| ID | Fire Severitya | % Sandb (2-0.05 mm) | % Siltc (53-2 μm) | % Clayc| Cr | mg/kg Fe | mg/g Mn | mg/kg Ni | mg/kg Ca | mg/g Mg | mg/g Na | mg/g K | mg/g |
| Surface Soil-Ash | ||||||||||||
| A1 | L | 1147 | 64.7 | 1351 | 1528 | 15.5 | 77.3 | 4.63 | 3.76 | |||
| A2 | L | 1532 | 69.1 | 1203 | 2530 | 10.2 | 158 | <0.1 | 1.65 | |||
| A3 | M/H | 1606 | 84.2 | 1480 | 3117 | 9.3 | 148 | <0.1 | 1.54 | |||
| A4 | M/H | 87.5 | 11.7 | 0.8 | 2256 | 78.9 | 1438 | 3380 | 8.7 | 159 | <0.1 | 0.90 |
| A5 | M/H | 999 | 57.7 | 1022 | 1849 | 10.2 | 125 | 2.10 | 2.48 | |||
| A6 | M/H | 78.2 | 20.2 | 1.6 | 1970 | 79.2 | 1555 | 2726 | 29.9 | 174 | <0.1 | 2.86 |
| A7 | M/H | 85.7 | 12.9 | 1.4 | 4829 | 102 | 1543 | 2643 | 17.9 | 211 | <0.1 | 2.29 |
| Less than 53 μm size fraction | ||||||||||||
| A4 | M/H | 1133 | 94.7 | 1975 | 3042 | 41.5 | 124 | <0.1 | 3.38 | |||
| A6 | M/H | 946 | 82.7 | 1910 | 2294 | 44.2 | 133 | <0.1 | 4.41 | |||
| A7 | M/H | 1643 | 105 | 2547 | 3489 | 34.0 | 189 | 2.02 | 3.91 | |||
| Bulk Soild | ||||||||||||
| Rhyolitic \((n=7)\) | 162 | 34.5 | 796 | 88 | 10.7 | 7.9 | 13.6 | 9.70 | ||||
| Melange \((n=16)\) | 314 | 50.0 | 855 | 259 | 10.7 | 37.3 | 8.17 | 13.2 | ||||
| Serpentine \((n=10)\) | 2373 | 87.1 | 1511 | 2929 | 4.4 | 150 | 3.61 | 1.69 |
| ID | Fire Severitya | % Sandb (2-0.05 mm) | % Siltc (53-2 μm) | % Clayc| Cr | mg/kg Fe | mg/g Mn | mg/kg Ni | mg/kg Ca | mg/g Mg | mg/g Na | mg/g K | mg/g |
| Surface Soil-Ash | ||||||||||||
| A1 | L | 1147 | 64.7 | 1351 | 1528 | 15.5 | 77.3 | 4.63 | 3.76 | |||
| A2 | L | 1532 | 69.1 | 1203 | 2530 | 10.2 | 158 | <0.1 | 1.65 | |||
| A3 | M/H | 1606 | 84.2 | 1480 | 3117 | 9.3 | 148 | <0.1 | 1.54 | |||
| A4 | M/H | 87.5 | 11.7 | 0.8 | 2256 | 78.9 | 1438 | 3380 | 8.7 | 159 | <0.1 | 0.90 |
| A5 | M/H | 999 | 57.7 | 1022 | 1849 | 10.2 | 125 | 2.10 | 2.48 | |||
| A6 | M/H | 78.2 | 20.2 | 1.6 | 1970 | 79.2 | 1555 | 2726 | 29.9 | 174 | <0.1 | 2.86 |
| A7 | M/H | 85.7 | 12.9 | 1.4 | 4829 | 102 | 1543 | 2643 | 17.9 | 211 | <0.1 | 2.29 |
| Less than 53 μm size fraction | ||||||||||||
| A4 | M/H | 1133 | 94.7 | 1975 | 3042 | 41.5 | 124 | <0.1 | 3.38 | |||
| A6 | M/H | 946 | 82.7 | 1910 | 2294 | 44.2 | 133 | <0.1 | 4.41 | |||
| A7 | M/H | 1643 | 105 | 2547 | 3489 | 34.0 | 189 | 2.02 | 3.91 | |||
| Bulk Soild | ||||||||||||
| Rhyolitic \((n=7)\) | 162 | 34.5 | 796 | 88 | 10.7 | 7.9 | 13.6 | 9.70 | ||||
| Melange \((n=16)\) | 314 | 50.0 | 855 | 259 | 10.7 | 37.3 | 8.17 | 13.2 | ||||
| Serpentine \((n=10)\) | 2373 | 87.1 | 1511 | 2929 | 4.4 | 150 | 3.61 | 1.69 |
| Remarks to Author | Author Responses | |
| Reviewer #1: | ||
| The manuscript "Can temperate forests deliver both future wood demand and climate-change mitigation?" by Forster et al. presents the results of the lifecycle assessments of the global warming potential of wood supply under the growing wood demand scenarios. The analysis is fairly clear and thorough, however I have several reservations outlined below: | We thank the reviewer for the positive comments and valuable specific suggestions to further enhance the paper. | |
| 1 | There are no uncertainty estimates around the wood supply and demand as well as GWP. Incorporating the uncertainty analysis into the study would provide insight into where the future research efforts should be directed in order to reduce the uncertainty, as well as help evaluate the confidence in the reported estimates. For an example of uncertainty analysis using CBM-CFS please refer to J.M. Metsaranta, C.H. Shaw, W.A. Kurz, C. Boisvenue, and S. Morken. 2017. Uncertainty of inventory-based estimates of the carbon dynamics of Canada's managed forest (1990-2014). Canadian Journal of Forest Research. 47(8): 1082-1094. https://doi.org/10.1139/cjfr-2017-0088 | The demand profiles modelled are based on the outcome of an extensive literature review of wood demand modelling studies. We recognise and account for the significant uncertainty sourrounding future wood demand projections (indicated by the wide range of demand projections published in the literature) by modelling two different demand profiles in the present study. These two demand profiles capture the range in the published literature. LCA of forest management scenarios under each of these demand profile senarios indicates the degree of uncertainty and sensitivity of results to future demand projection assumptions. In dealing with uncertainties for the supply side, we have taken the simple and clear approach of testing multiple scenarios of variation in future forest productivity (yield). We believe that this is an efficient way of integrating all of these causes of uncertainty listed by the reviewer 1 comment 2. We have also added the following text to the 'Discussion section' lines 375-379: "There is an urgent need for more integrated evidence that incorporates holistic assessment of prospective forestry value chains alongside landscape dynamics (including forest management and expansion), at both national and global scales, including improved estimates of the potential impacts of climate change-linked threats to the future productivity of both temperate forests and the other forest biomes providing wood production." See continuation of this response in response to reviewer 1 comment 2 below. |
| Remarks to Author | Author Responses | |
| 2 | Although authors did state in the methods section that risks of pests disease, wind, fire, warming and other climate change effects would not significantly alter the study findings because the risks are highly uncertain and would apply similarly across the study, I don't think it is the case. Fire, drought, changing productivity due to climate change would not change the wood demand, however they could profoundly affect the wood supply. Given the length of the projections in the study, warming would substantially increase heterotrophic respiration, and therefore would alter GWP projections. Drought events could substantially and repeatedly reduce forest yield due via inhibition of the photosynthetic rate and increasing mortality rate. Warming temperatures and changing precipitation regimes are also likely to affect forest net primary production. Increasing fire frequency and severity could profoundly affect the wood supply. I don't think these effects are negligible for GWP and wood supply estimates, and therefore should be considered in the study, especially given the 100-year projection time. | As described in response to the previous comment, we believe that the approach to test multiple scenarios of variation in future forest productivity (yield) is an efficient way of integrating many of the causes of uncertainty listed by the reviewer. It is the approach that is most compatible with the LCA methodology of our study. Therefore, to address this specific reviewer requirement we have introduced a further pair of scenarios ('lower productivity' than the reference scenario) to model impact of possible ecosystem shocks (leading to yield reduction) that could arise from natural disturbance such as pests, disease, wind, drought and fire. These new scenarios are a modification of the 'reference rotation' existing forest (YC18, 50-yr rotation) + afforestation combination. The new scenarios are a transition of a) 15% and b) 30% of the existing forest from YC18 to YC12 ramping up over a 15 year period (remaining as a 50-yr harvest rotation), followed by recovery back to YC18 after one harvest rotation, ramping back down over a 15-yr period. (The afforestation assumptions were unchanged). Essentially the scenario represents a rapid shock of -two different intensities (affect on 15% & 30% of the total area), followed by recovery after harvest (at 50-yrs old). A detailed description of the basis for the parameterisation of these two scenarios (two and a half pages of text) based on a substantial new literature review (32 references cited) is provided in the new Supplementary Information file "Modelling of natural disturbances - 'reduced productivity'". We recognise that natural disturbances, the intensity and rate of impact, and management responses to these are complex and therefore the modelling options available are vast. We believe the new scenarios selected correspond to contrasting realistic disturbance patterns presented in the reviewed literature and therefore offer additional insights to the other scenarios already modelled. We have included the new results in an updated version of 'Supplementary Data 3', alongside the results of all the other scenarios modelled in this paper. We modelled these two new scenarios under high and low demand projections and for the variation in afforestation rates and planting periods. We found that the new 'lower productivity' scenarios in this set led to higher net forest carbon loss (domestic and overseas) than the 'reference rotation' scenarios, as well as other changes, yet these disturbances do not change the overall results or findings of the study. We have added text on this to the methodology (lines 701-708) and results section of the manuscript |
| Remarks to Author | Author Responses (lines 114-118; 169-172; and 201-204) and have included a more comprehensive description in the new Supplementary Information file (Supplementary Methods 1) in order to minimise additional word count. |
| Remarks to Author | Author Responses | |
| 3 | The study results are generalized for a “temperate country”, however most of the data used in the study are for the UK, why not make the study focused on the UK? It would make it easier to assess the feasibility of the proposed scenarios and allow to avoid generalized statements (as a reader, I had a little trouble with those). | We intentionally kept the analysis generic for a hypothetical temperate country rather than a UK-specific case study in order to provide evidence that has broad relevance. (The UK is not 'typical' in that it has a high baseline of imports and low domestic wood production.) This study is prospective in nature and focuses on the impacts of change, based on transparent assumptions for future changes (e.g. rate of decarbonisation, projected wood demand increase, shifts in forest management, different afforestation rates) that are not unique to the UK (or any other country) or dependant on historic conditions that may be unique to the UK. In order to perform the LCA we needed to make assumptions on typical product breakouts at the forest gate and at a sawmill and a representative wood product flow. Given limitations on public availability of this kind of data, we used data available for the UK as the basis for these assumptions. These assumptions are broadly representative across temperate countries beyond the UK given the similarities in technology and wood value chains. For example, typical rates of conversion of softwood to sawtimber at sawmills is similar across temperate regions. We have edited the Methodology 'Scope of LCA' section (e.g. lines 622-628) to try and improve the reconciliation between our use of UK data and generalised statements. |
| Remarks to Author | Author Responses | |
| Reviewer #2 | ||
| Summary: In this manuscript, authors present a combined application of forest carbon models and life cycle assessment (LCA) to estimate global warming (GW) impacts (+/-) of harvested wood products' (HWPs) value chain in a temperate country (appears to be UK). For projected high and low wood demands, the GW impacts are assessed for different scenarios within the country (changing rotation length of forests, increasing rate of production, and expansion of forest area) and overseas imports from non-temperate forests (tropical country). This kind of study is an interesting attempt to integrate two areas of the forest products value chain (forestry and HWPs) for policy implications. The results indicate that increased wood use is not a climate-change solution unless afforestation, increasing forest productivity under sustainable forest management, and mitigating demand increases through enhanced circularity and cascading of wood use are also integrated into the strategy. I am offering below some comments/suggestions to improve the manuscript. | We thank the reviewer for the positive comments and endorsement of the important findings of the paper. | |
| Major comments | ||
| 1 | 103-104: The current production and consumption levels of this temperate country should have been characterized to visualize the gap between demand and supply for the reference year 2023, and the how different scenarios or intended decisions might close this gap and influence GW impacts. | We intentionally defined a hypothetical scenario in which domestic production equals domestic consumption in the reference year 2023, as indicated in Figure 1. As described in our response to reviewer 1 comment 3 above, we have taken this hypothetical approach rather than characterising the actual production and consumption levels of a specific country in order to provide evidence that has broad relevance. (For example, the UK is not 'typical' in that it has a high baseline of imports and low domestic wood production.) It makes sense for a counterfactual lifecycle assessment (which is concerned with change from a baseline) to start the scenario with production = consumption rather that starting |
| Remarks to Author | Author Responses | |
| with a deficit or surplus, which would make interpretation of results more challenging. We then visualise how the different scenarios close the gap between future (increasing) demand and supply in Figure 1 by colour coding the supply deficit in red (and the supply surplus in pink). We have also added some additional text in the Figure 1 title to improve this description. | ||
| 2 | 107-108: What was the reason for selection of Sitka spruce forest in afforestation? The authors might discuss whether the results would be different if the forests were Douglas-fir or Western hemlock. | Sitka spruce was selected as it grows widely (native to west coast of Canada and the United States; now planted in 16 countries worldwide, including as the predominant plantation species in the UK, Ireland and Denmark - all countries with low forest cover). Sitka spruce also grows well in degraded upland sites where land is most economically viable for afforestation, unlike productive Douglas fir or western hemlock, which require lower altitude land that is of higher value for food production. LCA results would not be different if different species (or species mixes) were modelled unless a significantly different yield class (growth curve) was also assumed. It is the yield rather than the species choice that drives the GWP impact results (which of course would be related in specific geographic contexts but in a hypothetical forest study such as this, the yield class is specified independently by the modeller as a representative average across contexts). In this way, the choice of Sitka spruce is simply a proxy for commercial conifer species. However, we believe that the clarity of the manuscript benefits from naming the case species, which links to the UK sawmill and-representative wood product flow data used in the study. |
| 3 | Figure 2: Please change y-axis units to Tg CO2e. | We have made this change. |
| 4 | 234: More interpretations could be added in 'high wood demand projection' results. | We are pleased that Reviewer 2 sees further possible interpretations that we could include. We would welcome the opportunity to add more interpretations, however because of the journal's strict word limits this would necessitate making significant cuts to other parts of the text, which would sacrifice their clarity and content. Given the importance of these other components of the paper that would have to be cut, with regret we have concluded that adding more |
| Remarks to Author | Author Responses | |
| interpretations to the 'high wood demand projection' results would be of net cost to the value of the paper. | ||
| 5 | 264-267: Not clearly explained how GWP impact of alternative increased due to increase in overseas wood supply. Prolonged use of non-wood product and fuel alternatives? | We have added words in this sentence (line 290) to clarify that the GWP impact of the alternative is "curtailed HWP supply" i.e. prolonged use of non-wood product and fuel alternatives. We have also added further description to indicate the relative impact of the specific overseas scenarios ('Boreal 1,2&3, Tropical CVL and Tropical (afforestation)' in Fig. 4) in which emissions associated with supplying the wood shortfall are higher or lower that the emissions from 'curtailed HWP supply' (prolonged use of non-wood and fuel alternatives). |
| 6 | 278-279: Please add explanation to this sentence, it seems confusing that more imports from tropical afforestation is better. | It is unclear to us why this is confusing as it seems to be clearly shown in the results reported in Fig. 4. We do acknowledge the potential disbenefits of new tropical afforestation in lines 363-364 ("numerous socio-economic51 and biodiversity conservation20 caveats". In essence, increasing demand for fast-growing tropical tree species, which could be established on the large areas of degraded land in the tropics (if local socio-economic conditions make them available), could actually enhance terrestrial carbon stocks. |
