences were found to be significant. Source data are provided as a Source Data file. The neurons and brain depictions in all panels are from the Janelia MouseLight project.\n\nNext, we asked whether the axons from neurons of distinct projection classes converging onto their shared targets had different path distances. With the sole exception of the dentate gyrus, all target regions displayed a significant dependence of path distance on the presubicular neuron class (Fig.\u00a07). For the ipsilateral medial entorhinal cortex, dorsal zone, projections from E6 and D19 have shorter distances than those from B27 and A38, and projections from B27 have significantly shorter distances than those from A38. For the contralateral medial entorhinal cortex, in contrast, projections from B27 have significantly longer distances than those from A38 (Fig.\u00a07a). For the ipsilateral parasubiculum, path distances from D19 are significantly longer than those from B27 (Fig.\u00a07b). Finally, for the contralateral subiculum, parasubiculum, and lateral entorhinal cortex, path distances from B27 are significantly longer than those from A38 (Fig.\u00a07b\u2013d).\n\na Box and whisker plot depicting the range of the path distances from neurons in the various classes to the ipsilateral (I) and contralateral (C) medial entorhinal cortex, dorsal zone (A38 to (I)dMEC: n\u2009=\u20096194 independent axonal path lengths; B27 to (I)dMEC: n\u2009=\u200925,005; D19 to (I)dMEC: n\u2009=\u20091030; E6 to (I)dMEC: n\u2009=\u200918; A38 to (C)dMEC: n\u2009=\u20094301; B27 to (C)dMEC: n\u2009=\u200915,800; C3 to (C)dMEC: n\u2009=\u2009183). Based on CCF-registered reconstructions, the axonal distance of an archetype neuron from class B27 (red, AA0526), from its soma in the presubiculum (green) to the ipsilateral dMEC (purple), is significantly longer than the comparable distance of an archetype neuron from class D19 (brown, AA0875). b Box and whisker plot depicting the path distances from neurons in various classes to the ipsilateral and contralateral parasubiculum (B27 to (I)ParaS: n\u2009=\u20091622 independent axonal path lengths; D19 to (I)ParaS: n\u2009=\u20092031; A38 to (C)ParaS: n\u2009=\u200976; B27 to (C)ParaS: n\u2009=\u20091666). Based on CCF-registered reconstructions, the axonal distance of an archetype neuron from class B27 (red, AA0377), from its soma in the presubiculum (green) to the ipsilateral ParaS (purple), is significantly shorter than the comparable distance of an archetype neuron from class D19 (brown, AA0385). c Box and whisker plot of the path distances from neurons in various classes to the contralateral subiculum (A38 to (C)Sub: n\u2009=\u2009273 independent axonal path lengths; B27 to (C)Sub: n\u2009=\u20091389). Based on CCF-registered reconstructions, the axonal distance of an archetype neuron from class A38 (blue, AA0528), from its soma in the presubiculum (green) to the contralateral Sub (purple), is significantly shorter than the comparable distance of an archetype neuron from class B27 (red, AA0526). d Box and whisker plot depicting the path distances from neurons in various classes to the ipsilateral and contralateral lateral entorhinal cortex (A38 to (I)LEC: n\u2009=\u200918,720 independent axonal path lengths; B27 to (I)LEC: n\u2009=\u20092002; D19 to (I)LEC: n\u2009=\u200927; A38 to (C)LEC: n\u2009=\u200917,142; B27 to (C)LEC: n\u2009=\u20094532). Based on CCF-registered reconstructions, the axonal distance of an archetype neuron from class D19 (brown, AA0912), from its soma in the presubiculum (green) to the ipsilateral LEC (purple), is significantly shorter than the comparable distance of an archetype neuron from class A38 (blue, AA0878). See Fig.\u00a04 for abbreviation definitions. The red horizontal lines in the box and whisker plots depict the medians. The first quartiles (Q1) and the third quartiles (Q3) are represented, respectively, by the lower and upper bounds of the boxes. Error bars represent the data range, where the lower line is Q1\u2009\u2212\u20091.5\u2009\u00d7\u2009(Q3\u2009\u2212\u2009Q1) and the upper line is Q3\u2009+\u20091.5\u2009\u00d7\u2009(Q3\u2009\u2212\u2009Q1). Red pluses are outlier data points that are greater than Q3\u2009+\u20091.5\u2009\u00d7\u2009(Q3\u2009\u2212\u2009Q1) or less than Q1\u2009\u2212\u20091.5\u2009\u00d7\u2009(Q3\u2009\u2212\u2009Q1). In all depicted comparisons, significant differences in distances were calculated using a two-sided Wilcoxon Signed Rank Test performed on neuronal path distances and multiple testing was corrected for by False Discovery Rate to determine the significance of the resultant p-values. A * indicates that the path differences were found to be significant. Source data are provided as a Source Data file. The neurons and brain depictions in all panels are from the Janelia MouseLight project.",
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+            ]
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+        {
+            "section_name": "Discussion",
+            "section_text": "This study introduced an original method to objectively identify projection-based neuronal classes by pairing the Levene\u2019s test with unsupervised hierarchical clustering. We first conducted a confirmatory study on layer 6 of the primary motor cortex to verify that the proposed technique could reproduce known projection types in a previously explored area of the mammalian brain. The results yielded two clusters with axonal projections consistent with those of the corticothalamic and intratelencephalic neuron classes found in past studies, thereby confirming the validity of the technique23.\n\nLevene\u2019s test was chosen because it is not dependent on the data distributions being normal. Given the size of current available data, normality cannot be assured. As the accumulation of data increases by several orders of magnitude, it is possible that other statistical tests, such as an F-test, could be used instead. Another form of unsupervised clustering, such as K-means, could be utilized to achieve similar ends to what we were able to achieve. The important aspect is that the method be unsupervised, such that the data themselves direct the clustering without any user input.\n\nTo test whether the technique could lead to novel insights, we then applied it to the presubiculum, a region with crucial cognitive function24, yet few studies on its circuitry25. The results yielded five clusters, indicating distinct neuron classes, which led us to reject the null hypothesis that projection neurons exhibit random variation within the constraints of regional connectivity from the presubiculum. In an earlier study26, retrograde tracing identified five classes of neurons projecting from the presubiculum, which target the retrosplenial cortex (corresponding to our class A38), contralateral subiculum (class C3), medial entorhinal cortex (class B27), anterior thalamic nucleus (class E6), and lateral mammillary nucleus (class D19). Our results confirm the existence of these five classes and add new information that reveals patterns of divergence (e.g., class A38 projects to the retrosplenial cortex, dentate gyrus, subiculum, and entorhinal cortex), convergence (e.g., the subiculum receives projections from classes A38, contralateral C3, and D19), and specificity (e.g., class E6 projects exclusively to the medial geniculate nucleus, and all hypothalamic regions receive projections solely from class D19; see summary Fig.\u00a08).\n\nThe diagram summarizes the divergence of projections from the classes of the presubiculum and the convergence into parcels distributed throughout the brain. The sizes of the class nodes are proportional to the population sizes of the given classes. The correspondence to prior classification of presubicular convergence targets are listed to the left of the class nodes. The thickness of the arrows is proportional to the number of axonal points in the destination parcel. The dashed arrows represent contralateral connections. The intensity of blue of the destination parcel nodes corresponds to the number of converging connections, where darker corresponds to more connections. From within each cluster, the arrow lengths are ranked according to the path distance to target. See Fig.\u00a04 for the parcel abbreviation definitions.\n\nThe proposed clustering technique correctly distinguishes cortical (classes A38, B27, and C3) from subcortical (D19 and E6) pathways in the second binary split in the hierarchical classification. These results also add cellular level details to previously reported presubicular projections to retrosplenial cortex and thalamic reticular nuclei27, as well as a broader circuit context to the characterization of individual presubicular neurons targeting the medial entorhinal cortex28.\n\nFurthermore, our findings reveal that several target regions are spatially subdivided according to the differing inputs between classes. These regions include the entorhinal cortex (lateral projections mainly from class A38 and medial projections primarily from class B27), retrosplenial cortex (dorsoventral granular projections almost exclusively from class A38 and lateral agranular projections solely from class D19), and thalamus (medial anterior thalamic nucleus and lateral geniculate nucleus projections principally from class D19 and dorsoventral anterior thalamic nucleus and medial geniculate nucleus projections predominantly from class E6). Some of these regional subdivisions also have known functional distinctions: for instance, the medial entorhinal cortex specializes in spatial representation while the lateral entorhinal cortex specializes in integrating sensory input29. Among the thalamic geniculate nuclei, the medial geniculate nucleus is part of the auditory pathway, whereas the lateral geniculate nucleus is part of the visual pathway4.\n\nFrom a comparison of divergent path distances from one presubicular class to its major targets, along with a comparison of convergent path distances from each presubicular class to collectively major targets, we found that path distances to the same targets were significantly different between classes, as were the path distances to distinct targets within most classes. This might imply that electrical impulses reach different targets with varying delays, both within the same class and between classes.\n\nTopographic analysis of presubicular classes revealed spatial separation between the somata of each class. Grid cells are co-localized with head-direction and border cells in dorsal presubiculum as compared to the ventral presubiculum30, in a manner similar to that found in the deeper layers of the medial entorhinal cortex31, implying that grid cells are more likely to be found in class A38 and E6 neurons than in class B27 neurons. Topographic analysis also suggests the possibility of anatomically mapping the input and output of the circuitry specializing in head direction computations32. Our reported topography of presubicular projections classes is consistent with the recently observed local modularity of the head-direction microcircuit33, and may help clarify the relationship between the egocentric and allocentric spatial and episodic representations of the cortico-hippocampal system34. Previous studies found head-direction cells in layer 3 of dorsal presubiculum33. Since class D19 neurons are found in layer 3, whereas class A38 neurons are mostly confined to layer 2, this would imply that head-direction cells make up part of the composition of class D19, but less so for class A38.\n\nAs with many secondary data analyses, we have limited knowledge of, and control over, artifactual shortcomings in the utilized datasets due to possible idiosyncrasies in labeling, imaging, tracing, registration, and mapping. However, the technique introduced with this work is applicable to many disparate sources of data besides MouseLight, including fMOST13,14,15 and even MapSeq/BarSeq35,36. These data sources follow separate experimental and computational protocols, allowing independent validation for the source regions in which these datasets overlap. Our results so far, in the cases of the mouse primary motor cortex and presubiculum, indicate that the executed analysis is robust to these possible confounding variables22.\n\nOverall, this study revealed that neurons can be divided into distinct classes based on axonal projection patterns, as demonstrated in layer 6 of the primary motor cortex and the presubiculum. Our applied analyses can be used to similarly analyze neurons projecting from all other mouse brain regions with sufficient data. There are currently approximately 40 regions fitting this criterion in the existing datasets, but this number is expected to grow in the near future. Furthermore, we suggest the application of pairing Levene\u2019s test and unsupervised hierarchical clustering to other complementary datasets, such as single-cell transcriptomic datasets, to classify neurons across a molecular domain, in addition to an anatomical domain, as demonstrated here. Moreover, all these complementary datasets are broadly expected to continue to grow in sample size, brain coverage, and acquisition pace37,38, supporting a call to establish cloud-based, community accessible pipelines for robust, rigorous, and systematic neuronal characterization39,40.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-45741-x/MediaObjects/41467_2024_45741_Fig8_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The axonal reconstructions utilized in this study are represented in the Janelia MouseLight public repository21 (available at http://ml-neuronbrowser.janelia.org) as SWC-formatted files41. This standard data structure captures each neuronal tracing point with a set of numerical values that include the three-dimensional coordinates, the local neurite radius, and the identity of the next point in the path to the root42. The spacing between consecutive points can be computed as 3D Euclidean distance of their locations, and the length of the axon as the sum of those distances.\n\nIt may be tempting to assume that length constitutes the most natural metric to quantify axonal extent in each brain region. However, it is important to remember that this dataset was collected by light microscopy and does not capture the distribution of presynaptic boutons. Therefore, it is not directly possible to distinguish synapse-bearing portions of the axonal arborization from fibers of passage. It is arguably the connectivity target regions that should guide classification rather than the regions through which the projection simply travels to reach its destinations. This can be a critical confounding factor as the longest unbranching stretches of cortical projecting neurons often correspond precisely to fibers of passage43.\n\nIn our own axonal reconstruction experience, we noticed that, while tracking branches from the image stack, it is natural to increase the density of tracing points when the arbor meanders in the synaptic neuropil than when it traverses layers devoid of potential postsynaptic partners10. Moreover, when we painstakingly identified and annotated the position of all axonal boutons in a different study, we found a tendency to utilize more tracing points per unit of length in bouton-rich branches than otherwise44. These observations are consistent with the need for greater sampling rates in the presence of larger signal gradients or first derivatives in terms of axonal curvature (neuropil meandering), radius (bouton swelling vs. shaft), or both (bifurcation points).\n\nIn the MouseLight dataset analyzed here (Source data are provided as a Source Data file), the number of points in an axonal branch and the corresponding branch length are significantly linearly correlated (Pearson R\u2009=\u20090.742; N\u2009=\u200919,847; p\u2009<\u200910\u221299). To determine whether the average spacing between points varies non-uniformly between supposed fibers of passage and putative synapse-bearing axons, we separated the axonal branches in each presubiculum neuron based on Strahler (centripetal) order, namely order 1\u20133 (terminal, pre-terminal, and pre-pre-terminal branches) from order 4\u20136 (those more than 2 bifurcations away from an ending). This choice is justified by converging experimental evidence that cortical axons make most presynaptic contacts at Strahler orders 1\u20133, while boutons are substantially sparser at orders 4\u2013645,46. This is also consistent with the strongly non-uniform distribution of average branch length in the dataset investigated in this study, indicating more likely fibers of passage at Strahler order 4\u20136 (964.4\u2009\u00b1\u20091037.1\u2009\u00b5m) than at Strahler order 1\u20133 (144.7\u2009\u00b1\u200980.2\u2009\u00b5m; one-tail t-test p\u2009=\u20094.8\u2009\u00d7\u200910\u221211; t-value\u2009=\u2009\u22127.35; df\u2009=\u200988). We found indeed that the average spacing of tracing points is significantly smaller at order 1\u20133 (22.54\u2009\u00b1\u200910.61\u2009\u00b5m) than at Strahler order 4\u20136 (39.07\u2009\u00b1\u200927.43\u2009\u00b5m; one-tail t-test p\u2009=\u20093.5\u2009\u00d7\u200910\u22127; t-value\u2009=\u2009\u22125.26; df\u2009=\u2009111). This again supports the notion that the number of tracing points is a better proxy indicator of synapse-bearing axonal extent than total length. We thus chose to utilize the number of tracing points, and not arbor length, as the metric to classify axonal projections.\n\nThe location of each axonal data point for nearly 1100 neurons was extracted from JSON files from the MouseLight dataset21 using the freeware JSONLab v1.5 (v2.0 is now available at https://sourceforge.net/projects/iso2mesh/files/jsonlab/2.0%20%28Magnus%20Prime%29/jsonlab-2.0.zip/download), where the three-dimensional coordinates and parcel information were provided for each axonal point of the neuron. The number of axonal points in each brain parcel were tabulated for all neurons and were stored in a matrix, in which each row represents a neuron, each column represents a parcel, and the values in each cell represent the axonal counts of a particular neuron in a particular region (Fig.\u00a01; Source data are provided as a Source Data file).\n\nTo determine whether distinct projection classes of neurons exist from a particular parcel of the brain, hypothesis HA, we tested the pairwise differences between neurons from the experimental matrices described above. If only a single class of neurons exists, then only a single distribution of differences between neurons will be generated (Fig.\u00a02a). If two hypothetical classes exist, then the differences between neurons, evaluated two at a time, will be smaller within a given class than across the two classes (Fig.\u00a02b, c). In a multi-class scenario, a histogram of the differences between neurons should be wider than the distribution generated when all the neurons belong to just a single class (Fig.\u00a02d). To generate the distribution of differences for the null hypothesis, H0, a randomized control matrix was generated from the original experimental matrix through multiple iterations of the stochastic pairwise swapping of axonal counts from two neurons across two target regions (Fig.\u00a02e). This method randomized the projection patterns, yielding a continuum consistent with the regional connectivity of Fig.\u00a02a, while preserving axonal sizes (row sums) and regional targeting (column sums) of the original experimental matrix.\n\nWe assessed the hypothesis that the variance of experimental data was significantly larger than the variance of randomized data (\u03b1\u2009=\u20090.05). For both the experimental and randomized matrices, we computed the arccosine between a pair of neuronal vectors, each composed of the axonal counts across all target regions (https://github.com/Projectomics/MATLAB). These angles measure the projection difference of two neurons across all brain parcels. We then performed a 1-tailed Levene\u2019s test47 on the angle distributions of the experimental and randomized matrices to assess whether their variances differed significantly. To this aim, we used the MATLAB function vartestn with the TestType parameter set to LeveneAbsolute. If the experimental data had a greater variance than the randomized data, then the experimental data could be further divided into classes, consistent with the scenario presented in Fig.\u00a02b.\n\nWe used unsupervised agglomerative hierarchical clustering to determine a biologically accurate division of neuron classes based on axonal projection patterns. Specifically, the MATLAB linkage function, with the average algorithm for computing distance between clusters, was utilized on the 93 MouseLight neurons originating in the presubiculum and the 52 MouseLight neurons originating in layer 6 of the primary motor cortex. The initial assumption (null hypothesis) was that all neurons were part of a single class. If Levene\u2019s test yielded significant results, the number of class divisions was incremented, and the technique was again repeated on each class division. This iterative process continued until none of the subdivided classes yielded significant results, thereby yielding the final class divisions (Fig.\u00a02f).\n\nTo estimate the fractional counts of cells in each of k projection classes in each region, we matched their respective single-cell axonal patterns against the regional connectivity from anterograde tracing to the m known targets, as presented in the Allen Mouse Brain Connectivity Atlas (http://connectivity.brain-map.org/projection). The problem is equivalent to a set of constrained, weighted, linear equations that can be solved numerically by non-negative least-square (NNLS) optimization48. NNLS finds the values x that minimizes the Euclidean norm of (Ax - b) with the constraint x\u2009\u2265\u2009049, where x is the k-dimensional vector representing the fractions of neurons in each class; b is the m-dimensional vector representing the weights of the regional projections to each target; and A is a k-by-m matrix with rows representing the projections of each class (the sum of the summary vectors of the corresponding neurons) and columns representing target regions. NNLS was computed using the lsqnonneg function in MATLAB.\n\nMatrix A and vector b were based on data from the MouseLight dataset (Source data are provided as a Source Data file) and the Allen Mouse Brain Connectivity Atlas, respectively. Setting the target region to the whole brain in the Connectivity Atlas and the source region to the presubiculum resulted in 7 tracing experiments, which included projection volumes and projection densities for each target brain region. Cross referencing the targeted regions of the MouseLight axonal projections with target regions that appeared in all 7 anterograde tracing experiments resulted in a listing of 66 regions. Matrix A was created with rows representing these 66 brain regions and columns representing the 5 neuron classes found by pairing Levene\u2019s test with unsupervised hierarchical clustering of the presubiculum data (Source data are provided as a Source Data file). The average projection volume and density values for each of the 66 regions were calculated from the 7 experiments, and the averages were multiplied to populate the columns of vector b.\n\nTo obtain the highest confidence in the NNLS analysis, matrix A was sequentially bi-normalized first by axonal length and then by invaded region (Source data are provided as a Source Data file). Specifically, first each cell in matrix A was normalized so that each row summed to one. Next, each value was divided by the number of regions, 66, and multiplied by the number of clusters, 5, such that the sum of all values in matrix A equaled 5. Subsequently, each cell in matrix A was normalized so that each column summed to one. Vector b was normalized such that the sum of all values equaled to one. Finally, the squared Euclidean norm of the residual of the MATLAB function lsqnonneg was calculated as a proxy for the uncertainty of the analysis.\n\nTo quantify the spatial separation among the somata among the neuron projection classes in the presubiculum, we performed a convex hull analysis for the location of the soma centers in each class using MATLAB. To create the convex hull, outliers were removed by iteratively going through all points in each class and calculating the volume of the convex hull without each point. If the volume differed by more than 1/n of the volume of the original convex hull, which included all points, the point was considered an outlier and removed from the dataset. This established an algorithmic thresholding that corresponded well with the visual inspection of potential outliers. However, if removing the outliers resulted in fewer than four somata, the minimal number of points required to conduct a convex hull analysis, all points were considered. Between each pair of convex hulls, the proportion of the volume of overlap to the volume of the union of the convex hulls was used to assess the similarity between topographic locations.\n\nUtilizing the original JSON data files, for every neuron in each presubiculum class, we measured the path distance from the soma to each axonal point in the target region. We then calculated the median path distance to each target region across all neurons in the class, and performed a Wilcoxon Signed Rank Test50, using the MATLAB function ranksum, to assess whether the path distances to each characteristic target of a particular class were significantly different. Using the same data files, we also performed a Wilcoxon Signed Rank Test to assess whether the path distances to each characteristic target between all clusters were significantly different. In both sets of comparisons, multiple testing was corrected for by False Discovery Rate to determine the significance of the resultant p-values.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "Source Data for Figs.\u00a01, 3b, 4b, 6, and 7 are provided with the paper, as are the neurons depicted in Figs.\u00a03d and 4e, data for the Strahler order analysis, the axonal counts for the layer 6 of the primary motor cortex, and the non-negative least squares analysis The Janelia MouseLight reconstructions from layer 6 of the primary motor cortex and the presubiculum are available for citation by way of their individual digital object identifiers, as provided in the Source data Supplement file.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "All code is available in the GitHub repository at https://github.com/Projectomics.",
+            "section_image": []
+        },
+        {
+            "section_name": "Change history",
+            "section_text": "A Correction to this paper has been published: https://doi.org/10.1038/s41467-024-53487-9",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "We thank Dr. Rodrigo Mu\u00f1oz-Casta\u00f1eda for help with validating the mapping of neuronal reconstructions to anatomical coordinates. This work was supported in part by NIH grants R01NS39600, U01MH114829, and RF1MH128693, all to G.A.A.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Center for Neural Informatics, Krasnow Institute for Advanced Studies and Bioengineering Department, College of Engineering & Computing, George Mason University, Fairfax, VA, USA\n\nDiek W. Wheeler,\u00a0Shaina Banduri,\u00a0Sruthi Sankararaman,\u00a0Samhita Vinay\u00a0&\u00a0Giorgio A. Ascoli\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nD.W.W., S.B., S.S., and S.V. contributed to the analysis and interpretation of data, to the writing of software, and to the writing of the manuscript. G.A.A. contributed to the conception of the project, to the analysis and interpretation of data, and to the writing of the manuscript.\n\nCorrespondence to\n                Diek W. Wheeler or Giorgio A. Ascoli.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Charles Gerfen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional information",
+            "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",
+            "section_image": []
+        },
+        {
+            "section_name": "Source data",
+            "section_text": "",
+            "section_image": []
+        },
+        {
+            "section_name": "Rights and permissions",
+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions",
+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Wheeler, D.W., Banduri, S., Sankararaman, S. et al. Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint.\n                    Nat Commun 15, 1555 (2024). https://doi.org/10.1038/s41467-024-45741-x\n\nDownload citation\n\nReceived: 22 June 2023\n\nAccepted: 01 February 2024\n\nPublished: 20 February 2024\n\nVersion of record: 20 February 2024\n\nDOI: https://doi.org/10.1038/s41467-024-45741-x\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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12696181428465838111cfb9bc03699d1113262f9765d95778f49019f4af4f2b/metadata.json

@@ -0,0 +1,130 @@
+{
+    "title": "Electrically empowered microcomb laser",
+    "pre_title": "Electrically empowered microcomb laser",
+    "journal": "Nature Communications",
+    "published": "17 May 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48544-2/MediaObjects/41467_2024_48544_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-48544-2/MediaObjects/41467_2024_48544_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [],
+    "code": [],
+    "subject": [
+        "Frequency combs",
+        "Integrated optics",
+        "Microwave photonics",
+        "Mode-locked lasers",
+        "Semiconductor lasers"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3518051/v1.pdf?c=1716030427000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3518051/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-48544-2.pdf",
+    "preprint_posted": "13 Dec, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Optical frequency comb underpins a wide range of applications from communication, metrology, to sensing. Its development on a chip-scale platform -- so called soliton microcomb -- provides a promising path towards system miniaturization and functionality integration via photonic integrated circuit (PIC) technology. Although extensively explored in recent years, challenges remain in key aspects of microcomb such as complex soliton initialization, high threshold, low power efficiency, and limited comb reconfigurability. Here we present an on-chip laser that directly outputs microcomb and resolves all these challenges, with a distinctive mechanism created from synergetic interaction among resonant electro-optic effect, optical Kerr effect, and optical gain inside the laser cavity. Realized with integration between a III-V gain chip and a thin-film lithium niobate (TFLN) PIC, the laser is able to directly emit mode-locked microcomb on demand with robust turnkey operation inherently built in, with individual comb linewidth down to 600 Hz, whole-comb frequency tuning rate exceeding 2.4\u00d71017 Hz/s, and 100% utilization of optical power fully contributing to comb generation. The demonstrated approach unifies architecture and operation simplicity, high-speed reconfigurability, and multifunctional capability enabled by TFLN PIC, opening up a great avenue towards on-demand generation of mode-locked microcomb that is expected to have profound impact on broad applications.Physical sciences/Optics and photonics/Lasers, LEDs and light sources/Mode-locked lasersPhysical sciences/Optics and photonics/Lasers, LEDs and light sources/Semiconductor lasersPhysical sciences/Optics and photonics/Other photonics/Frequency combsPhysical sciences/Optics and photonics/Applied optics/Microwave photonicsPhysical sciences/Optics and photonics/Applied optics/Integrated optics",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplementaryInformationforsolitonlasing.pdfSupplementary Information for \u201dElectrically enpowered micro-comb laser\u201d",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Optical microcomb underpins a wide range of applications from communication, metrology, to sensing. Although extensively explored in recent years, challenges remain in key aspects of microcomb such as complex soliton initialization, low power efficiency, and limited comb reconfigurability. Here we present an on-chip microcomb laser to address these key challenges. Realized with integration between III and V gain chip and a thin-film lithium niobate (TFLN) photonic integrated circuit (PIC), the laser directly emits mode-locked microcomb on demand with robust turnkey operation inherently built in, with individual comb linewidth down to 600\u2009Hz, whole-comb frequency tuning rate exceeding 2.4\u2009\u00d7\u20091017\u2009Hz/s, and 100% utilization of optical power fully contributing to comb generation. The demonstrated approach unifies architecture and operation simplicity, electro-optic reconfigurability, high-speed tunability, and multifunctional capability enabled by TFLN PIC, opening up a great avenue towards on-demand generation of mode-locked microcomb that is of great potential for broad applications.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Optical frequency comb is a coherent light source that consists of many highly coherent single-frequency laser lines equally spaced in the frequency domain. Its development has revolutionized many fields including metrology, spectroscopy, and clock1. In recent years, significant interest has been attracted to the generation of phase-locked optical frequency comb in on-chip nonlinear microresonators2,3,4. The superior coherence offered by these mode-locked microcombs has rendered a variety of important applications including data communication5, spectroscopic sensing6, optical computing7,8, range measurement9,10,11,12, optical13 and microwave14 frequency synthesis, with many others expected in the years to come.\n\nDespite this great progress, challenges remain in the development and application of microcombs. The first is the difficulty in triggering comb mode-locking due to the intrinsic device nonlinearities. Recently, self-starting operations have been demonstrated to address this issue15,16,17,18. Their implementations, however, require sophisticated system pre-configuration and careful balance of specific nonlinear dynamics, which are difficult to apply in most practical devices. The second is the low power efficiency of soliton microcomb generation due to the pump-laser-cavity frequency detuning induced by soliton pulsing. Although pulse pumping19 or auxiliary-resonator enhancement20,21 can improve the generation efficiency, they require delicate synchronization in time or resonance frequency and the difficulty of soliton initialization remains the same. The third is the limitation in the comb controllability due to the monolithic nature of the comb generator that is difficult to change after the device is fabricated. Piezoelectric effect could be used to deform the comb resonator12, which, however, exhibits limited tuning speed and efficiency due to its slow mechanical response. To date, the majority of comb generators still have to rely on external laser control to adjust the microcomb state.\n\nRecently, there are significant advances in chip-scale integration of semiconductor lasers and nonlinear comb generators16,22,23,24, in which a diode laser produces single-frequency laser emission to pump a hybridly or heterogeneously integrated external nonlinear resonator to excite microcombs. Such a fully integrated system shows great promise in improving the size, weight, and power consumption. However, the nature of soliton comb generation remains essentially the same, with all the above challenges persistent. Up to now, the realization of an integrated comb source free from these challenges remains elusive.\n\nHere we present a fundamentally distinctive approach to resolve these challenges in a single device. Figure\u00a01a shows the device concept. In contrast to conventional approaches that rely solely on a single mechanism\u2014either optical Kerr or electro-optic effect\u2014for comb generation while with external pumping, we utilize the resonantly enhanced electro-optic (EO) modulation to initiate the comb generation, the resonantly enhanced optical Kerr effect to expand the comb bandwidth and phase-lock the comb lines, and the embedded III-V optical gain to sustain and stabilize the comb operation. Moreover, the resulting coherent microwave (via optical detection) is fed back to the EO comb to further enhance the mode-locking, leading to unique self-sustained comb operation.\n\na Conceptual illustration of the comb generation and mode-locking principle, in which electro-optic (EO) comb generation, Kerr comb generation, and broadband optical gain all work synergistically together inside a single laser cavity for on-demand generation of mode-locked soliton comb. In addition, the laser comb output is detected and fed back to the laser cavity for resonant EO modulation to realize a self-sustained operation. b Schematic of comb laser cavity structure formed by hybrid integration between a RSOA chip and a LN external cavity chip. Two different configurations are employed: A, cavity-enhanced (CE) comb laser structure in which the LN external cavity is formed mainly by an embedded high-Q racetrack resonator together with a broadband Sagnacloop mirror; B, Fabry-Perot (FP) comb laser in which the LN external cavity is formed by an EO phase modulation section together with an a broadband Sagnac loop mirror. c Photo of a CE comb laser, showing that the RSOA is edge-coupled to the LN external cavity chip. d Zoom-in photo showing the edge-coupling region between the RSOA and the LN chip. e Photo of the racetrack resonator and the loop mirror in a CE comb laser. f Photo of the EO phase modulator and the loop mirror in an FP comb laser.\n\nWe realize this approach by integrating a III-V gain element with a thin-film lithium-niobate (LN) photonic integrated circuit (PIC) to produce a III-V/LN comb laser (Fig.\u00a01b). LN PIC has attracted significant interest recently25,26,27,28 for a variety of applications including high-speed modulation29,30, frequency conversion31,32,33, optical frequency comb15,34,35, and single-frequency lasers36,37,38,39. Here, we unite active EO modulation with passive four-wave mixing (FWM) in a dispersion-engineered high-Q laser cavity for the on-demand generation of mode-locked soliton microcomb, which naturally leads to self-starting full turnkey operation simply by turning on/off either the RF signal driving the comb resonator or the electric current driving the gain element. As the comb modes extract energy directly from material gain, all of the optical power obtained from the III-V gain medium contributes fully to the comb generation, distinct from conventional microcombs in which the majority of the optical power remains in the pump wave. Moreover, the strong electro-optic effect of the LN cavity enables high-speed tunability of comb frequencies and electro-optic reconfigurability of comb spectrum and mode spacing. With this approach, we are able to produce broadband highly coherent microcombs, with individual comb linewidth down to 600\u2009Hz, frequency tunability of over 2.4\u2009\u00d7\u20091017 Hz/s for the entire microcomb, microwave phase noise down to -115\u2009dBc/Hz at 500\u2009kHz frequency offset, and a wall-plug efficiency exceeding 5.6%. The simplicity of the demonstrated approach opens up a new path for on-demand generation of mode-locked microcombs that is expected to have profound impact on the broad applications in high-precision metrology, telecommunications, remote sensing, clocking, computing, and beyond.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "The III-V/LN microcomb laser is formed by integrating an InP reflective semiconductor optical amplifier (RSOA) with an LN external cavity chip via facet-to-facet coupling. We employ two types of laser cavity structures for the purpose, as shown in Fig.\u00a01b. Chip A laser structure is embedded with a dispersion-engineered EO microresonator and Chip B laser structure consists of a simple EO phase modulation waveguide section. The advantage of Chip-A resonator-type structure is that the high-Q microresonator offers strong cavity enhancement for comb generation and mode-locking, which we term as the cavity-enhanced (CE) comb laser. The benefit of the Chip-B Fabry-Perot-type structure is that it offers flexibility of mode-locking operation, which we term as the Fabry-Perot (FP) comb laser.\n\nIn the CE comb laser, the group-velocity dispersion (GVD) of the racetrack microresonator (intrinsic optical Q\u2009~\u20091.6\u2009\u00d7\u2009106) is engineered to be small but slightly anomalous to support broadband comb generation. At the same time, the pulley coupling regions are specially designed for uniform close-to-critical coupling to the resonator over a broad telecom band. Such a design ensures high loaded optical Q (\u2009~\u20095\u2009\u00d7\u2009105) of the resonator uniformly across a wide spectral range which is crucial both for enhancing the comb generation and mode-locking and for efficient light coupling into/out of the microresonator. Moreover, we also engineer the GVD of straight waveguide sections outside the racetrack resonator to compensate for that of the RSOA section so as to minimize the overall laser cavity GVD. At the same time, the overall optical path length of the laser cavity is designed to be an integer multiple of the racetrack resonator\u2019s for matching their resonance mode frequencies and round-trip group delay. The GVD of the FP comb laser is engineered in a similar fashion. The free-spectral range (FSR) of the racetrack resonator is designed to be around 40 GHz to better accommodate bandwidths of RF filter and amplifier after detection of comb beating signal. In contrast, the FP comb laser is designed to have a small FSR of around 10 GHz for easy operation of harmonic mode locking.\n\nTo support the broadband operation, the Sagnac loop mirror employs an adiabatic coupling design40 to achieve high reflection and feedback with a reflectivity of\u2009>\u200995% over a broad spectral band of 1500\u20131600\u2009nm. On the other hand, a horn taper waveguide is designed on the LN chip to minimize the coupling loss between the LN chip and the RSOA gain chip. The RSOA exhibits a broadband gain in the telecom L-band, with a 3-dB bandwidth of\u2009>\u200940 nm. Figure\u00a01c\u2013f shows the device structures. Details about the design parameters and the characterization of the laser structures are provided in the Supplementary Information (SI).\n\nTo excite the mode-locked microcomb, we first launch a single-frequency RF signal to drive the racetrack resonator of the CE comb laser (Fig.\u00a02a), with a frequency of 39.58 GHz that matches its FSR. Before the RF signal is applied, the device exhibits single-mode or multi-mode lasing, with an example shown in the blue curve of Fig.\u00a02b. However, A microcomb is readily produced as soon as the RF signal is applied, with an optical spectral bandwidth of about 20\u2009nm (Fig.\u00a02b, red curve). Mode-locking of the comb is verified by the clean RF tone at 39.58\u2009GHz detected from the beating between comb lines (Fig.\u00a02c), as well as the autocorrelation trace from the laser output pulses (Fig.\u00a02b, inset). The 39.58-GHz RF tone exhibits a high signal-to-noise ratio of 79 dB (Fig.\u00a02c, inset), whose phase noise spectrum matches identically the driving RF source (Fig.\u00a02d), showing the preservation of the relative phase coherence between comb lines via mode-locking. Mode-locking of the comb is also clearly evident by the clean noise floor around the DC region in the RF spectrum (Fig.\u00a02c see\u00a0SI for details), where the zero extra noise from the mode-locked comb state infers that all the comb lines of the entire comb are phase-locked together.\n\na Schematic of the experimental setup for comb laser characterization. OSA: optical spectrum analyzer; AC: autocorrelator; PD: photo-detector; ESA: electrical spectrum analyzer; OSC: real-time oscilloscope; PNA: phase noise analyzer. b Optical spectrum of the comb laser output. Off state: single mode lasing with the RF driving off. On state: comb lasing with the RF driving on. Inset: Autocorrelation trace of the laser output pulses, in the \u201ccomb on\" state, in which the blue curve shows the experimental data, dotted curves show the the fitted autocorrelation profiles from individual sech2 pulses, and the dashed curve show the overall fitted autocorrelation trace. The autocorrelation is recorded directly from the laser output pulses without dispersion compensation. c Electrical spectrum of the beat note detected from the comb laser output. The red and blue curves show the comb-on and comb-off (single-mode lasing) states, respectively, corresponding to those in b. Gray curve shows the noise background of the optical detector, as a comparison. The inset shows the detailed spectrum of the RF beat note at 39.58 GHz. d Phase noise spectrum of the 39.58-GHz RF beat note (red) and the RF driving signal (blue). e Phase noise spectrum of the CE comb laser output measured with a self-heterodyne method. Red and green curves show for two different comb states, and blue curve shows for the comb-off (single-mode lasing) state. Inset shows the corresponding frequency noise spectrum of the three laser states. f P\u2013I\u2013V curve of the CE comb laser, in which the red and blue curves show the L-I and I-V curves, respectively. g\u2013j Turnkey operation of the comb laser at two different speed of 2 Hz g, h and 200 Hz i, j, respectively. Red curve shows the normalized driving RF power and blue curve shows the beating signal between the comb laser ouput and an external reference laser at 1582\u2009nm. h, j show the zoomed-in signal for the on/off states, respectively.\n\nThe underlying mechanism responsible for mode-locking dominantly contributes from the combined resonant EO modulation and optical Kerr effect, in which the EO modulation produces EO sidebands to initiate the comb generation while the optical Kerr effect broadens the comb spectrum and phase-locks the comb lines (Fig.\u00a01a). This unique comb generation mechanism distinguishes the comb laser from other approaches15,16,17,18,19,20,21 (see\u00a0SI for more details). Indeed, the laser is able to produce mode-locked soliton pulses in the absence of EO modulation (while with a narrower spectrum), in which only the optical Kerr effect is responsible for mode-locking. The detailed theoretical modeling and testing results are provided in\u00a0SI. In Fig.\u00a02c, the small RF tone around the half-harmonic at 19.79\u2009GHz indicates certain comb dynamics. It can be eliminated by reconfiguring the laser and one example is shown in SI which exhibits a clean single RF beating tone and a well-defined sech2-shaped soliton pulse spectrum. The two lasers mainly differ in their overall dispersion of the laser cavity, indicating that the device dispersion plays an important role on the comb spectrum. The two-sidelobe feature of the comb spectrum in Fig.\u00a02b implies that the output pulses are likely to be mode-locked two-color pulses in which the two color pulses bounds with each other via certain interpulse interaction41. Its exact nature, however, will require further exploration. The details for comb spectrum reconfiguration based RF tuning are provided in the\u00a0SI.\n\nIn addition to the high coherence between the comb lines, the comb laser also exhibits narrow linewidth on its individual comb lines. To show this feature, we employ the correlated self-heterodyne method42,43 to characterize the overall linewidth of the whole comb laser by launching the entire comb for linewidth measurement (rather than characterizing individual comb lines themselves) (See\u00a0SI for details). The recorded phase noise spectrum is shown in Fig.\u00a02e, which indicates a white frequency-noise floor of\u2009~\u2009350\u2009Hz2/Hz (Fig.\u00a02e, inset) that corresponds to a laser linewidth of\u2009~\u20092 kHz. The linewidth of the comb lines can be decreased further and an example is shown in Fig.\u00a02e for a slightly different comb state produced from the same laser, which exhibits a white frequency-noise floor of\u2009~\u2009100\u2009Hz2/Hz (Fig.\u00a02e, inset) that corresponds to a laser linewidth as low as\u2009~\u2009600\u2009Hz. Note that these values represent the overall linewidth contributed from the entire comb, which indicates the averaged intrinsic linewidth of individual comb line.\n\nFigure\u00a02f shows the current-dependent characteristics of the comb laser, which exhibits a low threshold current of 50\u2009mA, indicating the low overall loss of the integrated laser. The comb laser produces an optical output power of 11\u2009mW, in which the major individual comb lines exhibit a power in the range of 0.25\u20132\u2009mW. The power is measured at a pumping current of 275\u2009mA and a pumping voltage of 1.4\u2009V, which corresponds to a wall-plug efficiency of 2.8%, defined as the ratio between the output optical power and the electrical power used to drive the RSOA. As the laser has two output ports (Fig.\u00a01b) that emit the same amount of optical power, the total wall-plug efficiency of the laser is thus 5.6%. This level of wall-plug efficiency is on par with other integrated external-cavity semiconductor lasers recently developed44,45. Intriguingly, the comb power increases with increased driving RF power, whose details are provided in the\u00a0SI. Note that the total optical power contributes fully to the generated comb, in strong contrast to conventional Kerr solitons or EO combs in which the major optical power remains in the residual pump wave with low comb generation efficiency.\n\nA distinctive feature of the comb laser is that the produced comb can be switched on/off on demand by simply switching on/off the driving RF signal. To show this feature, we beat the comb with a reference single-frequency laser operating at the wavelength of 1582 nm that is inside the comb spectrum, and monitor the beating signal with the RF driving signal being turned on/off. As shown in Fig.\u00a02g and i, the beating signal follows faithfully the driving RF signal. The coherent beating signal shows up readily when the RF driving is on, indicating the generation of the mode-locked comb. The beating signal disappears right after the RF driving is off, indicating the shutoff of the comb state. Same phenomenon is observed when the reference laser is tuned to other wavelengths within the comb spectrum.\n\nSimilar phenomena are observed in the FP comb laser, while generally with smaller spectral extents due to the lack of cavity enhancement. The FP comb laser, however, exhibits a distinctive feature in that it can be flexibly mode-locked at higher harmonics of the laser cavity FSR. Figure\u00a03 shows this feature. We are able to achieve third- and fourth-order harmonic mode-locking by applying an RF signal to the phase modulation section of the FP comb laser, with a frequency of 29.45 and 39.27\u2009GHz, respectively, that are three and four times of the laser FSR (9.817\u2009GHz). Again, mode locking of the combs is clearly verified by the detected RF beating signal from the combs with a SNR of 77\u2009dB, as well as by the autocorrelation traces from the laser output pulses (Fig.\u00a03b,c, insets).\n\nThird harmonics (29.45\u2009GHz) and fourth harmonics (39.27\u2009GHz) are separately used as driving signals. a Schematic of the experimental setup for harmonic mode-locking of the comb laser. b Optical spectrum of the laser output with third-harmonic mode-locking, by driving the phase modulator with a RF signal at 29.45 GHz. c Optical spectrum of the laser output with fourth-harmonic mode-locking, by driving the phase modulator with a RF signal at 39.27\u2009GHz. In b, c, the left insets shows the electrical spectrum of the RF beat note detected from the output laser comb, and the right insets show the autocorrelation trace of the laser output pulses with dashed curve showing the fitted individual pulses. Same as Fig.\u00a02b, autocorrelation is recorded directly from the laser output pulses without dispersion compensation or pulse shaping.\n\nAnother distinctive characteristic of the comb laser is that the laser frequencies of the entire mode-locked comb can be tuned cohesively at a high speed. To show this feature, we apply a triangular-waveform electric signal\u2014together with the 39.58-GHz RF driving signal\u2014to the racetrack resonator of the CE comb laser as shown in Fig.\u00a04a. While the 39.58-GHz RF driving signal supports the mode-locking process, the triangular-waveform electric signal will adiabatically tune the resonance frequencies of the racetrack resonator, thus tuning the laser frequencies of the entire mode-locked comb together as a whole.\n\na Left panel: Schematic of the setup for comb frequency tuning, in which a triangular-waveform electrical signal produced by an arbitrary waveform generator (AWG) is used to drive the racetrack resonator of the CE comb laser together with the 39.58-GHz mode-locking RF signal. Middle panel: Conceptual illustration of the comb frequency tuning process, showing the laser frequencies of the comb are tuned together as a whole. Right panel: Schematic showing the corresponding sideband creation around the comb lines, introduced by triangular-waveform frequency modulation. b\u2013d Time-frequency spectra of the beatnote between the comb laser output and a referenced laser operating at a fixed wavelength of 1582 nm, at the modulation speed of 1, 10, and 100\u2009MHz, respectively. The dashed curves show the corresponding triangular-waveform EO tuning signal. Bottom panels: Corresponding relative frequency deviation at each modulation speed. e, f, g. Electrical spectrum of the 39.58-GHz beat note detected from the laser output comb, at modulation speed of 1\u2009MHz e, 10\u2009MHz f, and 100\u2009MHz g, respectively. h. Laser frequency tuning efficiency recorded at different modulation speeds.\n\nTo show this feature, we beat the comb with a narrow-linewidth reference CW laser at 1582 nm that is about 15 GHz away from a comb line, and monitor the beating signal in real time. At the same time, we monitor the spectrum of the recorded 39.58-GHz RF tone from the beating between the comb lines (See\u00a0SI for details of the setup). The frequency dynamics of the 15-GHz beating signal with the reference laser show the frequency tuning of the comb line nearby while the 39.58-GHz RF tone from the comb line beating indicates the quality of mode locking during the frequency tuning. Figure\u00a04b\u2013d shows the temporal variation of the 15-GHz beating signal at different modulation speeds of 1, 10, 100\u2009MHz. They show clearly that the frequency tuning of the comb line follows faithfully the waveform of the driving triangular-waveform electric signal at all modulation speeds, with a deviation of no more than 5%. In particular, the recorded 39.58-GHz RF tone from the comb line beating (Fig.\u00a04e\u2013g) remains unchanged during the frequency tuning, except with created modulation sidebands that simply results from the laser frequency modulation (see also Fig.\u00a04a, right figure). This observation confirms that the phase-locking between the comb modes is fully preserved during the high-speed frequency tuning process, indicating that the entire mode-locked comb is tuned in its frequencies as a whole, without any perturbation to the comb mode spacing. This is in strong contrast to other comb modulation approaches11,12,46 where the comb mode spacing is seriously impacted by external modulation. The frequency tuning range of 1.2\u2009GHz at the modulation speed of 100\u2009MHz (Fig.\u00a04d) corresponds to a frequency tuning rate as high as 2.4\u2009\u00d7\u20091017\u2009Hz/s for the comb. Both the frequency tuning rate and tuning speed are orders of magnitudes higher than the piezoelectric tuning and the external pump modulation approaches11,12, which are constrained only by the photon lifetime of the high-Q racetrack resonator. As shown in Fig.\u00a04h, the device exhibits a frequency tuning efficiency of about 0.2\u20130.8\u2009GHz/V depending on the modulation speed, which is more than an order of magnitude higher than the piezoelectric approach12. The tuning efficiency can be further doubled by employing both sets of driving electrodes of the racetrack resonator (Fig.\u00a04a).\n\nSo far, the comb laser utilizes an external RF signal to support the mode-locking. This signal, however, can be removed by feeding the coherent 39.58-GHz RF tone detected from the comb mode beating directly back to the comb laser cavity to sustain the mode-locking, resulting in unique stand-alone self-sustained comb lasing operation. Figure\u00a05a illustrates this approach. The comb laser output is detected by a high-speed optical detector whose output RF signal is amplified to an adequate amplitude, filtered to suppress excess low-frequency noises, adjusted with appropriate phase, and then fed back to drive the racetrack resonator of the CE comb laser. This approach is somewhat similar to47,48 but the feedback modulation here is directly applied to the enhancing microresonator inside the laser cavity. The combined EO and optical Kerr effects lead to significantly broader comb spectrum, in contrast to the pure EO comb in47,48.\n\na Schematic of self-feedback locking of the CE comb laser. b Optical spectrum of the comb laser output at a driving current of 285 mA. c\u2013e Optical spectrum d and optical power e of the comb laser output as a function of the RSOA driving current c. f Electrical spectrum of the 39.58-GHz beat note detected from the laser output comb, with a driving current of 60\u2009mA. g Phase noise spectrum of the detected 39.58-GHz beat note.\n\nAs shown in Fig.\u00a05b, a broadband microcomb with a spectrum covering about 50\u2009nm and an optical power of 8.5\u2009mW is produced on chip with a driving current of 285\u2009mA. Indeed, the microcomb is readily produced on demand as soon as the driving current is turned on, with a driving current as low as 60\u2009mA, as shown in Fig.\u00a05c\u2013e. Mode locking of the comb is clearly evident by the clean 39.58-GHz RF tone detected from the comb mode beating (Fig.\u00a05f), which exhibits a SNR of 65 dB and a narrow 3-dB linewidth of 1.5\u2009kHz. The phase noise of the RF beating signal reaches a level of -90\u2009dBc/Hz at an offset frequency of 60\u2009kHz, which is considerably lower than the laser heterodyne beating approach49 and is comparable to that of free-running optical Kerr soliton microcombs46,50,51. The optical spectral bandwidth and the output power of the comb laser increase considerably with increased driving current (Fig.\u00a05d). This is expected since the increased optical power of the mode-locked comb inside the high-Q racetrack resonator would significantly enhance the optical Kerr effect and the resulting four-wave-mixing process to broaden the comb spectrum. No saturation is observed on the comb spectral bandwidth as the current increases.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "The attainable extent of the microcomb spectrum or soliton pulse width in current devices is primarily limited by the available optical power inside the cavity and the group delay mismatch between the enhancing resonator and the main laser cavity. For the former, it can be improved by either reducing the loss (e.g., improving the RSOA-LN chip coupling efficiency) or increasing the optical gain (e.g., using a higher-power RSOA) inside the laser cavity. For the latter, our theoretical modeling (see\u00a0SI) shows that the formation of ideal ultrashort soliton pulses would require that the roundtrip time of the main laser cavity be integer times that of the enhancing racetrack resonator. In current devices, however, there is a certain amount of mismatch which limits the comb spectrum and the coherence of the mode-beating RF tone. This problem can be resolved by further optimization of the roundtrip length of the main laser cavity, for example, via heterogeneous integration24 in which the roundtrip length of the main laser cavity can be precisely defined by the fabrication process. It can also be resolved by introducing a certain group-delay tuning element52 into the external laser cavity. With these optimizations, we expect that ultra-broadband highly coherent soliton microcomb can be produced.\n\nTo conclude, we have introduced a chip-scale microcomb laser that can be flexibly mode-locked with either active-driving or passive-feedback approaches and that can be tuned/reconfigured at an ultrafast speed, with robust turnkey operation inherently built in. The demonstrated integrated comb laser exhibits remarkable reconfigurability and performance significantly beyond the reach of conventional on-chip mode-locked semiconductor lasers53,54,55,56. The demonstrated devices combine elegantly the simplicity of integrated laser structure, robustness of mode-locking operation, and electro-optically enhanced tunability and controllability, opening up a new avenue towards on-demand generation of soliton microcombs with high power efficiency that we envision to be of great promise for a wide range of applications including ranging, communication, optical and microwave synthesis, sensing, metrology, among many others.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The device fabrication begins with a congruent x-cut thin film lithium-niobate-on-insulator (LNOI) wafer, with a 600\u2009nm LN layer on a 4.7\u2009\u03bcm silica-coated silicon substrate. E-beam lithography (EBL) and Ar-ion milling are used to etch the waveguide with ZEP-520A as mask. Etching thickness ranges from 300\u2009nm (CE comb laser) to 350\u2009nm (FP comb laser) for dispersion engineering. Second EBL is applied on PMMA for deposition of 400\u2009nm gold-evaporated electrodes, which are placed 2.5\u2009\u03bcm from the waveguide. The distance between the waveguide and electrode is chosen to balance the EO modulation frequency with loss from metal absorption. Dicing and polishing of LN chip are employed at last to acquire optimized fiber-to-chip and amplifier-to-chip coupling, with both coupling losses around 6 dB.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data are available in the main text or the supplementary information.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "We would like to thank Lin Chang and William Renninger for their helpful discussions. This work is supported in part by the Defense Advanced Research Projects Agency (DARPA) QuICC program under Agreement No. FA8650-23-C-7312 and LUMOS program under Agreement No. HR001-20-2-0044, and the National Science Foundation (NSF) under Grant No. OMA-2138174 and ECCS-2231036. This work was performed in part at the Cornell NanoScale Facility, a member of the National Nanotechnology Coordinated Infrastructure (National Science Foundation, ECCS-1542081); and at the Cornell Center for Materials Research (National Science Foundation, Grant No. DMR-1719875).",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Jingwei Ling, Zhengdong Gao.\n\nDepartment of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA\n\nJingwei Ling,\u00a0Zhengdong Gao,\u00a0Shixin Xue,\u00a0Mingxiao Li\u00a0&\u00a0Qiang Lin\n\nInstitute of Optics, University of Rochester, Rochester, NY, USA\n\nQili Hu,\u00a0Kaibo Zhang,\u00a0Usman A. Javid,\u00a0Raymond Lopez-Rios,\u00a0Jeremy Staffa\u00a0&\u00a0Qiang Lin\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.L. and Z.G. designed and fabricated the devices. J.L. and Z.G. performed the device characterization. S.X and Q.H. assisted in the device fabrication. S.X., M.L., K.Z., U.J., R.L., and J.S. assisted in experiments. J.L., Z.G., and Q.L. wrote the manuscript with contributions from all authors. Q.L. supervised the project. Q.L. conceived the concept.\n\nCorrespondence to\n                Qiang Lin.",
+            "section_image": []
+        },
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+            "section_image": []
+        },
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+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Cheng Wang and Lin Chang for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
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+            "section_text": "Ling, J., Gao, Z., Xue, S. et al. Electrically empowered microcomb laser.\n                    Nat Commun 15, 4192 (2024). https://doi.org/10.1038/s41467-024-48544-2\n\nDownload citation\n\nReceived: 05 December 2023\n\nAccepted: 02 May 2024\n\nPublished: 17 May 2024\n\nVersion of record: 17 May 2024\n\nDOI: https://doi.org/10.1038/s41467-024-48544-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+{
+    "title": "Unveiling the intermediate hydrated proton in water through vibrational analysis on the 1750 cm\u22121 signature",
+    "pre_title": "Deciphering the Vibrational Features of Hydrated Proton in Water",
+    "journal": "Nature Communications",
+    "published": "01 July 2025",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60794-2/MediaObjects/41467_2025_60794_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60794-2/MediaObjects/41467_2025_60794_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60794-2/MediaObjects/41467_2025_60794_MOESM3_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-60794-2/MediaObjects/41467_2025_60794_MOESM4_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-025-60794-2#ref-CR31",
+        "/articles/s41467-025-60794-2#ref-CR48",
+        "/articles/s41467-025-60794-2#Sec24"
+    ],
+    "code": [],
+    "subject": [
+        "Infrared spectroscopy",
+        "Method development",
+        "Molecular dynamics"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4829060/v1.pdf?c=1751455459000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4829060/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-025-60794-2.pdf",
+    "preprint_posted": "15 Aug, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Hydration of proton is the key to understand the acid-base chemistry and biochemical processes, for which the Zundel and Eigen cations have been recognized as the foundation. However, their dominance remains contentious due to the challenge of attributing the infrared signature at ${\\sim}1750\\ \\mathrm{cm^{-1}}$, stemming from the theoretical dilemma of balancing structural diversity and solvent fluctuations. Herein, we circumvented this obstacle by devising an integrated approach for computing frequency-specific vibrational vectors via inverse Fourier transform of the vibrational density of states. When applied to aqueous acid, it unveiled an additional ``Intermediate'' configuration, linked to the aforementioned spectral signature, which exhibit a higher population (44%) and longer lifetime (51 fs), compared to Zundel-like (28%, 25 fs) and Eigen-like (28%, 36 fs), benefitting from the local electric field induced by surrounding solvent molecules. This work reshapes the basic understanding of aqueous proton, and offers a universal solution for characterizing transient species in liquids.Physical sciences/Chemistry/Theoretical chemistry/Molecular dynamicsPhysical sciences/Chemistry/Physical chemistry/Optical spectroscopy/Infrared spectroscopy",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "structureopt.zipDataset 1SI.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Hydration of proton is the key to understand the acid-base chemistry and biochemical processes, for which the Zundel and Eigen cations have been recognized as the foundation. However, their dominance remains contentious due to the challenge of attributing the infrared signature at \u00a0~1750\u00a0cm\u22121, stemming from the theoretical dilemma of balancing structural diversity and solvent fluctuations. Herein, we circumvent this obstacle by devising an integrated approach for computing frequency-specific vibrational vectors via inverse Fourier transform of the vibrational density of states. When applied to aqueous acid, it unveils an additional \u201cIntermediate\u201d configuration, linked to the aforementioned spectral signature, which exhibits a higher population (44%) and longer lifetime (51 fs), compared to Zundel-like (28%, 25 fs) and Eigen-like (28%, 36 fs), benefitting from the local electric field induced by surrounding solvent molecules.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Acid-base chemistry and most biological redox chemistry are closely related to proton transfer (PT) through water, which has been believed to proceed with a structural diffusion mechanism that was firstly proposed by Grotthuss more than two centuries ago1. According to the modern picture of the Grotthuss mechanism, PT in aqueous solution undergoes ultrafast interconversion between the Eigen and Zundel cations, both of which were suggested as the probable states of the hydrated protons by Wicke2 and Zundel3, respectively. While these seminal works settled the foundation of proton dynamics in aqueous solution4,5, intensive structural dynamics in the vicinity of the excess proton seemed to blurred the boundary between Zundel-like and Eigen-like configurations, raising sustained dispute on assessing the importance of the two limiting structures in shaping the behavior of hydrated protons6,7,8,9,10.\n\nBased on the Eigen-Zundel two-structure model, it has been noticed that the experimentally determined relaxation decay of the 1750 cm\u22121 signature11, along with its intricate coupling with other vibrational frequencies7, cannot be thoroughly interpreted9,11. On the other hand, molecular dynamics (MD) simulations had uncovered a substantial portion of untypical configurations that are intermediate between Zundel and Eigen configurations4,5,9,12,13,14,15,16. By using various specifically designed descriptors to measure the structural deviation from the two limiting structures, many efforts have been made to divide structures into additional classes8,9,15,16,17. However, the unique spectral feature of these intermediating configurations has not been unambiguously captured from the proton-relevant band across 1000-3000 cm\u22121, raising concerns about the existence of \u201chidden configurations\u201d.\n\nTo decipher the vibrational spectra of hydrated proton in aqueous solution presents significant challenges. Plenty of previous studies simplified the aqueous solution to various static structures of protonated water clusters, and averaging the results to mimic the aqueous spectrum8,9,10,12,17,18,19. This approach establishes the explicit structure-spectroscopy relationship, but is typically unable to account for the anharmonic effects caused by the structural dynamics. While the distinctive spectral signatures emerged above 2000 cm\u22121 and below 1500 cm\u22121 has been unambiguously associated with Eigen-like and Zundel-like configurations, respectively, consensus on interpreting the prominent spectral signature near 1750 cm\u22121 is still absent.\n\nTo fully account for the dynamical effect, several vibrational analysis methods based on MD trajectories have been developed, including those involving diagonalizing the velocity covariance matrix20 and the dipole velocity cross-correlation matrices21. However, the methods have some limitations in dealing with aqueous acidic solutions due to the fast transport of the hydrated protons via Grotthuss mechanism, as discussed in the Methods section. Recent studies simulated the vibrational spectrum through the Fourier transform of the autocorrelation function of dipole moment or velocity of aqueous proton13,14. By simulating the vibrational spectra utilizing short time segments of MD trajectory presenting distinct configurational affiliations, the spectrum corresponds to an ensemble of dynamic structures. Thus, one-to-one assigning the vibrational signatures to specific vibrational modes of distinct structures remains an open problem.\n\nIn this work, we design an approach that inversely translates spectral signatures back to frequency-specific vibrational movements and re-establishes the linkage between vibrational characteristics and the underlying structural features. This theoretical scheme is combined with the atomic neural network force field (ANNFF) for HCl solutions, which is constructed from the machine learning of the energies and forces computed with the density functional theories (DFT)22. The constructed ANNFF offers excellent reproduction of the radial distribution functions (RDFs) and the proton diffusion coefficient, presented in Supplementary Figs.\u00a01\u20132 and Supplementary Table\u00a01. The computational scheme is highly efficient to allow for implementing nanosecond scale of MD simulations on highly dilute HCl solutions, with the convenience of eliminating the concentration effect. Based on our vibrational spectrum assignment approach and proton structure decomposition, we unveil a distinct \u201cIntermediate\u201d configuration that bridges the Zundel-like and Eigen-like conformations. These structures are characterized by a proton stretching mode frequency of 1770 cm\u22121 and comprise a significant 44% of the overall distribution, in comparison to the Zundel-like (28%) and Eigen-like (28%) configurations. Through further research utilizing local electric field analysis and time correlation function analysis, we determine that the Intermediate persists for 51 fs, outlasting both the Zundel-like (25 fs) and Eigen-like (36 fs). The enhanced stability of these intermediate configurations is attributed to the stabilizing effects of the local electric field induced by the surrounding hydrogen-bond (HB) network.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results and discussion",
+            "section_text": "To overcome the challenge of attributing vibrational signatures to specific vibrational modes of distinct structures in liquids, we develop an integrated approach that reconstructs frequency-specific vibrational vectors through inverse fast Fourier transform (IFFT) of the vibrational density of states (VDOS). While the details of the integrated numerical scheme are introduced in the Methods section, it is outlined as the flowchart shown in Fig.\u00a01, which comprise four distinct steps.\n\nThe entire MD trajectory is divided into segments of 3 ps, achieving a balance of spectral resolution and number of effective segments. The segments where the special pair motif is well sustained will be selected to form the dataset for further analysis. The average structure of the motif for each selected segment is determined.\n\nFor a spectral signature of interest at a specific frequency, one degree of freedom of the most relevant atom will be selected as the reference. The velocity autocorrelation function (VACF) for this reference degree of freedom will be calculated, as well as the velocity cross-correlation functions (VCCFs) between it and all other degrees of freedom. These functions are subsequently converted into the VDOS and cross spectra using fast Fourier transform (FFT).\n\nAfter performing dimensional conversion on the VDOS and cross spectra, and selecting a specific frequency, IFFT is applied to quantify the vibrational amplitudes for all degrees of freedom, along with their phase differences. Within each segment, these amplitudes and phase differences are mapped onto the average structure to generate the vibrational vectors.\n\nA set of vibrational coordinates are defined appropriately for all atoms in the special pair moiety to facilitate the averaging of the vibrational vectors across segments. The segment-specific vibrational vectors are transformed from the original Cartesian coordinate system to the defined vibrational coordinate system, averaged across all selected segments, and casted onto the average structure of all segments.\n\nA \u201cspecial pair'', consisting of the excess proton and its two flanking water molecules, is employed as the fundamental moiety for interpreting the solvated proton\u2019s vibrational spectrum49. For an arbitrary frequency in the vibrational spectrum, the vibrational vectors of all atoms in the moiety can be derived via the four distinct steps: a Segmentation of the MD trajectory. b Calculation of vibrational density of states (VDOS) and cross spectra using fast Fourier transform (FFT). vi(t) and vj(t) denote the velocity evolutions of the ith and jth degrees of freedom within the moiety. c Derivation of segment-specific vibrational vectors through inverse fast Fourier transform (IFFT). d Averaging of vibrational vectors across segments.\n\nThe core of this integrated approach lies in utilizing the IFFT to determine, for any given frequency, the vibrational amplitudes and phases associated with all atoms within a specified structural motif of interest. Through a carefully designed strategy that involves averaging the vibrational vectors over different segments, this method offers a feasible framework for assigning vibrational spectra in highly flexible structures, such as liquids. This scheme integrates the merits of Fourier transform-based vibrational spectra simulation techniques, encompassing concurrently anharmonic and dynamical effects13,14, and the merit of static cluster-based vibrational analysis methods in generating frequency-specific vibrational vectors8,9,10,12,17,18,19.\n\nExperimental infrared (IR) spectra of HCl solutions were usually obtained with concentrations above 1 M6,7,23,24,25, inevitably introducing a concentration effect that may obscure the intrinsic behavior of excess protons. To investigate this influence, we conduct an attenuated total reflection Fourier-transform infrared (ATR-FTIR) experiment on a series of HCl solutions with concentrations down to 0.01 M. As shown in Fig.\u00a02a, the 0.1 M of dilute HCl solution can still present explicitly the markedly broadened \u201cproton continuum\u201d previously observed in more concentrated solutions25, a spectral signature being recognized as a key indicator of the high complexity of proton dynamics in water. The concentration effect exerts non-negligible impact on this spectral signature, likely due to the relatively weaker capacity of chloride ions to share the excess proton, compared to water oxygen14. This results in more pronounced positive and negative signals around 3000 cm\u22121 in the more dilute solution.\n\na The dark blue trace indicates the experimental infrared (IR) difference spectrum between 0.1 M HCl and water. The red trace indicates the theoretical vibrational density of states difference spectrum between 0.1 M HCl and water. The light blue trace indicates the experimental IR difference spectrum between 4 M HCl and water from ref. 25. The horizontal dashed line is the zero line for the difference spectra. All intensities of the difference spectra have been normalized to the same number of protons with arbitrary units (Arb. Units). b The experimental IR spectra of 0.1 M HCl (red trace) and water (dark blue trace). The shaded areas represent the proton stretching bands for Zundel-like and Eigen-like configurations in refs. 8,13,17 with different patterns. c The average vibrational density of states (VDOS) spectra of the excess proton (red), the flanking water (FW) hydrogens (dark blue), and the bulk water hydrogens (light blue). d The cyan trace is the same as the red one in (c). The yellow trace is the average VDOS spectrum of proton stretch. Peak 1\u20136 are the Gaussian peaks obtained by fitting the cyan trace. The blue dashed line is the sum of these Gaussian peaks.\n\nDue to the limitations imposed by the instrumental signal-to-noise ratio, the difference spectrum of the 0.01 M solution fails to clearly reproduce the proton continuum (refer to Supplementary Fig.\u00a03). Nevertheless, an analysis of the distance distribution between the chlorine ion and the hydrated proton based on the MD simulation trajectory reveals non-random aggregation of this ionic pair even in the 0.1 M solution (Supplementary Fig.\u00a04), as well as its impact on the vibrational spectrum of the proton (Supplementary Fig.\u00a05). The concise discussion on the influence of chloride ions is in the\u00a0Supplementary Information. Consequently, the subsequent theoretical investigation into the local structures of the excess proton will focus on the MD trajectory of the more dilute 0.01 M HCl solution, ensuring the minimization of the interference from the chloride anion.\n\nFor comparative purposes, the theoretical IR difference spectrum between a 0.9 M HCl solution and pure water is computed (refer to Supplementary Information and Supplementary Fig.\u00a06). This theoretical spectrum demonstrates excellent agreement with the experimental IR difference spectrum in terms of lineshape, thereby validating the accuracy of the trajectory-based vibrational spectral analysis methodology. Despite the recent progress in machine learning techniques for predicting the polarization properties of molecules, which has enabled the direct simulation of IR spectra for liquids26, earlier research has demonstrated that the VDOS spectrum, particularly the difference spectrum between HCl solution and pure water, can effectively capture the majority of the characteristic spectral features observed in the experimental proton continuum14. The simulated VDOS difference spectrum for 0.1 M HCl aligns well with the experimental IR spectrum, as illustrated in Fig.\u00a02a. Since the VDOS spectrum is intrinsically linked to the nuclear velocities required for the calculation of vibrational vectors, it serves as a fundamental starting point in the design of a scheme for assigning the vibrational spectrum of the hydrated proton in water.\n\nFor a better understanding of the spectral features in IR spectrum, we first separate spectral features of the excess proton and hydrogen atoms in its flanking waters, as well as hydrogen atoms in bulk water. Similarity between the spectra of the flanking water hydrogens and bulk water hydrogens is witnessed, though with obvious red-shift of the OH stretching band broadened down to below 2000 cm\u22121, as shown in Fig.\u00a02c, which was interpreted as the effect of identity exchange between the excess proton and flanking water hydrogen due to \u201cspecial pair dance\u201d5,12, as well as the blue shift of the libration and intermolecular vibration bands below 1000 cm\u22121 17,27. The influence of the excess proton on the spectral feature of its flanking waters is softening the O-H bonds and hindering their libration style of motions, similar to the effects of the external electric field on water28. On the other hand, the VDOS spectrum of the excess proton is distinctively different from the others, presenting clearly the broad continuum across the whole mid-infrared region, the most important feature in the difference spectrum.\n\nBy defining proton stretch as the oscillatory motion of the proton between the two oxygen atoms within the flanking water molecules, it becomes straightforward to isolate the partial VDOS spectrum of proton stretch. Subsequently, this partial spectrum is fitted to three Gaussian-type peaks, as illustrated in Fig.\u00a02d. The residual obtained after subtracting the stretching motion from the total proton VDOS spectrum is then fitted with three additional Gaussian-type peaks. Overall, the VDOS spectrum of the excess proton, within the frequency range of 500\u20134000 cm\u22121, is comprehensively decomposed into six Gaussian-type peaks, achieving a high coefficient of determination of R2\u00a0=\u00a00.990 (see Fig.\u00a02d).\n\nThe robust fit of the partial VDOS of proton stretch into three Gaussian functions underscores the necessity of incorporating additional hydrated proton configuration type beyond the conventionally recognized Zundel and Eigen cations. Earlier MD simulations have disclosed that almost half of the configurations in the trajectory possess atypical features, characterized by a moderate degree of asymmetry, thus precluding their classification into either Zundel-like or Eigen-like configurations4. The nearly barrierless interconversion between Zundel-like and Eigen-like configurations also indicates the increased probability in finding these atypical configurations, as well as their importance in shaping the vibrational signature in the proton continuum.\n\nVarious schemes of classifying the excess proton defect have been proposed previously. Among them, spatial coordinates proven to be straightforward for constructing structural descriptors to evaluate the asymmetry of the excess proton\u2019s local environment8,9,12,17,18,19. Alternatively, the number of special pairs10,13, or the smooth overlap atomic positions (SOAPs)29, or even the energetics and hydrogen bonding character relevant to the proton transfer between two flanking waters30, have also been utilized as descriptors to distinguishing between configurations. In this study, we prefer to employ the spatial coordinates of the \u201cspecial pair\u201d, since previous studies confirmed their relevance to the frequency of proton stretch8,9,13.\n\nThe distribution presented in Fig.\u00a03a exhibits a significant broadening along the \\({R}_{{{{{\\rm{O}}}}}_{2}{{{{\\rm{H}}}}}^{*}}\\) axis, indicating the substantial fluctuations in the HB length associated with proton rattling. Although the overall structure distribution exhibits a single-peak characteristic, which is consistent with the results from the global free energy surface analysis29, a careful error analysis reveals that at least three two-dimensional (2D) Gaussian functions are indispensable for precisely fitting the distribution with substantial overlap between adjacent functions, achieving an excellent coefficient of determination of R2\u00a0=\u00a00.999 in Fig.\u00a03c. Attempts to reduce the number of Gaussian functions lead to a substantial increase in the fitting error, whereas adding more Gaussian functions does not yield notable improvements (see Supplementary Fig.\u00a07). As shown in Fig.\u00a03b, based on the distinct asymmetries of the proton\u2019s position, the decomposed 2D Gaussian distributions can be rationally attributed to Zundel-like, intermediate, and Eigen-like configurations, with weights of 28%, 44%, and 28%, respectively. The populations of the three types are in line with the earlier MD simulation4, only if the atypical configurations are named as the intermediate class. The existence of the intermediate gains supported from geometrical optimizations of the snapshots from the MD trajectories (details are described in ref. 31). As discussed in the\u00a0Supplementary Information, under dilute conditions below 1 M of concentration, the population ratio of three configurations remains essentially stable (refer also to Supplementary Figs.\u00a08\u20139, as well as Supplementary Table\u00a02).\n\na The 2D probability density (PD) distribution in 0.01 M HCl, which is extracted and mapped onto a coordinate space defined by \\({R}_{{{{{\\rm{O}}}}}_{1}{{{{\\rm{H}}}}}^{*}}\\) and \\({R}_{{{{{\\rm{O}}}}}_{2}{{{{\\rm{H}}}}}^{*}}\\), the distances between the excess proton (H*) and its two adjacent oxygen atoms (O1 and O2) of flanking water molecules (assuming \\({R}_{{{{{\\rm{O}}}}}_{1}{{{{\\rm{H}}}}}^{*}} < {R}_{{{{{\\rm{O}}}}}_{2}{{{{\\rm{H}}}}}^{*}}\\)). b The three 2D Gaussian-type fitting functions are assigned to Zundel-like (blue), Intermediate (yellow) and Eigen-like (green) configurations. The black lines illustrate the contour plot of the sum of the three types. c The absolute error (AE) distribution of the 2D Gaussian-type fitting, defined as the difference between the probability density distribution and the sum of the three 2D Gaussian-type fitting functions. The unit of root mean square error (RMSE) and AE is \u00c5\u22122.\n\nThe relatively balanced abundance of each configuration type highlights the importance of considering all three types when discussing the properties of the excess proton. To rationally associate these configurations with the three proton stretching signatures in the vibrational spectrum, we calculate the periods of proton stretch for carefully selected segments of MD trajectories belong to distinct types (refer to the\u00a0Supplementary Information and Supplementary Fig.\u00a010 for details). The Zundel-like, intermediate, and Eigen-like configurations exhibited average proton stretching periods of 25.3 fs, 18.7 fs, and 13.7 fs, respectively, equivalent to frequencies of 1320 cm\u22121, 1780 cm\u22121, and 2430 cm\u22121. Clear differences in the average proton stretching frequencies were observed, with a progressive increase from Zundel-like to Eigen-like configurations. This trend is consistent with the peaks 1\u20133 in Fig.\u00a02d, thereby resolving the controversy regarding the structural assignment of proton stretching frequencies as shown in Fig.\u00a02b. While previous studies have suggested the potential presence of intermediate states based on structural classifications9,15, they did not fully substantiate these claims, particularly in terms of definitive vibrational signatures. The vibrational signal at 1770 cm\u22121 has long been a subject of intense research, but its corresponding proton configuration assignment remains controversial, and it has never been assigned to an intermediate state. Our work successfully identifies the 1770 cm\u22121 peak as a unique proton stretching vibration corresponding to the intermediate state. This finding contributes to a deeper understanding of linear IR spectra in acid solutions and prompts a re-evaluation of existing interpretations in 2D IR spectroscopy for such systems.\n\nWith the established connection between the vibrational signatures and configurations of the excess proton, the vibrational vectors associated with the frequencies at the centers of peaks 1 to 6 are generated and illustrated in Fig.\u00a04. The vectors in Fig.\u00a04a\u2013c clearly indicate the mixing of the proton stretch with other types of vibrational motions, which is evident from the overlap observed among the fitted Gaussian peaks. Additionally, the vectors shown in Fig.\u00a04d\u2013f highlight the non-stretching motions present in the hydrated proton moiety. All of the assignments of these six spectral signatures to specific vibrational modes are listed in Table\u00a01. As the vibrational vectors were computed using segments of the MD trajectory rather than static structures, the motions of \\({{{{\\rm{H}}}}}_{5}{{{{\\rm{O}}}}}_{2}^{+}\\) inherently preserve the dynamical impact of the surrounding water molecules. This underscores one of the advantages of our approach: the chosen cluster size does not affect the vibrational vectors of the concerned motifs, as detailed in the\u00a0Supplementary Information and Supplementary Fig.\u00a011.\n\nFrom a to c, the vibrational vectors for three proton-stretch signatures located in different frequency regions, which are casted onto their corresponding configuration types. From d to f, the vibrational vectors for libration (d)17, umbrella (e)8,17, and bending (f)13,18, respectively. The latter three vectors are imposed on the average structure of all configurations, since they are insensitive to the configuration changes.\n\nMixing of the spectral signatures of proton stretch with other motion styles hindered comprehensive understanding of the spectrum and the arrival of a unanimous consensus regarding the assignment of proton dynamics from molecular spectrum. Being one of the major focuses of dispute, a commonly accepted assessment of the spectral features near 1750 cm\u22121 has not been fully achieved. Conventionally, both theoretical and experimental studies attributed this spectral signature primarily to the flanking-water bending mode7,11,12,18,25. However, several studies have also indicated the involvement of the proton stretch8,13,17,19,32. The vibrational vectors displayed in Fig.\u00a04b clearly indicate that the signals around 1770 cm\u22121 are mainly contributed from both proton stretch and flanking water bend. Our analysis reveals that both vibrational modes contribute approximately equally to the observed spectral feature. However, since the proton stretch typically exhibits more intensive IR intensity than the flanking-water bend8, the former mode ought to dominate the IR spectrum at 1770 cm\u22121. The controversy at \u00a0~1750 cm\u22121 might arise due to the accuracy limitation of the semiempirical theories, which sometimes resulted in hundreds of wavenumbers of deviations in predicted vibrational peak positions compared to both ab initio MD (AIMD) trajectory and experimental results10,13. This inconsistency, which undermines the reliability of the vibrational mode assignments, has been essentially avoided in our simulations using machine-leaning neural network force fields from first-principles level of calculations. This finding resolves the long-standing controversy regarding the assignment of this spectral feature and provides a more accurate understanding of the vibrational dynamics in aqueous proton systems.\n\nThe mixed nature of the vibrational signal at 1750 cm\u22121, particularly, confirming the distinctive contribution from the proton stretching mode of intermediate configurations, offers a fresh perspective on interpreting state-of-the-art experimental 2D IR spectra for HCl solutions reported by Tokmakoff and colleagues. When assigning the signature solely to the flanking water bending mode of Zundel-like configurations7,11,25, its anisotropy decay should be perfectly fitted with a single exponential similar to water bending33, contrary to the reported biexponential fitting11. By including the proton stretching mode of intermediate configurations, the biexponential fitting can be smoothly interpreted. An additional advantage of introducing this distinct vibrational feature can also be harvested concerning the interpretation of the cross peaks in 2D IR spectra. The cross peak (1200 cm\u22121, 1750 cm\u22121) previously attributed to the coupling of proton stretch and flanking water bend in Zundel-like configurations7,9 should also involve the contribution of the chemical exchange effect relevant to Zundel-like and intermediate configurations. The ultrafast interconversion between these configurations, which occurs at a timescale of sub-100 fs5,6,24,34,35, can be observed when using a waiting time of 150 fs in 2D IR experiments, in contrast to the commonly used picoseconds of waiting time for observing most chemical exchange processes. Notably, incorporating the chemical exchange effect does not contradict the established interpretation based on the experimental observations. For instance, the proton stretching modes of Zundel-like and intermediate configurations can also perfectly rationalize the strong parallel polarization preference observed experimentally7.\n\nAnalysis of the local electric field is helpful to exploring how the surrounding water HB network is involved in shaping the behaviour of a hydrated proton. Figure\u00a05 graphically illustrates the relationship between the local electric field intensity and the collective coordinates, specifically ql and qs, which represent the long (major) and short (minor) axes of 2D Gaussian-type structural distribution functions (see Supplementary Fig.\u00a012 and Supplementary Tables\u00a03\u20136), respectively. Figure\u00a05a illustrates, for each category of hydrated proton configuration, how the electric field intensity evolves along ql. Notably, while all three categories exhibit an enhanced electric field along ql, the ranges of intensities are distinctly different and essentially non-overlapping. This observation holds even when using the same collective coordinate ql of the Intermediate state for all three configurations, as shown in Supplementary Fig.\u00a014. This finding underscores that the observed variations in the local electric field distributions are not artifacts of the choice of collective variables but rather reflect intrinsic differences in the structural parameters of the three configurations. On the other hand, qs appears to emphasize the uniformity of configurations within a given configuration, when plotting the electric field intensity along qs, essentially horizontal lines are observed for all configurations, as demonstrated in Fig.\u00a05b. The two collective variables incline to focusing on either the distinctiveness among categories of configurations or the tendency of gradual change with each category. Their excellent performance highly validates the structure decomposition scheme via 2D Gaussian-type fitting, and more importantly, reveals the feasibility of utilizing ql as the sole collective coordinate in exploring each category of configurations.\n\na The local electric field distributions along collective coordinate ql, for Zundel-like (0\u20130.13 V/\u00c5), Intermediate (0.13\u20130.28 V/\u00c5) and Eigen-like (0.28\u20130.40 V/\u00c5) configurations, respectively. b The local electric field distributions along collective coordinate qs, for Zundel-like, Intermediate and Eigen-like configurations, respectively. The color bar of the scatter points signifies the structural probability densities (PD), and it is worth noting that the color bar is uniquely defined for each sub-graph. The local electric field surrounding the excess proton is evaluated using Coulomb\u2019s law within the first solvation shell of the \\({{{{\\rm{H}}}}}_{5}{{{{\\rm{O}}}}}_{2}^{+}\\) moiety, as long-range contributions have minor impact on the internal electric field37. The atomic charges utilized in this estimation are derived from the SPC/E water model, which are set as \u00a0\u22120.8476e for oxygen and \u00a0+0.4238e for hydrogen50. This model has been demonstrated to provide a valid and efficient approximation, with no significant impact on the robustness of the conclusions in our previous work37. Specifically, E1 is defined as the projection of the local electric field onto the O1-H* bond, where a positive value of E1 would stabilize the current configuration by hindering the proton\u2019s transfer towards the opposite atom O2. The introduction of an additional \u201cintermediate\u201d configuration can be necessitated by inspecting the plot of local electric field distribution presented as Supplementary Fig.\u00a013, classifying configurations merely into Zundel-like and Eigen-like cations will result in non-negligible dependence of the field intensity on qs and overlapped electric field between categories of configurations.\n\nThe current study contributes significantly to the understanding of the mechanisms underlying the remarkable structural diversity of protons in water. Our findings reveal a notable correlation between local electric field intensity and HB network asymmetry surrounding the proton\u2019s two flanking water molecules (refer to Supplementary Information, Supplementary Figs.\u00a015, 16 and Supplementary Tables\u00a07\u20139). Specifically, from Zundel-like to Eigen-like configurations, there is a progressive enhance in the local electric field intensity, which coincides with an increase in HB network asymmetry. These observations remind us that the emergence of three distinct types of hydrated proton could potentially stem from a finite set of HB network patterns in the immediate environment. Fluctuations in the HB network can induce local electric fields, which in turn stabilize these critical configurations. Conversely, when the proton cation mismatches with its local environment, the surrounding HB networks tend to promote configurational transformations. As detailed in the\u00a0Supplementary Information, biexponential fitting of the correlation functions reveals lifetimes of the configurations, which are 25 fs, 51 fs, and 36 fs for the Zundel-like, intermediate, and Eigen-like configurations, respectively (see Supplementary Fig.\u00a017). These ultra-short timescales rationalize the experimentally observed sub-100 fs process of structural interconversions5,6,24,34,35.",
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+            ]
+        },
+        {
+            "section_name": "Conclusions",
+            "section_text": "This study has successfully delved into the dynamical behavior of hydrated protons in water through a theoretical exploration that capitalized on the reliability and efficiency of atomic neural network representations of the force field. This approach has allowed us to perform MD simulations on models encompassing thousands of water molecules, effectively minimizing the confounding effects of concentration and enhancing our comprehension of the protons\u2019 fundamental characteristics. Furthermore, our proposed IFFT scheme has proven to be an expedient tool for extracting vibrational vectors from MD trajectories, enabling the direct characterization of frequency-specific vibrational features within the broad proton continuum. While the interpretation of these features remains a subject of ongoing discussions, our findings have noteworthily contributed to this debate. By integrating vibrational spectrum assignment with structural distribution analysis, we have presented compelling evidence for the existence of distinct intermediate configurations of hydrated protons, alongside the established Zundel-like and Eigen-like configurations. Notably, these intermediate configurations exhibit a unique proton stretching peak at 1770 cm\u22121, which is approximately equally mixed with the flanking water bending mode. We hypothesize that the local electric field generated by the surrounding HB network in solution plays a pivotal role in stabilizing these intermediate configurations, a phenomenon that is absent in smaller gaseous protonated water clusters. In essence, this study provides a fresh perspective for understanding the dynamical behavior of hydrated protons in water, laying the groundwork for future investigations into the intricate interactions and structures that govern this seemingly simple but profoundly fascinating system.\n\nAlthough the nuclear quantum effects (NQEs) hold recognized importance in the aqueous proton system, we prefer not to consider it when the currently used generalized gradient approximation (GGA) functional satisfactorily replicates the experimental IR spectrum of HCl solution and the proton\u2019s diffusion coefficient. Notably, the GGA functional appears to offset errors in electron correlation and NQEs14,36. Previous research also warns that combining the GGA functional with NQEs may lead to notable discrepancies compared to experimental observations37,36.",
+            "section_image": []
+        },
+        {
+            "section_name": "Method",
+            "section_text": "",
+            "section_image": []
+        },
+        {
+            "section_name": "Vibrational spectrum assignment approach",
+            "section_text": "We began by locating the excess proton in the simulation box for each snapshot in the 1.2 ns MD trajectory. Water molecules were identified by grouping each oxygen atom with its two closest hydrogen atoms, leaving the excess proton as the only remaining unpaired hydrogen atom. To balance spectral resolution and the consistency of the excess proton\u2019s local environment, the entire trajectory was divided into 3 ps segments, given the frequent identity switching between an excess proton and a flanking water hydrogen atom. Applying a criterion that the determined excess proton maintains its identity in more than 60% of the snapshots within a segment, 26 segments were retained for further analysis. In these retained segments, the proton and its two flanking waters were found to form a stable special-pair motif to hold the excess proton, relying on which the vibrational properties of the proton will be analyzed. These segments, totaling 6.5% of the entire trajectory, are outlined in Supplementary Fig.\u00a018 and the\u00a0Supplementary Information.\n\nTo streamline the mathematical derivation, we abstracted atomic motions into harmonic vibrations represented by sinusoidal functions. However, it\u2019s important to note that our approach also contains anharmonic components and other atomic motions.\n\nFor the purpose of maximizing information retention, we picked a reference degree of freedom that exhibits strong relative intensity within the chosen frequency range. The position of this reference degree of freedom, denoted as xref(t), can be described as\n\nwhere xref(0) is the initial position at time t\u00a0=\u00a00, Nref represents the total number of vibrational modes encompassed by xref(t), \\({A}_{{{{\\rm{ref}}}}}^{k}\\), \\({\\omega }_{{{{\\rm{ref}}}}}^{k}\\) and \\({\\varphi }_{{{{\\rm{ref}}}}}^{k}\\) are the amplitude, frequency and phase of the vibrational mode k. The mass-weighted velocity vref(t) was calculated as\n\nTherefore, the VACF of the degree of freedom ref can be expressed as\n\nThe VACFref(t) loses the initial phase \u03c6ref of vref(t). Note that summation replaced all integrations in our practical computations. By performing the FFT on VACFref(t), we obtained the VDOS spectrum Pref(\u03c9),\n\nTo rescale the VDOS spectrum to the dimension of mass-weighted velocity, we introduced the dimension conversion factor S(\u03c9), which is defined as\n\nThe position xi(t) of the degree of freedom i at time t can be expressed as\n\nwhere Ni is the number of vibrational modes. The mass-weighted velocity vi(t) was calculated as\n\nWe calculated the velocity correlation function (VCF) between vi(t) and vref(t). If i is ref, it is the VACF; otherwise, it is the VCCF.\n\nwhere L represents the number of vibrational modes shared by both vi(t) and vref(t). The VCFi(t) preserves the phase difference between these two velocities. By performing the FFT on VCFi(t), we obtained the Pi(\u03c9),\n\nFor convenience, the VDOS spectra were rescaled to the dimension of mass-weighted velocity. We divided Pi(\u03c9) by the dimension conversion factor S(\u03c9) whenever Pi(\u03c9) is non-zero,\n\nSelecting a specific frequency \u03c9s, we obtained \\({P}_{i}^{s}(\\omega )\\) by retaining only the value of \\({P}_{i}^{{{{\\rm{Scale}}}}}(\\omega )\\) at \u00a0\u00b1\u00a0\u03c9s, and zeroing out all other frequencies.\n\nBy performing the IFFT on \\({P}_{i}^{s}(\\omega )\\), we obtained the velocity \\({v}_{i}^{s}(t)\\) corresponding to the selected frequency \u03c9s,\n\nFrom \\({v}_{i}^{s}(t)\\), we can straightforwardly determine the amplitude \\({A}_{i}^{s}\\) and the phase difference \\({\\varphi }_{i}^{s}-{\\varphi }_{{{{\\rm{ref}}}}}^{s}\\) for the degree of freedom i at selected frequency \u03c9s. Subsequently, we can associate the amplitudes and phase differences with their corresponding average structure to generate the vibrational vectors.\n\nThe diagonalization of the velocity covariance matrix, as described in\u00a0ref.\u00a020, is generally effective for systems exhibiting distinct and well-separated vibrational peaks, such as those found in gaseous or certain solid-state substances. Nevertheless, this method becomes less adequate when applied to the investigation of proton vibrations in acids, where the vibrational modes are intricately coupled due to complex proton dynamics. In stark contrast, our approach allows for the mixing of vibrational modes, rendering it particularly suitable for the study of the present system.\n\nBy diagonalizing the dipole velocity cross-correlation matrix, it is possible to extract local vibrational vectors for condensed systems, as demonstrated in ref. 21. However, in the case of HCl solutions, frequent proton hopping necessitates the division of MD trajectories into very short segments. This segmentation complicates the extraction of vibrational features for the entire trajectory, as the vibrational modes and corresponding frequency distributions can vary significantly between segments. In contrast, our approach centers on specific frequencies within the vibrational spectrum, allowing us to average the vibrational vectors at the same frequency across multiple segments.\n\nWhile effective modes analysis based on MD trajectories of small protonated water clusters can provide vibrational vectors for normal modes38, it cannot fully account for the dynamic effect on the vibrational spectrum of hydrated proton in water. It is noteworthy that the utilization of velocity correlation functions, rather than velocity or position time evolution functions as in previous research39, significantly enhances the signal-to-noise ratio in the constructed VDOS spectrum. This advancement ensures a more accurate and reliable analysis of the vibrational properties of the system.\n\nTo obtain the average vibrational vectors across various segments, we have devised the vibrational coordinates for the \\({{{{\\rm{H}}}}}_{5}{{{{\\rm{O}}}}}_{2}^{+}\\) moiety. For the hydrogen atoms in the flanking water molecules, we utilized three unit vectors, one aligned with the O-H axis, another situated within the H2O plane and perpendicular to the O-H axis, and a third one perpendicular to the H2O plane. For the oxygen atoms in the flanking water molecules and the proton, our internal coordinates contain three unit vectors as well, one parallel to the O-O axis, one that signifies the mean projection of the two flanking water H-H connections onto the plane orthogonal to the O-O axis, and a third vector perpendicular to the first two. Afterward, we decomposed the vibrational vectors of each segment at the same frequency onto these vibrational coordinates.\n\nFor all selected trajectory segments, in cases where \\({R}_{{{{{\\rm{O}}}}}_{1}{{{{\\rm{H}}}}}^{*}} < {R}_{{{{{\\rm{O}}}}}_{2}{{{{\\rm{H}}}}}^{*}}\\), the proton configurations were classified in accordance with the boundary lines shown in Supplementary Fig.\u00a019. Subsequently, the average structure for each configuration type was computed utilizing internal coordinates.",
+            "section_image": []
+        },
+        {
+            "section_name": "MD simulation details",
+            "section_text": "AIMD simulations were performed utilizing the VASP 5.4.4 package40,41. The valence electron wave functions were expanded in a plane wave basis with an energy cutoff of 400 eV. For the description of core electrons, pseudopotentials were employed using the projector augmented wave (PAW) method42. The exchange and correlation energy were computed using the RPBE functional, formulated within the GGA43,44. To incorporate van der Waals (vdW) interactions, the DFT-D3/zero correction method was utilized45. The Brillouin zone sampling was simplified to a single \u0393 point.\n\nIn the case of 0.9 M HCl, our cubic simulation box (measuring 12.42 \u00c5 in length) encompassed 63 water molecules, accompanied by one proton and chloride ion pair, resulting in an approximate density of 1 g/cm3, which is consistent with the experimental density. The convergence criterion for DFT energy and wave function was set to 10\u22126 eV. By executing three uncorrelated simulations within the canonical ensemble (NVT), maintaining a temperature of 298 K, a pressure of 1 atm, and utilizing a time step of 1 fs, we successfully generated a total of 150 ps trajectories.\n\nTo enhance computational efficiency and extend simulation durations with low ion concentration, we constructed the ANNFFs for HCl solutions22. The scalability of ANNFFs is well-established, as they decompose the total energy into the sum of contributions from individual atoms22. This allows an ANNFF trained on smaller systems to predict the potential energy surface for larger configurations simply by adding new components to the total energy, provided the atomic environments are adequately represented in the training dataset. The ANNFF based on RPBE-D3 functional has been successfully employed in modeling water dissociation, providing RDFs and self-diffusion coefficient for liquid water that show good agreement with experimental results37.\n\nFor the training process, we utilized the n2p2 package46. Our ANNFFs comprised a series of feedforward neural networks, each featuring two hidden layers with 25 nodes apiece. The local chemical environments of H, O, and Cl atoms were described using a total of 36, 39, and 26 symmetry functions (SFs), respectively, encompassing both radial and angular types, with a cutoff radius of 6 \u00c5. The initial training set contained 1945 configurations randomly chosen from AIMD trajectories. Following this, preliminary ANNFFs were created and employed in NVT MD simulations using the LAMMPS program equipped with the ANNFF library22,47. Structures exhibiting extrapolation warnings during MD were filtered, recalculated using DFT, and subsequently added to the training set. Several iterations of ANNFF training and configuration selection were conducted until the ANNFFs were ready for ns-scale MD simulations with few extrapolation warnings. The final training set contained 3259 configurations. For comparison, the test set encompassed all AIMD configurations. Three parallel ANNFFs were trained and tested, exhibiting promising parallelism and accuracy, as detailed in Supplementary Tables\u00a010 and 11.\n\nBy utilizing the first ANNFF listed in Supplementary Table\u00a010, we conducted MD simulations within three cubic simulation boxes of lengths 12.42, 24.84, and 49.67 \u00c5, which contains 63, 511, and 4095 water molecules, respectively, alongside a proton and chloride ion pair48. Additionally, a simulation for pure water was conducted with 512 water molecules using a cubic box with a side length of 24.84 \u00c5. To maintain consistency across all simulations, the cell length for each ANNFF MD simulation was adjusted to align with an experimental density of 1 g/cm3. Following a 0.6 ns equilibration phase at 298 K and 1 atm pressure, with a time step of 0.3 fs, the simulations were extended for an additional 1.2 ns to generate trajectories for subsequent analysis.\n\nFor comparison, a system comprising 63 water molecules, a proton, and a chloride ion pair (equivalent to 0.9 M HCl) is presented in the\u00a0Supplementary Information. Additionally, a system containing 511 water molecules, a proton, and a chloride ion pair (0.1 M HCl), as well as a system with 512 water molecules, were utilized to generate the theoretical difference VDOS spectrum shown in Fig.\u00a02a. The geometrical optimizations were performed on the snapshots from the MD trajectory of 0.1 M HCl. These systems are also included in the\u00a0Supplementary Information for comparison. All other analyses presented in the manuscript are based on a 0.01 M HCl solution, which consists of 4095 water molecules, a proton, and a chloride ion pair.",
+            "section_image": []
+        },
+        {
+            "section_name": "Experimental details",
+            "section_text": "The acid solutions were prepared by diluting concentrated HCl aq. (GR, 36.0\u201338.0% Sinopharm Chemical Reagent Co., Ltd) with water (Millipore, 18.2 M\u03a9) to proton concentrations at 0.1 M and 0.01 M.\n\nThe IR spectra were recorded on an ATR-FTIR vacuum spectrometer (Bruker VERTEX 80v) equipped with a deuterated triglycine sulfate (DTGS) detector in the range of 650\u20134000 cm\u22121. Each spectrum was averaged over 64 scans with a resolution of 4 cm\u22121. A Platinum ATR unit A 225 with diamond crystal accessory was used. The background spectrum of the bare diamond crystal was recorded and used for the subsequent measurements at room temperature. In all cases a 6 \u03bcL of sample solution was placed on the ATR prism. In order to prevent evaporation of the liquid, the sample was sealed with a lid that was pressed tightly against the platform by a Pressure application device.\n\nTo ensure accurate results, we collected multiple samples of water and HCl solutions and obtained five parallel spectra for each. Afterward, we carefully chose the spectrum sets exhibiting the smallest standard deviation, enabling us to compute the average spectra and the difference spectra between HCl solutions and water.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The snapshots from the MD trajectories for geometrical optimizations have been deposited in the figshare database31. The initial and final configurations of MD trajectories generated in this study have been deposited in the github48. The data generated in this study are provided in the Source Data file.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "The code used in this study is available from the corresponding author on request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This work is supported by the National Key Research and Development Program of China (2023YFA1506902 to C.L. and X.Y., 2023YFA1506903 to Y.G.) and the National Natural Science Foundation of China (22073041 to C.L. and X.Y., 62075225 to H.Z. and Y.W.). We thank the High Performance Computing Center of Nanjing University for computational resources. C.L. and X.Y. are thankful to Prof. Minghui Yang, Prof. Jian Liu, and Prof. Lu Wang for constructive suggestions. We would also thank the staff at BL06B beamline of the Shanghai Synchrotron Radiation Facility for their help in data collection.",
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+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry of the Ministry of Education (MOE), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China\n\nXuanye Yang\u00a0&\u00a0Chungen Liu\n\nShanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, 201800, China\n\nYu Wu\u00a0&\u00a0Hongwei Zhao\n\nUniversity of Chinese Academy of Sciences, Beijing, 100049, China\n\nYu Wu\n\nComputer Science Department, School of Engineering, University of Texas at El Paso, El Paso, TX, 79968, USA\n\nSiyu Deng\n\nState Key Laboratory for Artificial Microstructure and Mesoscopic Physics, Frontier Science Center for Nano-optoelectronics and School of Physics, Peking University, Beijing, 100871, China\n\nLing Liu\n\nShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201210, China\n\nHongwei Zhao\u00a0&\u00a0Yi Gao\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nX.Y. and C.L. conceived and designed the project. X.Y. developed the method for computing the vibrational vectors, wrote the code, performed all calculations, and analyzed the data. Y.W. and H.Z. performed the ATR-FTIR measurements, and analyzed the experimental data. S.D. provided help in optimizing the code. X.Y. prepared the manuscript. L.L., Y.G. and C.L. supervised the project and revised the manuscript. All authors commented on the manuscript.\n\nCorrespondence to\n                Ling Liu, Hongwei Zhao, Yi Gao or Chungen Liu.",
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+            "section_text": "Yang, X., Wu, Y., Deng, S. et al. Unveiling the intermediate hydrated proton in water through vibrational analysis on the 1750 cm\u22121 signature.\n                    Nat Commun 16, 5764 (2025). https://doi.org/10.1038/s41467-025-60794-2\n\nDownload citation\n\nReceived: 01 August 2024\n\nAccepted: 03 June 2025\n\nPublished: 01 July 2025\n\nVersion of record: 01 July 2025\n\nDOI: https://doi.org/10.1038/s41467-025-60794-2\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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190ab9cb7e233308a2370fa0ea0649b0ef2427b44d8fbd487673ada88f62905e/metadata.json