| 7 | 295: Clarity is needed on what type of non-wood product is considered substituted by HWPs. Because substitution credits for two type of non-wood products can be different for same HWP. | In response to this point, to avoid adding significant additional word count, we have added text to the methodology (line 616-617) and in the Fig. 5 title to direct readers to supplementary information for more detail on the substitution credits assumptions (supplementary figure 1 and supplementary Table 1). We also direct readers to Forster et al. (2021) where the methodology was originally published. We have also improved figure 5 in the present paper to convey, among other things, the product substitution assumptions more clearly. |
| 8 | 340: BECCS is associated with 'energy substitution' like non-wood products avoided is associated with 'product substitution'. There aren't a lot of interpretations in the results and discussion that focused on energy substitution. | We have expanded the sentence referring to BECCS to mention product substitution and emphasise the permanent geological storage of biogenic carbon that contributes a carbon sink. Lines 368-369. |
| Remarks to Author | Author Responses | |
| 9 | 600: there is no ‘ix’ in the components of LCA system boundary. Including via ...? | We have checked the numbering of components of the LCA system boundary in figure 5 and in the text and cannot find anything missing. |
| 10 | 724-740: This whole paragraph does not seem to be fit for methodology. Its more suitable for introduction. | We believe that the paragraph is important for justifying the important selection of assumptions for the projected rate of demand increase used in the study, which is a core aspect of the study's methodology. We have edited and changed the order of the text to make the inclusion of this text in the methodology more fluent and justified. E.g. lines 779-785 have been moved from earlier in the section. |
| 11 | 747-748: What is the per capita timber consumption in UK, which translates to 30% increase in demand by 2050? | We do not believe that including per capita timber consumption in the UK is relevant to the study. The 30% increase by 2050 statistic was included as this is a date that many published projections focus on, because it is an important date for many 'net zero' targets as mandated by the 2015 Paris Agreement of UNFCCC, and therefore a statistic that readers are likely to be interested in. As stated in our response to reviewer 2 comment 1, we have prioritised assumptions that are non-country-specific and forward looking wherever possible, to maximise the transferability of results. In essence, the critical (and transferable) mathematical/biophysical dynamic is the rate of demand increase from the baseline relative to the expansion rate of baseline forest area (through afforestation) and the (change in) productivity of existing (and new) forest. |
| 12 | 754: In this paragraph, it would be good if authors give a brief about the different HWP end-uses (primary and cascading uses) considered and maybe a justification for selecting HWP uses. | The primary HWP end-uses are described in the methodology and further in the SI, including in a revised system diagram (Figure 5) indicating major processes and products. Details of cascading uses and also justification for the assumptions used are provided in Forster et al. (2021), which we cite in the methodology. We believe there is insufficient justification to repeat all of this detail here, especially on account of the word count restrictions. |
| 13 | 766: More clarity on defining the overseas forest type and carbon storage. Also, was the transportation distance and mode of transport included in the analysis? | Description of the overseas forest types is provided in the following paragraphs (lines 827-856). We did not include assumed differences in transport distances between overseas and domestic HWPs because, in a previous study (Forster et al., 2023), we found that these differences made a very small contribution (less than 0.01%) relative to the net GWP impact of the value chain. |
| Remarks to Author | Author Responses | |
| 14 | 785: Why 'GWP (forest C) impact...'? Forest carbon can be stored, emitted, or removed but cannot be equated directly to GWP. | GWP (forest C) impact is defined in the previous paragraph as the 'GWP impact of fluxes in forest carbon stocks'. We have added '(GWP (forest C))' after this definition to add clarity. It refers to the GWP impact associated with net CO2e fluxes to/from the forest. Line 828. |
| 15 | Overall, the methods section appears weak to me and needs a thorough revision to ensure that work can be reproduced. | We have carefully addressed each of the reviewers specific comments and assume that in doing so we have met the need expressed here. |
| Minor comments | ||
| 16 | 325 and 330: should it be trip or tip? | Tip means to tilt, tumble or topple. In this context, 'the balance... can tip from sink to source', means the balance can tilt or shift from sink to source. |
| 17 | 723: why question marks in middle of the sentence? | We have removed these. |
| 18 | Double numbering in references | We have removed these. |
| 19 | Some of the links in supplementary excel file are broken. Please check. | We have checked the supplementary Excel files and assume that the reviewer is referring to the 'HWP calculation module' (Supplementary Data 2). The broken links are the emissions factors cells that link to the original LCA worksheet (Supplementary Data 1) that generated them. These links do not work once the workbook is resaved in a different location. However, in the final version of these supplementary data files, which will be stored in a permanent repository or in the publisher's website, we will endeavour to ensure the links all work. |
| Remarks to Author | Author Responses | |
| Reviewer #3: | ||
| 1 | The aim of this case study is to quantify the GHG mitigation potential of different measures or forest management options (in particular afforestation) in terms of meeting an increasing demand for wood, assuming that existing models for the forest sector tend to "underestimate" forest carbon fluxes. |
| Remarks to Author | Author Responses | |
| 2 | In fact, in addition to the development of forest carbon stocks, the delayed release of biogenic carbon through the use of wood as material as well as potential shifting effects of the GHG emission balances associated with the life cycle of these wood-containing product systems and their potential product alternatives do also have an impact on the overall GHG balance. | We are not clear on whether this forms part of the reviewer's summary of the paper or is a comment requiring our response. In case it is the latter, we provide the following clarification. We agree with the reviewer about the importance of impacts throughout the wood products life cycle. Yet, the full climate mitigation effects of forestry are often under-represented because: (i) LCA approaches that (sometimes) better represent substitution and possible end-of-life carbon storage effects are usually only applied to specific products (partial wood flows out of forests); (ii) inventory approaches that attempt to represent all wood products, typically neglect short-lived wood products used for energy, and don't attribute substitution effects back to the forest sector; (iii) both LCA and inventory approaches often disregard cascading uses of wood, and associated second (and possible further) substitutions, along with extended carbon storage effects; (iv) only very prospective analyses consider future BECCS deployment that could lock up biogenic carbon indefinitely. |
| 3 | However, the implementation of the presented approach to estimate the total GHG impact of different management scenarios compared to the defined reference, in our view appears to be completely inadequate in this study. It does not comply with applicable international standards and existing state-of-the-art knowledge. | We are surprised by this comment, which we strongly believe does not reflect the content and rigour of our study or its compliance with the highest international standards. International standards apply to specific types of accounting, such as Environmental Product Declarations for (LCA of) wood products or UNFCCC guidelines for national GHG inventory accounting. In contrast, there are no such "standards" for the prospective consequential LCA of entire forest and multiple downstream product systems, which is the innovative methodological approach of our study. That is precisely what makes this approach so valuable - it transcends the accounting rules that are applied to deal with truncated system boundaries (e.g. how to allocate forest effects to specific downstream products). This paper builds on the state-of-the-art approach applied in our earlier, highly-cited manuscript published in Nature Communications (Forster et al., 2021), and applies it to a temperate forest system under different management and expansion regimes; this is highly novel. To reiterate, we are not attempting to calculate product footprints as per various international standards, but instead (as is befitting for a submission to Nature Communications) to provide a novel, holistic and rigorous analysis of the climate mitigation efficacy of different forest |
| Remarks to Author | Author Responses | |
| management strategies, providing key new evidence for policy makers and an innovative novel approach for researchers. | ||
| 4 | While the modeling of carbon storage development in the forest using the internationally recognized Carbon Budget Model for the Canadian Forestry Service appears adequate, the methodological inadequacies relate in particular to the life cycle assessment methodology, on the basis of whose standard-compliant, consistent and transparent implementation avoided emissions through "product substitution" can be estimated in the first place. In order to adequately consider process chain emissions and based on those also "avoided emissions from product substitution", it is crucial to meet the internationally standardized requirements for life cycle assessment (including ISO 14040/44 and ISO 21930). The mere summation of unrelated LCA process information from a background database (here: Ecolnvent) is inadequate - at least for the processes outside the forest along the processing and value chain. The data used for calculating "primary avoided emissions (FF/product substitution)" are also completely unsuitable. | As explained in our previous response, our study is categorically not an attributional LCA, which seems to have been the assumption of the reviewer. Instead it is a consequential LCA. That said, this study does fully comply with ISO 14040 and 14044 standards (which in reality are a basic framework to structure LCA), insofar as it: provides a clear goal and scope of the study; calculates an inventory that accounts for relevant changes within the expanded system boundary; applies GWP100 characterisation factors for the life cycle impact assessment phase; interprets results with clear regard for the question and methods used (now including important sensitivity analyses). Furthermore, this study uses system expansion in preference to allocation, as recommended in the allocation hierarchy advocated by the ISO standards. The study categorically does not sum "unrelated" LCA process information - incurred or avoided processes are clearly indicated in the system boundary (Supplementary Fig. 1 and in Fig. 5, also now improved), based on a consequential LCA approach that incorporates all changes associated with the scenarios vs the baseline counterfactual situation. |
| Remarks to Author | Author Responses | |
| 5 | Furthermore, the simplified calculation of assumed carbon storage effects through the use of wood as a material (harvested wood products) contradicts central core requirements in the calculation as set out in the methodological guidelines and requirements provided by IPCC (incl. e.g. the consideration of inherited emissions). In consequence, the HWP contribution trough biogenic carbon storage as well as potential “avoided emissions” appear to be massively overestimated. | Our methodology does not contradict core requirements of the IPCC and from this comment we do not understand how the reviewer considers that it does so. The IPCC provide guidelines for a number of approaches to deal with HWP C storage at a national scale. All of them are based on the fact that biogenic carbon harvested from the forest is "lost" from the terrestrial system, but some of this carbon "reappears" in longer-lived HWPs (with a large deficit reflecting biomass assumed to be immediately oxidised via combustion for bioenergy or for kiln drying of wood in sawmills). Our approach fully respects the biogenic carbon balance - in fact much more explicitly than typical national inventory accounting (with which we are fully familiar), because flows into all main products, both short- and long-lived, are accounted for, and the duration of carbon storage in each of these products, and any subsequent products, is explicitly accounted for in our methodology. Again, this is the benefit of our expanded boundary approach (across products and through time) - it minimises the influence of "cut-off" rules and associated value judgements, which often profoundly influence carbon footprints at the wood product level. |
| 6 | While it seems undisputed, even without the present study, that "the expansion of the (industrial) bioeconomy should be linked to the availability of raw materials in order to avoid unrealistic supply expectations, " statements to the effect that "considerable HWP-C storage and product substitution credits can be achieved simultaneously" are not at all tenable on the basis of this simple and, in our view, completely methodologically inadequate implementation. | Without any further insight from the reviewer as to where our methodology is inadequate, it is difficult to address this comment. We acknowledge that our original system diagram did not fully represent the scope of our methodology (although it was described fully in the text and illustrated in Supplementary Fig.1). Therefore, we hope that the improved system diagram in the resubmitted manuscript helps understanding of precisely how we have linked forest C storage with HWP-C storage and substitution, and long-term CCS C-storage effects in a coherent manner. As previously mentioned, this builds on the methodology of Forster et al. (2021) published in Nature Communications. |
| 7 | The simple comparison of a changing supply of forest wood with a modeled demand for this raw material (including potential gaps) can also be carried out without balancing all GHG emissions relevant to the forestry and wood sector. | It is true that a simple comparison of changing supply of forest wood with demand could be carried out in isolation. However, that would not address the important policy question of what the climate mitigation effect would be of alternative forest strategies that aim to minimise the gap between future supply and demand. That is where this paper provides a unique and robust contribution to the scientific literature, for which its rigorous methodological consequential LCA approach is essential. |
| Remarks to Author | Author Responses | |
| Reviewer #1: | ||
| The manuscript "Can temperate forests deliver both future wood demand and climate-change mitigation?" by Forster et al. presents the results of the lifecycle assessments of the global warming potential of wood supply under the growing wood demand scenarios. The analysis is fairly clear and thorough, however I have several reservations outlined below: | We thank the reviewer for the positive comments and valuable specific suggestions to further enhance the paper. | |
| 1 | There are no uncertainty estimates around the wood supply and demand as well as GWP. Incorporating the uncertainty analysis into the study would provide insight into where the future research efforts should be directed in order to reduce the uncertainty, as well as help evaluate the confidence in the reported estimates. For an example of uncertainty analysis using CBM-CFS please refer to J.M. Metsaranta, C.H. Shaw, W.A. Kurz, C. Boisvenue, and S. Morken. 2017. Uncertainty of inventory-based estimates of the carbon dynamics of Canada's managed forest (1990-2014). Canadian Journal of Forest Research. 47(8): 1082-1094. https://doi.org/10.1139/cjfr-2017-0088 | The demand profiles modelled are based on the outcome of an extensive literature review of wood demand modelling studies. We recognise and account for the significant uncertainty sourrounding future wood demand projections (indicated by the wide range of demand projections published in the literature) by modelling two different demand profiles in the present study. These two demand profiles capture the range in the published literature. LCA of forest management scenarios under each of these demand profile senarios indicates the degree of uncertainty and sensitivity of results to future demand projection assumptions. In dealing with uncertainties for the supply side, we have taken the simple and clear approach of testing multiple scenarios of variation in future forest productivity (yield). We believe that this is an efficient way of integrating all of these causes of uncertainty listed by the reviewer 1 comment 2. We have also added the following text to the 'Discussion section' lines 375-379: "There is an urgent need for more integrated evidence that incorporates holistic assessment of prospective forestry value chains alongside landscape dynamics (including forest management and expansion), at both national and global scales, including improved estimates of the potential impacts of climate change-linked threats to the future productivity of both temperate forests and the other forest biomes providing wood production." See continuation of this response in response to reviewer 1 comment 2 below. |
| Remarks to Author | Author Responses | |
| 2 | Although authors did state in the methods section that risks of pests disease, wind, fire, warming and other climate change effects would not significantly alter the study findings because the risks are highly uncertain and would apply similarly across the study, I don't think it is the case. Fire, drought, changing productivity due to climate change would not change the wood demand, however they could profoundly affect the wood supply. Given the length of the projections in the study, warming would substantially increase heterotrophic respiration, and therefore would alter GWP projections. Drought events could substantially and repeatedly reduce forest yield due via inhibition of the photosynthetic rate and increasing mortality rate. Warming temperatures and changing precipitation regimes are also likely to affect forest net primary production. Increasing fire frequency and severity could profoundly affect the wood supply. I don't think these effects are negligible for GWP and wood supply estimates, and therefore should be considered in the study, especially given the 100-year projection time. | As described in response to the previous comment, we believe that the approach to test multiple scenarios of variation in future forest productivity (yield) is an efficient way of integrating many of the causes of uncertainty listed by the reviewer. It is the approach that is most compatible with the LCA methodology of our study. Therefore, to address this specific reviewer requirement we have introduced a further pair of scenarios ('lower productivity' than the reference scenario) to model impact of possible ecosystem shocks (leading to yield reduction) that could arise from natural disturbance such as pests, disease, wind, drought and fire. These new scenarios are a modification of the 'reference rotation' existing forest (YC18, 50-yr rotation) + afforestation combination. The new scenarios are a transition of a) 15% and b) 30% of the existing forest from YC18 to YC12 ramping up over a 15 year period (remaining as a 50-yr harvest rotation), followed by recovery back to YC18 after one harvest rotation, ramping back down over a 15-yr period. (The afforestation assumptions were unchanged). Essentially the scenario represents a rapid shock of -two different intensities (affect on 15% & 30% of the total area), followed by recovery after harvest (at 50-yrs old). A detailed description of the basis for the parameterisation of these two scenarios (two and a half pages of text) based on a substantial new literature review (32 references cited) is provided in the new Supplementary Information file "Modelling of natural disturbances - 'reduced productivity'". We recognise that natural disturbances, the intensity and rate of impact, and management responses to these are complex and therefore the modelling options available are vast. We believe the new scenarios selected correspond to contrasting realistic disturbance patterns presented in the reviewed literature and therefore offer additional insights to the other scenarios already modelled. We have included the new results in an updated version of 'Supplementary Data 3', alongside the results of all the other scenarios modelled in this paper. We modelled these two new scenarios under high and low demand projections and for the variation in afforestation rates and planting periods. We found that the new 'lower productivity' scenarios in this set led to higher net forest carbon loss (domestic and overseas) than the 'reference rotation' scenarios, as well as other changes, yet these disturbances do not change the overall results or findings of the study. We have added text on this to the methodology (lines 701-708) and results section of the manuscript |
| Remarks to Author | Author Responses (lines 114-118; 169-172; and 201-204) and have included a more comprehensive description in the new Supplementary Information file (Supplementary Methods 1) in order to minimise additional word count. |
| Remarks to Author | Author Responses | |
| 3 | The study results are generalized for a “temperate country”, however most of the data used in the study are for the UK, why not make the study focused on the UK? It would make it easier to assess the feasibility of the proposed scenarios and allow to avoid generalized statements (as a reader, I had a little trouble with those). | We intentionally kept the analysis generic for a hypothetical temperate country rather than a UK-specific case study in order to provide evidence that has broad relevance. (The UK is not 'typical' in that it has a high baseline of imports and low domestic wood production.) This study is prospective in nature and focuses on the impacts of change, based on transparent assumptions for future changes (e.g. rate of decarbonisation, projected wood demand increase, shifts in forest management, different afforestation rates) that are not unique to the UK (or any other country) or dependant on historic conditions that may be unique to the UK. In order to perform the LCA we needed to make assumptions on typical product breakouts at the forest gate and at a sawmill and a representative wood product flow. Given limitations on public availability of this kind of data, we used data available for the UK as the basis for these assumptions. These assumptions are broadly representative across temperate countries beyond the UK given the similarities in technology and wood value chains. For example, typical rates of conversion of softwood to sawtimber at sawmills is similar across temperate regions. We have edited the Methodology 'Scope of LCA' section (e.g. lines 622-628) to try and improve the reconciliation between our use of UK data and generalised statements. |
| Remarks to Author | Author Responses | |
| Reviewer #2 | ||
| Summary: In this manuscript, authors present a combined application of forest carbon models and life cycle assessment (LCA) to estimate global warming (GW) impacts (+/-) of harvested wood products' (HWPs) value chain in a temperate country (appears to be UK). For projected high and low wood demands, the GW impacts are assessed for different scenarios within the country (changing rotation length of forests, increasing rate of production, and expansion of forest area) and overseas imports from non-temperate forests (tropical country). This kind of study is an interesting attempt to integrate two areas of the forest products value chain (forestry and HWPs) for policy implications. The results indicate that increased wood use is not a climate-change solution unless afforestation, increasing forest productivity under sustainable forest management, and mitigating demand increases through enhanced circularity and cascading of wood use are also integrated into the strategy. I am offering below some comments/suggestions to improve the manuscript. | We thank the reviewer for the positive comments and endorsement of the important findings of the paper. | |
| Major comments | ||
| 1 | 103-104: The current production and consumption levels of this temperate country should have been characterized to visualize the gap between demand and supply for the reference year 2023, and the how different scenarios or intended decisions might close this gap and influence GW impacts. | We intentionally defined a hypothetical scenario in which domestic production equals domestic consumption in the reference year 2023, as indicated in Figure 1. As described in our response to reviewer 1 comment 3 above, we have taken this hypothetical approach rather than characterising the actual production and consumption levels of a specific country in order to provide evidence that has broad relevance. (For example, the UK is not 'typical' in that it has a high baseline of imports and low domestic wood production.) It makes sense for a counterfactual lifecycle assessment (which is concerned with change from a baseline) to start the scenario with production = consumption rather that starting |
| Remarks to Author | Author Responses | |
| with a deficit or surplus, which would make interpretation of results more challenging. We then visualise how the different scenarios close the gap between future (increasing) demand and supply in Figure 1 by colour coding the supply deficit in red (and the supply surplus in pink). We have also added some additional text in the Figure 1 title to improve this description. | ||
| 2 | 107-108: What was the reason for selection of Sitka spruce forest in afforestation? The authors might discuss whether the results would be different if the forests were Douglas-fir or Western hemlock. | Sitka spruce was selected as it grows widely (native to west coast of Canada and the United States; now planted in 16 countries worldwide, including as the predominant plantation species in the UK, Ireland and Denmark - all countries with low forest cover). Sitka spruce also grows well in degraded upland sites where land is most economically viable for afforestation, unlike productive Douglas fir or western hemlock, which require lower altitude land that is of higher value for food production. LCA results would not be different if different species (or species mixes) were modelled unless a significantly different yield class (growth curve) was also assumed. It is the yield rather than the species choice that drives the GWP impact results (which of course would be related in specific geographic contexts but in a hypothetical forest study such as this, the yield class is specified independently by the modeller as a representative average across contexts). In this way, the choice of Sitka spruce is simply a proxy for commercial conifer species. However, we believe that the clarity of the manuscript benefits from naming the case species, which links to the UK sawmill and-representative wood product flow data used in the study. |
| 3 | Figure 2: Please change y-axis units to Tg CO2e. | We have made this change. |
| 4 | 234: More interpretations could be added in 'high wood demand projection' results. | We are pleased that Reviewer 2 sees further possible interpretations that we could include. We would welcome the opportunity to add more interpretations, however because of the journal's strict word limits this would necessitate making significant cuts to other parts of the text, which would sacrifice their clarity and content. Given the importance of these other components of the paper that would have to be cut, with regret we have concluded that adding more |
| Remarks to Author | Author Responses | |
| interpretations to the 'high wood demand projection' results would be of net cost to the value of the paper. | ||
| 5 | 264-267: Not clearly explained how GWP impact of alternative increased due to increase in overseas wood supply. Prolonged use of non-wood product and fuel alternatives? | We have added words in this sentence (line 290) to clarify that the GWP impact of the alternative is "curtailed HWP supply" i.e. prolonged use of non-wood product and fuel alternatives. We have also added further description to indicate the relative impact of the specific overseas scenarios ('Boreal 1,2&3, Tropical CVL and Tropical (afforestation)' in Fig. 4) in which emissions associated with supplying the wood shortfall are higher or lower that the emissions from 'curtailed HWP supply' (prolonged use of non-wood and fuel alternatives). |
| 6 | 278-279: Please add explanation to this sentence, it seems confusing that more imports from tropical afforestation is better. | It is unclear to us why this is confusing as it seems to be clearly shown in the results reported in Fig. 4. We do acknowledge the potential disbenefits of new tropical afforestation in lines 363-364 ("numerous socio-economic51 and biodiversity conservation20 caveats". In essence, increasing demand for fast-growing tropical tree species, which could be established on the large areas of degraded land in the tropics (if local socio-economic conditions make them available), could actually enhance terrestrial carbon stocks. |
| 7 | 295: Clarity is needed on what type of non-wood product is considered substituted by HWPs. Because substitution credits for two type of non-wood products can be different for same HWP. | In response to this point, to avoid adding significant additional word count, we have added text to the methodology (line 616-617) and in the Fig. 5 title to direct readers to supplementary information for more detail on the substitution credits assumptions (supplementary figure 1 and supplementary Table 1). We also direct readers to Forster et al. (2021) where the methodology was originally published. We have also improved figure 5 in the present paper to convey, among other things, the product substitution assumptions more clearly. |
| 8 | 340: BECCS is associated with 'energy substitution' like non-wood products avoided is associated with 'product substitution'. There aren't a lot of interpretations in the results and discussion that focused on energy substitution. | We have expanded the sentence referring to BECCS to mention product substitution and emphasise the permanent geological storage of biogenic carbon that contributes a carbon sink. Lines 368-369. |
| Remarks to Author | Author Responses | |
| 9 | 600: there is no ‘ix’ in the components of LCA system boundary. Including via ...? | We have checked the numbering of components of the LCA system boundary in figure 5 and in the text and cannot find anything missing. |
| 10 | 724-740: This whole paragraph does not seem to be fit for methodology. Its more suitable for introduction. | We believe that the paragraph is important for justifying the important selection of assumptions for the projected rate of demand increase used in the study, which is a core aspect of the study's methodology. We have edited and changed the order of the text to make the inclusion of this text in the methodology more fluent and justified. E.g. lines 779-785 have been moved from earlier in the section. |
| 11 | 747-748: What is the per capita timber consumption in UK, which translates to 30% increase in demand by 2050? | We do not believe that including per capita timber consumption in the UK is relevant to the study. The 30% increase by 2050 statistic was included as this is a date that many published projections focus on, because it is an important date for many 'net zero' targets as mandated by the 2015 Paris Agreement of UNFCCC, and therefore a statistic that readers are likely to be interested in. As stated in our response to reviewer 2 comment 1, we have prioritised assumptions that are non-country-specific and forward looking wherever possible, to maximise the transferability of results. In essence, the critical (and transferable) mathematical/biophysical dynamic is the rate of demand increase from the baseline relative to the expansion rate of baseline forest area (through afforestation) and the (change in) productivity of existing (and new) forest. |
| 12 | 754: In this paragraph, it would be good if authors give a brief about the different HWP end-uses (primary and cascading uses) considered and maybe a justification for selecting HWP uses. | The primary HWP end-uses are described in the methodology and further in the SI, including in a revised system diagram (Figure 5) indicating major processes and products. Details of cascading uses and also justification for the assumptions used are provided in Forster et al. (2021), which we cite in the methodology. We believe there is insufficient justification to repeat all of this detail here, especially on account of the word count restrictions. |
| 13 | 766: More clarity on defining the overseas forest type and carbon storage. Also, was the transportation distance and mode of transport included in the analysis? | Description of the overseas forest types is provided in the following paragraphs (lines 827-856). We did not include assumed differences in transport distances between overseas and domestic HWPs because, in a previous study (Forster et al., 2023), we found that these differences made a very small contribution (less than 0.01%) relative to the net GWP impact of the value chain. |
| Remarks to Author | Author Responses | |
| 14 | 785: Why 'GWP (forest C) impact...'? Forest carbon can be stored, emitted, or removed but cannot be equated directly to GWP. | GWP (forest C) impact is defined in the previous paragraph as the 'GWP impact of fluxes in forest carbon stocks'. We have added '(GWP (forest C))' after this definition to add clarity. It refers to the GWP impact associated with net CO2e fluxes to/from the forest. Line 828. |
| 15 | Overall, the methods section appears weak to me and needs a thorough revision to ensure that work can be reproduced. | We have carefully addressed each of the reviewers specific comments and assume that in doing so we have met the need expressed here. |
| Minor comments | ||
| 16 | 325 and 330: should it be trip or tip? | Tip means to tilt, tumble or topple. In this context, 'the balance... can tip from sink to source', means the balance can tilt or shift from sink to source. |
| 17 | 723: why question marks in middle of the sentence? | We have removed these. |
| 18 | Double numbering in references | We have removed these. |