@@ -0,0 +1,158 @@
+{
+    "title": "In situ training of an in-sensor artificial neural network based on ferroelectric photosensors",
+    "pre_title": "In situ training of an in-sensor artificial neural network based on ferroelectric photosensors",
+    "journal": "Nature Communications",
+    "published": "07 January 2025",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55508-z/MediaObjects/41467_2024_55508_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55508-z/MediaObjects/41467_2024_55508_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55508-z/MediaObjects/41467_2024_55508_MOESM3_ESM.pdf"
+        },
+        {
+            "label": "Supplementary Movie 1",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55508-z/MediaObjects/41467_2024_55508_MOESM4_ESM.mp4"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-55508-z/MediaObjects/41467_2024_55508_MOESM5_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-024-55508-z#Sec12"
+    ],
+    "code": [],
+    "subject": [
+        "Ferroelectrics and multiferroics",
+        "Information storage"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4791621/v1.pdf?c=1736341665000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4791621/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-55508-z.pdf",
+    "preprint_posted": "29 Jul, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (<30 \u03bcs), and multilevel (>4 bits) photoresponses, as well as long retention (15 days), high endurance (109), high write speed (100 ns), and small cycle-to-cycle and device-to-device variations (~0.66% and ~2.72%, respectively), all of which are desirable for the in situ training. Additionally, a bi-directional closed-loop programming scheme is developed, achieving a precise and efficient weight update for the FE-PS. Using this programming scheme, an in-sensor ANN based on the FE-PSs is trained in situ to recognize traffic signs for commanding a prototype autonomous vehicle. Moreover, this in-sensor ANN operates 50 times faster than a von Neumann machine vision system. This study paves the way for the development of in-sensor computing systems with in situ training capability, which may find applications in new data-streaming machine vision tasks.Physical sciences/Materials science/Materials for devices/Information storagePhysical sciences/Materials science/Condensed-matter physics/Ferroelectrics and multiferroics",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplementaryInformation.pdfSupplementaryVideo1.mp4Supplementary Video 1",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "In-sensor computing has emerged as an ultrafast and low-power technique for next-generation machine vision. However, in situ training of in-sensor computing systems remains challenging due to the demands for both high-performance devices and efficient programming schemes. Here, we experimentally demonstrate the in situ training of an in-sensor artificial neural network (ANN) based on ferroelectric photosensors (FE-PSs). Our FE-PS exhibits self-powered, fast (<30 \u03bcs), and multilevel (>4 bits) photoresponses, as well as long retention (50 days), high endurance (109), high write speed (100\u2009ns), and small cycle-to-cycle and device-to-device variations (~0.66% and ~2.72%, respectively), all of which are desirable for the in situ training. Additionally, a bi-directional closed-loop programming scheme is developed, achieving a precise and efficient weight update for the FE-PS. Using this programming scheme, an in-sensor ANN based on the FE-PSs is trained in situ to recognize traffic signs for commanding a prototype autonomous vehicle. Moreover, this in-sensor ANN operates 50 times faster than a von Neumann machine vision system. This study paves the way for the development of in-sensor computing systems with in situ training capability, which may find applications in new data-streaming machine vision tasks.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Conventional von Neumann machine vision systems, composed of physically separated image sensing, memory, and processing units, are becoming time and energy inefficient in time-critical applications like autonomous driving. A promising solution to this problem is the recently emerging in-sensor computing technique1,2. In this technique, visual information is directly processed within image sensors, resulting in significantly reduced latency and energy consumption. So far a variety of in-sensor computing systems, mimicking both the low-level (e.g., contrast enhancement and noise suppression)3,4,5,6,7,8 and high-level (e.g., recognition and classification)9,10,11,12,13,14,15,16,17,18,19 image processing functions of human visual systems, have been demonstrated. Among them, in-sensor artificial neural networks (ANNs), consisting of interconnected programmable photosensors with tunable photoresponsivities (weights), are of particular interest because they can realize real-time image sensing and recognition9,10,11,18,19. Such capability is highly demanded for the time-critical applications.\n\nNotably, most previous studies implemented the in-sensor ANNs by using ex situ training17,18,19,20,21,22, where weights are computed by software models deployed on digital computers and, then, programmed into the hardware (Fig.\u00a01a). By contrast, in situ training, where weights are updated directly in the hardware (Fig.\u00a01b), was rarely implemented for in-sensor ANNs9,11. In fact, the in situ training is increasingly seen as a more efficient approach due to its advantages as follows23,24,25,26,27. First, the in situ training enhances the area and energy efficiencies of the hardware because it avoids the use of additional digital computers. In addition, the in situ training can tolerate some device non-idealities (e.g., stuck fault and device-to-device variation) owing to the self-adaptive weight adjustment, thus resulting in improved training performance. Besides, the in situ training empowers the system with an online learning capability, which is important for handling new data-streaming scenarios like autonomous driving.\n\nSchematics illustrating a ex situ training and b in situ training on in-sensor ANNs. This figure highlights the major advantages of the in situ training, including avoiding the use of additional digital computers, incorporating self-adaptive weight update, and being able to handle new data.\n\nNevertheless, the in situ training places high demands on the device performance. Specifically, nonvolatile multilevel weights, linear and symmetric weight update, fast write and read speeds, high endurance, and small variations are typically required by the in situ training28. However, the existing programmable photosensors9,11 struggle to satisfy all of these performance requirements. In addition, the in situ training also demands an efficient programming scheme for weight update29,30. While the programming schemes for in-memory ANNs have been extensively studied31,32,33, there is a lack of research on identifying efficient programming schemes for in-sensor ANNs. The demands for both high-performance devices and efficient programming schemes pose a significant challenge to the implementation of in situ training on in-sensor ANNs.\n\nHere, we experimentally demonstrate the in situ training of an in-sensor ANN based on ferroelectric photosensors (FE-PSs) using a bi-directional closed-loop (BD-CL) programming scheme. The FE-PS is a programmable photosensor which operates through polarization control of photoresponsivity. It has been recently exploited as a building block of an in-sensor ANN, referred to as the ferroelectric photosensor network (FE-PS-NET), which demonstrates capability to implement real-time image sensing and recognition18,19,20. However, the in situ training of the FE-PS-NET remains unexplored hitherto, leaving uncertainty about whether the performance of the FE-PS is sufficient for the in situ training. We first show that the FE-PS with a device structure of Pt/Pb(Zr0.2Ti0.8)O3 (PZT)/SrRuO3 (SRO) exhibits symmetrically switchable photovoltaic responses as controlled by the remanent polarization. Besides, the FE-PS displays outstanding performance critical for in situ training, including self-powered, fast (<30\u2009\u03bcs), and multilevel (>4 bits) photoresponses, as well as long retention (50 days), high endurance (109), high write speed (100\u2009ns), and small cycle-to-cycle (C2C) and device-to-device (D2D) variations (~0.66% and ~2.72%, respectively). Then, a BD-CL programming scheme is developed, achieving a precise and efficient weight update for the FE-PS. Based on the high-performance FE-PSs and the BD-CL programming scheme, we demonstrate that the FE-PS-NET is capable of being trained in situ to recognize traffic signs for commanding a prototype autonomous vehicle. Besides, the trained FE-PS-NET also exhibits high reliability (retaining 100% recognition accuracy for up to 50 days), high inference speed (50 times faster than a von Neumann machine vision system), and zero energy consumption for inference (excluding contributions from peripheries). These results, therefore, showcase that the FE-PS-NET is a promising candidate for the development of in-sensor ANNs with in situ training capability.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-55508-z/MediaObjects/41467_2024_55508_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Figure\u00a02a schematically illustrates the device structure of our FE-PS, consisting of an epitaxial ferroelectric PZT film sandwiched between top Pt and bottom SRO electrodes. The detailed fabrication processes of the Pt/PZT/SRO FE-PS are presented in the \u201cMethods\u201d section. The Pt electrodes are ~10\u2009nm thick and ~200\u2009\u03bcm in diameter. The PZT and SRO layers (thicknesses: ~120 and ~40\u2009nm, respectively) are epitaxially grown on the STO substrate. The high epitaxial quality of the PZT/SRO film is revealed by the X-ray diffraction (XRD) results (Supplementary Fig.\u00a0S1) and transmission electron microscopy (TEM) images (Supplementary Fig.\u00a0S2). The atomic force microscopy (AFM) image (Supplementary Fig.\u00a0S3a) shows a smooth surface of the PZT film with a small root-mean-square roughness of ~440\u2009pm, further confirming the good film quality. The piezoresponse force microscopy (PFM) imaging (Fig.\u00a02b) and hysteresis loops (Supplementary Fig.\u00a0S3b) demonstrate the ferroelectricity of the PZT film. The high-quality ferroelectric PZT film provides a prerequisite for the manipulation of polarization and photoresponsivity in the Pt/PZT/SRO FE-PS.\n\na Schematic of the device structure of the Pt/PZT/SRO FE-PS. b PFM phase image after writing a box-in-box pattern (outer: +5\u2009V; inner: \u20135\u2009V) on the bare PZT film. Vp-dependent c bipolar, d positive monopolar, and e negative monopolar P\u2013V hysteresis loops for the device. f Top and bottom panels: illuminated I\u2013V curves measured after applying positive and negative pulses with different Vp, respectively. Middle panel: schematics of the different polarization states corresponding to the different Vp, which produce photocurrents with different magnitudes and directions. These schematics are drawn based on the results in (d, e). In (d\u2013f) and anywhere else, the States I to IV refer to the full Pup, half Pup, near-zero-polarization, half Pdown, and full Pdown states, respectively.\n\nThe polarization switching behavior in the Pt/PZT/SRO FE-PS is characterized by measuring bipolar and monopolar polarization-voltage (P\u2013V) hysteresis loops. In these measurements, the voltage is applied to the top Pt electrode while the bottom SRO electrode is grounded. The applied voltage pulses have a fixed width (0.15\u2009ms) and varied amplitudes (Vp). The measured Vp-dependent bipolar P\u2013V loops are shown in Fig.\u00a02c. The loop begins to open when Vp reaches 2.2\u2009V. As Vp continues to increase, the loop grows larger, eventually becoming nearly saturated at Vp\u2009=\u20092.9\u2009V. The saturated P\u2013V loop displays a remanent polarization (Pr) as large as ~80\u2009\u03bcC/cm2, consistent with the Pr values of high-quality epitaxial PZT films34,35,36. In addition, the saturated P\u2013V loop exhibits almost no voltage offset, suggesting that imprint field in our device is negligible. Because the imprint field is often defect-induced37,38,39, its absence in turn implies the high quality of the PZT film.\n\nFigure\u00a02c also demonstrates that multiple intermediate polarization states can be accessed by applying Vp in the range of 1.8\u20132.9\u2009V. To further verify the accessibility of intermediate polarization states, monopolar P\u2013V loops are measured. Before measuring each monopolar P\u2013V loop, a full polarization up (Pup) or down (Pdown) state is preset by applying a \u22123.5\u2009V or +3.5\u2009V pulse, respectively. As shown in Fig.\u00a02d, the device remains in the full Pup state (~\u221280\u2009\u03bcC/cm2) when applying positive pulses with Vp\u2009\u2264\u2009+1.8\u2009V. By increasing Vp, the device enters into 3 distinct intermediate states: half Pup (~\u221240\u2009\u03bcC/cm2), near-zero-polarization, and half Pdown (~+40\u2009\u03bcC/cm2), at Vp\u2009=\u2009+2.2, +2.4, and +2.6\u2009V, respectively. Further enhancing Vp to +2.9\u2009V and above results in the full Pdown state (~+80\u2009\u03bcC/cm2). Conversely, applying negative pulses with increasing Vp can switch the device from the full Pdown state to the full Pup state gradually (Fig.\u00a02e). Notably, the up-to-down and down-to-up polarization switching processes are rather symmetric, benefiting from the aforementioned absence of imprint field. In addition, the loops in Fig.\u00a02d, e feature relatively flat tops and bottoms, respectively, suggesting that the polarizations are nonvolatile when external voltages return to zero. The polarization nonvolatility is further verified by the retention test, where a long retention time of at least 24\u2009h is observed (Supplementary Fig.\u00a0S4).\n\nThe outstanding polarization switching characteristics exhibited by the Pt/PZT/SRO FE-PS, including large Pr, symmetric switching, multi-states, and nonvolatility, make it a promising candidate for investigating the polarization-modulated photovoltaic behavior. We first write the device into a specific polarization state by applying a \u22123.5\u2009V (+3.5\u2009V) preset pulse followed by a positive (negative) pulse Vp (the protocol is the same as that used in Fig.\u00a02d,e). In each polarization state, the current\u2013voltage (I\u2013V) characteristics are measured under 365\u2009nm ultraviolet (UV) illumination. Note that this light wavelength agrees well with the bandgap of PZT (~3.6\u2009eV)35,40,41. Hereafter unless otherwise specified, the applied light intensity and corresponding optical power (i.e., light intensity times electrode area) are ~85\u2009mW/cm2 and ~26.7\u2009\u03bcW, respectively. As shown in the top panel of Fig.\u00a02f, the device exhibits switchable photovoltaic responses apparently. More specifically, in the \u22123.5\u2009V-written state, the illuminated I\u2013V curve mainly locates in the second quadrant, displaying a short-circuit current (ISC) of ~15\u2009nA and an open-circuit voltage (VOC) of ~\u22120.6\u2009V. This ISC is 3 orders of magnitude higher than the dark current (\u22120.015\u2009nA @ \u22120.6\u2009V; see Supplementary Fig.\u00a0S5). The illuminated I\u2013V curve shows little change after applying positive pulses with Vp\u2009\u2264\u2009+1.8\u2009V. This is in accordance with the unchanged full Pup state at Vp\u2009\u2264\u2009+1.8\u2009V (Fig.\u00a02d). As Vp increases to +2.2, +2.4, +2.6, and +2.9\u2009V, the illuminated I\u2013V curve shifts from the second quadrant to the fourth quadrant. This is reminiscent of the polarization state evolution as shown in Fig.\u00a02d. Further increasing Vp to +3.2\u2009V and above causes almost no shift of the illuminated I\u2013V curve. This agrees with the saturation of the full Pdown state at such large Vp (Fig.\u00a02d). One can therefore establish a one-to-one correlation between the photoresponsivity states and the polarization states (see the top and middle panels of Fig.\u00a02f).\n\nIn addition, applying negative pulses can switch the photoresponsivity states reversely (see the bottom panel of Fig.\u00a02f), which correspond one-to-one to the polarization states from the full Pdown state to the full Pup state (Fig.\u00a02e). These results demonstrate that the photoresponse in our FE-PS is well controlled by the remanent polarization. The detailed mechanism underlying the polarization control of photoresponse is described in Supplementary Fig.\u00a0S6. Notably, using the remanent polarization as the control knob of the photoresponse, the FE-PS is essentially a self-powered photosensor consuming zero energy for photosensing.\n\nAnother important feature of the FE-PS is the symmetric switching of photoresponse. As shown in Fig.\u00a02f, the ISC (or VOC) values in a pair of opposite polarization states have almost the same magnitude but opposite signs. For example, the ISC value in the full Pup state is ~15\u2009nA, which is just opposite to that in the full Pdown state, i.e., ~\u221215\u2009nA. Such symmetry is attributed to the fact that the control knob of the photoresponse, i.e., the polarization, exhibits symmetric switching owing to the absence of imprint field (Fig.\u00a02c). Note that the symmetric switching of photoresponse is particularly useful, because it enables a single FE-PS to represent both positive and negative weights. Consequently, there is no need to use a pair of devices to represent a signed weight, greatly reducing the hardware overhead for network construction.\n\nNext, the performance of the FE-PS as a programmable photosensor for in situ training is comprehensively investigated. In the in situ training, the FE-PS is mainly used for inference and weight update. We first focus on the performance metrics related to the inference. The inference accuracy is largely determined by the linearity of photocurrent versus light intensity. Figure\u00a03a illustrates the measured relationships between photocurrent and light intensity for the 5 representative states, all of which can be well represented by linear fits (average coefficient of determination: 0.9997). This linear dependency of photocurrent on light intensity has been validated across different devices (Supplementary Fig.\u00a0S7), allowing for precise multiplication between photoresponsivity and optical power. A high inference accuracy also requires a long retention of photoresponsivity. As shown in Fig.\u00a03b, the photocurrents in different states are rather stable and can be retrieved after 24\u2009h. Moreover, the photocurrent responses are highly reproducible in the cyclic test (Supplementary Fig.\u00a0S8), and the photoresponsivity retention time can even be extended to 50 days (Supplementary Fig.\u00a0S9). These results demonstrate the nonvolatility of the programmed photoresponsivities, which is well attributed to the polarization nonvolatility (Supplementary Fig.\u00a0S4).\n\na Light intensity dependence and b long-term stability of photocurrents of the device in the States I to IV. c Transient current responses to illumination for the device in the full Pup (upper panel) and Pdown (lower panel) states. d Photoresponsivities of the device in the full Pup and Pdown states after different endurance cycles. e Photoresponsivity as a function of write pulse width. The device is preset into a fully Pup (Pdown) state before each application of a\u2009+\u200910\u2009V (\u201310\u2009V) write pulse with a varied width. f LTP/LTD characteristics measured with an amplitude-increasing pulse scheme. g Performance comparison between our FE-PS and other emerging programmable photosensors for in-sensor computing. The \u201c0\u201d and \u201c1\u201d on the \u201cself-powered\u201d axis represents \u201cnot self-powered\u201d and \u201cself-powered\u201d, respectively. The \u201c0\u201d and \u201c1\u201d on the \u201cIph-Ilight relationship\u201d represents \u201cnonlinear\u201d and \u201clinear\u201d relationships between photocurrent and light intensity, respectively. The \u201c\u20131\u201d on the \u201cphotoresponse time\u201d and \u201cwrite energy\u201d axes refers to that the value is not reported.\n\nBesides the inference accuracy, the inference speed and energy consumption are also important concerns. A high inference speed demands a short photoresponse time. As seen from Fig.\u00a03c, the average 10\u201390% photocurrent generation and decay times are both ~30\u2009\u03bcs. In fact, these times are limited by the amplifying circuit used for measurement (Supplementary Fig.\u00a0S10). Also considering that previous studies observed an ultrashort photoresponse time of <1\u2009ns in FE-PS42,43. We therefore infer that the photoresponse time of our FE-PS may be far below 30 \u03bcs, thus allowing a fast inference speed. In addition, as mentioned earlier, our FE-PS works in the self-powered photovoltaic mode without external biases. Consequently, zero energy is consumed at the device level for the inference.\n\nThen, we investigate the performance metrics related to the weight update. During the in situ training, the weight, i.e., photoresponsivity, needs to be frequently updated. Endurance is thus our first concern. The endurance test for the FE-PS is performed by applying cyclic \u00b13.5\u2009V/0.5 \u03bcs pulses. As displayed in Fig.\u00a03d and Supplementary Figs.\u00a0S11 and S12, both the photoresponsivity and polarization show little changes after 109 cycles. This is, to the best of knowledge, the highest endurance among the reported values for programmable photosensors19,44. Besides, the FE-PS also demonstrates a high speed for weight update (i.e., write speed), which is conducive to accelerating the training process. As illustrated in Fig.\u00a03e, the photoresponsivity of the FE-PS can be tuned by \u00b110\u2009V pulses with widths as short as 100\u2009ns. This write speed is at least one order of magnitude faster than those of other programmable photosensors19,20,44. However, the energy consumption for weight update (i.e., write energy) of our FE-PS is relatively high, reaching ~1.77\u2009nJ per operation (Supplementary Fig.\u00a0S13). The relatively high write energy is mainly attributed to the large area of the present FE-PS (3.14\u2009\u00d7\u2009104\u2009\u03bcm2). To address this issue, device downscaling is a viable solution. For example, by scaling down the FE-PS to ~1\u2009\u03bcm2 as demonstrated previously45, its write energy could be reduced to ~56\u2009fJ per operation, a sufficiently low value compared with those of other programmable photosensors14,17,19.\n\nWhen performing the weight update in an open-loop manner, linear and symmetric modulation of multilevel photoresponsivities is preferred. To investigate it, long-term potentiation and depression (LTP and LTD, respectively) characteristics of the FE-PS are measured by employing an amplitude-increasing pulse scheme. Specifically, 21 positive pulses (amplitude: from 1.1 to 2.1\u2009V in increments of 0.05\u2009V; width: 3\u2009ms) and 21 negative pulses (amplitude: from \u20131.1 to \u20132.1\u2009V in decrements of \u20130.05\u2009V; width: 3\u2009ms) are applied successively to modulate the photoresponsivity. The use of a relatively large pulse width is due to the limitation of the pulse generator used in this measurement (see \u201cMethods\u201d). At this pulse width, the applied pulse voltages are around the coercive voltages (Supplementary Fig.\u00a0S14), enabling them to switch the polarization and associated photoresponsivity. As shown in Fig.\u00a03f, the photoresponsivity reduces from 0.56 to \u20130.56\u2009mA/W with the positive pulses, and then increases back to 0.56\u2009mA/W with the negative pulses. Such LTD and LTP processes are repeatable. Each LTP or LTD process contains 21 distinct photoresponsivity states, confirming the FE-PS\u2019s capability to store multi-bit weights (>4 bits). In fact, such a weight number is relatively moderate when compared to those reported previously14,19,22. To increase the weight number, a viable solution is to design a pulse scheme that enables a more gradual modulation of photoresponsivity, which warrants further investigation. In addition, the modulation of photoresponsivity is observed to be symmetric yet nonlinear, which is well associated with the symmetric, nonlinear polarization switching (Fig.\u00a02c). The nonlinearity in photoresponsivity modulation can deviate a weight from its target value when updating the weight in an open-loop manner. To address this issue, one approach is to enhance the linearity of polarization switching through domain engineering46, a topic that warrants further exploration. Alternatively, a closed-loop programming scheme30,47 can also be employed as a solution, as demonstrated later in this study. Besides, this scheme can also alleviate the adverse impacts of C2C and D2D variations on the precision of weight update, although the C2C and D2D variations of the FE-PS are quite small (only ~0.66% and ~2.72%, respectively; see Supplementary Fig.\u00a0S15).\n\nFigure\u00a03g summarizes the performance of our FE-PS in comparison to other programmable photosensors for in-sensor computing, with a more detailed summary presented in Supplementary Table\u00a0S1. It is shown that our FE-PS is one of the few self-powered devices showing simultaneously linear Iph-Ilight relationship and fast photoresponse. Furthermore, our FE-PS exhibits significantly longer retention, higher endurance, faster write speed, and smaller C2C and D2D variations compared to other programmable photosensors, particularly those used for in situ training9,11. Such superior performance of our FE-PS can mainly be attributed to its distinctive operation mechanism and high-quality epitaxial PZT film. While other programmable photosensors mainly rely on a volatile gating effect5,9,10,11,12,48 or a kinetically slow defect migration effect13,49 to tune photoresponsivity, our FE-PS operates through the polarization control of photoresponsivity. The photo-excited charge carriers in the FE-PS are separated by a polarization-induced asymmetric potential that is electrically switchable. This intrinsic process, without involving defect-mediated effects such as charge trapping/detrapping, allows a fast and self-powered photoresponse as well as a linear Iph-Ilight relationship. In addition, the high-quality epitaxial PZT film offers large nonvolatile polarization and excellent polarization switching properties for the FE-PS. As the polarization is the control knob of the photoresponsivity, our FE-PS could thus exhibit exceptionally long retention, high endurance, and fast write speed. These results demonstrate that our FE-PS is a competitive candidate for constructing an in-sensor ANN with in situ training capability.\n\nBesides the performance of FE-PS, the programming scheme is also important for the implementation of in situ training. In the previous studies reporting the in situ training of in-sensor ANNs9,11, only an open-loop (OL) programming scheme was used for weight update. In contrast, this study aims to explore the optimal programming scheme, and hence 3 different programming schemes: OL, uni-directional closed-loop (UD-CL), and BD-CL, are comparatively investigated. Figure\u00a04a shows the schematic of the OL scheme, where write pulses are applied to the device without verifying its photoresponsivity. However, both the BD-CL and UD-CL schemes apply a read operation after each write pulse to verify the device\u2019s photoresponsivity (see Fig.\u00a04b, c, respectively). The main difference between the BD-CL and UD-CL schemes is the polarity of write pulses. In the UD-CL scheme, write pulses with the same polarity are applied to the device until its photoresponsivity reaches the target value within a certain margin of error. If the photoresponsivity exceeds the target value, known as being over-written, a refresh pulse is applied to re-initialize the photoresponsivity, followed by the repetition of unipolar write pulses (Fig.\u00a04c). By contrast, in the BD-CL scheme, write pulses with an opposite polarity are applied to correct any over-written photoresponsivity and bring it back towards the target value (Fig.\u00a04b). The detailed flows of these programming schemes and their implementation platform are shown in Supplementary Figs.\u00a0S16 and S17, respectively.\n\nSchematics illustrating the modulation of photoresponsivity using a OL, b BD-CL, and c UD-CL programming schemes. d Measured photoresponsivities of the FE-PS after programming into 3 target photoresponsivities (from top to bottom: 0.267, 0, and \u20130.267\u2009mA/W, respectively, as indicated by the dotted lines) using different programming schemes. e Numbers of pulses used to achieve the target photoresponsivities in (d) for different programming schemes. In (d, e), the reported photoresponsivity and pulse number values are averaged from 20 independent tests. The error bars in (d) indicate standard deviations. f Distribution histogram of 19 photoresponsivity states statistically obtained by repeatedly writing the device for 20 times\u00a0(20 data points per one state per one time) using the BD-CL programming scheme. g Retention behavior of the 19 photoresponsivity states in (f).\n\nThe above 3 programming schemes are compared in terms of programming precision and pulse consumption. A high programming precision is beneficial for achieving training convergence, while a low pulse consumption can help to save the time and energy costs in the training. For a fair comparison, the FE-PS is initialized at the same photoresponsivity of 0.56\u2009mA/W. After the initialization, the device is programmed into 3 target photoresponsivities: 0.267, 0, and \u20130.267\u2009mA/W, by using the 3 programming schemes. For each programming scheme, the initialization and programming are repeated for 20 times. Figure\u00a04d shows that the UD-CL and BD-CL schemes achieve much lower discrepancies between actual and target photoresponsivities, i.e., higher programming precisions, compared to the OL scheme. This improvement is attributed to the use of verification with a small standard deviation margin (2.5%) in the CL schemes, which is absent in the OL scheme. This in turn confirms that the CL schemes can well address the nonlinearity in photoresponsivity modulation as well as C2C and D2D variations. On the other hand, as depicted in Fig.\u00a04e, the pulse number increases as the programming scheme varies from OL to BD-CL and UD-CL. The lowest pulse consumption in the OL scheme is due to the absence of verification, and the combined use of verification and refresh causes the highest pulse consumption in the UD-CL scheme. These results demonstrate that the BD-CL scheme achieves the best tradeoff between programming precision and pulse consumption. In other words, the BD-CL scheme can realize a precise and efficient weight update for the FE-PS, and hence it is used hereafter for further study.\n\n19 photoresponsivity states in the range of \u20130.48 to 0.48\u2009mA/W are repeatedly written for 20 times by using the BD-CL programming scheme. A standard deviation margin of 2.5% is used for the photoresponsivity verification. Figure\u00a04f presents the distribution histogram of the 19 photoresponsivity states, which are well separated from each other without any overlap. In addition, these photoresponsivity states are demonstrated to be nonvolatile (Fig.\u00a04g). These results further showcase the efficacy of the BD-CL programming scheme in attaining multi-bit weights in the FE-PS.\n\nThe availability of both the high-performance FE-PSs and the BD-CL programming scheme allows the construction of an FE-PS-NET with in situ training capability. To experimentally demonstrate it, 4 individual FE-PSs are connected in parallel to form a 4\u2009\u00d7\u20091 FE-PS-NET, which acts as a \u201cvisual system\u201d of a prototype autonomous vehicle, as shown in Fig.\u00a05a. The photographs of the FE-PS-NET and its peripheries in the vehicle are shown in Supplementary Fig.\u00a0S18. The FE-PS-NET is trained in situ to learn the real-time recognition of traffic signs. The recognition result produced by the FE-PS-NET is directly sent to the motor system of the vehicle to control its movement. 4 traffic signs (2\u2009\u00d7\u20092 pixels) are used for training, which represent the 4 commands: \u201cgo\u201d, \u201cstop\u201d, \u201cturn left\u201d, and \u201cturn right\u201d, respectively (Fig.\u00a05b). These traffic signs are temporarily implemented by a 2\u2009\u00d7\u20092 array of UV light-emitting diodes (LEDs), with one LED (i.e., one pixel) focusing on one FE-PS. The pixel values of \u201c1\u201d and \u201c0\u201d correspond to the optical powers of 50 and 0\u2009mW/cm2, respectively.\n\na Photography of the vehicle, along with schematic circuit diagrams. The output current of the FE-PS-NET is fed to the neuron unit, and then sent to the motor system of the vehicle. b Four traffic signs used for training and test. c Flowchart of the in situ training of the FE-PS-NET. The operations in the light blue boxes are implemented in hardware, while those in the orange boxes are temporarily implemented in software on a PC and can be implemented by on-chip integrated circuits in the future. Evolutions of d MSE, e weights, and f normalized Iouts for different input traffic signs with the training epoch.\n\nFigure\u00a05c depicts the flowchart of the in situ training, which mainly involves two processes: inference and weight update. In the inference process, a traffic sign is projected onto the FE-PS-NET. Each FE-PS in a pixel produces a photocurrent (or zero photocurrent) by multiplying its photoresponsivity by the optical power in this pixel, as shown in Fig.\u00a05a. Simultaneously, the photocurrents of all the FE-PSs are summed based on the Kirchhoff\u2019s law. The output current Iout is therefore given by\n\nwhere Rn is the weight (i.e., photoresponsivity) at the n-th pixel, Pn is the input optical power at the n-th pixel, and N is the number of pixels. Equation (1) indicates that the FE-PS-NET can implement an in-sensor multiply-accumulate (MAC) operation, which is the basis for the real-time image sensing and recognition. The Iout is subsequently sent to a neuron unit comprising an amplifying circuit and a comparison circuit, as shown in Fig.\u00a05a. The neuron unit converts the Iout to a voltage signal Vout, which is further represented by one of\u00a04 levels: \u20130.75, \u20130.25, +0.25, and +0.75\u2009V\u00a0through voltage comparison. These 4 voltage levels represent the recognition results of \u201cturn right\u201d, \u201cstop\u201d, \u201cgo\u201d, and \u201cturn left\u201d, respectively.\n\nThe goal of the training is to minimize the discrepancy between the recognition results and the true labels of the traffic signs, which is measured by a mean square error (MSE) cost function implemented in software:\n\nwhere yi is the output after feeding the dimensionless value of the Vout for the i-th input image to a tanh activation function, \u0177i is the true label of the i-th input image, and M is the number of input images. To minimize the cost function, the weights need to be updated to their optimal values. We use the gradient descent algorithm to guide the weight update. In this algorithm, the target weights are calculated in software based on the gradients of the cost function:\n\nwhere the sign \u201c:=\u201d denotes assigning the value on the right side to the left side and \u03b1 is learning rate. Next, the photoresponsivity of each FE-PS is experimentally adjusted to its corresponding target weight by using the BD-CL programming scheme. At this stage, one epoch of training is completed. With the platform shown in Supplementary Fig.\u00a0S17, the training can proceed automatically for multiple epochs until the network converges.\n\nFigure\u00a05d shows the evolution of the MSE during the in situ training of the FE-PS-NET. It is seen that the MSE decreases with the training epoch and becomes minimized after 10 epochs. The decrease in MSE is attributed to the smooth convergence of the weights, as presented in Fig.\u00a05e. Notably, the training convergence behavior exhibited by the FE-PS-NET is well consistent with that of a software-based ANN (see comparison between the solid and dotted lines in Fig.\u00a05d, e), confirming the capability of the FE-PS-NET to implement the in situ training. Figure\u00a05f displays the output currents of the FE-PS-NET for the 4 traffic signs at different training epochs. At Epoch #0, all the output currents fall into the range corresponding to the \u201cstop\u201d sign, indicating that the FE-PS-NET can only recognize the \u201cstop\u201d sign. As the training proceeds, more traffic signs are correctly recognized. The 100% recognition accuracy is achieved at Epoch #10.\n\nThe trained FE-PS-NET is then used for the test with the 4 traffic signs same as those used in the training. Figure\u00a06a shows the navigation of the vehicle based on the real-time recognition results from the trained FE-PS-NET. The vehicle takes correct actions in response to all the traffic signs, well attributed to the accurate recognition provided by the trained FE-PS-NET. The behavior of the vehicle at the different training levels of the FE-PS-NET can be found in Supplementary Movie\u00a01.\n\na Photographs of the vehicle when executing different motions (top) along with the corresponding Iouts of the FE-PS-NET (bottom). b Long-term stability of the Iouts of the FE-PS-NET. c Recognition accuracies of the FE-PS-NET at different noise levels (noise level refers to the standard deviation of the Gaussian noise). d Schematic architecture of the FE-PS-NET (left) and its inference speed (right). e Schematic architecture of a von Neumann machine vision system (left) and its inference speed (right).\n\nWe further investigate the reliability of the trained FE-PS-NET. Figure\u00a06b demonstrates that the trained FE-PS-NET retains 100% recognition accuracy after 50 days. Introducing noises into the test traffic signs (Supplementary Fig.\u00a0S19) results in lower accuracies for the trained FE-PS-NET (Fig.\u00a06c). However, it is worth noting that the accuracy at each noise level remains close to its corresponding theoretical upper limit (see comparison between the solid and dotted lines in Fig.\u00a06c). These results underscore the high reliability of the trained FE-PS-NET, which is well attributed to the nonvolatility of the programmed weights.\n\nThe weight nonvolatility also allows the FE-PS-NET to store the weights locally without the need for an external memory. Consequently, the FE-PS-NET can essentially integrate the sensing, memory, and processing functions. More importantly, it can function as an in-sensor ANN capable of performing high-level image processing with boosted speed and energy efficiency. The inference speed of the FE-PS-NET is expected to be high as the image sensing and processing occurs simultaneously in the FE-PS-NET. Figure\u00a06d shows the rising and falling delay times of the output current of the FE-PS-NET, both of which are as short as 12\u2009\u03bcs. The rising (falling) delay time is defined as the time lag between the 50% points of the rising (falling) edges of the input voltage (applied to the LED) and the output current. It should be noted that the measured delay times are indeed limited by the amplifying circuit used for measurement, similar to the issue encountered when measuring the photoresponse time of a single FE-PS. Nevertheless, the inference speed of the FE-PS-NET is still 50 times faster than that of a von Neumann machine vision system consisting of grayscale sensors and a microcontroller unit (MCU) (see comparison between Fig.\u00a06d, e). Besides the high inference speed, the FE-PS-NET (excluding peripheries like the neuron unit) also exhibits zero energy consumption for inference because its constituent FE-PSs operate in the self-powered photovoltaic mode.\n\nThe speed and energy consumption of the FE-PS-NET for training are currently limited by the minimum pulse width (i.e., 3\u2009ms) available in the in situ training experiment (see \u201cMethods\u201d). However, in the specific write speed and energy tests, the FE-PS demonstrates a fast write speed (100\u2009ns; see Fig.\u00a03e) and a potentially low write energy (~56 fJ per operation assuming device downscaling; see Supplementary Fig.\u00a0S13), suggesting that the FE-PS-NET has the potential for achieving high training speed and energy efficiency. In addition, using on-chip integrated circuits to implement the operations which are temporarily realized by software in the in situ training process (Fig.\u00a05c) could further boost the training speed and energy efficiency.\n\nAlthough the FE-PS-NET demonstrated here is a small-scale network, it has the potential to be scaled up due to several factors. First, it has been demonstrated that the FE-PS could exhibit tunable photoresponsivity when downscaled to ~1\u2009\u03bcm2\u00a045,. Further downscaling the FE-PS is viable because the commercial ferroelectric capacitor with the same device structure has been fabricated using the standard 130-nm complementary metal-oxide semiconductor (CMOS) process50. In addition, other merits of the FE-PS-NET, such as the local weight storage and the representation of both positive and negative weights in a single device, can substantially reduce the hardware overhead when scaling up the network. Therefore, the construction of a large-scale FE-PS-NET with a high area efficiency appears to be technologically feasible. Note that the large-scale FE-PS-NET empowered by the in situ training capability is competent for handling complicated machine vision tasks (see Supplementary Fig.\u00a0S20 for demonstration). Besides the large-scale network construction, both the flexible design51 of the FE-PS-NET and its integration with memristor-based neurons52 are topics of great interest for future research.",
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "In summary, we have experimentally demonstrated the in situ training of the FE-PS-NET using the BD-CL programming scheme. It is first shown that the building block of the FE-PS-NET, i.e., the Pt/PZT/SRO FE-PS, exhibits symmetrically switchable photovoltaic responses as controlled by the remanent polarization. Then, several key performance metrics related to both the inference and weight update in the in situ training are investigated for the FE-PS. In particular, the FE-PS displays self-powered, fast (<30 \u03bcs) and multilevel (>4 bits) photoresponses, as well as linear dependency of photocurrent on light intensity, long retention (50 days), high endurance (109), high write speed (100\u2009ns), and small C2C and D2D variations (~0.66% and ~2.72%, respectively). Next, several programming schemes are designed to implement the weight update in the FE-PS. Among them, the BD-CL programming scheme achieves the best tradeoff between programming precision and pulse consumption. Thanks to the high-performance FE-PSs and the BD-CL programming scheme, the in situ training of the FE-PS-NET to recognize traffic signs for commanding a prototype autonomous vehicle is successfully implemented. Besides, the trained FE-PS-NET also shows high reliability (retaining 100% recognition accuracy for up to 50 days), high inference speed (50 times faster than a von Neumann machine vision system), and zero energy consumption for inference (excluding contributions from peripheries). Our study marks a significant advancement in the development of in-sensor computing systems with in situ training capability, which may be particularly useful for handling new data-streaming machine vision tasks.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "Approximately 120\u2009nm PZT thin films together with ~40\u2009nm SRO bottom electrodes were epitaxially grown on STO (001) substrates by pulsed laser deposition (PLD). The PLD system mainly comprised a KrF excimer laser source (\u03bb\u2009=\u2009248\u2009nm) (Coherent COMPexPro 205) and a customized chamber. Different laser energy fluences of 0.90\u2009J/cm2 and 0.97\u2009J/cm2 were used for the deposition of PZT and SRO films, respectively, while the repetition rates were kept the same at 5\u2009Hz. The SRO films were first deposited at a substrate temperature\u00a0of 680\u2009\u00b0C under an oxygen pressure of 15\u2009Pa, followed by the deposition of PZT films at a lower substrate temperature of 620\u2009\u00b0C under the same oxygen pressure. Subsequently, the PZT/SRO films were cooled to room temperature at a 10\u2009\u00b0C/min cooling rate under 1000\u2009Pa oxygen pressure. Then, Pt top electrodes (thickness: ~10\u2009nm; diameter: ~200\u2009\u03bcm) were ex situ deposited on the PZT/SRO films through a shadow mask by sputtering at room temperature and under vacuum. The resulting Pt/PZT/SRO capacitor-like heterostructures are the desired FE-PSs. To construct a FE-PS-NET, multiple FE-PSs were connected to a test board containing pre-fabricated interconnections between different cells (Supplementary Fig.\u00a0S17).\n\nThe phases and crystalline structures of the fabricated films were characterized by XRD (\u201cX\u201d Pert PRO, PANalytical). The microstructures were further investigated using TEM (Tecnai G2-F20). AFM and PFM studies were carried out on an integrated scanning probe microscope (Asylum Research MFP-3D) with Pt-coated silicon tips (Nanoworld EFM Arrow). The PFM images and hysteresis loops were measured by using an AC driving voltage of 0.8\u2009V in the dual a.c. resonance tracking (DART) mode.\n\nFerroelectric P\u2013V hysteresis loops were recorded using a ferroelectric workstation (Radiant Precision Multiferroic). DC I\u2013V characteristics and low-speed photoresponses were measured with a Keithley 6430 SourceMeter. High-speed photoresponses were measured by using a combination of an amplifying circuit and an oscilloscope (LeCory 64Xi-A). In all the photoresponse measurements, optical inputs were supplied by 365\u2009nm UV LEDs with tunable light intensities. Voltage pulses were applied to tune the polarization and associated photoresponsivity; however, different pulse generators were used in different experiments. When performing the LTP/LTD measurement and implementing various programming schemes, the pulses (minimum width: 3\u2009ms) were provided by 12-bit digital-to-analog converters (DACs) as controlled by an STM32 MCU. In the measurements of endurance and write speed, a function generator (Agilent 33250\u2009A) was used to generate the pulses (minimum width: 10\u2009ns). In the rest cases, the ferroelectric workstation was used as the pulse generator.\n\nThe setup for the in situ training of the FE-PS-NET is shown in Supplementary Fig.\u00a0S17. It mainly included a test board carrying the FE-PS-NET, an amplifying circuit, an STM32 MCU, a 8-channel 16-bit analog-to-digital converter (ADC), 12-bit DACs, and a personal computer (PC). When performing the training, as shown in Fig.\u00a05c, the FE-PS-NET produced a photocurrent upon the application of illumination. Subsequently, the current signal was sent to the amplifying circuit for current\u2013voltage conversion and amplification. The resulting voltage signal was further directed to the ADC, the MCU, and ultimately the PC. The PC implemented the gradient descent algorithm, and relayed the result back to the MCU. Communication between the PC and MCU was conducted via a universal asynchronous receiver/transmitter (UART). The MCU then instructed the DACs to apply pulses to the FE-PS-NET to update the weights. To realize a precise weight update, closed-loop programming schemes were executed (Supplementary Fig.\u00a0S16). The training proceeded automatically for multiple epochs until the network became converged.\n\nThe FE-PS-NET trained at different levels was used to perform the real-time image recognition for a prototype autonomous vehicle. Besides the FE-PS-NET, the vehicle also comprised a motor system, a STM32 MCU, 2 batteries, and a vehicle body (Supplementary Fig.\u00a0S18). The real-time recognition result of the FE-PS-NET was directly sent to the motor system of the vehicle to control its movements, while the MCU was used only for controlling LEDs.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The data that support the findings of this study are available in the article and the Supplementary Information. Additional data related to this study can be requested from the corresponding authors.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "All the codes that support the findings of this study are available from the corresponding authors upon request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "The authors would like to thank the National Key Research and Development Programs of China (Grant No. 2022YFB3807603), the National Natural Science Foundation of China (Grant Nos. 92163210 and 52172143), the Science and Technology Projects in Guangzhou (Grant Nos. 202201000008 and 2022A04J00031), and the Guangdong Natural Science Funds for Distinguished Young Scholar (Grant No. 2024B1515020053).",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Haipeng Lin, Jiali Ou.\n\nInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China\n\nHaipeng Lin,\u00a0Jiali Ou,\u00a0Zhen Fan,\u00a0Wenjie Hu,\u00a0Boyuan Cui,\u00a0Wenjie Li,\u00a0Zhiwei Chen,\u00a0Kun Liu,\u00a0Linyuan Mo,\u00a0Meixia Li,\u00a0Xubing Lu,\u00a0Xingsen Gao\u00a0&\u00a0Jun-Ming Liu\n\nKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei Key Laboratory of Photo-Electricity Information and Materials, Hebei University, Baoding, China\n\nXiaobing Yan,\u00a0Jikang Xu\u00a0&\u00a0Biao Yang\n\nNational Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou, China\n\nGuofu Zhou\n\nLaboratory of Solid State Microstructures and Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China\n\nJun-Ming Liu\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nZ.F. conceived the research. Z.F. and X.Y. supervised the project. H.L., J.O., and B.C. prepared the devices. H.L., J.O., W.H., W.L., G.Z., and X.G. performed the XRD, TEM, and PFM characterizations. H.L., J.O., J.X., Z.C., B.Y., K.L., L.M., M.L., X.L., and G.Z. conducted the electrical measurements. H.L. and J.O. carried out the simulations. H.L., J.O., Z.F., X.Y., X.G., and J.-M.L. wrote and revised the manuscript.\n\nCorrespondence to\n                Zhen Fan or Xiaobing Yan.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Ming Wang, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
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+        {
+            "section_name": "About this article",
+            "section_text": "Lin, H., Ou, J., Fan, Z. et al. In situ training of an in-sensor artificial neural network based on ferroelectric photosensors.\n                    Nat Commun 16, 421 (2025). https://doi.org/10.1038/s41467-024-55508-z\n\nDownload citation\n\nReceived: 23 July 2024\n\nAccepted: 11 December 2024\n\nPublished: 07 January 2025\n\nVersion of record: 07 January 2025\n\nDOI: https://doi.org/10.1038/s41467-024-55508-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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+        {
+            "section_name": "Associated content",
+            "section_text": "Focus",
+            "section_image": []
+        }
+    ]
+}