| 19 | Some of the links in supplementary excel file are broken. Please check. | We have checked the supplementary Excel files and assume that the reviewer is referring to the 'HWP calculation module' (Supplementary Data 2). The broken links are the emissions factors cells that link to the original LCA worksheet (Supplementary Data 1) that generated them. These links do not work once the workbook is resaved in a different location. However, in the final version of these supplementary data files, which will be stored in a permanent repository or in the publisher's website, we will endeavour to ensure the links all work. |
| Remarks to Author | Author Responses | |
| Reviewer #3: | ||
| 1 | The aim of this case study is to quantify the GHG mitigation potential of different measures or forest management options (in particular afforestation) in terms of meeting an increasing demand for wood, assuming that existing models for the forest sector tend to "underestimate" forest carbon fluxes. |
| Remarks to Author | Author Responses | |
| 2 | In fact, in addition to the development of forest carbon stocks, the delayed release of biogenic carbon through the use of wood as material as well as potential shifting effects of the GHG emission balances associated with the life cycle of these wood-containing product systems and their potential product alternatives do also have an impact on the overall GHG balance. | We are not clear on whether this forms part of the reviewer's summary of the paper or is a comment requiring our response. In case it is the latter, we provide the following clarification. We agree with the reviewer about the importance of impacts throughout the wood products life cycle. Yet, the full climate mitigation effects of forestry are often under-represented because: (i) LCA approaches that (sometimes) better represent substitution and possible end-of-life carbon storage effects are usually only applied to specific products (partial wood flows out of forests); (ii) inventory approaches that attempt to represent all wood products, typically neglect short-lived wood products used for energy, and don't attribute substitution effects back to the forest sector; (iii) both LCA and inventory approaches often disregard cascading uses of wood, and associated second (and possible further) substitutions, along with extended carbon storage effects; (iv) only very prospective analyses consider future BECCS deployment that could lock up biogenic carbon indefinitely. |
| 3 | However, the implementation of the presented approach to estimate the total GHG impact of different management scenarios compared to the defined reference, in our view appears to be completely inadequate in this study. It does not comply with applicable international standards and existing state-of-the-art knowledge. | We are surprised by this comment, which we strongly believe does not reflect the content and rigour of our study or its compliance with the highest international standards. International standards apply to specific types of accounting, such as Environmental Product Declarations for (LCA of) wood products or UNFCCC guidelines for national GHG inventory accounting. In contrast, there are no such "standards" for the prospective consequential LCA of entire forest and multiple downstream product systems, which is the innovative methodological approach of our study. That is precisely what makes this approach so valuable - it transcends the accounting rules that are applied to deal with truncated system boundaries (e.g. how to allocate forest effects to specific downstream products). This paper builds on the state-of-the-art approach applied in our earlier, highly-cited manuscript published in Nature Communications (Forster et al., 2021), and applies it to a temperate forest system under different management and expansion regimes; this is highly novel. To reiterate, we are not attempting to calculate product footprints as per various international standards, but instead (as is befitting for a submission to Nature Communications) to provide a novel, holistic and rigorous analysis of the climate mitigation efficacy of different forest |
| Remarks to Author | Author Responses | |
| management strategies, providing key new evidence for policy makers and an innovative novel approach for researchers. | ||
| 4 | While the modeling of carbon storage development in the forest using the internationally recognized Carbon Budget Model for the Canadian Forestry Service appears adequate, the methodological inadequacies relate in particular to the life cycle assessment methodology, on the basis of whose standard-compliant, consistent and transparent implementation avoided emissions through "product substitution" can be estimated in the first place. In order to adequately consider process chain emissions and based on those also "avoided emissions from product substitution", it is crucial to meet the internationally standardized requirements for life cycle assessment (including ISO 14040/44 and ISO 21930). The mere summation of unrelated LCA process information from a background database (here: Ecolnvent) is inadequate - at least for the processes outside the forest along the processing and value chain. The data used for calculating "primary avoided emissions (FF/product substitution)" are also completely unsuitable. | As explained in our previous response, our study is categorically not an attributional LCA, which seems to have been the assumption of the reviewer. Instead it is a consequential LCA. That said, this study does fully comply with ISO 14040 and 14044 standards (which in reality are a basic framework to structure LCA), insofar as it: provides a clear goal and scope of the study; calculates an inventory that accounts for relevant changes within the expanded system boundary; applies GWP100 characterisation factors for the life cycle impact assessment phase; interprets results with clear regard for the question and methods used (now including important sensitivity analyses). Furthermore, this study uses system expansion in preference to allocation, as recommended in the allocation hierarchy advocated by the ISO standards. The study categorically does not sum "unrelated" LCA process information - incurred or avoided processes are clearly indicated in the system boundary (Supplementary Fig. 1 and in Fig. 5, also now improved), based on a consequential LCA approach that incorporates all changes associated with the scenarios vs the baseline counterfactual situation. |
| Remarks to Author | Author Responses | |
| 5 | Furthermore, the simplified calculation of assumed carbon storage effects through the use of wood as a material (harvested wood products) contradicts central core requirements in the calculation as set out in the methodological guidelines and requirements provided by IPCC (incl. e.g. the consideration of inherited emissions). In consequence, the HWP contribution trough biogenic carbon storage as well as potential “avoided emissions” appear to be massively overestimated. | Our methodology does not contradict core requirements of the IPCC and from this comment we do not understand how the reviewer considers that it does so. The IPCC provide guidelines for a number of approaches to deal with HWP C storage at a national scale. All of them are based on the fact that biogenic carbon harvested from the forest is "lost" from the terrestrial system, but some of this carbon "reappears" in longer-lived HWPs (with a large deficit reflecting biomass assumed to be immediately oxidised via combustion for bioenergy or for kiln drying of wood in sawmills). Our approach fully respects the biogenic carbon balance - in fact much more explicitly than typical national inventory accounting (with which we are fully familiar), because flows into all main products, both short- and long-lived, are accounted for, and the duration of carbon storage in each of these products, and any subsequent products, is explicitly accounted for in our methodology. Again, this is the benefit of our expanded boundary approach (across products and through time) - it minimises the influence of "cut-off" rules and associated value judgements, which often profoundly influence carbon footprints at the wood product level. |
| 6 | While it seems undisputed, even without the present study, that "the expansion of the (industrial) bioeconomy should be linked to the availability of raw materials in order to avoid unrealistic supply expectations, " statements to the effect that "considerable HWP-C storage and product substitution credits can be achieved simultaneously" are not at all tenable on the basis of this simple and, in our view, completely methodologically inadequate implementation. | Without any further insight from the reviewer as to where our methodology is inadequate, it is difficult to address this comment. We acknowledge that our original system diagram did not fully represent the scope of our methodology (although it was described fully in the text and illustrated in Supplementary Fig.1). Therefore, we hope that the improved system diagram in the resubmitted manuscript helps understanding of precisely how we have linked forest C storage with HWP-C storage and substitution, and long-term CCS C-storage effects in a coherent manner. As previously mentioned, this builds on the methodology of Forster et al. (2021) published in Nature Communications. |
| 7 | The simple comparison of a changing supply of forest wood with a modeled demand for this raw material (including potential gaps) can also be carried out without balancing all GHG emissions relevant to the forestry and wood sector. | It is true that a simple comparison of changing supply of forest wood with demand could be carried out in isolation. However, that would not address the important policy question of what the climate mitigation effect would be of alternative forest strategies that aim to minimise the gap between future supply and demand. That is where this paper provides a unique and robust contribution to the scientific literature, for which its rigorous methodological consequential LCA approach is essential. |
| Comments | # | Part | Point by Point Answers & Actions |
| Editor comments found in the separate cover letter. | 1-8 | All | See author cover letter. |
| REVIEWER COMMENTS | |||
| Reviewer #1 (Remarks to the Author) | |||
| The article systematically reviews sharing of electronic health records in a cloud. The text is original, follows PRISMA protocol, and presents exciting insights to the scientific community. The authors considered the most significant articles on the subject. The protocol is adequately done and gives a discussion pointing out the main challenges and issues. | 9 | All | We thank the reviewer for constructive and helpful comments that has ameliorated the manuscript. |
| Following, I list some suggestions to improve the article: GPOC is not a universally accepts term. It is a term that the authors have used in previous publications. Although I do not see a problem using this term in the article, the authors could better define what it means and why it could not use just PHR to represent this idea. Another doubt is what is a co-owned cloud. Is it a public cloud? Is it co-owned by the patient and its health providers? | 10 | Abst. & Intro. | It is important to define the unicity of the GPOC concept. This has now been implemented into the abstract and introduction. Hence, now there is a better definition of GPOC, what the co-ownership is, and why it is trisected for legal reasons. It has also been highlighted why a just PHR cannot be used - the unicity of the GPOC concept of cloud, ownership, AI integration, blockchain, foundation, globality, sharing, independence, interaction, legal foundation status and substrate for development of ML and its global dissemination has been elaborated. |
| - Pg. 3, line 45: The authors discuss centralized approaches. It would be interesting to mention fog-based and peer-to-peer/hierarchical methods for completeness. | 11 | Disc. | These have now been implemented as valuable new angles. Now the fog-based and peer-to-peer/hierarchical methods are mentioned. |
| - Pg. 3, line 63: How to deal with the language barrier among cross-border travel? I would appreciate a discussion on this subject. | 12 | Disc. | This is now included in the discussion among the challenges and importance of AI integration into GPOC. We exemplify how inbuilt AI in the GPOC can solve language barriers. |
| - When discussing security and privacy, one of the focuses of the article, you could analyze the implications of HIPAA and GDPR. There are many aspects that these kinds of legislation affect privacy regarding health records and their relation to cross-border travel. | 13 | Disc. | Thank you, we have now added a comment about and discuss this. After this comment we refer to the GPOC Ethics article 4 in the reference list. This article focuses entirely on ethics, policy, legal, regulation, etc. This is a self-contained article within the GPOC series. GDPR & HIPAA now mentioned (without infringing into the Ethics' article), but now highlighted. Indeed, we cross reference to the article that focuses in this area. |
| - Security is mainly discussed in terms of encryption and decryption of data. A more comprehensive analysis is needed, including the three main aspects of confidentiality, integrity, and availability. | 14 | Disc. | Now included and analysed articles of the systematic review elaborating these main aspects of confidentiality (19), integrity (11), and availability (12). Within parenthesis the number of articles bringing this up. |
| - The method is well-defined and based on PRISMA. I did not find the range period for the search, the bases used, and the specific terms (on page 5, line 94, you only mention some keywords). | 15 | Meth. & Supp. | The PRISMA form and other search documents have now all been included as supplements and referred to as supplementary files S1 and S3. In S1 the full search strategy is visible, with search strings etc. In S3 the PRISMA checklist is presented. |
| - Interoperability is an important issue that is not discussed at all. What is the rationale, and why was it not considered in the article? A global trend of adopting HL7 FHIR and other standards could influence the field. | 16 | Disc. | Now interoperability has been implemented into the discussion. Now also HL7 FHIR is mentioned. Also, as cross referencing here has been made to the technical EcoTech Article.2 |
| - Security-based parameters are based on times in ms: time for encryption, time for decryption, and the ratio of means. These times depend on the infrastructure: CPU, memory, network bandwidth, etc. Just showing the number of effective time adds little value since it depends on the infrastructure used. | 17 | Disc. | Now this point has been added to the big picture of the results, as a decisive factor to consider and weigh in. The infrastructure for GPOC is further elaborated in the technical EcoTech Article.2 It is cross referenced to this now too. |
| - I missed a big picture of the main issues in the discussion section. The discussion is based on the more traditional "Author X studied that; Author Y discusses." You could start with a big picture presenting the main topics and then more implicitly bringing the related studies. | 18 | Disc. | Now the big picture has been added in an initial overview, but also in the discussion. Al the places with the traditionally mention "author X studied Y", etc, have been altered and the result is a much better flow. Now, with an accompanying big picture before and after, it is more readable. As advised the big picture is presented mainly first. Also, the traditional author mentioning has been partly altered where possible now, so that the sentences are more neutral as flow better for the reader. |
| - You cite types of encryptions on lines 281-282 (page 14). A | 19 | Disc. | A discussion has now been added on encryption. Also, referring further the technical EcoTech Article.2 |
| discussion could be added because it could influence many essential aspects of the study. | |||
| - Blockchain is cited throughout the article, and I consider it a trend. You could discuss this technology further and its impact with more emphasis. Other crucial subjects still need to be explored, including Federated Learning, Fog Computing, and the Internet of Things (how to combine data from wearables from patient EHR, for instance). | 20 | Disc. | Now a discussion on blockchain has been added. Federated Learning, Fog Computing, and the Internet of Things are mentioned, but also a refence for further reading of the technical EcoTech Article.2 |
| - On page 19 you discuss machine learning and the use of data for decision, preserving privacy, and not disclosing patients' details. Anonymization and obfuscation, among other methods, could be commented on here. Although machine learning is not a focus of the article, it could influence GPOC in the services provided and organization/storage of data. | 21 | Disc. | Now anonymization and obfuscation and also fully homomorphic encryption is mentioned. There is now also a refence for further reading, where this is elaborated, in the technical EcoTech Article.2 |
| - Line 386, Pg. 20, you cite "four main areas," but it seems to be five. It would be nice to see some future directions. | 22 | Disc. | It has now been changed to eight (8) areas. Moreover, the initial keywords of each numbered paragraph have been italicised for increased clarity and readability. Also, the number of directions has been expanded and are now also synched with the six problems statements mentioned before and also referred to in the GPOC series. Again, without any overlap. |
| Reviewer #2 (Remarks to the Author) | |||
| I appreciate and congratulate all authors for adding knowledge to the Global Patient co-Owned Cloud (GPOC) a Cloud-based infrastructure that shares information in the Personal Health Records (PHRs) space. This topic is timely and requires significant effort to establish particularly the concern on data security and privacy. I believe the meta-analysis and results presented cover the area to some extent however the term "use-ubiquity globally" that appears in the abstract does not take any advantages and/or support to the claims (refer to the conclusion). | 23 | Abst. | Many thanks. And yes, the term "use-ubiquity globally" that appears in the abstract, has been deleted and the whole abstract has been completely rewritten. It is now within the required limits of <150 words. Decreased from 244 words. The abstract now starts to explain GPOC, present the series to get a context for the reader, and then focuses on the article and what it shows. |
| Some areas to improve based on the Electronic Health Records (EHRs) use are: 1) Validity and Bias: This is very | 24 | Res. | Now under the headline "Validity and Bias" it has now been explained how the risk minimisation was managed. |
| critical to explain how the risk minimisation was managed. | |||
| (2) Perhaps, the language tone could be lower to support the general audience and/or readers (since this is Open Access Forum) | 25 | All | As the editor also requested (and referred to reviewer 2) above in comment #1, very sentence of the whole manuscript has been put under revision and all paragraphs and nearly all sentences have been altered or polished. Hence, the language is now more suitable to a general audience and the sentences are shorter. |
| (3) In addition, Blockchain protocol-embedded PHRs might be another approach that did not dominate the claim hence expanding such idea and thinking would be an added advantage to the paper. | 26 | Disc. | Now the approach with Blockchain protocol-embedded PHRs have been mentioned. Reference added for further reading of the technical EcoTech Article.² |
| (4) The conclusion section: I believe there are standards when sharing EHRs. Such standards are depending on the Geographical locations, the Country's data protection, and/or breaches of legislation and their maturity (this is a very challenging area indeed). | 27 | Disc. & Con. | Now a hint about this is added. This is the full subject and ranger of the Ethics Article⁴. This article fully delves into geographical variations of ethics, legislation, regulations and policies. It is an additional combinatory literature review and interview series on this subject. It could not have been included into the present systematic review and meta-analysis and is self-contained and stands on its own legs. But a cross referencing has been added with the mentioned hint that this is important, and worth an independent article. See also the last point 8 of the discussion summary. |
| ADDITIONAL COMMENTS | |||
| The manuscripts share some form of information from the literature search. While they are interconnected, scientifically it is appropriate and valid to share the information however, the merit of the research contributions is somewhat diluted. Hence, it would be advisable to realign the findings specifically to the papers titled, for the readers' comfort and benefits. | 28 | Abst. & Intro | Absolutely. The overlaps have now been minimised. Hopefully eradicated. For instance, it is requested in the revision to delve into a technical or regulatory area, and these have now been added, but only very brief and with a reference to the article which has this area as its main subject. It is hence important that the required technical additions to the systematic review do not infringe or dilute other manuscripts. Note: all mentioning of subjects belonging to another manuscript has been deleted. Similarly other subjects belonging to other self-contained parts of the series have been deleted and referred to only once for information to the reader (ref 1,2,3,4). Hence, a total realignment has been carried out. |
| In general, the body of work completed was commendable. At the same time, the written language may not be palatable to a wider audience and readers. It would be advisable to make it simple where possible since there are several paragraphs confounded with each other. | 29 | All | The whole text has now been rewritten. The language has been improved and the text flows better. It is adapted to the general audience. The sentences are less complex and shorter. Easier terminology has been used, whenever possible. When comparing the previous version and the present article manuscript all paragraphs and a majority of sentences have been rewritten to ease the flow for the general audience, which is very important. The paragraphing has been changed to eradicate any confusion. Note that now both the abstract (in two short sentences) and the introduction's first paragraph briefly mention the GPOC concept and the context of the GPOC series. Hence, the reader will immediately know what to expect from the articles regarding scope and contents. At the onset the reader will be aware that the five GPOC series entities will display distinct and non-overlapping foci. Hence, this sets the tone and contributes to the realignment of the articles. Several paragraphs hence became superfluous and have been deleted. |
| One of the classic examples in the paper titled "Systematic Review and Meta-Analysis for a Global Patient co-Owned Cloud (GPOC)" uses the term "use-ubiquity globally" in the abstract. This term is misleading based on the conclusion. | 30 | Abst. | This term has been deleted. The abstract has been completely rewritten. It has also been shortened from 244 to the required 150 words maximum. All intricate sentences with complex syntax and prosody have been altered. The message is clearer. Sentences shorter. Language simpler. |
| Comments | # | Part | Point by Point Answers & Actions |
| Editor comments found in the separate cover letter. | 1-8 | All | See author cover letter. |
| REVIEWER COMMENTS | |||
| Reviewer #1 (Remarks to the Author) | |||
| The article systematically reviews sharing of electronic health records in a cloud. The text is original, follows PRISMA protocol, and presents exciting insights to the scientific community. The authors considered the most significant articles on the subject. The protocol is adequately done and gives a discussion pointing out the main challenges and issues. | 9 | All | We thank the reviewer for constructive and helpful comments that has ameliorated the manuscript. |
| Following, I list some suggestions to improve the article: GPOC is not a universally accepts term. It is a term that the authors have used in previous publications. Although I do not see a problem using this term in the article, the authors could better define what it means and why it could not use just PHR to represent this idea. Another doubt is what is a co-owned cloud. Is it a public cloud? Is it co-owned by the patient and its health providers? | 10 | Abst. & Intro. | It is important to define the unicity of the GPOC concept. This has now been implemented into the abstract and introduction. Hence, now there is a better definition of GPOC, what the co-ownership is, and why it is trisected for legal reasons. It has also been highlighted why a just PHR cannot be used - the unicity of the GPOC concept of cloud, ownership, AI integration, blockchain, foundation, globality, sharing, independence, interaction, legal foundation status and substrate for development of ML and its global dissemination has been elaborated. |
| - Pg. 3, line 45: The authors discuss centralized approaches. It would be interesting to mention fog-based and peer-to-peer/hierarchical methods for completeness. | 11 | Disc. | These have now been implemented as valuable new angles. Now the fog-based and peer-to-peer/hierarchical methods are mentioned. |
| - Pg. 3, line 63: How to deal with the language barrier among cross-border travel? I would appreciate a discussion on this subject. | 12 | Disc. | This is now included in the discussion among the challenges and importance of AI integration into GPOC. We exemplify how inbuilt AI in the GPOC can solve language barriers. |
| - When discussing security and privacy, one of the focuses of the article, you could analyze the implications of HIPAA and GDPR. There are many aspects that these kinds of legislation affect privacy regarding health records and their relation to cross-border travel. | 13 | Disc. | Thank you, we have now added a comment about and discuss this. After this comment we refer to the GPOC Ethics article 4 in the reference list. This article focuses entirely on ethics, policy, legal, regulation, etc. This is a self-contained article within the GPOC series. GDPR & HIPAA now mentioned (without infringing into the Ethics' article), but now highlighted. Indeed, we cross reference to the article that focuses in this area. |
| - Security is mainly discussed in terms of encryption and decryption of data. A more comprehensive analysis is needed, including the three main aspects of confidentiality, integrity, and availability. | 14 | Disc. | Now included and analysed articles of the systematic review elaborating these main aspects of confidentiality (19), integrity (11), and availability (12). Within parenthesis the number of articles bringing this up. |
| - The method is well-defined and based on PRISMA. I did not find the range period for the search, the bases used, and the specific terms (on page 5, line 94, you only mention some keywords). | 15 | Meth. & Supp. | The PRISMA form and other search documents have now all been included as supplements and referred to as supplementary files S1 and S3. In S1 the full search strategy is visible, with search strings etc. In S3 the PRISMA checklist is presented. |
| - Interoperability is an important issue that is not discussed at all. What is the rationale, and why was it not considered in the article? A global trend of adopting HL7 FHIR and other standards could influence the field. | 16 | Disc. | Now interoperability has been implemented into the discussion. Now also HL7 FHIR is mentioned. Also, as cross referencing here has been made to the technical EcoTech Article.2 |
| - Security-based parameters are based on times in ms: time for encryption, time for decryption, and the ratio of means. These times depend on the infrastructure: CPU, memory, network bandwidth, etc. Just showing the number of effective time adds little value since it depends on the infrastructure used. | 17 | Disc. | Now this point has been added to the big picture of the results, as a decisive factor to consider and weigh in. The infrastructure for GPOC is further elaborated in the technical EcoTech Article.2 It is cross referenced to this now too. |
| - I missed a big picture of the main issues in the discussion section. The discussion is based on the more traditional "Author X studied that; Author Y discusses." You could start with a big picture presenting the main topics and then more implicitly bringing the related studies. | 18 | Disc. | Now the big picture has been added in an initial overview, but also in the discussion. Al the places with the traditionally mention "author X studied Y", etc, have been altered and the result is a much better flow. Now, with an accompanying big picture before and after, it is more readable. As advised the big picture is presented mainly first. Also, the traditional author mentioning has been partly altered where possible now, so that the sentences are more neutral as flow better for the reader. |
| - You cite types of encryptions on lines 281-282 (page 14). A | 19 | Disc. | A discussion has now been added on encryption. Also, referring further the technical EcoTech Article.2 |
| discussion could be added because it could influence many essential aspects of the study. | |||
| - Blockchain is cited throughout the article, and I consider it a trend. You could discuss this technology further and its impact with more emphasis. Other crucial subjects still need to be explored, including Federated Learning, Fog Computing, and the Internet of Things (how to combine data from wearables from patient EHR, for instance). | 20 | Disc. | Now a discussion on blockchain has been added. Federated Learning, Fog Computing, and the Internet of Things are mentioned, but also a refence for further reading of the technical EcoTech Article.2 |
| - On page 19 you discuss machine learning and the use of data for decision, preserving privacy, and not disclosing patients' details. Anonymization and obfuscation, among other methods, could be commented on here. Although machine learning is not a focus of the article, it could influence GPOC in the services provided and organization/storage of data. | 21 | Disc. | Now anonymization and obfuscation and also fully homomorphic encryption is mentioned. There is now also a refence for further reading, where this is elaborated, in the technical EcoTech Article.2 |
| - Line 386, Pg. 20, you cite "four main areas," but it seems to be five. It would be nice to see some future directions. | 22 | Disc. | It has now been changed to eight (8) areas. Moreover, the initial keywords of each numbered paragraph have been italicised for increased clarity and readability. Also, the number of directions has been expanded and are now also synched with the six problems statements mentioned before and also referred to in the GPOC series. Again, without any overlap. |
| Reviewer #2 (Remarks to the Author) | |||
| I appreciate and congratulate all authors for adding knowledge to the Global Patient co-Owned Cloud (GPOC) a Cloud-based infrastructure that shares information in the Personal Health Records (PHRs) space. This topic is timely and requires significant effort to establish particularly the concern on data security and privacy. I believe the meta-analysis and results presented cover the area to some extent however the term "use-ubiquity globally" that appears in the abstract does not take any advantages and/or support to the claims (refer to the conclusion). | 23 | Abst. | Many thanks. And yes, the term "use-ubiquity globally" that appears in the abstract, has been deleted and the whole abstract has been completely rewritten. It is now within the required limits of <150 words. Decreased from 244 words. The abstract now starts to explain GPOC, present the series to get a context for the reader, and then focuses on the article and what it shows. |
| Some areas to improve based on the Electronic Health Records (EHRs) use are: 1) Validity and Bias: This is very | 24 | Res. | Now under the headline "Validity and Bias" it has now been explained how the risk minimisation was managed. |
| critical to explain how the risk minimisation was managed. | |||
| (2) Perhaps, the language tone could be lower to support the general audience and/or readers (since this is Open Access Forum) | 25 | All | As the editor also requested (and referred to reviewer 2) above in comment #1, very sentence of the whole manuscript has been put under revision and all paragraphs and nearly all sentences have been altered or polished. Hence, the language is now more suitable to a general audience and the sentences are shorter. |
| (3) In addition, Blockchain protocol-embedded PHRs might be another approach that did not dominate the claim hence expanding such idea and thinking would be an added advantage to the paper. | 26 | Disc. | Now the approach with Blockchain protocol-embedded PHRs have been mentioned. Reference added for further reading of the technical EcoTech Article.² |
| (4) The conclusion section: I believe there are standards when sharing EHRs. Such standards are depending on the Geographical locations, the Country's data protection, and/or breaches of legislation and their maturity (this is a very challenging area indeed). | 27 | Disc. & Con. | Now a hint about this is added. This is the full subject and ranger of the Ethics Article⁴. This article fully delves into geographical variations of ethics, legislation, regulations and policies. It is an additional combinatory literature review and interview series on this subject. It could not have been included into the present systematic review and meta-analysis and is self-contained and stands on its own legs. But a cross referencing has been added with the mentioned hint that this is important, and worth an independent article. See also the last point 8 of the discussion summary. |
| ADDITIONAL COMMENTS | |||
| The manuscripts share some form of information from the literature search. While they are interconnected, scientifically it is appropriate and valid to share the information however, the merit of the research contributions is somewhat diluted. Hence, it would be advisable to realign the findings specifically to the papers titled, for the readers' comfort and benefits. | 28 | Abst. & Intro | Absolutely. The overlaps have now been minimised. Hopefully eradicated. For instance, it is requested in the revision to delve into a technical or regulatory area, and these have now been added, but only very brief and with a reference to the article which has this area as its main subject. It is hence important that the required technical additions to the systematic review do not infringe or dilute other manuscripts. Note: all mentioning of subjects belonging to another manuscript has been deleted. Similarly other subjects belonging to other self-contained parts of the series have been deleted and referred to only once for information to the reader (ref 1,2,3,4). Hence, a total realignment has been carried out. |