1937f6db2acf9b19fa459af2d346c13087288b6309d1e307bb84c4357db8c669/metadata.json

@@ -0,0 +1,120 @@
+{
+    "title": "Evidence of electron interaction with an unidentified bosonic mode in superconductor CsCa2Fe4As4F2",
+    "pre_title": "Evidence of electron interaction with an unidentified bosonic mode in superconductor CsCa2Fe4As4F2",
+    "journal": "Nature Communications",
+    "published": "31 July 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50833-9/MediaObjects/41467_2024_50833_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-50833-9/MediaObjects/41467_2024_50833_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [],
+    "code": [],
+    "subject": [
+        "Electronic properties and materials",
+        "Superconducting properties and materials"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3916454/v1.pdf?c=1722510844000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3916454/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-50833-9.pdf",
+    "preprint_posted": "05 Feb, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Using angle-resolved photoemission spectroscopy, we report the evidence of the observation of a kink structure in the band dispersion of Fe-based superconductor CsCa2Fe4As4F2. The kink shows an orbital selective and momentum dependent behavior, which is located at 15 meV below Fermi level along the \u0393 - M direction at the band with dxz orbital character and vanishes when approaching the \u0393 - X direction, correlated with a slight decrease of the superconducting gap. Most importantly, this kink structure disappears when the superconducting gap closes, indicating that the corresponding bosonic mode (~ 9 \u00b1 1 meV) is closely related to superconductivity. However, the origin of this mode remains unidentified, since it cannot be related to phonons or the spin resonance mode (~ 15 meV) observed by inelastic neutron scattering. The behavior of this mode is rather unique and challenges our present understanding of the superconducting paring mechanism of the bilayer FeAs-based superconductors.Physical sciences/Physics/Condensed-matter physics/Electronic properties and materialsPhysical sciences/Physics/Condensed-matter physics/Superconducting properties and materials",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "CsCa2Fe4As4F2SupplementarymaterialsNC2.docx",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "The kink structure in band dispersion usually refers to a certain electron-boson interaction, which is crucial in understanding the pairing in unconventional superconductors. Here we report the evidence of the observation of a kink structure in Fe-based superconductor CsCa2Fe4As4F2 using angle-resolved photoemission spectroscopy. The kink shows an orbital selective and momentum dependent behavior, which is located at 15\u2009meV below Fermi level along the \u0393\u2212M direction at the band with dxz orbital character and vanishes when approaching the \u0393\u2212X direction, correlated with a slight decrease of the superconducting gap. Most importantly, this kink structure disappears when the superconducting gap closes, indicating that the corresponding bosonic mode (~9\u00b11 meV) is closely related to superconductivity. However, the origin of this mode remains unidentified, since it cannot be related to phonons or the spin resonance mode (~15\u2009meV) observed by inelastic neutron scattering. The behavior of this mode is rather unique and challenges our present understanding of the superconducting paring mechanism of the bilayer FeAs-based superconductors.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Electron-boson coupling is the cornerstone for pairing in superconductors. In Bardeen\u2013Cooper\u2013Schrieffer theory for conventional superconductors, phonons are identified as the pairing glue. While for unconventional superconductors, such as cuprates and Fe-based superconductors, antiferromagnetic (AFM) magnons or spin excitations are often considered as the potential pairing bosonic mode. Particularly, spin resonance mode observed by inelastic neutron scattering (INS) correlates with superconductivity. For example, it emerges below the superconducting transition temperature (TC), its energy (ER) correlates with TC and its momentum connects different parts of the Fermi surface1,2,3,4,5,6,7,8,9,10,11,12. The resonance energy ER approximately follows an empirical ratio ER/2\u0394\u223c0.64, where \u0394 is the superconducting gap3,13,14. The observation of a smaller ER compared to 2\u0394 provides evidence for the existence of a spin exciton. On the other hand, numerous experiments reported the evidence of electron-phonon coupling in these materials15,16,17,18,19,20,21,22,23,24,25, and other proposals of pairing glue such as orbital fluctuations26,27,28, add more complexity to the investigation of unconventional superconductivity.\n\nRecently, a stochiometric Fe-based superconductor, ACa2Fe4As4F2 (A\u2009=\u2009K, Rb, Cs)29, has attracted attention due to their multiple-layer structures and a high TC of around 30\u2009K. However, its pairing symmetry remains controversial. Several muon-spin relaxation (\u03bcsR) measurements30,31,32 suggested the possible nodal superconducting gaps, while other methods indicated the nodeless gap feature33,34,35,36,37,38. Interestingly, INS experiments reported resonance peaks with energies even higher than 2\u0394 which cannot be understood by the spin exciton picture13,39. This sets them apart from other Fe-based superconductors, and the presence of such atypical resonance peaks appears to challenge the AFM spin fluctuation scenario, sparking renewed interest in their pairing mechanisms.\n\nIn this Letter, we present a comprehensive angle-resolved photoemission spectroscopy (ARPES) investigation of the low-energy electronic structure of CsCa2Fe4As4F2 (TC\u2009~\u200929\u2009K). Our findings reveal a pronounced kink structure that is strongly correlated with superconductivity. It appears at a binding energy (EB) of 15\u2009meV along the \u0393\u2212M direction exclusively in the \u03b1 band (dxz), and disappears above the superconducting gap closing temperature. The kink exhibits strong angular dependent behavior, and diminishes in the \u0393\u2212X direction, giving a dramatic change in the electron-boson coupling strength (\u03bbe\u2212b). Additionally, we observe that the anisotropic superconducting gap of the \u03b1 band closely follows the anisotropic behavior of this kink. Based on the size of the corresponding superconducting gap and kink energy, we propose a bosonic mode with an energy of around 9\u2009meV which is highly related to superconductivity in this bilayer system. However, unlike the kink observed in Ba0.6K0.4Fe2As240, the kink in CsCa2Fe4As4F2 cannot be directly attributed to the reported spin resonance mode and its origin remains mysterious.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results and discussion",
+            "section_text": "The crystal structure of CsCa2Fe4As4F2, depicted in Fig.\u00a01a, can be regarded as a composite of CaFeAsF and KFe2As2. It exhibits a total doping level of 0.25 hole/Fe due to the self-doping effect29. Figure\u00a01b\u2013d show the basic electronic structure of the sample. The measured Fermi surface using 50\u2009eV photons at 10\u2009K is presented in Fig.\u00a01b. Around the \u0393 point, there are three hole pockets (\u03b1, \u03b2 and \u03b3) while four hot spot-like pockets (\u03b4) surround the M point, more details could be found in Supplementary Fig.\u00a01 and Supplementary Fig.\u00a02. One should notice that as a bilayer system, there should be two sets of Fe orbitals, and we neglect this in our discussion hereafter due to the fact that the interlayer coupling is weak in this system and the splitting of these bands is beyond our experimental resolution35. The quasi two-dimensional electronic structure of this compound is confirmed by kz-dependent measurements in Supplementary Fig.\u00a03, which is consistent with previous transport measurements where there was huge resistivity anisotropy of \u03c1c(T)/\u03c1ab(T) up to 10341. In addition to the primary bands, there exists a weak large pocket around the M point, as indicated by the white dashed curves. This is believed to be the folded \u03b3 band from the \u0393 point (black solid circle) due to surface reconstruction, similar to other alkali metal intercalated Fe-based superconductors42,43, indicating most likely the measured samples are with Cs terminated surface. Both linear-horizontal (LH) and linear-vertical (LV) polarizations are utilized to detect the orbital characters of these bands, as shown in Fig.\u00a01b, c. The LV polarization exhibits a stronger intensity of the \u03b1 band, indicating its dxz orbital nature44. The \u03b2 and \u03b3 bands around \u0393 are formed by dyz and dxy orbitals, respectively, while the \u03b4 and \u03b5 bands at the M point consist of dyz and dxy orbitals, respectively. Notably, a distinct kink structure is observed in the \u03b1 band well below TC as depicted in Fig.\u00a01e, located at 15\u2009meV below the Fermi level. This kink anomaly is distinct from a Bogoliubov bending band since the energy positions differs (Supplementary Fig.\u00a04) and it is also confirmed by our scanning tunneling microscopy (STM) result in Supplementary Fig.\u00a05. More details about the kink will be discussed in the later.\n\na The crystal structure of CsCa2Fe4As4F2. b, c ARPES spectra along the \u0393-\u039c direction by using both LH and LV polarized 60\u2009eV photons. The bands are denoted by \u03b1, \u03b2, \u03b3, \u03b4 and \u03b5. d Fermi surface map obtained at 10\u2009K using 50\u2009eV photons, superposed with 2Fe-Brillouin zone (BZ) boundary (yellow solid lines) and high symmetry points (black dots). e Kink structure on the \u03b1 band measured with 7\u2009eV laser. The black arrow indicates the kink position and the black dashed line indicates the bare band in normal state.\n\nFigure\u00a02 displays the superconducting gaps and their angular dependence across different bands. The symmetrized spectra along the \u0393\u2212M direction measured at 7\u2009K shown in Fig.\u00a02a,b reveal a distinct gap opening at \u03b1, \u03b2 and \u03b4 bands. It should be noted that the \u03b3 band may exhibit a tiny gap beyond energy resolution. The superconducting gap values (\u0394) for the\u03b1, \u03b2 and \u03b4 bands are 6\u2009meV, 6\u2009meV, and 5.5\u2009meV, respectively (Fig.\u00a02c and Supplementary Fig.\u00a06), determined from the fitting of the symmetrized energy distribution curves (EDCs) in a BCS-based phenomenological model45. The temperature evolution of these bands can be found in Supplementary Fig.\u00a07. Figure\u00a02d,e present momentum distributions of the symmetrized EDCs for the \u03b1 and \u03b2 bands. The extracted gap values reveal a slight anisotropy in the \u03b1 band, with the gap size gradually decreasing from 6\u2009meV in the \u0393\u2212M direction to 4\u2009meV in the \u0393\u2212X direction as shown in Fig.\u00a02f. However, the \u03b2 band is nearly isotropic, with a constant gap size of 6\u2009meV as shown in Fig.\u00a02g. More details can be found in Supplementary Fig.\u00a08.\n\na Symmetrized ARPES spectrum along the \u0393-\u039c direction measured at 7\u2009K with 23\u2009eV photons, where \u03b1, \u03b2 and \u03b3 denote distinct bands. The color bar shows the ARPES spectra intensity. b Symmetrized ARPES spectrum of the \u03b4 band measured near the M point along the \u0393-\u039c direction measured at 7\u2009K with 50\u2009eV photons. c Symmetrized EDCs of the \u03b1 (7\u2009eV, blue curve), \u03b2 (7\u2009eV, green curve) and \u03b4 (50\u2009eV, black curve) bands. The red lines are fits using phenomenological model. Their superconducting gap sizes are annotated above. d, e Symmetrized EDCs of the points noted in the insets of the \u03b1, \u03b2 pockets, respectively. f Distribution of the in-plane superconducting gap of \u03b1 (upper panel), \u03b2 (lower panel) pockets, respectively. The red and black arrows indicate the \u0393-\u039c and \u0393-\u03a7 directions, respectively. The error bars reflect the uncertainty in determining the gap sizes.\n\nThe kink feature is exclusively observed at the \u03b1 band and strongly coupled with superconducting phase (Fig.\u00a03). The criterion of the existence of kink is that both the real (Re\u2211) and imaginary part (Im\u2211) of the self-energy (\u2211) appear anomalies around the kink energy. When the superconducting gap closes, both the raw spectrum (the lower two panels of Fig.\u00a03a) and the extracted peak positions (Fig.\u00a03d) indicate an absence of this kink, where we use multiple Lorentzian peaks to present the fitted bands as shown in Supplementary Fig.\u00a09. In the light of the extracted momentum distribution curves (MDCs) for the \u03b1 band at 7\u2009K (Fig.\u00a03b), we have identified the kink position located at 15\u2009meV below EF highlighted by the blue curve, where there is a dramatic change in slope. The self-energy analysis of the \u03b1 band is shown in Fig.\u00a03c. Both the real and imaginary parts of the self-energy exhibit anomalies around this energy, and the Re\u2211 displays a relatively broad feature (ranging from 6\u2009meV to 35\u2009meV) with a maximum at 15\u2009meV. Such a broad energy span is peculiar and intriguing (the data can be repeated on another sample in Supplementary Fig.\u00a010). The Kramers-Kronig (KK) transformation analysis is shown in Supplementary Fig.\u00a011, which presents that both Re\u2211 and Im\u2211 match well with each other under the KK transformation, indicating the self-consistency of the extracted self-energies. The strength of electron-boson coupling (\u03bbe\u2212b) of the kink can be determined by analyzing the slope change in both bare and renormalized band dispersions, where \u03bbe\u2212b is defined as:\n\na ARPES Spectra measured along the \u0393-\u039c direction at 7\u2009K, 35\u2009K and 65\u2009K. The presence of a kink in the \u03b1 band is indicated by the black arrow at 7\u2009K. The color bar indicates the photoemission intensity. b Corresponding MDCs of the \u03b1 band measured at 7\u2009K in panel a, where peak positions are marked with circles and the kink position is highlighted in blue. c The self-energy analysis of the kink reveals anomalies in both Re\u2211 (blue circle curve) and Im\u2211 (red circle curve) at an energy of approximately 15\u2009meV below the Fermi level. The color bar shows the ARPES spectra intensity. d The peak positions of the \u03b1 band, extracted from MDCs at various temperatures. e The corresponding electron-boson coupling strength \u03bbe\u2212b as a function of temperature. The red arrow marks the TC. The error bars reflect the uncertainty in determining the coupling strength.\n\nWe use the curve at 100\u2009K as the bare band, and the energy range of the renormalized band lies between the gap energy and the kink energy. The extracted \u03bbe\u2212b values gradually decrease with the increasing temperature, and eventually disappears above the superconducting gap closing temperature, as shown in Fig.\u00a03e. Details of the kink structure evolution with temperature can be found in Supplementary Figs.\u00a012\u201314. One could notice that the kink seems to persist slightly above the transition temperature 29\u2009K and vanishes around 35\u2009K, which is possible due to the existence of pseudogap in this material as reported by several other experiments46,47.\n\nWe further analyze the angular distribution of the kink in the \u03b1 band well below TC in Fig.\u00a04. Figures\u00a04a,d depict the representative high symmetry cuts along the \u0393\u2212X and \u0393\u2212M directions, respectively (more details can be found in Supplementary Fig.\u00a015). The angle offset from the \u0393\u2212M direction is defined as \u03b8. The kink structure is present along the \u0393\u2212M direction, while it is absent along the \u0393\u2212X direction. The self-energy analysis also reveals discernible distinctions between these two directions, as illustrated in Figs.\u00a04b,c. In contrast to the observable anomalous signals around 15\u2009meV in the \u0393\u2212M direction, no significant variation is observed in both Re\u2211 and Im\u2211 in the \u0393\u2212X direction. The peak positions of the \u03b1 band are extracted step by step from the \u0393\u2212M direction to the \u0393\u2212X direction with a 5-degree increment, as summarized in Fig.\u00a04c with momentum shifts. The dashed straight lines serve as guides for the kink positions at each curve and the determination of the kink position is in Supplementary Fig.\u00a016. Figure\u00a04e shows that both the kink energy position Ekink and the corresponding coupling strength \u03bbe\u2212b exhibit remarkable decreasing trends as \u03b8 increases. The kink vanishes beyond 25\u00b0, accompanied by a slight energy shift from 15\u2009meV at 0\u00b0 to 13\u2009meV at 25\u00b0. Notably, the coupling strength also dramatically decreases towards zero at the edge of 25\u00b0. In spectroscopic experiments, electron-boson interactions may give rise to low-energy anomalies near the Fermi level, appearing at energies of \u0394+\u03a9 in principle, where \u0394 represents the superconducting gap and \u03a9 denotes the corresponding bosonic energy. We extract the boson energies at various angles by subtracting the \u0394\u03b1 from Ekink. As shown in Fig.\u00a04f, the extracted boson energies are located at a constant value of 9\u00b11 meV. Therefore, it is reasonable to infer that the distribution of the electron-boson coupling correlates with the anisotropic superconducting gap of the \u03b1 band48.\n\na, d The ARPES spectra measured along the \u0393-\u03a7 direction with \u03b8=45\u2218 and the \u0393-\u039c direction with \u03b8=0\u2218, respectively. The color bar indicates the photoemission intensity. b Re\u03a3 and Im\u03a3 of the \u03b1 band for \u03b8=45\u2218 which show negligible anomaly. c In-plane dispersions of the \u03b1 band extracted from their corresponding MDC curves presented at 5\u2218 intervals from the \u0393-\u039c to the \u0393-\u03a7 directions, which are stacked with momentum offsets for a better illustration. e The kink energy positions Ekink and electron-boson coupling strength \u03bbe\u2212b, extracted from c, as a function of \u03b8 angles. They both exhibit suppression upon approaching the \u0393-\u03a7 direction. f A summary of the Ekink (red dots) and the superconducting gap \u0394\u03b1 (light blue dots) in the\u03b1 band. The kink-corresponding bosonic mode energies are presented as the black dots with a near constant energy of 9\u00b11 meV. The error bars reflect the uncertainty in determining the gap sizes and coupling strength.\n\nA common origin of such a kink in band dispersion is electron-phonon coupling, which renormalizes the Fermi velocity around the phonon energy \u03a949. For ACa2Fe4As4F2 (A\u2009=\u2009K, Rb, Cs), numerous phonon modes have been detected in various experiments34,46,50,51, and these modes were observed in both the normal and superconducting phases. In our ARPES experiment, however, the absence of kink behavior above TC exclude phonon as a candidate. The 9\u2009meV bosonic mode is directly associated with a mode that should only emerge when the superconducting condensation is ready, and several other experiments have also reported a potentially superconducting correlated bosonic mode in ACa2Fe4As4F236,38,52.\n\nThe spin resonance has been discovered to emerge in the superconducting states of cuprates and iron-based superconductors2,4,6,7,11,53,54,55,56,57,58,59,60. While the correlation between kink structures and the corresponding spin resonance modes have been widely reported in cuprates9,61,62,63, in which these kinks are located at the energy Ekink=\u0394+ER63, only a few Fe-based superconductors, such as Ba0.6K0.4Fe2As240 and (Sr/Ba)1-xKxFe2As264, were found to exhibit this correspondence. Notably, the kink discovered in CsCa2Fe4As4F2 which presents a broad distribution of Re\u2211 cannot be accounted for by the sharp spin resonance mode at 15\u2009meV observed by the neutron scattering13,39 as the ARPES determined kink corresponds to a boson energy of 9\u00b11meV. Therefore, we exclude the possibility of the reported spin resonance mode as being the physical origin of the kink. However, it is noteworthy that the Re\u2211 of the \u03b1 band presents a broad feature in energy where the spin resonance mode may exist within this broad spectrum, but with a limited impact.\n\nThese bilayer superconductors (ACa2Fe4As4F2) are quite unique among all iron-based superconductors. On one hand, it shows giant anisotropy between the in-plane and out-of-plane resistivity which is different from most iron pnictides and mimic cuprates41. On the other hand, the spin resonance modes possess a unique downward dispersion that is similar to cuprates in previous INS report, and its energy exceeds the limit of 2\u039413, challenging the conventional understanding of the resonance modes. Due to the similarities to cuprates, one possible candidate for causing such a broad Re\u2211 in energy may be the existence of paramagnon, which is broad in energy and only observed in numerous cuprates by the resonant inelastic soft X-ray scattering but not by neutron scattering65. Another possible mode that emerges in the superconducting state is Josephson plasmon, which only exists in the multiple-layered superconductors66,67,68,69. Further optical measurements on thick samples are needed to study the Josephson plasmon. If true, the correlation between momentum-dependent gap and \u03bbe\u2212b may suggest that the electron-Josephson plasmon can further facilitate pairing in addition to other correlation effects. One could notice that, similar to spin resonance mode and electron-magnon coupling in multi orbital system40,70, this mode either originated from paramagnon or Josephson plasmon also has a strong orbital selectivity here. Additionally, the extracted boson energy closely aligns with the spin gap energy (~ 10\u2009meV) of this compound13, suggesting that the spin gap might be also the possible candidate similar to the case in the overdoped Bi2Sr2CaCu2O8+\u03b471. Considering that the strong superconductivity correlated kink structures are rather rare in other iron-based superconductors, such a remarkable kink in band dispersion is a unique feature observed for the bilayer system.\n\nIn conclusion, our ARPES study of CsCa2Fe4As4F2 presents a unique case that deviates from many other studied Fe-based superconductors. We reveal an anomalous kink in CsCa2Fe4As4F2 that exists just at the \u03b1 band, which is most likely caused by electron interacting with certain bosons. This kink exhibits strong coupling with superconductivity and correlates with the superconducting gap of the \u03b1 band. However, it cannot be attributed to phonons and the spin resonance found by INS. Our results may provide a counterexample that challenges the critical role of spin resonance in Fe-based superconductors. Finally, we believe that both the unique spin resonance mode in INS and the unique bosonic mode in our ARPES measurements can uncover some anomalous but critical aspects. The identity of this unique bosonic mode calls for further study which could be crucial to understand the superconducting mechanism in Fe-based superconductors.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "High-quality CsCa2Fe4As4F2 single crystals were synthesized by the solid-state reaction method29,39. Synchrotron-ARPES measurements were performed at beamline BL03U of Shanghai Synchrotron Radiation Facility (SSRF) and BL13U of National Synchrotron Radiation Laboratory (NSRL) in China. The overall energy resolution for the gap measurement was set to be better than 6\u2009meV at 23\u2009eV photon energy and the angular resolution is ~ 0.2 degree for the gap and kink measurements. The crystals were cleaved in-situ and measured with a base pressure better than 6\u00d710\u221211 Torr. Lab-based laser ARPES measurements were carried out using 6.999\u2009eV light source at University of Science and Technology of China. The overall energy resolution was set to be better than 2\u2009meV the angular resolution is ~ 0.2 degree for the gap and kink measurements. All the data presented in this paper were taken within a few hours after cleavage ensuring the results were not affected by the aging effect.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data needed to evaluate the conclusion in the paper are present in the paper and the Supplementary information. All raw data generated during the current study are available from the corresponding author upon request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+        },
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+            "section_name": "Acknowledgements",
+            "section_text": "This work is supported by the National Natural Science Foundation of China (Grant No. 12174362 and No. 11888101, No. 92065202), the Innovation Program for Quantum Science and Technology (Grant No. 2021ZD0302800, No. 2021ZD0302803), the New Cornerstone Science Foundation. Part of this research used Beamline 03U of the Shanghai Synchrotron Radiation Facility, which is supported by ME2 project under contract no. 11227902 from National Natural Science Foundation of China. The authors would like to express their gratitude for the insightful discussions with H. Q. Luo, X. Y. Lu. S. S. Qin, K. Jiang, and J. P. Hu.",
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+        },
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+            "section_name": "Author information",
+            "section_text": "Shool of Emerging Technology, University of Science and Technology of China, Hefei, 230026, China\n\nPeng Li,\u00a0Sen Liao,\u00a0Shiwu Su,\u00a0Jiakang Zhang,\u00a0Ziyuan Chen,\u00a0Yajun Yan,\u00a0Juan Jiang\u00a0&\u00a0Donglai Feng\n\nKey Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, 211189, China\n\nZhicheng Wang\n\nSchool of Physics, Zhejiang University, Hangzhou, 310058, China\n\nHuaxun Li\u00a0&\u00a0Guanghan Cao\n\nNational Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, 230026, China\n\nZhicheng Jiang,\u00a0Shengtao Cui,\u00a0Zhe Sun,\u00a0Dawei Shen\u00a0&\u00a0Donglai Feng\n\nSchool of Nuclear Science and Technology, University of Science and Technology of China, Hefei, 230026, China\n\nZhicheng Jiang,\u00a0Dawei Shen\u00a0&\u00a0Donglai Feng\n\nShanghai Synchrotron Radiation Facility, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201210, China\n\nZhengtai Liu\n\nState Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing, 100084, China\n\nLexian Yang\n\nCAS Key Laboratory of Strongly-coupled Quantum Matter Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China\n\nLinwei Huai\u00a0&\u00a0Junfeng He\n\nNew Cornerstone Science Laboratory, University of Science and Technology of China, Hefei, 230026, China\n\nDonglai Feng\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nP.L. and J.J. conceived the experiments. P.L., S.L., S.W.S. and J.J. carried out ARPES measurements with the synchrotron assistance from Z.C.L., Z.T.L. and D.W.S. at SSRF, S.T.C. and Z.S. at NSRL and laser-ARPES assistance from L.X.Y., L.W.H. and J.F.H., Z.C.W., H.X.Li. and G.H.C. synthesized single crystals. J.K.Z., Z.Y.C. and Y.J.Y. conducted the STM experiments. P.L., J.J. and D.L.F. wrote the manuscript. All authors contributed to the scientific planning and discussions.\n\nCorrespondence to\n                Juan Jiang or Donglai Feng.",
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+            "section_text": "Li, P., Liao, S., Wang, Z. et al. Evidence of electron interaction with an unidentified bosonic mode in superconductor CsCa2Fe4As4F2.\n                    Nat Commun 15, 6433 (2024). https://doi.org/10.1038/s41467-024-50833-9\n\nDownload citation\n\nReceived: 01 February 2024\n\nAccepted: 22 July 2024\n\nPublished: 31 July 2024\n\nVersion of record: 31 July 2024\n\nDOI: https://doi.org/10.1038/s41467-024-50833-9\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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19575552a546c4ee86f64361be6623827ceca335468badc96205c9c3aab785d2/metadata.json

@@ -0,0 +1,140 @@
+{
+    "title": "Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit",
+    "pre_title": "Probabilistic Computing with NbOx Mott Memristor-based Self-oscillatory pbit",
+    "journal": "Nature Communications",
+    "published": "08 November 2023",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-43085-6/MediaObjects/41467_2023_43085_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-43085-6/MediaObjects/41467_2023_43085_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [],
+    "code": [],
+    "subject": [
+        "Electrical and electronic engineering",
+        "Electronic devices"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3027417/v1.pdf?c=1699535659000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3027417/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-023-43085-6.pdf",
+    "preprint_posted": "08 Jun, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Energy-based computing is a promising approach for solving NP-hard problems. Probabilistic computing using pbits, which can be fabricated through the semiconductor process and integrated with conventional processing units, can be an efficient candidate for fulfilling these demands. Here, we propose a novel pbit unit comprising a NbOx mott memristor-based oscillator, capable of generating probabilistic bits in a self-clocking manner. The noise-induced mott transition causes the probabilistic behavior, which can be effectively modeled using a multi-noise-induced stochastic process around the mott transition temperature. We demonstrate a memristive Boltzmann machine based on our proposed pbit and validate its feasibility by solving NP-hard problems. Furthermore, we propose a streamlined operation methodology that considers the autocorrelation of individual bits, enabling energy-efficient high-performance probabilistic computing.Physical sciences/Materials science/Materials for devices/Electronic devicesPhysical sciences/Nanoscience and technology/Nanoscale devices/Electronic devicesPhysical sciences/Engineering/Electrical and electronic engineering",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SupplNbOxpbitfin.docx",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Energy-based computing is a promising approach for addressing the rising demand for solving NP-hard problems across diverse domains, including logistics, artificial intelligence, cryptography, and optimization. Probabilistic computing utilizing pbits, which can be manufactured using the semiconductor process and seamlessly integrated with conventional processing units, stands out as an efficient candidate to meet these demands. Here, we propose a novel pbit unit using an NbOx volatile memristor-based oscillator capable of generating probabilistic bits in a self-clocking manner. The noise-induced metal-insulator transition causes the probabilistic behavior, which can be effectively modeled using a multi-noise-induced stochastic process around the metal-insulator transition temperature. We demonstrate a memristive Boltzmann machine based on our proposed pbit and validate its feasibility by solving NP-hard problems. Furthermore, we propose a streamlined operation methodology that considers the autocorrelation of individual bits, enabling energy-efficient and\u00a0high-performance probabilistic computing.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "The hyper-connected era, characterized by the Internet of Things, big data, and artificial intelligence, demands efficient computing solutions for solving combinatorial optimization problems such as route search, network optimization, etc1. However, these problems are often non-deterministic polynomial-time-hard (NP-hard), posing a challenge for conventional deterministic computing, which requires vast resources and yields incorrect local minimum solutions2,3. Energy-based computing has emerged as a potential solution to this challenge. One example is adiabatic quantum computing (AQC), which encodes problems into energy landscapes and leverages quantum mechanics to identify the lowest energy state corresponding to the correct answer4,5. AQC can effectively achieve global minima by escaping local minima with quantum-mechanical principles. However, its requirement for an ultra-low temperature environment limits its application to edge devices.\n\nProbabilistic computing (p-computing) has recently emerged as a promising energy-based computing system. Unlike other approaches, p-computing is operable at room temperature and compatible with CMOS technology6,7,8,9, making p-computing highly feasible and attainable. It employs probabilistic bits (pbits), which fluctuate probabilistically between 0 and 1, like the probabilistic behavior of qubits. The first CMOS-compatible pbit device was proposed using a magnetic tunnel junction (MTJ) structure10. The MTJ cell possesses two energetically equal states (i.e., parallel and antiparallel spins) separated by an energy barrier. As this energy barrier is sufficiently low so that the intrinsic thermal noise flips its state, it results in fluctuation between the two states. In addition, the energy level of each state can be controlled by externally applied voltage, thereby modulating the probability of having a particular state. This work implies that any physical systems exhibiting bi-stability can potentially be used as pbits for energy-efficient computing.\n\nMetal-insulator transition (MIT) in transition metal oxides such as NbOx or VOx is a phenomenon that exhibits bi-stability between the metal and insulator phases at a certain transition temperature (TMIT)11. MIT dynamics are highly complex, involving electrical and thermal dynamics coupling, which can be used in various emerging physical computing devices, such as biomimetic/neuromorphic artificial intelligence and cryptography devices12,13,14. The MIT at the TMIT can be easily disturbed by slight irregularities, making the device offer pbit functionality. In addition, the TMIT can be attained by Joule heating15,16,17,18, allowing for electrically modulable thermal dynamics. Furthermore, with a series resistor, NbOx volatile memristors generate oscillating current outputs under a direct current (DC) bias19,20.\n\nThus, by combining the probabilistic behavior of bi-stability with the oscillation characteristics, it is possible to obtain the probabilistic oscillation, which can be potentially used as a new type of pbits. Moreover, differing from conventional pbits9,21,22, such oscillator-based pbits can generate a self-sustaining bitstream without bit-generating signal pulses. This can reduce power consumption and increase the stability of the system. Therefore, it is worthwhile to investigate developing the pbit from the metal-insulator transition and evaluate its potential for next-generation p-computing.\n\nIn this study, we propose an oscillatory pbit device embodying an NbOx volatile memristor. We observed that the oscillation is probabilistic when the MIT is involved during oscillation. Furthermore, the oscillation probability (posc) is controllable by modulating the Vext, resulting in a sigmoidal posc-Vext relation suitable for p-computing. We also propose a model that accurately reproduces the experimental results, indicating that thermal and electrical noises trigger the probabilistic oscillation. Then, we demonstrate a memristive Boltzmann machine to validate its p-computing capability by solving graph-based combinatorial optimization problems. Lastly, we present the inherent autocorrelation issue and propose solutions for energy-efficient problem-solving.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Figure\u00a01a shows the current-voltage (I-V) behavior (black line) of the NbOx volatile memristor (TiN/NbOx/TiN-via) measured from a current sweep from 0 to 1\u2009mA. For the device integration, a square-shaped 40 nm-width TiN-via bottom electrode had been prepared from a commercial foundry. Then, a 20 nm-thick NbOx layer and a 50 nm-thick TiN top electrode were deposited by reactive sputtering (device structure is shown in the inset). The device capacitance was 6.47\u2009pF and it exhibited repeatable double negative differential resistance (NDR) behaviors. The constant device capacitance and almost identical I-V characteristics after electroforming with respect to the device\u2019s area (Supplementary Fig.\u00a0S1) suggested that the switching is associated with a localized region, following a core-shell model15,16,17,18,20,23,24. The first NDR (NDR-1) at a low current (from 0.03 to 0.07\u2009mA) is accompanied by the thermally activated conduction mechanism, and box-shaped second NDR (NDR-2) at a high current range (from 0.55 to 0.8\u2009mA) is attributed to the MIT of NbOx. During the DC I-V sweep, the operating current was sufficiently low not to involve non-volatile resistance changes caused by the electrochemical reaction, such as oxygen vacancy formation25,26. As shown in Fig.\u00a01b, when a resistor (RL) is connected serially, the circuit comprises a so-called self-oscillator and generates current oscillations under the DC voltage input (Vext). Interestingly, the oscillating output current exhibited probabilistic behavior at a certain Vext. The colored dashed lines in Fig.\u00a01a indicate the load lines with RL\u2009=\u20091.7 k\u03a9 and Vext\u2009=\u20091.32\u2009V (blue), 1.38\u2009V (red), and 1.44\u2009V (violet), whose intersections are operating points giving different types of oscillating outputs.\n\na An I-V curve of the NbOx memristor (TiN/NbOx/TiN-via) with a current sweep mode and load lines of RL\u2009=\u20091.7 k\u03a9. b Schematic of NbOx oscillator circuit and layer structure. c Oscillation probability (posc) distribution to the external voltage (Vext) from 1.32 to 1.46\u2009V with a 0.01\u2009V interval. Error bars show standard deviation. d Three representative oscillation samples with varying Vext; Vext\u2009=\u20091.32\u2009V (posc\u2009=\u20091), 1.38\u2009V (posc\u2009=\u20090.71), and 1.44\u2009V (posc\u2009=\u20090.003). e Histogram of the peak interval from 5 datasets in case of the Vext\u2009=\u20091.38\u2009V.\n\nFigure\u00a01c plots oscillating probabilities (posc) as a function of the Vext from 1.32 to 1.46\u2009V with a 0.01\u2009V interval. The posc is calculated by dividing the number of observed oscillation peaks (Nobs) by the number of available peaks at the given frequency (Navail). Each posc value was an average from 5 datasets recorded for 30 \u03bcs (Supplementary Fig.\u00a0S2). The probabilistic oscillation generates probabilistic bits in 260\u2009ns with an energy of 114 pJ per oscillation or 141 pJ per non-oscillation (staying in the metallic state), which is faster than reported diffusive memristor-based9. Furthermore, the self-clocking nature allowed a compact implementation of the pbit in the circuit12. The Vext - posc curve fits well with the sigmoidal function, posc\u2009=\u2009\\(1/(1+{e}^{a\\left({V}_{{ext}}+b\\right)})\\) showed that the probabilistic oscillation satisfied the requirements of the pbit; the occurrence of oscillation at a certain time could be probabilistically 0 or 1, where the probability could be controlled by Vext. Figure\u00a01d shows three distinct oscillation behaviors with varying Vext as Fig.\u00a01a; Vext\u2009=\u20091.32\u2009V (posc\u2009=\u20091), 1.38\u2009V (posc\u2009=\u20090.71), and 1.44\u2009V (posc\u2009=\u20090.003). At Vext\u2009=\u20091.32\u2009V, the NbOx oscillator generates periodic oscillation. Whereas, at Vext\u2009=\u20091.38\u2009V and 1.44\u2009V, it generates irregular, probabilistic oscillation, with varying intervals (colored boxes) between peaks. Figure\u00a01e plots the peak interval distribution of the Vext\u2009=\u20091.38\u2009V. The interval ranges from 260\u2009ns to 3.8 \u03bcs, indicating that the probabilistic oscillation is irregular and random. More details on the measurement system configuration and its influence on the device characterization can be found in Supplementary Information section\u00a03.\n\nIdeally, the self-oscillator should either generate a periodic oscillation or not. The probabilistic oscillation suggests that some irregular dynamics are present, perturbing the deterministic behavior27,28,29,30. To identify the factors leading to this new type of non-ideal phenomenon, we designed a probabilistic oscillator (p-osc) model based on the deterministic model suggested by Suhas Kumar et al.13,14,31 with the Ornstein-Uhlenbeck (OU) process32.\n\nThe adopted deterministic model comprises a three-dimensional Poole-Frenkel conduction model (Eq.\u00a01), Newton\u2019s cooling law (Eq.\u00a02), a nonlinear function of thermal resistance describing the metal-insulator transition (Eq.\u00a03), and Kirchhoff\u2019s law (Eq.\u00a04) in the circuit of Fig.\u00a01b. These equations are described as follows.\n\nwhere A is the lateral area of the cell, d is the thickness, kb is the Boltzmann constant, and \u03c9 and \u03c30 are material constants. Tamb is an ambient temperature, Cth is a thermal capacitance, Rth is a temperature-dependent thermal resistance composed of an insulating phase (Rth,I) and a metallic phase (Rth,M), and TMIT is a metal-insulator transition temperature. Cm is an electrical capacitance of the NbOx layer (Table\u00a0S2). Figure\u00a02a shows the I-V curve (black) and load line (blue dotted) obtained from Eqs.\u00a01\u20134, where dT/dt\u2009=\u20090, dv/dt\u2009=\u20090, RL\u2009=\u20091.2 k\u03a9, and Vext\u2009=\u20091.41\u2009V. The temperature sweep curve (red line) crosses the box-shaped I-V curve of the NDR-2 region, meaning the phase transition between insulating and metallic phases at TMIT (1070\u2009K).\n\na I-V curves of deterministic NbOx memristor model obtained by current sweep (black) and temperature sweep (red). Load line (blue dashed) at Vext\u2009=\u20091.41\u2009V, RL\u2009=\u20091.2 k\u03a9. b T-t (upper panel) plot and the corresponding T-V plot (lower panel) of the p-osc model highlighting two cases, non-oscillating (blue\u00a0line) and oscillating (red\u00a0line). c Schematic of the temperature distribution induced by noises in the p-osc model. d posc-Vext of the noise-free model (green), p-osc model (black), mean-field model (red), and experimental result of RL\u2009=\u20091.7 k\u03a9 (blue). Error bars in d, e show standard deviation. e, f Experimental and simulation results on voltage range (\u0394Vprob) of the posc-Vext plots with various RL. g The posc-Vext plots with varying voltage noise amplitude (\u03c3v) and electrical capacitance (Cm).\n\nUnder the given RL and Vext conditions, the operating point was formed in the on state above NDR-2, so no oscillation occurs as such. However, when noises were involved, oscillation could occur, and probabilistic oscillation could be understood through the p-osc model as follows. We introduced a time-dependent OU process since white noises were involved in the thermal and electrical conditions of our device (Supplementary Fig.\u00a0S4). Then, Eqs.\u00a02 and 4 can be replaced by Eqs.\u00a05 and 6.\n\nIn these equations, the stochastic process can originate from the addition of the stochastic diffusion term, \\(\\sigma {dW}(t)\\), where \\(W(t)\\) denotes the Wiener process, and \\({\\sigma }^{2}\\) is the variance of the noise.\n\nThe noise caused the equilibrium state to be pushed into a metastable state, and the accumulated behaviors could lead to an oscillating state when they reached a specific threshold30,33, that is TMIT (Supplementary Information section\u00a06). Figure\u00a02b shows T-t plot (upper panel) and T-V plot (lower panel) obtained from the p-osc model, with \\({\\sigma }_{T}{W(t)}\\)\u2009=\u20091\u2009mK and \\({\\sigma }_{v}{W(t)}\\)\u2009=\u20090.1\u2009mV. On both panels, non-oscillating and oscillating cases are highlighted in blue and red, respectively. The noise continuously perturbs the equilibrium state of the oscillator, pushing it toward a metastable state out of the equilibrium state (blue line). When the accumulated result reaches the TMIT, MIT occurs, thus resulting in a rapid temperature drop until the device reaches the off state. Then, oscillation spike is generated as it spontaneously turns on and returns to the initial equilibrium.\n\nThe p-osc model allowed for the calculation of the posc from the T-t plot and, thus, the posc-Vext plot by collecting posc at various Vext, similar to calculating the posc from experiments in Fig.\u00a01c (p-osc model-based oscillation data is included in Supplementary Fig.\u00a0S6). The p-osc model-based oscillation results could reasonably reproduce the experimental data (Fig.\u00a01c, d and Supplementary Fig.\u00a0S2), confirming the credibility of introducing the OU process. However, obtaining the posc-Vext plot through this method is quite complex, and as a result, it is burdensome to get the posc-Vext plot under various conditions.\n\nTo effectively design a pbit, more compact theoretical model for the posc-Vext plot is required. Thus we introduced a mean-field approximation that simplifies the noise behavior by representing it in terms of its mean and standard deviation. Then, the mean (\\({\\mathbb{E}}\\left[T\\right]\\)) and standard deviation (\\(\\sigma [T]\\)) of the metastable temperatures by the noise are given by Eqs.\u00a07 and 8. (A detailed calculation process is described in Supplementary Information section\u00a08)\n\nFigure\u00a02c plots the normal distribution of metastable temperatures (NT) (right panel) at the given conditions (left panel). The device starts oscillation when the metastable temperatures drop below TMIT. Therefore, the oscillation probability is given as a function of the area below the TMIT in the distribution. Figure\u00a02d plots posc-Vext of the noise-free model (green), p-osc model (black), mean-field model (red), and experimental result of RL\u2009=\u20091.7 k\u03a9 (blue, upper x axis) reproducing the sigmoidal curve accurately.\n\nFrom Eq.\u00a08, the voltage range (\u0394Vprob) of the sigmoidal posc-Vext curve is modulable by adjusting oscillator circuit parameters. Figure\u00a02e, f shows \u0394Vprob with various RL in experiments with NbOx oscillator and simulation with p-osc model. As RL increased, \u0394Vprob increased, confirming the tunability of the pbit characteristics. In addition, the dependence of \u0394Vprob with the voltage noise amplitude and electrical capacitance is shown in Fig.\u00a02g.\n\nHere, we implemented a Boltzmann machine34 adopting the established p-osc model-based pbits (NbOx pbit) for solving NP-hard problems through simulations. Figure\u00a03a schematically illustrates the memristive Boltzmann machine (MBM), where Pi refers to NbOx pbit. Memory and process units are the necessary components for storing the outputs and calculating the next inputs for each pbit, respectively. Also, input and output controllers are shown for applying input voltages and reading the output signals.\n\na Operation scheme of the constructed MBM. b A graph example of G(6, 7) with 6 vertices and 7 edges. c Two MVC solutions, X(X1, X2, X3, X4, X5, X6)\u2009=\u2009\u2018011001\u2019 and \u2018101001\u2019 obtained by a brute-force algorithm. d Histogram of X collected over 2000 iterations of the MBM. Most frequent Xs (red) are same as MVC in c.\n\nHere, one operation cycle of the MBM at the t-th iteration comprises the following three steps: (Step 1) The NbOx pbits (Pi) receive voltage inputs (\\({V}_{{{{{{\\rm{ext}}}}}},i}^{t}\\)) respectively and return probabilistic digital outputs as an oscillation spike. Process unit converts the presence or absence of a spike to a 1 or 0 (\\({X}_{i}^{t}\\in \\{0,1\\}\\)). At each iteration, the output vector \\({{{{{{\\bf{X}}}}}}}^{t}({X}_{1}^{t},\\ldots,{X}_{n}^{t})\\) is obtained, which corresponds to a potential answer of the t-th iteration. These output vectors are collected in the memory for the final evaluation after all iterations are completed. (Step 2) The processing unit calculates the Hamiltonian gradient (\\({D}_{{{{{{\\rm{i}}}}}}}^{t}=-\\partial h\\left({{{{{{\\bf{X}}}}}}}^{t}\\right)/\\partial {X}_{i}\\)) as following the neuronal dynamics between neurons34,35. (Step 3) For obtaining the subsequent input voltages, the \\({D}_{{{{{{\\rm{i}}}}}}}^{t}\\) is linearly transformed to the \\({V}_{{{{{{\\rm{ext}}}}}},i}^{t+1}\\) by the following equation36;\n\nwhere \\(a=\\frac{{V}_{{{{{{\\rm{osc}}}}}},\\min }-{V}_{{{{{{\\rm{osc}}}}}},\\max }}{{D}_{\\max }-{D}_{\\min }}\\), \\(b={V}_{{{{{{\\rm{osc}}}}}},\\min }-a{D}_{\\max }\\), in which \\({V}_{{{{{{\\rm{osc}}}}}},\\max }\\) and \\({V}_{{{{{{\\rm{osc}}}}}},\\min }\\) are the maximum and minimum input voltages of the pbit. Dmax and Dmin are the positive and negative values of the largest absolute value in the range of \\({D}_{i}^{t}\\). This operating cycle repeats for \\(N\\) iterations. Consequently, N sets of Xs are collected in the memory, and the majority X is determined as an answer.\n\nUsing the designed MBM, we solved a minimum vertex covering (MVC) problem. The objective of the MVC problem is to find a subset of vertices that encompasses at least one endpoint of every edge of the undirected and non-weighted graph37. When the graph is non-bipartite, the MVC problem is an NP-hard problem, which lies beyond the capabilities of classical algorithms to solve within polynomial time as the problem size increases38,39. For example, in the case of the brute-force algorithm, if a graph consists of n vertices and m edges, it requires 2n \u00d7 n \u00d7 m computations to find the answer in all possible cases (Algorithm S1).\n\nFigure\u00a03b shows a non-bipartite graph, G(6, 7), with six vertices connected by seven edges. Although this problem falls under the category of NP-hard problems, its size (n\u2009=\u20096, m\u2009=\u20097) is small enough that the solution can be obtained using a classical brute-force algorithm. The MVCs were \\({{{{{\\bf{X}}}}}}({X}_{1},{X}_{2},{X}_{3},{X}_{4},{X}_{5},{X}_{6})\\)\u2009=\u2009\u2018011001\u2019 and \u2018101001\u2019 as shown in Fig.\u00a03c. Here, note that Xi represents each vertex and the number 0 or 1 shows which of the two groups that vertex is part of.\n\nNext, we derived a solution using our designed NbOx pbit-based MBM. We adopted an Ising model approach3, where each vertex is assigned as a pbit, and the Hamiltonian is defined as Eq.\u00a010.\n\nHere V and E are the edge and vertex set in graph G(V, E). \\({X}_{u}\\) and \\({X}_{v}\\) are binary variables on each vertex u (or v), and \\(\\alpha\\) and \\(\\beta\\) are arbitral parameters. Details are described in the Supplementary Information section\u00a09. Figure\u00a03d is a histogram of \\({{{{{\\bf{X}}}}}}{{{\\rm{s}}}}\\) collected over 2000 iterations. It shows that the correct answers, \\({{{{{\\bf{X}}}}}}\\)\u2009=\u2009\u2018011001\u2019 and 101001\u2019, appear most frequently, demonstrating that the MBM solves the given MVC problem.\n\nThe MBM produces a potential answer at each iteration, and the majority of the answers become the final answer. Therefore, to obtain a reliable final answer, it is necessary to perform a large number of iterations, but this leads to significant time and energy consumption. Consequently, finding the answer within the fewest iterations possible is critical. However, we found that the autocorrelation of pbit significantly decreased the accuracy of the answer and increased the required number of iterations to obtain the answer. We highlight here the autocorrelation issue of pbit outputs and propose a compensating method considering this issue.\n\nAutocorrelation is a serial correlation of time series data. Thus, the autocorrelation involved during the iterative processes can influence the output and change the output probability, deviating from the target probability. This may hinder the efficient search for the solution space. Figure\u00a04a shows probabilistic oscillations at Vext\u2009=\u20091.37\u2009V (upper panel) and the corresponding bitstream (lower panel). From the bitstream, the autocorrelation at lag k (\\({\\rho }_{k}\\)) can be defined as;\n\nwhere yt is the t-th bit value, and \\(\\bar{y}\\) is the mean of a bitstream. Figure\u00a04b plots the autocorrelations of the bitstream in Fig.\u00a04a as a function of lags from 0 to 20 (Data for all Vext cases are in Supplementary Fig.\u00a0S8.). At lag 0, the autocorrelation is 1 because it compares to itself, and as the lag increases, the autocorrelation decreases, suggesting the bits at greater distances are highly independent.\n\na Probabilistic oscillation plot of NbOx oscillator at Vext\u2009=\u20091.37\u2009V (upper panel) and corresponding bitstream (lower panel). b The corresponding autocorrelation (\u03c1k) of bitstream as a function of lag k. c Difference between the occurrence of correct answers (Ycor) and the average of the top 5 incorrect answers (\u0232incor) for uncorrelated case, highly autocorrelated case (\u0394\u2009=\u20090), and autocorrelation-relieved case (\u0394\u2009=\u20092) as a function of iterations during solving the MVC problem. d Normalized (Ycor - \u0232incor) value as a function of iterations. e Average energy during bit generation (black), iterations to solution (red), and energy to solution (green bars) as a function of \u0394.\n\nThe reason autocorrelation appears can be understood as follows: when the device undergoes oscillation, significant fluctuations occur in temperature and voltage. However, these fluctuations may not fully relax until the subsequent oscillation generates, thereby influencing the initial state of the following oscillation cycle. Therefore, to obtain independent outputs from NbOx pbits, it is necessary to allow sufficient time between data selection. Thus, we put some delay period (\u0394) when determining the pbit output to compensate for autocorrelation. For example, when \u0394\u2009=\u20092, the third data of the probabilistic oscillation is chosen for the output. Then, we evaluated the MBM performance with varying \u0394. Here, we propose defining the performance as the difference between the occurrence of correct answers (Ycor) and the average of the top 5 incorrect answers (\u0232incor). This method compares the number of correct answers with the number of major incorrect answers, demonstrating how clear the correct answer is. Detailed explanations are described in Supplementary Information section\u00a012. Figure\u00a04c shows the (Ycor - \u0232incor) of one of the answers, \\({{{{{\\bf{X}}}}}}\\)\u2009=\u2009\u2018011001\u2019, over 1000 iterations during solving MVC for the ideal case (unautocorrelated, blue) and for the autocorrelated cases without the delay period (\u0394\u2009=\u20090, red) and with a delay period of 2 (\u0394\u2009=\u20092, violet). When \u0394\u2009=\u20090, the (Ycor - \u0232incor) was below 0 until more than 600 iterations, indicating that the MBM system gave wrong answers due to the autocorrelation. Whereas when \u0394\u2009=\u20092, the (Ycor - \u0232incor) was higher than 0, meaning that it could potentially yield the correct answer, which is highly comparable to the ideal case. Figure\u00a04d plots the normalized (Ycor - \u0232incor) over 2000 iterations for one of the answers. When \u0394\u2009=\u20092, it gives the correct answer steadily after 290 iterations, much smaller than 1310 iterations of \u0394\u2009=\u20090, meaning fewer iterations are needed to determine the answer.\n\nLastly, we have estimated the energy consumption of pbits for the MBM operation in solving the MVC problem. Here, the total energy consumption of pbits (energy to solution) can be approximately defined by (number of pbits, n) \u00d7 (energy during bit generation, Epbit) \u00d7 (number of iterations to solution, N). In the energy consumption calculation, we considered only the energy consumption in pbits, and we did not include the energy consumed in memory (storing output vectors) and process units (calculating the Hamiltonian gradient and obtaining the subsequent input voltages from it), as these parts are commonly required in p-computing and are handled by conventional digital computers. Here, Epbit is the energy required for generating the pbit\u2019s output. To obtain the exact Epbit, it is necessary to count the occurrence of oscillation and non-oscillation cases, which is very complicated. So, we assumed that each case occurs with a 50% probability. Then, Epbit can be set to 128 \u00d7 (\u0394\u2009+\u20091) pJ/bit, where 128 pJ is the average energy per oscillation or non-oscillation and (\u0394\u2009+\u20091) is a factor by the delay period. Figure\u00a04e compares the energy efficiency of the MBM as a function of \u0394 to get the correct answer for the G(6, 7) problem. Although the energy during bit generation (black) increases proportionally to \u0394, the required iteration to the correct answer (red) decreases and converges from \u0394\u2009=\u20092 due to sufficient relaxation of autocorrelation. Consequently, the total energy consumption is the lowest at \u0394\u2009=\u20092 with 34% less energy consumption than \u0394\u2009=\u20090.\n\nTo address the autocorrelation issue, we propose allowing sufficient time for state relaxation. However, this inevitably involves time wastage. Therefore, if we can identify the temperature and voltage fluctuation and relaxation characteristics accurately and develop methods to leverage them, the performance of MBMs can be enhanced without incurring temporal inefficiencies, which should be investigated further.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "We developed a new type of pbit using the probabilistic oscillation of the NbOx memristor that can generate a probabilistic bit in 260\u2009ns with an average energy of 128 pJ/bit in a self-clocking manner. Then, we developed a highly accurate compact model that can fully simulate the experimental results of probabilistic oscillation. We developed a memristive Boltzmann machine composed of NbOx oscillator-based pbits and solved the graph-based NP-hard problem, validating the feasibility of the proposed pbit. Furthermore, we proposed an autocorrelation issue on the pbit bitstreams and suggested efficient approaches to deal with the issue.\n\nAlthough this study showed the feasibility of NbOx oscillator-based pbits, there are still challenges to resolve before it can be practically used. One of the most crucial issues is device-to-device variation. Although our device shows a reliable variation in DC characteristics between cells, any variation may affect the probabilistic oscillation window. Our model suggested that the variation can originate from the difference in noise characteristics injected into each pbit device and from the intrinsic variation of the time-dependent components such as Cth and Cm. Therefore, future studies will explore related topics such as constant noise supply systems to ensure a consistent environment and low-variation devices with uniform device-to-device parameters to accelerate the development of practical p-computing hardware.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "A TiN/NbOx/TiN-via volatile memristor device was fabricated using the following process. For the 40\u2009nm TiN-via bottom electrode, a planarized substrate containing TiN-vias was prepared from a commercial foundry. A 20 nm-thick NbOx blanket layer was deposited by reactive sputtering at 100\u2009\u00b0C under the mixed gas flow of Ar and O2 (Ar:O2\u2009=\u200948:2) using an Nb target. Afterward, a 50 nm-thick TiN top electrode and a 20 nm-thick Pt contact electrode were sequentially deposited and patterned by a lift-off process, where the TiN electrode was deposited by reactive sputtering at room temperature using a TiN target, and the Pt electrode was by E-beam evaporation.\n\nAll electrical characterizations were performed using a semiconductor analyzer (Keithley 4200A-SCS) and a probe station system. The I-V characteristics were obtained in a current sweep using two SMUs (Source Measurement Units). For the self-oscillation characteristics, a 30 \u03bcs width of voltage pulses with various levels were applied and measured using a Keithley 4225-PMU (Pulse Measurement Unit) and 4225-RPM (Remote Amplifier/Switch). The C-V characteristics were measured using Keithley 4210-CVU (Capacitance Voltage Unit) module.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All the relevant data are available from the corresponding author upon reasonable request.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "Simulation results were processed using Python and LTspice software. All the relevant codes are available from the corresponding author upon reasonable request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This research was supported by the National Research Foundation of Korea (NRF) (Grant numbers: RS-2023-00216619, RS-2023-00216992, 2022M3F3A2A01076569, 2022M3I7A4085484, and 2023R1A2C2005159), NNFC (Grant number: 1711160154), and UP program of KAIST (Grant number: N10230061).",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea\n\nHakseung Rhee,\u00a0Gwangmin Kim,\u00a0Hanchan Song,\u00a0Woojoon Park,\u00a0Do Hoon Kim,\u00a0Jae Hyun In,\u00a0Younghyun Lee\u00a0&\u00a0Kyung Min Kim\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.R. generated ideas, performed experiments and simulations, and wrote the manuscript. G.K. and J.H.I. provided the initial concept and analyzed its characteristics. Y.L. contributed to the device characteristics analysis during the manuscript revision. H.S. and W.P. fabricated the NbOx device. H.S. and D.H.K. discussed the autocorrelation analysis. K.M.K. supervised this work.\n\nCorrespondence to\n                Kyung Min Kim.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Farooq A. Khanday, Su-in Yi, and the other, anonymous, reviewer for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional information",
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+            "section_image": []
+        },
+        {
+            "section_name": "Rights and permissions",
+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nReprints and permissions",
+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Rhee, H., Kim, G., Song, H. et al. Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit.\n                    Nat Commun 14, 7199 (2023). https://doi.org/10.1038/s41467-023-43085-6\n\nDownload citation\n\nReceived: 06 June 2023\n\nAccepted: 30 October 2023\n\nPublished: 08 November 2023\n\nVersion of record: 08 November 2023\n\nDOI: https://doi.org/10.1038/s41467-023-43085-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "npj Unconventional Computing (2025)\n\nScientific Reports (2025)\n\nNature Communications (2025)\n\nNature Reviews Electrical Engineering (2025)",
+            "section_image": []
+        },
+        {
+            "section_name": "Associated content",
+            "section_text": "Collection",
+            "section_image": []
+        }
+    ]
+}