| In general, the body of work completed was commendable. At the same time, the written language may not be palatable to a wider audience and readers. It would be advisable to make it simple where possible since there are several paragraphs confounded with each other. | 29 | All | The whole text has now been rewritten. The language has been improved and the text flows better. It is adapted to the general audience. The sentences are less complex and shorter. Easier terminology has been used, whenever possible. When comparing the previous version and the present article manuscript all paragraphs and a majority of sentences have been rewritten to ease the flow for the general audience, which is very important. The paragraphing has been changed to eradicate any confusion. Note that now both the abstract (in two short sentences) and the introduction's first paragraph briefly mention the GPOC concept and the context of the GPOC series. Hence, the reader will immediately know what to expect from the articles regarding scope and contents. At the onset the reader will be aware that the five GPOC series entities will display distinct and non-overlapping foci. Hence, this sets the tone and contributes to the realignment of the articles. Several paragraphs hence became superfluous and have been deleted. |
| One of the classic examples in the paper titled "Systematic Review and Meta-Analysis for a Global Patient co-Owned Cloud (GPOC)" uses the term "use-ubiquity globally" in the abstract. This term is misleading based on the conclusion. | 30 | Abst. | This term has been deleted. The abstract has been completely rewritten. It has also been shortened from 244 to the required 150 words maximum. All intricate sentences with complex syntax and prosody have been altered. The message is clearer. Sentences shorter. Language simpler. |
| Batch | Surrogate Mice | iPrEs Injected Blastocysts | Embryos at E12.5 | Embryos With GFP Labeled |
| 1 | 2 | 28 | 8 | 5 |
| 2 | 1 | 7 | 2 | 2 |
| 3 | 2 | 12 | 0 | / |
| 4 | 1 | 14 | 2 | 2 |
| 5 | 2 | 22 | 7 | 3 |
| SUM | 8 | 83 | 19 | 12 |
| Batch | Surrogate Mice | iPrEs Injected Blastocysts | Embryos at E12.5 | Embryos With GFP Labeled |
| 1 | 2 | 28 | 8 | 5 |
| 2 | 1 | 7 | 2 | 2 |
| 3 | 2 | 12 | 0 | / |
| 4 | 1 | 14 | 2 | 2 |
| 5 | 2 | 22 | 7 | 3 |
| SUM | 8 | 83 | 19 | 12 |
| MD trajectory | Zundel-like | Intermediate | Eigen-like |
| Classical GGA | 0.31 | 0.42 | 0.27 |
| Quantum hybrid | 0.33 | 0.42 | 0.25 |
| CND | CNA | CND - CNA | |
| O1 | 1.94 | 0.04 | 1.90 |
| O2 | 1.65 | 0.45 | 1.20 |
| Difference | 0.29 | -0.41 | 0.70 |
| CND | CNA | CND - CNA | |
| O1 | 1.93 | 0.07 | 1.86 |
| O2 | 1.75 | 0.30 | 1.45 |
| Difference | 0.18 | -0.23 | 0.41 |
| CND | CNA | CND - CNA | |
| O1 | 1.90 | 0.13 | 1.77 |
| O2 | 1.84 | 0.19 | 1.65 |
| Difference | 0.06 | -0.06 | 0.12 |
| MD trajectory | Zundel-like | Intermediate | Eigen-like |
| Classical GGA | 0.31 | 0.42 | 0.27 |
| Quantum hybrid | 0.33 | 0.42 | 0.25 |
| CND | CNA | CND - CNA | |
| O1 | 1.94 | 0.04 | 1.90 |
| O2 | 1.65 | 0.45 | 1.20 |
| Difference | 0.29 | -0.41 | 0.70 |
| CND | CNA | CND - CNA | |
| O1 | 1.93 | 0.07 | 1.86 |
| O2 | 1.75 | 0.30 | 1.45 |
| Difference | 0.18 | -0.23 | 0.41 |
| CND | CNA | CND - CNA | |
| O1 | 1.90 | 0.13 | 1.77 |
| O2 | 1.84 | 0.19 | 1.65 |
| Difference | 0.06 | -0.06 | 0.12 |
| Region | Pearson correlation between early occupancy and occupancy change index | P value |
| Austria | -0.042 | <0.05 |
| Czech Republic | 0.040 | 0.087 |
| Denmark | -0.005 | 0.871 |
| Flanders | -0.026 | 0.454 |
| Germany | 0.036 | 0.137 |
| Great Britain | 0.176 | <0.01 |
| Ireland | 0.063 | 0.057 |
| The Netherlands | 0.016 | 0.595 |
| Switzerland | 0.003 | 0.889 |
| Thiérache | -0.014 | 0.703 |
| # regions where species is present | # species |
| 10 | 288 |
| 9 | 186 |
| 8 | 144 |
| 7 | 130 |
| 6 | 165 |
| 5 | 218 |
| 4 | 356 |
| 3 | 487 |
| 2 | 685 |
| 1 | 1261 |
| Regions | # total grid cells | # species present in all grid cells | # species present in more than 95% grid cells | # total native species used in analysis |
| Austria | 2600 | 0 | 2 | 2419 |
| Czech Republic | 2551 | 0 | 0 | 1834 |
| Denmark | 263 | 0 | 5 | 921 |
| Flanders | 985 | 0 | 29 | 861 |
| Germany | 12024 | 0 | 65 | 1715 |
| Great Britain | 2852 | 0 | 0 | 1355 |
| Ireland | 1007 | 0 | 8 | 910 |
| The Netherlands | 1685 | 0 | 30 | 1115 |
| Switzerland | 1827 | 0 | 0 | 2307 |
| Thérache | 129 | 0 | 0 | 775 |
| Region | Pearson correlation between early occupancy and occupancy change index | P value |
| Austria | -0.042 | <0.05 |
| Czech Republic | 0.040 | 0.087 |
| Denmark | -0.005 | 0.871 |
| Flanders | -0.026 | 0.454 |
| Germany | 0.036 | 0.137 |
| Great Britain | 0.176 | <0.01 |
| Ireland | 0.063 | 0.057 |
| The Netherlands | 0.016 | 0.595 |
| Switzerland | 0.003 | 0.889 |
| Thiérache | -0.014 | 0.703 |
| # regions where species is present | # species |
| 10 | 288 |
| 9 | 186 |
| 8 | 144 |
| 7 | 130 |
| 6 | 165 |
| 5 | 218 |
| 4 | 356 |
| 3 | 487 |
| 2 | 685 |
| 1 | 1261 |
| Regions | # total grid cells | # species present in all grid cells | # species present in more than 95% grid cells | # total native species used in analysis |
| Austria | 2600 | 0 | 2 | 2419 |
| Czech Republic | 2551 | 0 | 0 | 1834 |
| Denmark | 263 | 0 | 5 | 921 |
| Flanders | 985 | 0 | 29 | 861 |
| Germany | 12024 | 0 | 65 | 1715 |
| Great Britain | 2852 | 0 | 0 | 1355 |
| Ireland | 1007 | 0 | 8 | 910 |
| The Netherlands | 1685 | 0 | 30 | 1115 |
| Switzerland | 1827 | 0 | 0 | 2307 |
| Thérache | 129 | 0 | 0 | 775 |
| Study | Topical intervention | Lumen reduction | Mean percent improvements in lumen stenosis | |
| Control group | Experimental group | |||
| Kurt 2017[7] | 40 mg Triamcinolone, submucosal injection, once | 91.0% | 49.0% | 46.2% |
| Chong 2011[8] | 0.1 mg Rapamycin, urethral irrigation, daily for 28 days | 69.1% | 56.6% | 18.1% |
| 1 mg Rapamycin, urethral irrigation, daily for 28 days | 69.1% | 36.5% | 47.2% | |
| Kurt 2017[7] | 0.5 mg/mL Mitomycin-C (MMC), hydropathic compress, once | 91.0% | 45.0% | 50.5% |
| 0.2 mg Insulin-like growth factor 1 (IGF-1), impregnated collagen sutured to the catheter, catheterization for 14 days | 79.8% | 39.3% | 50.8% | |
| Shinchi 2019[9] | 0.01 mg Docetaxel, urethral irrigation, daily for 28 days | 84.7% | 48.0% | 43.3% |
| 0.1 mg Docetaxel, urethral irrigation, daily for 28 days | 84.7% | 36.9% | 56.4% | |
| This research | 3 mg Rapamycin, sustained release coating modified catheter, catheterization for 30 days | 51.8% | 10.6% | 79.2% |
| Study | Topical intervention | Lumen reduction | Mean percent improvements in lumen stenosis | |
| Control group | Experimental group | |||
| Kurt 2017[7] | 40 mg Triamcinolone, submucosal injection, once | 91.0% | 49.0% | 46.2% |
| Chong 2011[8] | 0.1 mg Rapamycin, urethral irrigation, daily for 28 days | 69.1% | 56.6% | 18.1% |
| 1 mg Rapamycin, urethral irrigation, daily for 28 days | 69.1% | 36.5% | 47.2% | |
| Kurt 2017[7] | 0.5 mg/mL Mitomycin-C (MMC), hydropathic compress, once | 91.0% | 45.0% | 50.5% |
| 0.2 mg Insulin-like growth factor 1 (IGF-1), impregnated collagen sutured to the catheter, catheterization for 14 days | 79.8% | 39.3% | 50.8% | |
| Shinchi 2019[9] | 0.01 mg Docetaxel, urethral irrigation, daily for 28 days | 84.7% | 48.0% | 43.3% |
| 0.1 mg Docetaxel, urethral irrigation, daily for 28 days | 84.7% | 36.9% | 56.4% | |
| This research | 3 mg Rapamycin, sustained release coating modified catheter, catheterization for 30 days | 51.8% | 10.6% | 79.2% |
| Materials | Targeted strategy | Treatment strategy | Brain targeting efficiency | Ref |
| NP-siRNA-CTX. | molecular recognition | gene therapy | / | 1 |
| BPLP-PLAs NPs | molecular recognition | immunotherapy | / | 2 |
| iRGD-loaded SPNPs | molecular recognition | gene therapy | / | 3 |
| siRNA micelles. | molecular recognition | gene therapy | ~15% ID/g | 4 |
| MTX@MnO2-Opca | molecular recognition | chemotherapy | / | 5 |
| Iron oxide nanoparticle | molecular recognition | radiotherapy | / | 6 |
| CpG-exo/TGM | molecular recognition | immunotherapy | / | 7 |
| TrQβ@b-3WJ iSCRLet-7g | molecular recognition | gene therapy and radiotherapy | / | 8 |
| BSO-CAT@MOF-199 @DDM | molecular recognition | immunotherapy | / | 9 |
| ABNPs@mRNA | molecular recognition | gene therapy | 7.22% ID/g | 10 |
| Cannabidiol prodrug | molecular recognition | molecularly targeted therapy | / | 11 |
| \(Cu_{2}-xSe\)NPs | homing | immunotherapy | / | 12 |
| \(Au@Cu_{2}-xSe\)NPs | molecular recognition | immunotherapy | 13 | |
| CRISPR/Cas12a nanocapsule system | / | gene therapy and molecularly targeted therapy | 6.1% ID/g | 14 |
| Lipid nanoparticles | / | gene therapy | / | 15 |
| AMVY@NPs | molecular recognition | molecularly targeted therapy | / | 16 |
| Silk fibroin microneedle | / | molecularly targeted therapy | / | 17 |
| ANCss(Cas9/sgRNA) | molecular | gene therapy | 11.8% ID/g | 18 |
| recognition | ||||
| CSI@Ex-A | molecular recognition | sonodynamic therapy | ~ 7.5% ID/g | 19 |
| Fe-CDs@Ang | molecular recognition | / | 4.5% ID/g | 20 |
| poly(MIs)/PTX@PEI/siPGK1@CCM | homing | chemotherapy and radiotherapy | ~ 6.0% ID/g | 21 |
| Ang-NP@RNP | molecular recognition | gene therapy | 12.9% ID/g | 22 |
| D-iGSNPs | molecular recognition | radiotherapy | ~ 9.0% ID | 23 |
| Ang-NCss(siRNA) | molecular recognition | gene therapy | 6.69% ID/g | 24 |
| NO-Lip@PAC@Cur | chemotaxis | mitochondrial mineralization | 29.3% ID/g | This work |
| Material composition | Loaded drugs | Specificity | Targeted strategy | Mitochondria-targeting strategy | Penetration strategy | Treat ment appro ach | Ref. |
| Ang-PAMS/T LND | chloridr amine | unique metabolic pathways in brain tumor tissues | brain endothelial cell recognition + chemotactic targeting of brain tumor cells | material modification with positively charged triphenylphosphine (TPP) through charge attraction targeting mitochondria | nanomotor movement achieves deep penetration | immunother apy | Nat. Com mun . 202 3, 14, 941. |
| NO-Lip@PAC@Cur | curcum in | the high calcium environmen t unique to brain tumor tissue | chemotaxis targeting the microenvironment of brain tumor | utilizing size-dependent effects and chemotactic targeting effects of high levels of iNOS specific to mitochondria | variable sizes and nanomotor movement achieve further penetration | mitochondrial miner alizati on | This work |
| Experiment name | Experiment purpose | Cells used | Reasons for selection |
| assessment of NO-Lip@PAC degradation behavior | evaluate the selective degradation behavior of NO-Lip@PAC in normal cell and cancer cell environments | HUVECs | simulate normal vascular cells in blood circulation |
| the dynamic behavior of different samples | explore the motility behavior of Lip@PAC and NO-Lip@PAC in normal cell and cancer cell environments | bEnd.3 | simulate normal vascular cells surrounding the tumor microenvironment |
| chemotactic behavior of different samples | explore the chemotactic behavior of Lip@PAC and NO-Lip@PAC in normal cell and cancer cell environments | bEnd.3 | simulate normal vascular cells surrounding the tumor microenvironment |
| evaluation of ability to penetrate the BBB | evaluate the ability of different samples to cross the BBB | bEnd.3 | bEnd.3 can form a dense cell layer that can be used to simulate the BBB in vitro |
| Ca2+ content | evaluate the effects of different samples on Ca2+ content in cells | bEnd.3 | simulate normal cells in the brain to contrast with brain tumor cells |
| cell activity | investigate cell viability after treating different samples with bEnd.3 and Gl261 cells | bEnd.3 | simulate normal cells in the brain to contrast with brain tumor cells |
| Materials | Targeted strategy | Treatment strategy | Brain targeting efficiency | Ref |
| NP-siRNA-CTX. | molecular recognition | gene therapy | / | 1 |
| BPLP-PLAs NPs | molecular recognition | immunotherapy | / | 2 |
| iRGD-loaded SPNPs | molecular recognition | gene therapy | / | 3 |
| siRNA micelles. | molecular recognition | gene therapy | ~15% ID/g | 4 |
| MTX@MnO2-Opca | molecular recognition | chemotherapy | / | 5 |
| Iron oxide nanoparticle | molecular recognition | radiotherapy | / | 6 |
| CpG-exo/TGM | molecular recognition | immunotherapy | / | 7 |
| TrQβ@b-3WJ iSCRLet-7g | molecular recognition | gene therapy and radiotherapy | / | 8 |
| BSO-CAT@MOF-199 @DDM | molecular recognition | immunotherapy | / | 9 |
| ABNPs@mRNA | molecular recognition | gene therapy | 7.22% ID/g | 10 |
| Cannabidiol prodrug | molecular recognition | molecularly targeted therapy | / | 11 |
| \(Cu_{2}-xSe\)NPs | homing | immunotherapy | / | 12 |
| \(Au@Cu_{2}-xSe\)NPs | molecular recognition | immunotherapy | 13 | |
| CRISPR/Cas12a nanocapsule system | / | gene therapy and molecularly targeted therapy | 6.1% ID/g | 14 |
| Lipid nanoparticles | / | gene therapy | / | 15 |
| AMVY@NPs | molecular recognition | molecularly targeted therapy | / | 16 |
| Silk fibroin microneedle | / | molecularly targeted therapy | / | 17 |
| ANCss(Cas9/sgRNA) | molecular | gene therapy | 11.8% ID/g | 18 |
| recognition | ||||
| CSI@Ex-A | molecular recognition | sonodynamic therapy | ~ 7.5% ID/g | 19 |
| Fe-CDs@Ang | molecular recognition | / | 4.5% ID/g | 20 |
| poly(MIs)/PTX@PEI/siPGK1@CCM | homing | chemotherapy and radiotherapy | ~ 6.0% ID/g | 21 |
| Ang-NP@RNP | molecular recognition | gene therapy | 12.9% ID/g | 22 |
| D-iGSNPs | molecular recognition | radiotherapy | ~ 9.0% ID | 23 |
| Ang-NCss(siRNA) | molecular recognition | gene therapy | 6.69% ID/g | 24 |
| NO-Lip@PAC@Cur | chemotaxis | mitochondrial mineralization | 29.3% ID/g | This work |
| Material composition | Loaded drugs | Specificity | Targeted strategy | Mitochondria-targeting strategy | Penetration strategy | Treat ment appro ach | Ref. |
| Ang-PAMS/T LND | chloridr amine | unique metabolic pathways in brain tumor tissues | brain endothelial cell recognition + chemotactic targeting of brain tumor cells | material modification with positively charged triphenylphosphine (TPP) through charge attraction targeting mitochondria | nanomotor movement achieves deep penetration | immunother apy | Nat. Com mun . 202 3, 14, 941. |
| NO-Lip@PAC@Cur | curcum in | the high calcium environmen t unique to brain tumor tissue | chemotaxis targeting the microenvironment of brain tumor | utilizing size-dependent effects and chemotactic targeting effects of high levels of iNOS specific to mitochondria | variable sizes and nanomotor movement achieve further penetration | mitochondrial miner alizati on | This work |
| Experiment name | Experiment purpose | Cells used | Reasons for selection |
| assessment of NO-Lip@PAC degradation behavior | evaluate the selective degradation behavior of NO-Lip@PAC in normal cell and cancer cell environments | HUVECs | simulate normal vascular cells in blood circulation |
| the dynamic behavior of different samples | explore the motility behavior of Lip@PAC and NO-Lip@PAC in normal cell and cancer cell environments | bEnd.3 | simulate normal vascular cells surrounding the tumor microenvironment |
| chemotactic behavior of different samples | explore the chemotactic behavior of Lip@PAC and NO-Lip@PAC in normal cell and cancer cell environments | bEnd.3 | simulate normal vascular cells surrounding the tumor microenvironment |
| evaluation of ability to penetrate the BBB | evaluate the ability of different samples to cross the BBB | bEnd.3 | bEnd.3 can form a dense cell layer that can be used to simulate the BBB in vitro |
| Ca2+ content | evaluate the effects of different samples on Ca2+ content in cells | bEnd.3 | simulate normal cells in the brain to contrast with brain tumor cells |
| cell activity | investigate cell viability after treating different samples with bEnd.3 and Gl261 cells | bEnd.3 | simulate normal cells in the brain to contrast with brain tumor cells |
| gene | nucleotide | amino acid |
| ORF1ab | C3267T | T1001I |
| C5388A | A1708D | |
| T6954C | I2230T | |
| 11288-11296 deletion | SGF 3675-3677 deletion | |
| spike | 21765-21770 deletion | HV 69-70 deletion |
| 21991-21993 deletion | Y144 deletion | |
| A23063T | N501Y | |
| C23271A | A570D | |
| C23604A | P681H | |
| C23709T | T716I | |
| T24506G | S982A | |
| G24914C | D1118H | |
| Orf8 | C27972T | Q27stop |
| G28048T | R52I | |
| A28111G | Y73C | |
| N | 28280 GAT->CTA | D3L |
| C28977T | S235F |
| gene | nucleotide | amino acid |
| ORF1ab | C3267T | T1001I |
| C5388A | A1708D | |
| T6954C | I2230T | |
| 11288-11296 deletion | SGF 3675-3677 deletion | |
| spike | 21765-21770 deletion | HV 69-70 deletion |
| 21991-21993 deletion | Y144 deletion | |
| A23063T | N501Y | |
| C23271A | A570D | |
| C23604A | P681H | |
| C23709T | T716I | |
| T24506G | S982A | |
| G24914C | D1118H | |
| Orf8 | C27972T | Q27stop |
| G28048T | R52I | |
| A28111G | Y73C | |
| N | 28280 GAT->CTA | D3L |
| C28977T | S235F |
| total beta | total alpha | beta/alpha | |
| WT1 | 11.157 | 10.584 | 1.05413832 |
| WT2 | 13.261 | 12.17 | 1.08964667 |
| M83-1-B1 | 11.05 | 10.122 | 1.09168149 |
| M83-2-B2 | 8.995 | 8.329 | 1.07996158 |
| M83-3-B3 | 11.208 | 10.248 | 1.09367681 |
| M83-4-B4 | 10.538 | 9.761 | 1.0796025 |
| M83-5-B5 | 10.459 | 10.076 | 1.03801112 |
| M83-6-B6 | 12.021 | 9.743 | 1.23380889 |
| M85-1-C1 | 11.694 | 10.992 | 1.06386463 |
| M85-2-C2 | 14.149 | 13.024 | 1.08637899 |
| M85-3-C3 | 14.53 | 13.875 | 1.04720721 |
| M85-4-C4 | 14.287 | 13.315 | 1.07300038 |
| M85-5-C5 | 15.969 | 15.049 | 1.06113363 |
| M85-7-C7 | 14.178 | 13.037 | 1.08752014 |
| M85-8-C8 | 12.498 | 11.984 | 1.04289052 |
| M85-9-C9 | 13.527 | 12.556 | 1.0773355 |
| total beta | total alpha | beta/alpha | |
| WT1 | 11.157 | 10.584 | 1.05413832 |