19b1d6b59efd7b19368f5003a25f24e86cf87b8704c446aba11acfc2ee49035d/metadata.json

@@ -0,0 +1,148 @@
+{
+    "title": "Timing of antibiotic administration determines the spread of plasmid-encoded antibiotic resistance during microbial range expansion",
+    "pre_title": "Timing of antibiotic administration determines the spread of plasmid-encoded antibiotic resistance during microbial range expansion",
+    "journal": "Nature Communications",
+    "published": "14 June 2023",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-39354-z/MediaObjects/41467_2023_39354_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-39354-z/MediaObjects/41467_2023_39354_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-39354-z/MediaObjects/41467_2023_39354_MOESM3_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [
+        "https://opendata.eawag.ch/",
+        "https://doi.org/10.25678/0008EB."
+    ],
+    "code": [
+        "https://opendata.eawag.ch/",
+        "https://doi.org/10.25678/0008EB"
+    ],
+    "subject": [
+        "Antimicrobial resistance",
+        "Biofilms",
+        "Microbial ecology"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-2674610/v1.pdf?c=1686827438000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-2674610/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-023-39354-z.pdf",
+    "preprint_posted": "13 Mar, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Plasmids are the main vector by which antibiotic resistance (AR) is transferred between bacterial cells within surface-associated communities. In this study, we ask whether there is an optimal time to administer antibiotics to minimize plasmid spread in new bacterial genotypes during community expansion across surfaces. We addressed this question using consortia of two Pseudomonas stutzeri strains, where one is an AR-encoding plasmid donor and the other a potential recipient. We allowed the strains to co-expand across a surface and administered antibiotics at different times. We found that plasmid transfer and transconjugant proliferation have unimodal relationships with the timing of antibiotic administration, where they reach maxima at intermediate times. These unimodal relationships result from the interplay between the probabilities of plasmid transfer and loss. Our study provides mechanistic insights into the transfer and proliferation of AR-encoding plasmids within microbial communities and identifies the timing of antibiotic administration as an important determinant.Biological sciences/Microbiology/BiofilmsBiological sciences/Microbiology/Antimicrobials/Antimicrobial resistanceBiological sciences/Ecology/Microbial ecology",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SIMaTiming.pdfSupplementary InformationNCOMMS23104031rs.pdfReporting Summary",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Plasmids are the main vector by which antibiotic resistance is transferred between bacterial cells within surface-associated communities. In this study, we ask whether there is an optimal time to administer antibiotics to minimize plasmid spread in new bacterial genotypes during community expansion across surfaces. We address this question using consortia of Pseudomonas stutzeri strains, where one is an antibiotic resistance-encoding plasmid donor and the other a potential recipient. We allowed the strains to co-expand across a surface and administered antibiotics at different times. We find that plasmid transfer and transconjugant proliferation have unimodal relationships with the timing of antibiotic administration, where they reach maxima at intermediate times. These unimodal relationships result from the interplay between the probabilities of plasmid transfer and loss. Our study provides mechanistic insights into the transfer and proliferation of antibiotic resistance-encoding plasmids within microbial communities and identifies the timing of antibiotic administration as an important determinant.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "The spread of antibiotic resistance (AR) is a global health problem whose causes and potential mitigation measures remain unclear1,2. The conjugation-mediated transfer of AR-encoding plasmids is a mechanism by which AR genes can spread between bacterial cells located within close spatial proximity to each other3,4,5,6. The frequencies of plasmid-free and plasmid-carrying cells within a microbial community will change over time depending on the probability of plasmid transfer from a plasmid donor to a potential recipient cell and the probability of plasmid loss upon cell division7,8,9,10,11,12,13. The frequencies will also depend on the relative fitness of plasmid-free and -carrying cells, where AR-encoding plasmids typically incur a fitness cost in the absence of antibiotic pressure14,15,16,17,18. The time during which the community is not exposed to antibiotic pressure is therefore expected to select against plasmid-carrying cells16,19. This leads to the expectation that a negative relationship exists between the timing of antibiotic administration and the transfer and proliferation of AR-encoding plasmids in new genotypes, as longer times should result in smaller frequencies of plasmid-carrying cells due to out-competition by fitter plasmid-free cells.\n\nIn host-associated microbiomes, microbial communities often proliferate on surfaces (e.g. the gut lumen, skin, mucosae, etc.) where AR is typically conferred by conjugative plasmids20,21. AR in these systems can be maintained by plasmid transfer even in the absence of antibiotic pressure22,23. In patients receiving antibiotic treatment, these communities undergo frequent spatial reduction\u2013expansion dynamics as a consequence of growth and death during which plasmid-free and plasmid-carrying individuals frequently (re)mix and expand together24,25,26. Work in the mouse gut has shown that the spread of AR-encoding plasmids is maximized in situations where pools of persistent AR genotypes in the gut lumen mix with invading plasmid-free enteric pathogens27,28. It can be expected that the successional stage of these communities when antibiotics are applied can determine whether AR genotypes are likely to proliferate or not. The pervasiveness of mixed proliferation of plasmid-free and plasmid-carrying cells indicates that efforts to eradicate recalcitrant infections could benefit from a better temporal understanding of the spread of AR-encoding plasmids in relation to its main mechanisms of plasmid transfer and loss.\n\nSurface-associated microbial communities, such as those associated with hosts, are considered hotspots for the conjugation-mediated transfer of AR-encoding plasmids4,29,30, notably because surface association promotes the close physical cell\u2013cell contacts that are required for the conjugation process5,31. A universal feature of surface-associated communities is that as cells within a community grow and divide, the community as a whole expands across space in a process referred to as range expansion32,33,34. During this process, growth is confined to only a thin layer of cells located at the expansion frontier where nutrients that diffuse from the periphery are readily available35. One consequence of this process is that different populations become increasingly spatially segregated over time32,36,37,38. This reduces the number of interspecific cell\u2013cell contacts (e.g., between plasmid donors and potential recipients), thus also reducing the number of potential plasmid transfer events (Fig.\u00a01a). Because spatial intermixing decays during range expansion and reduces the number of interspecific cell\u2013cell contacts32,36,37,38,39, this again leads to the expectation that a negative relationship exists between the time of antibiotic administration and the transfer and proliferation of AR-encoding plasmids in new genotypes.\n\na Different populations (in this case plasmid donors and potential recipients) become increasingly spatially segregated over time as a consequence of stochastic drift at the expansion frontier. This reduces the number of interspecific cell\u2013cell contacts and the potential for plasmid transfer, as plasmid transfer can only occur along the interfaces of plasmid donors and potential recipients. b Our experimental system consists of pairs of strains of the bacterium Pseudomonas stutzeri. One strain is the plasmid donor that expresses red fluorescent protein from its chromosome and carries conjugative plasmid pAR145 that encodes for blue fluorescent protein and chloramphenicol resistance (Cell type 1; magenta cell). The other strain is the potential recipient that expresses green fluorescent protein from its chromosome and is plasmid-free (Cell type 2; green cell). If the potential recipient receives the plasmid, it will express both green and blue fluorescent proteins and appear in the composite color cyan. Plasmid carriers can also be cured of the plasmid during cell division and return to their plasmid-free states (magenta to red and cyan to green). Solid curved arrows indicate successful plasmid transfer while dashed curved arrows indicate plasmid loss. Inter-plasmid gain refers to plasmid transfer between different cell types, while intra-plasmid gain refers to plasmid transfer within the same cell type.\n\nIn this study, we test the hypothesis that a negative relationship does indeed exist between the time of antibiotic administration and the transfer and proliferation of AR-encoding plasmids, where the negative relationship is driven by selection against plasmid-carrying cells in the absence of antibiotics and the decay in spatial intermixing during the range expansion process (Fig.\u00a01a). Testing this hypothesis is especially paramount because, in clinical settings, infections generally need to be treated promptly, while our hypothesis would suggest that early treatment times might have negative consequences on the spread of AR-encoding plasmids in new genotypes (Fig.\u00a01a). To test our hypothesis, we performed range expansion experiments with pairs of strains of the bacterium Pseudomonas stutzeri, where one strain carries the chloramphenicol resistance-encoding conjugative plasmid pAR145 (referred to as the plasmid donor strain) while the other is plasmid-free (referred to as the potential recipient strain) (Fig.\u00a01b). After the initiation of range expansion, we applied chloramphenicol at different times and quantified the transfer and proliferation of pAR145. We then used an individual-based computational model to quantify how the probabilities of plasmid transfer and loss interact with each other to determine the spread of AR-encoding plasmids during range expansion. This enabled us to test the generality of our experimental results and establish a causal relationship between the timing of antibiotic administration and the spread of AR-encoding plasmids in new genotypes.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-023-39354-z/MediaObjects/41467_2023_39354_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "We first quantified the dynamics of pAR145 during range expansion in the absence of chloramphenicol, and thus in the absence of positive selection for pAR145. We defined the pAR145 load as the frequency of pAR145-carrying cells at the expansion frontier where cells are actively growing (approximately a radial ring with a width of 35\u2009\u00b5m located at the expansion periphery35). We performed range expansion experiments with consortia composed of two derivative strains of P. stutzeri A1601 (Fig.\u00a01b). One expresses red fluorescent protein from its chromosome40 and carries pAR145, which encodes for chloramphenicol resistance and blue fluorescent protein41, 42 (referred to as the pAR145 donor). The other expresses green fluorescent protein from its chromosome40 but does not carry pAR145 (referred to as the potential recipient). The pAR145 donor strain expresses both red and blue fluorescent proteins and appears as the composite color magenta, whereas the potential recipient only expresses green fluorescent protein and appears as green (Fig.\u00a01b). If the potential recipient receives pAR145 (referred to as a transconjugant), then it will express both green and blue fluorescent proteins and appear as the composite color cyan (Fig.\u00a01b). This system allows us to identify the spatial locations of pAR145 donors, potential recipients, and transconjugants, and to quantify the pAR145 load during range expansion.\n\nWe observed five important outcomes from the range expansion experiment. First, pAR145 donor and potential recipient cells rapidly segregated during range expansion to form a sectorized spatial pattern with reduced spatial intermixing (Fig.\u00a02a, b), which is consistent with previous studies investigated pattern formation by other competing bacterial strains32,37,38. Second, abundant cyan sectors emerged during the early stages of sector formation (Fig.\u00a02a), which demonstrates extensive pAR145 transfer to potential recipient cells and the formation of transconjugants during the initial stages of range expansion when spatial intermixing was high. Third, the newly formed transconjugants were rapidly displaced by plasmid-free cells (green) thereafter (Fig.\u00a02a, c). Fourth, extensive pAR145 loss occurred from pAR145 donor cells, which is evident by the rapid displacement of magenta sectors and the formation of red sectors (Fig.\u00a02a and Supplementary Fig.\u00a01a). Finally, carrying pAR145 incurs a fitness cost in the absence of chloramphenicol pressure (Supplementary Fig.\u00a01b). This caused pAR145 donor cells to be gradually displaced by potential recipient cells (Fig.\u00a02c), resulting in an increase in the ratio of green-to-red during range expansion (Fig.\u00a02d). We also performed range expansion experiments with the pAR145 donor alone to verify that the decline in the pAR145 load was not dependent on the presence of the potential recipient (Supplementary Fig.\u00a01a).\n\na Representative microscopy image of a one-week range expansion for a pair of P. stutzeri strains. One strain is the pAR145 donor that expresses red fluorescent protein from its chromosome and carries pAR145 encoding for blue fluorescent protein and chloramphenicol resistance (appears magenta). The other strain is the potential recipient that expresses green fluorescent protein from its chromosome. The white dashed ring indicates the boundary between pAR145-carrying and largely pAR145-free regions. The yellow square frame is a magnified region. b Intermixing index, c pAR145 load, and d ratio of green (potential recipient) to red (donor cured of pAR145) pixels as a function of expansion radius (expansion time) beginning at the edge of the inoculation area (1500\u2009\u00b5m) to the edge of the final expansion frontier (4000\u2009\u00b5m) at radial increments of 10\u2009\u00b5m. Different colored data points correspond to measurements for different independent biological replicates (n\u2009=\u20096).\n\nOverall, we observed a sharp decline in the pAR145 load during range expansion, where the decline began after ~24\u2009h (corresponding to a radius of ~1750\u2009\u00b5m) and the load approached zero after 48\u2009h (corresponding to radii >2250\u2009\u00b5m) (Fig.\u00a02c). Our data indicate that the decline in the pAR145 load is caused by two processes. First, potential recipient cells that never received pAR145 (green cells) displaced pAR145-carrying cells, which is evident by the emergence and persistence of green sectors (Fig.\u00a02a, d). Second, a subset of pAR145 donor cells lost pAR145 (red cells) and subsequently displaced pAR145-carrying cells, which is evident by the emergence and persistence of red sectors (Fig.\u00a02a, c). Taken together, our data demonstrate that both the relative growth rates of pAR145-carrying and pAR145-free cells and the probability of pAR145 loss upon cell division are important for understanding and predicting pAR145 dynamics during range expansion.\n\nBecause the pAR145 load rapidly declines during range expansion in the absence of chloramphenicol (Fig.\u00a02c), we expected that the time at which chloramphenicol is administered after the onset of range expansion, and thus the time at which we apply positive selection for pAR145, determines the subsequent proliferation of transconjugant cells. More specifically, we hypothesized that the frequency of transconjugant cells at the expansion frontier would decline monotonically with time before applying chloramphenicol. To test this, we added chloramphenicol at 13 time points between 0 and 108\u2009h after the onset of the range expansion experiment and allowed the consortia to expand thereafter for seven days. Thus, we fixed the chloramphenicol exposure time while varying the extent of range expansion prior to chloramphenicol administration. Note that the chloramphenicol concentration that we applied prevents further growth of plasmid-free cells. At the end of the experiment, we quantified the frequency of transconjugant cells at the expansion frontier.\n\nContrary to our expectation, we observed a unimodal relationship between the frequency of transconjugant cells at the expansion frontier seven days after chloramphenicol administration and the time at which we added chloramphenicol (two-sample two-sided Welch test; P1\u2009=\u20096.3\u2009\u00d7\u200910\u22126, P2\u2009=\u20098.8\u2009\u00d7\u200910\u22126, n\u2009=\u20095) (Fig.\u00a03a, b). To test for a unimodal relationship, we computed P1 and P2 by comparing the maximum observed transconjugant frequency with the frequencies measured for chloramphenicol administration times at 0\u2009h (P1) and 108\u2009h (P2). Thus, we tested whether the maximum transconjugant frequency occurs at an intermediate chloramphenicol administration time. Furthermore, we found that the transconjugant frequency increased with the administration time up to the time at which we observed the maximum transconjugant frequency (Pearson correlation test; r\u2009=\u20090.72, P\u2009=\u20098.8\u2009\u00d7\u200910\u221212, n\u2009=\u20095) and decreased thereafter (Pearson correlation test; r\u2009=\u2009\u22120.81, P\u2009=\u20092.0\u2009\u00d7\u200910\u221210, n\u2009=\u20095) (Fig.\u00a03b). At earlier chloramphenicol administration times, the spatial patterns that emerged after chloramphenicol administration consisted of contiguous discrete sectors of pAR145 donor and transconjugant cells (Fig.\u00a03a). Thus, all cells that contributed to community expansion carried pAR145. At later chloramphenicol administration times (84\u201396\u2009h), the sectorized patterns became discontiguous and were composed of spatially isolated bubble-like structures of pAR145 donor or transconjugant cells (Fig.\u00a03a), presumably because only a few pAR145-carrying cells remained at the expansion frontier at the point when we administered chloramphenicol. We found that the level of spatial intermixing, which we reasoned is a determinant of pAR145 transfer, also has a unimodal relationship with the time of antibiotic administration (Fig.\u00a03c).\n\na Representative microscopy image (from n\u2009=\u20095) of spatial patterns formed after chloramphenicol was administered at different times after the onset of range expansion. The total time of chloramphenicol exposure was 7 days for all treatments. b Frequency of transconjugants (cyan) at the expansion frontier. c Global intermixing index measured as the sum of intermixing indices across the expansion area at radial increments of 10\u2009\u03bcm. d Extent of range expansion after chloramphenicol administration (7 days). e Ratio of transconjugant (cyan) to pAR145 donor (magenta) cells at the expansion frontier. For b\u2013e, each datapoint is a measurement for an independent biological replicate (n\u2009=\u20095).\n\nWe next quantified the extent of range expansion that occurred during the 7-day chloramphenicol treatment period (Fig.\u00a03d). Because the frequency of transconjugant cells reached a maximum value when chloramphenicol was administered at intermediate times after the onset of range expansion (Fig.\u00a03a, b), we also expected the ability of the consortia to expand (grow) during chloramphenicol treatment would also reach a maximum value at intermediate times. As expected, we observed a unimodal relationship between the extent of range expansion during chloramphenicol treatment and the time at which chloramphenicol was administered, with the maximum value occurring when chloramphenicol was administered 12\u2009h after the onset of range expansion (two-sample two-sided Welch test; P1\u2009=\u20090.011, P2\u2009=\u20091.5\u2009\u00d7\u200910\u22125, n\u2009=\u20095) (Fig.\u00a03d). We also observed a significant positive relationship between the frequency of transconjugant cells at the expansion frontier (data plotted in Fig.\u00a03b) and the ability of the consortia to expand after the administration of chloramphenicol (data plotted in Fig.\u00a03d) (Pearson correlation test; r\u2009=\u20090.65, P\u2009=\u20091.5\u2009\u00d7\u200910\u22126, n\u2009=\u20095).\n\nWe finally quantified the extent to which pAR145 transferred into potential recipient cells by quantifying the ratio of transconjugant (cyan) to pAR145 donor (magenta) cells at the expansion frontier 7 days after chloramphenicol administration (Fig.\u00a03e). We expected a positive relationship between the time of range expansion prior to chloramphenicol administration and the ratio of transconjugants-to-pAR145 donor cells. Briefly, short times before chloramphenicol administration should be insufficient to generate numerous transconjugant cells resulting in smaller ratios, while longer times should allow the generation of more transconjugant cells and higher accumulation of pAR145 donor cells that had lost pAR145 resulting in larger ratios. However, the ratio of transconjugant-to-pAR145 donor cells did not follow a monotonically increasing trend, but instead saturated at later chloramphenicol administration times (two-sample two-sided Welch test; P\u2009=\u20090.17, n\u2009=\u20095) (Fig.\u00a03e). In this case, we computed P by comparing the maximum observed ratio of transconjugant-to-pAR145 donor cells with the ratio measured at the longest chloramphenicol administration time (Fig.\u00a03e).\n\nWe next sought to test whether the increased numbers of transconjugant cells at intermediate antibiotic administration times were due to more transconjugant cells being created (transfer events) or better proliferation of individual transconjugant cells. To test this, we performed individual-based computational simulations to gain insights using the CellModeller framework43. Briefly, we positioned plasmid donor (magenta) and potential recipient (green) cells at an ~1:1 ratio according to a checkerboard arrangement with a uniform distance between cells and random rotational orientation of cells along a two-dimensional plane. We assigned plasmid-free cells to have a 17% higher growth rate than plasmid-carrying cells in the absence of antibiotics, which is in accordance with our experimental data (Supplementary Table\u00a01 and Supplementary Fig.\u00a02). We applied a constant probability of plasmid transfer (Pc\u2009=\u20090.002) when a plasmid donor and a potential recipient cell come into physical contact with each other, whereupon successful plasmid transfer causes the recipient cell to become a transconjugant (cyan). We also applied a constant probability of plasmid loss upon cell division (Pl\u2009=\u20090.005) that can occur for any plasmid-carrying cell throughout the duration of range expansion. We administered antibiotics at various time steps (0, 100, 200, 400, 600, and 800) after initiating the simulations to mimic our experimental design, upon which only plasmid donor and transconjugant cells could continue growing. We used the same duration of \u201cantibiotic exposure\u201d for all simulations (1000 time steps) (Fig.\u00a04a, b). Finally, we quantified the number of unique transconjugant lineages that derived from a single plasmid transfer event and persisted at the expansion frontier (Fig.\u00a04c), the frequency of transconjugant cells at the expansion frontier (Fig.\u00a04d), and the mean size of transconjugant lineages (Fig.\u00a04e).\n\na Representative simulations of range expansions for different antibiotic administration times. Plasmid donor cells are magenta, transconjugant cells are cyan, potential recipient cells are green, and donor cells that lost the plasmid are red. T indicates the time step at which antibiotics were administered after the onset of range expansion. b Transconjugant lineages for the simulations presented in a where each color identifies a unique lineage. Effect of antibiotic administration time on c the number of transconjugant lineages at the expansion frontier, d the frequency of transconjugants at the expansion frontier, and e the mean size of transconjugant lineages. For c\u2013e, each datapoint is a measurement for an independent simulation (n\u2009=\u20095).\n\nWe observed a unimodal relationship between the number of transconjugant lineages and the time of antibiotic administration after the onset of range expansion (two-sample two-sided Welch test; P1\u2009=\u20090.0012, P2\u2009=\u20090.00068, n\u2009=\u20095) (Fig.\u00a04c) and a unimodal relationship between the frequency of transconjugant cells at the expansion frontier and the antibiotic administration time (two-sample two-sided Welch test; P1\u2009=\u20090.00058, P2\u2009=\u20090.00057, n\u2009=\u20095) (Fig.\u00a04d), which is consistent with our experimental observations (Fig.\u00a03b). We also observed a unimodal relationship between the mean size of transconjugant lineages and the antibiotic administration time (two-sample two-sided Welch test; P1\u2009=\u20095.6\u2009\u00d7\u200910\u22127, P2\u2009=\u20090.00028, n\u2009=\u20095) (Fig.\u00a04e). Although the number of transconjugant lineages and the frequency of transconjugant cells both show a unimodal relationship with the time of antibiotic administration, they do not correlate with each other (Spearman rank correlation test; rho\u2009=\u20090.32, P\u2009=\u20090.085). Instead, the mean size of transconjugant lineages is positively correlated with the frequency of transconjugant cells (Spearman rank correlation test; rho\u2009=\u20090.80, P\u2009=\u20091.3\u2009\u00d7\u200910\u22127), indicating that the increased frequency of transconjugants is largely associated with local proliferation of individual transconjugants.\n\nTo assert that our results were independent of the time allowed for proliferation after antibiotic administration, we performed further simulations for an additional 1000 time steps after administering antibiotics and again quantified the number of transconjugant cells at the expansion frontier (Supplementary Fig.\u00a03a), the frequency of transconjugant cells at the expansion frontier (Supplementary Fig.\u00a03b), and the mean size of transconjugant lineages (Supplementary Fig.\u00a03c) for different antibiotic administration times. We found that the unimodal relationships remain valid after the additional time steps for all of these measurements. Moreover, we observed two additional outcomes. First, compared to the number of transconjugant lineages, the frequency of transconjugants at the expansion frontier increased with a longer duration of antibiotic exposure (two-sample two-sided Welch test; P\u2009=\u20090.0044 at time point 600) (Supplementary Fig.\u00a03b). This is because as the antibiotic exposure duration increased, the \u201cbubble-like\u201d protrusions that we observed in both experiments and simulations gradually developed and merged together (Figs.\u00a03a and 4a), therefore generating a higher frequency of transconjugants at the expansion frontier. However, because each protrusion originated from a single transconjugant lineage (Fig.\u00a04b), the number of lineages remained constant. Second, the mean size of transconjugant lineages increased as the antibiotic exposure time prolonged (two-sample two-sided Welch test; P\u2009=\u20090.0099 at time point 600), which again verifies that the sizes of the newly formed transconjugant lineages can catch up provided there is a sufficiently long duration of antibiotic exposure.\n\nWe finally examined how the probabilities of plasmid transfer and loss, both of which can vary over orders of magnitude in nature44,45,46,47,48, affect the relationship between the timing of antibiotic administration after the onset of range expansion and the frequency of transconjugant cells at the expansion frontier. More specifically, we tested under what conditions a unimodal relationship is likely to occur. To achieve this, we varied the plasmid transfer and loss probabilities in our individual-based computational model and quantified the effects. When we set the plasmid loss probability to a low value (0.001), we observed a monotonically increasing relationship where the frequency of transconjugant cells at the expansion frontier increases with the plasmid transfer probability (Spearman rank correlation test; rho\u2009=\u20090.88, P\u2009=\u20091.8\u2009\u00d7\u200910\u221210) (Fig.\u00a05a, d), which is counter to our original expectation. This represents a scenario where the plasmid transfers at a faster rate than it is lost, thus ensuring its persistence in the system. In contrast, when we set the plasmid loss probability to a high value (0.015), we observed a monotonically decreasing relationship where the frequency of transconjugant cells at the expansion frontier decreases with the plasmid transfer probability (Spearman rank correlation test; rho\u2009=\u2009\u22120.63, P\u2009=\u20090.00017) (Fig.\u00a05c, d), which is consistent with our original expectation. This represents a scenario where the plasmid is lost from the plasmid donor cells at a faster rate than the plasmid can transfer, eventually leading to it being purged from the system. Finally, when we set the plasmid loss probability to an intermediate value (0.005) (Fig.\u00a05b), we observed a unimodal relationship where the frequency of transconjugant cells reaches a maximum at an intermediate antibiotic administration time (Fig.\u00a05d), which is qualitatively consistent with our experimental observations (Fig.\u00a03b). This represents a scenario where the plasmid transfer and loss processes are balanced and counteract each other.\n\nWe performed simulations with pairs of plasmid donor (magenta) and potential recipient (green) cells where we administered antibiotics at different times after the onset of range expansion and quantified the frequency of the transconjugant at the expansion frontier. Representative simulations when the plasmid loss probability was set to a 0.001, b 0.005, or c 0.015 with a fixed plasmid transfer probability of 0.002. d Quantification of the transconjugant frequencies at the expansion frontiers for (a\u2013c). Datapoints are measurements for independent simulations (n\u2009=\u20095). e Quantification of the relationship between the time of antibiotic administration and the frequency of transconjugant cells at the expansion frontier for different combinations of plasmid transfer and loss probabilities. For each pair of plasmid transfer and loss probabilities and for each antibiotic administration time, we performed five simulations.\n\nWe next expanded our modeling to investigate broader combinations of plasmid transfer and loss probabilities and quantified the resulting relationships between the time of antibiotic administration and the frequency of transconjugant cells at the expansion frontier. We varied the plasmid transfer probability from 0.001 to 0.007 and the plasmid loss probability from 0.0005 to 0.02 while fixing all other parameters. We then quantified the frequency of transconjugant cells at the expansion frontier. We found that combinations of plasmid transfer and loss probabilities can give rise to four distinct relationships (Fig.\u00a05e). Monotonically increasing relationships (colored light red) occur for high plasmid transfer probabilities and low plasmid loss probabilities, which is expected as such conditions enable prolonged plasmid persistence prior to antibiotic administration. Conversely, monotonically decreasing relationships (colored light green) occur for low plasmid transfer probabilities and high plasmid loss probabilities, which is again expected as such conditions reduce plasmid persistence prior to antibiotic administration. Flat relationships (colored yellow) can be considered as the extreme case of monotonically decreasing relationships where the high plasmid loss probabilities purge plasmids before the earliest antibiotic administration time. Finally, unimodal relationships (colored light blue) lie at intermediate combinations of plasmid transfer and loss probabilities, which emphasizes that unimodal relationships emerge only when the plasmid transfer and loss processes are relatively balanced. Thus, predicting the spread of plasmid-encoded AR during range expansion requires knowledge of both plasmid transfer and loss probabilities.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "Combining experiments with individual-based computational modeling, we demonstrated how the timing of antibiotic administration drives the spread of AR-encoding plasmids as surface-associated microbial communities expand across space. We showed that plasmid spread into AR-sensitive cells peaks at intermediate antibiotic administration times. These intermediate times are nested in a narrow window when the spatial intermixing of plasmid donors and potential recipients is maximal. The counterbalancing effects of plasmid transfer and loss predict the impact of the timing of antibiotic administration on the spread of AR.\n\nIn surface-associated microbial communities experiencing antibiotic pressure, the spread of plasmid-encoded AR is maximized for patterns of spatial organization displaying large numbers of contacts between plasmid donors and recipients (Fig.\u00a03). The emergence of such patterns is dependent on the successional stage of the community; at early stages of community development the expansion frontier becomes rapidly dominated by AR types due to a fitness advantage over sensitive individuals (Fig.\u00a03a). At late stages of community development, the long period of antibiotic-free conditions allows sensitive cells to dominate the expansion frontier due to the fitness cost derived from plasmid maintenance in their AR counterparts49 (Fig.\u00a03a). Antibiotic administration at a late stage of community development, therefore, occurs after the purging of plasmid-encoded AR and is expected to be the point when the community is most vulnerable to antibiotic stress. This vulnerability does not imply that late antibiotic administration times can completely eradicate the microbial community of interest50,51, but it marks a point when the active AR fraction is at a minimum. Intermediate stages of community succession have allowed the preferential proliferation of plasmid-free individuals without completely outcompeting the AR fraction. This maximizes the contacts between plasmid-carrying and plasmid-free cells that promote plasmid transfer and results in the maximal spread of plasmid-encoded AR (Fig.\u00a03a). The frequent mixing and proliferation of plasmid-carrying and plasmid-free populations of enterobacterial pathogens is considered an important factor of AR persistence and spread in the gut lumen25. A temporal perspective on plasmid-encoded AR spread in the gut could thus improve the understanding of the processes leading to recalcitrant AR populations on surfaces.\n\nThe proliferation of new transconjugants was highly predictable at intermediate stages of community succession, but stochastic at early and late stages. Small population sizes are susceptible to stochastic drift52, which during range expansion can drive deleterious genetic variants to fixation53. We observed small and unpredictable numbers of transconjugant lineages at the expansion frontier at both early and late antibiotic administration times. At early administration times, a few \u201clucky\u201d sensitive lineages were able to obtain the AR-encoding plasmid and benefit from antibiotic administration to colonize the expansion frontier. Likewise, at later stages of community succession, a few AR lineages had drifted to the expansion frontier and proliferated upon antibiotic administration (Supplementary Fig.\u00a04). This is an example of how the persistence of a deleterious mutation drifting at the frontier of a range expansion can proliferate when environmental conditions change54. The lineage diversity of an expanding population sets the basis for its subsequent adaptation to novel conditions55, a factor that can determine the probability of AR spread into different environments. Our results suggest that the timing of antibiotic administrations can be important for controlling the heterogeneity (i.e. lineage diversity) of AR, which should be a key determinant for predicting the potential threat of a community carrying AR genes56,57,58,59. The number of unique transconjugant cell lineages followed the same unimodal trend as population sizes, where lineage diversity peaked at intermediate successional stages. However, the size and genetic diversity of the transconjugant population decoupled over time after antibiotic administration (Supplementary Fig.\u00a03), where some lineages drifted to extinction while others kept growing. These observations are conceptually similar to those by Stevenson et al.60, who showed that mercury resistance encoded by conjugative plasmids spreads predominantly horizontally in the absence of mercury stress (here time before antibiotic administration), while resistance spreads predominantly vertically via clonal expansion in the presence of mercury stress (plasmid spread after antibiotic administration). Consistent with dynamics in range expansions under the strong effects of genetic drift32, our findings indicate that the diversity of newly formed AR populations is determined by the time lapsed before antibiotic administration, but that this diversity of the AR lineages surviving the treatment decreases rapidly over time.\n\nThe counterbalancing effects of plasmid transfer and loss determine the time when antibiotic administration most effectively promotes the proliferation of plasmid-encoded AR. The transfer and loss rates of plasmids can offset the influence of plasmid fitness costs in the maintenance of AR46,48,60. For example, Lopatkin et al.10 showed experimentally that nine common plasmids across six incompatibility groups can persist in microbial consortia in the absence of positive selection provided transfer rates are sufficiently high. Similarly, Porse et al.61 found that plasmid loss rates offset the fitness cost of 14 plasmids found in E. coli strains commonly involved in urinary tract infections, driving AR persistence in those strains. We show that the interplay between these factors determines the spread of AR-encoding plasmids differently depending on the successional stage of the community. Very high plasmid transfer or very low loss rates can both lead to the preservation of plasmids at the expansion frontier over time, leading to maximal AR spread at later times of antibiotic administration (Fig.\u00a05d, e). This finding confirms previous work showing that it only takes the presence of a highly proficient donor (one with high plasmid transfer and low plasmid loss rates) to maintain plasmids in adjacent poor recipient populations (those with low plasmid transfer and high plasmid loss rates)8. On the other end, very low plasmid transfer or very high plasmid loss rates rapidly purge the plasmid from the expansion frontier and prevent AR spread (Fig.\u00a05d, e), and it is only when plasmid transfer and loss rates balance each other that maximal AR spread occurs at intermediate stages of community succession.\n\nPlasmid loss rates are highly variable even among strains of the same species46, and in complex communities, plasmid persistence in the absence of positive selection is associated with the proportion of highly proficient versus poor strains at maintaining and transferring the plasmid62. Intuitively, the presence of poor plasmid recipients is higher in complex communities, which could hamper the maintenance of AR. However, Kottara et al.62 found that plasmid transfer and loss rates can offset the influence of the selective environment and of specific plasmid features to determine plasmid spread in soil microbial communities. Similar experiments to those shown here that track plasmid dynamics in more complex communities containing taxa with different plasmid transfer and loss rates would help understand how varying levels of plasmid transfer and loss rates influence the spread of plasmid-encoded AR in natural systems. We are yet aware that estimating the plasmid transfer probability is not trivial as numerous abiotic and biotic factors need to be considered such as nutrient level, pH, and temperature63,64,65. Especially in spatially structured environments, individual cells likely encounter different environmental conditions and thus have different transfer and loss rates at different locations.\n\nOur results can aid the mechanistic understanding of the spread of plasmid-encoded AR on surfaces colonized by microbial communities while acknowledging that plasmid dynamics are likely far more complex in natural systems. The growing body of studies that identify high plasmid transfer or low plasmid loss rates as the primary mechanism for AR maintenance in the absence of antibiotic pressure (e.g., Lopatkin et al.10) suggests cessation of antibiotic use will not be sufficient to eradicate plasmid-encoded AR over prolonged periods of time. Because the timing of antibiotic administration can be a factor that varies under different circumstances66,67,68, we believe it is important to understand the relationships between antibiotic administration times and the spread of AR. We thus believe that a better temporal understanding of the interplay between plasmid transfer and loss in more complex microbial communities is essential to better understand the problem of AR persistence in efforts to tackle the global AR crisis.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "We provide the genotypes of all the P. stutzeri strains used in this study in Supplementary Table\u00a02. Detailed descriptions of the construction of our strains can be found elsewhere37,40,69. Briefly, the donor and recipient strains are identical except that they carry different isopropylthio-\u03b2-galactoside (IPTG)-inducible fluorescent protein-encoding genes on their chromosome. The donor strain contains a red fluorescent protein-encoding gene (echerry) while the recipient strain contains a green fluorescent protein-encoding gene (egfp)40. This enables us to distinguish and quantify the abundances of different strains when grown together37,38,39. The donor strain additionally carries an R388-derivative plasmid pAR145 (pSU2007 aph::cat-PA1/04/03-cfp\u2217-T0) that encodes for chloramphenicol resistance and an IPTG-inducible ecfp gene (encoding for blue fluorescent protein)41,42.\n\nWe used a modified version of the range expansion experimental protocols reported elsewhere32,37. We first grew the pAR145 donor strain overnight with liquid lysogeny broth (LB) medium amended with chloramphenicol (25\u2009\u00b5g\u2009mL\u22121) to maintain pAR145 and the potential recipient strain overnight in LB medium without chloramphenicol. After growth, we adjusted the optical density at 600\u2009nm (OD600) of each overnight culture to 2.0 with 0.89% (w/v) sodium chloride solution, mixed the pAR145 donor and potential recipient cultures together at a volumetric ratio of 1:1, and deposited 1\u2009\u00b5L aliquots of the mixture onto the surfaces of separate replicated LB agar plates amended with 0.1\u2009mM IPTG. We then incubated the LB agar plates at room temperature. Note that in order to ensure appropriate growth rates for investigation, we used 30% LB agar plates (1.5% agar) to reduce the nutrient level and slow the expansion velocity. We administered chloramphenicol at 13 different time points after the onset of range expansion, which are: 0, 2, 4, 6, 12, 24, 36, 48, 60, 72, 84, 96, and 108\u2009h. We administered chloramphenicol (the final concentration within the agar plate was 25\u2009\u00b5g/mL) by depositing a total volume of 10\u2009\u00b5L to each LB agar plate as four 2.5\u2009\u00b5L point sources located ~2\u2009mm away from the expansion frontier. This concentration of chloramphenicol completely represses the growth of plasmid-free cells under our experimental conditions. We performed five independent biological replicates for each chloramphenicol administration time.\n\nWe acquired confocal laser scanning microscopy (CLSM) images of our range expansions using a Leica TCS SP5 II confocal microscope (Leica Microsystems, Wetzlar, Germany). We used objectives \u00d75/0.12na (dry) and \u00d710/0.3na (dry) (Etzlar, Germany). We set the laser emission at 458\u2009nm for the excitation of blue fluorescent protein, at 488\u2009nm for the excitation of green fluorescent protein, and at 514\u2009nm for the excitation of red fluorescent protein. We used an image frame size of 1024\u2009\u00d7\u20091024 and a pixel size of 3.027\u2009\u00b5m.\n\nWe analyzed the images in ImageJ (https://imagej.nih.gov/ij/) using Fiji plugins (v. 2.1.0/1.53c) (https://fiji.sc). We first auto-thresholded channel four to obtain outlines of the expansion areas using the \u2018Otsu dark\u2019 function and used this information to quantify expansion size and signals at the expansion frontier. We next auto-thresholded channel three (the green channel) using the \u2018Intermodes dark\u2019 function, followed by applying the functions \u201cFill Holes\u201d and \u201cDespeckle\u201d to remove noise. To segment and extract the red channel, we auto-thresholded channel five using the \u201cHuang dark\u201d function followed by the same noise-removing steps. We used the same approach to quantify the plasmid load by segmenting channel one (the blue channel). We multiplied the binarized blue channel with the red channel to obtain the signal for the pAR145 donor (composite magenta color) or the blue channel with the green channel to obtain the signal for transconjugants (composite cyan color). The composite channels for magenta and cyan register all the signals of either pAR145 donors or transconjugants across the whole expansion area. Next, we multiplied the composite channels with the extracted expansion outline to obtain the donor or transconjugant at the expansion frontier. Because all the images are binary images, the outline image is a \u201cring\u201d that only contains values of 255 at the periphery, while the rest are all 0. Therefore, by multiplying the outline image with the composite channel of magenta or cyan, we are able to capture signals lying at the expansion frontier. We finally applied the \u2018analyze particle\u2019 function to obtain counts and sizes of desired objects such as the expansion size, frequency of transconjugants at the expansion frontier, and the ratio of two strains at the expansion frontier.\n\nWe quantified spatial intermixing (referred to as the intermixing index) between strains from the CLSM images using Fiji plugins (v. 2.1.0/1.53c) (https://fiji.sc). We first cropped all the images to squares and applied the \u2018Intermodes dark\u2019 function to channel three (green channel) followed by the \u201cDespeckle\u201d function twice to remove noise. We then used the Sholl analysis plugin70 on the binarized channel three to calculate the number of intersections between the background and information-containing parts of the image. We next extracted data over defined ranges (for Fig.\u00a02b, between radii of 2500 and 5000\u2009\u00b5m; for Fig.\u00a03c, between radii of 2000 and 4000\u2009\u00b5m). We excluded radii <2500 or 2000\u2009\u00b5m for two reasons: first, they do not accurately capture the spatial features caused by the range expansion process (i.e., they capture the inoculation area). Second, fluorescent signals at smaller radii are difficult to precisely resolve, thus creating noise. To obtain the global intermixing index, we summed the individual intermixing indices at 10\u2009\u00b5m radial increments within the desired ranges and then normalized the sum by the number of radial increments that contained non-zero values. We quantified individual intermixing following the descriptions provided elsewhere37.\n\nWe used a colony collision assay as described elsewhere71 to measure the relative growth rate, or plasmid cost, of plasmid-carrying and plasmid-free strains in spatially structured environments. We first grew monocultures of the pAR145-carrying and pAR145-free strains independently and adjusted the OD600 of each overnight culture to 2.0 with 0.89% (w/v) sodium chloride solution. We then used a pipetting robot to place two 1\u2009\u00b5L drops, one of which was the pAR145 donor and the other the potential recipient, 3\u2009mm apart from each other onto replicated LB agar plates (1.5% agar). We next incubated the LB agar plates at room temperature for 96\u2009h to allow the drops to form colonies and the colonies to collide with each other. We then estimated the relative growth rates of strains based on the arc of the collision boundary between the two corresponding colonies and the radii of the colonies using Eq. (1) below:\n\nwhere \\(l\\) is the distance between two colonies, \\(s\\) is the selective advantage or the cost that pAR145 confers, \\({v}_{1}\\) is the expansion velocity of the pAR145-free colony that is growing faster, and \\({v}_{2}\\) is the expansion velocity of the pAR145-carrying colony that is growing slower. \\({R}_{\\rm {{b}}}\\) is the radius of the circle generated by the arc at the boundary, and knowing \\({R}_{\\rm {{b}}}\\) and \\(l\\) is sufficient to derive \\(s\\). We quantified \\({R}_{\\rm {{b}}}\\) and \\(l\\) for 4 replicates using Adobe Illustrator (version 27.0.1) to manually draw lines and circles and to extract values. Image scale can differ among replicates, but since \\({R}_{\\rm {{b}}}\\) and \\(l\\) are proportional in one image, \\(s\\) will not be affected. We provide all of the data in Supplementary Table\u00a01\n\nWe customized a spatially explicit individual-based computational model to mimic our experimental system using the CellModeller 4.3 framework43. CellModeller is a Python-based, open-source platform for modeling large-scale multi-cellular systems, such as biofilms, plant tissue, and animal tissue. We modeled individual rod-shaped bacterial cells as three-dimensional capsules that grow by extending their length. Capsules experience viscous drag and cannot grow into one another. As they grow, cells add a constant volume until they reach a critical size where they then divide into two daughter cells, ensuring cell size homeostasis. In CellModeller, each cell is abstracted as a computational object referred to as a cellState (cs) that contains all the information regarding that individual cell, including its spatial position (pos[x, y, z]), rotational orientation (dir[x, y, z]), cell length (length), growth rate (growthRate), and cell type (cellType). The cell-type is an arbitrary label that allows us to simulate different cellular behaviors. Our model contains four cell types: cellType 0 simulates a potential recipient cell colored green; cellType 1 simulates a plasmid donor cell colored magenta; cellType 2 simulates the plasmid-free status of cellType1, colored red; and cellType 3 simulates the plasmid-carrying status of cellType 0, colored cyan. These four cellTypes allow us to distinguish each type from the others and record information, for example, on spatial positioning during the simulation.\n\nIn CellModeller, individual cells are modeled as cylinders of length \\(l\\) capped with hemispheres that result in a capsule shape, with both hemispheres and the cylinder having a radius \\(r\\). At each simulation step, a cell increases in length based on its growth rate parameter, which is physically constrained by the other cells in its physical proximity. In this work, we initiated cells to have \\(r=0.04\\) and \\(l=2\\) and set cells to divide when their length reaches the critical division length \\({l}_{{{\\rm {div}}}}\\) with Eq. (2) below:\n\nwhere \\({l}_{0}\\) is the initial cell length at birth and \\(G\\) is a random Gaussian distribution with mean \\(\\triangle\\) \\(=2\\) and standard deviation \\(\\sigma=0.45\\). Therefore, when a cell divides, the two daughter cells are initiated with \\({l}_{{{\\rm {div}}}}\\) /2 and a new target division length is assigned to each daughter cell calculated from the equation above. The addition of constant mass has been found to accurately model bacterial division while maintaining cell size homeostasis as described elsewhere72.\n\nWe modified our model to integrate plasmid transfer and loss. As part of the biophysics in CellModeller, physical contacts between cells are calculated at each step to minimize any overlap between cells43. We altered the code such that each cell kept track of its contacts, thus allowing us to model plasmid transfer when cells were in contact. This function is activated by setting the argument \u2018compNeighbours\u2009=\u2009True\u2019 when initiating the biophysical model. When plasmid donor and recipient cells were in contact, we applied a constant probability per unit time of plasmid transfer. For all figures except for Fig.\u00a05e, we applied a constant probability (Pc\u2009=\u20090.002) for plasmid transfer and varied the probability of plasmid loss to investigate the interplay between the two. We applied \u201cantibiotics\u201d at six different time steps: 0, 100, 200, 400, 600, and 800. We modified the self-defined function of updating cell status where we only allowed plasmid-carrying cells to continue growing after \u201cantibiotic treatment\u201d. Plasmid transfer and loss can occur during the entire simulation regardless of whether \u201cantibiotics\u201d were applied or not.\n\nWe ran all high-resolution simulations in parallel on Piz Daint, a supercomputer located at the Swiss National Supercomputing Center (CSCS). We loaded the modules \u201cdaint-mc\u201d and the high throughput scheduler \u201cGREASY\u201d for high throughput simulations. We used the Slurm workload manager to submit jobs via the command \u201csbatch\u201d. We generated job scripts following the template scripts on Slurm jobscript generator.\n\nWe extracted and generated simulation data on Piz Daint and converted all necessary information from pickle files to csv files. We then performed all statistical analyses using R Studio Version 1.3.1073 (https://www.rstudio.com). We used the two-sample two-sided Welch test for all pair-wise comparisons, and we therefore did not make any assumptions regarding homogeneity of variances among our datasets. To identify the trend of unimodality, monotonic increasing, monotonic decreasing, or flat, we obtained the maximum average value and compare it with the earliest treatment time point and the latest treatment time point. If the maximum value emerges at an intermediate time point, then the values at the beginning and end should be significantly lower than the maximum observed value, thus indicating a unimodal trend. If the maximum value is only significantly higher than the earliest treatment point, then it indicates a monotonically increasing trend. If it is only significantly higher than the latest treatment point, then it indicates a monotonically decreasing trend. Finally, if it is not significantly higher than either of these two endpoints, then it indicates a flat trend. All sample sizes (n) reported in the results are the number of independent biological replicates.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data generated in this study have been deposited in the Eawag Research Data Institutional Collection (ERIC) repository (https://opendata.eawag.ch/) at the following https://doi.org/10.25678/0008EB.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "All codes used in this study are publicly available in the Eawag Research Data Institutional Collection (ERIC) repository (https://opendata.eawag.ch/) at the following https://doi.org/10.25678/0008EB.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "We thank Stuart Dennis for assistance with the simulation performance on Piz Daint. We acknowledge access to Piz Daint at the Swiss National Supercomputing Centre (CSCS) under Eawag\u2019s share with the project ID em09. We thank Daniel Angst for assistance with the pipetting robot at ETH Z\u00fcrich. Y.M. was supported by grants from the Swiss National Science Foundation (31003A_176101 and 310030_207471) awarded to D.R.J. J.R. was supported by an Early PostDoc Mobility grant from the Swiss National Science Foundation (P2EZP3_199849) awarded to J.R.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Department of Environmental Microbiology, Swiss Federal Institute of Aquatic Science and Technology (Eawag), 8600, D\u00fcbendorf, Switzerland\n\nYinyin Ma,\u00a0Josep Ramoneda\u00a0&\u00a0David R. Johnson\n\nDepartment of Environmental Systems Science, Swiss Federal Institute of Technology (ETH), 8092, Z\u00fcrich, Switzerland\n\nYinyin Ma\n\nCooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, 80309, USA\n\nJosep Ramoneda\n\nInstitute of Ecology and Evolution, University of Bern, 3012, Bern, Switzerland\n\nDavid R. Johnson\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nY.M. and D.R.J. conceived and developed the main research question. Y.M. and D.R.J. designed the laboratory experiments. All authors designed the in silico experiments. Y.M. performed the experiments and individual-based model simulations. All authors analyzed and interpreted the data. All authors wrote and revised the manuscript. All authors reviewed and approved the final version of the manuscript.\n\nCorrespondence to\n                Yinyin Ma or David R. Johnson.",
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+            "section_text": "Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.",
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+            "section_name": "About this article",
+            "section_text": "Ma, Y., Ramoneda, J. & Johnson, D.R. Timing of antibiotic administration determines the spread of plasmid-encoded antibiotic resistance during microbial range expansion.\n                    Nat Commun 14, 3530 (2023). https://doi.org/10.1038/s41467-023-39354-z\n\nDownload citation\n\nReceived: 09 March 2023\n\nAccepted: 08 June 2023\n\nPublished: 14 June 2023\n\nVersion of record: 14 June 2023\n\nDOI: https://doi.org/10.1038/s41467-023-39354-z\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Cell Communication and Signaling (2025)\n\nNature Communications (2024)\n\nCommunications Biology (2024)\n\nEnvironmental Geochemistry and Health (2024)",
+            "section_image": []
+        }
+    ]
+}

1a87241a9dec799841c8d77ab69577ec395483dcd133ec4e1f83098d8e5db6b9/metadata.json

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+{
+    "title": "Spontaneous shock waves in pulse-stimulated flocks of Quincke rollers",
+    "pre_title": "Spontaneous shock waves in pulse-stimulated flocks of Quincke rollers",
+    "journal": "Nature Communications",
+    "published": "03 November 2023",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM3_ESM.pdf"
+        },
+        {
+            "label": "Supplementary Movie 1",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM4_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 2",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM5_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 3",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM6_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 4",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM7_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 5",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM8_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 6",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM9_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 7",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM10_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 8",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM11_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 9",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM12_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 10",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM13_ESM.mp4"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42633-4/MediaObjects/41467_2023_42633_MOESM14_ESM.zip"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-023-42633-4#Sec10"
+    ],
+    "code": [],
+    "subject": [
+        "Colloids",
+        "Fluids"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3011793/v1.pdf?c=1699096228000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3011793/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-023-42633-4.pdf",
+    "preprint_posted": "08 Jun, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Active matter demonstrates complex spatiotemporal self-organization not accessible at equilibrium and the emergence of collective behavior. Fluids comprised of microscopic Quincke rollers represent a popular realization of synthetic active matter. Temporal activity modulations, realized by modulated external electric fields, have been recently suggested as an effective tool to expand the variety and complexity of accessible dynamic states in active ensembles. Here, we report on the emergence of shock wave patterns composed of coherently moving particles energized by a pulsed electric field. The shock waves emerge spontaneously and move faster than the average particle speed. Combining experiments, theory, and simulations, we demonstrate that the shock waves originate from intermittent spontaneous vortex cores due to a vortex meandering instability. They occur when the rollers' translational and rotational decoherence times, regulated by the electric pulse durations, become comparable. The phenomenon does not rely on the presence of confinement, and multiple shock waves continuously arise and vanish in the ensemble. Our findings highlight the importance of the interaction timescales in the emergence of dynamic patterns under temporally modulated energy injection. The results may stimulate design strategies for reconfigurable self-assembled active architectures.Physical sciences/Materials science/Soft materials/ColloidsPhysical sciences/Materials science/Soft materials/Fluids",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SI.pdfmovieS1Ripples.mp4movie S1movieS2Shockwaves.mp4movie S2movieS3Shockwaves.mp4movie S3movieS4Flocks.mp4movie S4movieS5Vortices.mp4movie S5movieS7Simulation2D.mp4movie S7movieS8Simulation3D.mp4movie S8",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Active matter demonstrates complex spatiotemporal self-organization not accessible at equilibrium and the emergence of collective behavior. Fluids comprised of microscopic Quincke rollers represent a popular realization of synthetic active matter. Temporal activity modulations, realized by modulated external electric fields, represent an effective tool to expand the variety of accessible dynamic states in active ensembles. Here, we report on the emergence of shockwave patterns composed of coherently moving particles energized by a pulsed electric field. The shockwaves emerge spontaneously and move faster than the average particle speed. Combining experiments, theory, and simulations, we demonstrate that the shockwaves originate from intermittent spontaneous vortex cores due to a vortex meandering instability. They occur when the rollers\u2019 translational and rotational decoherence times, regulated by the electric pulse durations, become comparable. The phenomenon does not rely on the presence of confinement, and multiple shock waves continuously arise and vanish in the system.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Active matter encompasses a broad class of interacting self-propelled particles that transduce energy from the environment into mechanical motion1,2,3,4,5. With the increase in particle concentration, active matter exhibits a transition from individual to collective behavior manifested by various patterns of coherent locomotion: jets, bands, flocks, vortices6,7,8,9. This behavior was observed in many realizations of active matter, from macroscopic bird flocks, fish schools to microscopic bacterial suspensions, cytoskeletal extracts, and field-driven Janus particles, spinners and rollers10,11,12,13,14,15,16,17,18,19,20.\n\nMicroscopic Quincke rollers are a popular realization of synthetic active matter. Quincke rollers\u2014dielectric colloids suspended in a weak electrolyte and energized by a static (DC) electric field\u2014utilize the electrohydrodynamic Quincke rotation phenomenon21,22 and inject energy and angular momentum into the system at the microscopic level. In the presence of a solid surface, the Quincke rotation is transformed into a horizontal translation. Quincke rollers demonstrate a remarkable level of complex collective behaviors and self-organization ranging from the emergence of correlated flocks to the formation of global vortices, polar bends, and oscillating flows under confinement7,23,24,25. Temporal modulation of the activity of Quincke rollers via a pulsed electric field is an effective technique to control the persistence lengths and collective behavior of rollers26,27,28. By manipulating the duration \u03c4on and intervals \u03c4off between the pulses of the same polarity, a set of novel dynamic states, such as multiple localized vortices and lattices emerge28. The new patterns are often attributed to dynamic system memory and changing interparticle force balances at time scales comparable to the Maxwell-Wagner polarization relaxation time \u03c4MW\u2009=\u2009(\u03f5p\u2009+\u20092\u03f5f)/(\u03c3p\u2009+\u20092\u03c3f), where \u03f5p,f and \u03c3p,f are respective particle and fluid permittivities and conductivities26,28.\n\nThe suspending media also plays a significant role in active ensembles dynamics29,30,31,32,33. In the case of Quincke rollers, the observed complex dynamics of rolling colloids is always accompanied by electrohydrodynamic flows induced by the applied electric field powering the system. The strength of the flows grows with the amount of charge in the media, which in the case of the majority of Quincke experimental systems is regulated by the ionic surfactant AOT (aerosol dioctyl sulfosuccinate sodium) salt and the absorbed water content34,35,36.\n\nHere, we report on the emergence of spontaneous shockwaves that became accessible under temporal activity modulations in crowds of colloidal Quincke rollers with the increased strength of the electrohydrodynamic flows. In response to the increased media conductivity, the electrohydrodynamic flows are no longer negligible and promote intermittent rollers densifications (dynamic ripples) in the system at the uncorrelated gas state. The particle shockwaves continuously emerge and dissipate on the background of spontaneous density variations in the transition region between the gas and vortex states. The shockwaves originate in local high-density regions where rollers develop velocity correlations and spontaneously start to move collectively faster than the average particle speed in the ensemble. The dependent velocity distributions have also been observed in related magnetic roller systems37. We combine experiments and continuum computational modeling to demonstrate that the shock waves originate from the transient vortex cores due to vortex meandering instability and occur when the active rollers\u2019 translational and rotational decoherence times become comparable. Multiple shock waves continuously appear and vanish in the system. Our work highlights the crucial importance of the interaction timescales in the emergence of dynamic patterns under temporal modulation of the activity and suggests pathways to manipulate and enrich collective dynamics in active systems.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "In our experiments, we use polystyrene spheres (d\u2009=\u20094.8 \u03bcm) dispersed in a weakly conductive liquid that are sandwiched between two ITO-coated glass slides and energized by a static (DC) electric field (see \u201cMethods\u201d for the details). Above a certain threshold value of the field strength, Ec, the particles start to spontaneously rotate due to the electrohydrodynamic Quincke rotation phenomenon22 and turn into rollers exploring the bottom plate of the experimental cell. A typical velocity of the rollers under a static electric field E\u2009=\u20093.2 V/\u03bcm used in our experiment is 1.9 mm/s. The motion of the particles proceeds over the background of the electrohydrodynamic (EHD) flows between the electrodes. The EHD flows are always present in the energized system and scale with the amount of charge available in the liquid and the applied electric field. However, the effect of the EHD flows on the observed particles dynamics under typical field conditions for the Quincke rollers is often negligible. At high electric field, the EHD flows may become dominant and result in lifting off the particles and three-dimensional patterns33. To manipulate the electrohydrodynamic flows in our experiments, we increase the medium\u2019s conductivity by absorbing water to \u03c3\u2009=\u20097.8\u2009\u00d7\u200910\u22128 S/m.\n\nThe behavior of the rollers becomes significantly different if the activity of particles is modulated by the pulsed electric field26,28. Figure\u00a01A demonstrates the dynamic phase diagram in our system under pulsed electric field excitations as a function of the pulse duration, \u03c4on, and the interval between the pulses, \u03c4off. The magnitude of the pulses was fixed at E\u2009=\u20093.2 V/\u03bcm. The system now exhibits a new striking collective response\u2014spontaneous shockwaves\u2014contentiously emerging at different locations of the ensemble, propagating and dissipating. Typical shockwave fronts are shown in Fig.\u00a01B (see also Supplementary Movie\u00a01). The new dynamic phase has not been previously observed at lower medium conductivity (\u03c3\u2009=\u20095.4\u2009\u00d7\u200910\u22128 S/m) under identical activity modulations28 where only flocks, vortices, and lattices have been observed (Supplementary Fig.\u00a01). Noticeably, the shock waves propagate on top of the spontaneous local particle number densifications, ripples, clearly visible in Supplementary Movies\u00a01 and 2. The state of the system at exactly the same driving field conditions as the ripples state demonstrated but at lower media conductivity (\u03c3\u2009=\u20095.4\u2009\u00d7\u200910\u22128 S/m) is shown in Supplementary Movie\u00a09. Instead of ripples, the system organizes in a vortical motion without significant densifications. In principle, the ripple-free gas state at low conductivity is also achievable but at different driving conditions, see Supplementary Movie\u00a010. The formation of the ripples is driven by electrohydrodynamic flows between the conductive plates that, in the presence of the particles at the electrode distorting the local electric field, result in tangential fluid flows directed towards the particles giving rise to an effective interparticle attraction and local particle densifications33,38,39. Shockwaves are spontaneously excited from the local transient densification regions of ripples generating propagating spiral-like wavefronts that will eventually dissipate or be interrupted by another shockwave propagating in the system, see Fig.\u00a01D and Supplementary Movie\u00a03.\n\nA Phase diagram as a function of pulse duration \u03c4on and the pulse interval \u03c4off. The phases in the gray area marked with * were reported previously in ref. 26. The average particle area fraction \u03d50\u2009=\u20090.11. The conductivity of the medium \u03c3\u2009=\u20097.8\u2009\u00d7\u200910\u22128 S/m. The field magnitude E\u2009=\u20093.2 V/\u03bcm. See Supplementary Movies\u00a01\u20135 and Supplementary Fig.\u00a03 for more details on dynamic phases. B Experimental snapshot of multiple shock waves. The red arrows indicate the propagation directions of two major waves. The scale bar is 1 mm. C Representative probability distribution functions (PDFs) of the particle velocities for different dynamic phases: gas (\u03c4on = 4.0 ms; \u03c4off = 2.7 ms), shock waves (\u03c4on = 4.9 ms; \u03c4off = 1.8 ms), flocks (\u03c4on = 5.3 ms; \u03c4off = 1.4 ms) and vortices (\u03c4on = 6.6 ms; \u03c4off = 0.1 ms). The period of excitation T\u2009=\u20096.7 ms. The second peak of PDF for the shock waves regime is shaded by pink, indicating fast particles (\u2223u\u2223/\u2223u0\u2223\u2009>\u20092) involved in the shock waves. \u2223u0\u2223 is the average particle speed in the first peak in the shock waves regime and the average speed of all particles in other phases. D Snapshots of a shock wave propagating from top right to bottom left. Particles with (\u2223u\u2223/\u2223u0\u2223\u2009>\u20092) are colored in red. The scale bar is 0.5 mm. Source data are provided as a Source Data file.\n\nThe build-up of the particle velocity correlations in the system resulting in collective phases is controlled by the activity modulations. A particle activity and retained polarization memory increase with increasing \u03c4on and/or shortening \u03c4off (see Supplementary Fig.\u00a02). The probability distribution function (PDF) of the particle velocities in different dynamic phases of the system are shown in Fig.\u00a01C. The regime of shockwaves has two distinctive peaks in its distribution corresponding to the particles in a gas phase performing uncorrelated motion (low-velocity peak) and fast particles involved in the intermittent shockwaves characterized by a short-lived correlated motion of the particles (high-velocity peak). The short-lived correlations between the particles in a shockwave are promoted by the particle densifications of the ripples driven by the electrohydrodynamic flows. Those correlations decay as the interparticle distances increase with the wave propagation, resulting in the eventual dissipation of the wavefront. The shock waves appear at the narrow transition region before the system switches to a vortex phase.\n\nThe excitation process of a shockwave is illustrated in Fig.\u00a02 and Supplementary Movie\u00a06. Rollers first slowly accumulate to dense vertices of ripples via constrained random walks under electrohydrodynamic flows (Fig.\u00a02C). Accidentally, rollers gain high velocities (\u2223u\u2223/\u2223u0\u2223\u2009>\u20092) and form small dynamic clusters with particle velocities aligned (t\u2009=\u20090). Most dynamic clusters dissipate over time or explode as ripples while a small cluster fraction merges (t\u2009=\u20090.02 s), grows (t\u2009=\u20090.05 s), and eventually forms a vortex (t\u2009=\u20090.1 s). Due to the vortex\u2019s meandering instability in an unconfined environment, the unstable vortex quickly breaks into a spiral shock wave (t\u2009=\u20090.2 s) which propagates (t\u2009=\u20090.3 s) and eventually dissipates. The excitation process is also presented by typical particle trajectories, which show the circular motion of the initial transient vortex and spiral trajectories of the final shock wave (Fig.\u00a02D).\n\nA Snapshots illustrating the excitation of a shockwave. Fast particles (\u2223u\u2223/\u2223u0\u2223\u2009>\u20092) are colored in red. The scale bar is 0.2 mm. B Velocity vectors of fast particles colored according to the velocity directions. Insets are zoomed-in region containing fast particles. A transient vortex is formed before transformation into a shockwave. C, D Trajectories of the particles inside of a square indicated in (A) before (C) and after (D) the shockwave excitation. Particle positions at t\u2009=\u20090 are marked with circles. d is the particle diameter. Only 10% particle trajectories are shown for a better visualization.\n\nRollers accelerate when a shockwave propagates through and forms a densified region associated with higher particle velocities. The shape of the shockwave is shown in the corresponding microscopy image, the particle density, and the particle velocity map in Fig.\u00a03A\u2013C. The shockwave bulges in the propagation direction, causing typical asymmetric density and velocity profiles with sharply curved wavefronts and relatively shallow tails. To better understand the effect of the shockwave on individual rollers, we track a few selected particles and measure their instantaneous positions and velocities when a shock wave passes through (see Fig.\u00a03D\u2013E). Rollers first perform Brownian-like movements, showing random trajectories and slow speeds. When the shock wave arrives, particles move smoothly and collectively for about 50d before returning to their random motion (Fig.\u00a03D). The distance for collective displacement is comparable to the width of the shockwave indicated by the particle density map or velocity map. During this process, the particle speeds dramatically increase to about 5 times and then decay relatively slowly to the original level, see Fig.\u00a03E.\n\nA\u2013C Microscopy image, particle density, and particle velocity maps of a propagating shock wave. The inset visualizes 9 particles in the red square (5d\u2009\u00d7\u20095d) at t\u2009=\u20090.25 s. The scale bar is 0.5 mm. D Trajectories of particles in the red square are shown in (A). The trajectories' color code is the same as shown in (A) inset. The smooth and aligned middle parts of the trajectories indicate the passing of a shock wave, while the rest of the trajectories demonstrate uncorrelated random motions. E Corresponding speed evolution of particles (colored lines) shown in (D) and average velocity of particles in the fixed red square (dark red line with square symbols). The horizontal dashed line indicates the speed of the shock wave uwave\u2009=\u20092.1 mm/s. The baseline of average velocity in the square is lower than \u2223u0\u2223 due to the average of velocities rather than speeds. See details about the calculation of the wave speed in Supplementary Fig. 4. F Variations of the particle local area fraction in the red square in the process of the shock wave passage. Source data are provided as a Source Data file.\n\nBesides tracking the particles, the influence of shock waves is also monitored by the evolution of the average velocity (dark red line with square symbols in Fig.\u00a03E) and local particle area fraction (Fig.\u00a03F) at a fixed position indicated by a red square in Fig.\u00a03A. The shape of the average velocity evolution is similar to those of individual particles (colored lines) with an exception of a slightly delayed increase due to different objects of measurements. When the shock wave arrives at the selected position (t\u2009\u2248\u20090.2 s), the average velocity increases dramatically, accompanied by an abrupt increase in local particle density. The wave nature is confirmed by the fact that the speed of the shock waves (uwave = 2.1 mm/s) is about 40% higher than the peak particle speed (u\u2009=\u20091.5 mm/s). This makes the shock waves very different from other traveling density bands observed in many active matter systems where the particle velocity is the same as the band front velocity7,8,40.\n\nWhile the phenomenology seems somewhat similar to the activity waves reported recently in ref. 36, there are several crucial differences between our shockwaves and the activity waves observed in populations of subcritical Quincke rollers. Firstly, the shock waves of rollers are driven by a pulsed electric field with a field amplitude higher than the critical field strength Eq. In contrast, activity waves are observed for a constant electric field slightly lower than Eq. Therefore, rollers in shock waves perform steady rotations and induce hydrodynamic flows during \u03c4on. In contrast, in activity waves, the transient motion of particles is triggered by repulsion from nearest neighbors due to the Quincke instability. Secondly, the wave propagation mechanisms are also different. Rollers in shock waves interact via electrostatic and hydrodynamic interactions, while electrostatic interactions and hard-core collisions are dominant for the particle motion in activity waves. Due to long-range interactions via hydrodynamic flows, shock waves can be excited in relative dilute systems (\u03d5\u2009~\u20090.1), while activity waves are only observed at very high-density systems (\u03d5\u2009~\u20090.4) to trigger a domino-like effect due to hard-core collisions.\n\nTo study and understand the dynamical behavior of the system in response to variations of the \u03c4on and \u03c4off of the excitation electric field in the experiments, we investigate the behavior of the roller system using a continuum model and perform computational studies. Within the model, variations of the translational and rotational diffusion constants are directly affected by the activity modulation procedure. See the \u201cMethods\u201d section for details of the computational model. We study the steady-state behavior of the system depending on the parameter \\(\\delta=\\tilde{D}/{\\tilde{D}}_{r}\\), where the tilde denotes dimensionless parameters.\n\nThe particle density becomes homogeneous for smaller \u03b4\u2009\u2272\u20090.5, which we associate with the gas states, whereas for large \u03b4\u2009\u2273\u20091.16, the system develops a stable global vortex. We find shockwave states for the region \u03b4\u2009\u2208\u2009[0.6,\u20091.16], as shown by the snapshots of the steady-states for different \u03b4 values in Fig.\u00a04A\u2013D. The shockwave state becomes fully developed with multiple waves traversing the system at \u03b4 approaching 1, corresponding to the state where the roller\u2019s translational and rotational decoherence times become comparable. The dynamics of the shockwaves, as obtained in the simulations, is also shown in Supplementary Movies\u00a07 and 8.\n\nA\u2013D Snapshots of the steady-state particle density \u03c1 for different ratios \u03b4\u2009=\u2009D/Dr. The system shows a transition from a homogeneous state at low \u03b4 over to the state with the spontaneous shock waves (A\u2013C) when the roller\u2019s translational and rotational decoherence times become comparable and further to a single vortex state for \u03b4\u2009\u2265\u20091.16 (D). E Close-up of a shock wave traveling through a small analysis region (outlined) as 3D density isosurface. See also Supplementary Movies\u00a07 and 8. F, G Time evolution of the particle speed \u2223u\u2223 (F) and particle density \u03c1 (G) in the process of a shockwave passage through the analysis region shown in (E). At t\u2009~\u200910, the shockwave passes through the region resulting in a maximum speed of 0.68, while the wave travels at a speed of 0.97 (horizontal dashed line), i.e., 42% faster than the local particle speed. Source data are provided as a Source Data file.\n\nSince we are particularly interested in the shockwave regime, we performed a detailed analysis of this state in analogy to the experiments. Figure\u00a04E, G shows the evolution of the particle density in the selected region of interest. In this analysis region (outlined), we calculate the particle speed \u2223u\u2223 as a function of time while a shock wave travels through it. A detailed animation of this process can be found in the Supplementary Movie 8. For the particular shock wave shown in Fig.\u00a04F, the wave passes through the analysis region at time t\u2009~\u200910 with a maximum local particle speed \u2223u\u2223 of \\({u}_{\\max }=0.68\\) (Fig.\u00a04G), while the wave travels at speed uwave\u2009=\u20090.97 (dashed black line), i.e., 42% faster than the local particle speed. The analysis was performed for several shock waves with comparable ratios of \\({u}_{{{{{{{{\\rm{wave}}}}}}}}}/{u}_{\\max }\\), which agrees with the experimental observation.\n\nAnalysis of the shockwave excitation in the simulations reveals local instabilities that lead to non-zero vorticity, producing localized densification of particles forming short-lived vortices. The emission of radial shockwaves then dissolves these whirling, denser spots. Local densifications leading to the emergence of shockwave fronts also occur when flocks collide with each other or with the boundaries of the confinement potential creating intermittent density hot spots in the density map.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "We have demonstrated that active ensembles of Quincke rollers with enhanced role of electrohydrodynamic flows exhibit the onset of spontaneous shockwaves that became accessible under temporal modulations of activity by a pulsed electric field. The shock waves continuously emerge, propagate, and dissipate at different locations of the ensemble. The electrohydrodynamic flows are no longer negligible and promote intermittent rollers densifications (dynamic ripples) in the system. We have shown that shockwaves emerge at high-density regions and, like shockwaves in gases, propagate at a speed exceeding the average particle speed. These emergent waves originate from the transient vortex cores due to vortex meandering instability. The computational modeling sheds light on the origin of the observed shockwaves and reveals that this unconventional dynamic state becomes accessible when the translational and rotational decoherence times are comparable. The presented computational model does not consider the 3D electrohydrodynamic flows33. The current version of the model operates with the hydrodynamic flows in the shallow water approximation, i.e., quasi-two-dimensional geometry. The effect of the electrohydrodynamic flows can be further included via an additional vertical fluid velocity component similar to that in ref. 41. The extended model will likely provide a better agreement with the experimental observations.\n\nIn the context of shock waves, the Burgers equation is often used to describe their formation due to the competition between the viscosity and the convective nonlinearity v\u2009\u2207\u2009v. Since our equations contain the convective nonlinear terms, it is reasonable to assume, at least, at the qualitative level, some resemblance of the shock wave formation mechanism as in the Burgers equation. The main difference, however, is that the shock waves are not the steady-state solutions in the Burgers equation. On the contrary, due to energy injection in our system, the shock waves propagate without change of shape. It would also be interesting to compare our results to a Burgers\u2019 equation approach akin ref. 42, since the nonviscous Burgers\u2019 equation gives rise to discontinuities resulting in shockwaves. Our findings highlight the importance of the interplay between transient system memory, manipulated by a pulsed field, and electrohydrodynamic flows in accessing unconventional dynamic phases that are not accessible under a continuous energy input. The results suggest new approaches for controlling and manipulating active colloidal materials at the microscale.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "In our experiments, spherical polystyrene particles (d\u2009=\u20094.8 \u03bcm) are suspended in 0.15 mol/L AOT/hexadecane solution and injected into an open cell constructed by two parallel ITO-coated glass slides and spacers with a typical thickness of 95 \u03bcm. The electric field is supplied by a function generator (Agilent 33210A, Agilent Technologies) and a power amplifier (BOP 1000M, Kepco Inc.). The water content of the host fluid is controlled by the relative humidity (RH) of the closed environment and monitored by real-time conductivity measurements. The pulsed field amplitude was set to E\u2009=\u20093.2 V/\u03bcm. The sample cell is observed under a microscope with a 4\u00d7 microscope objective. Videos are recorded by a fast-speed camera (IL 5, Fastec Imaging) at 1057 frames per second (FPS). Particle tracking velocimetry (PTV) and further data analysis are carried out with custom codes in Python and Trackpy43.\n\nHere we employ a continuum model for Quincke rollers. The approach is adapted from our previous work44,45, which we developed for magnetic roller systems. Within this model, the particles are described by a coarse-grained particle density field \u03c1(r) and their velocity-field u(r). The fluid is described by the fluid velocity v(r) and fluid height profile h(r). The host fluid is described by the depth-averaged in-plane velocity \\(\\bar{{{{{{{{\\bf{v}}}}}}}}}=\\bar{{{{{{{{\\bf{v}}}}}}}}}(x,y,t)\\), i.e., we use a shallow water approximation here, and the depth of the solvent h\u2009=\u2009h(x,\u2009y,\u2009t). The gravity role is auxiliary and used for simplification purpose. The term ~g\u2009\u2207\u2009h in Eq. (2) is due to the shallow-wave approximation and is required to satisfy the fluid incompressibility condition, Eq. (3). It is possible to set h\u2009=\u2009const, and, correspondingly, \\(\\nabla \\bar{{{{{{{{\\bf{v}}}}}}}}}=0\\). However, it would result in a more computationally challenging algorithm without affecting the observed behavior. The particle dynamics is described by a Ginzburg-Landau-like equation for u:\n\nwhere m0 is the mass of a roller, \\(D={u}_{0}^{2}{\\tau }_{{{{{{{{\\rm{dif}}}}}}}}}/4\\) is the translational diffusion coefficient of the particles. The Ginzburg-Landau parameters are determined by the mean collision and diffusion times, \\({\\tau }_{{{{{{{{\\rm{col}}}}}}}}}={(2\\rho {a}_{0}{u}_{0})}^{-1}\\) and \u03c4dif, respectively, as \\(\\alpha={\\tau }_{{{{{{{{\\rm{col}}}}}}}}}^{-1}-{\\tau }_{{{{{{{{\\rm{dif}}}}}}}}}^{-1}\\) and \\(\\beta={({u}_{0}^{2}{\\tau }_{{{{{{{{\\rm{col}}}}}}}}})}^{-1}\\). The former is described in dimensionless units as \\(\\tilde{\\alpha }=\\eta {u}_{0}\\tilde{\\rho }-{\\tilde{D}}_{r}\\), where \\({\\tilde{D}}_{r}\\) is the dimensionless rotational diffusion constant, \\(\\sim {\\tau }_{{{{{{{{\\rm{dif}}}}}}}}}^{-1},\\eta\\) a numerical constant, and \\(\\tilde{\\rho }\\) the dimensionless particle density. The last two terms characterize the coupling between active rollers and a passive host fluid (solvent), where the \\(\\gamma=\\frac{3}{4}\\frac{{a}_{0}}{h}{\\tau }_{{{{{{{{\\rm{col}}}}}}}}}^{-1}\\) term results from the over-damped roller dynamics, and the last term describes the rotation of rollers in a hydrodynamic flow with vorticity \\({{{{{{{\\boldsymbol{\\Omega }}}}}}}}=\\frac{1}{2}\\nabla \\times \\bar{{{{{{{{\\bf{v}}}}}}}}}\\)46. For the Quincke roller system, the stress tensor \\({{{\\Pi }}}_{}^{}\\) takes the form \\({{\\Pi }}=\\frac{3}{64{a}_{0}}\\left({{{{{{{\\boldsymbol{p}}}}}}}}\\otimes {{{{{{{\\boldsymbol{p}}}}}}}}-\\frac{3}{2}{p}^{2}{\\mathsf{I}}\\right)-P{\\mathsf{I}}\\), where p(r) is the polarization field, I is the identity tensor, and P the pressure, which phenomenologically accounts for the finite size of colloids. The latter results in a term \u2212Q(\u03c1)\u2009\u2207\u2009\u03c1 in Eq. (1), where Q(\u03c1) takes into account hard-core repulsion at high densities (i.e., when two particle overlap) and attraction for intermediate densities, which accounts for polarization effects being linear in \u03c1, and a small repulsion at very low densities47. The polarization field p is itself a dynamic quantity, similar to u, and therefore described by a related Ginzburg-Landau equation with Landau-Lifshitz-like term aligning p and u, see ref. 44.\n\nIn weakly-conducting fluids, the interactions between dipoles scale as separation distance in power four48. In the so-called leaky-dielectric model48,49, the fluid\u2019s conductivity decreases the dipole strength. The dipolar interactions are small compared to the hydrodynamic interactions that decay much slower41. Thus, unlike in a magnetic system, the electrostatic dipolar interactions between Quincke rollers become negligible compared to the hydrodynamic interactions and we can neglect the dipolar contribution to the stress tensor \u03a0 in Eq. (1).\n\nFor the dynamics of suspending fluid, we use the two-dimensional depth-averaged Navier-Stokes equation (shallow water approximation)50\n\nwhere g is the gravitational acceleration and \u03bd the kinematic viscosity. The last two terms on the RHS originate from the no-slip condition at the rollers-solvent interface. u0 also determines the scale of the fluid velocity.\n\nEquations (1) and (2) have to be solved together with the continuity equations for the\n\nAll equations are integrated using quasi-spectral split-step methods, which calculate all second-order spatial derivatives in Fourier space. Technically, the solver is implemented on the general-purpose graphics processing units (GPU) using complex fast-Fourier-transforms (FFT; here the cuFFT implementation) for the x and y components of u,\u2009\\(\\bar{{{{{{{{\\bf{v}}}}}}}}}\\), and the combined (h,\u2009\u03c1) vector. Compared to general-purpose CPU finite-element solvers, this method allows for an integration speed-up of 3 to 4 orders of magnitude and naturally uses periodic boundary conditions due to the FFTs.\n\nUsing \u03c4dif as unit of time and u0\u03c4dif as unit of length the above equations are rewritten in dimensionless units. The roller density is normalized by the mean value \\(\\bar{\\rho }={\\nu }_{p}/(\\pi {a}_{0}^{2})\\), where \u03bdp is the surface fraction of the particles. A dimensionless parameter \u03c10\u2009<\u20091 determines then the average density in the system.\n\nThe units are defined by their experimental values, which set the following dimensionless parameter ranges for the simulations\n\nTo solve the above equations numerically, a time unit is discretized in 250 steps, and the system is partitioned spatially on a regular, square mesh with up to 2048\u2009\u00d7\u20092048 grid points. Additionally, the equation for the particle velocity has an additional circular confinement force, which is zero inside the circular region of diameter comparable to linear system size. This confinement is used to mimic the experimental geometry and to avoid an overall transversal mode due to the needed periodic boundary conditions for the FFT used to solve the equations of motion. The equations are then integrated for up to 107 time steps, corresponding to about 5\u2009min experimental time.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data that support the findings of this study are provided in this paper and the Supplementary Information.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "Custom codes used for numerical modeling are available at github.com/activematerials/Shockwave_continuum.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "The research at Argonne National Laboratory was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. Use of the Center for Nanoscale Materials, an Office of Science user facility, was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. The research of ISA was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award no. DE-SC0020964.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Bo Zhang\n\nPresent address: Collaborative Innovation Center of Advanced Microstructures, National Laboratory of Solid State Microstructure, and Department of Physics, Nanjing University, Nanjing, 210093, China\n\nMaterials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA\n\nBo Zhang,\u00a0Andreas Glatz\u00a0&\u00a0Alexey Snezhko\n\nDepartment of Physics, Northern Illinois University, DeKalb, IL, 60115, USA\n\nAndreas Glatz\n\nDepartment of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16802, USA\n\nIgor S. Aranson\n\nDepartment of Chemistry, Pennsylvania State University, University Park, PA, 16802, USA\n\nIgor S. Aranson\n\nDepartment of Mathematics, Pennsylvania State University, University Park, PA, 16802, USA\n\nIgor S. Aranson\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nA.S. and B.Z. conceived the research, B.Z. performed the experiments, A.G. and I.S.A. conducted the numerical simulations and formulation of the model. All authors analyzed the data and wrote the manuscript.\n\nCorrespondence to\n                Bo Zhang or Alexey Snezhko.",
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+            "section_text": "Zhang, B., Glatz, A., Aranson, I.S. et al. Spontaneous shock waves in pulse-stimulated flocks of Quincke rollers.\n                    Nat Commun 14, 7050 (2023). https://doi.org/10.1038/s41467-023-42633-4\n\nDownload citation\n\nReceived: 01 June 2023\n\nAccepted: 16 October 2023\n\nPublished: 03 November 2023\n\nVersion of record: 03 November 2023\n\nDOI: https://doi.org/10.1038/s41467-023-42633-4\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Communications Physics (2025)",
+            "section_image": []
+        }
+    ]
+}

1aef848e87fd0e818342b7e242f3ced67536966ecf13157ee525e499c5814330/metadata.json

@@ -0,0 +1,167 @@
+{
+    "title": "A crosslinked eutectogel for ultrasensitive pressure and temperature monitoring from nostril airflow",
+    "pre_title": "Independent yet simultaneous sensing of pressure and temperature from nostril airflow for ultrasensitive respiratory monitoring",
+    "journal": "Nature Communications",
+    "published": "08 April 2025",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Description of Addtional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Supplementary Movie 1",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM3_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 2",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM4_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 3",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM5_ESM.mp4"
+        },
+        {
+            "label": "Supplementary Movie 4",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM6_ESM.mp4"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM7_ESM.pdf"
+        },
+        {
+            "label": "Transparent Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM8_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_MOESM9_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-025-58631-7#Sec24"
+    ],
+    "code": [],
+    "subject": [
+        "Gels and hydrogels",
+        "Sensors and biosensors"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-4897034/v1.pdf?c=1744196827000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-4897034/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-025-58631-7.pdf",
+    "preprint_posted": "21 Aug, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Accurately detection of nostril airflow is vital for real-time respiratory monitoring. However, the developed methods only rely on single stimulus sensing for nostril airflow, which is extremely susceptible to interference in the complexed environment, and severely affects the accuracy of detection results. Here, a multimodal integrated eutectogel sensor was explored to simultaneously sense the pressure and temperature stimuli of nostril airflow, via independently outputting capacitance and resistance, respectively, yet without cross-coupling. The completely physical crosslinking and the synergistic interaction of HAp and tannic acid (TA) within the network endow this eutectogel with extremely low modulus, remarkable self-healing efficiency, robust adhesion, excellent environmental stability and bio-compatibility. By integrating this synthetic eutectogel with circuit design, a multimodal sensor was developed, which exhibited superior pressure sensitivity to other reported gel-based sensors. As a proof of concept, this sensor was further explored to diagnose a traditional respiratory disease of obstructive sleep apnea syndrome (OSAS) via simultaneously detecting five kinds of stimuli in the sleeping process, greatly improving the accuracy and reliability of the detection results. This work provides a highly effective strategy for achieving ultrasensitive respiratory monitoring and forecasting respiratory diseases.Health sciences/Health care/Health servicesPhysical sciences/Materials science/Materials for devices/Sensors and biosensorseutectogelflexible sensornostril airflowrespiratory monitoringwearable  electronics",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "Supportinginformation.pdfMovieS1.mp4Movie S1MovieS2.mp4Movie S2MovieS3.mp4Movie S3",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Accurate detection of nostril airflow is vital for real-time respiratory monitoring. However, the developed methods only rely on single stimulus sensing for nostril airflow, which is extremely susceptible to interference in the complex environment, and severely affects the accuracy of detection results. Here, a multimodal integrated eutectogel sensor is explored to simultaneously sense the pressure and temperature stimuli of nostril airflow, by independently outputting capacitance and resistance, respectively, without cross-coupling. The completely physical crosslinking and the synergistic interaction of hydroxyapatite and tannic acid within the network endow this eutectogel with extremely low modulus, remarkable self-healing efficiency, robust adhesion, good environmental stability, and bio-compatibility. A multimodal sensor is developed by integrating this synthetic eutectogel with circuit design, which exhibits superior pressure sensitivity compared to other reported gel-based sensors. As a proof of concept, this sensor is further explored to diagnose the traditional respiratory disease of obstructive sleep apnea syndrome by simultaneously detecting five kinds of stimuli in the sleeping process, greatly improving the accuracy and reliability of the detection results. This work provides a highly effective strategy for achieving ultrasensitive respiratory monitoring and forecasting respiratory diseases.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Respiration is an essential behavior for sustaining human life1,2. Respiratory behavior reflects the human health and physical or mental activity3. Real-time respiration monitoring can forecast, rule out, and diagnose some related diseases, such as obstructive sleep apnea syndrome (OSAS), cardiovascular disorders (CVD), asthma, or epilepsy4,5. Hence, highly efficient respiratory monitoring is vital for managing personal health and even saving human lives. However, the commercially available polysomnography (PSG) equipment can only be used in hospitals for patients owing to its cumbersome operation, high cost, and poor wearability6, which severely restricts the flexibility and extensive of respiratory monitoring for people with different needs.\n\nDeveloping wearable population-wide electronics for continuous respiratory monitoring is urgently needed. Flexible electronic sensors have recently attracted great attention in personal healthcare, electrical skins, and artificial intelligence, Researchers benefit from their ability to transform different stimuli (strain, pressure, thermal, humidity, etc.) into directly readable electrical signals (current, voltage, resistance, capacitance, etc.)4,7,8,9,10,11,12,13. Great efforts have been devoted to studying respiratory-related behaviors using different modal sensors, such as monitoring the nostril airflow9,12,14,15,16, or recording the chest and abdomen movement in the inhaling/exhaling process8,11. However, current works on respiratory monitoring usually focus on transforming a single stimulus from a nostril airflow or chest/abdomen movement, which is extremely susceptible to interference from an uncontrollable external environment. This significantly hinders its potential application in continuous respiratory monitoring and in-home healthcare. Compared to sensing the single stimuli of pressure variation for the chest/abdomen, the simultaneously detectable signals from the nostril airflow during breathing are much more diverse, such as the temperature, humidity, pressure, or gas. Furthermore, some common breath-related diseases (Nasal Polyps, Nasal Septum, etc.) can be directly diagnosed by measuring individual airflow signals from the two nostrils in clinical settings17. Therefore, effective monitoring of the nostril airflow is extremely important for evaluating respiration status. If two or more types of signals from the nostril airflow are simultaneously detected but independently output as different electrical signals by one integrated sensor, the obtained results will be much more reliable and accurate. On the one hand, it can eliminate the interference of the external environment with temperature or humidity when it just senses one stimulus signal from nostril airflow. On the other hand, it can effectively avoid invalid diagnoses induced by motion artifacts when only chest/abdomen variation is monitored, which is susceptible to random body movements, especially in long-term sleeping respiratory monitoring.\n\nAdditionally, wearable comfortability, simple fabrication, and high sensitivity of the resultant sensor are the other key parameters that will help the practicability of respiratory monitoring equipment. Compared to the classical metal18, conductive fabric8,12, layer-by-layer composited nanogenerator1,9,11, or on-mask based sensors19, the conductive hydrogel-based resistive/capacitive sensor that is directly adhered to the skin possesses great advantages. This includes simple assembly and much higher comfortability and wearability due to the bio-compatibility, bio-tissue compliance, and comparable modulus to the natural skin10,20,21,22,23. Although some works tried to monitor the respiration by recording the humidity variation of nostril airflow or the chest/abdomen movement4,24, the single detection signal severely reduced the reliability and accuracy of results as mentioned above. The key cause is that the reported sensor cannot meet the requirement of high-pressure sensitivity for the weak nostril airflow. In addition, the appropriate modulus, high tissue adhesion, good environmental stability, and fast self-healing rate are beneficial to the high sensitivity and long service life for respiratory sensors, which also remains a great challenge for the currently developed gel sensors, especially for the hydrogel-based devices. Hence, developing a high-performance respiratory monitoring sensor with independent/simultaneous sensing of multiple signals from nostril airflow, high sensitivity, and good comfortability and usability is highly desired for meeting the increasing requirement of respiratory monitoring.\n\nHerein, a multimodal ultrasensitive eutectogel-based respiratory sensor was developed, which independently and simultaneously detected pressure and temperature variations from nostril airflow, with negligible cross-coupling between these two detected stimuli. This exciting characteristic is ascribed to the remarkable performance of the synthesized eutectogel combined with the adjustment of the circuit connection for this sensor. As depicted in Fig.\u00a01, a poly (N-Acryloyl 2-Glycine) composited eutectogel (labeled as ATH) was developed by physically crosslinking with tannic acid (TA) using hydroxyapatite (HAp) as the crosslinker in the deep eutectic solvents (DESs) of choline chloride (ChCl) and glycol. The optimized ATH6 gel exhibited comparable low modulus and superior self-healing efficiency due to the complete physical crosslinking interactions of the gel network. The strong adhesive strength to bio-tissue and other substrates was achieved, attributed to the multiple strong interactions between the gel and substrates. Besides, the utilization of DESs and bio-friendly raw materials endowed this gel with high ionic conductivity, good environmental stability, and high cell viability. The resulting sensor was simply fabricated by sandwiching a dielectric layer between two gel layers and attaching three wires, integrating a resistive sensor into a capacitive sensor. Notably, the much higher pressure sensitivity of this sensor compared to other reported gel-based pressure sensors ensured the good sensing ability for the weak airflow pressure of nasal breathing due to the low modulus and high ionic conductivity of this eutectogel. Combing its good durability and bio-compatibility, this integrated eutectogel sensor shows great advantages in continuous respiratory monitoring, and significantly improves the accuracy and reliability of the detected result for diagnosing respiration-related diseases by simultaneously displaying five monitoring channels, effectively avoiding environmental interference and motion artifacts when only a single stimulus is detected.\n\nThis eutectogel is prepared by completely physical crosslinking via the multiple interaction of PACG, TA, HAp and the solvent of ChCl/EG, which displays extremely low modulus, remarkable self-healing efficiency, robust adhesion, good environmental stability, and bio-compatibility. A multimodal sensor is developed by integrating this synthetic eutectogel with circuit design, which exhibits superior pressure sensitivity and can simultaneously sense the pressure and temperature stimuli of nostril airflow, by independently outputting capacitance and resistance, respectively, with non-interference.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-025-58631-7/MediaObjects/41467_2025_58631_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "ATH eutectogel was synthesized through photo-initiated free radical polymerization by mixing ACG (N-Acryloyl 2-Glycine) monomer, TA, and HAp in the ChCl/ glycol (1:2, M/M) solvent upon 365-nm irradiation, which was labeled as ATHx (x represents the weight ratio of HAp to ACG). The detailed information is presented in the Experimental section and Supporting information (Supplementary Table\u00a0S1). In this ATH gel, the HAp played a key role in the crosslinking interaction via the strong coordination bonds with the polymer chains. At the same time, the TA also contributed to the hydrogen bonding of the gel network. As shown in Fig.\u00a02a, the crosslinking network could be generated in the ATH6 system, and no gel formation was observed without HAp (ATH0). This result was also verified by testing the storage modulus (G\u2032) and loss modulus (G\u2033) for the ATH6 and ATH0 systems, respectively (Fig.\u00a02b). The higher value of G\u2032 compared to G\u2033 clearly demonstrated the formation of the crosslinked gel in ATH6. Subsequently, the mechanical performance of the ATH gels, such as tensile and compressive properties, were systematically measured. The tensile stress (Fig.\u00a02c) and fracture toughness (Supplementary Fig.\u00a01a) increased as the weight ratio of HAp was increased from 2% to 20% for ATHx. Similar results were obtained in the compressive curves (Fig.\u00a02e) and Young\u2032s modulus (Fig.\u00a02h), attributed to the much higher crosslinking density with increasing HAp. The tensile fracture strain of ATH2 to ATH20 was from 1062% to 544%, and the corresponding tensile fracture stress was from 9.7\u2009kPa to 75.4\u2009kPa (Fig.\u00a02c). The elasticity of ATH was evaluated by taking ATH6 as the sample. Only a little hysteresis loop was observed in the successive stepwise tensile and compressive curves (Fig.\u00a02d, f). The tensile and compressive stress increased stepwise as the strain increased from 100% to 700% for tensile (Fig.\u00a02d) and from 20% to 80% for compression (Fig.\u00a02f), respectively, meanwhile displaying the steady cyclic curves at a certain strain (Supplementary Fig.\u00a01b, c). Moreover, ATH6 could recover to its original shape without any fracture when applying successive compression at 80% strain for at least 30 cycles (Fig.\u00a02g), demonstrating the good fatigue resistance and elasticity of the gel. Notably, the ATH (ATH2 \u223c ATH20) gel exhibited extremely lower Young\u2032s modulus (1.48\u2009kPa to 12.87\u2009kPa) compared to other reported gels for electrode or sensor application (Fig.\u00a02h, i)25,26,27,28,29,30,31, which was similar to that of soft biological tissues and was a benefit to the skin compliance of the gel. To demonstrate the advantages of eutectogel over hydrogel, the corresponding ATH6 hydrogel (labeled as Hydrogel in this paper) was prepared just by replacing the ChCl/glycol deep eutectic solvent with the same amount of H2O. It showed much higher tensile stress, Young\u2032s and storage modulus than that of ATH6 eutectogel (Supplementary Fig.\u00a02a, b, c). As shown in Fig.\u00a02j, the ATH6 and ATH20 eutectogels could transfer the fingerprint, and the softer the gel, the cleaner the fingerprint, while it was difficult for the corresponding hydrogel. The good elasticity and soft modulus of ATH eutectogel are vital to capture the brittle pressure variation of nostril airflow and also important for the tightness of the skin-sensor interface.\n\nThe gelation behavior (a) and the rheology test (b) of ATH6 and ATH0. Scale bars: 2\u2009cm. The tensile (c) and the compressive (e) stress-strain curves for ATHx when varying the HAp content from 2% to 20%. The stepwise tensile (d) and compressive (f) curves of ATH6 eutectogel. g The successive compressive test at 80% strain for ATH6 eutectogel. h The Young\u2019s modulus for ATHx eutectogel. Data are presented as the mean values\u2009\u00b1\u2009SD, n\u2009=\u20095 independent samples. i Comparison of Young\u2032s modulus for this work with other reported related works. j The fingerprint transformation by using ATH6 and ATH20 eutectogels and the corresponding hydrogel.\n\nSelf-adhesive performance is extremely vital for fixing the sensor on the skin and avoiding interference signals from motion artifacts. The standard lap-shear test was utilized to quantitatively evaluate the adhesion property of ATH eutectogel. As depicted in Fig.\u00a03a, b, the shear stress of ATH eutectogel to porcine skin was first increased and then decreased as the HAp content increased. This is because the adhesive performance was co-influenced by the bulk strength of the gel and the interfacial strength. The ATH6 displayed the highest shear strength (34.6\u2009kPa) because of its higher bulk strength compared to that of ATH2, while the stiffer ATH12 and ATH20 decreased its interfacial strength. Considering the mechanical strength and adhesive performance results, the ATH6 was chosen as the optimized sample for subsequent tests. ATH6 exhibited strong adhesion to the smooth skin and the hairy skin, and reached ultra-conformal and seamless contact with the skin surface which was characterized using an optical microscope (Fig.\u00a03e). Moreover, different kinds of substrates could also adhere strongly with ATH6 eutectogel, which exhibited much higher adhesive capability than hydrogel, especially for the nonpolar substrates (Supplementary Fig.\u00a03a\u2013d). Additionally, different external mechanical stimulus (such as the twisted treatment or continuously bending) generates negligible influence on the adhesion of ATH6 eutectogel to porcine skin (Supplementary Fig.\u00a03e, f and Movie\u00a01). Although the sweat on the skin has negative influence on the adhesion performance of ATH6 eutectogel, the adhesion strength on the sweaty skin (30\u2009\u03bcL sweat deposited on the porcine skin, area: 1\u2009\u00d7\u20091\u2009cm2) still attained 23\u2009kPa which was higher than that of many adhesive gels. The influences of temperature and storage time were investigated to demonstrate the adhesive stability in different environments. Considering the respiratory monitoring application, the varied temperature range was from 37\u2009\u00b0C to \u221220\u2009\u00b0C (Fig.\u00a03c, d). The shear strength of ATH6 eutectogel and hydrogel nearly kept the same value at 25\u2009\u00b0C and 37\u2009\u00b0C. For ATH6 eutectogel, the shear strength increased sharply at lower temperatures and reached as high as 125.7\u2009kPa at \u221220\u2009\u00b0C. This is due to more intense hydrogen interactions between the polymer chains at lower temperatures and its good anti-freezing performance (Supplementary Fig.\u00a04a\u2013c). In contrast, the adhesion strength of hydrogel was seriously degraded, and it was even lost below zero because of water freezing, which could be detected by differential scanning calorimetry (DSC). Meanwhile, no obvious shear strength loss was observed for ATH6 eutectogel with a prolonged storage time of at least one week. However, the hydrogel sample lost the adhesion capability after 1 day due to the evaporation of water in the open-air environment (Fig.\u00a03f). The result of the environmental stability test for ATH6 eutectogel and hydrogel could support the above explanation. The eutectogel kept the original state and weight at RT and 40% relative humidity (RH), while almost all the water within the hydrogel evaporated and a film sample was left after one week (Fig.\u00a03g and Supplementary Fig.\u00a05a). In addition, the ATH6 eutectogel was also put in the outdoor from 8:00 a.m. to 8:00 p.m. to mimic the practical application environment. It also kept the original weight, but the hydrogel severely lost the solvent after 3\u2009h (Supplementary Fig.\u00a05b). The good adhesion stability and the high adhesion strength of ATH6 eutectogel were ascribed to the synergistic effect of low modulus, the abundant adhesive groups (carboxyl, catechol, amide) and the utilization of DESs. They not only formed multiple interactions (mechanical anchoring, hydrogen bonding, electrostatic interaction, metal coordination interaction, and van der Waals interactions) with different substrates, but also maintained the original network structure in different environments.\n\nThe curves of lap shear test (a) and the calculated shear strength (b) of ATH to porcine skin at RT. Data are presented as the mean values\u2009\u00b1\u2009SD, n\u2009=\u20095 independent samples. The curves of lap shear test (c) and the calculated shear strength (d) of ATH6 to porcine skin at different temperatures. Data are presented as the mean values\u2009\u00b1\u2009SD, n\u2009=\u20095 independent samples. e The visual demonstration of adhesive performance for ATH6 eutectogel to smooth skin and hairy skin, and the seamless adhesion to porcine skin. Scale bars: 1\u2009mm. f The shear strength of ATH6 eutectogel and hydrogel increases the storage time. Data are presented as the mean values\u2009\u00b1\u2009SD, n\u2009=\u20095 independent samples. g The pictures of ATH6 eutectogel and hydrogel at different storage times. The cell viability (L929) demonstration of ATH6 eutectogel by varying the different concentrations (h) and co-culturing time (i). Data are presented as the mean values\u2009\u00b1\u2009SD, n\u2009=\u20095 independent samples. j Photographs of the mouse attached with ATH6 eutectogel before and after 2 days (left, Scale bars: 5\u2009mm). HE staining images (Scale bars: 200\u2009\u03bcm) of skin from the mice attached without (control) and with ATH6 eutectogel for 2 days. Three animals per group.\n\nBiocompatibility is also a key parameter for the material that directly adheres to the skin in practical applications. As shown in Fig.\u00a03h, i, the ATH6 eutectogel displayed high cell viability (L929) of 94% even at 500\u2009\u03bcg/mL and similar metabolic activity with the control sample on days 1, 3, and 5. After 24\u2009h of firm adhesion with the skin of a volunteer, ATH6 eutectogel could be peeled off without any residue and irritative reaction (Supplementary Fig.\u00a06). The skin irritation test was supplemented to further demonstrate the bio-compatibility of eutectogel by animal experiment (approval number 2024b177). As shown in Fig.\u00a03j, ATH6 eutectogel has adhered to the depilated dorsal skin of mice. After 2 days, no irritative reaction was observed and the HE (hematoxylin-eosin) staining analysis shows this eutectogel did not induce any structural change in the skin. The good biocompatibility of ATH6 eutectogel was ascribed to the utilization of bio-friendly crude materials and solvent, including the monomer of N-Acryloyl 2-Glycine (ACG), HAp, and TA, and the DESs solvent of choline chloride and ethylene glycol, ensuring the bio-safety in the following nostril airflow monitoring.\n\nThe fast and efficient self-healing capability is important for the long service life of the device. The tensile test was utilized to quantitatively measure the self-healing efficiency of ATH6 eutectogel. It reached 96.5% of self-healing efficiency after 3\u2009h at RT (Fig.\u00a04a, b), superior to other reported self-healing gels (eutectogels, hydrogel, and organogels) and elastomers which needed longer healing time or additional stimulus to reach the high healing efficiency (Fig.\u00a04c)32,33,34,35,36,37,38,39,40,41,42,43,44,45,46. Additionally, two pieces of ATH6 eutectogel could be integrated into one just by attaching them together gently (Fig.\u00a04f and Supplementary Fig.\u00a07). This self-healed gel still demonstrated good stretchability, even resisting the sharp scissor for the healed junction and inflating the gel balloon to 16 times of original volume (Fig.\u00a04f), and also recovering to its original conductivity (Supplementary Fig.\u00a08). Furthermore, the cycling strain sweeping was conducted from 0.1% to 300% to evaluate the rheological recovery behavior of ATH6 eutectogel (Supplementary Fig.\u00a09). When the larger strain of 300% was applied, the value of G\u2032 became smaller than that of G\u2033 because of the collapse of the gel network. While retaining the sample at 300% strain only for 100\u2009s, the G\u2032 could completely return to the initial value immediately when the strain was back to 0.1% due to the rapid restoration of the gel structure. These periodical and stable changes in modulus further verified the fast reconstruction for the ATH6 gel network. Furthermore, the self-healing efficiency of ATH6 under different temperatures was quantitatively detected as shown in Fig.\u00a04d, and achieving high healing efficiency even at \u221220\u2009\u00b0C (73.25%) (Fig.\u00a04e). This good self-healing capability was ascribed to the completely physical crosslinking interactions of hydrogen bonds and coordination bonds within the ATH6 which were dynamically reversible, endowing it with superior self-healing performance (Fig.\u00a04g).\n\na The tensile stress-strain curves of original ATH6 and self-healed eutectogels. b The calculated self-healing efficiency of ATH6 eutectogel with different self-healing time at RT. Data are presented as the mean values\u2009\u00b1\u2009SD, n\u2009=\u20095 independent samples. c The comparison of the self-healing efficiency and time of this work with the previously reported works. d The tensile stress-strain curves of original and self-healed ATH6 eutectogel at different self-healing temperatures. e The calculated self-healing efficiency of ATH6 eutectogel at different temperatures. Data are presented as the mean values\u2009\u00b1\u2009SD, n\u2009=\u2009 5 independent samples. f The visual demonstration of self-healed ATH6 eutectogel which can resist different kinds of extreme mechanical stimulus. Scale bars: 5\u2009mm. g The self-healing mechanism of ATH6 eutectogel.\n\nA sandwiched structure was designed by sandwiching a dielectric film (the commercial VHB 4905) between two ATH6 eutectogels and embedding three metal electrodes to construct the dual-modal eutectogel sensor (Fig.\u00a05a). When the two electrodes were connected to the same piece of eutectogel, a temperature-sensitive resistive sensor was fabricated, because the ionic dissociation and transportation within the gel were sensitive to temperature. Moreover, the resistance change ratio was calculated using the equation of \u0394R/R0\u2009=\u2009(Rx\u2013R0)/R0, where Rx and R0 represented the initial and real-time resistance, respectively. The parallel-plate capacitive sensor was formed by connecting two electrodes to the separated gels at the top and bottom. The applied pressure could cause the distance variation between two electrodes and generate the capacitance change. Furthermore, the capacitance change value was defined as \u0394C/C0\u2009=\u2009(Cx\u2013C0)/C0, the Cx and C0 correspond to the initial and real-time capacitance, respectively. To detect the sensory relationship between the stimulus of pressure/thermal and the electrical signal of resistance and capacitance, and considering the practical application of nostril airflow monitoring, this integrated sensor was compressed under 0\u2013250\u2009Pa at different temperatures from 20 to 35\u2009\u00b0C by utilizing the compression and heating mode of the Dynamic Mechanical Analyzer (DMA). In these defined ranges of pressure and temperature variation, the resistive change of this integrated sensor displayed a linear increase with the increasing temperature, and nearly no response of resistive change when the pressure was varied from 0 to 250\u2009Pa (Fig.\u00a05b). While the capacitive change was mainly influenced by the pressure stimulus with negligible influence of temperature (Fig.\u00a05c). Hence, this result demonstrates the pressure and thermal stimuli can be simultaneously but independently sensed by this one integrated sensor by outputting separate capacitive and resistive signals, respectively, via utilizing the different sensitivity of capacitive and resistive sensor to pressure and temperature in this defined range. Compared to the previously reported multimodal sensors, this work not only decouples the pressure and temperature by this integrated capacitive and resistive sensor at a certain range21,24,47,48, but also has the advantages of utilizing just one kind of eutectogel which has robust tissue adhesion, superior self-healing, and good bio-compatibility, and this extremely facile fabrication of sensor could avoid the complex fabrication and multiple functional materials49, which is easy for achievement transformation. This remarkable performance of this sensor provides the great possibility for independent and simultaneous sensing of pressure and temperature from nostril airflow.\n\na The schematic design of dual-modal eutectogel-based sensor, and the sensing mechanism of resistance and capacitance for this sensor upon thermal or pressure stimulus. The response curves of resistance (b) and capacitance (c) for pressure vibration from 0 to 250\u2009Pa and temperature change from 20 to 35\u2009\u00b0C.\n\nBefore simultaneously detecting the two signals from nostril airflow using this dual-modal eutectogel-based sensor, the separate sensing capability of pressure and temperature through capacitance and resistance was investigated, respectively. High-pressure sensitivity is vital for monitoring the weak pressure signal from nostril airflow. Hence, the pressure sensitivity S was first calculated by drawing the curve of the capacitance change versus pressure, and the slop of this curve was defined as S (0.42\u2009kPa\u22121, R2\u2009=\u20090.99, Fig.\u00a06a). Additionally, the fast response time of 15\u2009ms and recovery time of 17.5\u2009ms were displayed when touching the sensor under a successively instantaneous pressing behavior, and the stable periodical signal of capacitive change was present (Fig.\u00a06b). A radar map (Fig.\u00a06c)13,24,37,48,50,51,52 and a detailed table (Supplementary Table\u00a02) were made to compare the comprehensive performance of this work with other related gel or elastomer -based flexible sensors. This ATH6 based sensor could decouple the compressive pressure and temperature under a certain range, simultaneously possessing good self-adhesion performance and comparable pressure and thermal sensitivity. When a certain pressure from 40\u2009Pa to 190\u2009Pa was applied to the sensor, the capacitive change displayed stepwise increase with increasing the pressure and steady cyclic signals at a certain pressure, respectively (Fig.\u00a06d). Even the subtle difference in the moving rate for the eyeball was clearly recognized from the capacitance signal when the sensor was adhered to the left eyelid, strongly demonstrating the high sensitivity of this sensor (Fig.\u00a06e). Furthermore, the durability was tested by compressing this sensor at a simulating nostril airflow pressure of 70\u2009Pa under the successive loading-unloading mode (Fig.\u00a06f). The almost constant of capacitance change was observed at least 10,000th cycle, showing the superior durability and environmental stability of this sensor, due to the strong interactions between the gel network and the stable DESs. The above good pressure sensitivity and durability of this sensor are critical for the long-time service life and use-cost in its practical application.\n\na The sensing sensitivity (S) of capacitance for this sensor upon pressure stimulus from 0 to 200\u2009Pa. b The response curve of capacitive variation when applying successively instantaneous pressing behavior to the sensor. c A radar map for comparing this work and the previously reported gel or elastomer-based sensors. d The response curves of capacitive variation for this sensor upon applying different pressures. e Detecting different rates of eye movement by adhering this sensor on the upper eyelid. f Demonstration of the sensing stability for this sensor upon applying pressure of 70\u2009Pa for 10,000 cycles.\n\nRespiratory behavior involves the nostril airflow and the chest/abdomen movement. The respiratory rate and intensity are the key parameters for evaluating the status of a person and are extremely important for early diagnosis of related diseases. In this work, four different respiratory states were stimulated, including apnea, fast, deep, and normal breathing behavior. The sensor was self-adhered on the skin below the nostril, as well as the chest and abdomen positions. Firstly, the pressure variation from nostril airflow and chest and abdomen movement in the respiratory behavior was continuously monitored in the capacitive mode. The relative capacitive change was constant when holding breath to simulate the apnea. In contrast with the electrical signal at normal state (\u0394C/C0\u2009=\u20090.30%, 24\u2009bpm), the bigger capacitive variation (\u0394C/C0\u2009=\u20090.38%, 17\u2009bpm) of deep respiratory and the shorter time interval (\u0394C/C0\u2009=\u20090.20%, 30 bpm) of fast respiratory were displayed, clearly indicating the stronger intensity and faster frequency corresponded to the deep and fast respiration, respectively (Fig.\u00a07a). A similar change pattern of capacitive variation was displayed in real-time monitoring of chest and abdomen movement for respiratory behavior (Fig.\u00a07b, c, Supplementary Movie\u00a02 and 3). In contrast, the relative capacitive change of chest/abdomen breath was much higher than that of nose breath. This demonstrates that the weak pressure stimulus from nostril airflow could indeed be precisely recorded by this sensor. Specifically, the change in the absolute value for capacitance was the opposite for inhaling and exhaling behavior in respiratory monitoring via nostril airflow and chest/abdomen breath (the insets of Fig.\u00a07a, b, and c). For nose breathing, the exhalation induced an increase in capacitance because of the generated pressure from the nostril airflow. Conversely, the act of exhalation via chest/abdomen breathing resulted in a decrease of capacitive signal, ascribing to the comparable volume shrinkage of the chest and abdomen when exhaling. Interestingly, the sensor even detected the difference between the left and right nostrils (Fig.\u00a07d). One of the nostrils, called the main breathing nostril, exhibited much higher respiratory intensity than that of another accessory breathing nostril. Meanwhile, the different physiological activities of a person could also be differenced by monitoring the pulse, which was calculated from the stable and clear capacitive signal. Figure\u00a07e showed the pulse could be gradually recovered to the normal state by increasing the rest time after running. The above results strongly confirm that this sensor possesses extraordinary sensitivity for weak pressure detection, providing the potential application of this sensor in disease diagnosis via monitoring the pressure variation.\n\nThe curves of capacitive variation for monitoring the respiratory behavior from nostril airflow (a), chest (b), and abdomen (c) movement by mimicking different respiratory behaviors. d The capacitive variation for detecting left and right nostril airflow by adhering the sensor on the corresponding subnostril. e Monitoring pulse variation after resting different time on capacitive mode. f Detecting nostril or mouth airflow by mimicking different respiratory behavior on resistive mode, via monitoring the thermal variation of airflow. Scale bars: 1\u2009cm. g Demonstration of the sensing performance for the self-healed sensors by monitoring the temperature variation of nostril airflow on resistive mode.\n\nBased on the precise detection of pressure stimulus for respiratory behavior in the capacitive mode, the thermal variation of nostril airflow was further investigated in the resistive mode. The temperature coefficient of resistance (TCR), which represents the thermal sensitivity, was evaluated according to the equation (TCR\u2009=\u2009[(RT\u2013R0)/R0]/\u03b4T). The calculated TCR was \u22121.67%\u2009\u00b0C\u22121 (R2\u2009=\u20090.99, Supplementary Fig.\u00a012). When applying the increased temperature stimulus to the sensor, not only activating the ionic transport within the gel network, but also promoting the dissociation of Ca2+ within the polymer network and increasing the concentration of charge carriers, both of them contributed to the decreased resistance at higher temperature, which is beneficial to detect the small thermal variation of nostril airflow. When monitoring the thermal variation of airflow from the nose and mouth by changing four kinds of respiratory patterns, a similar law was displayed (Fig.\u00a07f). No electrical variation was observed when simulating apnea. The larger signal and faster frequency appeared when simulating the deep and fast respiration, respectively, compared to the normal state. Specifically, at deep and normal respiration states, the resistive variation of airflow from mouth breathing was obviously bigger than that of nose breathing. This is attributed to the much more exhaled hot airflow from the mouth, which induces a larger temperature difference than that of nostril airflow. This can be used to distinguish breathing behavior from nose or mouth and is important for evaluating personal sleep quality. In order to investigate the effect of eutectogel to the capacitive sensing. Two control capacitive sensors were fabricated which utilizing the polyethylene (PE) film and metal Cu sheet as the control dielectric layer and control electrode, respectively, ascribing to their incompressibility and super high modulus and stiffness. And the corresponding schematic illustration for sensor structure was shown in Supplementary Fig.\u00a011a, which were labeled as sensor I, II, and III, respectively. When applied pressure of 15, 30 or 150\u2009Pa to the sensor, the relative value of capacitive variation of sensor I and II is periodical for one certain pressure and stepwise increased as increasing the pressure (Supplementary Fig.\u00a011b). Notably, in contrast to sensor I, the sensor II displayed the similar value of \u0394C/C0 at comparable small pressure of 15 and 30\u2009Pa, but showed smaller value of \u0394C/C0 at 150\u2009Pa (Supplementary Fig.\u00a011b). Meanwhile, the sensor III cannot detect the capacitive variation at 15\u2009Pa, and also exhibited much smaller value of \u0394C/C0 at 30 and 150\u2009Pa compared to that of sensor I and II (Supplementary Fig.\u00a011b). This experiment clearly demonstrated the capacitive variation was mainly ascribed to the expanding area of eutectogel at comparable small pressure (C\u2009=\u2009\u03b5S/4\u03c0kd, S is the effective area of the conducting layer, d is the thickness of the dielectric layer, \u03b5 is the dielectric constant of the dielectric layer, and k is the electrostatic constant). While for large pressure, the capacitive variation was ascribed to the synergistic effect of expanding area of eutectogel and the reduced thickness variation of VHB. And the result of nostril airflow monitoring by using these three sensors also verified this effect (Supplementary Fig.\u00a011c). As mentioned above, this eutectogel possessed superior self-healing efficiency. To investigate the reusability of this fabricated gel sensor, the sensor was deliberately cut into two halves, and the resistance value of the original and self-healed sensor for monitoring the successive nose breathing behavior was compared. As shown in Fig.\u00a07g, the outputted electrical signal could recover to the original value when the gel was self-healed, and no electrical signal degraded even after the 5th healing. And the healed sensor could also reproduce the extremely same resistive variation when mimicking the different respiratory patterns (Supplementary Fig.\u00a013). Even after healing at an extreme environment of \u221220\u2009\u00b0C, the sensor could display good sensing capability (Supplementary Fig.\u00a014), ascribing to the excellent self-healing efficiency at \u221220\u2009\u00b0C (Fig.\u00a04e) and the remarkable ionic conductivity at \u221220\u2009\u00b0C (Supplementary Fig.\u00a010b). This excellent self-healing performance could also be reflected in the mode of the capacitive pressure sensor (Supplementary Fig.\u00a015). The above results demonstrate not only the weak pressure stimuli but also the small thermal variation could be precisely detected by outputting different kinds of electrical signals. This provides a solid foundation for monitoring the nostril airflow via the dual mode.\n\nBased on the above results, the nostril airflow was monitored real-time by adhering to the bimodal sensor below the left nose (Fig.\u00a08a). The airflow field and the pressure distribution from exhaled airflow were simulated (ANSYS WORKBENCH CFX), respectively, and the maximum pressure to the sensor was less than 10\u2009Pa. Simultaneously, a higher temperature stimulus from exhaled airflow was applied to the sensor, and the highest temperature value was about 35\u2009\u00b0C. As shown in Fig.\u00a05b, c, the pressure variation from 0 to 10\u2009Pa and the thermal stimulus from 20 to 35\u2009\u00b0C to the corresponding electrical signal of capacitance and resistance displayed a linear relationship without cross-coupling. Hence, the capacitive signal reflected the pressure variation of nostril airflow, while the resistive signal represented the thermal stimuli of nostril airflow. As shown in Fig.\u00a08a, the pressure and temperature of nostril airflow were indeed simultaneously detected by outputting independent electrical signals (Supplementary Movie\u00a04). Notably, the two monitored signals matched perfectly, and the peak trend of capacitive and resistive variation was the opposite, ascribing to the exhaled airflow generated pressure stimuli, which increased the capacitance value while the higher temperature made the resistance decrease, which was consistent with the above-mentioned result. Moreover, the different respiratory behaviors were mimicked and monitored. In the apnea state, a straight line was observed because of no variation in capacitance and resistance. When switching into the deep, normal, and fast respiratory mode, both the capacitive and resistive variation curves changed accordingly. The corresponding respiratory frequencies for the deep, normal, and fast states were 14, 19, and 32 bpm, respectively. To further investigate the superior sensitivity for simultaneous and independent detection of pressure and temperature, the subtle water droplets with different weights and temperatures were applied to this dual-modal sensor. As shown in Fig.\u00a08b, when fixing the temperature of water droplets at 27\u2009\u00b0C and just increasing the weight of water droplets from 5, 15 to 30\u2009mg, the capacitive variation value increased stepwise. The resistive variation was nearly kept constant, and the little difference in resistance was ascribed to the different contact areas when varying the weight of the droplet. When fixing the weight of water droplets at 30\u2009mg and just increasing their temperature from 27, 28 to 29\u2009\u00b0C, the capacitive variation was nearly the same while the resistive variation increased stepwise. To demonstrate the advantage of this bimodal sensor, the pressure and temperature variation of nostril airflow were simultaneously monitored by this bimodal sensor at different environmental temperatures (18, 25, 40\u2009\u00b0C). As shown in Supplementary Fig.\u00a016, the outside temperature indeed generates severe influence on the thermal detection of nostril airflow via resistive mode, while the capacitive signal sensing the pressure variation of nostril airflow keeps a stable value at these different outside temperatures. These results strongly demonstrate the pressure and temperature stimulus from one subject could be simultaneously but independently detected by this fabricated bimodal eutectogel-based sensor, via outputting different electrical signals without cross-coupling, thereby effectively eliminating interference between the different stimuli when relying on one kind of electrical signal output.\n\na Simultaneous detection of the pressure and temperature variation of nostril airflow by outputting different kinds of electrical signals of capacitance and resistance, respectively, the airflow field and pressure distribution of exhaled nostril airflow was simulated, and the thermal distribution of nostril airflow was visualized by an infrared camera. b The sensing test of water droplets to the dual-modal sensor by varying the weight or temperature of the water droplet.\n\nObstructive sleep apnea syndrome (OSAS) is one typical respiratory disease in clinical practice, that is likely to induce some severe illness. Effectively monitoring respiratory behavior in sleep is extremely crucial for assessing individual health and even saving human lives. Figure\u00a09 shows the evident characteristic of OSAS is the intermittent cessation of breathing during sleep because of the pharyngeal narrowing. In clinical practice, sleep apnea is defined as a complete airflow cessation period of at least 10\u2009s. Herein, the bimodal sensor was further developed to monitor OSAS by adhering four sensors on the corresponding body positions, below the nose, chest, abdomen, and wrist, to independently but simultaneously detect the nostril airflow, chest/abdomen movement, and pulse. As proof of concept, the breathing was first conducted at a normal state for 36\u2009s. Then, it was deliberately held for 24\u2009s to simulate the apnea state and recovered to normal breathing again, and this process was repeated two times. Figure\u00a09 shows the consistent, stable, and periodical electrical signal at the initial normal state for the five kinds of stimulus from four different body positions. This included the temperature response of nostril airflow and the pressure detection from nostril airflow, chest, abdomen, and pulse. The calculated respiratory rate and pulse rate were about 25\u2009rpm and 72\u2009bpm, respectively. For the second stage of apnea, nearly flat curves of capacitive variation for the pressure stimulus of nostril airflow, chest, and abdomen were observed. This was because no pressure variation was observed for these body positions at this state. In contrast, the value of resistive variation increased until a platform was achieved, ascribed to the gradually increased resistance when stopping the thermal stimulus from nostril airflow. Meanwhile, the pulse rate evidently decreased to 62\u2009bpm because of the airflow obstruction, further strongly demonstrating the occurrence of an apnea event. And the corresponding resistive and capacitive variation gradually returned to the normal state when recovering normal breathing for these five outputted electrical signals. Furthermore, a similar curve appeared when applying the repeated behaviors. The severity of OSAS was evaluated by recording the number and duration time of apnea events per hour in the personal sleep. The above results clearly verified that this kind of eutectogel-based bimodal sensor could achieve superior sensing capability for simultaneously monitoring five different stimuli by outputting independent electrical signals. This significantly improved the accuracy and reliability of OSAS diagnosis and effectively avoided the motion artifact and interference of the environment, which usually occurred when solely a single stimulus is monitored or only the same electrical signal is output for different stimuli.\n\nThe schematic illustration of the symptoms of OSAS disease, and monitoring the apnea state and the respiratory recovery process when mimicking OSAS via simultaneously displaying five kinds of channels.",
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "In this work, a ATH eutectogel was synthesized by completely physical crosslinking of PACG, TA, and HAp in DESs. The optimized ATH6 gel exhibited low modulus and superior self-healing efficiency compared to other reported gel or elastomers. While the strong adhesive strength to the porcine skin or other substrates was achieved, simultaneously possessing good environmental stability and bio-compatibility. The fabricated dual-mode sensor displayed pressure sensitivity superior to previous reports, which could precisely detect the weak pressure stimulus of nostril airflow. Hence, this kind of ATH6 gel-based sensor could simultaneously detect the pressure and thermal stimuli from the nostril airflow, while outputting independent electrical signals of capacitance and resistance, respectively, without cross-coupling. This remarkable benefit significantly improved the accuracy and reliability of the detection result for nostril airflow, which effectively excluded the interference of the external environment when solely a single stimulus is monitored or only the same electrical signal is output for different stimuli. In contrast with the clinical instrument of polysomnography (PSG) which needs two kinds of sensors to be simultaneously fixed to monitor the pressure and temperature of nostril airflow, this kind of bimodal sensor integrated into one single sensor, greatly improving the comfortability, wearability, and portability. In proof of concept, this constructed sensor was applied to real-time monitor and evaluate the severity of OSAS, via simultaneously monitoring five kinds of stimuli in sleeping behavior, demonstrating superior sensing capability and highly improving the accuracy and reliability of detection results. This work provides a potentially effective approach to real-time monitoring of sleeping respiration and is a strong tool for forecasting respiratory or sleep-related diseases.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "Nano-sized HAp was purchased from Macklin Biochemical Technology Co., Ltd (Shanghai, China). TA and 2-Hydroxy-4\u2032-(2-hydroxyethoxy)-2-methylpropiophenone (I2959) were purchased from Sigma Aldrich Life Science & Technology Co., Ltd (Shanghai, China). Choline chloride (CHCl), ethylene glycol (EG), and acryloyl chloride were purchased from Adamas-beta Co., Ltd N-Acryloyl 2-Glycine (ACG) was synthesized according to previous work (glycine, acryloyl chloride and NaOH were as the crude materials, which reacted at RT for 1\u2009h)53. All other chemicals and solvents were analytical reagents. All animal experiments were conducted with the permission of the Laboratory Animal Ethics Committee of South China Agricultural University (approval number 2024b177). Six female SD rats with an average body weight of 160\u2009g were used. All animals were purchased and raised by the animal management of South China Agricultural University. The rats were housed in the controlled animal facilities with relative humidity at 55\u201360% and temperature conditions at 22\u201325\u2009\u00b0C under 12/12-h light/dark cycle, with access to food and water ad libitum. Each animal was used for only one experiment. And the rats were euthanized at second day via intraperitoneal injection of an excessive amount of anesthetic. And the treated skins were photographed and processed for HE staining.\n\nSimply, choline chloride and ethylene glycol were mixed at a 1:2\u2009M/M ratio. The mixture was continuously stirred at 80\u2009\u00b0C until a transparent liquid was obtained.\n\nAppropriate amounts of ACG, HAp, and TA were first dissolved in DESs, according to Table\u00a0S1. Then, 1\u2009wt% I2959 (relative to the total mass of ACG) was added and stirred thoroughly until complete dissolution. The mixture was added to a polytetrafluoroethylene (PTFE) mold, covered with a glass sheet, and then irradiated with UV light (\u03bb\u2009=\u2009365\u2009nm) for 20\u2009min. A series of ATH eutectogels were obtained and labeled as ATHx, in which x represents the weight ratio of HAp to ACG.\n\nThe tensile and compression tests were carried out using a universal testing machine (model 2 kN, CMT1203). For the tensile tests, dumbbell-shaped strip specimens of 3\u2009mm thick, 3\u2009mm wide, and 65\u2009mm length were prepared. The tensile rate was 100\u2009mm/min. For the compression test, the shape of the eutectogel samples was cylindrical, with a diameter of 12\u2009mm and a height of 10\u2009mm. The compression rate was fixed at 10\u2009mm/min. Cyclic tests were carried out as follow-up tests immediately after the initial loading. For the high- and low- temperature tensile tests, the eutectogels were allowed to balance at the tested temperatures for 6\u2009h. Then, the tensile tests were performed at a speed of 100\u2009mm/min. Data are expressed as mean\u2009\u00b1\u2009s. d (n\u2009=\u20095).\n\nThe adhesive shear strength of the gel for different substrates was determined by the lap shear test on a universal testing machine equipped with a 2\u2009kN load cell at a constant speed of 50\u2009mm*min\u22121 according to ASTM F2255-05 standards. The gels were cut into a size of 10\u2009mm\u2009\u00d7\u200910\u2009mm\u2009\u00d7\u20091\u2009mm, sandwiched between two substrates, and pressed with a 500\u2009g weight for 1\u2009min. The substrates included fresh porcine skin, steel, copper, wood, rubber, and plastic. Moreover, the fresh porcine skin was purchased from a local grocery shop. For the high-low temperature lap-shear test, the gels were equilibrated at the corresponding temperature for 2\u2009h and then tested at 50\u2009mm/min.\n\nA dumbbell-shaped eutectogel (3\u2009mm thick, 3\u2009mm wide, and 65\u2009mm long) was cut into two parts with a razor blade to evaluate the self-healing ability of the ATH gel. The two separated parts were placed in contact with each other in a PTFE mold for a certain time, and tested the tensile curve at the same temperature by using the high-low temperature chamber of the universal testing machine. To evaluate the self-healing efficiency, the tensile fracture strain of the original and self-healed eutectogels were measured under the same conditions. The self-healing efficiency (HE) was defined as Eq. (1):\n\nwhere SH and SO are the tensile fracture strain of the self-healed eutectogel and the original eutectogel, respectively.\n\nThe long-term stability of the gel was assessed by recording the weight change of the gel in different environments. The relative humidity (RH) of the indoor environment (RT) was about 40%, and the temperature was 25\u2009\u00b0C, while the outdoor RH was 55%\u201370% and the temperature was 25\u2009\u00b0C\u201330\u2009\u00b0C. Weight retention was calculated by Wt/W0 \u00d7100%.\n\nWt and W0 represent the post-storage weight and the initial weight of the gel, respectively. Five parallel specimens were tested.\n\nThe anti-freezing properties of ATH6 eutectogel and the corresponding hydrogel were determined using differential scanning calorimetry (DSC, Netzsch 214 polyma, Germany) under an N2 atmosphere. The temperature range was from 20 to \u221280\u2009\u00b0C at a cooling rate of \u221210\u2009\u00b0C min\u22121.\n\nRheological tests were carried out on a rotational rheometer (TA Discovery HR-2) with parallel plates (diameter of 20\u2009mm). In the linear viscoelastic region, a constant strain amplitude of 0.5% and a constant frequency of 1\u2009Hz were tested for time sweep. To evaluate the self-healing properties of the gels, large strain of 300% and small strain of 0.1% were respectively maintained for 100\u2009s, and alternately repeated four times. All gel samples were prepared as circles with a diameter of 20\u2009mm and a thickness of 1.5\u2009mm.\n\nThe CCK-8 method was used to evaluate the cell viability of this eutectogel. The gel samples were soaked in Duchenne Modified Eagle\u2019s Medium (DMEM) and left at 37\u2009\u00b0C for 24\u2009h to obtain the leach liquor, and the pure DMEM as the control sample. Fibroblasts were inoculated into 96-well plates at 5\u2009\u00d7\u2009103 cells per well. After the cells adhered to the bottom of the well plates, the different concentrations of leach liquor (50\u2009\u00b5g/mL, 100\u2009\u00b5g/mL, 200\u2009\u00b5g/mL, 500\u2009\u00b5g/mL) were co-incubated in a CO2 incubator (37\u2009\u00b0C, 5% CO2) for 1, 3 and 5 days. At each predetermined time point, the optical density values at 450\u2009nm were measured using the microplate reader.\n\nA dielectric film (VHB 4905, 3\u2009M) was sandwiched between two eutectogels, and three wires were inserted into the eutectogels. The capacitance and resistance signals were detected by a digital multimeter (LinkZill TruEbox 01RC, China). To detect the sensory relationship between the stimulus of pressure/thermal and the electrical signal of resistance and capacitance, DMA (DMA850, TA) was utilized by varying the pressure from 0 to 250\u2009Pa at different temperatures from 20 to 35\u2009\u00b0C. The infrared images of the sensors were recorded by an infrared thermal imager (FLUKE Tis75).\n\nAll experiments were repeated independently with similar results at least five times.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data supporting the findings of this study are available within this article, its supplementary information, and source data file.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This work was financially supported by the Guangdong Basic and Applied Basic Research Foundation (2025B1515020031, 2023A1515012167), National Key Research and Development Program of China (2024YFA1107600). No formal approval for the experiments involving human volunteers was required, which was waived by the Medical Ethics Committee of Guangzhou Medical University. The volunteers took part following informed consent.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Tao Liu, Qinan Wu.\n\nThe Third Affiliated Hospital, School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China\n\nTao Liu,\u00a0Huansheng Liu,\u00a0Xiyang Zhao\u00a0&\u00a0Zhenzhen Liu\n\nCollege of Food Science, South China Agricultural University, Guangzhou, China\n\nTao Liu\u00a0&\u00a0Zhenzhen Nong\n\nInstitute of Biomass Engineering, College of Materials and Energy, South China Agricultural University, Guangzhou, China\n\nQinan Wu,\u00a0Huansheng Liu,\u00a0Xiyang Zhao,\u00a0Xin Yi,\u00a0Jing Liu,\u00a0Qingwen Wang\u00a0&\u00a0Zhenzhen Liu\n\nJoint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Macau, China\n\nBingpu Zhou\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nT.L. and Q.W. contributed equally to this work, and they carried out most experiments. Z.L. conceived this idea, supervised the work, and wrote the manuscript. H.L., X.Z., X.Y., J.L., and Z.N. assisted in characterization of some materials. B.Z. and Q.W. assisted in supervising the work. All authors discussed the results and revised the manuscript.\n\nCorrespondence to\n                Zhenzhen Liu.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The authors declare no competing interests.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks the anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional information",
+            "section_text": "Publisher\u2019s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",
+            "section_image": []
+        },
+        {
+            "section_name": "Source data",
+            "section_text": "",
+            "section_image": []
+        },
+        {
+            "section_name": "Rights and permissions",
+            "section_text": "Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.\n\nReprints and permissions",
+            "section_image": []
+        },
+        {
+            "section_name": "About this article",
+            "section_text": "Liu, T., Wu, Q., Liu, H. et al. A crosslinked eutectogel for ultrasensitive pressure and temperature monitoring from nostril airflow.\n                    Nat Commun 16, 3334 (2025). https://doi.org/10.1038/s41467-025-58631-7\n\nDownload citation\n\nReceived: 11 August 2024\n\nAccepted: 29 March 2025\n\nPublished: 08 April 2025\n\nVersion of record: 08 April 2025\n\nDOI: https://doi.org/10.1038/s41467-025-58631-7\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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1b056ca46005a72aa11fb33f92b8e90ea66cd07bd3261fcafbc752224b68dc95/metadata.json

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+{
+    "title": "The polyketide to fatty acid transition in the evolution of animal lipid metabolism",
+    "pre_title": "The polyketide to fatty acid transition in the evolution of animal lipid metabolism",
+    "journal": "Nature Communications",
+    "published": "03 January 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Description of Additional Supplementary Files",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM3_ESM.pdf"
+        },
+        {
+            "label": "Supplementary Dataset 1",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM4_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 2",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM5_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 3",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM6_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 4",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM7_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 5",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM8_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 6",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM9_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 7",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM10_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 8",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM11_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 9",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM12_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 10",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM13_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 11",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM14_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 12",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM15_ESM.txt"
+        },
+        {
+            "label": "Supplementary Dataset 13",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM16_ESM.txt"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM17_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_MOESM18_ESM.xlsx"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "/articles/s41467-023-44497-0#MOESM1",
+        "https://doi.org/10.6084/m9.figshare.24066234",
+        "https://www.ncbi.nlm.nih.gov/sra/SRR22547485",
+        "https://www.ncbi.nlm.nih.gov/sra/SRR22547486",
+        "/articles/s41467-023-44497-0#MOESM1",
+        "/articles/s41467-023-44497-0#Sec24"
+    ],
+    "code": [
+        "https://doi.org/10.5281/zenodo.10125497"
+    ],
+    "subject": [
+        "Molecular evolution",
+        "Transferases"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-2978988/v1.pdf?c=1704374110000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-2978988/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-023-44497-0.pdf",
+    "preprint_posted": "18 Jun, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Lipids are crucial to all life, but the biochemical origins of many animal lipids remain unclear. We unveil a previously uncharacterized class of enzymes that is widely occurring in animals, and responsible for producing elaborate lipid- and polyketide-like compounds. These enzymes, the animal FAS-like PKSs (AFPKs), share a common ancestor with the animal fatty acid synthase, which produces the fats needed for animal survival, providing a plausible evolutionary bridge between the drug-like polyketides and the fatty acids. Vertebrates lack AFPKs, but excepting the placental mammals contain PKSs. In contrast, molluscs and arthropods contain abundant AFPKs, correlated with their rich polyketide chemistry. Molluscan AFPKs are associated with a lack of other defenses, consistent with the hypothesis that AFPKs provide a chemical defense for some lineages. By contrast, shelled molluscs (physically defended) generally contain PKSs instead. Arthropods have few PKSs, likely originating in parasites or recent horizontal gene transfer, but their abundant AFPKs potentially contributed to their ecological and evolutionary success. Although animal metabolism is well studied, surprising new metabolic enzyme classes still await discovery and may be unrecognized drivers of hyperdiverse animal radiations including gastropods, beetles and spiders.Biological sciences/Evolution/Molecular evolutionBiological sciences/Computational biology and bioinformatics",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "datasetS1Figure26ANIMALks.hmm.txtDataset 1datasetS2Figure26FASkshmm.txtDataset 2datasetS3Figure26FASIIHMM.txtDataset 3datasetS4Figure3Atree.txtDataset 4datasetS5Figure3BmollskFAS.hmm.txtDataset 5datasetS6Figure3Bmolluskpks.hmm.txtDataset 6datasetS7Figure6Btree1.txtDataset 7datasetS8Figure6Btree2.txtDataset 8datasetS9Figure6Carclade3hmm.txtDataset 9datasetS10Figure7tree.txtDataset 10NEESI.docxSupplementary Information",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Animals synthesize simple lipids using a distinct fatty acid synthase (FAS) related to the type I polyketide synthase (PKS) enzymes that produce complex specialized metabolites. The evolutionary origin of the animal FAS and its relationship to the diversity of PKSs remain unclear despite the critical role of lipid synthesis in cellular metabolism. Recently, an animal FAS-like PKS (AFPK) was identified in sacoglossan molluscs. Here, we explore the phylogenetic distribution of AFPKs and other PKS and FAS enzymes across the tree of life. We found AFPKs widely distributed in arthropods and molluscs (>6300 newly described AFPK sequences). The AFPKs form a clade with the animal FAS, providing an evolutionary link bridging the type I PKSs and the animal FAS. We found molluscan AFPK diversification correlated with shell loss, suggesting AFPKs provide a chemical defense. Arthropods have few or no PKSs, but our results indicate AFPKs contributed to their ecological and evolutionary success by facilitating branched hydrocarbon and pheromone biosynthesis. Although animal metabolism is well studied, surprising new metabolic enzyme classes such as AFPKs await discovery.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Fatty acids are required by living organisms, yet strikingly different branches of the tree of life have acquired convergent solutions to fatty acid biosynthesis. The animal fatty acid synthase (FAS) in particular has an independent origin and distinct domain architecture compared to other FASs, even those found in close relatives such as fungi1. Instead, the animal FAS has the same domain organization and clear sequence and structural homology with a class of enzymes known as the type I polyketide synthases (PKSs) (Fig.\u00a01A). Widely found throughout the tree of life, including within animals2,3,4,5, type I PKSs produce complex secondary (specialized) metabolites such as antibiotics, pigments, and many other biologically and commercially important compounds6 (Fig. 1C). While both PKSs and FASs polymerize acetate and its chemical relatives, in contrast to PKSs the animal FAS produces fully saturated lipids (Fig.\u00a01B). The current model is that animal FAS shares a common ancestor with fungal type I PKS1, but the evolutionary origins of fatty acid biosynthesis in animals remain surprisingly unclear.\n\nA The canonical functional domain architecture of the animal type I fatty acid synthase (FAS) is shown, with one of its major products, the fully saturated lipid oleic acid. Below, the type II FAS, such as those found in mitochondria and in bacteria, plants, and elsewhere, are encoded on individual proteins. B The catalytic cycle of a FAS or polyketide synthase (PKS) enzyme. The acyltransferase (AT) loads substrates, most commonly malonate, onto the acyl carrier protein (ACP). Substrates are then condensed by the ketosynthase (KS), creating an elongated product with a ketone. The ketoreductase (KR) reduces the ketone to an alcohol, while the dehydratase (DH) eliminates water to produce the olefin. Finally, the enoylreductase (ER) reduces the olefin to a fully saturated lipid. The R group on the growing lipid is methyl in the starter unit and becomes elongated in further iterative reaction cycles. C Known animal PKS and animal FAS-like PKS (AFPK) products. Note that variable substrates (methylmalonate) can be used and that variable reduction can lead to ketones, polyenes, and other features not normally synthesized by the animal FAS. The animal PKSs and AFPKs reported to date have similar domain architectures to the animal FAS, with exceptions at their C-termini. An AFPK product is shown in the box, while PKS products are outside the box.\n\nRecently, the enzyme EcPKS1 from sacoglossan molluscs was described which seemed to bridge these two types of metabolism. EcPKS1 is phylogenetically closely related to animal FAS, but instead of saturated fats, it made complex products similar to those produced by PKSs7; thus, a potentially new family of enzymes was designated, the animal FAS-like PKSs (AFPKSs). EcPKS1 was part of specialized metabolism, making unique compounds so far found only in sacoglossans, where they are associated with the ability of the animals to perform photosynthesis8,9. A provisional phylogenetic analysis indicated that there might be more FAS-like enzymes in molluscs. However, technical difficulties made it difficult to discover new AFPKs, since they are very similar to animal FASs and are often misassembled and/or misannotated in omics databases. In addition, some of these very FAS-like enzymes were identified as FAS paralogs in insects and associated by genetic methods with cuticular (branched-chain) hydrocarbons and pheromones10,11,12. This mystery spurred us to ask whether AFPKs were widespread in the animal world and potential evolutionary intermediates bridging FAS and PKS metabolism. If so, these largely uncharacterized enzymes might explain the vast number of lipid-like molecules found in the animals for which no biosynthetic pathways have been defined.\n\nHere, we developed bioinformatics methods that reliably differentiate mitochondrial type II FASs, animal type I FASs, PKSs, and the phylogenetically intermediate AFPK enzymes. We demonstrate that AFPKs are widespread in molluscs and in arthropods but rare or absent from other animal taxa investigated. AFPKs share a common ancestor with the animal type I FAS, indicating a single origin early in the development of the animal phyla. Further, these results reinforce previous ideas that fungal type I PKSs and animal FAS share a common ancestor. Finally, the methods clarified the phylogeny of KS-containing enzymes in the animals, revealing key aspects of their evolution, origin, and distribution. In some cases, patterns clearly reflect biological and ecological roles, while for the most part, these are new and uncharacterized enzymes. Taken together, these results reveal an unexpected enzymatic repertoire across major animal phyla that may underlie much of the chemical richness of diverse groups. The designation of AFPKs as a distinct group is supported by their presence in a derived clade with animal FAS on the global KS tree, coupled with the distinctive biochemical features of the AFPKs characterized to date, which together distinguish the AFPKs from canonical animal PKSs.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-023-44497-0/MediaObjects/41467_2023_44497_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "We aimed to globally identify AFPKs in animals, but we anticipated four technical problems. First, animal FAS/PKS proteins are often poorly assembled due to their lengths and, sometimes, the presence of multiple closely related copies in a genome/transcriptome5. Therefore, our workflow involved downloading all sequence read archives (SRAs) from GenBank for taxa of interest (Supplementary Data\u00a010 and Supplementary Table\u00a01), (re)assembling them and then performing further analyses. Because of our interest in their elaborate polyketide chemistry13, we also sequenced a representative of Siphonaria (NCBI accession numbers: SRR22547485 and SRR22547486) and added its transcriptome and genome to the same workflow as used for SRAs14. Second, many animal datasets contain sequences originating in co-occurring organisms. Contaminating contigs from bacteria, fungi, plants, and algae were removed by the taxonomy assignment pipeline in the Autometa package15. Third, type I FAS/PKS are multidomain enzymes (~500\u2009kDa in the dimeric state) that are difficult to align, except for the N-terminal ketosynthase (KS) domains. Thus, we analyzed only KS domains. Finally, with EcPKS1 and EcPKS2 as the only biochemically characterized AFPKs to the best of our knowledge7,16, it was difficult to identify and distinguish AFPKs from FAS enzymes. To solve this problem, we first developed profile hidden Markov models (HMMs) using animal PKSs and FASs that were previously identified5,7. To mitigate any potential bias in HMM scores for KSs originating from various animal species, the training of HMMs incorporated FAS sequences from a diverse range of species, spanning the phyla Cnidaria, Nematoda, Annelida, Tardigrada, Mollusca, Echinodermata, and subphylum Vertebrata. Using these profiles, we employed the distribution pattern of HMM scores to automate the identification of animal cytoplasmic and mitochondrial FAS, PKSs, and related enzymes in animal datasets. Simultaneously, we distinguished between hypothetical AFPKs and animal type I FAS. Subsequent phylogenetic analyses supported the identification of AFPKs. We used only full-length, well-assembled KSs with a validated origin in animal genomes for all further analyses described in this study.\n\nProfile HMM analysis using 558 mollusc transcriptome assemblies led to the identification of contigs potentially encoding KS-containing enzymes. Nonredundant KS-containing genes were then used as queries to search against the nr database in NCBI to identify further mollusc KS-containing genes. These were initially employed to identify 1390 KS-encoding enzymes in Mollusca, as well as 18,039 KS-encoding genes in >5000 specimens from Arthropoda (representing the two major protostome lineages). The algorithm was used to unveil all KS-containing enzymes in 896 sponge transcriptome assemblies from the SRA database (phylum Porifera). It was also applied to Chordata (482 transcriptomes from 282 species from subphylum Vertebrata, as well as 232 transcriptomes from subphylum Tunicata) for comparison. Sponges were chosen to represent basal animals given sufficient genomic and transcriptomic resources for our analyses compared to other candidate groups (e.g., Ctenophora); vertebrates were used to represent deuterostomes given their relevance to human physiology and medicine. Finally, we identified FAS genes in other representative metazoan taxa.\n\nStrikingly, while mitochondrial type II FAS enzymes were readily identified in Porifera, we could not find other KS-containing proteins encoded within sponge genomes. Our pipeline is designed to differentiate sponge-encoded genes from those in the abundant bacteria that are often present, making this a robust analysis. Previously, type I FAS was found to be very rare or absent in sponges17 and type I FAS genes identified in our analysis appear to originate in dinoflagellate symbionts. Our result uses a much larger dataset but is otherwise consistent with these previous analyses. We also failed to detect type I FAS in the limited available ctenophore datasets. In contrast, all other animal groups investigated, from placozoans to chordates, harbor the cytoplasmic FAS. Further, animal FAS could not be identified in the choanoflagellates, thought to be the sister group of animals18. This implies that the animal FAS may have originated in the ParaHoxazoa19.\n\nAlthough type I FAS-like enzymes were identified in molluscs7,16, it was initially difficult to differentiate true FAS enzymes from the AFPKs, especially because robust phylogenetic tree methods are not practical at the scale needed to parse omics data. Therefore, we applied an HMM method. Three different HMMs (animal type I FAS, PKS, and mitochondrial type II FAS) were generated using protein sequences from GenBank (Supplementary Data\u00a01\u20133). The alignment scores of the KS domains were compared in each of the three models. The data was visualized in a dot plot in comparison to the ordered FAS HMM alignment bit scores (Fig.\u00a02). We focused on the discrete region bridging the animal type I FAS and the animal PKSs in the HMM score dot plot, with scores for EcPKS1 and ECPKS2 helping to roughly reference the region. Although not as accurate as phylogenetic analysis, this method was extremely rapid and enabled us to readily differentiate potential AFPKs, PKSs, and type I and II animal FASs. These methods were applied to the available data from molluscs, arthropods, and vertebrates.\n\nIn this method, we noticed a discrete region bridging the animal type I FAS and the animal PKSs. The yellow bar shows a region in which putative animal FAS-like PKSs (AFPKs) dominate; for the hmm scores in the order FAS, PKS, FASII for EcPKS1 (587.7, 274.6, 123.1) and EcPKS2 (581.7, 285.2, 133.5); these were further evaluated and validated using additional methods including phylogenetic analyses. A Mollusk KSs; B Arthropod KSs; C Vertebrate KSs. The x-axes indicate sequence number, ordered by relationship to cytoplasmic FAS; the y-axes indicate HMM bit scores.\n\nIn the molluscs, using EcPKS1 and EcPKS2 as references, the type I FASs, type II FASs, AFPKs, and PKSs could be readily distinguished visually, with abundant AFPKs discovered across molluscan groups. The identified AFPKs were used in further analyses that confirmed their distinctness from related FASs (see below).\n\nSimilar to molluscs, the arthropods appeared to contain a large number of extremely diverse, unanticipated AFPKs. However, unlike what was observed in molluscs, there was not a clear distinction between FASs and AFPKs; instead, a continuum was observed spanning the FAS\u2013PKS transition. This made it difficult to use this method to firmly define AFPKs (further details below), as could be done with molluscs. Further, very few PKSs were identified in arthropods.\n\nThe chordate KS-containing proteins revealed very clear patterns reflecting PKS and lipid diversity in the group. No AFPKs were discovered in any available vertebrate transcriptome, genome, or the GenBank nr database, despite exhaustive searches using this HMM method, maximum likelihood (ML) phylogenetic analyses, and even manually screening datasets. In contrast, 2014 FAS or PKS genes were detected in vertebrates. Thus, AFPKs are not universal in animals but may be restricted to protostomes. We also investigated all tunicate SRAs (another group of chordates), finding that they contained only animal FAS, and not PKSs or AFPKs. By contrast, PKSs are relatively widespread among vertebrates, including many found in mammals. Only one of these vertebrate PKSs has been characterized: a bird PKS that synthesizes polyenes, coloring budgerigars green20. The roles of other vertebrate PKSs are unknown, except that a fish PKS with an unknown product is required for otolith (ear) formation21. No PKSs were seen in any placental mammal; all were in marsupial genomes, implying that PKSs might have been lost in the transition to eutherian mammals. Overall, the vertebrate data expands previous knowledge of PKSs and reveals many proteins of unknown function or biological significance.\n\nPKSs are broadly distributed in the animal kingdom, present in every phylum investigated except for sponges and ctenophores (Supplementary Fig.\u00a01). Aside from vertebrate proteins mentioned above, PKSs have been characterized in phylum Echinodermata5, where at least one makes aromatic pigments, and in phylum Nematoda, where a Caenorhabditis elegans PKS-nonribosomal peptide synthetase makes a complex hormone/signaling compound2. In contrast to the PKSs, AFPKs were only identified in molluscs and arthropods.\n\nTo further demonstrate that the HMM model applied to vertebrates and that we were not missing something due to the model sequence set, we examined how different KS training sequences from less diverse species affected the medium score of AFPKs. To do this, we downloaded all vertebrate protein sequences annotated as FAS or PKS from GenBank. We extracted the KS domains, removed redundant sequences, and subjected them to analysis using an ML tree. We only detected FAS and PKS genes in our analysis. Subsequently, we constructed new HMMs for FAS and animal PKS using the newly detected sequences exclusively from vertebrates sourced from GenBank. These HMMs were then utilized to generate HMM score dot plots for molluscs and vertebrate KSs. Remarkably, the resulting HMM score plots are very similar to the ones in Fig.\u00a02 (Supplementary Fig.\u00a05), reinforcing the robustness of the analytical method. For example, only the FAS HMM scores for EcPKS1 and EcPKS2 are ~80 lower in comparison to the corresponding values in Fig.\u00a02, but these two KSs are still in a discrete region bridging the animal type I FAS and the animal PKSs.\n\nWe focused on Mollusca because AFPKs have been biochemically characterized7,16 from sacoglossan molluscs, a group of sea slugs including chloroplast-retaining species8,9 in which AFPK products are likely to be important for photosynthesis. To better define the phylogenetic diversity of molluscan AFPKs, two methods were performed. First, we picked the region shown in Fig.\u00a02 with FAS HMM scores from 400 to 600, based on the scores of EcPKS1 and EcPKS2; potential AFPK protein sequences were selected and then aligned with randomly selected molluscan FAS and PKS sequences. The resulting alignment was used to create an ML tree. Phylogenetic analysis differentiated molluscan FASs, AFPKs, and PKSs into seven different clades (Fig.\u00a03A). Two of these clades represented the canonical animal type I FAS and PKS groups. Outside of those groups were four clades (mo-clades 1-4) that were more similar to FAS than to PKS, which we categorized as AFPKs. The functionally characterized EcPKS1 and EcPKS2 reside in mo-clade 1. mo-clade 1 proteins are closely related to the animal FAS, even though they make products that are much different than expected from FAS chemistry. The mo-clade 1 AFPKs produce partially reduced pyrone polyenes derived from methylmalonate7,16 instead of linear, saturated fats derived from acetate that are produced by FASs. It was not initially clear whether mo-clade 5 was more FAS-like or PKS-like, but it appeared to be more closely related to the animal PKSs than were the other AFPK clades.\n\nA Maximum-likelihood phylogenetic trees of fatty acid synthases (FASs), polyketide synthases (PKSs), and animal FAS-like PKSs (AFPKs) from molluscs (Supplementary Data\u00a04). B Hidden Markov model (HMM) alignment bit scores of the ketosynthase (KS) domains reveal different subclasses of mollusc PKS, AFPK, and FAS (Supplementary Data\u00a05 and 6).\n\nWith the goal of creating a practical, accurate, and rapid method for categorizing AFPKs, we randomly selected FAS and PKS sequences from the initial phylogeny (Fig.\u00a03A) to create two training sets: one from the FASs, and one from the PKSs. The training sets were used to generate HMMs, which were applied to analyze all mollusc KSs (Fig.\u00a03B). The HMM score of each KS-containing protein sequence was plotted in a scatter graph, where any protein sequence above the line y\u2009=\u2009x is more closely related to PKSs, while AFPKs and FASs are below the line. Using these models, we identified 113 nonredundant putative AFPKs in mo-clades 1\u20134 from existing mollusc SRA datasets. Because mo-clade 5 was above the line comprising y\u2009=\u2009x, it was tentatively identified as a PKS clade.\n\nThe domain architecture of KS-containing proteins was predicted by antiSMASH22 and Interpro23 (Fig.\u00a04). All animal FAS proteins contain a thioesterase (TE) domain that is responsible for hydrolyzing the final product. However, AFPKs in mo-clades 1\u20133 lacked a TE domain. In the case of mo-clade 1 proteins EcPKS1 and EcPKS2, offloading is accomplished without a TE, possibly by the spontaneous formation of a pyrone ring system7. However, for the majority of these proteins, the offloading mechanism is unknown.\n\nDomain architecture (A) and intron pattern (B) of fatty acid synthases (FASs), polyketide synthases (PKSs), and animal FAS-like PKSs (AFPKs) found in molluscs. KS ketosynthase, AT acyltransferase, DH dehydratase, MT methyltransferase, ER enoylreductase, KR ketoreductase, ACP acyl carrier protein, TE thioesterase, C condensation.\n\nStrikingly, no domain was predicted by antiSMASH for the protein sequences in mo-clade 4, while Interpro was only able to predict the KS-AT-TE domains. The majority of mo-clade 4 enzymes lack predictable sequence similarity with other proteins, indicating as yet unknown biochemistry. All but one of the clades had ketoreductase (KR) domains predicted to be active by the algorithm in antiSMASH22. Animal FAS enzymes contain pseudo-methyltransferase (\u03a8-MT) domains that are structurally important but catalytically inactive24. They may have evolved from the active MT present in fungal type I PKSs. Underscoring the close relationships between FAS and AFPKs, many AFPKs retain identifiable \u03a8-MT domains. mo-clade 5 is the only one that is likely to contain active MT domains, further supporting its phylogenetic placement amongst the PKSs and not the AFPKs. mo-clade 2 proteins were predicted to encode inactive KR domains; such proteins should likely lead to the formation of aromatic compounds. Tandem acyl carrier protein (ACP) domains were detected in some of the proteins in mo-clade 3. In summary, the AFPKs have diverse domain architectures, the chemical products of which are currently unpredictable.\n\nThe mollusc PKS clade contains two distinct domain architectures: those with condensation (C) domains from nonribosomal peptide biosynthesis, and those without C domains (Fig.\u00a04A, Supplementary Data\u00a011). In taxa (i.e. Siphonaria) that contain both types of PKSs, the KS portions are phylogenetically very closely related. Overall, this result and the domain architecture differences seen in AFPKs suggested that the N-terminal regions encoding the KSs are relatively conserved, while the C-terminal regions arise through recombination at least in some cases.\n\nFrom the available genome sequences, we observed that the exon density of mollusc PKS genes is much higher than that found in FAS and AFPK genes. The few introns observed in mollusc PKS genes were concentrated at the N-terminus (Fig.\u00a04B, Supplementary Data\u00a012). In contrast, AFPK and FAS genes have high intron densities through their entire lengths, with the exception of rather large exons in the \u03a8-MT domains. Based upon their position between conserved and variable parts of the proteins, these \u03a8-MT domain regions might be sites of recombination.\n\nIn the 1081 KSs detected from 558 mollusc SRA assemblies, there are 525 FASs, 178 AFPKs and 378 PKSs (including canonical PKSs\u2009+\u2009mo-clade 5). Up to nine KSs were found in a given species (Fig.\u00a05A), but the distribution of AFPK/PKS clades differed drastically among SRA samples. Plotting clade distribution by molluscan class and genus (Fig.\u00a05B), mo-clades 1\u20133 were only detected in a few genera within Gastropoda, while other mo-clades are widely distributed. Strikingly, mo-clades 1\u20133 do not co-occur with PKSs in most analyzed genera.\n\nA Bar plot of the counts of ketosynthase-(KS)-containing genes detected in each of the 558 SRA specimens. The bar color indicates the KS clade. B Heat map plot of AFPK and PKS clades distribution in 143 genera analyzed. The inner black ring indicates the clade detected in each genus. The outside ring color indicates the class to which the genera belong. C Mapping AFPKs and PKSs to a\u00a0current gastropod\u00a0phylogeny. The colored circles indicate each AFPK/PKS clade. A colored circle on top of a node in the phylogenetic tree indicates that the AFPK/PKS clade is found only within that taxonomic group.\n\nPhylogenetic evidence indicated that the repertoire of AFPK diversity increased in concert with progressive reduction of the ancestral shell in Heterobranchia, a major gastropod lineage including sea slugs and traditional pulmonate (air-breathing) snails and slugs (Fig.\u00a05C). Indeed, almost all molluscan families known to contain polypropionate or polyene polyketides contained AFPKs (mo-clades 1\u20133) (Supplementary Fig.\u00a02)25. Canonical animal PKS enzymes were sampled from most major molluscan lineages (Polyplacophora, Gastropoda, Bivalvia, Scaphopoda), while mo-clade 4 AFPKs were present in all surveyed molluscan classes including Cephalapoda and Monoplacophora (Fig.\u00a05B, C). mo-clade 4 AFPKs were also expressed in all major gastropod subclasses (Patellogastropoda, Vetigastropoda, Neritimorpha, Caenogastropoda, and Heterobranchia). In contrast, mo-clades 1\u20133 were phylogenetically restricted to Heterobranchia (Fig.\u00a05C). Major evolutionary trends within Heterobranchia were (a) convergent reduction and loss of the ancestral shell (a physical defense) in many \u2018sea slug\u2019 lineages, and (b) the transition to air-breathing and invasion of freshwater and terrestrial habitats in Pneumopulmonata, culminating in the explosive radiation of stylommatophoran snails and slugs. By facilitating the biosynthesis of small molecules used as anti-predator defenses, sunscreens, and in other chemical signaling roles, AFPKs may have facilitated shell loss and the colonization of novel habitats in heterobranchs, which comprise about one-third of molluscan species diversity.\n\nWithin Heterobranchia, mo-clade 4 enzymes were sampled in lower heterobranchs, euopisthobranch sea slugs, freshwater snails (Hygrophila), and amphibious members of Amphipulmonata, sister group to the terrestrial Stylommatophora14 (Fig.\u00a05C). mo-clade 2 was found only in Heterobranchia, but was present in diverse groups: the lower heterobranchs; a pleurobranch; euopisthobranchs including the model organism Aplysia; and basal pneumopulmonates, including Siphonaria and an acochlidiacean. This distribution indicates the ancestor of mo-clade 2 was present in the most recent common ancestor of Heterobranchia. In contrast, mo-clade 3 AFPKs were only sampled in Euopisthobranchia: cephalaspideans (bubble shells and kin), sea hares (e.g., Aplysia), and pteropods (sea butterflies). Euopisthobranchia had the richest repertoire of biosynthetic potential, expressing enzymes from three mo-clades as well as the canonical animal PKS lineage. As euopisthobranchs underwent repeated, parallel reductions in the ancestral shell and radiated into habitats including the pelagic realm26, further exploration of the potential role of polyketides in defense and adaptation to planktonic life is warranted27,28,29,30. The second phylogenetically restricted AFPK lineage was mo-clade 1 AFPKs, expressed solely in shell-less sacoglossans (clade Plakobranchacea). These sea slugs expressed only mo-clade 1 AFPKs, and many of the species analyzed had multiple AFPKs within this lineage.\n\nAlternative chemical defenses may have\u00a0 selected against AFPK expression or gene retention. Strikingly, no PKS or AFPK genes were detected in nudibranchs, which typically deploy diet-derived chemicals or cnidarian nematocysts for defense, in lieu of a shell27,31. mo-clade 1 AFPKs were not detected in the shelled sacoglossans (superfamily Oxynooidea). The shells in this group are thin and likely provide little defense, but most species store defensive compounds from their host, the \u201ckiller algae\u201d Caulerpa32,33. The only other major group lacking PKS expression was the Neogastropoda, in which complex venoms and a heavy shell may have favored the loss of ancestral polyketide chemistry34. These phylogenetic trends further implicate a role for AFPKs and the compounds they produce in defensive strategies, and highlight the interplay between phenotypic tradeoffs, genome evolution, and diversification dynamics across Gastropoda.\n\nArthropods contained numerous potential AFPKs but they were more difficult to distinguish from FASs than were the mollusc AFPKs, necessitating refinement of methods (Fig.\u00a02B). The KSs originated in 2622 different arthropod species, and as a result, the observed sequence diversity was much greater than found in mollusc and vertebrate data sets. For this reason, the slope of the FAS HMM bit score was virtually continuous, without discrete transitions between enzyme classes as in the mollusc and vertebrate analyses. We hypothesized that the arthropod KSs with FAS HMM bit scores between 500 and 200 (Fig.\u00a02B, shaded region) comprised AFPKs. However, this area included some PKS genes (Fig.\u00a02B, red dots above the green line). Compared to the majority of\u00a0AFPKs in the area, those PKS genes had higher PKS HMM scores. For example, one of them was previously identified as a\u00a0horizontally acquired PKS gene35 (GenBank accession: OXA62418.1) in the\u00a0springtail Folsomia candida genome, which has HMM scores in the order FAS, PKS, FASII: 272.0, 325.6, 132.6 in the plot. We hypothesized that, as found in Folsomia, many arthropod PKS genes in this region of the plot potentially result from horizontal gene transfer. We therefore predicted much of the polyketide repertoire of arthropods likely derives from the biosynthetic activity of AFPKs, which subsequent analyses revealed are widespread in arthropods.\n\nTo resolve the arthropod AFPKs, we first took a subset of the data, comprising all 477 KSs from beetle species. The dot plot of HMM bit scores (Fig.\u00a06A) showed a very similar trend to that for the whole arthropod KSs, but with a sharper break point between the FAS and AFPK sequences. It also revealed at least two different types of AFPKs in beetles, since there are two regions with different slopes. Indeed, evolutionary relationships among 477 beetle KSs (Fig.\u00a06B, tree1, Supplementary Data\u00a07) supported three KS clades (ar-clades 1\u20133) that are phylogenetically distant from the FAS clade common among animals. The HMM scores of the KSs from these three clades suggested that they are AFPKs (Figs.\u00a02B and 6A).\n\nA The hidden Markov model (HMM) score distribution of beetle KSs has a similar trend to that for the whole arthropod KSs shown in Fig.\u00a02B but with a clearer breakpoint from the FASs. B Congruence between beetle KS tree (tree1) and selected arthropod KS tree (tree2). The \u201cother KSs\u201d include PKSs from bacteria and molluscs, ar-clades1 and 4 are close to mollusca AFPKs. Alignment files are provided in Supplementary Data\u00a07 and 8.\n\nTo determine whether this pattern was recapitulated throughout arthropods, the KSs (>5000) from SRA assemblies were sorted according to the FAS HMM score, and every tenth sequence was selected to provide 497 KS sequences that were analyzed by ML (tree 2 in Fig.\u00a06B, Supplementary Data\u00a08). The resulting phylogeny is highly congruent with the beetle KS tree (tree1 in Fig.\u00a06B), with two of the AFPK clades (ar-clades 2 and 3) distributed throughout the arthropods. ar-clade 1 was only sampled in beetles, and therefore had reduced representation on the all-arthropod tree (tree2 in Fig.\u00a06B). The ar-clade 1 lineage may be restricted to the spectacular radiation of beetles, one of the major sources of terrestrial biodiversity36,37,38, and thus warrants special attention given the unknown role of these enzymes39,40. In addition, a fourth AFPK clade (ar-clade 4) was identified only in spiders, another exceptional animal radiation41,42,43,44. This spider clade was closely related to mollusc AFPKs, reflecting the ancient origin of AFPKs prior to the divergence of major bilaterian lineages. Because many arthropod AFPKs are very closely related to FASs, we took a subset of sequences from each of the AFPK clades (ar-clades1\u20134) identified in tree2. These subsets were used as reference points to better understand the distribution patterns of all arthropod KSs. The dataset consisted of a total of 6542 nonredundant KS sequences obtained from selected arthropod SRA datasets and the GenBank nr database. These sequences were randomly divided into 11 groups, each containing approximately 600 KS sequences. Combining each of these groups with the corresponding reference sequence, we conducted a thorough analysis using ML methods (see Supplementary Fig.\u00a03). The resulting phylogenetic trees showed remarkable consistency across the set of 11 trees and when compared to tree1 and tree2 presented in Fig.\u00a06b. Notably, the reference sequences for ar-clades1\u20134 were distributed in all of the major clades of the phylogenetic trees; thus, these four ar-clades represented all major AFPK lineages detected in available arthropod transcriptomes.\n\nArthropod AFPKs have a similar domain architecture (including a TE domain) to that found in FASs. A few of the sequences we investigated had alternative termination domains, including the reductive (R) domains that often terminate fungal and bacterial PKS and peptide synthetase enzymes45. In many cases, this implies a much more complex lipid metabolism in these animals than is currently appreciated; potentially many of the unusual lipids isolated from arthropods might originate from the activity of as-yet uncharacterized AFPK sequences46. These include ethers, aldehydes, alcohols, and branched-chain lipids47. We found that some of those in ar-clades 2 and 3 have been previously associated with insect-specialized metabolism. For example, in Locusta migratoria, there are three different type I FAS orthologs that are annotated as \u201cFAS\u201d12. Knockout and expression studies showed that one LmFAS2 (QNU13193), which we recovered in the FAS clade, is expressed systemically as the normal type I FAS, while the other two, LmFAS1 (QNU13192, ar-clade2) and LmFAS3 (QNU13194, ar-clade3) were expressed in the integument. Knockout of LmFAS1/LmFAS3 altered the cuticular hydrocarbon and/or inner hydrocarbon profile. Paralogous \u201cFAS\u201d enzymes were similarly associated with specialized metabolism in several other insects10,11, but based on our findings those genes are predicted to be AFPKs. We hypothesize that the biochemical characterization of arthropod AFPKs will reveal the source of many unusual lipids and hormones distributed throughout the phylum.\n\nApplying the HMMs used above in this study, we identified only mitochondrial (type II) FASs in sponges and ctenophores. In neither group did we find the type I FAS/AFPK/PKS enzymes. By contrast, we identified type I FASs in all phyla of ParaHoxozoa. Both ctenophores and sponges are noted for the prevalence of fatty acid elongases48, which makes the unique suite of lipids known only in the sponges. In higher animals and yeast, the mitochondrial FAS is specialized to produce octanoic acid needed for lipoate biosynthesis49. We hypothesize that sponges and ctenophores might use the type II FAS to produce short-chain octanoate, which is matured by cytoplasmic elongases50. Alternatively, an unknown lipid biosynthetic route may yet be found in these animals or their microbial symbionts17. By contrast, the ancestor of ParaHoxozoa used a specialized type I FAS, not found in any other lineage or domain on the Tree of life, to synthesize long-chain lipids.\n\nTo further investigate the evolutionary history of AFPK diversity, we inferred the relationships among AFPKs, FASs, and PKSs from a range of eukaryotes (animals, fungi, amoebae) as well as archaea and eubacteria (Fig.\u00a07, Supplementary Data\u00a09, Supplementary Fig.\u00a04 and Supplementary Data\u00a013). The resulting phylogeny reinforces previous suggestions that the animal FAS shares a common ancestor with fungal highly-reducing PKSs)1. However, animal FAS shared a more recent common ancestor with the AFPKs, which formed a grade paraphyletic with respect to animal FASs (Fig.\u00a07). The animal FASs were a derived clade nested within the AFPKs, most closely related to ar-clades 2 and 3. These findings suggest an ancestral fungal-like type I PKS was retained in animals and diversified into the AFPK/FAS enzyme family. Based on their phylogenetic distributions, AFPKs and animal FAS likely diverged in the ancestor of ParaHoxozoa. Apparent paraphyly of AFPKs with respect to FAS could be an artifact of rooting, or it may reflect the diversification of AFPKs in speciose radiations promoted by the ecological roles of polyketides while constraints of primary metabolism limited FAS diversity. However, our findings demonstrate that AFPK and FAS lineages share a much more recent common ancestor than either share with PKS enzymes, and suggest a shared evolutionary history of enzyme function between primary and secondary metabolism during animal evolution.\n\nNodes were supported by the Shimodaira-Hasegawa likelihood ratio test and ultrafast bootstrap, given as percent values. Alignment files are provided in Supplementary Data\u00a09.\n\nThe ML phylogeny also supported a sister relationship for mollusc mo-clade 5 and ameba PKSs, reenforcing that clade 5 belongs to the PKS cluster, and not to AFPKs. We propose that the \u201cmollusc\u201d clade 5 might actually originate in a symbiotic organism living in the host molluscs; alternatively, it could be a true molluscan PKS.\n\nThrough the use of HMM score sorting methods followed by extensive phylogenetic tree analysis, we identified hundreds of AFPKs, forming eight different clades (mo-clades 1\u20134 and ar-clades 1\u20134). Nonetheless, this process was highly time-consuming and heavily dependent on the precise alignment of hundreds of protein sequences, which often required manual curation. We aimed to develop a model using well-defined AFPKs described above to rapidly ascertain the probability that a given KS domain sequence is an AFPK. Such a method would be widely useful in delineating the unexpectedly rich and complex lipid and polyketide metabolism found in animals.\n\nConsidering the above limitations, we created AFPK-Finder (DOI:10.5281/zenodo.10125497) to rapidly distinguish AFPKs from PKS and FAS sequences with excellent computational efficiency. First, we prepared a panel of different HMMs using two different resources: we downloaded PKS-related sequences from different organisms from NCBI, and PKS-related HMMs/conserved domains from Pfam and CDD. Next, we used the AFPKs (mo-clades 1-4) identified from mollusks as training data and aligned them to a random subset of the HMMs. The resulting data matrix, which contained the HMM alignment scores, was then normalized and analyzed using Rtsne for dimension reduction (Fig.\u00a08A). The Rtsne 2D plot generated from 30 HMMs showed that the datapoints clustered exactly according to their PKS types (Fig.\u00a08B). This finding indicates that although a KS may not show significant alignment with an HMM, it still provides useful information that helps to annotate its function. We evaluated the model\u2019s robustness by utilizing the arthropod AFPKs as the test dataset. ar-clades 1\u20134 were submitted to AFPK-Finder. The sequences from ar-clades 1, 3, and 4 clustered very well with mollusc AFPKs. Most of the sequences in ar-clade 2 formed their own cluster, with only a small subset of them clustering well with mollusc AFPKs. In comparison with other ar-clades, ar-clade 2 cluster is much closer to the FAS cluster, which is consistent with the observation in trees 1 and 2 in Fig.\u00a06B. This suggests that ar-clade 2 might have a function very similar to FAS. Furthermore, ar-clade 2 pulled four mollusc FAS sequences out of the FAS cluster in the plot, indicating that mollusks may also contain the same type of proteins (Fig.\u00a08C). It is possible that the small number of these sequences makes it difficult to observe a separate clade in the phylogenetic tree.\n\nA The training process involved using mollusc AFPKs to align with different combinations of polyketide synthase-related hidden Markov models, and the resulting data matrices of alignment scores were analyzed for dimension reduction. The resulting clusters were evaluated by comparison to the clades in the phylogeny tree in Fig.\u00a03A. B The correct model was selected based on the congruence between the clusters and clades. C The model was then tested using arthropod AFPKs (purple INPUT_KS) that were identified in Fig.\u00a06.\n\nOverall, AFPK-Finder precisely and rapidly recapitulated our findings from the much more time-consuming HMM-phylogeny analysis shown in Figs.\u00a02 and 3 above. No AFPKs defined in the more rigorous method were missed by AFPK-Finder, but the algorithm rapidly identified a few new AFPKs that were not observed in our initial survey. Moreover, AFPK-Finder readily distinguished and classified all KSs found in animal transcriptomes. Thus, it should be broadly useful in understanding lipid metabolism in the animal kingdom.\n\nThere are several limitations to this study. First, in several cases, we have interpreted an absence of a gene or pathway from taxonomic groups in our analysis as indicating a true absence of those genes. This could also result from several other causes, such as a limited sample set, a lack of expression in the tissues analyzed, or the presence of unanticipated orthologs that are not accounted for in the models. Nonetheless, due to the large number of samples, KS types, and sequences in this study, the overall trends identified are likely to be robust. A second limitation is that our current state of knowledge is not complete, and lipid/polyketide biogenesis and evolution are exceptionally complex. For example, some mussels (molluscs) contain two different varieties of type I FAS. The two FASs share a common ancestor in the phylogenetic tree, but they are not very similar (<50% identity) in protein sequence in comparison to the FAS isoforms detected in other species. Potentially, one of these could be a specialized enzyme arising from within the FAS clade. If validated in further work, such evolution downstream of the AFPK/FAS branch point would indicate a more complex evolutionary pathway to diverse lipids than reflected in the current study, which reflects available sequences and our present understanding of PKS/FAS biochemistry.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "Here, we show that polyketide biosynthesis in arthropods and molluscs is likely dominated by AFPKs, a family of proteins that spans the phylogenetic gap between the type I PKSs and the animal FASs. AFPKs and animal FASs form a single clade, with AFPK subfamilies diversifying in specific molluscan and arthropod lineages. Overall, from available transcriptome data, the sum of the methods described above led to the identification of 6122 AFPKs in arthropods and 277 in molluscs. In the few cases where their functions are known, AFPKs in sacoglossan molluscs and in insects contribute to specialized metabolism, producing unusual polyketide-like lipids that are ecologically important to the producing animal. Their biochemical features are intermediate between those of the animal FAS and the PKSs. For these reasons, we propose that the AFPKs comprise a single, true family of KS-containing enzymes.\n\nWhile polyketide metabolites are well studied in bacteria, fungi, and plants, in animals they represent a largely overlooked group with significant future potential. The methods presented here will enable the biochemical interrogation of this widespread enzyme class and its role in the biology, ecology, and diversification of animals, especially given the association between AFPK diversity and species richness in several major radiations.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "This research complies with ethical regulations. No institutional approval was required for this research.\n\nLive specimens of Siphonaria sp. were purchased and shipped from AlgaeBarn.com to the University of Utah in aquarium bags with seawater, inflated with oxygen. The shell was removed, and the whole animal was cut into small pieces (<2\u2009mm2) and homogenized in sterilized nuclease-free water. RNA was extracted using TRIzol (Invitrogen) followed by a DNA-free DNA removal kit (Invitrogen). The quality of the extracted total RNA was evaluated by electrophoresis and QC RIN using the Agilent RNA screen tape assay. An Illumina library was prepared at the Huntsman Cancer Institute\u2019s High-Throughput Genomics (HCI-HTG) facility at the University of Utah using Illumina TruSeq Stranded mRNA Library Preparation Kit with polyA selection, and sequenced using an Illumina NovaSeq 6000 sequencer with a\u2009~450\u2009bp insert size and 150\u2009\u00d7\u2009150\u2009bp paired-end runs to produce 100M read-pairs. Raw reads were trimmed and adaptors removed by trimmomatic51, then assembled using rnaSPAdes52. Genes were predicted using Prodigal53 in metagenome mode.\n\nSiphonaria gDNA from the homogenized tissue was extracted using the Qiagen DNeasy Blood & Tissue Kit. Illumina library preparation and sequencing were performed at the HCI-HTG. Sequencing library preparation was performed using an NEBNext Ultra II DNA Library Prep Kit with a 450\u2009bp mean insert size. Sequencing used an Illumina NovaSeq 6000 sequencer with 2\u2009\u00d7\u2009150\u2009bp runs. Raw reads were trimmed and adaptors were removed by trimmomatic and then assembled using metaSPADES52. The animal genes were predicted using AUGUSTUS 3.354 with the transcriptome assembly as training data.\n\nSRA fastq raw reads were downloaded from NCBI and assembled using rnaSPAdes. All available gastropod SRA datasets available as of January 2022 were downloaded. For non-gastropod molluscs, the SRA data was sorted in the SRA Run Selector by the Bytes column. Only the top two SRA in byte size were selected for each species and then downloaded. For arthropods and vertebrates, only one SRA data set (the top one in the bytes size) for each species was downloaded. Raw reads for each SRA data were trimmed and adaptors removed by trimmomatic, then assembled using rnaSPAdes. The genes in each assembly were predicted using Prodigal. SRA datasets with low quality were removed if they did not contain at least one KS-containing protein. SRAs used in this study are listed in Supplementary Information.\n\nOrthologous genes were aligned using t-Coffee55 (-mode mcoffee -output\u2009=\u2009msf, fasta_aln). To remove poorly aligned regions, the resulting alignment was subsequently trimmed with Clipkit56 with model parameter \u2018-m kpi-gappy\u2019. The trimmed alignment was then manually inspected to remove any remaining poorly aligned regions. The maximum-likelihood tree was constructed using iqtree57 (./iqtree -nt AUTO -st AA -alrt 1000 -bb 1000). The ML tree was visualized using ggtree library58.\n\nTo generate KS profile HMMs for type I FASs and animal PKSs, seed sequences were selected from previously identified sequences and from well-annotated animal sequences from GenBank. KS domains of these sequences were predicted using antiSMASH. These KS sequences were used as a query to blastp search against the SRA protein data prepared above. Top hits from the blastp search were analyzed using an ML tree with the seed sequences. The KSs that clade with the seed sequences (FAS and PKS) were, respectively, aligned using t-Coffee (-mode mcoffee -output\u2009=\u2009msf, fasta_aln) to make HMMs using \u2018hmmbuild\u2019 in the hmmer3 package59. Other HMMs were generated using the standard method for \u2018hmmbuild\u2019.\n\nThe SRA protein database was searched with the KS HMMs (FAS and PKS) prepared above. A bit score\u2009=\u2009180 was set as the threshold for a KS hit. To remove any contamination from the SRA transcriptome assemblies, the corresponding contigs that contain the KS hits were analyzed using the taxonomy assignment pipeline in the Autometa15 package (make_taxonomy_table.py -a ks_hit_contigs.fa -l 700). The output \u2018.lca\u2019 file gave taxonomy ID for the lowest common ancestor of each contig. Based on the taxonomy ID, contigs for bacteria, fungi, plants, and algae were removed. The KS domains of the remaining contigs were predicted by antiSMASH and InterPro. To access KS-containing proteins from GenBank, manually selected, full-length KS domains from the SRA KS hits in each phylum were used as query to search against a standalone nr database, with an output format (-outfmt \u20186 qseqid sseqid pident length qcov qlen slen mismatch gapopen evalue bitscore staxids sscinames scomnames sskingdoms sblastnames stitle sseq\u2019). The nr hits were filtered using a threshold \u2018qcov>85\u2019 and the staxids matching the animal phylum demand. Here, the nr hit sequences was extracted directly from the \u2018sseq\u2019 output. For each nr sequence ID, there are multiple hits in the blastp output; only the one with the longest \u2018sseq\u2019 was chosen. Finally, the KSs from both the SRA database and no. database were combined, and the duplicated sequences were removed by Sequence Dereplicator and Database Curator (SDDC)60.\n\nKS domain protein sequences were aligned to different HMM models using hmmsearch function in the HMMER3 package (http://hmmer.org/), with the output option \u2018hmmsearch --tblout\u2019. The full sequence score for each KS sequence in the output was used for further comparison in dotplot/scatter plot using gglpot library61.\n\nParameters for data normalization and perplexity selection were based on the Rtsne.r script in YAMB62.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The alignment files for HMMs and trees are provided in the\u00a0Supplementary Information. KSs sequences from molluscs, arthropods, and vertebrates used in this study are deposited in figshare: https://doi.org/10.6084/m9.figshare.24066234. Raw sequencing data for the mollusc Siphonaria is available in genbank (SRR22547485 and SRR22547486). The original data for plotting in figures is provided in Source Data file. The lists of SRA accession numbers that were used in this paper are provided in Supplementary Data\u00a010.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "The code for AFPK-finder is available on GitHub (https://doi.org/10.5281/zenodo.10125497).",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "We thank the Center for High Performance Computing, University of Utah, for computational support and J.P. Torres for critically reading the manuscript. This work was funded by NSF IOS 2127111 and 2127110.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Zhenjian Lin, Feng Li.\n\nDepartment of Medicinal Chemistry, University of Utah, Salt Lake City, UT, 84112, USA\n\nZhenjian Lin,\u00a0Feng Li\u00a0&\u00a0Eric W. Schmidt\n\nDepartment of Biological Sciences, California State University, Los Angeles, CA, 90032, USA\n\nPatrick J. Krug\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nE.W.S., P.J.K., and Z.L. designed the research; Z.L. and F.L. designed the strategies for KS sequencing data collection and analysis; Z.L. performed the experiments and analyzed the data. Z.L. and F.L. developed the AFPK-Finder tool; P.J.K. performed the study of the current\u00a0gastropod phylogeny; E.W.S., Z.L., and P.J.K. wrote the paper.\n\nCorrespondence to\n                Eric W. Schmidt.",
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+            "section_text": "Lin, Z., Li, F., Krug, P.J. et al. The polyketide to fatty acid transition in the evolution of animal lipid metabolism.\n                    Nat Commun 15, 236 (2024). https://doi.org/10.1038/s41467-023-44497-0\n\nDownload citation\n\nReceived: 30 May 2023\n\nAccepted: 15 December 2023\n\nPublished: 03 January 2024\n\nVersion of record: 03 January 2024\n\nDOI: https://doi.org/10.1038/s41467-023-44497-0\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Nature (2025)",
+            "section_image": []
+        }
+    ]
+}