| WT2 | 13.261 | 12.17 | 1.08964667 |
| M83-1-B1 | 11.05 | 10.122 | 1.09168149 |
| M83-2-B2 | 8.995 | 8.329 | 1.07996158 |
| M83-3-B3 | 11.208 | 10.248 | 1.09367681 |
| M83-4-B4 | 10.538 | 9.761 | 1.0796025 |
| M83-5-B5 | 10.459 | 10.076 | 1.03801112 |
| M83-6-B6 | 12.021 | 9.743 | 1.23380889 |
| M85-1-C1 | 11.694 | 10.992 | 1.06386463 |
| M85-2-C2 | 14.149 | 13.024 | 1.08637899 |
| M85-3-C3 | 14.53 | 13.875 | 1.04720721 |
| M85-4-C4 | 14.287 | 13.315 | 1.07300038 |
| M85-5-C5 | 15.969 | 15.049 | 1.06113363 |
| M85-7-C7 | 14.178 | 13.037 | 1.08752014 |
| M85-8-C8 | 12.498 | 11.984 | 1.04289052 |
| M85-9-C9 | 13.527 | 12.556 | 1.0773355 |
| Total recording duration [min] | Movement recording duration [min] | Rest recording duration [min] | N samples at 10 Hz for decoding | ||||
| Cohort-ID | Age | Gender | UPDRS | ||||
| Berlin-001 | 50 | f | n.a. | 7.91 | 2.8 | 5.11 | 4737 |
| Berlin-002 | 62 | m | 21 | 15.78 | 2.2 | 13.58 | 9459 |
| Berlin-003 | 56 | f | 31 | 13.56 | 2.56 | 11 | 8127 |
| Berlin-004 | 45 | f | 36 | 26.42 | 7.85 | 18.58 | 15843 |
| Berlin-005 | 43 | f | 31 | 95.3 | 21.44 | 73.86 | 57171 |
| Berlin-006 | 57 | m | 36 | 40.91 | 3.15 | 37.76 | 24537 |
| Berlin-007 | 55 | m | 59 | 27.46 | 4.55 | 22.91 | 16467 |
| Berlin-008 | 66 | m | 24 | 40.97 | 4.64 | 36.34 | 24573 |
| Berlin-009 | 59 | m | 27 | 30.12 | 2.36 | 27.76 | 18063 |
| Berlin-010 | 66 | f | 30 | 10.77 | 1.8 | 8.97 | 6453 |
| Berlin-011 | 67 | m | 39 | 17.51 | 3.14 | 14.37 | 10497 |
| Berlin-012 | 54 | m | 31 | 19.31 | 2.25 | 17.06 | 11577 |
| Beijing-001 | 66 | f | 61 | 10.92 | 1.74 | 9.18 | 6543 |
| Beijing-002 | 70 | m | 70 | 16.19 | 0.41 | 15.78 | 9705 |
| Beijing-003 | 73 | f | 51 | 9.91 | 0.71 | 9.19 | 5937 |
| Beijing-004 | 67 | f | 52 | 6.99 | 0.74 | 6.24 | 4185 |
| Beijing-005 | 59 | f | 58 | 23.89 | 1.08 | 22.82 | 14325 |
| Beijing-007 | 76 | m | 41 | 21.77 | 0.79 | 20.98 | 13053 |
| Beijing-008 | 66 | f | 55 | 22.69 | 1.24 | 21.45 | 13605 |
| Beijing-009 | 66 | f | 39 | 18.62 | 1.41 | 17.21 | 11163 |
| Beijing-010 | 67 | f | 27 | 11.58 | 2.84 | 8.74 | 6939 |
| Beijing-011 | 72 | f | 47 | 10.62 | 0.61 | 10.02 | 6363 |
| Pittsburgh-001 | 60.3 | m | 28 | 12.62 | 0.93 | 11.7 | 7563 |
| Pittsburgh-002 | 51.2 | m | 27 | 19.46 | 1.03 | 18.43 | 11667 |
| Pittsburgh-003 | 63.8 | m | 40 | 18.93 | 1.98 | 16.95 | 11349 |
| Pittsburgh-004 | 54.7 | f | n.a. | 10.55 | 0.42 | 10.13 | 6321 |
| Pittsburgh-005 | 53.8 | m | 33 | 7.6 | 0.56 | 7.04 | 4551 |
| Pittsburgh-006 | 44.2 | m | 31 | 16.24 | 0.86 | 15.38 | 9735 |
| Pittsburgh-007 | 63.6 | m | 32 | 17.93 | 1.47 | 16.46 | 10749 |
| Pittsburgh-008 | 59.6 | m | 52 | 5.78 | 0.39 | 5.4 | 3459 |
| Pittsburgh-009 | 71.6 | m | 55 | 10.8 | 0.3 | 10.5 | 6471 |
| Pittsburgh-010 | 52.5 | m | 50 | 12.99 | 0.88 | 12.12 | 7785 |
| Pittsburgh-011 | 66.8 | f | 62 | 22.99 | 1.68 | 21.31 | 13785 |
| Pittsburgh-012 | 65.2 | f | 54 | 17.75 | 0.75 | 17 | 10641 |
| Pittsburgh-013 | 54.4 | m | 34 | 27.61 | 1.52 | 26.09 | 16557 |
| Pittsburgh-014 | 67.9 | m | 48 | 8.22 | 0.47 | 7.75 | 4923 |
| Pittsburgh-015 | 69 | f | 31 | 12.3 | 1.63 | 10.67 | 7371 |
| Pittsburgh-016 | 67 | m | 42 | 15.25 | 1.71 | 13.55 | 9141 |
| Cross validation type | Number of samples Mean ± Std [a.u.] | Duration Mean ± Std [min] |
| Within subjects | 11616 ± 9101 | 19.37 ± 15.18 |
| Leave one subject out within cohorts | 137991± 41745 | 230 ± 69.6 |
| Leave one subject out across cohort | 430017 ± 9213 | 716,71 ± 15.4 |
| Within cohorts CEBRA vs Grid Points | Within cohorts CEBRA vs Connectomics | Across cohorts CEBRA vs Grid Points | Across cohorts CEBRA vs Connectomics | |
| Balanced accuracy | 0.02 (p=0.03) | 0.02 (p=0.003) | 0.02 (p<10-5) | 0.02 (p<10-5) |
| Movement detection rate | 0.01 (p=0.69) | 0.01 (p=0.7) | 0.03 (p=0.04) | 0.03 (p=0.04) |
| CEBRA vs Grid Points | LOCO within cohorts CEBRA vs Connectomics | |
| Balanced accuracy | 0.03 (p=0.0002) | 0.03 (p<10-5) |
| Movement detection rate | 0.02 (p=0.33) | 0.02 (p=0.35) |
| Patient RNS ID | Group | Location (lead 1) | Location (lead 2) | Months postop | Number Recording | Ictal recordi ng duration [min] | Total recordi ng duration [min] | Sex | Age |
| RNS1529 | Hippo-campus | Left Hippo-campus | Right Hippo-campus | 11 | 676 | 131.38 | 754.89 | F | 22.7 |
| RNS1603 | Neocortex | Right premotor area | Right primary motor area | 11 | 545 | 38.97 | 747.49 | F | 34.5 |
| RNS1836 | Migrational Dysplasia | Left heterotopia | Right heterotopia | 10 | 1059 | 258.63 | 956.8 | F | 39.2 |
| RNS1090 | Migrational Dysplasia | Right heterotopia | Right heterotopia | 23 | 590 | 132.22 | 575.25 | M | 21.8 |
| RNS9536 | Migrational Dysplasia | Left heterotopia | Right heterotopia | 26 | 356 | 65.16 | 431.73 | F | 46.4 |
| RNS1440 | Neocortex | Basal temporal (depth) | Temporo occipital (strip) | 35 | 1058 | 195.21 | 1215.61 | F | 41.4 |
| RNS1534 | Hippo-campus | Left Hippo-campus | Right Hippocampus | 7 | 228 | 23.46 | 301.04 | F | 39.5 |
| Patient RNS ID | ch | Mni-x | Mni-y | Mni-z |
| RNS1090 | 0 | 52.21 | -16.72 | 7.4 |
| 1 | 60.52 | -17.1 | 8.5 | |
| 2 | 51.99 | -33.88 | 20.04 | |
| 3 | 59.98 | -31.44 | 22.27 | |
| RNS1440 | 0 | -36.28 | -33.73 | -12.62 |
| 1 | -57.77 | -39.8 | -11.08 | |
| 2 | -49.65 | -78.96 | 7.2 | |
| 3 | -57.8 | -59.93 | -2 | |
| RNS1529 | 0 | 26.78 | -9.03 | -3.15 |
| 1 | 30.4 | -31.5 | -7.3 | |
| 2 | -27.2 | -5.3 | -5.27 | |
| 3 | -33.37 | -25.71 | -2.56 | |
| RNS1534 | 0 | -24.88 | -27.1 | -20.72 |
| 1 | -29.12 | -26.11 | -0.61 | |
| 2 | 25.5 | -32.13 | -15.17 | |
| 3 | 28.27 | -29.48 | 5.27 | |
| RNS1603 | 0 | 6.94 | -27.61 | 41.9 |
| 1 | 12.39 | -20.99 | 64.41 | |
| 2 | 3.15 | -21.45 | 38.83 | |
| 3 | 4.77 | -19.21 | 62.13 | |
| RNS1836 | 0 | -30.25 | 29.23 | 25.37 |
| 1 | -33.1 | 6.45 | 27.23 | |
| 2 | -40.77 | -12.16 | 27.73 | |
| 3 | -42.18 | -35.04 | 31.49 | |
| RNS9536 | 0 | -26.55 | -12.51 | -27.03 |
| 1 | -30.04 | -30.35 | -18.61 | |
| 2 | 24.19 | -34.09 | -14.51 | |
| 3 | 29.47 | -52.34 | -9.33 | |
| Total recording duration [min] | Movement recording duration [min] | Rest recording duration [min] | N samples at 10 Hz for decoding | ||||
| Cohort-ID | Age | Gender | UPDRS | ||||
| Berlin-001 | 50 | f | n.a. | 7.91 | 2.8 | 5.11 | 4737 |
| Berlin-002 | 62 | m | 21 | 15.78 | 2.2 | 13.58 | 9459 |
| Berlin-003 | 56 | f | 31 | 13.56 | 2.56 | 11 | 8127 |
| Berlin-004 | 45 | f | 36 | 26.42 | 7.85 | 18.58 | 15843 |
| Berlin-005 | 43 | f | 31 | 95.3 | 21.44 | 73.86 | 57171 |
| Berlin-006 | 57 | m | 36 | 40.91 | 3.15 | 37.76 | 24537 |
| Berlin-007 | 55 | m | 59 | 27.46 | 4.55 | 22.91 | 16467 |
| Berlin-008 | 66 | m | 24 | 40.97 | 4.64 | 36.34 | 24573 |
| Berlin-009 | 59 | m | 27 | 30.12 | 2.36 | 27.76 | 18063 |
| Berlin-010 | 66 | f | 30 | 10.77 | 1.8 | 8.97 | 6453 |
| Berlin-011 | 67 | m | 39 | 17.51 | 3.14 | 14.37 | 10497 |
| Berlin-012 | 54 | m | 31 | 19.31 | 2.25 | 17.06 | 11577 |
| Beijing-001 | 66 | f | 61 | 10.92 | 1.74 | 9.18 | 6543 |
| Beijing-002 | 70 | m | 70 | 16.19 | 0.41 | 15.78 | 9705 |
| Beijing-003 | 73 | f | 51 | 9.91 | 0.71 | 9.19 | 5937 |
| Beijing-004 | 67 | f | 52 | 6.99 | 0.74 | 6.24 | 4185 |
| Beijing-005 | 59 | f | 58 | 23.89 | 1.08 | 22.82 | 14325 |
| Beijing-007 | 76 | m | 41 | 21.77 | 0.79 | 20.98 | 13053 |
| Beijing-008 | 66 | f | 55 | 22.69 | 1.24 | 21.45 | 13605 |
| Beijing-009 | 66 | f | 39 | 18.62 | 1.41 | 17.21 | 11163 |
| Beijing-010 | 67 | f | 27 | 11.58 | 2.84 | 8.74 | 6939 |
| Beijing-011 | 72 | f | 47 | 10.62 | 0.61 | 10.02 | 6363 |
| Pittsburgh-001 | 60.3 | m | 28 | 12.62 | 0.93 | 11.7 | 7563 |
| Pittsburgh-002 | 51.2 | m | 27 | 19.46 | 1.03 | 18.43 | 11667 |
| Pittsburgh-003 | 63.8 | m | 40 | 18.93 | 1.98 | 16.95 | 11349 |
| Pittsburgh-004 | 54.7 | f | n.a. | 10.55 | 0.42 | 10.13 | 6321 |
| Pittsburgh-005 | 53.8 | m | 33 | 7.6 | 0.56 | 7.04 | 4551 |
| Pittsburgh-006 | 44.2 | m | 31 | 16.24 | 0.86 | 15.38 | 9735 |
| Pittsburgh-007 | 63.6 | m | 32 | 17.93 | 1.47 | 16.46 | 10749 |
| Pittsburgh-008 | 59.6 | m | 52 | 5.78 | 0.39 | 5.4 | 3459 |
| Pittsburgh-009 | 71.6 | m | 55 | 10.8 | 0.3 | 10.5 | 6471 |
| Pittsburgh-010 | 52.5 | m | 50 | 12.99 | 0.88 | 12.12 | 7785 |
| Pittsburgh-011 | 66.8 | f | 62 | 22.99 | 1.68 | 21.31 | 13785 |
| Pittsburgh-012 | 65.2 | f | 54 | 17.75 | 0.75 | 17 | 10641 |
| Pittsburgh-013 | 54.4 | m | 34 | 27.61 | 1.52 | 26.09 | 16557 |
| Pittsburgh-014 | 67.9 | m | 48 | 8.22 | 0.47 | 7.75 | 4923 |
| Pittsburgh-015 | 69 | f | 31 | 12.3 | 1.63 | 10.67 | 7371 |
| Pittsburgh-016 | 67 | m | 42 | 15.25 | 1.71 | 13.55 | 9141 |
| Cross validation type | Number of samples Mean ± Std [a.u.] | Duration Mean ± Std [min] |
| Within subjects | 11616 ± 9101 | 19.37 ± 15.18 |
| Leave one subject out within cohorts | 137991± 41745 | 230 ± 69.6 |
| Leave one subject out across cohort | 430017 ± 9213 | 716,71 ± 15.4 |
| Within cohorts CEBRA vs Grid Points | Within cohorts CEBRA vs Connectomics | Across cohorts CEBRA vs Grid Points | Across cohorts CEBRA vs Connectomics | |
| Balanced accuracy | 0.02 (p=0.03) | 0.02 (p=0.003) | 0.02 (p<10-5) | 0.02 (p<10-5) |
| Movement detection rate | 0.01 (p=0.69) | 0.01 (p=0.7) | 0.03 (p=0.04) | 0.03 (p=0.04) |
| CEBRA vs Grid Points | LOCO within cohorts CEBRA vs Connectomics | |
| Balanced accuracy | 0.03 (p=0.0002) | 0.03 (p<10-5) |
| Movement detection rate | 0.02 (p=0.33) | 0.02 (p=0.35) |
| Patient RNS ID | Group | Location (lead 1) | Location (lead 2) | Months postop | Number Recording | Ictal recordi ng duration [min] | Total recordi ng duration [min] | Sex | Age |
| RNS1529 | Hippo-campus | Left Hippo-campus | Right Hippo-campus | 11 | 676 | 131.38 | 754.89 | F | 22.7 |
| RNS1603 | Neocortex | Right premotor area | Right primary motor area | 11 | 545 | 38.97 | 747.49 | F | 34.5 |
| RNS1836 | Migrational Dysplasia | Left heterotopia | Right heterotopia | 10 | 1059 | 258.63 | 956.8 | F | 39.2 |
| RNS1090 | Migrational Dysplasia | Right heterotopia | Right heterotopia | 23 | 590 | 132.22 | 575.25 | M | 21.8 |
| RNS9536 | Migrational Dysplasia | Left heterotopia | Right heterotopia | 26 | 356 | 65.16 | 431.73 | F | 46.4 |
| RNS1440 | Neocortex | Basal temporal (depth) | Temporo occipital (strip) | 35 | 1058 | 195.21 | 1215.61 | F | 41.4 |
| RNS1534 | Hippo-campus | Left Hippo-campus | Right Hippocampus | 7 | 228 | 23.46 | 301.04 | F | 39.5 |
| Patient RNS ID | ch | Mni-x | Mni-y | Mni-z |
| RNS1090 | 0 | 52.21 | -16.72 | 7.4 |
| 1 | 60.52 | -17.1 | 8.5 | |
| 2 | 51.99 | -33.88 | 20.04 | |
| 3 | 59.98 | -31.44 | 22.27 | |
| RNS1440 | 0 | -36.28 | -33.73 | -12.62 |
| 1 | -57.77 | -39.8 | -11.08 | |
| 2 | -49.65 | -78.96 | 7.2 | |
| 3 | -57.8 | -59.93 | -2 | |
| RNS1529 | 0 | 26.78 | -9.03 | -3.15 |
| 1 | 30.4 | -31.5 | -7.3 | |
| 2 | -27.2 | -5.3 | -5.27 | |
| 3 | -33.37 | -25.71 | -2.56 | |
| RNS1534 | 0 | -24.88 | -27.1 | -20.72 |
| 1 | -29.12 | -26.11 | -0.61 | |
| 2 | 25.5 | -32.13 | -15.17 | |
| 3 | 28.27 | -29.48 | 5.27 | |
| RNS1603 | 0 | 6.94 | -27.61 | 41.9 |
| 1 | 12.39 | -20.99 | 64.41 | |
| 2 | 3.15 | -21.45 | 38.83 | |
| 3 | 4.77 | -19.21 | 62.13 | |
| RNS1836 | 0 | -30.25 | 29.23 | 25.37 |
| 1 | -33.1 | 6.45 | 27.23 | |
| 2 | -40.77 | -12.16 | 27.73 | |
| 3 | -42.18 | -35.04 | 31.49 | |
| RNS9536 | 0 | -26.55 | -12.51 | -27.03 |
| 1 | -30.04 | -30.35 | -18.61 | |
| 2 | 24.19 | -34.09 | -14.51 | |
| 3 | 29.47 | -52.34 | -9.33 | |
| Outcome: log weekly vaccine first doses, V<sub>it</sub> | ||||
| p-values in [ ] | (1) | (2) | (3) | (4) |
| mandate announced, P<sub>it</sub> | 0.506 *** [0.001] | 0.505 *** [0.001] | ||
| week 0 | 0.359 *** [0.005] | 0.377 *** [0.003] | ||
| week 1 | 0.543 *** [0.001] | 0.557 *** [0.001] | ||
| week 2 | 0.498 ** [0.010] | 0.476 *** [0.007] | ||
| week 3 | 0.705 *** [0.001] | 0.681 *** [0.001] | ||
| week 4 | 0.713 ** [0.018] | 0.677 *** [0.005] | ||
| week 5+ | 0.651 * [0.056] | 0.591 ** [0.019] | ||
| log weekly cases, C<sub>it</sub> | 0.002 [0.967] | 0.002 [0.958] | -0.010 [0.816] | -0.010 [0.814] |
| log weekly deaths, D<sub>it</sub> | 0.048 [0.365] | 0.046 [0.415] | 0.085 [0.100] | 0.085 [0.116] |
| R-squared | 0.820 | 0.821 | 0.808 | 0.809 |
| sample size, N | 920 | 920 | 920 | 920 |
| province fixed effects | X | X | ||
| province characteristics | X | X | ||
| date fixed effects | X | X | X | X |
| Outcome: log weekly first doses, Vk | |
| mandate announced | 0.506 |
| clustered SE (asymptotic) | (0.001) *** |
| wild bootstrap one-way clustered SE | [0.001] *** |
| wild bootstrap two-way clustered SE | {0.000} *** |
| Outcome: | log weekly cases | log weekly deaths |
| mandate announced (lagged) | -0.202 | 0.408 |
| [0.315] | [0.184] | |
| R-squared | 0.937 | 0.864 |
| sample size | 780 | 780 |
| province fixed effects | X | X |
| date fixed effects | X | X |
| Outcome: log weekly vaccine first doses, V<sub>it</sub> | ||||
| p-values in [ ] | (1) | (2) | (3) | (4) |
| mandate announced, P<sub>it</sub> | 0.506 *** [0.001] | 0.505 *** [0.001] | ||
| week 0 | 0.359 *** [0.005] | 0.377 *** [0.003] | ||
| week 1 | 0.543 *** [0.001] | 0.557 *** [0.001] | ||
| week 2 | 0.498 ** [0.010] | 0.476 *** [0.007] | ||
| week 3 | 0.705 *** [0.001] | 0.681 *** [0.001] | ||
| week 4 | 0.713 ** [0.018] | 0.677 *** [0.005] | ||
| week 5+ | 0.651 * [0.056] | 0.591 ** [0.019] | ||
| log weekly cases, C<sub>it</sub> | 0.002 [0.967] | 0.002 [0.958] | -0.010 [0.816] | -0.010 [0.814] |
| log weekly deaths, D<sub>it</sub> | 0.048 [0.365] | 0.046 [0.415] | 0.085 [0.100] | 0.085 [0.116] |
| R-squared | 0.820 | 0.821 | 0.808 | 0.809 |
| sample size, N | 920 | 920 | 920 | 920 |
| province fixed effects | X | X | ||
| province characteristics | X | X | ||
| date fixed effects | X | X | X | X |
| Outcome: log weekly first doses, Vk | |
| mandate announced | 0.506 |
| clustered SE (asymptotic) | (0.001) *** |
| wild bootstrap one-way clustered SE | [0.001] *** |
| wild bootstrap two-way clustered SE | {0.000} *** |
| Outcome: | log weekly cases | log weekly deaths |
| mandate announced (lagged) | -0.202 | 0.408 |
| [0.315] | [0.184] | |
| R-squared | 0.937 | 0.864 |
| sample size | 780 | 780 |
| province fixed effects | X | X |
| date fixed effects | X | X |
| Isotopic distribution for UN@C82 | ||
| Theoretical simulation | UN@C2(5)-C82 | UN@C8(6)-C82 |
| Neutral Mass | Intensity | m/z | Intensity | m/z | Intensity |
| 1233.045 | 0.73 | ||||
| 1234.045 | 0.65 | ||||
| 1235.045 | 0.28 | ||||
| 1236.045 | 100.00 | 1236.160 | 939 (100.00%) | 1236.334 | 7936 (100.00%) |
| 1237.045 | 89.00 | 1237.161 | 818 (87.11%) | 1237.334 | 6488 (81.75%) |
| 1238.045 | 39.15 | 1238.170 | 335 (35.68%) | 1238.338 | 2759 (34.76%) |
| 1239.045 | 11.34 | 1239.180 | 39 (4.15%) | 1239.348 | 602 (7.59%) |
| 1240.045 | 2.43 | 1240.343 | 78 (0.98%) | ||
| 1241.045 | 0.41 | ||||
| 1242.045 | 0.06 |
| UN@C2(5)-C82 | UN@C8(6)-C82 | ||
| site | occupancy | site | occupancy |
| U1 | 0.312 | U1 | 0.6442 |
| U2 | 0.188 | U2 | 0.1903 |
| U3 | 0.1087 | ||
| U4 | 0.0566 | ||
| Isotopic distribution for UN@C82 | ||
| Theoretical simulation | UN@C2(5)-C82 | UN@C8(6)-C82 |
| Neutral Mass | Intensity | m/z | Intensity | m/z | Intensity |
| 1233.045 | 0.73 | ||||
| 1234.045 | 0.65 | ||||
| 1235.045 | 0.28 | ||||
| 1236.045 | 100.00 | 1236.160 | 939 (100.00%) | 1236.334 | 7936 (100.00%) |
| 1237.045 | 89.00 | 1237.161 | 818 (87.11%) | 1237.334 | 6488 (81.75%) |
| 1238.045 | 39.15 | 1238.170 | 335 (35.68%) | 1238.338 | 2759 (34.76%) |
| 1239.045 | 11.34 | 1239.180 | 39 (4.15%) | 1239.348 | 602 (7.59%) |
| 1240.045 | 2.43 | 1240.343 | 78 (0.98%) | ||
| 1241.045 | 0.41 | ||||
| 1242.045 | 0.06 |
| UN@C2(5)-C82 | UN@C8(6)-C82 | ||
| site | occupancy | site | occupancy |
| U1 | 0.312 | U1 | 0.6442 |
| U2 | 0.188 | U2 | 0.1903 |
| U3 | 0.1087 | ||
| U4 | 0.0566 | ||
| Gene | Fold change in 16p11.2 | Fold change in 15q11-13 |
| RPS14 | 1.09 | 1.11 |
| PCDHGB6 | 0.54 | 0.54 |
| TUBGCP5 | 1.18 | 1.72 |
| CYFIP1 | 1.05 | 1.35 |
| ELAVL2 | 0.68 | 0.52 |
| SNHG5 | 1.15 | 1.3 |
| NAP1L5 | 0.75 | 0.53 |
| MYL6B | 0.97 | 0.92 |
| Gene | Fold change in 16p11.2 | Fold change in 15q11-13 |
| RPS14 | 1.09 | 1.11 |
| PCDHGB6 | 0.52 | 0.54 |
| ELAVL2 | 0.72 | 0.52 |
| SNHG5 | 1.15 | 1.3 |
| CTNNA2 | 0.59 | 0.58 |
| NAP1L5 | 0.69 | 0.53 |
| Gene | Fold change in 16p11.2 | Fold change in 15q11-13 |
| RPS14 | 1.09 | 1.11 |
| PCDHGB6 | 0.54 | 0.54 |
| TUBGCP5 | 1.18 | 1.72 |
| CYFIP1 | 1.05 | 1.35 |
| ELAVL2 | 0.68 | 0.52 |
| SNHG5 | 1.15 | 1.3 |
| NAP1L5 | 0.75 | 0.53 |
| MYL6B | 0.97 | 0.92 |
| Gene | Fold change in 16p11.2 | Fold change in 15q11-13 |
| RPS14 | 1.09 | 1.11 |
| PCDHGB6 | 0.52 | 0.54 |
| ELAVL2 | 0.72 | 0.52 |
| SNHG5 | 1.15 | 1.3 |
| CTNNA2 | 0.59 | 0.58 |
| NAP1L5 | 0.69 | 0.53 |
| Material | Temp. (K) | Vbd (V) | Idark (A) | M @ Vd | Ref. |
| Si | 300 | 40 | - | 6×106 @ 40 V | 1 |
| Si/Ge | 300 | 10.3 | 1×10-10 | 1.2×102 @ 10.3 V | 2 |
| InGaAs/InP | 225 | 61.0 | 1×10-11 | 1.0×105 @ 61.0 V | 3 |
| InAlAsSb | 300 | 19.6 | 1×10-7 | 7.0×103 @ 19.6 V | 4 |
| InAlAsSb/GaSb | 300 | 45.0 | 1×10-6 | 1.5×102 @ 45.0 V | 5 |
| WSe2/MoS2 | 300 | 6.5 | 5×10-10 | 1.0×103 @ 30 V | 6 |
| WSe2 | 300 | 1.6 | 1×10-11 | 4.7×102 @ 3 V | 7 |
| WSe2 | 300 | 24 | 1×10-14 | 5.0×105 @ 24 V | 8 |
| MoS2 | 100 | 4.5 | 1×10-12 | 1.2×103 @ 10 V | 9 |
| Graphite/InSe/Ti | 160 | 5.1 | 1×10-12 | 3×105 @ 5.1 V | 10 |
| Graphene/InSe/Cr | 100 | 3.9 | 1×10-13 | 2.3×107 @ 3.9 V | This work |
| Graphene/InSe/Cr | 300 | 2.9 | 1×10-11 | 3.6×106 @ 3 V | This work |
| Material | Temp. (K) | Vbd (V) | Idark (A) | M @ Vd | Ref. |
| Si | 300 | 40 | - | 6×106 @ 40 V | 1 |
| Si/Ge | 300 | 10.3 | 1×10-10 | 1.2×102 @ 10.3 V | 2 |
| InGaAs/InP | 225 | 61.0 | 1×10-11 | 1.0×105 @ 61.0 V | 3 |
| InAlAsSb | 300 | 19.6 | 1×10-7 | 7.0×103 @ 19.6 V | 4 |
| InAlAsSb/GaSb | 300 | 45.0 | 1×10-6 | 1.5×102 @ 45.0 V | 5 |
| WSe2/MoS2 | 300 | 6.5 | 5×10-10 | 1.0×103 @ 30 V | 6 |