1b2e838d88b0dff78e28ec13e5b301848878544d2cadd4988580960a569dc6b1/metadata.json

@@ -0,0 +1,158 @@
+{
+    "title": "Kinetics of the xanthophyll cycle and its role in photoprotective memory and response",
+    "pre_title": "Kinetics of the Xanthophyll Cycle and its Role in the Photoprotective Memory and Response",
+    "journal": "Nature Communications",
+    "published": "19 October 2023",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42281-8/MediaObjects/41467_2023_42281_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42281-8/MediaObjects/41467_2023_42281_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42281-8/MediaObjects/41467_2023_42281_MOESM3_ESM.pdf"
+        }
+    ],
+    "supplementary_1": [
+        {
+            "label": "Source Data",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-42281-8/MediaObjects/41467_2023_42281_MOESM4_ESM.zip"
+        }
+    ],
+    "supplementary_2": NaN,
+    "source_data": [
+        "https://doi.org/10.5281/zenodo.8284422",
+        "/articles/s41467-023-42281-8#Sec15"
+    ],
+    "code": [
+        "https://doi.org/10.5281/zenodo.8284422"
+    ],
+    "subject": [
+        "Biophysical chemistry",
+        "Non-photochemical quenching",
+        "Photobiology"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3044300/v1.pdf?c=1697800487000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3044300/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-023-42281-8.pdf",
+    "preprint_posted": "26 Jun, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Efficiently balancing photochemistry and photoprotection is crucial for survival and productivity of photosynthetic organisms in the rapidly fluctuating light levels found in natural environments. The ability to respond quickly to sudden changes in light level is clearly advantageous. In the alga Nannochloropsis oceanica we observed an ability to respond rapidly to sudden increases in light level which occur soon after a previous high-light exposure. This ability implies a kind of memory. In this work, we explore the xanthophyll cycle in N. oceanica as a photoprotective memory system. By combining snapshot fluorescence lifetime measurements with a biochemistry-based quantitative model we show that both short-term and medium-term \u201cmemory\u201d arises from the xanthophyll cycle. In addition, the model enables us to characterize the relative quenching abilities of the three xanthophyll cycle components. Given the ubiquity of the xanthophyll cycle in photosynthetic organisms the model described here will be of utility in improving our understanding of vascular plant photoprotection with important implications for crop productivity.Physical sciences/Chemistry/Photochemistry/PhotobiologyPhysical sciences/Chemistry/Physical chemistry/Biophysical chemistry",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SIRoleofXanthophyllsinNPQ20230608.pdfSupporting Information",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "Efficiently balancing photochemistry and photoprotection is crucial for survival and productivity of photosynthetic organisms in the rapidly fluctuating light levels found in natural environments. The ability to respond quickly to sudden changes in light level is clearly advantageous. In the alga Nannochloropsis oceanica we observed an ability to respond rapidly to sudden increases in light level which occur soon after a previous high-light exposure. This ability implies a kind of memory. In this work, we explore the xanthophyll cycle in N. oceanica as a short-term photoprotective memory system. By combining snapshot fluorescence lifetime measurements with a biochemistry-based quantitative model, we show that short-term memory arises from the xanthophyll cycle. In addition, the model enables us to characterize the relative quenching abilities of the three xanthophyll cycle components. Given the ubiquity of the xanthophyll cycle in photosynthetic organisms the model described here will be of utility in improving our understanding of vascular plant and algal photoprotection with important implications for crop productivity.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "In high-intensity light, photosynthetic organisms are unable to utilize all available energy for photochemistry. In order to minimize the formation of damaging reactive oxygen species, the excess energy is dissipated as heat through non-photochemical quenching (NPQ) pathways1,2. The eustigmatophyte alga Nannochloropsis oceanica has a relatively simple NPQ system3,4 in comparison to vascular land plants. It consists of two main components: a pH-sensing protein, potentially LHCX1, and the xanthophyll cycle. The xanthophyll cycle in N. oceanica is a shared feature with higher plants, but this alga lacks additional features like state transitions or pigments like lutein and chlorophyll-b5,6,7. This simplistic nature makes N. oceanica an ideal model organism for studying the essential components of NPQ.\n\nThe xanthophyll cycle in N. oceanica consists of the same de-epoxidation steps, from violaxanthin (V) to antheraxanthin (A) to zeaxanthin (Z), and reverse epoxidation steps, as seen in green algae and plants6,8. The enzyme violaxanthin de-epoxidase (VDE), located in the thylakoid lumen, converts V to A to Z upon protonation under high-light (HL) stress. Simultaneously, zeaxanthin epoxidase (ZEP), which is found in the stroma and thought to be constitutively active, reverses the VAZ cycle by epoxidizing Z to A to V9,10,11 (Fig.\u00a01). It is now well-established that the VAZ cycle correlates with activation of energy-dependent quenching, qE, in both N. oceanica3,4 and more complex organisms12,13,14. The fast activating, pH-dependent quenching, qE, in N. oceanica also depends on the protein LHCX14. The mechanism of sensing changes in the thylakoid membrane pH-gradient and whether or not LHCX1 can bind pigments is still under investigation4,15,16,17,18,19, however, the vital role of Z together with a pH-sensing protein in qE is widely achknowledged8,14. The accumulation of A and Z has been observed to correlate with an increase in NPQ throughout a diurnal cycle in plants10,11, and it has been proposed that an additional, slower activating and slow deactivating Z-dependent quenching process, also operates in the absence of a pH-gradient sensing protein12,13. However, the precise roles of the three xanthophylls and the kinetics of their interconversion in NPQ are not well understood, which is surprising given the prevalence of this widespread three-state photoprotective system in photosynthetic organisms.\n\nThe xanthophyll (X) binds to the protein (P) reversibly to form a protein-xanthophyll complex (PX). In response to light this can convert into an active quencher form (QX). When not bound to the protein, the xanthophylls interconvert between violaxanthin (V), antheraxanthin (A) and zeaxanthin (Z). The activation of the VDE enzyme, which controls the V\u2009\u2192\u2009A\u2009\u2192\u2009Z processes, is dependent on light conditions, which alter the ratio of the active VDE enzyme (VDEa) and its inactive from (VDEi). The light-sensitive steps in the model are highlighted in yellow. The species responsible for quenching, the QX complexes in qE and pool Z in qZ, are also indicated by red arrows.\n\nIn previous work20, we utilized a simplified kinetic model of the VAZ cycle that did not include the intermediate A to understand NPQ in N. oceanica. Despite this simplification, the model gave useful insights into the time scales of processes involved in NPQ activation, and it could quantitatively predict the quenching response, as well as qualitatively predict changes in V and Z concentrations, in response to a variety of regular and irregular light/dark illumination sequences. However, when exploring how the response changed when the dark period was progressively lengthened, it became clear that N. oceanica has short-term \u201cmemory\u201d of previous HL exposure which could not be captured by the simplified two-xanthophyll model. This type of memory of previous exposure to stressor events, wherein some organisms remain primed for an extended period to quickly respond to further stress, has been observed for other stressors such as in drought conditions21. Various plant species, including Smilax australis, Monstera deliciosa, Vinca minor, and Vinca major, have been shown to possess a long-term memory of growth light conditions, which is strongly species-dependent. This long-term memory manifests in xanthophyll pool size and composition as well as maximum NPQ levels22,23, an effect we also found evidence for previously in N. oceanica20. It has also been shown that in phytoplankton and algae possessing a simpler two-state xanthophyll cycle, the xanthophylls can act as a long-term memory of growth light conditons24,25,26. In this work we aim to explore the details of short-term photoprotective memory (operating on time scales\u2009\u2272\u20091 hour), complementing existing studies on connections between longer-term light exposure memory and the xanthophyll cycle.\n\nWe hypothesize that in response to light stress, the VAZ cycle, and the kinetics of the different de-epoxidation/epoxidation steps, may act as a memory of previous HL exposure27. Specifically, we propose that the presence of A in a system could keep plants and algae primed to respond to further HL stress, due to the slow rates of transforming A back to V. The role of the partially de-epoxidised xanthophyll A in photoprotection has been difficult to investigate directly, however, work on plants has suggested that both A and Z correlate with NPQ in plants22,28, but in this work, we also aim to further elucidate its role in photoprotection. Previous work has shown the ratio of the rates from A\u2009\u2192\u2009Z to V\u2009\u2192\u2009A ranges from 4.5\u20136.3 times faster in various plant species,29,30,31 and the rate of epoxidation has been measured to be 1.4 times faster for Z than A11. However precise measurements of these rates in N. oceanica and their functional significance in NPQ and short-term memory of light stress have not been fully explored.\n\nIn this work, we aim to fully understand the role of xanthophyll cycle kinetics in photoprotective memory by considering the full VAZ cycle in modeling NPQ, and we show that differential rates of interconversion between the three xanthophylls are responsible for the multiple time-scales of photoprotective memory. In a further step towards a comprehensive understanding of NPQ in N. oceanica, the full VAZ model allows us to assess the relative quenching abilities of the three xanthophylls in the qE process, estimate the relative abundance of quenchers in the thylakoid membrane, and also quantify the relative contributions of LHCX1-dependent qE quenching and zeaxanthin-dependent qZ quenching in NPQ. In what follows, we start by briefly presenting our expanded model, then show how it accurately describes the HL stress responses of N. oceanica, and how it encodes the functional role of the VAZ cycle in photoprotection.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-023-42281-8/MediaObjects/41467_2023_42281_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "Motivated by measurements of xanthophyll concentrations and NPQ in response to light exposure (as presented in the next section), we have developed a new model for the coupled LHCX1-xanthophyll cycle photoprotection system in N. oceanica, as is summarized schematically in Fig.\u00a01. Before presenting any results, we briefly summarize the features of the model (details of the kinetic equations are given in the SI). In the predecessor to this model20, we neglected several important features that are included in the new model presented here, such as the intermediate A, which we will show plays an essential role in photoprotective memory, and the capability of each xanthophyll to act as a quencher, facilitated by LHCX1, which will be important for understanding the immediate response of N. oceanica to light stress. Furthermore, we will show that the new model can quantitatively describe xanthophyll concentrations in cells, enabling us to estimate the absolute abundance of quenching sites in N. oceanica and estimate its absolute quenching rate.\n\nOverall the model involves 12 chemical species: the protein P, the three \u201cpool\u201d xanthophylls X\u2009=\u2009V, A, and Z, three xanthophyll-bound complexes PX in the non-quenching state and three in the quenching state QX, and the active (protonated) VDEa and inactive (unprotonated) VDEi forms of the VDE enzyme. Within the model, the protein P, binds the xanthophylls, X\u2009=\u2009V, A, Z, reversibly to form a complex PX. For simplicity, we assume a single labile xanthophyll binding site per P, which we have found is sufficient to interpret the available experimental data. This PX complex is activated under HL conditions to reversibly form an active quencher, establishing the PX\u21ccQX equilibrium, which we assume arises due to protonation and conformational changes. Previous work has identified LHCX1 as an essential component in activating the protein P, in the \u201cqE\u201d quenching mechanism4,11,20, although the actual active quencher PX/QX could involve other proteins, especially since it is not known if LHCX1 binds pigments, and alternatively, LHCX1 may just induce the conformational changes in P to activate quenching. Thus the precise identity of PX/QX is open to interpretation. The total fluorescence decay rate \u03c4F(t)\u22121\u00a0of chlorophylls in the membrane at a given time in the experiment t is assumed to be related linearly to the concentration of the QX species,\n\nwhere 1/\u03c4F,0 is the intrinsic fluorescence decay rate of chlorophyll (arising from both the dominant non-radiative and minor radiative pathways), and kqE is the quenching rate constant for the QX complexes that mediate the LHCX1 and \u0394pH-dependent qE quenching. We also incorporate zeaxanthin-dependent quenching, qZ, into the model by adding a quenching contribution that solely depends on the concentration of zeaxanthin in the \u201cpool\u201d. The quenching rate constant for Z is denoted kqZ. We assume that qE and qZ mechanisms are non-radiative, dissipating chlorophyll excitation energy as heat into the environment. From this we can obtain the experimentally measured NPQ\u03c4\u2009=\u2009(\u03c4F(0)\u2009\u2212\u2009\u03c4F(t))/\u03c4F(t). We assume that whilst the extent to which PX converts to QX under HL conditions is dependent on X, the quenching rate of each complex in the chloroplast is the same. With the available NPQ\u03c4 data, we found that it is not possible to ascertain whether the differences in total quenching capacity of the different QX species arise due to differences in quenching rate, or the positions of the PX\u21ccQX equilibrium under HL conditions. Therefore, for simplicity, we treat the quenching rate kqE as being identical for all QX, and we also assume that the equilibrium constant for this process is zero in the dark.\n\nThe interconversion of the xanthophylls is assumed to occur after unbinding of X from P, PX\u21ccP\u2009+\u2009X. The X species in the model should be regarded as X in the pool of xanthophylls not\u00a0bound to P. For example, X could be bound to other light-harvesting proteins from which it can unbind rapidly and reversibly. The xanthophylls in the pool can be de-epoxidised sequentially, from V\u2009\u2192\u2009A and then A\u2009\u2192\u2009Z, by VDEa, where the maximum turnover rate for the VDE enzyme is different for the two de-epoxidation steps. VDE is assumed to interconvert between VDEa and VDEi forms depending on light conditions. We model this as a simple two-state equilibrium with first-order rate laws for the activation and deactivation. We also treat the epoxidation steps as sequential, first from Z\u2009\u2192\u2009A then from A\u2009\u2192\u2009V, and we assume that each epoxidation by the ZEP enzyme can be treated as a first-order rate process, with different epoxidation rates for Z and A.\n\nIn order to investigate the response of the xanthophyll cycle to fluctuating light conditions, we have measured the changes in concentrations of these pigments in N. oceanica in response to four sequences of high-intensity light exposure: 5 HL- 10 D- 5 HL, 1 HL- 4 D- 7 HL- 5 D- 1 HL- 2 D, 10 HL- 10 D, and 1 HL-1 D, where HL denotes high light, D denotes darkness, and numbers indicate the duration of the exposure in minutes. The HPLC data showed a significant fraction of xanthophylls, particularly V, that remained constant over the time scale of the experiment, which we believe corresponds to xanthophylls strongly bound to proteins other than LHCX1. The samples were dark-acclimated for 30\u2009min prior to HL exposure to ensure minimal initial amounts of A and Z. Figure\u00a02 shows the change in VAZ cycle carotenoids relative to their initial dark-acclimated values (at t\u2009=\u20090), i.e. \u0394[X]\u2009=\u2009[X](t)\u2009\u2212\u2009[X](0) and [X](t) is the total\u00a0concentration of X at t. The experimental data show that \u0394[A] was greater than \u0394[Z] during HL exposures; \u0394[A] remained relatively constant during dark periods (Fig.\u00a02), which shows a more rapid dynamical response to reduction in light exposure. In the 5 HL- 10 D- 5 HL sequence (Fig.\u00a02A), during the 10-minute dark period \u0394[Z] decreased almost entirely back to its dark-acclimated value whilst \u0394[A] remained constant for the first five minutes of darkness before it began to diminish. Both \u0394[A] and \u0394[Z] increased in response to the second HL exposure, and the rate of Z accumulation was greater than during the first HL period. Similarly in the 10 HL-10 D sequence (Fig.\u00a02C), \u0394[A] remained at a constant level compared to \u0394[Z], which decreased more rapidly back to its dark-acclimated concentration. In the 5 HL- 10 D - 5 HL and 10 HL-10 D sequences, there was a small amount of continued accumulation of A and Z in the first dark phase for ~1\u2009min, indicating a delayed deactivation of the de-epoxidation process, as we found previously in modeling the NPQ\u03c4 response of N. oceanica20.\n\n\u0394[X] as a function of time for four HL exposure sequences: A 5 HL- 10 D- 5 HL, B 1 HL- 4 D- 7 HL- 5 D- 1 HL- 2 D, C 10 HL- 10 D, D 1 HL-1 D (yellow shaded regions indicate the HL phases). Experimental results are shown as points and model predictions are shown as solid lines. Predictions correspond to the total xanthophyll concentrations, \u0394[X]tot\u2009=\u2009\u0394[X]\u2009+\u2009\u0394[PX]\u2009+\u2009\u0394[QX]. Experimental error bars (shaded regions) correspond to two standard errors of the mean (from n\u2009=\u20093 technical replicates). RMSD (root mean square deviations) in the fits are A RMSDV\u2009=\u200911.2, RMSDA\u2009=\u20098.6, RMSDZ\u2009=\u200911.5 B RMSDV\u2009=\u20096.8, RMSDA\u2009=\u200911.5, RMSDZ\u2009=\u20093.1 C RMSDV\u2009=\u200914.6, RMSDA\u2009=\u20099.5, RMSDZ\u2009=\u20098.4, and D RMSDV\u2009=\u200911.7, RMSDA\u2009=\u200911.6, RMSDZ\u2009=\u200910.7 all in mmol/ mol Chl a.\n\nRate constants for xanthophyll interconversion in the model were parameterized based on a reduced form of the full model, fitted to the experimental HPLC data, as detailed in the\u00a0Supplementary Information (Sec.\u00a02). The full model predictions for the HPLC data are also shown in Fig.\u00a02, where we see the model mostly predicts the HPLC data within the experimental fluctuations, although in the 1 HL- 4 D- 7 HL- 5 D- 1 HL- 2 D sequence the model slightly overestimates \u0394[A] and \u0394[Z] after 1\u2009min of light exposure (it should be noted that the fluctuations in xanthophyll concentrations in Fig.\u00a02D do not correlate with the periodicity of light exposure on close inspection). In Table\u00a01 we summarize the maximum rates for the de-epoxidation processes, defined as \\({k}_{{{{{\\rm{X}}}}}\\to {{{{{\\rm{X}}}}}}^{{\\prime} }}{[\\,{{{{\\rm{VDEa}}}}}\\,]}_{\\max }^{{{{{{{{\\rm{light/dark}}}}}}}}}\\), and the epoxidation rates in the light and dark phases, and the rate constant for activation/deactivation (i.e. formation of VDEa from VDEi). We see that VDE activity increases by a factor of around 1000 in HL conditions, and that the VDE de-epoxidises A slightly faster than V, although the difference is small. Conversely for the epoxidation we see that Z is epoxidised nearly twice as fast as A. In our model, we find that the VDE enzyme takes just over 1\u2009min to activate and deactivate in both the light and dark phases, which is consistent with the continuing increase in A and Z concentrations observed at the start of the dark phases in the HPLC experiments.\n\nTime-correlated single photon counting (TCSPC) experiments were also performed on N. oceanica to measure NPQ\u03c4 in response to sequences of HL/dark exposure. In addition to 20-minute regular and irregular light sequences that were utilized in previous work20, seven new HL/dark cycles were utilized to ascertain how long algae retain their \u201cphotoprotective memory\u201d of previous HL exposure. The sequences had increasing dark durations (T\u2009=\u20091, 5, 9, 10, 12, 15, 20\u2009min) between two five-minute HL periods. The model was employed to describe NPQ\u03c4 dynamics of N. oceanica in response to various HL/dark exposure sequences, with parameters determined by fitting a subset of the NPQ\u03c4 sequences, namely the 5 HL-9 D-5 HL, 5 HL-15 D-5 HL, 3 HL-1 D-1 HL-3 D-9 HL-3 D, 1 HL-2 D-7 HL-5 D-1 HL-2 D, 2 HL-2 D sequences [Fig.\u00a03C, F, H, J]. Further details of this fitting procedure are given in the Methods section and Supplementary Information (Sec.\u00a01).\n\nYellow regions indicate HL phases of the experiments. Error bars correspond to two standard errors in the NPQ\u03c4 measurements (from n\u2009=\u20093 technical replicates). A\u2013G show data and model predictions for the 5 HL-T D- 5 HL sequences and H\u2013J show three additional sequences, where HL denotes HL exposure and D denotes darkness, with number indicating the exposure time in\u2009min. RMSD values for the fits are A 0.174 (n\u2009=\u20093), B 0.370 (n\u2009=\u20093), C 0.036 (n\u2009=\u20093), D 0.190 (n\u2009=\u20093), E 0.099 (n\u2009=\u20093), F 0.062 (n\u2009=\u20093), G 0.081 (n\u2009=\u20093), H 0.185 (n\u2009=\u20093), I 0.121 (n\u2009=\u20093), J 0.193 (n\u2009=\u20093).\n\nThe experimental NPQ\u03c4 data are shown in Fig.\u00a03. We see rapid NPQ activation and deactivation in response to changes in light levels, occurring on a timescale of <1\u2009min, together with a slower increase in NPQ\u03c4 during light exposure. The rapid component of NPQ\u03c4 activation and deactivation arising from the pH-sensing protein corresponds to the equilibration rate for the PX equilibrium in the model, given by \\({k}_{\\,{{{{\\rm{QX}}}}}\\,}^{{{{{{{{\\rm{light/dark}}}}}}}}}={k}_{\\,{{{{\\rm{QX}}}}}\\,,{{{{{{{\\rm{f}}}}}}}}}^{{{{{{{{\\rm{light/dark}}}}}}}}}+{k}_{\\,{{{{\\rm{QX}}}}}\\,,{{{{{{{\\rm{b}}}}}}}}}^{{{{{{{{\\rm{light/dark}}}}}}}}}\\). This equilibration rate is 2.1\u2009min\u22121 under light conditions and 4.7\u2009min\u22121 in the dark which gives an activation time of 29\u2009s and a deactivation time of 13\u2009s. Experimental data for the 5 HL-T D-5 HL sequences, Fig.\u00a03A\u2013G, show how NPQ\u03c4 recovers after various dark durations, directly probing the photoprotective memory of N. oceanica. The NPQ\u03c4 component recovered to its value at the end of the initial light period (t\u2009=\u20095 min) within 1\u2009min upon secondary light exposure when dark durations were up to T\u2009=\u20095\u2009min, and even with a 20\u2009min dark duration, the NPQ\u03c4 recovered within 3\u2009min.\n\nIn addition to the HPLC \u0394[X]tot data in Fig.\u00a02, the model is able to predict the average NPQ\u03c4 levels for all the sequences as shown in Fig.\u00a03, including sequences other than those in the training datasets. Differences between the model predictions and experiments were generally comparable to the variability between experimental runs. For example at the end of the first five minutes of light exposure, NPQ\u03c4 in the 5 HL- T D- 5 HL sequences (Fig.\u00a03A\u2013G) the experimental NPQ\u03c4 varies between around 0.8 and 1.4, although some discrepancies may be attributed to shortcomings of the model. Specifically the over-prediction of NPQ\u03c4 for the 1 HL-2 D-7 HL-5 D-1 HL-2 D, 2 HL-2 D sequence [Fig.\u00a03J] in the second light phase could be attributed to VDE activating too fast, as is seen in both the HPLC data and modeling [Fig.\u00a02B].\n\nIn the model, the position of the PX\u21ccQX equilibrium under HL conditions determines how well each of the xanthophylls can act as a quencher in qE. The maximum fraction of P-bound X that can exist in the QX state under HL conditions, denoted qX, determines the quenching capacity of each xanthophyll within our model. This can be expressed as\n\nwhere \\({K}_{\\,{{{{\\rm{QX}}}}}\\,}^{{{{{{{{\\rm{light}}}}}}}}}\\) is the equilibrium constant for the PX\u21ccQX process under HL conditions determined from fitting the model to the experimental data. In Table\u00a02 we list these values for our model under light and dark conditions, obtained from fitting the model to the experimental NPQ\u03c4 curves. From the qX values we find that A is approximately three times more effective as a quencher than V, and Z is nearly 10 times more effective than V. From the model we can also quantify the relative contributions of qE and qZ to the overall quenching, by the ratio of kqZ to kqE, which is found to be kqZ/kqE\u2009=\u20090.026\u2009\u00b1\u20090.005.\n\nTo further test the model, we have modified the wild type (WT) N. oceanica parameterized model to predict the NPQ\u03c4 response of two N. oceanica mutants: the vde and lhcx1 mutants. The NPQ\u03c4 response of the vde mutant, which has VDE knocked out preventing the accumulation of Z, was modeled utilizing parameters obtained from the WT model with kV\u2192A and kA\u2192Z to zero. The NPQ\u03c4 response was measured for three HL/D sequences, shown in Fig.\u00a04A\u2013C together with model predictions. Even in the absence of A and Z, NPQ\u03c4 increases near-instantaneously to around 0.3 in response to light, demonstrating the relevant role of LHCX1 in the vde mutant. However, because of V\u2019s low quenching capacity, the NPQ\u03c4 response is significantly smaller than that seen in WT, and there is no steady increase of NPQ\u03c4 over the duration of the experiment, unlike in the WT N. oceanica. The model captures the NPQ\u03c4 response of the vde mutant remarkably well, despite not being parameterized with these data.\n\nError bars correspond to two standard errors in the NPQ\u03c4 measurements (from n\u2009=\u20093 technical replicates). Light/dark sequences: A 1 HL-2 D-7 HL-5 D-1 HL-2 D (n\u2009=\u20092), B 10 HL-10 D (n\u2009=\u20092), and C 2 HL-2 D (n\u2009=\u20093)\u2009\u00d7\u20095. D\u2013F NPQ\u03c4 responses were measured for the lhcx1 mutant (black) together with model predictions (blue) for three sequences of light/dark exposure. Light/dark sequences: A 1 HL-2 D-7 HL-5 D-1 HL-2 D (n\u2009=\u20092), B 10 HL-10 D (n\u2009=\u20093), and C) 2 HL-2 (n\u2009=\u20093) D\u2009\u00d7\u20095. RMSD for the model predictions are A 0.134, B 0.136, C 0.118, D 0.227, E 0.139, F 0.142.\n\nWe have also modeled the NPQ\u03c4 response of the lhcx1 mutant, in which LHCX1 is not expressed and only zeaxanthin-mediated qZ quenching operates. This was modeled by simply setting [P]tot\u2009=\u20090, removing the qE quenching process, while holding the total xanthophyll concentration constant. The experimental NPQ\u03c4 data and model predictions are shown in Fig.\u00a04D\u2013F, where we see the model accurately captures the slow rise of NPQ\u03c4 in the light phases, arising from the build-up of Z during light exposure, and the slower decay in the dark phases due to slow epoxidation of Z. The success of the model in predicting the NPQ response of the lhcx1 mutant strongly supports the interpretation of the kinetic model species \u201cP\u201d as involving or at least requiring LHCX1 to function.",
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "Our combined experimental and kinetic model results suggest that photoprotective memory in N. oceanica can be explained qualitatively with a simple three-state model. The three-state system should consist of a poor quencher (V), a modest quencher (A), and a good quencher (Z). After a sample has sufficiently accumulated the good quencher, during brief dark/low-light periods, Z remains before being converted back to the modest quencher (A), acting as short-term memory. However, during extended dark durations, Z will be converted almost entirely to A. Whilst A is also converted back to V, the A\u2009\u2192\u2009V transition occurs at a slower rate such that during another HL exposure occurs, the Z pool can form more rapidly from the reservoir of A. We can also see this dynamic represented in the HPLC data (Fig.\u00a02). By adding the intermediate step in the VAZ cycle, the model not only more accurately reflects the biochemical processes but also allows for the short-term photoprotective memory, over time scales between 1\u2009min to ~30\u2009min, to be modeled and understood.\n\nFrom our experiments and modeling, we have also been able to determine the relative quenching capacities of the different xanthophylls. We find that V facilitates a weak but rapid response to changes in HL. The vde mutant demonstrates that even without an effective quencher like Z, there is still an NPQ\u03c4 response to fluctuating light. In very short bursts of HL, V may act as the main quencher where the switch between its roles in photochemistry and photoprotection is determined by the pH gradient, as suggested previously32.\n\nAs the intermediate step in the VAZ cycle, A\u2019s role as a potential quencher in qE is often overlooked. With a quenching capacity of around 30%, it is 3.5 times less efficient than Z (95%) at dissipating excess energy. However, it plays a crucial role in photoprotection in facilitating NPQ recovery after long dark durations. In Fig.\u00a05 we show a breakdown of the NPQ\u03c4 response predicted by the model for the 5 HL-10 D-5 HL sequence, where we see at short times the main quencher in qE is actually V complexed with LHCX1, with contributions from A emerging at t\u2009=\u20091\u2009min and Z at t\u2009=\u20092\u2009min. After light exposures of more than 2\u2009min, Z functions as the primary quencher, with small, but not insignificant, contributions from V and A. Whilst LHCX1-dependent qE makes the largest contribution to NPQ\u03c4, qZ also makes a small contribution, and within the model, this is the sole contributor to the long-lived NPQ\u03c4 response in the dark. Even for very long-time light exposure, the model predicts that LHCX1-dependent qE dominates over qZ, with qZ making up only ~23% of the total NPQ\u03c4 in this limit, whilst the LHCX1-Zeaxanthin qE accounts for the majority (~75%) of the limiting NPQ\u03c4. It should be noted that this limit is based on extrapolating the model to light exposure times beyond those that we have investigated, which may not be reliable, and we also expect the relative contributions of qE and qZ to depend strongly on species and growth conditions, as has been found in studies of plants22,33,34. We have not suggested a microscopic model for the qZ process, although in the SI, Sec. 4, we show how a quenching process depending on some other zeaxanthin binding protein (or protein complex) P\u2019 would be consistent with our simple model. Zeaxanthin binding to some other protein could activate qZ by directly quenching excitation energy, potentially via charge transfer, or inducing conformational changes in the protein that promote other quenching mechanisms35,36,37.\n\nA Contributions of each xanthophyll to the total NPQ\u03c4 as predicted by the model as a function of time for the 5 HL- 10 D-5 HL sequence. B\u2013D NPQ\u03c4, [A]tot\u2009=\u2009[A]\u2009+\u2009[PA]\u2009+\u2009[QA], and [Z]tot\u2009=\u2009[Z]\u2009+\u2009[PZ]\u2009+\u2009[QZ], predicted by the model for three 5 HL-T D-5 HL sequences of light/dark exposure: B T\u2009=\u20091\u2009min, C T\u2009=\u200910\u2009min and D T\u2009=\u200920\u2009min.\n\nAn essential element of the three-state photoprotective memory system observed in N. oceanica is the kinetics of xanthophyll cycle, which together with the quenching capacities of the xanthophylls creates an effective photoprotective system. Upon the first exposure to light, NPQ activation is limited by moving through two steps before Z, the primary quencher, is accumulated, where VDE activation and the V\u2009\u2192\u2009A step (with a half-life of ~7 min) control the initial rate of NPQ activation. Z may still function as a moderate quencher in the dark through qZ, so fast conversion of Z\u2009\u2192\u2009A by ZEP (half-life ~8\u2009min) in the dark is necessary to facilitate efficient photosynthesis under low-light conditions. The slower kinetics of A\u2009\u2192\u2009V in the dark (with a half-life ~20\u2009min) enables A to function as a buffer, facilitating rapid NPQ reactivation if light levels fluctuate again to damaging levels. The fast A\u2009\u2192\u2009Z conversion by VDE on light exposure (with a half-life of ~4\u2009min) also plays an essential role in photoprotective memory by enabling the buffer of A to be rapidly converted to an active quencher. Previous work in plants found the rate of de-epoxidation of A to be about 4 times faster than that of V29,30,31, which is a much larger difference compared to the de-epoxidation rates that we have found, with de-epoxidation of A being only about 1.5 times faster than that of V. However, VDE activity is influenced by the thylakoid lumen acidity, availability of ascorbate, and potentially unique species-specific differences, any of which could explain this discrepancy. Furthermore, because VDE is not active in the dark, the relative activity of ZEP on Z and A is far more relevant to photoprotective memory than the relative activity of VDE on V and A. On top of the slower time scale kinetics of the VAZ cycle, which control the maximum quenching capacity of the system, very rapid responses to light fluctuations, on time scales of around 1\u2009min or less, are facilitated by protonation and subsequent conformational changes of the quenching protein which binds the xanthophylls.\n\nFrom the model, we can directly probe how the total A and Z concentrations vary during the 5 HL-T D- 5 HL sequences to demonstrate the functional role of xanthophyll cycle kinetics in photoprotective memory. Here we show in Fig.\u00a05 the model NPQ\u03c4 and the total A and Z concentrations normalized by their values at t\u2009=\u20095\u2009min. For very short dark phase (T\u2009=\u20091\u2009min, Fig.\u00a05B) Z continues to accumulate (due to the finite deactivation time of VDE in our model), acting as short-term light exposure memory and the NPQ\u03c4 recovers very rapidly upon re-illumination. For intermediate and longer lengths of dark duration (T\u2009=\u200910\u2009min, Fig.\u00a05C and T\u2009=\u200920\u2009min, Fig.\u00a05D), the quencher Z decreases but A remains steady, presumably acting as a buffer, and thus as a short-term memory for excess light exposure, and facilitating a fast response to HL in the second light phase. In these cases, the NPQ\u03c4 response in the second HL phase correlates most strongly with the A concentration, and not the Z concentration. In the\u00a0Supplementary Information, Fig.\u00a0S2, we show the experimental and model NPQ\u03c4 recovery, averaged over the first minute of HL, in the second light phase for the 5 HL-T D- 5 HL sequences, as a function of dark duration T. From this we have extracted (see\u00a0Supplementary Information Sec.\u00a02 for details) an NPQ\u03c4 memory time scale of ~22\u2009min, which matches the model A\u2009\u2192\u2009V time scale given by 1/kA\u2192V\u2009=\u200919.9\u2009min. This strongly suggests that antheraxanthin acts as a short-term memory for light exposure, with the A\u2009\u2192\u2009V step of the xanthophyll cycle controlling the effective memory time scale. It has previously been observed that xanthophyll composition correlates with photoprotection, long- and medium-term light-exposure memory, and light levels during growth in plants23,34, phytoplankton24,25 and algae26. We can now however add to this picture that the kinetics of the xanthophyll cycle also plays an important role in short-term photoprotective memory.\n\nOne important quantity we can estimate from this study is the lifetime of Chl-a excitations on the active quenching complexes QX. Firstly from the HPLC data and model we obtain an estimate of the total concentration of P (possibly LHCX1 or LHCX1 in a complex with other proteins) in the system as ~0.6\u2009mmol/mol Chl. Assuming roughly ten Chl-a molecules per light-harvesting protein, this means the species P makes up ~1 in 30 light-harvesting proteins in N. oceanica. Using this ratio of P to the other light-harvesting proteins and assuming excitation energy diffusion between proteins is faster than quenching, we can estimate the lifetime of Chl-a* on the active quenchers to be less than ~10 ps (further details of this calculation are given in the SI, Sec. 4). This approximate time scale is roughly consistent the quenching time scale in HL acclimated N. oceanica observed in transient-absorption experiments of ~8\u2009ps4 (especially given the simplifying assumptions we use to deduce our estimate). Recent work has suggested that quenching can be limited by excitation energy redistribution within and between light-harvesting proteins36,38,39, so the actual quenching process (likely either excitation energy transfer or charge transfer quenching4) may need to occur on an even shorter time scale than this estimate.\n\nOverall in this work, we have presented a model of xanthophyll cycle mediated non-photochemical quenching in N. oceanica, which can both accurately describe the short and intermediate timescale NPQ\u03c4 responses of N. oceanica to HL stress and the accompanying changes in xanthophyll concentrations. Employing a combination of experiments and modeling we have developed a deeper understanding of the photoprotective roles of the xanthophylls together with LHCX1. From this, we have suggested a three-state model for short time scale photoprotection in N. oceanica, where the zeaxanthin-LHCX1 system acts as the primary quencher, with antheraxanthin acting as a short-term \u201cmemory\u201d of HL stress capable of facilitating rapid response to fluctuations in light levels, and violaxanthin deactivating quenching under low-light conditions. This adds to the established picture of xanthophyll composition correlating with long-term memory of light-exposure22. Although we cannot conclusively identify the qE quencher, PX/QX, we can say that LHCX1 is an essential component of this system. We have also been able to estimate the chlorophyll excitation lifetime on active quenching proteins as less than ~10\u2009ps, as well as the relative abundance of quenchers in the thylakoid membrane. Evidence for zeaxanthin-dependent but LHCX1-independent \u201cqZ\u201d quenching has also been found, although its contribution to NPQ appears to be much smaller than that of LHCX1-dependent \u201cqE\u201d quenching. However, the proportion of qE or qZ contributions is going to vary depending on the species40. In order to implement a similar model of NPQ for use in vascular plants, more components need to be incorporated such as quenching due to lutein and state transitions5,6,7, which are not present in N. oceanica. However, we believe the model presented here provides a basis for building a quantitative model of NPQ responses for plants and other photosynthetic organisms, which are mediated by the same xanthophyll cycle.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "N. oceanica CCMP17796 was obtained from the National Center for Marine Algae and Microbiota (https://ncma.bigelow.org/) and cultivated in F2N medium41. Liquid cultures were grown to 2\u20135\u2009\u00d7\u2009107 cells/mL in continuous light at a photon flux density of 60\u2009\u03bcmol photons\u2009m\u22122\u2009s\u22121 at 22\u2009\u00b0C or room temperature.\n\nThe knock-out mutants vde and lhcx1 (Ref. 4)\u00a0were generated using homologous recombination of a hygromycin resistance cassette, with the addition of Cas9 RNP for lhcx1.\u00a0Further details of how the mutants were generated will be presented in a separate manuscript.\n\nTime-correlated single photon counting results in a histogram of Chl-a fluorescence decay, which is then fit to a biexponential decay function yielding an average lifetime (\\(\\bar{\\tau }\\)). Fluorescence lifetime measurements were taken every 15 seconds to capture the change in fluorescence lifetimes as a function of HL exposure. The amplitude-weighted average lifetime of the Chl-a fluorescence decay is converted into a unitless form, similar to that measured in the conventional pulse-amplitude modulation technique using the following equation: where \\(\\bar{\\tau }(0)\\) and \\(\\bar{\\tau }(t)\\) are the average lifetimes in the dark and at any time point t, respectively, during the experiment.\n\nAn ultrafast Ti:sapphire coherent Mira 900 oscillator was pumped using a diode laser (Coherent Verdi G10, 532\u2009nm). The center wavelength of the oscillator was 808\u2009nm with a full width at half maximum of 9\u2009nm. After frequency doubling the wavelength to 404\u2009nm with a \u03b2-barium borate crystal, the beam was split between the sample and a sync photodiode, which was used as a reference for snapshot measurements. Three synchronized shutters controlled the exposure of actinic light and the laser to the sample as well as to the microchannel plate-photomultiplier tube detector (Hamamatsu106 R3809U). The shutters were controlled by a LABVIEW software sequence. The detector was set to 680\u2009nm to measure Chl-a emission. During each snapshot, the laser and detection shutters were opened, allowing an excitation pulse with a power of 1.7\u2009mW to saturate the reaction center for 1 second while the emission was recorded. During HL periods, samples were exposed to white light with an intensity of 885\u2009\u03bcmol photons\u2009m\u22122\u2009s\u22121 (Leica KL 1500 LCD, peak 648\u2009nm, FWHM 220\u2009nm) by opening the actinic light shutter. The N. oceanica sample was concentrated at 40\u2009\u03bcg\u2009Chl\u2009mL\u22121. To do this, 1\u2009mL of N. oceanica culture was pelleted for 5 minutes at room temperature at max speed, flash frozen in liquid nitrogen, thawed at room temperature, and broken using FastPrep-24 (MP Biomedicals LLC) at 6.5 m/s for 60 seconds. The pellet was flash-frozen and broken two more times. Chlorophyll was extracted from the broken cells using 1\u2009mL of 80% acetone, and total chlorophyll in the culture was quantified according to Porra et al.42. The culture was then concentrated by centrifuging for 5 minutes at room temperature at 3320 g. Samples were dark-acclimated for 30 minutes prior to the experiment and placed in the custom-built sample holder on a sample stage. The LABVIEW sequence was altered for each regular, irregular, and dark duration sequence run to control exposure to light fluctuations. For the NPQ\u03c4 experiments, three technical replicates were performed for the WT and three for each mutant. Two experimental replicates were performed for the 5 HL-T D-5 HL experiments and the training data for the model. Only one experimental replicate was performed for the mutants.\n\nAliquots of N. oceanica in F2N media were taken at various time points during several regular and irregular HL/dark duration actinic light sequences. Samples were then flash-frozen in liquid nitrogen. After thawing, the samples were pelleted for 5 minutes at 4\u2218C at maximum speed to reach a cell count of ~45\u201360\u2009\u00d7\u2009106. The cells were washed twice with dH2O and pelleted at maximum speed for 5 minutes. The cells were again flash-frozen and thawed at room temperature followed by breaking the cells using FastPrep-24 (MP Biomedicals LLC) at 6.5 m/s for 60 seconds. The bead beating step was repeated once before adding 200\u2009\u03bcL of 100% cold acetone. The samples were centrifuged for 10 minutes (maximum speed, 4\u2218C), and the supernatant was filtered (0.2\u2009\u03bcm nylon filter) into HPLC vials. The supernatant was separated on a Spherisorb S5 ODS1 4.6-\u2009\u00d7\u2009250\u2009mm cartridge column (Waters, Milford, MA) at 30\u2218C. Analysis was completed using a modification of Garc\u00eda-Plazaola and Becerril43. Pigments were extracted with a linear gradient from 14% solvent A (0.1M Tris-HCl pH 8.0 ddH20), 84% (v/v) solvent B (acetonitrile), 2.0% solvent C (methanol) for 15 minutes, to 68% solvent C and 32% solvent D (ethyl acetate) for 33\u2009min, and then to 14% solvent A (0.1M Tris-HCl pH 8.0 ddH2O), 84% (v/v) solvent B (acetonitrile), 2.0% solvent C (methanol) for 19\u2009min. The solvent flow rate was 1.2\u2009mL\u2009min\u22121. Pigments were detected by A445 with reference at 550\u2009nm by a diode array detector. Standard curves were prepared from isolated pigments. The HPLC peaks were normalized to the total Chl-a concentration.\n\nEach step of the model given in Fig.\u00a01 is treated as an elementary reaction step in the 12 species model. As described in our previous work20, we cannot determine from these experiments the absolute concentration of VDE, so we replace the VDE species with a dynamical quantity \u03b1V DE(t) representing the activity of VDE at a time t relative to its maximum possible value. We also work in a reduced unit system defined for species B by \\([\\widetilde{{{{{{{{\\rm{B}}}}}}}}}]={\\tau }_{F}(0){k}_{{{{{{{{\\rm{qE}}}}}}}}}[{{{{{{{\\rm{B}}}}}}}}]\\), where \u03c4F(0) is the fluorescence lifetime at t\u2009=\u20090. With these reduced variables \\({{{{{{{{\\rm{NPQ}}}}}}}}}_{\\tau }(t)=\\Delta [\\widetilde{{{{{{{{\\rm{QV}}}}}}}}}](t)+\\Delta [\\widetilde{{{{{{{{\\rm{QA}}}}}}}}}](t)+\\Delta [\\widetilde{{{{{{{{\\rm{QZ}}}}}}}}}](t)+({k}_{{{{{{{{\\rm{qZ}}}}}}}}}/{k}_{{{{{{{{\\rm{qE}}}}}}}}})\\Delta [\\widetilde{\\,{{{{\\rm{Z}}}}}\\,}](t)\\), where \\(\\Delta [\\widetilde{\\,{{{{\\rm{QX}}}}}\\,}](t)\\) is the change in reduced concentration of QX relative to the t\u2009=\u20090 value, and likewise for \\(\\Delta [\\widetilde{\\,{{{{\\rm{Z}}}}}\\,}](t)\\).\n\nThe model parameters were fitted by minimizing the sum of square differences between the model NPQ\u03c4 and the experimental NPQ\u03c4 for the 5 HL-9 D-5 HL, 5 HL-15 D-5 HL, 3 HL-1 D-1 HL-3 D-9 HL-3 D, 1 HL-2 D-7 HL-5 D-1 HL-2 D, 2 HL-2 D sequences. Parameters for the VAZ interconversion steps were estimated from a least squares fit of a reduced model, which is a simple first-order kinetic model with activation of the VDE enzyme, to the HPLC data (this is detailed in the SI). In the rest of the parameter fitting these parameters were constrained to only vary by 50%. Additionally, to reduce the number of free parameters, the forward and backward binding rate constants kPX,f and kPX,b, and the activation rate to form QX, \\({k}_{\\,{{{{\\rm{QX}}}}}\\,}^{{{{{{{{\\rm{light/dark}}}}}}}}}={k}_{{{{{{{{\\rm{QX,f}}}}}}}}}^{{{{{{{{\\rm{light/dark}}}}}}}}}+{k}_{{{{{{{{\\rm{QX,b}}}}}}}}}^{{{{{{{{\\rm{light/dark}}}}}}}}}\\), were set to be independent of the species X. This way the equilibrium constant KQX is the only parameter in the model controlling the quenching capacity of each xanthophyll. The remaining parameters were fitted first using Matlab\u2019s \u201cglobalsearch\u201d function from an initial guess based on our previous model. This was then refined using the \u201cpatternsearch\u201d algorithm. Errors in the fitted parameters were estimated by bootstrapping the experimental time series 1000 times. The conversion factor from reduced units to the mmol/mol Chl units the HPLC data are reported in was found using a simple least squares fit between the experimental HPLC and model \u0394[X]tot values shown in Fig.\u00a02. Full details of the model kinetic equations and the full parameter set are given in the SI.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "All data presented in this manuscript is available at https://doi.org/10.5281/zenodo.8284422.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "Code availability",
+            "section_text": "All Matlab code used to run the model and produce figures in this manuscript is available at https://doi.org/10.5281/zenodo.8284422.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "The experimental work was supported by the U.S. Department of Energy, Office of Science, Chemical Sciences, Geosciences, and Biosciences Division through FWP 449B to K.K.N. and G.R.F. T.P.F. and D.T.L. were supported by the US Department of Energy, Office of Science, Basic Energy Sciences, CPIMS Program Early Career Research Program under Award DE-FOA0002019. K.K.N. is an investigator of the Howard Hughes Medical Institute.\u00a0D.T.L. acknowledges support from the Alfred P. Sloan Foundation.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Audrey Short, Thomas P. Fay.\n\nGraduate Group in Biophysics, University of California, Berkeley, CA, 94720, USA\n\nAudrey Short\u00a0&\u00a0Graham R. Fleming\n\nMolecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA\n\nAudrey Short,\u00a0Thien Crisanto,\u00a0Krishna K. Niyogi\u00a0&\u00a0Graham R. Fleming\n\nKavli Energy Nanoscience Institute, Berkeley, CA, 94720, USA\n\nAudrey Short,\u00a0David T. Limmer\u00a0&\u00a0Graham R. Fleming\n\nDepartment of Chemistry, University of California Berkeley, Berkeley, CA, 94720, USA\n\nThomas P. Fay,\u00a0Ratul Mangal,\u00a0David T. Limmer\u00a0&\u00a0Graham R. Fleming\n\nDepartment of Plant and Microbial Biology, University of California, Berkeley, CA, 94720, USA\n\nThien Crisanto\u00a0&\u00a0Krishna K. Niyogi\n\nHoward Hughes Medical Institute, University of California, Berkeley, CA, 94720, USA\n\nThien Crisanto\u00a0&\u00a0Krishna K. Niyogi\n\nChemical Science Division Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA\n\nDavid T. Limmer\n\nMaterial Science Division Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA\n\nDavid T. Limmer\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nG.R.F. and A.S. conceived the research. A.S. performed all spectroscopic and HPLC experiments, and performed initial data analysis. T.P.F. developed and implemented the model and performed the final data analysis. T.C. prepared all algal samples and generated mutants. R.M. assisted A.S. in performing experiments. A.S., T.P.F., and G.R.F. wrote the manuscript. A.S., T.P.F., T.C., G.R.F., D.T.L., and K.K.N. discussed the results and commented on the manuscript. D.T.L., K.K.N., and G.R.F. procured funding.\n\nCorrespondence to\n                Graham R. Fleming.",
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+        },
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+            "section_text": "Nature Communication thanks Andrei Herdean, Johann Lavaud and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
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+            "section_name": "About this article",
+            "section_text": "Short, A., Fay, T.P., Crisanto, T. et al. Kinetics of the xanthophyll cycle and its role in photoprotective memory and response.\n                    Nat Commun 14, 6621 (2023). https://doi.org/10.1038/s41467-023-42281-8\n\nDownload citation\n\nReceived: 09 June 2023\n\nAccepted: 05 October 2023\n\nPublished: 19 October 2023\n\nVersion of record: 19 October 2023\n\nDOI: https://doi.org/10.1038/s41467-023-42281-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Nature Communications (2025)",
+            "section_image": []
+        }
+    ]
+}

1c29e904ebb06d5a2a8a29a8380fd8c5d5776c717f4d416c74958058c835952b/metadata.json

@@ -0,0 +1,135 @@
+{
+    "title": "Activating lattice oxygen in high-entropy LDH for robust and durable water oxidation",
+    "pre_title": "Activating Lattice Oxygen in High-Entropy LDH for Robust and Durable Water Oxidation",
+    "journal": "Nature Communications",
+    "published": "27 September 2023",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-41706-8/MediaObjects/41467_2023_41706_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Peer Review File",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-41706-8/MediaObjects/41467_2023_41706_MOESM2_ESM.pdf"
+        },
+        {
+            "label": "Reporting Summary",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-023-41706-8/MediaObjects/41467_2023_41706_MOESM3_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [],
+    "code": [],
+    "subject": [
+        "Electrocatalysis",
+        "Electrochemistry"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-2947613/v1.pdf?c=1696022702000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-2947613/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-023-41706-8.pdf",
+    "preprint_posted": "29 May, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "The oxygen evolution reaction (OER) is known to be a kinetic bottleneck for water splitting. Triggering the lattice oxygen oxidation mechanism (LOM) can break the theoretical limit of the conventional adsorbate evolution mechanism (AEM) and enhance the OER kinetics, yet the unsatisfied stability remains a grand challenge. Here, we report a novel high-entropy MnFeCoNiCu layered double hydroxide decorated with Au single atoms and O vacancies (AuSA-MnFeCoNiCu LDH), which not only displays a low overpotential of 213 mV at 10 mA cm\u22122 and high mass activity of 732.925 A g\u22121 at 250 mV overpotential in 1.0 M KOH, but also delivers exceptional stability with 700 hours of continuous operation at ~100 mA cm\u22122. Combining the advanced spectroscopic techniques and density functional theory (DFT) calculations, it is demonstrated that the synergistic interaction between the incorporated Au single atoms and O vacancies leads to an upshift in the O 2p band and weakens the metal-O bond, thus triggering the LOM, reducing the energy barrier, and boosting the intrinsic activity.Physical sciences/Materials science/Materials for energy and catalysis/ElectrocatalysisPhysical sciences/Chemistry/Electrochemistry",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "Supportinginformation.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "The oxygen evolution reaction is known to be a kinetic bottleneck for water splitting. Triggering the lattice oxygen oxidation mechanism\u00a0(LOM) can break the theoretical limit of the conventional adsorbate evolution mechanism and enhance the oxygen evolution reaction kinetics, yet the unsatisfied stability remains a grand challenge. Here, we report a high-entropy MnFeCoNiCu layered double hydroxide decorated with Au single atoms and O vacancies (AuSA-MnFeCoNiCu LDH), which not only displays a low overpotential of 213\u2009mV at 10\u2009mA\u2009cm\u22122 and high mass activity of 732.925\u2009A\u2009g\u22121 at 250\u2009mV overpotential in 1.0\u2009M KOH, but also delivers good stability with 700\u2009h of continuous operation at ~100\u2009mA\u2009cm\u22122. Combining the advanced spectroscopic techniques and density functional theory calculations, it is demonstrated that the synergistic interaction between the incorporated Au single atoms and O vacancies leads to an upshift in the O 2p band and weakens the metal-O bond, thus triggering the LOM, reducing the energy barrier, and boosting the intrinsic activity.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Hydrogen generation through water electrolysis is an ideal way to utilize and store intermittent renewable energy sources, such as solar and wind power, which can effectively tackle the energy crisis and carbon emission issues1,2. However, the anodic oxygen evolution reaction (OER) involved in the process presents a complex four-electron transfer process coupled with protons transfer, leading to sluggish kinetics that limit the water splitting process3,4. Hence, a fundamental understanding of the OER mechanism is essential for boosting OER kinetics and exploring highly efficient and durable OER electrocatalysts. According to the conventional adsorbate evolution mechanism (AEM), the adsorption energies of the OER intermediates (*OH and *OOH) on metal active sites follow a linear relationship (\u0394GOOH\u2009=\u2009\u0394GOH\u2009+\u20093.2\u2009\u00b1\u20090.2\u2009eV), which sets a theoretical limit on the overpotential of around 370\u2009mV5,6,7. Recently, a lattice oxygen oxidation mechanism (LOM) has been proposed, which involves the activation and redox of the lattice oxygen during water oxidation, and can circumvent the limitation of the linear AEM relationship, thereby reducing the energy barrier8,9. Therefore, it\u2019s highly desirable to exploit robust and durable OER electrocatalysts based on LOM, which has become a research hotspot in the field of water splitting10,11.\n\nTransition-metal oxides/(oxy)hydroxides have been extensively studied for their potential to trigger the LOM during OER. These include defect-rich RuO2, (La, Sr)CoO3, CoZn (oxy)hydroxides, CoAl2O4, NaxMn3O7, NiFe (oxy)hydroxides, MoNiFe (oxy)hydroxides et al. 7,12,13,14. Experimental and theoretical calculation studies have shown that upshifting the O 2p band to approach the Fermi level (EF) is the key to triggering the LOM of transition-metal oxides/hydroxides. This leads to increased orbital overlap between the O 2p band and metal (M) d band, and strengthens the M-O covalent bond, which makes the redox of lattice oxygen more thermodynamically favorable15,16. However, the participation of the lattice oxygen in OER electrocatalysts can result in the leaching of the metal species on the surface that induces the collapse or phase transition of the bulk phase, leading to poor stability11. As a result, obtaining high catalytic activity and good stability simultaneously, especially under large-current-density OER conditions, remains a grand challenge for most OER electrocatalysts based on LOM17.\n\nHigh-entropy materials (HEMs), such as high-alloy oxides, LDH, sulfides, and fluorides, are now emerging as a versatile platform for electrochemical OER due to their unique properties, including the high-entropy effect, cocktail effect, and sluggish diffusion effect18,19,20,21,22,23. Compared to traditional unitary, binary, and ternary OER electrocatalysts, HEMs exhibit similar catalytic activity but better stability24, which may be attributed to the entropy-stabilized effect and sluggish diffusion effect that prevent phase transition and metal leaching25. Motivated by these findings, we hypothesize that triggering lattice oxygen activation in HEMs electrocatalysts may enable to obtain both high activity and good stability that traditional LOM-based OER electrocatalysts can\u2019t do. Previous studies validate that increasing the covalency of metal-oxygen bonds is critical to triggering lattice-oxygen oxidation26. Given that the covalency of metal-oxygen bonds is determined by the electronegativity of metal, and Au has the greatest electronegativity (2.54) of all metals, we speculate the incorporation of Au atoms into high-entropy LDH may increase metal-oxygen covalency, triggering LOM. Also, Au is a kind of inert metal element that has strong alkali resistance and electrochemical stability27, possibly avoiding the leaching during anodic oxidation, which is beneficial for the structure stability of the catalyst. Additionally, the introduction of a small amount of Au single atoms on the surface of high-entropy LDH instead of doping in the lattice with large amounts of Au atoms may be more realistic in consideration of the high cost of Au and the big difference in ion radius between Au and 3d transition elements28.\n\nIn this study, we report a high-entropy electrocatalyst, MnFeCoNiCu LDH decorated with Au single atoms and oxygen vacancies (denoted as AuSA-MnFeCoNiCu LDH), which exhibits both high OER activity and promising stability. The hard X-ray absorption fine structure spectroscopy (XAFS) and high-angle annular dark field-scanning transmission electron microscopy (HAADF-STEM) characterizations demonstrate that the atomically dispersed Au atoms are doped in Fe sites, while X-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR) measurements validate the existence of oxygen vacancies. The combination of in situ Raman spectroscopy, 18O isotope labeling mass spectroscopy, and density functional theory (DFT) calculations suggest that the synergistic effect of doped Au single atoms and generated oxygen vacancies transforms the OER mechanism of MnFeCoNiCu LDH from AEM to LOM, which reduces the energy barrier and enhances the intrinsic activity. The AuSA-MnFeCoNiCu LDH catalyst exhibits a significantly enhanced OER activity with an overpotential of 213\u2009mV at 10\u2009mA\u2009cm\u22122, which is 110\u2009mV lower than that of pristine MnFeCoNiCu LDH. Most importantly, AuSA-MnFeCoNiCu LDH shows remarkable stability, sustaining 700\u2009h at ~100\u2009mA\u2009cm\u22122 with only 6.4% degradation. This work not only successfully regulate the OER pathway of high-entropy LDH from traditional AEM to LOM, but also sheds light on the active mechanism on the lattice oxygen in high-entropy LDH, providing a way for the design of high-entropy-based OER electrocatalysts with high activity and durability based on LOM.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "First, the high-entropy MnFeCoNiCu LDH electrocatalyst was prepared using a typical hydrothermal method (see schematic in Fig.\u00a01a). X-ray diffraction (XRD) analysis shows that the MnFeCoNiCu LDH conforms to the hexagonal hydrotalcite structure (PDF#50-0235) (Fig.\u00a0S1a). In addition, the MnFeCoNiCu LDH exhibited a nanosheet morphology with smooth surfaces and sharp edges, as observed in the field emission scanning electron microscope (FE-SEM) and transmission electron microscope (TEM) images (Fig.\u00a0S1b, c). The energy dispersion spectrum (EDS) result and element mapping images (Fig.\u00a0S1d, e) show that Mn, Fe, Co, Ni, Cu, O elements were uniformly distributed throughout a typical nanosheet, with the molar content of five metal elements (Mn, Fe, Co, Ni and Cu) being 3.31 %, 7.88%, 4.16 %, 8.54 % and 7.38 %, respectively (Table\u00a0S1, calculated from the EDS). All these results indicate that we have successfully prepared a high-entropy MnFeCoNiCu LDH nanosheet electrocatalyst through a facile hydrothermal method.\n\na Synthesis schematic of AuSA-MnFeCoNiCu LDH. b TEM image of AuSA-MnFeCoNiCu LDH. The scale bar is 1\u2009\u03bcm. c AFM image. The scale bar is 0.3\u2009\u03bcm. d HRTEM image. The scale bar is 10\u2009nm. e SAED pattern. f AC-HAADF-STEM image. The scale bar is 1\u2009nm. g The corresponding intensity profiles of L1 and L2 in (f). h EDS elemental mapping of Mn, Fe, Co, Ni, Cu, O, Au. The scale bar is 1\u2009\u03bcm.\n\nSubsequently, Au single atoms were incorporated into MnFeCoNiCu LDH by an electrochemical cyclic voltammetry (CV) method. XRD analysis shows that the introduction of Au single atoms did not change the crystalline structure of MnFeCoNiCu LDH (Fig.\u00a0S2). FE-SEM and TEM images (Fig.\u00a0S3a and Fig.\u00a01b) validate that AuSA-MnFeCoNiCu LDH still maintained the nanosheet structure, and no obvious metal or metal oxide particles were observed, implying that Au atoms may exist in the form of single atoms. Atomic force microscopy (AFM) results show that a typical AuSA-MnFeCoNiCu LDH nanosheet has a thickness of 3.4\u2009nm (Fig.\u00a01c). Recent studies have demonstrated that the thickness of single layer of high-entropy LDH is about 0.7\u20130.9\u2009nm29,30,31. Moreover, the layer spacing of LDH can be reflected by the diffraction peak of (003) plane in the XRD pattern (Fig.\u00a0S2)32,33, and the calculated value based on Bragg formula is 0.74\u2009nm. Therefore, the as-prepared AuSA-MnFeCoNiCu LDH is comprised of 3 layers. The clear lattice fringe with an interplanar distance of 0.261\u2009nm shown in the high-resolution TEM (Fig.\u00a01d) corresponds to the (101) plane of AuSA-MnFeCoNiCu LDH. In contrast, the lattice spacing of MnFeCoNiCu LDH is 0.258\u2009nm (Fig.\u00a0S4). The slight expansion of AuSA-MnFeCoNiCu LDH in terms of interplanar distance relative to MnFeCoNiCu LDH may be attributed to the larger atomic radius of Au than other metals. Moreover, the selected area electron diffraction pattern (Fig.\u00a01e), collected along the [12\\(\\bar{1}\\)] crystal belt axis, also demonstrates the single crystal property of AuSA-MnFeCoNiCu LDH with a hexagonal hydrotalcite structure (PDF#50-0235), agreeing well with the XRD result. Aberration-corrected high-angle annular dark-field scanning transmission electron microscope (AC-HAADF-STEM) images (Fig.\u00a01f) confirm the atomic-level dispersion of Au atoms on the MnFeCoNiCu LDH substrate. Notably, the bright Au atoms (marked with yellow circles) were regularly located at the lattice point of metal atoms in MnFeCoNiCu LDH, suggesting that Au atoms may have replaced the metal atoms in MnFeCoNiCu LDH or were vertically anchored above the metal atoms, which will be discussed later. In addition, the intensity distribution curve (Fig.\u00a01g) from Fig.\u00a01f also confirms the successful incorporation of Au single atoms34. EDS elemental analysis shows that Au atoms were uniformly dispersed in MnFeCoNiCu LDH nanosheet (Fig.\u00a01h, Fig.\u00a0S3b and Table\u00a0S1). The mass content of Au single atoms measured by inductively coupled plasma-mass spectrometry (ICP-MS) was 1.1 wt.%.\n\nTo investigate the chemical state of MnFeCoNiCu LDH and AuSA-MnFeCoNiCu LDH, XPS analysis was conducted35. The high-resolution XPS spectra of all elements in samples were examined, revealing that the Mn, Fe, Co, Ni and Cu elements in AuSA-MnFeCoNiCu LDH all shifted to higher binding energy than those in pristine MnFeCoNiCu LDH (Fig.\u00a0S5). This shift suggests an enhanced chemical valence for metallic ions after the incorporation of Au single atoms19. Increasing the metal valence will enhance the orbital hybridization between the metal 3d and O 2p orbitals, resulting in a strengthened M-O covalent bond that favors the LOM rather than AEM in terms of the OER mechanism7. For atomically dispersed Au in AuSA-MnFeCoNiCu LDH, the high-resolution Au 4f spectrum (Fig.\u00a0S5f) can be deconvoluted into two characteristic peaks at 87.39 and 83.69\u2009eV, which are lower than the standard peak positions of Au (dotted line), implying a lower valence state. XPS results demonstrate a strong electronic coupling between Au single atoms and MnFeCoNiCu LDH36. Additionally, we further analyzed the XPS spectra of O 1s (Fig.\u00a02a) and identified a peak of 531.0\u2009eV corresponding to oxygen vacancies (VO), which confirms the existence of VO in AuSA-MnFeCoNiCu LDH. This conclusion was further supported by the EPR test result (Fig.\u00a0S6).\n\na O 1s XPS spectra of AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH. b Normalized Au L3-edge XANES spectra of AuSA-MnFeCoNiCu LDH and Au foil. (The inset is an enlarged view of the white line peak). c Ni K-edge EXAFS k2\u03c7(k) Fourier transform (FT) spectra of AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH, where the insert is the fitting model (blue for Nickel, navy for Oxygen). d EXAFS k2\u03c7(k) Fourier transform (FT) spectra of AuSA-MnFeCoNiCu LDH and Au foil (model: golden yellow for Gold, navy for Oxygen). e Wavelet transform (WT) contour map for EXAFS k2\u03c7(k) of AuSA-MnFeCoNiCu LDH and Au foil.\n\nTo further study the electronic structure and coordination structure of AuSA-MnFeCoNiCu LDH, the hard XAFS measurement was performed. Compared with those in MnFeCoNiCu LDH, all the absorption edges of Mn, Fe, Co, Ni and Cu in AuSA-MnFeCoNiCu LDH shift to higher energies (Fig.\u00a0S7), implying an increased valence of 3d transition metals, which is consistent with the XPS result34. Moreover, the decreased intensity of the white line peak on the Au L3-edge of AuSA-MnFeCoNiCu LDH indicates a reduced oxidation state of Au (Fig.\u00a02b), which also agrees well with the XPS result. To investigate the local coordination geometry of transition metals in the samples, the Fourier transformed extended X-ray absorption structure measurement was conducted. It was found that the peak intensity of the Ni-O bond became significantly weaker after the introduction of Au (Fig.\u00a02c), while the changes in Mn-O, Fe-O, Co-O and Cu-O were almost negligible (Fig.\u00a0S8), suggesting the existence of oxygen vacancies near the Ni site, which is coinciding with the XPS result. The fitted curve of the Ni K-edge EXAFS (Fig.\u00a02c) indicates that the coordination number (CN) of Ni-O bond in AuSA-MnFeCoNiCu LDH is 5, in contrast to the CN (6) of Ni-O bond in MnFeCoNiCu LDH, implying that the oxygen vacancies will also be generated during electrochemical deposition process, which is well consistent with the XPS and EPR results.\n\nFurthermore, as shown in Fig.\u00a02d, AuSA-MnFeCoNiCu LDH exhibits a distinct Au-O coordination and no characteristic peak of Au-Au bond (2.57\u2009\u00c5) is observed37, which verifies the presence of atomically dispersed Au atoms. The wavelet transform plot for the Au EXAFS k2\u03c7(k) of AuSA-MnFeCoNiCu LDH shows a maximal intensity of 6.56\u2009\u00c5\u22121, which is contributed by the Au-O scattering (Fig.\u00a02e)38,39. The combination of the AC-HAADF-STEM (Fig.\u00a01f) and EXAFS (Fig.\u00a02d) results suggest that there exist two possible coordination geometries for Au atoms. One possible configuration is that the Au atom is anchored on the surface of MnFeCoNiCu LDH, bonding with four oxygen atoms, among which three oxygen atoms are located at the superficial lattice and one is located over the Au atom, as illustrated in Fig.\u00a0S9a. However, the fitted result based on the above configuration is significantly different from the experimental data (Fig.\u00a0S9b), implying that Au atoms are not anchoring on the surface. The other possible coordination geometry is for the Au atom is that it may replace a superficial metal site, as shown in Fig.\u00a0S10. Given that the Au atom could potentially replace any of the metal atoms in MnFeCoNiCu LDH, we calculated the substitution formation energies of AuM (M\u2009=\u2009Mn, Fe, Co, Ni, and Cu) via DFT. The calculated result (Fig.\u00a0S11) indicates that the Au atom prefers to occupy the superficial Fe site in terms of thermodynamics. Therefore, we constructed a structure model in which the Au atom occupies the Fe site in MnFeCoNiCu LDH and then fitted the EXAFS curve (Fig.\u00a0S12 and Fig.\u00a02d). Notably, the fitted result (Table\u00a0S2) matches the experimental data well, confirming the doping of a single Au atom at the superficial Fe site.\n\nTo further uncover how the incorporated Au single atom occupies the Fe site experimentally, we conducted the ICP-MS test in the electrolyte after CV electrochemical deposition and on the electrode before CV. The result indicates that the dissolution percentage of Fe ions is far more than that of other metals (Fig.\u00a0S13), which also means the abundant Fe vacancies on the surface of MnFeCoNiCu LDH. This experimental finding is supported by DFT calculations on the formation energies of metal vacancies in MnFeCoNiCu LDH (Fig.\u00a0S14). Hence, it is reasonable to assume that the incorporated Au single atom tends to occupy the Fe site due to the abundant Fe vacancies on the surface of the catalyst. Finally, by combining the XPS and XAFS analysis, we have verified the successfully incorporation of Au single atoms and oxygen vacancies in high-entropy MnFeCoNiCu LDH, and the enhanced valence of the transition metals in high-entropy LDH caused by the strong electronic coupling may alter the OER mechanism40.\n\nAfter determining the geometric and electronic structure of AuSA-MnFeCoNiCu LDH, we further performed the electrochemical test towards OER in 1.0\u2009M KOH using a standard three-electrode system. Before the OER testing, electrolyte was purified to eliminate the impact of trace amounts of Fe according to previously reported methods41. The polarization curve of AuSA-MnFeCoNiCu LDH with 95% iR correction exhibits a significantly boosted OER activity compared to MnFeCoNiCu LDH and commercial IrO2 (Fig.\u00a03a). For AuSA-MnFeCoNiCu LDH, it only requires a low overpotential (\u03b7) of 213, 260, and 263\u2009mV to reach current densities of 10, 100, and 250\u2009mA\u2009cm\u22122, respectively. These values are 110, 183, and 229\u2009mV lower than those of pristine MnFeCoNiCu LDH (Fig.\u00a03b). We also carried out LSV tests for AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH with different iR compensations (Fig.\u00a03a and Fig.\u00a0S15), which are discussed in Supplementary note\u00a01. To further investigate the OER reaction kinetics, the Tafel slopes were extracted from the polarization curve (Fig.\u00a03c). Compared with that of MnFeCoNiCu LDH (85.5\u2009mV\u2009dec\u22121) and IrO2 (59.1\u2009mV\u2009dec\u22121), the Tafel slope of AuSA-MnFeCoNiCu LDH is dramatically reduced to 27.5\u2009mV\u2009dec\u22121, which not only indicates a faster reaction kinetics, but also implies a possible change in the OER reaction mechanism42. Besides AuSA-MnFeCoNiCu LDH, we also successfully synthesized 20 Au-decorated high-entropy LDHs materials (AuSA-HE LDHs) with different quinary transition-metals composition (Cr, Mn, Fe, Co, Ni Cu or Zn), and tested their OER performance (Figs.\u00a0S16\u2013S20 and Table\u00a0S3), as shown in Supplementary note\u00a02. Considering the low \u03b7 and the small Tafel slope, the as-synthesized AuSA-MnFeCoNiCu LDH exhibits better OER activity than other AuSA-HE LDHs in this works (Fig.\u00a0S18) and most reported high-entropy electrocatalysts (Fig.\u00a03d)19,20,43,44,45,46,47,48,49.\n\na LSV curves (the scan rate is 2\u2009mV\u2009s\u22121) with 95% iR correction. The loading of catalysts is ~1\u2009mg\u2009cm\u22122, and the solution resistance is 2.0 \u03a9. b Overpotential comparison (error bar: standard error of three repeated measurements). c Tafel plots of AuSA-MnFeCoNiCu LDH, MnFeCoNiCu LDH and IrO2. d OER performance comparison between AuSA-MnFeCoNiCu LDH and other reported high-entropy materials at j\u2009=\u200910\u2009mA\u2009cm\u22122. e EIS curves (the applied potential is 0.459\u2009V vs. Hg/HgO, and the frequency range is 106\u201310\u22122\u2009Hz) of AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH (inset is equivalent circuit). f Mass activity and normalized (by ECSA) current density of AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH at \u03b7\u2009=\u2009250\u2009mV (error bar: standard error of three repeated measurements). g Stability test of AuSA-MnFeCoNiCu LDH.\n\nAdditionally, the kinetics difference of MnFeCoNiCu LDH and AuSA-MnFeCoNiCu LDH was further verified by the electrochemical impedance spectroscopy (EIS) test. The Nyquist plot with an equivalent circuit is shown in Fig.\u00a03e, where AuSA-MnFeCoNiCu LDH delivers a smaller charge-transfer resistance (Rct) value (1.1\u2009\u03a9) than MnFeCoNiCu LDH (9.8\u2009\u03a9). This suggests a much faster charge transfer at the solid-liquid interface using AuSA-MnFeCoNiCu LDH, which is beneficial for boosting the OER kinetics7,50. Besides, the wettability of the electrode is also crucial as the rapid generation of bubbles at high current densities may block the active sites and impede the mass transfer. As shown in Fig.\u00a0S21, the droplet contact angle of AuSA-MnFeCoNiCu LDH is 22\u00b0, significantly smaller than that of MnFeCoNiCu LDH (51.9\u00b0), implying better hydrophilicity. The hydrophilic property is beneficial for eliminating gas bubbles and facilitating mass transfer, especially under large current density conditions51,52.\n\nTo further compare the intrinsic activity of different electrocatalysts, the polarization curves were normalized by the electrochemical active surface area (ECSA), where the ECSA values were calculated by the electric double-layer capacitance (Cdl) (Fig.\u00a0S22a), as described in the experimental section53. The ECSA-normalized LSV curves of AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH (Fig.\u00a0S22b) demonstrate that the intrinsic activity of AuSA-MnFeCoNiCu LDH is still superior to that of MnFeCoNiCu LDH. At \u03b7\u2009=\u2009250\u2009mV, AuSA-MnFeCoNiCu LDH shows a specific activity of 0.100\u2009mA\u2009cm\u22122, which is 12.500 times higher than that of MnFeCoNiCu LDH (0.008\u2009mA\u2009cm\u22122). Moreover, the mass activity of AuSA-MnFeCoNiCu LDH (732.925\u2009A\u2009g\u22121) at an overpotential of 250\u2009mV is 10.566 times higher than that of MnFeCoNiCu LDH (69.365\u2009A\u2009g\u22121) (Fig.\u00a03f and Table\u00a0S4). All these results prove that the incorporation of Au single atoms and oxygen vacancies could significantly improve the intrinsic activity of MnFeCoNiCu LDH. It is noteworthy that, in addition to the activity, stability is also a crucial factor for most electrocatalysts. Encouragingly, the synthesized AuSA-MnFeCoNiCu LDH shows good stability for OER. Even after a 700\u2009h chronoamperometry test (I-t) at a constant potential of 1.53\u2009V (vs. RHE), the current density (~100\u2009mA\u2009cm\u22122) only decays by 6.4% (Fig.\u00a03g), suggesting significantly better long-term stability than most LDHs and oxyhydroxides OER catalysts (Table\u00a0S5).\n\nIt is also noteworthy that the activity decay is primarily concentrated in the initial 50\u2009h, with an activity decay of 5.7% (Fig.\u00a0S23), so we conclude the high-entropy catalyst may undergo a surface reconstruction process during the early stage. Hence, the structure characterization of AuSA-MnFeCoNiCu LDH after 50\u2009h I-t test was carried out. The XRD results (Fig.\u00a0S24) show that AuSA-MnFeCoNiCu LDH still maintains its initial crystal structure. The HRTEM result (Fig.\u00a0S25) validates the generation of an amorphous layer with a thickness of about 10\u2009nm, which may be an amorphous oxyhydroxides36,54. To further identify the amorphous layer, an in-situ Raman spectrum test was performed, as illustrated in Fig.\u00a0S26, which confirms the formation of amorphous oxyhydroxides. Moreover, the element mapping analysis (Fig.\u00a0S27) also shows that Mn, Fe, Co, Ni, Cu, O, and Au elements are still uniformly distributed in the catalyst, suggesting the maintenance of the high-entropy structure. These results indicate that the surface of the high-entropy LDH catalyst undergoes a surface reconstruction during OER, i.e., transforming from high-entropy LDH to high-entropy oxyhydroxides. Further extending the I-t test time up to 700\u2009h had little impact on the structure of the high-entropy catalyst, as shown in Figs.\u00a0S28\u2013S30, suggesting the good structure stability for our as-synthesized high-entropy catalyst after the initial surface reconstruction. Additionally, the AC-HAADF-STEM and XPS results of the high-entropy catalyst after the stability test also demonstrate the existence of Au single atoms and O vacancies. (Fig.\u00a0S31)\n\nRecent studies have shown that the dissolution of Fe species in Fe-based LDH or oxyhydroxides can lead to the OER performance decay41,55. In this study, our high-entropy catalyst exhibited robust stability, we thus hypothesize that the dissolution of Fe species in our high-entropy catalyst might have been inhibited. To validate this hypothesis, we conducted the ICP-MS test on the electrolyte after the stability test to quantify the dissolution percentage of metal ions. Stability tests were also performed on NiFe LDH as a comparison (Fig.\u00a0S32). As shown in Fig.\u00a0S33, the dissolution percentage of Fe ions for AuSA-MnFeCoNiCu LDH is only 3.5%, obviously lower than that for NiFe LDH (30%), supporting our hypothesis that the unique high-entropy effect and sluggish diffusion effect of HEMs contribute to the catalyst\u2019s stability56. Moreover, the HER performance of AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH were also explored, see Supplementary note\u00a03 and Fig.\u00a0S34.\n\nNoting the dramatic reduction in the Tafel slope of AuSA-MnFeCoNiCu LDH relative to MnFeCoNiCu LDH (Fig.\u00a03c) indicates a substantial change in reaction kinetics, which could be attributed to the variation of OER mechanism. To verify this conjecture, we conducted the LSV measurement under different pH values ranging from 12.5 to 14.011,57. As illustrated in Fig.\u00a04a, it is found that the OER activity of AuSA-MnFeCoNiCu LDH enhances significantly with increasing pH values, while MnFeCoNiCu LDH exhibits slight pH-dependent activity. To precisely clarify the correlation between the activity and pH values, the proton reaction orders on RHE scale (\u03c1RHE, \u03c1RHE\u2009=\u2009\u2202log(j)/\u2202pH) was used, which reflects the dependence of OER reaction kinetics on proton activity. The detailed calculation process was described in the electrochemical measurements section58,59. When this value is closer to 1, the pH-dependent property of the catalyst is stronger60. Considering that the \u03c1RHE value of AuSA-MnFeCoNiCu LDH (0.89) is closer to 1 comparing with that of MnFeCoNiCu LDH (0.36), AuSA-MnFeCoNiCu LDH shows a stronger pH-dependent property, implying that it may undergo LOM rather than the traditional AEM during OER61.\n\na LSV curves measured in KOH electrolytes with pH\u2009=\u200912.5, 13, 13.5, and 14 (left), j at 1.45\u2009V vs. RHE plotted in log scale as a function of pH (right), from which the proton reaction orders (\u03c1RHE\u2009=\u2009\u2202logj/\u2202pH) were derived. The loading of catalysts is ~1\u2009mg\u2009cm\u22122, and the solution resistance is 2.0\u2009\u03a9. b Polarization curves of AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH in 1.0\u2009M KOH and 1.0\u2009M TMAOH (left), shift of overpotential at 100\u2009mA\u2009cm\u22122 (\u0394\u03b7100) and Tafel slopes from KOH to TMAOH (right). c Raman spectra of AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH, measured after running at 1.45\u2009V vs. RHE for 30\u2009min in 1.0\u2009M TMAOH and 1.0\u2009M KOH solutions and washing with deionized water. d In situ Raman spectra of MnFeCoNiCu LDH. e In situ Raman spectra of AuSA-MnFeCoNiCu LDH. f Mass spectrometric results by 18O isotope-labeling experiments. The signals were normalized by initial intensity of 16O2.\n\nUnlike AEM, the LOM will generate O22\u2212 species during OER, so detecting O22\u2212 species is important for verifying the OER mechanism11. Therefore, tetramethylammonium cation (TMA+), which can strongly bind to O22\u2212 species and hinder OER kinetics, was introduced as a detector of O22\u2212 species58. As shown in Fig.\u00a04b, AuSA-MnFeCoNiCu LDH shows significantly reduced OER activity in 1.0\u2009M TMAOH electrolyte relative to 1.0\u2009M KOH electrolyte (\u0394\u03b7100\u2009=\u20090.102\u2009V, \u0394Tafel\u2009=\u2009\u200912.3 1\u2009mV dec-1), implying the binding between TMA+ and O22\u2212 and validating that AuSA-MnFeCoNiCu LDH undergoes LOM during OER. Inversely, MnFeCoNiCu LDH exhibits a slight change (\u0394\u03b7100\u2009=\u20090.037\u2009V, \u0394Tafel\u2009=\u20091\u2009mV\u2009dec\u22121), indicating an AEM pathway.\n\nRaman spectroscopy was also leveraged to validate the presence of O22\u2212 species (Fig.\u00a04c). MnFeCoNiCu LDH and AuSA-MnFeCoNiCu LDH were operated in 1.0\u2009M TMAOH and 1.0\u2009M KOH solutions at 1.45\u2009V vs. RHE for 30\u2009min, respectively, and then washed with deionized water prior to the Raman spectroscopy measurement. The AuSA-MnFeCoNiCu LDH exhibits two peaks at 751.73 and 950.54\u2009cm\u22121, corresponding to the characteristic peaks of TMA+7. In contrast, no characteristic peaks are observed in MnFeCoNiCu LDH, validating the existence of O22\u2212 species in AuSA-MnFeCoNiCu LDH during OER7,58. The two major Raman peaks located at 400\u2013600\u2009cm\u22121 (Fig.\u00a04c) are assigned to the Eg bending vibration of M-O (\u03b4(M-O)) and Ag stretching vibration (\u03bd(M-O))40,62. Furthermore, the presence of O22\u2212 was directly verified by in situ electrochemical Raman spectroscopy (Fig.\u00a04d). For AuSA-MnFeCoNiCu LDH, when the applied potential reaches 1.3\u2009V (vs. RHE), a broad Raman peak around 1089\u2009cm\u22121 can be observed, which is ascribed to the stretching vibration of O22\u2212 (*-O-O-*) species60. As the applied potential increases, the characteristic Raman peaks of O22- species become stronger and sharper, indicating the substantial generation of O22\u2212 species and confirming the involvement of LOM during OER. In contrast, this characteristic Raman peak of O22\u2212 species is tiny for the pristine MnFeCoNiCu LDH (Fig.\u00a04e), implying a dominated AEM pathway60,63.\n\nThe afore-mentioned discussions reveal that AuSA-MnFeCoNiCu LDH follows the LOM pathway during OER while MnFeCoNiCu LDH mainly follows the AEM pathway. To further reveal the participation of lattice oxygen in the OER process, the 18O-labeled gas chromatography-mass spectrometer (GC-MS) measurements were performed, and the test details were described in the experimental section. Both AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH were firstly activated by electrochemical CV method in 18O-labeled KOH electrolyte, and then carried out OER test in 1.0\u2009M KOH with regular H2O. The collected oxygen products measured by GC-MS (Fig.\u00a04f) validate the existence of 18O-labeled products such as 16O18O and 18O2 for both samples, suggesting that both AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH involve in LOM. However, the content of 16O18O product in AuSA-MnFeCoNiCu LDH is significantly higher than that in MnFeCoNiCu LDH, implying that more lattice oxygen in AuSA-MnFeCoNiCu LDH was involved in the OER reaction compared with that in MnFeCoNiCu LDH. In other words, AuSA-MnFeCoNiCu LDH is more inclined to follow the LOM during OER while MnFeCoNiCu LDH favors the AEM.\n\nConsidering the greatest electronegativity of Au (2.54) among all transition metals, we conclude that the high electronegativity of Au may be responsible for the activation of the lattice oxygen in high-entropy LDH, because it may induce high metal-oxygen covalency26. To further reveal the relationship between the electronegativity of the incorporated metal atoms and the oxygen evolution mechanism, we choose other three metals with different electronegativity (Ru: 2.2, Pt: 2.3 and Ag: 1.9) for comparison, and the results validate that incorporating the single-atom metal with higher electronegativity is more conductive to triggering LOM in high-entropy LDH (Fig.\u00a0S35\u2013S39, Table\u00a0S6), as detailed in Supplementary note\u00a04.\n\nIn order to gain insight into the intrinsic reasons for the OER pathway conversion, we performed DFT\u2009+\u2009U calculations. Due to the surface reconfiguration of the catalyst in the OER process discussed in the previous section, we selected AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH instead of LDH as the computational models, and the (100) faces as the active surface due to their high activity30,64. To reveal the activity of lattice oxygen in AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH, the density of states of O\u00a02p and M\u00a03d orbits were calculated (Fig.\u00a05a). Given that the distance between the O 2p band center and the Fermi level (EF) was regarded an important parameter to identify the activity of the lattice oxygen, we calculated the O 2p band center (\u03b5O-2p) for AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH, and the values were \u22122.691 and \u22122.720\u2009eV, respectively. The O 2p band center after introducing Au shifts towards EF, promoting the release of the lattice oxygen from the lattice, which will facilitate the LOM10,65.\n\na Projected density of states (EF: Fermi level, \u03b5O\u22122p: O 2p band center); b Schematic band diagrams (UHB: upper Hubbard band, LHB: lower Hubbard band, N(e): state density); c The LHB center positions of AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH. d Adsorbate evolution mechanism (AEM) and oxygen oxidation mechanism (LOM) on MnFeCoNiCuOOH. e Computed free energies (\u0394G) of OER steps on AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH.\n\nAccording to molecular orbital theory, the bond between M and O in MOOH will lead to the formation of M-O bonding bands with oxygen character and M-O* antibonding bands with M character. For late transition metal oxyhydroxides, the strong d-d Coulomb interaction in M-O* antibonding orbitals will give rise to the Mott-Hubbard splitting, generating an empty upper Hubbard band (UHB) and a lower Hubbard energy band (LHB) full of electrons, as illustrated in Fig.\u00a05b7,66. It is noted that the energy difference between the UHB center and the LHB center (\u0394U) is also a useful descriptor to evaluate the lattice oxygen activity67. A larger \u0394U implies a stronger d-d Coulomb interaction, resulting in the LHB penetrating into the M-O bonding band. This enables the electrons to mainly remove from M-O bonding band rather than from the LHB under anodic potential, which will weaken the metal-oxygen bonds due to the decrease of M-O bond order and facilitate the lattice oxygen oxidation7. Therefore, we compared the LHB center and \u0394U values of AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH, as shown in Table\u00a0S7 and Table\u00a0S8. The DFT\u2009+\u2009U calculations show that Au incorporation weakens the metal-oxygen bonds in MnFeCoNiCuOOH, as indicated by the increased \u0394U values of M-O bands including Mn-O\u3001Fe-O\u3001Co-O\u3001Ni-O and Cu-O bonding bands in MnFeCoNiCuOOH relative to MnFeCoNiCuOOH. This weakened metal-oxygen bonding implies that AuSA-MnFeCoNiCuOOH favors the LOM than MnFeCoNiCuOOH. Furthermore, the LHB center value of Ni-O bonding band (\u22122.908\u2009eV) is the most negative among all M-O LHB centers (Mn-O, \u22122.779\u2009eV; Fe-O, \u22122.885\u2009eV; Co-O, \u22122.859\u2009eV; Cu-O, \u22122.832\u2009eV) in AuSA-MnFeCoNiCuOOH, suggesting the weakest Ni-O bond (Fig.\u00a05c). In a word, AuSA-MnFeCoNiCuOOH possesses the upshifted O 2p band, the increased \u0394U value, and the weakened M-O bond compared with MnFeCoNiCuOOH, implying a higher propensity for triggering the lattice oxygen activation and a preference for LOM, agreeing well with the experimental result7,67.\n\nIn general, AEM consists of four basic steps with three different intermediates (*OH, *O, *OOH), while LOM involves five basic steps including four different intermediates (*O, *OOH, *OO, VO), as shown in Fig.\u00a05d. Based on the AEM and LOM pathways, the Gibbs adsorption free energy diagrams of AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH were calculated. Firstly, we calculated the adsorption free energy of OER intermediates on different transition metal sites in AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH (Fig.\u00a05e, Figs.\u00a0S40\u2013S43 and Table\u00a0S9). The results indicate that Ni sites exhibit the lowest energy barrier (1.30\u2009eV and 1.44\u2009eV) for both samples in the AEM pathway. For comparison, the Gibbs free energy profiles of AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH following LOM are also provided (Fig.\u00a05e, Figs.\u00a0S44, S45 and Table\u00a0S10). Here, the oxygen atoms coordinated with Ni sites were chosen as the active sites due to the weakest Ni-O bond among all M-O bonds mentioned above. It can be observed that the first electrochemical deprotonation steps in the LOM pathway are the rate-determining steps for both AuSA-MnFeCoNiCuOOH and MnFeCoNiCuOOH, with energy barriers of 0.81\u2009eV and 0.99\u2009eV, respectively. From the perspective of thermodynamics, AuSA-MnFeCoNiCuOOH prefers to follow LOM\u00a0relative\u00a0to\u00a0MnFeCoNiCuOOH, which is well consistent with the experimental results.\n\nTo further reveal the synergistic effect between the Au single atoms and the oxygen vacancies in AuSA-MnFeCoNiCuOOH, we also calculated the Gibbs free energy profiles of MnFeCoNiCuOOH with only Au single atom and MnFeCoNiCuOOH with only oxygen vacancy following the LOM pathway, as illustrated in Figs.\u00a0S46\u2013S48 and Table\u00a0S10. The LOM energy barriers for MnFeCoNiCuOOH with only Au atom and MnFeCoNiCuOOH with only O vacancy are 1.23 and 0.95\u2009eV, respectively, both of which are larger than that of AuSA-MnFeCoNiCuOOH (0.81\u2009eV). This further confirms the synergistic effect of Au single atoms and O vacancies in triggering the LOM in high-entropy MnFeCoNiCuOOH. In order to elucidate the effect of high entropy on the OER process from the viewpoint of theoretical studies, we constructed a high-entropy AuSA-MnFeCoNiCuOOH model with disordered 3d transition metals and a non-high-entropy AuSA-MnFeCoNiCuOOH model with ordered 3d transition metals, as illustrated in Fig.\u00a0S49. We calculate the formation energies of the high-entropy and non-high-entropy samples, respectively, and the results show that the formation energy of the high-entropy sample (\u22121.75\u2009eV) is significantly lower than that of the non-high-entropy sample (\u22120.21\u2009eV), suggesting that the high-entropy effect can strengthen the structure stability of the catalyst (Fig.\u00a0S50). Moreover, given that the easy leaching of metal species next to oxygen vacancies in LOM-based electrocatalysts during OER10,68, we also calculated the binding energies of Ni species next to oxygen vacancies in high-entropy and non-high-entropy samples, which indicates that the binding energy of Ni next to oxygen vacancies in high-entropy sample (\u22121.35\u2009eV) is more negative than that of non-high-entropy sample (\u22120.25\u2009eV), implying that high-entropy effect can inhabit the leaching of metal species and boost the structure stability during lattice-oxygen oxidation process, which is responsible for the good OER stability (Fig.\u00a0S51).",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "In summary, we report a high-entropy MnFeCoNiCu LDH decorated with Au single atoms and O vacancies, which is fabricated by hydrothermal and electrochemical deposition methods. This catalyst delivers a remarkable enhancement in OER activity compared to MnFeCoNiCu LDH, and display a superior long-term durability. The 18O isotope labeling mass spectroscopy in combination with ex/in situ Raman spectroscopy validates that the boosted activity is attributed to the transformation of OER mechanism from AEM to LOM. DFT\u2009+\u2009U calculations further confirm that the introduced Au single atoms and the oxygen vacancies can synergistically upshift O 2p orbits and weaken metal-oxygen bonds to activate the lattice oxygen and lower the energy barrier of LOM, which facilitates the lattice oxygen oxidation. Moreover, the high-entropy effect is responsible for the good OER stability. This work provides valuable insights for designing robust and stable high-entropy electrocatalysts for a host of catalytic reactions involving lattice oxygen.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "Materials. Ni(NO3)2\u00b76H2O, Fe(NO3)2\u00b79H2O, Co(NO3)2\u00b76H2O, Mn(NO3)2\u00b74H2O, Cu(NO3)2\u00b73H2O and HAuCl4 were obtained from Sigma\u2013Aldrich. NH4F and urea were obtained from Macklin.\n\nMnFeCoNiCu LDH preparation. MnFeCoNiCu LDH was prepared by a hydrothermal method. First, Ni(NO3)2\u00b76H2O (0.45\u2009mmol), Fe(NO3)2\u00b79H2O (0.45\u2009mmol), Co(NO3)2\u00b76H2O (0.45\u2009mmol), Mn(NO3)2\u00b74H2O (0.45\u2009mmol), and Cu(NO3)2\u00b73H2O (0.45\u2009mmol) were dissolved in 35\u2009mL of deionized water, and then NH4F (4\u2009mmol) and urea (10\u2009mmol) were added. After complete dissolution, the mixed solution was poured into an autoclave, and added a piece of treated carbon cloth (CC) inside also, followed by heating to 120\u2009\u00b0C for 6\u2009h.\n\nAu single-atom separation method. The Au single-atom loaded MnFeCoNiCu LDH was further prepared using the electrochemical CV method. The electrolyte used was a mixture of NaCl and HAuCl4 (4\u2009mmol), mixed in 100\u2009mL of deionized water. The working electrode was prepared MnFeCoNiCu LDH, the counter electrode was carbon rod, and the reference electrode was Hg/HgO. Then in the voltage range of \u22120.6 to \u22120.2\u2009V vs. RHE, conduct 5\u2009CV cycles with scan rate of 0.1\u2009mV\u2009s\u22121. After finishing, the working electrode was rinsed with deionized water and dried. The obtained AuSA-MnFeCoNiCu LDH was then washed with deionized water and dried.\n\nCharacterization. Crystal structure characterizations were performed using an automated multipurpose X-ray diffractometer (SmartLab, Rigaku), and the employed X\u2010ray source is 4\u2009kW rotating anode Cu-K\u03b1, with a wavelength of 1.54059\u2009\u00c5. The morphology of the catalysts was captured by the focused ion beam scanning electron microscope (FIB-SEM, GAIA3, TESCAN) and the field emission electron microscope (JEM-2100F, JEOL). STEM-HAADF images were obtained from an atomic resolution electron microscope (JEM-ARM300F GRAND ARM, JEOL) with an additional EDS. AFM imaging was performed on the scanning electron probe microscope (5500 AFM/STM, Agilent). XPS were measured on the X-ray photoelectron spectrometer (VersaProbe II, ULVAC-PHI) with an aluminum anode X-ray source, and all spectra were calibrated by using the binding energy of C 1s (284.8\u2009eV) as a reference. The Raman spectra were gathered using the Raman spectrometer (LabRAM HR Evolution, HORIBA France SAS) with the excitation wavelength set to 532\u2009nm. The X-ray absorption spectroscopy were recorded at BL14B2 beamline of the SPring-8 Synchrotron Radiation Facility.\n\nElectrochemical measurements. To eliminate the impact of trace iron ions in the electrolyte on the performance of OER, the KOH electrolyte was purified according to literature methods34 before the OER testing. In addition, we conducted Hg/HgO electrode calibration before the testing, during which platinum wire was used as the working/counter electrode: high-purity hydrogen was bubbled into the 1.0\u2009M KOH electrolyte for 30\u2009min at room temperature firstly, and then the CV method is used for scanning with the range of \u22121.2\u20130\u2009V vs. Hg/HgO and the speed of 1\u2009mV\u2009s\u22121. Using the CHI-604E electrochemical station, electrochemical measurements were made on the catalysts in the three-electrode system. Except when otherwise noted, electrochemical experiments were performed with an electrolyte of 1.0\u2009M aqueous KOH solution. The catalysts supported on CC was used as the working electrode, while the reference electrode and the counter electrode were the same as above. CV curves were collected at the scan rates of 100\u2009mV\u2009s\u22121 and 20\u2009mV\u2009s\u22121, and the linear sweep voltammetry (LSV) were measured at 2\u2009mV\u2009s\u22121 (95% iR corrected). The solution resistance is 2.0\u2009\u03a9. Double-layer capacitance (Cdl) was obtained from CV at different scan rates. The ECSA of the ample was derived from the Cdl according to the following equation:\n\nwhere CS get a value of 40\u2009\u03bcF\u2009cm\u22122. EIS was performed at 0.45926\u2009V vs. Hg/HgO, the amplitude and the frequency range were set to 10\u2009mV and 106\u201310\u22122\u2009Hz, respectively. Chronoamperometry tests were performed at a steady voltage of 1.5\u2009V vs. RHE to evaluate the stability.\n\nProton reaction order (\u03c1RHE) reflects the dependence of OER reaction kinetics on proton activity, and the formula is as follows.\n\nWhere the pH value ranges from 12.5 to 14 and log(j) is the logarithm of the current density at 1.45\u2009V vs. RHE. When proton coupled electron transfer reactions occur, the OER kinetics are almost independent on the pH value of the solution, resulting in a low \u03c1RHE. If non synergistic proton electron transfer is involved in OER, the OER kinetics would be strongly pH-dependent and have a large \u03c1RHE value.\n\n18O isotope labeling experiments: Both AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH were firstly activated by electrochemical CV method (0\u20130.8\u2009V vs. Hg/HgO, 10 CV cycles) in 18O-labeled KOH electrolyte, and then carried out OER test (10\u2009mA\u2009cm\u22122 for 30\u2009min) in 1.0\u2009M KOH with regular H2O. Subsequently, the gas was collected for GC-MS analysis.\n\nMass activity calculation:\n\nHere j is the current density at a certain voltage and m is the catalyst loading.\n\nDFT details. Using the Vienna ab initio (VASP) software, all DFT calculations were carried out by the projector-augmented wave method, the Perdew-Burke-Ernzerhof and the generalized gradient approximation were employed as the functional form and the description of the electron exchange and associated energies, respectively. For calculation parameters, the cut-off energy was set to 450\u2009eV, and the convergence criteria for force and energy were set at 0.01\u2009eV\u2009\u00c5\u22121 and 10\u22125\u2009eV, respectively. Using the Monkhorst-Pack scheme of (3\u2009\u00d7\u20093\u2009\u00d7\u20091), sampling of the Brillouin zone was carried out for all model optimizations, and all plates were added with a 15\u2009\u00c5 vacuum layer to separate their periodicity. For all calculations of density of states, the K point of the Brillouin zone is taken as (9\u2009\u00d7\u20099\u2009\u00d7\u20091). The Hubbard-U correction (DFT\u2009+\u2009U method) was applied to improve the description of localized metal d-electrons in the AuSA-MnFeCoNiCu LDH and MnFeCoNiCu LDH systems. the value of U was set as 3.9, 4.0, 3.3, 6.0 and 3.87\u2009eV for Mn, Fe, Co, Ni and Cu, respectively.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "The data supporting the findings of this study are available within the article and Supplementary Information. All other relevant source data are available from the corresponding authors upon reasonable request.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This work was supported by the Natural Science Foundation of Hebei Province (E2022202035, H.L., F.W., L.L.) and Natural Science Foundation of Hebei Province (B2021202019, Y.L.). P.Z., Y.Z, W.P., C.C. and S.Z. was unfunded. Wenhao Yuan is thanked for the contributions to figure typography and visual appeal.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Key Laboratory of Special Functional Materials for Ecological Environment and Information (Ministry of Education), Hebei University of Technology, Tianjin, 300130, PR China\n\nFangqing Wang,\u00a0Ying Li,\u00a0Hui Liu\u00a0&\u00a0Shijian Zheng\n\nSchool of Material Science and Engineering, Hebei University of Technology, Tianjin, 300130, PR China\n\nFangqing Wang,\u00a0Yangyang Zhang,\u00a0Ying Li,\u00a0Limin Liang,\u00a0Cong Chen,\u00a0Hui Liu\u00a0&\u00a0Shijian Zheng\n\nDepartment of Physics and Astronomy, University of California, Irvine, CA, 92697, USA\n\nPeichao Zou\n\nGraduate School of Human and Environmental Studies, Kyoto University, Yoshida-nihonmatsu-cho, Sakyo, Kyoto, 606-8501, Japan\n\nWenli Pan\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nH.L. conceived the research idea. F.W. designed the experiments, prepared the materials, performed most of the characterizations and conducted the theoretical calculations. Y.Z. assisted in the characterizations. W.P. provide the synchrotron radiation source. Y.L. assisted in the DFT\u2009+\u2009U calculations. L.L. provided the Raman instrument and C.C. provided the theoretical computational resources. F.W. and H.L. drafted the manuscript, and S.Z. and P.Z. contributed to extensive revisions. All the co-authors contributed to the discussion and commented on the manuscript.\n\nCorrespondence to\n                Hui Liu or Shijian Zheng.",
+            "section_image": []
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+        },
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+            "section_text": "Nature Communications thanks In Sun Cho, Yongping Zheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.",
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+            "section_name": "About this article",
+            "section_text": "Wang, F., Zou, P., Zhang, Y. et al. Activating lattice oxygen in high-entropy LDH for robust and durable water oxidation.\n                    Nat Commun 14, 6019 (2023). https://doi.org/10.1038/s41467-023-41706-8\n\nDownload citation\n\nReceived: 17 May 2023\n\nAccepted: 14 September 2023\n\nPublished: 27 September 2023\n\nVersion of record: 27 September 2023\n\nDOI: https://doi.org/10.1038/s41467-023-41706-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+        "http://www.ccdc.cam.ac.uk/data_request/cif",
+        "/articles/s41467-024-50844-6#MOESM4",
+        "https://doi.org/10.25345/C5FJ29Q6V",
+        "https://doi.org/10.25345/C5571803M",
+        "https://doi.org/10.2210/pdb2BUI/pdb",
+        "https://doi.org/10.2210/pdb2GFY/pdb",
+        "https://doi.org/10.2210/pdb2IX4/pdb",
+        "https://doi.org/10.2210/pdb6ROP/pdb",
+        "https://doi.org/10.2210/pdb7UK4/pdb",
+        "/articles/s41467-024-50844-6#Sec23"
+    ],
+    "code": [],
+    "subject": [
+        "Biobricks",
+        "Biocatalysis",
+        "Combinatorial libraries",
+        "Metabolic engineering",
+        "Protein engineering"
+    ],
+    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3157617/v1.pdf?c=1722596740000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3157617/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-50844-6.pdf",
+    "preprint_posted": "27 Jul, 2023",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "The modular nature of polyketide assembly lines and the significance of their products make them prime targets for combinatorial engineering. While short synthases constructed using the recently updated module boundary have been shown to outperform those using the traditional boundary, larger synthases constructed using the updated boundary have not been investigated. Here we describe our design and implementation of a BioBricks-like platform to rapidly construct 5 triketide, 25 tetraketide, and 125 pentaketide synthases from the updated modules of the Pikromycin synthase. Every combinatorial possibility of modules 2-6 inserted between the first and last modules of the native synthase was constructed and assayed. Anticipated products were observed from 60% of the triketide synthases, 32% of the tetraketide synthases, and 6.4% of the pentaketide synthases. Ketosynthase gatekeeping and module-skipping were determined to be the principal impediments to obtaining functional synthases. The platform was also used to create functional hybrid synthases through the incorporation of modules from the Erythromycin, Spinosyn, and Rapamycin assembly lines. The relaxed gatekeeping observed from a ketosynthase in the Rapamycin synthase is especially encouraging in the quest to produce designer polyketides.Biological sciences/Biochemistry/EnzymesBiological sciences/Chemical biology/BiosynthesisBiological sciences/Chemical biology/Biocatalysis",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        },
+        {
+            "section_name": "Supplementary Files",
+            "section_text": "SI.pdf",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "The modular nature of polyketide assembly lines and the significance of their products make them prime targets for combinatorial engineering. The recently updated module boundary has been successful for engineering short synthases, yet larger synthases constructed using the updated boundary have not been investigated. Here we describe our design and implementation of a BioBricks-like platform to rapidly construct 5 triketide, 25 tetraketide, and 125 pentaketide synthases to test every module combination of the pikromycin synthase. Anticipated products are detected from 60% of the triketide synthases, 32% of the tetraketide synthases, and 6.4% of the pentaketide synthases. We determine ketosynthase gatekeeping and module-skipping are the principal impediments to obtaining functional synthases. The platform is also employed to construct active hybrid synthases by incorporating modules from the erythromycin, spinosyn, and rapamycin assembly lines. The relaxed gatekeeping of a ketosynthase in the rapamycin synthase is especially encouraging in the quest to produce designer polyketides.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Modular polyketide synthases (PKSs) are multi-domain enzymatic assembly lines that help\u00a0biosynthesize many important medicines, such as the antibacterial erythromycin and the anticancer agent epothilone (Fig.\u00a01)1,2. Each PKS contains sets of domains known as modules that work together to extend and modify the growing polyketide. A typical\u00a0module contains an acyltransferase (AT) that selects an \u03b1-carboxyacyl extender unit, an acyl carrier protein (ACP) that uses this extender unit to collect\u00a0a polyketide chain from an upstream ketosynthase (KS), optional processing enzymes that perform chemistry on the elongated chain, and a KS that\u00a0receives the processed intermediate. Processing enzymes can include\u00a0a ketoreductase (KR) that reduces the KS-generated \u03b2-keto group, a dehydratase (DH) that eliminates the KR-generated \u03b2-hydroxyl group, and an enoylreductase (ER) that reduces the DH-generated trans-\u03b1,\u03b2-double bond3. These large synthases are usually housed on several polypeptides that have docking motifs on their N- and C-terminal ends (NDDs and CDDs) to mediate interpolypeptide interactions4,5. This machinery can theoretically provide access to a wide swath of chemical space with great significance to material, synthetic, and medicinal chemistry.\n\nThe 7 modules (P1\u2013P7, updated definition) of this natural polyketide assembly line collaborate to biosynthesize the heptaketide precursor of pikromycin, narbonolide. AT acyltransferase, KR ketoreductase, DH dehydratase, ER enoylreductase, ACP acyl carrier protein, KS ketosynthase, TE thioesterase, KSQ priming KS, KR0 epimerase, CDD & NDD docking domain motifs.\n\nFor three decades, most attempts to engineer PKSs have used the traditional module boundary immediately upstream of KS and have been unsuccessful6. However, a study of related aminopolyol synthases provided evidence that KSs evolutionarily co-migrate with processing domains upstream of this boundary7. Engineering with the updated module boundary downstream of KS has been much more successful8,9,10,11. Whereas traditionally-engineered triketide lactone synthases rarely yield titers greater than 10\u2009mg\u2009L\u2212112,13,14, triketide lactone synthases engineered in our lab using the updated module boundary yield up to 390\u2009mg\u2009L\u2212110. The higher activity of the updated module is attributed to keeping together processing enzymes that introduce functionality at the \u03b1- and \u03b2-positions with KSs that gatekeep for the introduced functionality15.\n\nAlthough our lab has been successful implementing the updated module boundary in the construction of trimodular synthases that generate small triketide products9,10, the modularity of updated modules and thus the synthetic potential of polyketide assembly lines constructed from them remain essentially unknown. In constructing larger synthases that more thoroughly test the updated boundary, we considered many potential impediments: KSs could gatekeep beyond the \u03b2-position15; protein-protein interactions between neighboring modules could be suboptimal3,16; introduced docking domains could associate more weakly than in their native context4,5; processing enzymes could ignore unnatural substrates17,18; polypeptide stoichiometries could be unbalanced19; and polypeptides could be degraded19. Although each of these factors could hamper the activities of engineered synthases, we hypothesized that the predominant impediments would be identified if enough synthases were investigated. Thus, we first aimed to engineer all possible triketide, tetraketide, and pentaketide synthases using modules from the pikromycin synthase20.\n\nHere we employ a BioBricks-like platform to ligate DNA fragments encoding updated modules in a sequential manner between regions encoding the first and last modules of the pikromycin synthase (P1 and P7)21. Each synthase possesses one fewer polypeptide than its number of modules, and each inserted module harbors docking motifs from the spinosyn synthase appropriate for the self-assembly of the synthase4,5,22. Five P1-X-P7, 25 P1-X-Y-P7, and 125 P1-X-Y-Z-P7 expression plasmids are constructed and transformed into E. coli K207-3, a strain metabolically engineered to activate PKS polypeptides and supply them with methylmalonyl extender units23. Polyketide products extracted from media are detected by high-resolution mass spectrometry, and several are isolated for further characterization by NMR and crystallography. Anticipated molecules are detected from 60% of the triketide synthases, 32% of the tetraketide synthases, and 6.4% of the pentaketide synthases. Our analysis reveals KS gatekeeping24 and module-skipping25 are the major impediments to constructing functional synthases. The platform is used to construct 4 of the more active synthases (P1-P2-P3-P7, P1-P5-P6-P7, P1-P2-P3-P4-P7, and P1-P2-P3-P6-P7) using the traditional module boundary. The P1-P2-P3-P4-P7 and P1-P2-P3-P6-P7 equivalents are functional but yield 10.4- and 5.9-fold lower titers. The platform is also employed to engineer hybrid synthases using updated modules from the erythromycin, spinosyn, and rapamycin synthases. The substrate promiscuity displayed by a module from the rapamycin assembly line is especially encouraging towards realizing the common goal of producing designer polyketides.",
+            "section_image": [
+                "https:////media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41467-024-50844-6/MediaObjects/41467_2024_50844_Fig1_HTML.png"
+            ]
+        },
+        {
+            "section_name": "Results",
+            "section_text": "A platform was designed so that module-encoding DNA, maintained on cloning plasmids, could be sequentially inserted into an expression plasmid during synthase construction (Fig.\u00a02, Supplementary Data\u00a01)26. Cloning plasmids were generated from pUC19 by inserting synthetic DNA encoding a T7 terminator, a T7 promoter, a lac operator, a ribosomal binding site, as well as CDDs and NDDs from the spinosyn PKS (from SpnB/SpnC, SpnC/SpnD, and SpnD/SpnE; ATG was used for the NDD start codons) (Supplementary Fig.\u00a01)4,5,22. These orthogonal, class 1a docking motifs help the PKS polypeptides self-assemble. DNA encoding the N- and C-terminal portions of modules 2\u20136 of the pikromycin synthase (P2\u2013P6) was PCR-amplified from Streptomyces venezuelae ATCC 15439 genomic DNA\u00a0and inserted between the SpeI and BmtI sites and MfeI and XbaI sites, respectively. On the amino acid level, the BmtI site introduces an alanine and serine in the flexible region between ACP and CDD, and the MfeI site encodes a proline and isoleucine at the start of the KS domain where these residues are highly conserved (Supplementary Fig.\u00a02). The P1-P7 expression plasmid was constructed by inserting DNA encoding P1 and P7 between the KpnI and AvrII sites of pCDF-1b (Supplementary Data\u00a01). At the junction of these modules (10 residues downstream of the PikKS1 GTNAH motif and 11 residues downstream of the PikKS6 GTNAH motif), HindIII and SpeI sites were engineered to enable the insertion of HindIII/XbaI-digested, module-encoding DNA from cloning plasmids into the HindIII/SpeI-digested expression plasmid. On the amino acid level, this introduces residues at the updated module boundaries (KLAPAPTS at the junction downstream of P1 and SS at the other junctions), which are known to be highly tolerant to insertions27. On the DNA level, the HindIII and SpeI sites are preserved upstream of the inserted module, and a nonfunctional XbaI/SpeI site is formed downstream of the inserted module.\n\na Cloning plasmids were made by inserting synthetic DNA between the HindIII and XbaI sites of pUC19. The insertion of amplicons encoding the N- & C-terminal portions of a module, such as the 6th module of the pikromycin synthase (P6), places cognate CDD and NDD docking motifs between its ACP and KS domains. b The P1-P7 expression plasmid was made by inserting DNA encoding P1, HindIII-N12-SpeI, and P7 between the KpnI and AvrII sites of pCDF-1b. Module-encoding DNA cut from the cloning plasmids with HindIII and XbaI can be sequentially inserted to construct synthases like P1-P2-P4-P6-P7 (Supplementary Fig.\u00a01, Supplementary Data\u00a01).\n\nThe platform was first employed to construct 5 triketide synthases (P1-X-P7) by inserting DNA encoding modules P2\u2013P6, each split by the SpnC/SpnD docking motifs. Next, 25 tetraketide synthases (P1-X-Y-P7) were constructed by further inserting DNA encoding modules P2\u2013P6, each split by the SpnB/SpnC docking motifs. Finally, 125 pentaketide synthases (P1-X-Y-Z-P7) were constructed by further inserting modules P2\u2013P6, each split by the SpnD/E docking motifs.\n\nE. coli K207-3 cells transformed with the triketide\u2013pentaketide synthase expression plasmids were incubated in shake flasks at 19\u2009\u00b0C23. After 7 days, ethyl acetate extracts of the cultures were analyzed by high-resolution LC/MS and LC/MS/MS (Supplementary Figs.\u00a03\u201360 and Supplementary Tables\u00a01\u20133). Masses detected by LC/MS were within 5 ppm of those calculated for the [M\u2009+\u2009H]+ of products 1\u201319 anticipated for 3 triketide synthases (P1-P2-P7, P1-P4-P7, and P1-P6-P7), 8 tetraketide synthases (P1-P2-P3-P7, P1-P2-P4-P7, P1-P2-P5-P7, P1-P2-P6-P7, P1-P3-P4-P7, P1-P3-P6-P7, P1-P4-P5-P7, and P1-P5-P6-P7), and 8 pentaketide synthases (P1-P2-P3-P4-P7, P1-P2-P3-P5-P7, P1-P2-P4-P5-P7, P1-P2-P5-P5-P7, P1-P4-P5-P5-P7, P1-P2-P3-P6-P7, P1-P2-P5-P6-P7, and P1-P4-P5-P6-P7) (Fig.\u00a03). The MS1 spectra containing [M\u2009+\u2009H]+ usually also contain sodiated ([M+Na]+) and dehydrated ([M-H2O\u2009+\u2009H]+) ions. MS2 fragmentation patterns for the lactones follow those previously reported for triketide lactones (Supplementary Figs.\u00a03\u201332)28,29. The patterns of MS2 fragmentation from the pyrone products were similar to one another, as were the patterns of MS2 fragmentation from the linear products (Supplementary Figs.\u00a033\u201360).\n\nGreen background for polyketides 1\u201319 indicates that the anticipated m/z was detected by high-resolution mass spectrometry (Supplementary Figs.\u00a03\u201360 and Supplementary Tables\u00a03 and 4). Stereochemistries are based on how modules operate within the pikromycin synthase. Because synthases in which P3 or P5 is the penultimate module generate \u03b2-keto acids that undergo spontaneous decarboxylation, their decarboxylated products are shown.\n\nYields were estimated for the lactone and pyrone products (\u03bbmax\u2009=\u2009247 and 285\u2009nm, respectively) using standard curves generated using purified P1-P5-P6-P7 lactone and synthetic 4-hydroxy-6-methyl-2-pyrone (Supplementary Figs.\u00a061\u201367 and Supplementary Tables\u00a03 and 4)19. They ranged from 2.4\u2009mg/L for P1-P3-P4-P7 to 91.3\u2009mg/L for P1-P2-P3-P7. Four products were purified for further characterization by 1H NMR, 13C NMR, and 1H-13C HSQC (the products of P1-P2-P3-P7, P1-P5-P6-P7, P1-P2-P3-P4-P7, and P1-P2-P3-P6-P7) and one for characterization by crystallography (the product of P1-P2-P3-P6-P7) (Fig.\u00a04 and Supplementary Figs.\u00a068\u201380).\n\na \u00b9H NMR spectrum of 4, the anticipated product of P1-P2-P3-P7. b Crystal structure of 17 (absolute stereochemistry), the anticipated product of P1-P2-P3-P6-P7 (CCDC number 2278377). AT acyltransferase, KR ketoreductase, DH dehydratase, ER enoylreductase, ACP acyl carrier protein, KS ketosynthase, TE thioesterase, KSQ priming KS, CDD & NDD docking domain motifs.\n\nP3\u2013P6 were observed to function with non-native modules upstream of them, and P2\u2013P5 were observed to function with non-native modules downstream of them. However, modules often did not lead to functional synthases as expected. Their behavior in different contexts was characterized to identify impediments to their modularity.\n\nP2 was observed to function in the 2nd position (P1-P2-P7, P1-P2-P3-P7, P1-P2-P4-P7, P1-P2-P5-P7, P1-P2-P6-P7, P1-P2-P3-P4-P7, P1-P2-P3-P5-P7, P1-P2-P4-P5-P7, P1-P2-P5-P5-P7, P1-P2-P3-P6-P7, P1-P2-P5-P6-P7) but not in the 3rd or 4th positions. P3 was observed to function in the 2nd position (P1-P3-P4-P7 and P1-P3-P6-P7), and in the 3rd position when preceded by P2 (P1-P2-P3-P7, P1-P2-P3-P4-P7, P1-P2-P3-P5-P7, P1-P2-P3-P6-P7), but not in the 4th position. P4 was observed to function in the 2nd (P1-P4-P7, P1-P4-P5-P7, P1-P4-P5-P5-P7, P1-P4-P5-P6-P7), 3rd (P1-P2-P4-P7, P1-P3-P4-P7, P1-P2-P4-P5-P7), and 4th positions (P1-P2-P3-P4-P7). P5 was observed to function in the 2nd (P1-P5-P6-P7), 3rd (P1-P2-P5-P7, P1-P4-P5-P7, P1-P2-P5-P5-P7, P1-P4-P5-P5-P7, P1-P2-P5-P6-P7, P1-P4-P5-P6-P7), and 4th positions (P1-P2-P3-P5-P7, P1-P2-P4-P5-P7, P1-P2-P5-P5-P7, P1-P4-P5-P5-P7), and was the only module that was observed to function downstream of another copy of itself (P1-P2-P5-P5-P7 and P1-P4-P5-P5-P7). P6 was observed to function in the 2nd (P1-P6-P7), 3rd (P1-P2-P6-P7, P1-P3-P6-P7, P1-P5-P6-P7), and 4th positions (P1-P2-P3-P6-P7, P1-P2-P5-P6-P7, P1-P4-P5-P6-P7). No synthase with P6 immediately upstream of P2, P3, P4, P5, or P6 was functional.\n\nThe engineered tetraketide and pentaketide synthases that apparently yielded their anticipated products were investigated for shunt product formation (Supplementary Table\u00a05). For example, in addition to producing its anticipated pentaketide product, P1-P2-P3-P6-P7 produces compounds with mass spectra equivalent to those of the tetraketide products apparently generated by P1-P3-P6-P7, P1-P2-P3-P7, and P1-P2-P6-P7 as well as the triketide products generated by P1-P2-P7 and P1-P6-P7. In no case was a shunt product observed at a titer higher than that of the anticipated product (Supplementary Tables\u00a04 and 5).\n\nOur lab has compared the activities of equivalent triketide synthases constructed using traditional and updated modules from the venemycin and pikromycin synthases9,10. In each comparison, synthases constructed with the updated boundary have outperformed those constructed with the traditional boundary. To determine if this trend would extend to tetra- and pentaketide synthases, P1-P2-P3-P7, P1-P5-P6-P7, P1-P2-P3-P4-P7, and P1-P2-P3-P6-P7 equivalents were constructed using the traditional boundary. This requires replacing PikKS3 with PikKS6 in P1-P2-P3-P7, PikKS1 with PikKS4 in P1-P5-P6-P7, PikKS4 with PikKS6 in P1-P2-P3-P4-P7, and PikKS3 with PikKS5 in P1-P2-P3-P6-P7. This was accomplished for P1-P2-P3-P7, P1-P2-P3-P4-P7, and P1-P2-P3-P6-P7 by swapping the KSs between the MfeI and XbaI sites of the P3- and P4-containing cloning plasmids and using HindIII/XbaI fragments from these plasmids during the construction of the expression plasmids (Fig.\u00a02). To replace PikKS1 with PikKS4 in P1-P5-P6-P7 it was necessary to alter the P1-P7 expression plasmid. This was accomplished through SLiCE assembly of DNA encoding PikKS4 with amplicons that contain all of the P1-P7 expression plasmid except PikKS130. The 4 pairs of synthases were tested for polyketide production.\u00a0Only the P1-P2-P3-P4-P7 and P1-P2-P3-P6-P7 equivalents synthesized the products detected from synthases constructed with the updated boundary, and their yields were 10.4- and 5.9-fold lower (Supplementary Fig.\u00a081 and Supplementary Table\u00a06).\n\nWe also sought to employ the platform to construct and assess the activities of hybrid synthases in which the updated modules from the pikromycin synthase collaborate with those from other synthases (Fig.\u00a05). To determine whether modules that are functionally equivalent in natural synthases behave equivalently within engineered hybrid synthases, the 6th module of the erythromycin PKS, E6, was swapped for P6 in functional P6-containing synthases to generate P1-E6-P7, P1-P2-E6-P7, P1-P3-E6-P7, P1-P5-E6-P7, P1-P2-P5-E6-P7, and P1-P4-P5-E6-P7. Each of these hybrid synthases produces compounds with equivalent mass spectra to those produced by their counterparts constructed entirely from pikromycin modules (Supplementary Figs.\u00a05\u20138, 11\u201322, 25\u201332 and Supplementary Table\u00a01). Similarly, the insertion of the 2nd module of the spinosyn PKS, S2, between P1 and P7 yielded the synthase P1-S2-P7, which produces a compound with mass spectral peaks that match those previously reported for its anticipated triketide lactone (Supplementary Figs.\u00a09 and 10, and Supplementary Table\u00a01)29.\n\nThe 6th module of the erythromycin synthase, E6, functions equivalently to P6 in the hybrid synthases shown in the top two rows. The 2nd module of the spinosyn synthase, S2, collaborates with pikromycin modules in P1-S2-P7 to yield the expected triketide lactone, 20. As no pikromycin module was observed to function immediately downstream of P6, the 4th module of the rapamycin synthase, R4, hypothesized to contain a more substrate-permissive KS, was placed downstream of P6 in P1-P6-R4-P7. Results suggest the anticipated tetraketide, 21, is produced (Supplementary Figs.\u00a082 and 83). That P1-P2-R4-P7 apparently also produces its anticipated tetraketide, 5, indicates that RapKS4 can accept stereoisomeric intermediates with opposite stereochemistries at the \u03b3- and \u03b4-carbons and is relatively promiscuous, as hypothesized. AT acyltransferase, KR ketoreductase, DH dehydratase, ER enoylreductase, ACP acyl carrier protein, KS ketosynthase, TE thioesterase, KSQ priming KS, KR0 epimerase in P4 but KR with unknown function in R4, DH0 DH with unknown function; ER0 ER with unknown function, CDD & NDD docking domain motifs.\n\nIn a recent KS gatekeeping study, most of the KSs from the rapamycin synthase were noted to possess an aromatic residue at position 2 characteristic of KSs that gatekeep less stringently15. Since this aromatic residue (phenylalanine in RapKS4 from the 4th module of the rapamycin synthase, R4) can nonspecifically interact with diverse polyketide intermediates, modules from the rapamycin synthase may be particularly useful in accessing designer polyketides31. As no pikromycin module was observed to accept an intermediate from P6, the ability of R4 to do so within the tetraketide synthase P1-P6-R4-P7 was tested (Fig.\u00a05). This synthase apparently generates its anticipated product, as its MS2 spectrum is equivalent to that of the enantiomeric product of P1-P2-P4-P7 (Supplementary Figs.\u00a038 and\u00a042). The triketide synthase generated during its construction, P1-R4-P7, is also functional. A further test of the substrate tolerance of R4 was made through the construction of P1-P2-R4-P7. The intermediate presented to RapKS4 in P1-P2-R4-P7 is hypothesized to be a stereoisomer of the intermediate presented to RapKS4 in P1-P6-R4-P7, containing oppositely-oriented \u03b3-methyl and \u03b4-hydroxy substituents. Indeed, P1-P2-R4-P7 apparently produces its anticipated tetraketide, as its MS2 spectrum is equivalent to that of the P1-P2-P4-P7 product (Supplementary Figs.\u00a038 and\u00a040).\n\nModeling was performed to help understand the gatekeeping activities of KSs employed in this study (PikKS2\u2013PikKS6, SpnKS2, EryKS6, RapKS4) (Fig.\u00a06 and Supplementary Figs.\u00a082 and 83). First, AlphaFold predictions were obtained for each homodimeric KS32. Next, the coordinates and restraint files for polyketide substrates were generated using the program Sketcher33. Finally, they were positioned with the program Coot34 in conformations equivalent to those observed in acyl-KS structures (PDB Codes 2BUI, 2GFY, 2IX4, 6ROP, 7UK4; N-C\u03b1-C\u03b2-S and O-C-C\u03b1-C\u03b2 dihedral angles were kept within the experimentally observed ranges, except for the \u03b1/\u03b2-unsaturated PikKS3 intermediate, where O-C-C\u03b1-C\u03b2\u2009=\u20090\u00b0)15,35,36,37,38,39. While the routes of the modeled polyketide chains through the KSs follow those in the acyl-KS structures, precise conformations will need to be determined through structural studies.\n\nOn the left, stereodiagrams show how native polyketide intermediates may be bound to the reactive cysteines of PikKS2\u2013PikKS6 (KS structures generated by AlphaFold). The \u03b1-, \u03b2-, \u03b3-, and \u03b4-carbons of the intermediates as well as important gatekeeping residues are labeled15. On the right, intermediates apparently accepted or excluded by KSs within the engineered PKSs are tabulated.",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Discussion",
+            "section_text": "While our lab has employed the updated module boundary to engineer trimodular synthases that outcompete equivalent synthases engineered using the traditional module boundary9,10,19, and other labs have used it to modify larger synthases11,40,41, the utility of the updated module boundary has not been systematically examined. In this study, we sought to test the limits of PKS engineering with updated modules and identify impediments to programming synthases that biosynthesize designer polyketides. Our platform enabled the rapid construction of the expression plasmids for 5 triketide, 25 tetraketide, and 125 pentaketide synthases using the updated modules from the pikromycin synthase and docking domains from the spinosyn synthase.\n\nMass spectrometry of the culture extracts of E. coli K207-3 transformed with these plasmids showed peaks with m/z values anticipated for the products of 60% of the triketide synthases, 32% of the tetraketide synthases, and 6.4% of the pentaketide synthases as well as shunt products for the majority of the synthases. The nature of the shunt products is revealing. For example, although P1-P2-P3-P6-P7 generates its anticipated pentaketide, it also generates compounds with mass spectra equivalent to those of the anticipated tetraketide products of P1-P3-P6-P7, P1-P2-P3-P7, and P1-P2-P6-P7. This module-skipping naturally occurs in the quartromicin synthase, in which the final domain of the assembly line is an ACP that is hypothesized to collaborate with the upstream KS to generate a hexaketide as well as the KS of the previous module to generate a pentaketide, both of which are components of quartromicin42,43. Another type of module-skipping occurs in the pikromycin synthase within S. venezuelae due an alternative start codon that inactivates the P6 KS and enables transfer of the polyketide from the P6 ACP to TE to yield the hexaketide 10-deoxymethynolide44. Our lab has not observed E. coli to use this alternative start site, and shunt products that would result from this type of module-skipping from the engineered triketide\u2013pentaketide synthases were not observed.\n\nThe most significant impediment to engineering is KS gatekeeping beyond the \u03b2-carbon15,24,40,45. Of the 5 tetraketide synthases that contain PikKS2 in the 3rd position and the 50 pentaketide synthases that contain PikKS2 in the 3rd or 4th position, none yielded their anticipated product. Of the 5 tetraketide synthases that contain PikKS3 in the 3rd position and the 25 pentaketide synthases that contain PikKS3 in the 4th position, only P1-P2-P3-P7 yielded its anticipated product. In the aforementioned KS gatekeeping study, we investigated how residues at 32 positions in the KS substrate tunnel help select for chemistries within polyketide intermediates15. Diketide-accepting KSs like PikKS2 possess relatively large residues at positions 2, 10, 14, and 22 that make favorable interactions with the tail of a diketide but sterically exclude longer intermediates (Fig.\u00a06). PikKS3 possesses a serine at position 14 that can form a hydrogen bond with the d-\u03b4-hydroxy group of its natural triketide intermediate but cannot make an equivalent hydrogen bond with an intermediate lacking a d-\u03b4-hydroxy group. The ethyl tail of the triketide may fit in a hydrophobic pocket adjacent to the glycine in position 10 not accessible to the tails of longer intermediates.\n\nPikKS4 and PikKS6 were observed to less stringently gatekeep for substituents beyond the \u03b2-carbon (Fig.\u00a06). PikKS4 naturally accepts an intermediate containing a trans-double bond between its \u03b3- and \u03b4-carbons. Additionally, it apparently accepts an intermediate without \u03b3- and \u03b4-substituents in P1-P4-P7, P1-P4-P5-P7, P1-P4-P5-P5-P7, and P1-P4-P5-P6-P7 and intermediates containing an l-\u03b3-methyl group and a d-\u03b4-hydroxyl group in P1-P2-P4-P7 and P1-P2-P4-P5-P7. PikKS6 naturally accepts an intermediate containing an l-\u03b3-methyl group and \u03b4-methylene. Additionally, it apparently accepts an intermediate without \u03b3- and \u03b4-substituents in P1-P6-P7, an intermediate containing an l-\u03b3-methyl group and a d-\u03b4-hydroxyl group in P1-P2-P6-P7, and intermediates containing a \u03b3,\u03b4-trans-double bond in P1-P3-P6-P7 and P1-P2-P3-P6-P7.\n\nPikKS5 may be the least stringent gatekeeper of the pikromycin KSs (Fig.\u00a06). It naturally accepts an intermediate containing an l-\u03b3-methyl group and a \u03b4-keto group. Additionally, it apparently accepts an intermediate without \u03b3- and \u03b4-substituents in P1-P5-P6-P7, intermediates containing an l-\u03b3-methyl group and a d-\u03b4-hydroxyl group in P1-P2-P5-P7 and P1-P2-P5-P6-P7, an intermediate containing a \u03b3,\u03b4-trans-double bond in P1-P2-P3-P5-P7, and intermediates containing an l-\u03b3-methyl group and a \u03b4-methylene group in P1-P2-P5-P5-P7 and P1-P4-P5-P5-P7. The only module from which it was not observed to accept an intermediate is P6. The tryptophan at position 2 of PikKS5 may help it nonspecifically interact with diverse polyketide intermediates15. When a tryptophan was introduced at this position in EryKS3, its substrate scope was observed to increase dramatically31.\n\nNearly 6 decades ago, Celmer noted that carbon skeletons of erythromycin, pikromycin, and related macrolide antibiotics possess similar stereochemical patterns46. Much has been learned about how assembly line enzymes exert stereocontrol47,48,49, yet the significance of polyketide intermediate recognition by KSs is still under investigation. The KSs of synthases that produce macrolide antibiotics have apparently evolved not only to recognize the chemistries at the \u03b1- and \u03b2-positions set by enzymes in the same module but also chemistries beyond the \u03b2-position set by enzymes in previous modules. While complementary interactions between KSs and features beyond the \u03b2-position would increase the productivity of an assembly line, from an engineering standpoint modules containing these more substrate-specific KSs are less desirable than modules containing KSs that gatekeep only at the \u03b1- and \u03b2-positions.\n\nThe first 12 modules of the rapamycin synthase synthesize the \u201cvariable region\u201d of rapamycin, and their highly identical KSs possess an aromatic residue (F or Y) at position 2 that is characteristic of less stringent gatekeepers15,50. The next 2 modules help synthesize the \u201cconstant region\u201d of rapamycin, and their KSs do not possess the aromatic residue. The equivalent constant regions of rapamycin, FK506, and WDB002 tightly bind eukaryotic prolyl isomerases, while their variable regions target a second eukaryotic protein \u2013 mammalian Target Of Rapamycin, calcineurin, and centrosomal protein 250, respectively50. The modules that synthesize the variable regions may be more portable and facilitate the evolution of motifs that target new secondary proteins. Indeed, a method that accelerates recombination within the rapamycin synthase genes gave rise to synthases with 1\u20136 fewer module(s) and 1 more module that produce rapamycin derivatives containing anticipated changes to the variable region in good yields51.\n\nAfter employing the described platform to generate functional hybrid synthases containing modules from the erythromycin and spinosyn synthases, we attempted to incorporate a module from the rapamycin synthase. Since none of the modules from the pikromycin synthase were observed to accept intermediates from P6, we positioned the 4th module of the rapamycin synthase, R4, immediately downstream of P6 within P1-P6-R4-P7. In this tetraketide synthase, RapKS4 apparently accepts an intermediate with a d-oriented \u03b3-methyl substituent10. It is the only KS in this study that appears to do so. PikKS3, PikKS5, and PikKS6 naturally accept intermediates with l-oriented \u03b3-methyl substituents and may have evolved to interact with those methyl groups in their l-orientations. RapKS4 is apparently quite tolerant to \u03b3- and \u03b4-substituents since it accepts an intermediate without \u03b3- and \u03b4-substituents in P1-R4-P7 as well as stereoisomeric intermediates with oppositely oriented \u03b3- and \u03b4-substituents in P1-P6-R4-P7 and P1-P2-R4-P710. If modules from the rapamycin synthase are permissive to intermediates with diverse chemistries beyond the \u03b2-carbon and yield the chemistries anticipated of them, they could play prominent roles in engineering PKSs to yield designer polyketides.\n\nThe first assembly lines constructed with the described platform only contain modules from the pikromycin synthase since we were concerned engineering would be impeded by adverse protein-protein interactions between modules, especially those of different synthases. However, the constructed hybrid synthases appear to be highly functional. The 6 synthases in which P6 was substituted by E6 (P1-E6-P7, P1-P2-E6-P7, P1-P3-E6-P7, P1-P5-E6-P7, P1-P2-P5-E6-P7, and P1-P4-P5-E6-P7) show similar activities, suggesting that, although P6 and E6 are from different synthases and different organisms, their differences do not prevent them from equivalently collaborating with an upstream P1, P2, P3, P5, or a downstream P7. One could argue that evolutionary relationships between modules of PKSs that synthesize macrolide antibiotics confer greater success rates to hybrid synthases comprised of them. However, the apparent collaboration between modules of the pikromycin synthase with modules of the spinosyn and rapamycin synthases indicates that even modules from more evolutionarily-distant synthases can be compatible52,53.\n\nIn the synthases constructed here, the ordered assembly of polypeptides was mediated by 3 class 1a spinosyn CDD/NDD pairs. They substituted for other class 1a pairs (in P3 and P5) and class 1b pairs (in P6 and S2) as well as enabled the splitting of modules naturally encoded on one polypeptide (in P2, P4, E6, and R4) (Supplementary Fig.\u00a02). While these docking motifs apparently function quite well overall, they may not always function as anticipated. We hypothesize that, similar to the quartromicin synthase42, module-skipping can occur when ACPs are not restrained (e.g., through the association of CDD/NDD pairs downstream of them). Imbalanced polypeptide stoichiometries could cause this, since an excess of an upstream polypeptide would mean some CDD motifs are not paired with their cognate NDD motifs on the downstream polypeptide19. While peptide connections could be employed to covalently anchor ACPs to downstream KSs to prevent module-skipping, this would result in lower polyketide titers due to the poor expression of long polypeptides in E. coli10,19. Further investigation is warranted to elucidate the mechanism(s) of module-skipping in engineered synthases and how to avoid this phenomenon54.\n\nIn this work a high-throughput, BioBricks-style platform was developed to combinatorially construct expression plasmids for engineered PKSs. Through it, 29 triketide\u2013pentaketide synthases, including hybrid synthases, were obtained that yield products with mass spectra consistent with their anticipated structures. Titers were at levels that enable characterization by NMR and crystallography. As few natural short synthases appropriate for in vivo and in vitro studies have been discovered, the triketide\u2013pentaketide synthases engineered here can serve as valuable model systems. Through analyzing the activities of these synthases, we identified KS gatekeeping as the most significant impediment to PKS engineering in our updated module boundary platform. We demonstrated that this may be circumvented by employing modules containing substrate-tolerant KSs, such as those from the rapamycin synthase. Strategies like this may help alleviate the compounding effects of KS gatekeeping and result in a higher percentage of functional engineered synthases. By combining updated modules from diverse synthases with platforms like the one described here, the goal of engineering PKSs to synthesize designer commodity chemicals and medicines is being realized.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "All restriction enzymes, HiFi DNA Assembly Master Mix, ligase, and enzyme buffers are from New England Biosciences (NEB). Yeast extract, glycerol, sodium chloride, potassium phosphate dibasic, hydrochloric acid, sodium sulfate, ethyl acetate, hexanes, methanol, acetonitrile, and formic acid are from Fisher Scientific. Potassium phosphate monobasic is from Sigma-Millipore. Sodium propionate is from Alfa Aesar. Isopropyl \u03b2-d-1-thiogalactopyranoside (IPTG) is from Carbosynth. Milk filters are from KenAG. Minipreps and gel extracts were performed with Promega Wizard SV kits. Streptomycin, 2,4-dinitrophenylhydrazine (DNPH), antifoam 204, and thin layer chromatography (TLC) plates are from Sigma-Aldrich. Luria-Bertani broth is from Fisher Bioreagents. Casein is from Thermo Scientific. Deuterated chloroform is from Camrbidge Isotopes. KAPA polymerase master mix is from Roche Biosciences. Nunc Omnitrays are from Thermo-Fisher. Growblocks are from Axygen. SiliaFlash F60 is from Silicylce. gBlocks were ordered from IDT. Primers for PCR amplification are from Sigma-Millipore (Supplementary Data\u00a01).\n\nAll high-resolution LC/MS/MS was obtained using an Agilent 6530 TOF LC/MS/MS connected to a Poroshell 300SB-C3 column (2.1 \u00d775\u2009mm, 5-\u03bcm particle size). UV data and corresponding LC/MS data was obtained using an Agilent 6230 TOF LC/MS, connected to a ZORBAX Eclipse Plus C18 column (2.1 \u00d750\u2009mm, 1.8-\u03bcm particle size). 1H NMR spectra were obtained on an Agilent MR400 NMR. 13C and HSQC NMR were obtained on a Bruker AVANCE III 500 NMR supported by NSF grant 1 S10 OD021508-01. The Echo 525 Acoustic Liquid Handler is from Beckman Coulter. The benchtop bioreactor is a New Brunswick Bioflo 115. The Agilent 6230 and 6530 TOFs were calibrated between 118.0863\u2009m/z and 2721.8948\u2009m/z, and the calibrant used was Agilent calibrant mix G1969-85000.\n\nThe 125 P1-X-Y-Z-P7 expression plasmids were generated with the help of an Echo 525 Acoustic Liquid Handler. Module-encoding DNA fragments were digested out of cloning plasmids using XbaI and HindIII, separated on a 1% agarose gel, and gel-extracted. P1-X-Y-P7 expression plasmids were digested with SpeI and HindIII and gel-extracted. Solutions containing 5 inserts and 25 backbones with varying concentrations were placed in the wells of a 384-well plate and using a self-designed cherry-picking list, 10 fmol of each backbone and insert were transferred by the Echo 525 into two 96-well destination PCR plates to cover the 125 possible combinations (one destination well for each assembly). Next, a master mix of T4 ligase, T4 ligase buffer, and nuclease-free water was added to bring each reaction volume to 20\u2009\u00b5L (0.25 Weiss units/\u00b5L). The plates were then sealed with adhesive foil and placed into a 96-well thermocycler set to 16\u2009\u00b0C for 1\u2009h. Next, using a multichannel pipette, 10\u2009\u00b5L from each of the wells was added to 50\u2009\u00b5L of chemically-competent E. coli DH5\u03b1 in a 96-deep-well grow block, incubated for 10\u2009min on ice, heat-shocked at 42\u2009\u00b0C for 1\u2009min, supplemented with 1\u2009mL of LB, and incubated for 1\u2009h at 37\u2009\u00b0C and 240\u2009rpm. The culture blocks were then centrifuged at 3220\u2009\u00d7\u2009g for 5\u2009min, and the supernatants were removed.\n\nEach well of transformed, outgrown, and pelleted DH5\u03b1 cells in 96-well grow blocks were resuspended in 50\u2009\u00b5L of ultra-pure water and diluted in series of 1x, 10x, 20x with nuclease-free water into more grow blocks. Then 5\u2009\u00b5L of each well of each grow block (1x, 10x, and 20x dilutions) was plated, dropwise, onto LB-Agar with 100\u2009\u00b5g\u2009mL-1 of streptomycin in Nunc Omnitrays. The plates are the same length and width as standard 96-well blocks/plates, so a 3D-printed grid was used to define the 12 \u00d78 space on each of the plates, and each of the 5\u2009\u00b5L drops was isolated to its own 1 \u00d71 partition for drop plating. The plates were then left open in a biosafety cabinet to ensure that the droplets would dry and not cross-contaminate one another. After droplets were dry, plates were incubated overnight at 37\u2009\u00b0C. The array of 1\u201320x dilution is to ensure that even if assembly efficiency is low, several colonies could be picked for screening. Picked colonies inoculated grow blocks containing LB media with 100\u2009\u00b5g\u2009mL-1 streptomycin and were cultured overnight at 37\u2009\u00b0C and 240\u2009rpm. DNA was isolated using the Promega Wizard SV 96 Plasmid DNA Purification Kit. Proper ligation was verified for each pentaketide construct by Sanger sequencing.\n\nPolyketide production from engineered synthases was assessed in vivo using E. coli K207-3. This strain overexpresses Bacillus subtilis Sfp to phosphopantetheinylate ACP domains as well as the E. coli propionyl-CoA ligase PrpE and the Streptomyces coelicolor propionyl-CoA carboxylase to convert propionate supplied to the media into methylmalonyl-CoA23. A single E. coli K207-3 colony transformed with an expression plasmid was used to inoculate 3\u2009mL of LB media containing 100\u2009\u00b5g/mL streptomycin. After 16\u2009h at 37\u2009\u00b0C, 300\u2009\u00b5L of this starter culture was transferred into 30\u2009mL of production media [5\u2009g\u2009L\u22121 yeast extract, 10\u2009g\u2009L\u22121 casein, 15\u2009g\u2009L\u22121 glycerol, 10\u2009g\u2009L\u22121 NaCl, and 100\u2009mM potassium phosphate buffer (pH 7.6)] containing 100\u2009\u00b5g/mL streptomycin in a 250\u2009mL Erlenmeyer flask covered with a milk filter disk. Cells were shaken at 240\u2009rpm at 37\u2009\u00b0C until OD600\u2009=\u20090.5. The cultures were then cooled to 19\u2009\u00b0C, provided with 20\u2009mM sodium propionate and 0.1\u2009mM IPTG, and incubated at 240\u2009rpm for 7 d.\n\n0.5\u2009mL aliquots of culture broth were acidified with the addition of 10 \u03bcL of concentrated HCl, extracted twice with 0.5\u2009mL of ethyl acetate and concentrated in vacuo. Each extract was resuspended in 50/50% (v/v) methanol/water and analyzed by high-resolution LC/MS/MS with an Agilent 6530 connected to a Poroshell 300SB-C3 column (75 \u00d72.1\u2009mm, 5-\u03bcm particle size, using a flow rate of 0.700\u2009mL/min [Solvent A: water with 0.1% (v/v) formic acid; Solvent B: acetonitrile with 0.1% (v/v) formic acid. 5\u201390% B for 12\u2009min, 90% B for 4\u2009min, positive mode]. MS2 data collection began after 3\u2009min, as products eluted thereafter. Samples were observed using AJS ESI using 150\u2009V and a gas temperature of 350\u2009\u00b0C. Each sample was set to the specific target mass of the parent ion identified by LC/HRMS. Fragments were generated through collision induced dissociation at 0\u201335\u2009V (depending on the stability of the target molecule different collision energies were selected to generate the most ion diversity for structural annotation). The MS1 and MS2 data is displayed in Supplementary Figs.\u00a03\u201360. Each precursor ion was isolated with a 4\u2009m/z isolation window.\n\n0.5\u2009mL aliquots of culture broth were acidified with the addition of 10\u2009\u03bcL concentrated HCl, extracted twice with 0.5\u2009mL of ethyl acetate, and concentrated in vacuo. Each extract was analyzed by high-resolution LC/MS with an Agilent 6230 TOF LC/MS connected to a ZORBAX Eclipse Plus C18 column (2.1 \u00d750\u2009mm, 1.8-\u03bcm particle size) using a flow rate of 1\u2009mL\u2009min-1 [Solvent A: water with 0.1% (v/v) formic acid; Solvent B: acetonitrile with 0.1% (v/v) formic acid. 5\u2013100% B for 15\u2009min, 100% B for 3\u2009min, positive mode]. Lactone and pyrone polyketides were detected using the default voltage and gas temperature (180\u2009V and 300\u2009\u00b0C), while linear polyketides were detected using 50\u2009V and 125\u2009\u00b0C. The retention time of the EIC peak for each sample was used to determine the corresponding UV peak from the diode array detector (DAD) data when estimating the concentration of the lactone and pyrone products (Supplementary Figs.\u00a066 and 67). All mass spectrometry and UV data was analyzed using Agilent MassHunter software.\n\nOnce the production of expected polyketides was detected with small scale experiments, the cultures were scaled up so that NMR spectra could be attained. A single E. coli K207-3 colony transformed with an expression plasmid was used to inoculate 3\u2009mL of LB media containing 100\u2009\u00b5g/mL streptomycin. After 16\u2009h at 37\u2009\u00b0C, 300\u2009\u00b5L of this starter culture was transferred into 300\u2009mL of production media [5\u2009g\u2009L\u22121 yeast extract, 10\u2009g\u2009L\u22121 casein, 15\u2009g\u2009L\u22121 glycerol, 10\u2009g\u2009L\u22121 NaCl, and 100\u2009mM potassium phosphate buffer (pH 7.6)] containing 100\u2009\u00b5g/mL streptomycin in a 3\u2009L flask covered with a milk filter disk. Cells were shaken at 240\u2009rpm at 37\u2009\u00b0C until OD600\u2009=\u20090.5. The cultures were then cooled to 19\u2009\u00b0C, provided with 20\u2009mM sodium propionate and 0.1\u2009mM IPTG, and incubated at 240\u2009rpm. After 7 d, cultures were acidified to pH 3 with HCl and extracted twice with ethyl acetate. The extract was dried with Na2SO4, filtered, and concentrated in vacuo. Products were purified by silica gel column chromatography with a 0\u2013100% ethyl acetate/hexanes gradient. Fractions were analyzed by thin-layer chromatography and LC/MS which contained the expected products. The purest fractions containing the products were combined, concentrated in vacuo, and characterized through a combination of 1H, 13C, and 1H-13C HSQC NMR spectroscopy.\n\nAfter synthases were verified for production, they were regrown in triplicate and analyzed using high-resolution LC/MS with UV detection. Calibration curves were generated so that peak areas could be converted to concentrations. For lactone products, the calibration curve was made using the purified product of P1-P5-P6-P7. 10\u2009mg was dissolved in 10\u2009mL and serially diluted to prepare concentrations of 400\u2009mg/L, 200\u2009mg/L, 100\u2009mg/L, 50\u2009mg/L, 25\u2009mg/L, 12.5\u2009mg/L, 6.25\u2009mg/L, and 3.125\u2009mg/L. Peak areas from UV absorption chromatograms were used to create the calibration curve (Supplementary Fig.\u00a061). For pyrone products, the synthetic standard, 4-hydroxy-6-methy-2-pyrone (CAS# 675-10-5) was used to prepare concentrations of 400\u2009mg/L, 200\u2009mg/L, 100\u2009mg/L, 50\u2009mg/L, 25\u2009mg/L, 12.5\u2009mg/L, 6.25\u2009mg/L, and 3.125\u2009mg/L. Peak areas from UV absorption chromatograms were used to create the calibration curve (Supplementary Fig.\u00a063). Molar concentrations of the products were estimated using the calibration curves, and production titers were calculated using their anticipated molecular weights. Lastly, the linear product of P1-P2-P3-P7 was quantified using EIC quantification due to its lack of UV absorbance. The purified product of P1-P2-P3-P7 was dissolved to make concentrations of 400\u2009mg/L, 200\u2009mg/L, 100\u2009mg/L, 50\u2009mg/L, 25\u2009mg/L, 12.5\u2009mg/L, 6.25\u2009mg/L, and 3.125\u2009mg/L. Peak areas from the EIC chromatogram were used to create a calibration curve (Supplementary Fig.\u00a065).\n\nA New Brunswick BioFlo 115 benchtop fermentor was used to scale up production from cells transformed with the P1-P2-P3-P6-P7 expression plasmid. A single E. coli K207-3 colony was used to inoculate 30\u2009mL of LB media containing 100\u2009\u00b5g/mL streptomycin. After 16\u2009h at 37\u2009\u00b0C and 240\u2009rpm, 15\u2009mL of this starter culture was injected into 1.5\u2009L of production media media [5\u2009g\u2009L\u22121 yeast extract, 10\u2009g\u2009L\u22121 casein, 15\u2009g\u2009L\u22121 glycerol, 10\u2009g\u2009L\u22121 NaCl, and 100\u2009mM potassium phosphate buffer (pH 7.6)] containing 100\u2009\u00b5g/mL streptomycin within a 3\u2009L hermetically-sealed, heat-blanketed, and autoclaved chamber. The culture was monitored continuously with pH, dissolved oxygen, and temperature probes. The cells were grown for 4\u2009h at 37\u2009\u00b0C, with ambient air (1.5\u20134.5 slpm) and internal rotor agitation (50\u2013250\u2009rpm) maintaining 20% dissolved oxygen. The chamber was then cooled to 19\u2009\u00b0C through pumping 50/50% (v/v) ethylene glycol/water through internal lines, and the culture was provided a final concentration of 0.1\u2009mM IPTG, 20\u2009mM sodium propionate, and 0.5\u2009mL of antifoam 204. Once a day, 1\u2009M NaOH was injected to return the pH to 7.6. After 7 d, cultures were acidified to pH 3 with HCl and extracted twice with ethyl acetate (2 \u00d71.5\u2009L). The extract was dried with Na2SO4, filtered, and concentrated in vacuo. Pentaketide 17\u00a0was purified by silica gel column chromatography with a 0\u2013100% ethyl acetate/hexanes gradient. Fractions were analyzed by thin-layer chromatography and LC/MS. The purest fractions were combined and concentrated in vacuo.\n\nAfter purifying the P1-P2-P3-P6-P7 pentaketide,\u00a017, crystals were obtained. They grew as colorless prisms by slow evaporation in 50% (v/v) methanol/water. Details about data collection and structure solution can be obtained from the\u00a0.cif file deposited at the Cambridge Structural Database under code 2278377.\n\nTo directly compare synthases containing either updated or traditional modules, traditional versions of P1-P2-P3-P7, P1-P5-P6-P7, P1-P2-P3-P4-P7, and P1-P2-P3-P6-P7 were constructed. For the traditional version of P1-P5-P6-P7, PikKS1 was swapped with PikKS4. To construct the plasmids for this synthase, primers were used to amplify the pCDF expression vector in two parts so that it lacked PikKS1 (Supplementary Data\u00a01). PikKS4 was also amplified and joined with the pCDF amplicons via SliCE assembly. The BioBricks-style approach allowed for the sequential insertion of P6 and P5, resulting in the traditional version of P1-P5-P6-P7. To construct the 3 other synthases, the MfeI and XbaI sites flanking the KS in each module-containing cloning plasmid were used to appropriately swap the KS-encoding DNA. Thus, to build the P1-P2-P3-P7 plasmid, PikKS3 was first removed from the P3 cloning plasmid and PikKS6 was ligated in. Sequential insertion of the HindIII/XbaI fragments from this plasmid and the P2 cloning plasmid into the P1-P7 expression plasmid then resulted in a \u201ctraditional P1-P2-P3-P7\u201d containing a traditional module boundary between P3 and P7. Similarly, to construct the P1-P2-P3-P4-P7 plasmid, PikKS4 was digested out of the P4 cloning plasmid and replaced with PikKS6, and this plasmid was used for the BioBricks-style assembly of the traditional P1-P2-P3-P4-P7. The P1-P2-P3-P6-P7 plasmid was assembled using a P3 cloning plasmid in which PikKS3 was replaced with PikKS5.\n\nTo test the functionality of the traditional and updated synthases, E. coli K207-3 was first transformed with the expression plasmids. Cultures were grown side-by-side in media and conditions described in the \u201cCulturing E. coli K207-3 transformed with expression plasmids\u201d section. Extractions and high-resolution LC/MS were performed as described in the \u201cConditions for high-resolution LC/MS and UV measurements on Agilent 6230\u201d section. The appropriate peak areas in EICs (171\u2212172\u2009m/z for P1-P2-P3-P7, 213-214\u2009m/z for P1-P5-P6-P7, 253-254\u2009m/z for P1-P2-P3-P4-P7, and 255-256\u2009m/z for P1-P2-P3-P6-P7) were integrated and compared (Supplementary Fig.\u00a081 and Supplementary Table\u00a06).\n\nNo statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized, and the investigators were not blinded to allocation during experiments and outcome assessment. All LC/MS was performed in triplicate as well as any measurements for quantification and comparison.\n\nFurther information on research design is available in the\u00a0Nature Portfolio Reporting Summary linked to this article.",
+            "section_image": []
+        },
+        {
+            "section_name": "Data availability",
+            "section_text": "Data supporting the findings of this work are available within the paper and its Supplementary Information files. A reporting summary for this article is available as a Supplementary Information file. The x-ray coordinates for the structure reported in this study were deposited at the Cambridge Crystallographic Data Centre (CCDC) under deposition number 2278377. These data can be obtained free of charge from CCDC via www.ccdc.cam.ac.uk/data_request/cif. Primer sequences are reported in Supplementary Data\u00a01. Data used to make Supplementary Figs. and Tables are provided in the Source Data file. The raw mass spectral data from the Agilent 6530 and the Agilent 6230 are available from the MassIVE repository under accession codes MSV000094913 [https://doi.org/10.25345/C5FJ29Q6V] and MSV000094892 [https://doi.org/10.25345/C5571803M]. Protein structures used in this work include 2BUI, 2GFY, 2IX4, 6ROP, and 7UK4.\u00a0Source data are provided with this paper.",
+            "section_image": []
+        },
+        {
+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "This work was supported by the NIH (GM145992, A.T.K.), the Welch Foundation (F-1712, A.T.K.), and the NSF through the Center for Dynamics and Control of Materials (MRSEC Cooperative Agreement No. DMR-1720595, J.D.L.). The structure of 17 was solved by the UT Austin X-ray Facility. The LC/MS/MS data was collected by the UT Austin Mass Spectrometry Facility.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "These authors contributed equally: Katherine A. Ray, Joshua D. Lutgens.\n\nDepartment of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA\n\nKatherine A. Ray,\u00a0Joshua D. Lutgens,\u00a0Ramesh Bista,\u00a0Jie Zhang,\u00a0Ronak R. Desai,\u00a0Takeshi Miyazawa,\u00a0Antonio Cordova\u00a0&\u00a0Adrian T. Keatinge-Clay\n\nDepartment of Chemistry, The University of Texas at Austin, Austin, TX, USA\n\nMelissa Hirsch\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nJ.D.L., K.A.R., R.B., J.Z., R.R.D., T.M., and A.C. performed all of the cloning. M.H., R.B., K.A.R., and J.D.L characterized products. K.A.R. and J.D.L. performed the analysis of the high-resolution LC/MS and LC/MS/MS data. J.D.L. and M.H. crystallized 17. A.T.K. performed the modeling of intermediates in KS active sites. K.A.R., J.D.L.,\u00a0R.R.D., and A.T.K. wrote the manuscript.\n\nCorrespondence to\n                Adrian T. Keatinge-Clay.",
+            "section_image": []
+        },
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+            "section_text": "Ray, K.A., Lutgens, J.D., Bista, R. et al. Assessing and harnessing updated polyketide synthase modules through combinatorial engineering.\n                    Nat Commun 15, 6485 (2024). https://doi.org/10.1038/s41467-024-50844-6\n\nDownload citation\n\nReceived: 24 July 2023\n\nAccepted: 23 July 2024\n\nPublished: 01 August 2024\n\nVersion of record: 01 August 2024\n\nDOI: https://doi.org/10.1038/s41467-024-50844-6\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
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+            ]
+        },
+        {
+            "section_name": "This article is cited by",
+            "section_text": "Microbial Cell Factories (2025)\n\nNature Chemical Biology (2025)\n\nNature Chemical Biology (2025)\n\nNature Chemical Biology (2025)\n\nCommunications Chemistry (2025)",
+            "section_image": []
+        }
+    ]
+}

1c7863a4cde849a1302b792c55b8a228d8ee172bfcd134f3ebd97740a10eef8f/metadata.json

@@ -0,0 +1,135 @@
+{
+    "title": "Effect of adaptive cruise control on fuel consumption in real-world driving conditions",
+    "pre_title": "Cruise Control Chronicles: The Fuel Consumption Saga Continues",
+    "journal": "Nature Communications",
+    "published": "19 November 2024",
+    "supplementary_0": [
+        {
+            "label": "Supplementary Information",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54066-8/MediaObjects/41467_2024_54066_MOESM1_ESM.pdf"
+        },
+        {
+            "label": "Transparent Peer Review file",
+            "link": "https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-024-54066-8/MediaObjects/41467_2024_54066_MOESM2_ESM.pdf"
+        }
+    ],
+    "supplementary_1": NaN,
+    "supplementary_2": NaN,
+    "source_data": [],
+    "code": [],
+    "subject": [
+        "Energy and behaviour",
+        "Energy efficiency",
+        "Energy modelling"
+    ],
+    "license": "http://creativecommons.org/licenses/by/4.0/",
+    "preprint_pdf": "https://www.researchsquare.com/article/rs-3778527/v1.pdf?c=1732107997000",
+    "research_square_link": "https://www.researchsquare.com//article/rs-3778527/v1",
+    "nature_pdf": "https://www.nature.com/articles/s41467-024-54066-8.pdf",
+    "preprint_posted": "06 Feb, 2024",
+    "research_square_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "This paper presents a comprehensive analysis of the impact of adaptive cruise control (ACC) on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level ACC benefit estimate that compares trips with and without ACC in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers.The results of this research show that the effect of ACC on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, ACC engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of ACC on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that ACC can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of ACC engagement to properly evaluate ACC effects.Scientific community and society/Energy and society/Energy and behaviourScientific community and society/Energy and society/Energy efficiencyPhysical sciences/Energy science and technology/Energy modellingEnergy consumptionautomated drivingadvanced driver assistance systemsadaptive cruise control.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional Declarations",
+            "section_text": "There is NO Competing Interest.",
+            "section_image": []
+        }
+    ],
+    "nature_content": [
+        {
+            "section_name": "Abstract",
+            "section_text": "This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects.",
+            "section_image": []
+        },
+        {
+            "section_name": "Introduction",
+            "section_text": "Adaptive cruise control (ACC) has emerged as a promising technology in the realm of advanced driver assistance systems (ADAS) and has the potential to improve driving safety, enhance driver comfort, and reduce energy consumption. ACC automatically adjusts the speed of a vehicle to maintain a safe distance from the vehicle ahead. Nowadays, nearly all major automotive manufacturers offer ACC on their new vehicle models, however the impact of this technology on vehicle energy consumption has sparked debate.\n\nDespite the growing body of research on the topic, there is still a need for further investigation using real-world driving data to better understand the real-world energy impact of ACC. Most studies performed to date have relied on limited datasets or simulation environments, which may not capture the full range of driving conditions, vehicle types, and driver behaviors that affect ACC performance and energy consumption in real-world scenarios.\n\nFor many decades, automakers could only improve vehicle energy efficiency by attacking physical sources of energy loss in a vehicle - aerodynamic drag, tire rolling resistance, engine friction, inertial losses due to mass, etc. In the past 20\u201330 years, the proliferation of electronic control systems and electrically controlled actuators in engines, transmissions, electric drive motors, and batteries has made significant optimization and loss reduction within propulsion systems possible through better software and control strategies.\n\nHowever, energy efficiency in the real world is determined as much or more by driver behavior as by a manufacturer\u2019s engineering decisions. Anecdotally, it is possible for two drivers (e.g., a parent and teenage child) to achieve vastly different performance levels in fuel economy, despite driving the exact same vehicle on similar routes. Thus, marginal improvements in baseline vehicle efficiency could be easily outweighed by the inefficient habits of a particular driver.\n\nUntil very recently, automakers have had virtually no control over how their vehicles are driven by end users, and thus, they have had limited control of their products\u2019 use-phase energy efficiency. Automated driving technologies present the first opportunity to date for automakers to exercise a greater degree of control over the use-phase emissions of their products. In fact, this opportunity is actively growing; recent usage rates show that the share of distance driven using automated systems has increased significantly as systems have become capable of operating without interruption in more types of road and traffic environments.\n\nThis opportunity is the central motivation for examining the spectrum of human driving habits, as well as the capabilities and typical behaviors of today\u2019s automated driving systems. In understanding the relative strengths and weaknesses of the two populations (humans and automated driving systems), we can better inform the development of next-generation automated driving systems to deliver vastly improved efficiency.\n\nInitial studies investigating the impact of ACC systems on energy consumption and efficiency showed promising results.1 and2 explored early research advances in adaptive cruise control, shedding light on its potential energy-saving benefits. More recent research has primarily focused on simulation studies3 and test-track experiments4. The energy impact of ACC and other automated driving technologies has typically been analyzed in free-flow or car-following modes, often using artificially constructed scenarios and relying on the questionable capabilities of common models to produce realistic vehicle dynamics and/or driving behavior5. For instance6, conducted a microsimulation study with a scenario-based approach, offering insights into the impact of automated vehicles on highway network emissions.\n\nIn general, the results have been mixed, depending on factors such as the tools employed, the methodology, the underlying control mechanisms, and the implementation7,8. For example9, demonstrated in a meta-analysis of ACC\u2019s environmental impacts that the outcomes were highly sensitive to time gap settings, and various ACC control strategies that influence the results have also been identified10,11 emphasized the importance of critically reviewing model assumptions and their practical applicability. Efforts to compare results and draw general conclusions from existing literature are challenging due to the differences in terminology, assumptions, scenarios, and evaluation criteria across studies. Simulation-based results are heavily dependent on internal models and assumptions, often focusing on theoretical potential in ideal conditions rather than on practical impacts. Experimental studies are potentially able to produce more reliable conclusions, but require more resources and offer more limited scope for generalizations. Experiments can also be prone to behavioral bias, where the participants change their behavior because they are aware that they are participating in a study. On this point, this particular study is able to avoid this bias because the data was collected in the background as part of GM\u2019s normal course of business, and the drivers did not have awareness of any studies that would make use of the driving data.\n\nMoreover12, highlighted in a systematic review the existing knowledge gap regarding interactions between human-driven and non-connected automated vehicles. Accurately representing these interactions in traffic models is challenging and can affect the results when assessing energy impacts.\n\nThe use of real-world driving data to analyze ACC systems\u2019 effects on energy consumption has become more prevalent and sophisticated in recent years. However, the literature on this topic remains limited. Despite advances in on-board measuring and high-performance computing, acquiring comprehensive data remains challenging.13 notes the growing prevalence of ACC systems in modern commercial vehicles but highlights the lack of information on their operation and impact on traffic dynamics. They propose a unified data structure for easier comparison across various tests, vehicles, and systems. The complete dataset is published as an open-access database called OpenACC, which is planned to evolve as more tests are conducted. This project is at attempt to engage the scientific community in understanding ACC vehicles\u2019 properties and potential impacts on traffic flow and energy consumption, identifying key differences between ACC systems and human drivers, and helping design new ACC car-following models for traffic microsimulation.\n\nSpecifically, studies like14 investigate the energy impact of ACC in real-world highway scenarios by comparing ACC driving behavior to human drivers. The research discovered that ACC followers contributed to string instability and had tractive energy consumption 2.7% \u201320.5% higher than human drivers individually and 11.2% \u201317.3% higher on a platoon level.4 also examines the impact of ACC systems on traffic flow, energy consumption, and safety in real-world driving conditions through the testing of 10 ACC-equipped vehicles from different brands and powertrains at low speeds in various configurations. This study confirmed previous findings regarding the string instability of ACC systems, suggesting that their current form may lead to increased energy consumption. However, other researchers such as15 found in a field test data evaluation that the fuel consumption rate for vehicles in ACC mode was about 5%\u20137% lower than for vehicles in non-ACC mode when traveling in similar conditions.\n\nDespite the scarcity of literature and lack of consensus on the impact of ACC systems on energy consumption, particularly those utilizing real-world driving data, it is clear that the energy-saving potential of ACC systems can vary depending on factors such as traffic conditions, specific algorithms, driving conditions, vehicle type, and driver behavior. Further research is necessary to fully understand the potential energy savings and drawbacks of ACC in different driving scenarios, particularly on a larger scale and at a fleet level.\n\nIn this study, we extend the existing body of research by analyzing a large and diverse dataset of real-world driving data collected from a fleet of General Motors (GM) vehicles and drivers in the United States. Our dataset includes powertrain data, sensor and ADAS data, and GPS data at 1-Hz resolution, providing a rich and detailed account of vehicle performance, driving conditions, and ACC usage. This time-series data is augmented with (1) encrypted driver logs in order to uniquely identify drivers, (2) decoders to extract detailed vehicle information from VINs, and (3) map matching capabilities via HERE Maps to retrieve the surrounding driving environment and route-level information. This large observational study allows us to gain valuable insights into the real-world energy impact of ACC across a wide range of scenarios. Understanding the impact of ACC on energy consumption on a large scale and in a real-world setting can inform the development of future vehicle technologies that further improve fuel efficiency and reduce emissions, create better automated driving controls, and allow for the study of trade-offs between safety and efficiency.\n\nThe remainder of this paper is organized as follows: First, we present the results of our data analysis, and discuss our findings at two different levels: a macroscopic, trip-level analysis in which the results show ACC\u2019s effect on energy use over the entirety of the fleet; and a more granular, situation-based analysis that segments trips for a higher-resolution understanding of ACC impact. We then present a discussion of the results, their implications, and their limitations, and suggest directions for future research. Before concluding, we detail the methods used in the study.",
+            "section_image": []
+        },
+        {
+            "section_name": "Results",
+            "section_text": "In this observational study, we investigated the treatment effect of engaging ACC on vehicle fuel consumption while controlling for potential confounding factors. The primary objective was to determine whether the engagement of ACC resulted in a significant difference in fuel consumption.\n\nWe examined the factors influencing vehicle fuel consumption in L/100 km using a linear mixed effect model, as a statistical modeling technique that allows for the analysis of hierarchical and clustered data. Model details can be found in the Methods section. An analysis of the results in Table\u00a01 reveals several significant relationships between the fixed effects and fuel consumption.\n\nFirst, we observed a strong positive relationship between inverse average vehicle speed and fuel consumption. Here we note that inverse average vehicle speed is transformed as 60 times the inverse of speed, so that units are in min/km. Specifically, a 1-unit increase in this variable was associated with an increase of 4.069 units in FC (t = 35.619). This can be interpreted as every additional min spent on a km (decrease in trip speed) increases the FC by roughly an additional 4 L/100 km. This finding confirms previous statements that lower average trip speeds contribute to increased FC.\n\nAdditionally, we find other significant and insightful associations between other covariates and fuel consumption. Specifically, we note that elevation change exhibits a small positive association with FC. An increase in elevation increases FC by +0.6 L/100 km for every added 100 m over the trip.\n\nAlso, we found a significant negative association between engine temperature and FC. Every 10-degree increase in engine temperature is linked to a decrease of 0.4 L/100 km in FC (t = \u221250.737). This result implies that higher engine temperatures are associated with lower fuel consumption due to improved engine efficiency at optimal operating temperatures.\n\nWe noticed that ambient temperature is not a statistically significant factor (t = 0.667) for fuel consumption change. This result indicates that higher ambient temperatures could marginally contribute to increased FC. Ambient temperature has a secondary effect on fuel consumption when engine temperature is controlled, as the latter has a more direct impact on FC.\n\nThe inverse distance covered showed a substantial positive relationship to FC. For each additional unit of inverse distance covered, fuel consumption increased by 6.1544 units (t = 65.713). This finding highlights the intuitive fact that as the distance traveled increases, FC usually improves (shorter trips generally exhibit higher fuel consumption due to the impact of cold start penalty, for example).\n\nAlso, it appears that a 1-unit increase in maximum vehicle speed corresponded to a 0.0513-unit increase in FC (t = 89.367). Provided everything else remains constant, this result suggests that vehicles reaching higher maximum speeds during a trip may consume more fuel. Similarly, trip level acceleration energy was another significant predictor of fuel consumption. A 1-unit increase in vehicle acceleration energy was associated with a 4.5827-unit increase in FC (t = 102.702). This result emphasizes that vehicles with higher acceleration energy levels at trip level are likely to consume more fuel.\n\nFinally, our analysis revealed a significant (t = 3.793) treatment effect of engaging adaptive cruise control on fuel consumption. After controlling for the other variables in the model, we found that when the adaptive cruise control was engaged, fuel consumption increased by 0.26 L/100 km compared to when it was not engaged (t = 3.793). This result indicates that at the fleet level, the use of adaptive cruise control may lead to a slight increase in FC. All else being equal, ACC engagement has a negative impact on fuel consumption (on average, i.e., across all vehicles, drivers, speeds, etc.), with an FC increase of 0.26 L/100 km. We observed that the average FC across the fleet was 14.7 L/100 km; by tying back this number we can conclude from this result that ACC may present about 2% FC penalty on the fleet.\n\nIn this section we focus on including an interaction term between ACC usage and average trip speed. Although we observed a +0.26 L/100 km penalty on average across all trips, we can further investigate the ACC effect on fuel consumption as a function of trip speed to better understand the results. In that case, the ATE of ACC on FC we are trying to extract depends on and may vary with the different trip speed profiles:\n\nwhere \u03c4i represents the average treatment effect of ACC on fuel consumption for the ith trip, Yi(1) represents the fuel consumption with ACC engaged, and Yi(0) represents the fuel consumption without ACC engaged. \u03b22 captures the treatment effect of engaging adaptive cruise control, and \u03b29 is the introduction of an additional coefficient for the cross interaction term. The \u03b2s are new estimates from the results of a fitted model that includes interactions, with \u03b22\u00a0=\u00a04.96 (t = 14.96) and \u03b29\u00a0=\u00a0\u2212\u00a03.91 (t = \u221210.71). Solving for a negative treatment effect on trip mean vehicle speed \\(\\bar{v}\\) when ACC is engaged leads to the following:\n\nIn this case, we find that the ATE of ACC on FC depends on a trip speed threshold of 0.79 km/min \u00a0\u2192\u00a0\u2009\u00a0\u2248 50 km/h; trips with lower average speeds see fuel consumption benefits from engaging ACC.\n\nFurther analysis (see Fig.\u00a01) revealed that trips that average less than 50 km/h represent trips with the following characteristics:\n\n\u2013 Higher functional class trips (\u00a0> 2.5 means trips that are mainly local/non-highway). Functional class is a road type indicator, reflecting traffic speed and volume, as well as the importance and connectivity of the road.\n\n\u2013 Maximum speed \u00a0< 90\u2013100 km/h.\n\nRelationship between trip speed and trip topology by looking at average trip functional class structure.\n\nOur analysis here reveals that the effect of ACC on fuel consumption varies with average trip speed. While ACC engagement generally results in a slight increase in fuel consumption (+0.26 L/100 km), it tends to be more fuel-efficient at lower speeds, particularly below 50 km/h. This indicates that ACC systems can provide fuel consumption benefits in urban and suburban driving conditions. However, at higher speeds, the rigid speed maintenance of ACC leads to increased fuel consumption compared to human drivers. This interaction between ACC engagement and trip speed underscores the importance of considering different driving conditions when evaluating the energy impact of ACC systems. Additionally, these benefits are limited to a smaller number of trip profiles, which connects with the overall negative impact that was noted previously.\n\nThe macro-level evidence presented above demonstrates that real-world use of ACC is overall not beneficial for trip-level fuel consumption. Furthermore, when benefits are present, they appear to be limited to certain trip conditions. A lower level analysis is needed to reinforce these findings and provide more granular explanations for ACC\u2019s impact on FC in distinct road and traffic conditions.\n\nOn a given trip, vehicle speed changes tend to be caused by certain external factors, under specific situations due to road events. Examples of these situations include braking, stopping, and accelerating due to red lights or stop signs, cruising for a while, braking and accelerating due to red lights or sudden lane changes by a preceding vehicle, and so on. Isolating those events by segmenting a given trip into specific situations allows us to obtain more targeted ACC benefit estimates for specific maneuvers, improving our overall understanding of the system and providing a more nuanced analysis.\n\nVarious situations occur in the driving of each trip. Figure\u00a02 shows an example of a composite drive cycle (in this case, the EPA HWFE cycle), after it has been run through our situation segmentation algorithm and broken up into distinct maneuvers. More details on the algorithm can be found in16.\n\n(Crs = Cruise, BSnA = Brake, Stop & Accelerate, BnA = Brake & Accelerate, A = Accelerate, B = Brake, Crp = Creep).\n\nThe algorithm enables situation-level and driving-level processing of the trips for the purpose of trip segmentation. By leveraging signals such as time, speed, yaw rate, position, acceleration, brake pedal position and accelerator pedal position, relative distance and speed with respect to the preceding vehicle, as well as ACC status, modes and settings, we can detect distinct situations within trips. We identified six different important situations, which we define as follows:\n\nCruise (Crs): Maintain speed with little variation.\n\nBrake and Accelerate (BnA): \n\n\u2013 Brake and accelerate again without stopping.\n\n\u2013 Due to traffic lights, turns, roundabouts, etc.\n\nBrake, Stop, and Accelerate (BSnA): \n\n\u2013 Brake, stop completely, and accelerate again.\n\n\u2013 Due to traffic lights, stop signs, etc.\n\nAcceleration (A): Accelerate due to speed limit increase.\n\nBraking (B): Brake due to speed limit decrease.\n\nCreeping (Crp): Move forward at very low speed with some stops.\n\nFigure\u00a03 provides an illustration of the situations detected over a given trip. For added nuance, situations involving braking can be further split into brake events with and without turning, and, more importantly, situations in which the driver is aware of the preceding vehicle\u2019s status. In fact, it is also relevant to separate situations with and without the presence of a preceding vehicle.\n\n(Crs = Cruise, BSnA = Brake, Stop & Accelerate, BnA = Brake & Accelerate, A = Accelerate, B = Brake, Crp = Creep).\n\nIn Fig.\u00a04 we show the distribution of detected driving situations over trips, as well as the distribution of ACC usage over these maneuvers. The figure shows that the most common driving situation is cruising mode, accounting for 50% of driving time. BnA and BSnA are observed with almost equal frequency in the dataset. The creeping situation is seldom detected by the algorithm. We also note that, as expected, ACC is predominantly used in cruising mode.\n\nPercentage of driving over each situation, i.e., share of each situation over entire dataset (left), distribution of adaptive cruise control usage over situations (right).\n\nIt is important to note that situation segmentation enhances the resolution of our analysis and yields a larger number of data points. Consequently, we observe a significant increase in the number of situations generated during a trip. This increased sample size ultimately leads to better model coefficient estimates, asymptotic statistical efficiency, and consistency.\n\nFor our analysis, we employ a linear mixed-effect model similar in structure to the one used in the macroscopic study, with some modifications to the variable selection design. In the earlier trip-level analysis, we controlled for acceleration energy to normalize the trip. This was acceptable at a macroscopic level, but with shorter and more stable segments, we need to ensure that we do not double count the effect of aggressiveness on FC in relation to ACC. To do this, we introduce four new variables to make situation segments more directly comparable: average speed, entry and exit speeds over the segment, and minimum and maximum speeds during the segment. We also account for variability in thermal conditions, such as engine and ambient temperature, as well as changes in elevation and segment distance.\n\nTable\u00a02 reveals several interesting findings (due to the small sample size, the Crp situation data were deemed unreliable and have been excluded from the analysis):\n\n\u2013 As elevation increases over a segment, FC also increases. This effect is most pronounced in acceleration situations and less so in BSnA and BnA situations. In cruising mode, a change in elevation has one-tenth the effect. Specifically, for every meter of change in elevation, FC is penalized by +0.37 L/100 km if the vehicle is in strong acceleration mode but only +0.037 L/100 km when cruising.\n\n\u2013 Higher engine temperatures primarily benefit BSnA modes, with a decrease in FC of \u22120.12 L/100 km. In BnA situations, the benefit is slightly lower at \u22120.084 L/100 km. This is likely due to the absence of idling events (no stop), since engine temperature is a dominant factor in idle fuel rates. In cruising mode, the impact is smaller, with a decrease of only \u22120.05 L/100 km.\n\n\u2013 The effect of ACC on fuel consumption varies depending on the situation. In cruising segments, the engagement of ACC results in a slight increase in FC (+0.14 L/100 km). In braking situations, the penalty that ACC offers is less clear (+0.334 L/100 km); however, we hypothesize that human drivers are better able to leverage coasting before an actual brake event, which may lead to efficiency benefits as the nominal fuel consumption of a deceleration event is spread over a greater distance traveled. Furthermore, some human drivers might utilize multi-anticipation, reacting to more than one vehicle ahead17.\n\n\u2013 Engaging ACC for acceleration-involved situations (such as BSnA, BnA, and A) seems to provide advantageous FC benefits. This is because the positive impact of ACC on FC during pure acceleration outweighs the negative impact observed during braking.\n\nSupplementary Table\u00a01 presents results with an additional layer of detail that differentiates maneuvers based on the presence or absence of a preceding vehicle. In all situations where no preceding vehicle is present during the segment, engaging ACC appears to increase FC. Conversely, when a vehicle is present, the engagement of ACC can provide some benefits, with the exception of braking situations. This can be calculated by combining ACC_engaged_cat and veh_ahead_cat along with their interaction term coefficient. The negative interaction term in all situations (except braking) suggests that ACC is advantageous when engaged in the presence of a preceding vehicle. It is important to note that the number of data points is significantly reduced in this design, leading to marginally statistically significant estimates in some cases.",
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+        {
+            "section_name": "Discussion",
+            "section_text": "This research has investigated the impact of adaptive cruise control on fuel consumption, shedding light on how this technology can affect driving efficiency. Our findings contribute to the growing body of literature on the subject, which includes several studies that have analyzed the impacts of ACC and other advanced driver assistance systems on fuel efficiency and emissions.\n\nIn examining the segmented results in Supplementary Table\u00a01, we find that certain maneuvers (BnA, BSnA, A) are better executed by automated driving systems, while others (Crs, B) are better executed by humans. The automated driving system\u2019s efficiency is also dramatically affected by the presence or absence of another vehicle ahead. In focusing on the dynamics of these individual maneuvers, we can assess the underlying causes of the efficiency discrepancies and theoretically design an automated system that can outperform humans in all types of driving maneuvers and road conditions.\n\nAccording to Table\u00a02, we observe a 0.14 L/100 km fuel consumption penalty for engaging adaptive cruise control in cruising situations. At first, the fact that current cruise control systems are less efficient on average than human drivers in \u201ccruising\u201d maneuvers may seem counter-intuitive. However, closer inspection of a typical cruising scenario illuminates the reasons for the relative shortfall of automated driving systems and presents opportunities for future improvement.\n\nFirst18, asserts that \u201cin today\u2019s cruise control systems, substantial energy is wasted by rigidly controlling to a single set speed regardless of the terrain or road conditions, [and] significant improvements in fuel economy and EV range can be achieved by relaxing the requirement that cruise control maintain a single constant speed at all times.\u201d We hypothesize that human drivers benefit from more flexibility compared to automated systems while in a cruising mode\u2014that is, they tend to hold relatively constant pedal position and allow vehicle speed to vary slightly (often without noticing), particularly over changing terrain and in open-road conditions where traffic is not a significant concern. This allows for more steady-state operation, which is particularly advantageous in ICE applications that can experience step changes in efficiency (e.g., powertrain downshifts, engine operating mode changes) as a result of small changes in vehicle load.\n\nThe mechanisms for improving efficiency through flexibility in cruise control were explored in detail in ref. 18. In this experimental study, a modified cruise control system was designed to let its speed vary within defined limits (\u00a0\u00b1 8 km/h) in response to changing road grades. This modified cruise control was tested back-to-back against standard cruise control on a grade schedule (taken from US-23 in Michigan) programmed into a dynamometer at the GM Proving Grounds. The study found that the modified cruise control uniformly achieved higher fuel economy than standard cruise control on all tested vehicles, by an average of 3.5% for the gasoline vehicle, 3.9% for the diesel vehicle, and 3.8% for the electric vehicle. It achieved these gains primarily by limiting engine braking on declines, limiting powertrain downshifts on inclines, and reducing overall tractive power requirements on inclines by around 15% by capping engine torque increases and allowing vehicle speed to drop temporarily. As automated driving systems evolve from simplistic cruise control to Level 2/3+ autonomy, there is evidence that human occupants are more tolerant of the system changing the cruising speed without human input. Therefore, future automated driving systems should fully capitalize on this flexibility that is deemed acceptable by passengers to achieve gains in energy efficiency.\n\nWe plan to conduct further investigations to support and explain the underlying mechanisms. Specifically, we are undertaking two studies: one analyzing the energy-saving benefits of ACC against different driver profiles, and another leveraging machine learning methods to model the relationship of vehicle dynamics to energy consumption with and without ACC at a microscopic level (second-by-second analysis). These studies will enhance our understanding of the efficiency improvements and provide more evidence for the hypotheses discussed in this paper.\n\nSupplementary Table\u00a01 expands on the results of Table\u00a02 by including the presence of a vehicle ahead as an interaction term. Introducing this new factor results in some notable changes in the effects of ACC engagement on fuel consumption. We now see fuel consumption penalties for engaging adaptive cruise in all studied maneuvers (no vehicle ahead), whereas Table\u00a02 showed some benefits for more dynamic maneuvers such as BnA and BSnA. However, in examining the ACC engaged effect only in cases where there is a vehicle ahead (by accounting for the interaction term), we observe fuel consumption benefits, though some maneuvers do not have statistically significant main effects (e.g., ACC system braking event with no vehicle ahead is not part of the technology). In other words, while ACC is less efficient than humans on average in the examined dataset, it is more efficient than humans on average when it is following another vehicle. This is a significant finding, and one that refines our understanding of the mechanisms of energy savings in automated driving. Our hypotheses regarding the differences in ACC impact on FC in open-road vs. following conditions are as follows.\n\nIn cruising maneuvers, open-road ACC suffers efficiency penalties (+0.14 L/100 km) as a result of its rigid control to a single set speed at all times. However, in the presence of a vehicle ahead, ACC allows vehicle speed to drop below the driver\u2019s set speed in order to maintain a comfortable following distance to the vehicle ahead. Therefore, in cases where human-driven vehicles ahead may naturally slow down due to inclines or other external factors, a vehicle with ACC will correspondingly slow down to maintain an appropriate following distance. In effect, the system temporarily mimics the more efficient behavior of the human driver ahead, and claims the associated efficiency benefits. On the other hand, a vehicle with ACC does not experience a symmetric FC penalty in cases where a human driver ahead is less efficient than ACC (e.g., surpassing the driver\u2019s set speed) since it is not permitted to exceed its set speed.\n\nWe assert that this asymmetric opportunity for efficiency improvement is the core driver of ACC\u2019s energy savings while cruising in the presence of a vehicle ahead\u2014the system matches the behavior of efficient human drivers ahead and collects the associated savings, but does not match the behavior of less efficient drivers ahead and thus avoids the associated penalties.\n\nIn BnA/BSnA maneuvers, which are common in dense traffic (including stop-and-go scenarios), we assert that there is a similarly asymmetric opportunity to improve efficiency through both flexible speeds and flexible acceleration rates. Open-road ACC has predefined calibrations that determine how the vehicle accelerates/decelerates in response to changes in set speed or initial engagement of ACC. Typically, these calibrations are set fairly aggressively, so that the vehicle achieves the driver\u2019s requested set speed as quickly as possible. Even with these fairly aggressive calibrations, ACC engagement results in an average \u22120.3 L/100 km impact to fuel consumption across the entire dataset. When there is a vehicle ahead, ACC is able to reduce fuel consumption even further in BnA and BSnA maneuvers. This is because ACC cannot ever accelerate any more aggressively than the open-road calibration limit even when a human driver ahead is particularly aggressive, but it has the opportunity to accelerate much more efficiently when following an efficient driver. This asymmetry results in a net savings in these maneuvers, when automated driving systems follow a sufficiently large number of distinct drivers with different behaviors.\n\nTable\u00a02 and Supplementary Table\u00a01 show that ACC engagement during strict acceleration maneuvers leads to a reduction in fuel consumption of \u22120.71 L/100 km. We contend that these savings arise from a calibrated limit of allowed acceleration while ACC is engaged. While human drivers are able to command up to the full capability of the engine during an acceleration maneuver (and incur massive fuel consumption penalties for doing so), ACC is limited to a maximum acceleration value that is much lower than the vehicle\u2019s full capability, even in open-road conditions. The data supports this point \u2014 the maximum acceleration observed in the dataset is 7.5 m/s2 for ACC, just about half of the 14.7 m/s2 maximum for human drivers. Likewise, the median positive commanded acceleration is 0.187 m/s2 for ACC compared to 0.316 m/s2 for human drivers (41% lower while in ACC). If we focus only on events where ACC is following a vehicle ahead, FC savings in acceleration maneuvers are significantly greater. This is another result of the asymmetric upside potential discussed in the previous section; ACC benefits from following efficient drivers, but incurs no penalty relative to open-road ACC for following inefficient drivers.\n\nTable\u00a02 and Supplementary Table\u00a01 show that ACC engagement during strict braking maneuvers leads to an increase in fuel consumption, +0.33 L/100 km. In these maneuvers, we hypothesize that this FC penalty is largely a result of ACC\u2019s high deceleration rates. These rates are intentionally set high by manufacturers because the downside risks of insufficient deceleration in cruise are severe. However, there is some opportunity to make these default deceleration rates less conservative (and thereby, more efficient) in future systems as sensing capabilities and control systems improve.\n\nIn open-road conditions, deceleration events can only be triggered by a decrease in the driver\u2019s requested set speed. As mentioned in an earlier section, the deceleration rates commanded in these maneuvers are predefined in calibration tables, and are generally set to be aggressive so the vehicle quickly responds to the driver\u2019s command. This means that braking maneuvers in ACC are generally shorter in both time and distance compared to equivalent maneuvers executed by human drivers. We see this reflected in the median deceleration commanded during braking events, which is \u22120.2 m/s2 for ACC and \u22120.18 m/s2 for human drivers.\n\nIn cases where there is a vehicle ahead, deceleration events are mostly triggered by the vehicle ahead slowing down. We observe from Table\u00a02 and Supplementary Table\u00a01 that the penalty for braking events when there is a vehicle ahead is about 30% less than the dataset average. We hypothesize that this is a result of the reduced capacity for a driver to coast when there is a slowing vehicle ahead. In other words, human drivers tend to slow down more rapidly when there is slowing traffic than in open-road conditions, so the capacity for human drivers to coast and outperform ACC shrinks in these particular situations but, notably, is not eliminated entirely.\n\nOur study in its current form has some limitations.\n\nAs an observational study, it cannot establish causality. While we have attempted to control for various factors, it remains possible that unobserved variables may have influenced the results. We feel that the hypotheses presented in the subsections above are the most plausible explanations for the observed impacts of ACC on fuel consumption in certain maneuvers, but further, more direct A-B comparisons of humans and automated driving systems in these specific maneuvers would be required to definitively establish the root causes for the observed phenomena.\n\nThere is a need for high-resolution, microscopic-level analysis (i.e., second-by-second) to better understand the nuances of ACC\u2019s impact on fuel consumption. Future research should explore these aspects in greater detail to validate and extend our findings. Specifically, more granular analyses of traffic conditions, powertrain types, differences in ACC settings or potentially control types, and regional regulatory differences in ACC performance are needed to build a more comprehensive understanding of the factors influencing fuel consumption in ACC.\n\nThe findings from the macroscopic trip level and the situation-based findings are interconnected and reinforce each other. While the macroscopic analysis indicates a slight increase in fuel consumption across the fleet when ACC is engaged, the situation-based results reveal that ACC has a negative impact on energy consumption specifically in cruising. Given that cruising is the most prevalent driving situation, the fuel penalty observed in the macroscopic analysis can be attributed primarily to the increased fuel consumption during cruising with ACC. This connection between the two levels of analysis highlights the importance of examining the effects of ACC on energy consumption in different driving situations to gain a comprehensive understanding of its overall impact.\n\nThe representativeness of our sample is also a potential limitation, as it consists of a single fleet of vehicles primarily used by GM employees and engineers. Although our data covers a large area of the U.S., it is not guaranteed that the findings can be generalized to the broader population. We believe that we have presented, in the data section, the details, key statistics and distributions pertaining to the population of vehicles in this study for full transparency.\n\nThe accuracy of our results is contingent upon the quality of the sensors, data collection processes, and the algorithms involved in the analysis. Despite our diligent data processing and cleaning, these factors may have introduced some degree of undetected error or bias.\n\nThe sufficiency of data for the ATE analysis is an important aspect, especially for researchers that would be interested in conducting such a study in the future. We did not conduct a specific data sufficiency study to determine the minimum amount of data needed for consistent ATE results. However, we can provide some general insights based on statistical principles.\n\nMore data generally leads to better results in statistical analysis. As the sample size (N) increases, the standard error decreases, leading to more precise estimates and reduced uncertainty. This principle, known as statistical power, is crucial for detecting small effects, such as the impact of ACC on fuel consumption. When the effect signal in the data is small, a significant amount of data is needed to overcome the noise. Additionally, statistical consistency implies that as the sample size increases, the bias in the estimations is reduced, providing more reliable results.\n\nThe effect of ACC on fuel consumption is relatively small, necessitating a significant amount of data to detect the signal amid the noise. But also, we control for many variables in our analysis, effectively slicing the data space across multiple dimensions. To ensure statistical significance and meaningful results in this high-dimensional space, a large number of data points are needed. Without sufficient data, the hypercubes within this multidimensional space would lack enough points to draw reliable conclusions.\n\nIt is worth noting that conducting a data sufficiency study is not as simple as it may seem, as there are various ways the data could be sampled, including random sampling, cluster sampling, or stratified sampling. Each of these methods can introduce different biases and complexities, requiring a sophisticated study design to offer relevant recommendations. In our case, we are dealing with a natural experiment from a purely observational study, making it challenging to predict how the results might differ under alternative sampling methods. Future research could include a well-crafted data sufficiency study to determine the minimum data requirements for robust ATE analysis. This study could include randomly reducing the existing dataset (e.g., by 20 percent) to test if the main findings are still uniformly observed in all reduced datasets.\n\nOur research offers valuable insights that can inform vehicle manufacturers, policymakers, and drivers about the potential effects of ACC on fuel consumption. Our findings indicate that ACC has the capacity to dramatically impact vehicle energy efficiency in both positive and negative directions, depending on driving situations, system behavior, and the presence of a vehicle ahead.\n\nMuch as flexibility in cruising speed can allow for more steady-state operation over changing grades, greater flexibility in following distance (instead of a single, driver-selectable \u201cgap setting\u201d as is common in today\u2019s vehicles) can allow for steadier, safer, more efficient operation in highly dynamic traffic conditions. For example, an automated driving system that detects a severe slowdown far ahead in its lane of travel could choose to coast and slow the vehicle preemptively, rather than travel at the set speed and command a severe braking event only when the vehicle is at imminent risk of not maintaining its minimum following distance.\n\nBoth human and automated drivers can theoretically pay attention to the trajectories of several vehicles ahead, scan multiple lanes of traffic, and modulate their speed proactively to prevent severe and costly braking and acceleration events. In practice, we find that few human drivers put in this level of thought and effort to achieve an efficient ride, but automated systems have the potential to consistently operate with high efficiency in traffic, if their sensing and planning capabilities are fully leveraged to achieve this objective. The tested ACC systems track both the first and second vehicle ahead as separate objects, and the position, velocity, and acceleration of these vehicles can be used as separate, distinct inputs in the ACC system\u2019s decision-making process. Particularly when traffic conditions are at their most unpredictable, automated systems can benefit substantially from their multi-modal sensing capabilities, which are always active and free of the distractions that can affect human drivers. When powerful data about surrounding traffic conditions is combined with a proactive control system designed to limit unnecessary speeding and acceleration in traffic, automated driving systems have the potential to vastly outperform human drivers in terms of safety, comfort, and energy efficiency.\n\nOverall, this study provides a deeper understanding of the interplay between ACC and fuel consumption in various driving situations. Our findings underscore the importance of considering the broader context when assessing the impact of advanced driver assistance systems. Future research should focus on overcoming the limitations of this study by conducting more controlled experiments, investigating a wider variety of vehicles and driving conditions, and refining data collection and analysis methods. Such research will contribute to the ongoing efforts to optimize ACC systems and other advanced driver assistance technologies for improved fuel efficiency and reduced greenhouse gas emissions.",
+            "section_image": []
+        },
+        {
+            "section_name": "Methods",
+            "section_text": "The work presented in this paper uses a significant amount of real-world data in various environments rather than simulations or small-scale, real-world experiments described earlier. First, we will take a close look at the previous related work conducted by GM in 201919. The approach, the data, and the methodology will be detailed to underscore how this current study extends the existing research.\n\nIn previous research conducted by GM engineers19, data were collected from the 2018 Cadillac CT6, the first vehicle with Super Cruise technology, which combined ACC and advanced lane-keeping functionality using cameras, sensors, and GPS locators. The study involved 51 vehicles driven by employees on their daily commutes for 62 days between November 16, 2017, and January 16, 2018, covering 320,742 km in 13,416 trips. The data contained information on fuel consumption, vehicle speed, and ACC state collected at a 1-Hz rate.\n\nIn this previous study, the researchers analyzed the impact of ACC on energy consumption by comparing fuel values when ACC was ON versus OFF at various speed intervals across the entire fleet. The method involved aggregating fuel consumed per mile at each speed interval for vehicles with ACC engaged and those without ACC engaged, regardless of differences in vehicle models, drivers, or trip/driving conditions. To account for differences in ACC usage and distance covered at various speeds, the researchers adjusted the fuel consumption benefits based on utilization rate and local distance traveled at each speed. The raw delta fuel consumption benefit was then weighted by the proportion of driving done at that speed interval relative to the total driving distance, resulting in a weighted adjusted average.\n\nThe method was deemed effective and the approach validated given the limited potential biases, the limited number of vehicle models and drivers involved in the study, and the extended period of data collection.\n\nIn our current study, we use a larger dataset collected from a fleet of 157 vehicles equipped with either traditional ACC or Super Cruise technology, noting that the longitudinal control system is identical between the two. Our extensive dataset includes 40,356 trips, covering 1,094,215 kilometers and 16,389 hours of driving by 95 different drivers. The data collection efforts are ongoing, but the results in this analysis cover the period of July 1, 2021, to September 1, 2022. With this richer dataset and larger fleet, we obtained more accurate \u201creal-world\u201d estimates of ACC benefits.\n\nHowever, to accurately isolate the true effect of ACC on energy consumption and mitigate potential biases in our findings, we have relied on a statistical approach with carefully controlled variables. This refined method enhances our ability to discern and quantify the energy-saving potential of ACC technology across a variety of real-world driving conditions.\n\nIn collaboration with GM through a Cooperative Research and Development Agreement (CRADA), we collected a large-scale dataset of real-world driving data, as described above. The dataset includes over 60 different signals at 1-Hz resolution, such as powertrain data (e.g., engine, fuel, transmission, thermal, etc.), automated driving assistance data (e.g., ACC, lane-keeping, gap settings, etc.), sensor data (e.g., relative lon/lat distance/speed with the vehicle ahead, time to collision, lane occupations, etc.), and GPS data. To efficiently handle and process this massive dataset, we developed a data management framework for ingesting, processing, and managing the data. We received weekly data streams from GM, processed the trips, generated summary-level quality assurance/quality control (QA/QC) reports, identified outliers, and cleaned the data.\n\nWe augmented the data by performing map matching using HERE Maps API to extract road information (e.g., speed limits, traffic patterns, traffic signs, grade, etc.). We leveraged a VIN decoder to obtain vehicle model and trim-level information, and used an internal vehicle information database20 to extract detailed vehicle specifications (e.g., vehicle mass, maximum engine power, frontal area, wheel details, etc.), and we integrated driver logs to identify the driver during each trip and the times at which drivers switched vehicles.\n\nAfter ingestion, the processed and cleaned data allowed us to perform thorough analysis at different granularity levels.\n\nFigure\u00a05 shows the distribution of some selected variables that provide high-level information about the data. The distribution of trip distances is highly skewed\u2014most trips are short (\u00a0< 200 km), with an average distance of around 25 km, and few trips are long-range. The mean trip speed is approximately 50 km/h, with an average travel time of 22 minutes. The overall fleet-level fuel consumption is around 15 L/100 km (15.7 mpg). Fuel consumption values can vary depending on factors such as trip distance, time of year (e.g., short trips during cold seasons can result in extremely high fuel consumption), and vehicle type and model. The fuel consumption is determined using the \u201cfuel injected rolling count\u201d signal which is calculated in the vehicle\u2019s Engine Control Module (ECM) and broadcast over the vehicle\u2019s internal CAN network. The CAN network is monitored by an on-board data recorder that logs all signals continuously while driving.\n\nA Trip Distances, (B) Average Trip Speeds, (C) Travel Times, and (D) Fuel Consumption. These histograms provide a comprehensive overview of the dataset, illustrating the range and distribution of the variables analyzed in this study. Trip Distances show most trips under 50 km with an average of 24.8 km, Average Trip Speeds peak around 125 km/h with an average of 49.4 km/h, Travel Times are mostly under 50 minutes with an average of 22.5 min, Fuel Consumption is mostly below 30 L/100 km with an average of 14.7 L/100 km but some extreme outlier trips.\n\nSupplementary Table\u00a02 provides a summary of the various vehicles included in the data. Each unique make/model/series may have multiple trim variants with different engine technologies and sometimes different fuel types (primarily gasoline and diesel). We used EPA Tier 3 87AKI certification fuel values for any fuel lower heating value and fuel density conversions to ensure consistent fuel comparisons. As the table shows, the fleet contains a few electric vehicles. The dataset also shows a relatively high use of automated driving technologies, with more than 35% of trips involving ACC usage. We do not believe that drivers were specifically instructed to use the automated features in their vehicles; instead, it is likely a result of their natural inclination to explore and try new technologies. Drivers typically used one vehicle type/model for extended periods, although multiple drivers may have used similar vehicles. A driver-vehicle matrix shows that drivers occasionally switch to different vehicle models throughout the year. The driver log enables us to track such events.\n\nMap-matching GPS coordinates to road attribute data on HERE maps revealed that most trips are high functional class driving, typically consisting of local, short journeys with occasional highway usage. Trips generally include fewer than 10 traffic lights and a few stop signs. Elevation changes during these trips are minimal, with delta elevation ranging between \u2212200 m and 200 m. Vehicles used in this study have been equipped with GPS antennas that report elevation as well as lat/long coordinates. Given the potential for noisy GPS signals, these elevation data were cross-referenced with our map-matched elevation data from HERE maps to identify and eliminate potential anomalies.These are used to calculate elevation delta between different points in a route. That is, in a given trip or segment, the elevation delta is simply the difference in elevation between the two end points. Figure\u00a06 provides a map view of the areas in the United States where the majority of trips occurred. As shown, most trips are concentrated in the southeast Michigan region, with some cross-country trips also taking place. As noted before, trips span a period of over a year of data collection; as a result, weather and ambient temperature levels span the range of conditions experienced in the lower 48 states in a typical calendar year. It was a priority to capture a wide range of temperatures and weather conditions in the data set, as weather can affect ACC engagement probability in two main ways. First, drivers may engage ACC at different rates depending on weather conditions - some may be more comfortable controlling the vehicle themselves in rainy/snowy conditions, while others with lower confidence or less driving experience may find the assistance of ACC to be helpful in inclement weather. Secondly, GM\u2019s ACC system cannot be engaged when the forward camera is obstructed, so system operation may be inhibited during severe precipitation events.\n\nMap data is available under the Open Database License(\u00a9 OpenStreetMap).\n\nFinally, an in-depth analysis of driver and trip aggressiveness (which resulted in its own manuscript21) considered factors such as acceleration energy, jerk energy, and trip-level standard deviation metrics to gain a better understanding of trip and driver profiles in relation to the percentage of ACC utilization during trips. These exploratory and descriptive analyses provided valuable insights and informed the subsequent statistical design to accurately model the fleet.\n\nIt is important to acknowledge that all vehicles in the study were produced by General Motors. While this is a limitation of the study, we do not expect that the inclusion of vehicles from other manufacturers would substantively change the results of the study, for a few reasons. First, although each automotive manufacturer has its own control algorithms for longitudinal and lateral control in cruise, we do not expect major differences in behavior because the high-level goals of these cruise systems are identical. The vehicles are programmed to maintain a single set speed unless traffic ahead requires them to slow down. While a vehicle is present ahead, driven vehicles are programmed to maintain a specified gap distance. Classical controls techniques (e.g., PI control) are employed nearly universally for maintaining open-road cruise speed and gap distance to the vehicle ahead. With regards to lateral control, some regions have unique regulations related to automatic lateral control that may on the surface imply differentiated cruise control performance between regions. However, in practice, the strategy for complying with UN Regulation 79 is likely identical across major automakers - vehicles remain under a prescribed lateral acceleration threshold by detecting upcoming curves in the roadway and slowing down in advance where necessary. Lastly, General Motors produces a wide variety of vehicles with different engine sizes, transmission types, masses and body styles. The full breadth of the GM portfolio (including performance cars, sedans, crossovers, pickup trucks and full-size SUVs) was leveraged in this study, which approximates the composition of vehicles on North American roadways very well.\n\nThe linear mixed-effect model was selected as the preferred technique for our analysis, as it is particularly suited to situations where the data have a nested structure, with observations grouped by certain factors. We accounted for the hierarchical structure of the data by considering random effects for vehicle type (VehicleType) and driver identification (DriverID), which are modeled as a representation of the variability associated with the grouping factors. Our model included eight fixed effects to be the variables of interest: inverse trip average vehicle speed in min/km (veh_spd_meanI), adaptive cruise control engagement (ACC_engaged_cat) as a binary variable, trip maximum vehicle speed in km/h (veh_spd_max), inverse trip distance covered in 1/km (dist_covered_kmI), trip-level vehicle acceleration energy in m2/s3 (veh_accel_nrg), trip elevation change in meters (elev_delta), trip average ambient temperature in degrees Celsius (amb_temp), and trip average engine temperature in degrees Celsius (eng_temp). These variables were included as covariates to control for their potential influence on fuel consumption while covariate transformations are meant to preserve a linear structure and ensure normality of model residuals.\n\nThe choice of a linear mixed effect model for this study allowed us to isolate the treatment effect of ACC engagement on fuel consumption as well as other covariates while controlling for the potential influence of VehicleType and DriverID in a cross-factored way. Note that, per our exploratory analysis, all variables included in this model exhibit fairly linear relationships with fuel \u2013 provided certain covariate transformation and under multiple controls. Normality of residuals and other model diagnostics revealed good model fit and no model assumption violation. Equation (3) represents the linear mixed effect model for the study:\n\nThe first line, \\({{\\mbox{FuelCons}}}_{i} \\sim N\\left(\\mu,{\\sigma }^{2}\\right)\\), indicates that the fuel consumption (FuelConsi) for each observation i is modeled as a normally distributed random variable with mean \u03bc and variance \u03c32.\n\nThe next few lines describe the fixed-effects part of the model:\n\na. \u03b1j[i],k[i] represents the intercept term for each observation i, accounting for both the random effects of DriverID j and VehicleType k.\n\nb. \u03b21k[i](veh_spd_meanI) captures the effect of the inverse average vehicle speed with the coefficient \u03b21k[i] specific to the VehicleType k.\n\nc. \u03b22k[i](ACC_engaged_catTRUE) represents the treatment effect of engaging ACC, with the coefficient \u03b22k[i] specific to the VehicleType k.\n\nd. \\({\\beta }_{3}({{\\rm{veh}}}\\_{{\\rm{spd}}}\\_\\max )\\), \u03b24(dist_covered_kmI), \u03b25(veh_accel_nrg), \u03b26(elev_delta), \u03b27(amb_temp), and \u03b28(eng_temp) represent the effects of maximum vehicle speed, distance covered, vehicle acceleration energy, elevation change, ambient temperature, and engine temperature, respectively, with their corresponding fixed coefficients.\n\nThe line \\({\\alpha }_{j} \\sim N({\\mu }_{{\\alpha }_{j}},{\\sigma }_{{\\alpha }_{j}}^{2})\\) describes the random effects for DriverID j. The intercept term \u03b1j follows a normal distribution with mean \\({\\mu }_{{\\alpha }_{j}}\\) and variance \\({\\sigma }_{{\\alpha }_{j}}^{2}\\).\n\nThe remaining lines describe the random effects for VehicleType k. The model includes random effects for the intercept term \u03b1k and the two fixed-effect coefficients \u03b21k and \u03b22k. These random effects follow a multivariate normal distribution with means \\({\\mu }_{{\\alpha }_{k}}\\), \\({\\mu }_{{\\beta }_{1k}}\\), and \\({\\mu }_{{\\beta }_{2k}}\\), and a covariance matrix that captures the variances (\\({\\sigma }_{{\\alpha }_{k}}^{2}\\), \\({\\sigma }_{{\\beta }_{1k}}^{2}\\), and \\({\\sigma }_{{\\beta }_{2k}}^{2}\\)) and correlations (\\({\\rho }_{{\\alpha }_{k}{\\beta }_{1k}}\\), \\({\\rho }_{{\\alpha }_{k}{\\beta }_{2k}}\\), and \\({\\rho }_{{\\beta }_{1k}{\\beta }_{2k}}\\)) among these random effects.\n\nThis section presents a macroscopic-level analysis that investigates the fuel consumption (FC) outcomes at the trip level while focusing on the impact of ACC engagement. We approached this as a study of counterfactuals, considering the potential outcomes of FC with ACC engaged and with ACC disengaged. To estimate the causal effect of ACC engagement on FC, we use the concept of average treatment effect (ATE).\n\nThe ATE is the average difference in outcome between the treated group (ACC engaged) and the control group (ACC disengaged) in a hypothetical situation in which we can control for all potential confounding factors. In our study, the treatment is the ACC engagement, and the ATE represents the average effect of ACC engagement on fuel consumption.\n\nThe fundamental challenge in estimating the ATE is that we observe only one state of ACC engagement at a given time in each trip, i.e., either ACC is engaged or it is disengaged, not both at once. In a hypothetical scenario, where each trip could be observed under identical conditions with both ACC states, we could directly compute the ATE. However, since this is not the case, we must rely on large samples to compute the ATE by comparing sample means, assuming that the sample mean differences generate an unbiased estimate of the ATE:\n\nThis is equivalent in Expectation to the following:\n\nWhere \u03c4i is an individual trip i treatment effect, and Yi(1),\u00a0Yi(0) are respectively the potential outcomes of trip i when ACC is engaged and counterfactually not engaged and where ACC engagement is defined as ACC being turned on at least once, regardless of the duration or frequency of its use during the trip. This equivalence is valid only if every trip has an equal chance of engaging ACC.\n\nIn our dataset, trips do not have equal probabilities of engaging ACC, leading to group-level biases. Ideally, random ACC assignment would result in a true ATE estimate, as the differences between trips would balance out, eliminating these biases. In our case, vehicles with ACC engaged typically exhibit better FC outcomes due to different driving profiles (e.g., longer trips, higher average speeds). The bias originates from the fact that, because of the kinds of trips in which ACC is engaged, the mean FC for vehicles that engaged ACC, had they not engaged it, would differ from the mean FC for vehicles that did not engage ACC. This omitted variable bias can be addressed by introducing control variables that account for differences between trips, allowing for an apples-to-apples comparison and equalizing the two groups.\n\nWithout random ACC engagement, treatment and control groups are not random subsets of all trips. Engaging ACC is systematically related to reduced FC outcomes for reasons other than ACC itself. Our estimate of the ATE, the expected difference between trips with ACC ON and trips with ACC OFF, is equal to the ATE among trips with ACC ON (if it can be observed) plus the mean difference between trips that engaged ACC if they hadn\u2019t, and the mean of the group that did not engage ACC. This difference is typically zero if randomization is present. This is best presented by the following equation:\n\nwhere Gi\u00a0=\u00a01 represents the group of trips in the data that actually engaged ACC, while potential outcomes Yi can be hypothetical. The first line is the expected difference between the ACC ON group and ACC OFF group, the second line is the ATE for the ACC ON group, and the last line represents the mean among the group that did engage ACC if they hadn\u2019t engaged ACC, which is different from the mean of the group that actually did not engage ACC. This last line captures the selection bias and would be nullified if Gi\u00a0=\u00a00 and Gi\u00a0=\u00a01 are similar. It is imperative to equalize the two groups in order to eliminate this bias term.\n\nBecause we want to study variation in ACC that is independent of FC, and because ACC engagement is not random among all trips, we need to equalize the trips by identifying variables that explain FC variation and that are related to ACC engagement likelihood. This process will help us to control for confounding factors and accurately estimate the impact of ACC on fuel consumption in real-world driving conditions.\n\nIn the following discussion, we leverage causal inference methods to estimate the effect of ACC on energy consumption. This approach involves identifying natural experiments in the data, such as trips where ACC was used versus others where ACC was not used. By comparing the fuel consumption of ACC-engaged trips to non-ACC trips, we could estimate the causal effect of ACC on energy consumption.\n\nIn our study, we consider two primary methodologies to analyze the impact of ACC on energy consumption: a controlled variable statistical model via regression and a propensity score matching (PSM). While PSM offers a more focused comparison by matching similar trips, there is complexity and sparsity in accurately matching and balancing trips. Conversely, regression analysis, less affected by these limitations, provides more comprehensive and applicable results, and thus, our findings will predominantly feature insights derived from the latter. In a multivariate approach (see section 2) we control for potential confounding factors that influence fuel consumption, such as trip distance, vehicle speed, driving conditions, vehicle type, and driver behavior, etc. (see section 5). We estimate the causal effects of the treatment (ACC engagement) on the outcome variable (fuel consumption) while controlling for these factors in order to isolate the effect of ACC engagement.\n\nCompared to the previously used methodology, this approach (typically employed in observational studies) not only leads to a more accurate estimate of the true ACC impact on energy consumption, but also offers flexibility in terms of the functional forms and interactions between the selected variables, allowing for the exploration of more complex relationships between the treatment, the control variables, and the outcome.\n\nWe carefully designed a set of variables to control in order to achieve near group equality and normalize trips with and without ACC engagement for comparison. This was achieved by learning the partial effects that each of the controlled variables has on fuel consumption and subtracting them to isolate the ACC effect. That is, we were trying to account for the effect of variables that we think have an impact on trip-level fuel consumption, learn that effect from the data and from the many examples, and remove their individual effects to isolate ACC impact.\n\nGiven the nature of this observational data, this variable selection endeavor boiled down to asking one fundamental question: What are the variables that explain FC variation that also could cause ACC to be engaged at the same time? That is, to understand the effects of ACC on FC, we have to think about not only the variables that cause FC variation but all the variables that cause ACC to be engaged. For example, Fig.\u00a07 shows that average trip-level speed is a strong predictor of FC, but also is a source of bias for ACC engagement as ACC is engaged at higher average trip speed (66 km/h vs 42 km/h). Higher average speed leads to lower FC, as noted before. In fact, the figure shows a 1/x relationship, that is, fuel consumption over speed is a non-linear function. To model this relationship correctly within our linear model framework, we employed a covariate transformation by including an inverse speed term in the model to linearize the relationship. This approach preserves the linearity regression assumption and allows us to account for the non-linear nature of the fuel consumption-speed relationship.\n\nTrip-average fuel consumption vs. speed, separated by ACC (adaptive cruise control) engagement modes.\n\nThe remaining selected variables are the results of careful data analysis. Primary factors such as the vehicle type and the driver ID are included, as well as first-order effects such as a speed and trip-level acceleration energy, defined as follows:\n\nNote that acceleration energy is calculated at the trip level, where T is a given trip length, vi is the speed at timestamp i, ti is the time signature at timestamp i, and some unit conversions are applied to provide m2/s3 unit values. We present the rest of the variables in Table\u00a03 along with their descriptions.\n\nIt is important to note that the electrical power consumption of the control modules and sensors required for Super Cruise remains constant whether the system is engaged or not. The control module is designed to stay active and continuously process real-time sensor inputs, ensuring readiness for immediate engagement. Therefore, the difference in electrical power consumption between the engaged and disengaged states of Super Cruise is zero. Consequently, the energy consumption associated with the Super Cruise system is implicitly included in the fuel consumption data analyzed in this study and not controlled for.",
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+        {
+            "section_name": "Data availability",
+            "section_text": "The data that support the findings of this study are not publicly available due to privacy and confidentiality concerns. The dataset includes personally identifiable information (PII) in the form of GPS data, which traces the driving patterns of GM employees, including their commutes to and from home. As such, sharing the data publicly compromises the privacy of the individuals involved. Researchers interested in the dataset for collaborative projects or further analysis may contact the corresponding author to discuss potential data sharing under specific agreements that ensure the protection of privacy and confidentiality. Any data sharing would require approval and agreement from General Motors (GM) and would be contingent upon compliance with applicable privacy regulations and institutional guidelines.",
+            "section_image": []
+        },
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+            "section_name": "Code availability",
+            "section_text": "The data used in this study was collected by General Motors using their proprietary data collection systems. Details on the specific software versions and technologies used for data collection are managed and maintained by GM. Additionally, the custom code and algorithms used for data processing and analysis and visualization were developed with Python 3.10, R 4.4.1 and Tableau 2024.1.5 software. Interested parties may contact the corresponding author for further information or potential collaboration, subject to approval.",
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+            "section_name": "References",
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+            "section_image": []
+        },
+        {
+            "section_name": "Acknowledgements",
+            "section_text": "The work described was sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The following DOE Office of Energy Efficiency and Renewable Energy (EERE) managers played important roles in establishing the project concept, advancing implementation, and providing ongoing guidance: Erin Boyd, Prasad Gupte, Alexis Zubrow, Jacob Ward, and David Anderson. Additionally, we would like to express our gratitude to the team at General Motors for their invaluable support and contributions, particularly in the areas of data collection and delivery. Their expertise and dedication were instrumental in the success of this project. The submitted manuscript was created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (\"Argonne\u201d). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.",
+            "section_image": []
+        },
+        {
+            "section_name": "Author information",
+            "section_text": "Vehicle and Mobility Simulation department at Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA\n\nAyman Moawad,\u00a0Jihun Han,\u00a0Dominik Karbowski,\u00a0Yaozhong Zhang\u00a0&\u00a0Aymeric Rousseau\n\n2050 Partners, 81 Coral Drive, Orinda, CA, 94563, USA\n\nMatthew Zebiak\n\nArgonne National Laboratory, Lemont, USA\n\nMatthew Zebiak\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nSearch author on:PubMed\u00a0Google Scholar\n\nD.K., A.R., and A.M. conceptualized the study. A.M. developed the methodology. A.M., J.H., Y.Z. contributed to the software. M.Z. validated the findings. A.M. performed the formal analysis. M.Z. and A.M. conducted the investigation, while A.M. curated the data. A.M., M.Z., and J.H. wrote the original draft, with all authors reviewing and editing the manuscript. A.M. handled the visualization. D.K. and A.R. provided supervision and project administration. D.K. and A.R. also secured the funding for the project.\n\nCorrespondence to\n                Ayman Moawad.",
+            "section_image": []
+        },
+        {
+            "section_name": "Ethics declarations",
+            "section_text": "The authors declare the following competing interests. This study was completed using proprietary, confidential data provided by General Motors through a Cooperative Research and Development Agreement (CRADA). All authors were involved in this CRADA and had access to the data.",
+            "section_image": []
+        },
+        {
+            "section_name": "Peer review",
+            "section_text": "Nature Communications thanks Michail Makridis and Konstantinos Mattas for their contribution to the peer review of this work. A peer review file is available.",
+            "section_image": []
+        },
+        {
+            "section_name": "Additional information",
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+            "section_image": []
+        },
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+            "section_name": "About this article",
+            "section_text": "Moawad, A., Zebiak, M., Han, J. et al. Effect of adaptive cruise control on fuel consumption in real-world driving conditions.\n                    Nat Commun 15, 10016 (2024). https://doi.org/10.1038/s41467-024-54066-8\n\nDownload citation\n\nReceived: 11 January 2024\n\nAccepted: 30 October 2024\n\nPublished: 19 November 2024\n\nVersion of record: 19 November 2024\n\nDOI: https://doi.org/10.1038/s41467-024-54066-8\n\nAnyone you share the following link with will be able to read this content:\n\nSorry, a shareable link is not currently available for this article.\n\n\n\n\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        ",
+            "section_image": [
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+            ]
+        },
+        {
+            "section_name": "Associated content",
+            "section_text": "Focus\n\nCollection",
+            "section_image": []
+        }
+    ]
+}