| WSe2 | 300 | 1.6 | 1×10-11 | 4.7×102 @ 3 V | 7 |
| WSe2 | 300 | 24 | 1×10-14 | 5.0×105 @ 24 V | 8 |
| MoS2 | 100 | 4.5 | 1×10-12 | 1.2×103 @ 10 V | 9 |
| Graphite/InSe/Ti | 160 | 5.1 | 1×10-12 | 3×105 @ 5.1 V | 10 |
| Graphene/InSe/Cr | 100 | 3.9 | 1×10-13 | 2.3×107 @ 3.9 V | This work |
| Graphene/InSe/Cr | 300 | 2.9 | 1×10-11 | 3.6×106 @ 3 V | This work |
| Reference | Window size | Data |
| (Lee et al., 2011) | 28 km (mean) | Site observation |
| (Li et al., 2015) | 50 × 28 km | Remote sensing observation |
| (Alkama and Cescatti, 2016) | 50 km | Remote sensing observation |
| (Duveiller et al., 2018) | 25 km | Remote sensing observation |
| (Chen et al., 2018) | 21.6 km (mean) | Site observation |
| (Barnes et al., 2024) | 30 km (maximum) | Site observation |
| dLEs dVPDs (w·m-2/hPa) | dLEs dVPDs (w·m-2/hPa) ignoring the impact of VPD on rs | Regression slope between VPDs and LEs (w·m-2/hPa) | |
| Forest (n=19) | 2.08 ± 1.85 | 5.92 ± 2.95 | 3.87 ± 2.87 |
| Openland (n=19) | 2.48 ± 1.71 | 4.87 ± 1.32 | 6.44 ± 2.67 |
| dLEs dVPDs (w·m-2/hPa) | dLEs dVPDs (w·m-2/hPa) ignoring the impact of VPD on rs | Regression slope between VPDs and LEs (w·m-2/hPa) | |
| Forest (n=19) | 2.08 ± 1.85 | 5.92 ± 2.95 | 3.87 ± 2.87 |
| Openland (n=19) | 2.48 ± 1.71 | 4.87 ± 1.32 | 6.44 ± 2.67 |
| Reference | Window size | Data |
| (Lee et al., 2011) | 28 km (mean) | Site observation |
| (Li et al., 2015) | 50 × 28 km | Remote sensing observation |
| (Alkama and Cescatti, 2016) | 50 km | Remote sensing observation |
| (Duveiller et al., 2018) | 25 km | Remote sensing observation |
| (Chen et al., 2018) | 21.6 km (mean) | Site observation |
| (Barnes et al., 2024) | 30 km (maximum) | Site observation |
| dLEs dVPDs (w·m-2/hPa) | dLEs dVPDs (w·m-2/hPa) ignoring the impact of VPD on rs | Regression slope between VPDs and LEs (w·m-2/hPa) | |
| Forest (n=19) | 2.08 ± 1.85 | 5.92 ± 2.95 | 3.87 ± 2.87 |
| Openland (n=19) | 2.48 ± 1.71 | 4.87 ± 1.32 | 6.44 ± 2.67 |
| dLEs dVPDs (w·m-2/hPa) | dLEs dVPDs (w·m-2/hPa) ignoring the impact of VPD on rs | Regression slope between VPDs and LEs (w·m-2/hPa) | |
| Forest (n=19) | 2.08 ± 1.85 | 5.92 ± 2.95 | 3.87 ± 2.87 |
| Openland (n=19) | 2.48 ± 1.71 | 4.87 ± 1.32 | 6.44 ± 2.67 |
| Site name | A1: Site side (m²) | A3: Opposite side (m²) | Percentage of area on opposite side contributing to site average estimate (%) |
| PAG-00379 | 340,157 | 7,268 | 2.1 |
| TUT-00376 | 257,666 | 195,131 | 43.1 |
| TUT-00516 | 343,405 | 109,209 | 24.1 |
| OFU-00437 | 176,259 | 1,865 | 1.0 |
| OAH-00300 | 356,730 | 18,672 | 5.0 |
| Geographic study region | Excludes sharks | Excludes sharks and semi-pelagics |
| Global | D'Agata et al. 2016 Roy Soc Proc B | MacNeil et al. 2015 Nature Cinner et al. 2016 Nature |
| Caribbean | Hawkins and Roberts 2004 Cons Biol | |
| Pacific | D'Agata et al. 2016 Nat Comms Gray et al. 2016 PLoS ONE | Williams et al. 2015 PLoS ONE Yeager et al. 2017 Glob Ecol Biogeog |
| Indian Ocean | McClanahan et al. 2009 MEPS | McClanahan et al. 2020 Aqu Conserv Samoyils et al. 2018 PLoS ONE Cowburn et al. 2018 Mar Pol Bull |
| Family | Species | Common name | Trophic group | Reason for omission |
| Carangidae | Carangoides ferdua | Blue trevally | Piscivore | Potential overinflation in estimates |
| Carangoides orthogrammus | Island trevally | Piscivore | Potential overinflation in estimates | |
| Caranx ignobilis | Giant trevally | Piscivore | Potential overinflation in estimates | |
| Caranx lugubris | Black jack | Piscivore | Potential overinflation in estimates | |
| Caranx melampygus | Bluefin trevally | Piscivore | Potential overinflation in estimates | |
| Caranpapuensis | Brassy trevally | Piscivore | Potential overinflation in estimates | |
| Caranx sexfasciatus | Bigeye trevally | Piscivore | Potential overinflation in estimates | |
| Decapterus macarellus | Mackerel scad | Planktivore | Potential overinflation in estimates | |
| Elagatis bipinnulata | Rainbow runner | Piscivore | Potential overinflation in estimates | |
| Scomberoides lysan | Doublespotted queenfish | Piscivore | Potential overinflation in estimates | |
| Selar crumenophthalmus | Bigeye scad | Planktivore | Potential overinflation in estimates | |
| Seriola dumerili | Greater amberjack | Piscivore | Potential overinflation in estimates | |
| Trachinotus bailloni | Smallspotted dart | Piscivore | Potential overinflation in estimates | |
| Caranx spp. | n/a | Piscivore | Potential overinflation in estimates | |
| Carcharhinidae | Carcharhinus amblyrhynchos | Grey reef shark | Piscivore | Potential overinflation in estimates |
| Carcharhinus galapagensis | Galapagos shark | Piscivore | Potential overinflation in estimates | |
| Carcharinus melanopterus | Blacktip reef shark | Piscivore | Potential overinflation in estimates | |
| Triaenodon obesus | Whitetip reef shark | Piscivore | Potential overinflation in estimates | |
| Chanidae | Chanos chanos | Milkfish | Primary consumer | Non-reef-associated |
| Congridae | Congridae spp. | Conger eel or Garden eel | Piscivore | Cryptobenthic |
| Muraenidae | Echidna nebulosa | Snowflake moray eel | Secondary consumer | Cryptobenthic |
| Enchelycore pardalis | Leopard moray eel | Piscivore | Cryptobenthic | |
| Gymnomuraena zebra | Zebra moray eel | Secondary consumer | Cryptobenthic | |
| Gymnothorax breedeni | Blackcheek moray eel | Piscivore | Cryptobenthic | |
| Gymnothorax eurostus | Abbott's moray eel | Secondary | Cryptobenthic |
| consumer | |||
| Gymnothorax flavimarginatus | Yellow-edged moray eel | Piscivore | Cryptobenthic |
| Gymnothorax javanicus | Giant moray eel | Piscivore | Cryptobenthic |
| Dwarf moray eel | Secondary consumer | Cryptobenthic | |
| Gymnothorax meleagris | Turkey moray eel | Piscivore | Cryptobenthic |
| Gymnothorax steindachneri | Steindachner's moray eel | Piscivore | Cryptobenthic |
| Gymnothorax undulatus | Undulated moray eel | Piscivore | Cryptobenthic |
| Moray eel | Piscivore | Cryptobenthic | |
| Myliobatidae | Manta birostris | Giant manta | Planktivore |
| Scombridae | Euthynnus affinis | Kawakawa | Piscivore |
| Gymnosarda unicolor | Dogtooth tuna | Piscivore | |
| Thunnus albacares | Yellowfin tuna | Piscivore | |
| Scombridae spp. | Striped bonito | Piscivore | |
| Sphyraenidae | Sphyraena qenie | Blackfin barracuda | Piscivore |
| Site name | A1: Site side (m²) | A3: Opposite side (m²) | Percentage of area on opposite side contributing to site average estimate (%) |
| PAG-00379 | 340,157 | 7,268 | 2.1 |
| TUT-00376 | 257,666 | 195,131 | 43.1 |
| TUT-00516 | 343,405 | 109,209 | 24.1 |
| OFU-00437 | 176,259 | 1,865 | 1.0 |
| OAH-00300 | 356,730 | 18,672 | 5.0 |
| Geographic study region | Excludes sharks | Excludes sharks and semi-pelagics |
| Global | D'Agata et al. 2016 Roy Soc Proc B | MacNeil et al. 2015 Nature Cinner et al. 2016 Nature |
| Caribbean | Hawkins and Roberts 2004 Cons Biol | |
| Pacific | D'Agata et al. 2016 Nat Comms Gray et al. 2016 PLoS ONE | Williams et al. 2015 PLoS ONE Yeager et al. 2017 Glob Ecol Biogeog |
| Indian Ocean | McClanahan et al. 2009 MEPS | McClanahan et al. 2020 Aqu Conserv Samoyils et al. 2018 PLoS ONE Cowburn et al. 2018 Mar Pol Bull |
| Family | Species | Common name | Trophic group | Reason for omission |
| Carangidae | Carangoides ferdua | Blue trevally | Piscivore | Potential overinflation in estimates |
| Carangoides orthogrammus | Island trevally | Piscivore | Potential overinflation in estimates | |
| Caranx ignobilis | Giant trevally | Piscivore | Potential overinflation in estimates | |
| Caranx lugubris | Black jack | Piscivore | Potential overinflation in estimates | |
| Caranx melampygus | Bluefin trevally | Piscivore | Potential overinflation in estimates | |
| Caranpapuensis | Brassy trevally | Piscivore | Potential overinflation in estimates | |
| Caranx sexfasciatus | Bigeye trevally | Piscivore | Potential overinflation in estimates | |
| Decapterus macarellus | Mackerel scad | Planktivore | Potential overinflation in estimates | |
| Elagatis bipinnulata | Rainbow runner | Piscivore | Potential overinflation in estimates | |
| Scomberoides lysan | Doublespotted queenfish | Piscivore | Potential overinflation in estimates | |
| Selar crumenophthalmus | Bigeye scad | Planktivore | Potential overinflation in estimates | |
| Seriola dumerili | Greater amberjack | Piscivore | Potential overinflation in estimates | |
| Trachinotus bailloni | Smallspotted dart | Piscivore | Potential overinflation in estimates | |
| Caranx spp. | n/a | Piscivore | Potential overinflation in estimates | |
| Carcharhinidae | Carcharhinus amblyrhynchos | Grey reef shark | Piscivore | Potential overinflation in estimates |
| Carcharhinus galapagensis | Galapagos shark | Piscivore | Potential overinflation in estimates | |
| Carcharinus melanopterus | Blacktip reef shark | Piscivore | Potential overinflation in estimates | |
| Triaenodon obesus | Whitetip reef shark | Piscivore | Potential overinflation in estimates | |
| Chanidae | Chanos chanos | Milkfish | Primary consumer | Non-reef-associated |
| Congridae | Congridae spp. | Conger eel or Garden eel | Piscivore | Cryptobenthic |
| Muraenidae | Echidna nebulosa | Snowflake moray eel | Secondary consumer | Cryptobenthic |
| Enchelycore pardalis | Leopard moray eel | Piscivore | Cryptobenthic | |
| Gymnomuraena zebra | Zebra moray eel | Secondary consumer | Cryptobenthic | |
| Gymnothorax breedeni | Blackcheek moray eel | Piscivore | Cryptobenthic | |
| Gymnothorax eurostus | Abbott's moray eel | Secondary | Cryptobenthic |
| consumer | |||
| Gymnothorax flavimarginatus | Yellow-edged moray eel | Piscivore | Cryptobenthic |
| Gymnothorax javanicus | Giant moray eel | Piscivore | Cryptobenthic |
| Dwarf moray eel | Secondary consumer | Cryptobenthic | |
| Gymnothorax meleagris | Turkey moray eel | Piscivore | Cryptobenthic |
| Gymnothorax steindachneri | Steindachner's moray eel | Piscivore | Cryptobenthic |
| Gymnothorax undulatus | Undulated moray eel | Piscivore | Cryptobenthic |
| Moray eel | Piscivore | Cryptobenthic | |
| Myliobatidae | Manta birostris | Giant manta | Planktivore |
| Scombridae | Euthynnus affinis | Kawakawa | Piscivore |
| Gymnosarda unicolor | Dogtooth tuna | Piscivore | |
| Thunnus albacares | Yellowfin tuna | Piscivore | |
| Scombridae spp. | Striped bonito | Piscivore | |
| Sphyraenidae | Sphyraena qenie | Blackfin barracuda | Piscivore |
| STD Copies | CCR5 | LTR U5 |
| 10^4 | 0.2% | 2.9% |
| 10^3 | 0.3% | 2.5% |
| 10^2 | 0.4% | 2.1% |
| 10^1 | 0.5% | 2.4% |
| 10^0 | 1.2% | 3.5% |
| STD Copies | CCR5 | LTR U5 |
| 10^4 | 0.2% | 2.9% |
| 10^3 | 0.3% | 2.5% |
| 10^2 | 0.4% | 2.1% |
| 10^1 | 0.5% | 2.4% |
| 10^0 | 1.2% | 3.5% |
| Groundwater wells | |||
| Total | With electric pumps | With diesel pumps | |
| Electrified households | 0.089** (0.044) | 0.180*** (0.051) | 0.002 (0.015) |
| Electrified households × PC | -0.0832** (0.039) | -0.1408*** (0.049) | 0.020 (0.025) |
| Total households | -0.050 (0.056) | -0.071 (0.053) | -0.015 (0.019) |
| Fixed-effects | |||
| District | Yes | Yes | Yes |
| Year | Yes | Yes | Yes |
| Fit statistics | |||
| Observations | 236 | 236 | 236 |
| R² | 0.96 | 0.93 | 0.93 |
| Within R² | 0.05 | 0.17 | 0.03 |
| Groundwater wells | |||
| Total | With electric pumps | With diesel pumps | |
| Electrified households | 0.058*** (0.020) | 0.101*** (0.025) | -0.028*** (0.008) |
| Electrified households × PC | -0.0543** (0.027) | -0.0709** (0.028) | 0.0201 (0.013) |
| Total households | 0.0294** (0.014) | 0.0036 (0.009) | 0.0101 (0.011) |
| Fixed-effects | |||
| District | Yes | Yes | Yes |
| State × Year | Yes | Yes | Yes |
| Fit statistics | |||
| Observations | 969 | 969 | 969 |
| R² | 0.74 | 0.93 | 0.87 |
| Within R² | 0.12 | 0.64 | 0.42 |
| Irrigated area (ha) | |||||||
| Annual (ha) | Kharif (ha) | Rabi (ha) | |||||
| (1) | (2) | (3) | (4) | (5) | (6) | ||
| Electrified households | 0.199*** (0.0665) | 0.050 (0.0527) | 0.117*** (0.0337) | 0.037 (0.0253) | 0.096*** (0.0320) | 0.034 (0.0275) | |
| Electrified Households × PC | -0.178*** (0.058) | -0.070 (0.043) | -0.063** (0.030) | -0.005 (0.021) | -0.010*** (0.029) | -0.060** (0.025) | |
| Wells with electric pumps | 1.492*** (0.182) | 0.801*** (0.111) | 0.594*** (0.075) | ||||
| Total households | 0.082 (0.060) | 0.079 (0.051) | 0.026 (0.025) | 0.025 (0.021) | -0.005 (0.025) | -0.006 (0.021) | |
| Cumulative Average Monthly Rain↑ | -11.43 (7.872) | -4.053 (7.458) | -11.91** (4.659) | -6.641 (4.252) | 23.00 (35.14) | 0.174 (32.52) | |
| Fixed-effects | |||||||
| District | Yes | Yes | Yes | Yes | Yes | Yes | |
| State × Year | Yes | Yes | Yes | Yes | Yes | Yes | |
| Fit statistics | |||||||
| Observations | 969 | 969 | 969 | 969 | 969 | 969 | |
| R² | 0.89 | 0.90 | 0.88 | 0.89 | 0.89 | 0.90 | |
| Within R² | 0.45 | 0.51 | 0.42 | 0.51 | 0.42 | 0.47 | |
| Non-PC districts | PC districts | p-value | |
| Average landless households (‘000) | 178.3621 | 211.9399 | 0.04 |
| Average land owning households (‘000) | 119.9451 | 165.4355 | 0.00 |
| Average total land in district (‘000 acre) | 1,414.8073 | 2,884.2886 | 0.02 |
| Groundwater wells with electric pumps | |||
| Deep tube-wells | Shallow tube-wells | Dug wells | |
| Electrified households | 0.045*** (0.008) | 0.074*** (0.013) | 0.040** (0.018) |
| Electrified households × PC | -0.038*** (0.010) | -0.041*** (0.014) | -0.059*** (0.018) |
| Total households | -0.007 (0.005) | -0.005 (0.004) | 0.001 (0.007) |
| Fixed-effects | |||
| District | Yes | Yes | Yes |
| Year | Yes | Yes | Yes |
| Fit statistics | |||
| Observations | 969 | 969 | 969 |
| R² | 0.52 | 0.78 | 0.87 |
| Within R² | 0.14 | 0.18 | 0.04 |
| Non-PC districts | PC districts | p-value | |
| Land owned (ha) | 2.23 | 1.42 | 0.00 |
| Land operated (owned and leased) (ha) | 2.61 | 1.55 | 0.00 |
| Land cultivated during Rabi | 1.66 | 1.01 | 0.00 |
| Land cultivated during Kharif | 2.09 | 1.17 | 0.00 |
| Groundwater wells | |||
| Total | With electric pumps | With diesel pumps | |
| Electrified households | 0.089** (0.044) | 0.180*** (0.051) | 0.002 (0.015) |
| Electrified households × PC | -0.0832** (0.039) | -0.1408*** (0.049) | 0.020 (0.025) |
| Total households | -0.050 (0.056) | -0.071 (0.053) | -0.015 (0.019) |
| Fixed-effects | |||
| District | Yes | Yes | Yes |
| Year | Yes | Yes | Yes |
| Fit statistics | |||
| Observations | 236 | 236 | 236 |
| R² | 0.96 | 0.93 | 0.93 |
| Within R² | 0.05 | 0.17 | 0.03 |
| Groundwater wells | |||
| Total | With electric pumps | With diesel pumps | |
| Electrified households | 0.058*** (0.020) | 0.101*** (0.025) | -0.028*** (0.008) |
| Electrified households × PC | -0.0543** (0.027) | -0.0709** (0.028) | 0.0201 (0.013) |
| Total households | 0.0294** (0.014) | 0.0036 (0.009) | 0.0101 (0.011) |
| Fixed-effects | |||
| District | Yes | Yes | Yes |
| State × Year | Yes | Yes | Yes |
| Fit statistics | |||
| Observations | 969 | 969 | 969 |
| R² | 0.74 | 0.93 | 0.87 |
| Within R² | 0.12 | 0.64 | 0.42 |
| Irrigated area (ha) | |||||||
| Annual (ha) | Kharif (ha) | Rabi (ha) | |||||
| (1) | (2) | (3) | (4) | (5) | (6) | ||
| Electrified households | 0.199*** (0.0665) | 0.050 (0.0527) | 0.117*** (0.0337) | 0.037 (0.0253) | 0.096*** (0.0320) | 0.034 (0.0275) | |
| Electrified Households × PC | -0.178*** (0.058) | -0.070 (0.043) | -0.063** (0.030) | -0.005 (0.021) | -0.010*** (0.029) | -0.060** (0.025) | |
| Wells with electric pumps | 1.492*** (0.182) | 0.801*** (0.111) | 0.594*** (0.075) | ||||
| Total households | 0.082 (0.060) | 0.079 (0.051) | 0.026 (0.025) | 0.025 (0.021) | -0.005 (0.025) | -0.006 (0.021) | |
| Cumulative Average Monthly Rain↑ | -11.43 (7.872) | -4.053 (7.458) | -11.91** (4.659) | -6.641 (4.252) | 23.00 (35.14) | 0.174 (32.52) | |
| Fixed-effects | |||||||
| District | Yes | Yes | Yes | Yes | Yes | Yes | |
| State × Year | Yes | Yes | Yes | Yes | Yes | Yes | |
| Fit statistics | |||||||
| Observations | 969 | 969 | 969 | 969 | 969 | 969 | |
| R² | 0.89 | 0.90 | 0.88 | 0.89 | 0.89 | 0.90 | |
| Within R² | 0.45 | 0.51 | 0.42 | 0.51 | 0.42 | 0.47 | |
| Non-PC districts | PC districts | p-value | |
| Average landless households (‘000) | 178.3621 | 211.9399 | 0.04 |
| Average land owning households (‘000) | 119.9451 | 165.4355 | 0.00 |
| Average total land in district (‘000 acre) | 1,414.8073 | 2,884.2886 | 0.02 |
| Groundwater wells with electric pumps | |||
| Deep tube-wells | Shallow tube-wells | Dug wells | |
| Electrified households | 0.045*** (0.008) | 0.074*** (0.013) | 0.040** (0.018) |
| Electrified households × PC | -0.038*** (0.010) | -0.041*** (0.014) | -0.059*** (0.018) |
| Total households | -0.007 (0.005) | -0.005 (0.004) | 0.001 (0.007) |
| Fixed-effects | |||
| District | Yes | Yes | Yes |
| Year | Yes | Yes | Yes |
| Fit statistics | |||
| Observations | 969 | 969 | 969 |
| R² | 0.52 | 0.78 | 0.87 |
| Within R² | 0.14 | 0.18 | 0.04 |
| Non-PC districts | PC districts | p-value | |
| Land owned (ha) | 2.23 | 1.42 | 0.00 |
| Land operated (owned and leased) (ha) | 2.61 | 1.55 | 0.00 |
| Land cultivated during Rabi | 1.66 | 1.01 | 0.00 |
| Land cultivated during Kharif | 2.09 | 1.17 | 0.00 |
| Residue | Residue atom | Ligand moiety | Ligand atom | Distance (Å) |
| Hydrogen bond | ||||
| R155 | NH2 | \(\beta \)-phosphate | O2B | 2.7 |
| K158 | NZ | O2B | 3.2 | |
| G19 | N | O1B | 3.2 | |
| H235 | ND1/N | \(\alpha \)-phosphate | O1A | 2.8/2.8 |
| V236 | N | O2A | 3.3 | |
| R22 | NH1/NE | Ribose ring | O3C | 3.0/4.0 |
| V236 | OE2 | O3C | 2.6 | |
| E239 | OE1 | O2C | 2.9 | |
| N17 | ND2 | Uracil ring | O2 | 2.8 |
| L209 | O/N | N3/O4 | 3.2/3.5 | |
| T214 | OG1 | N3 | 3.3 | |
| hydrophobic interaction | ||||
| V151 | CG1 | Uracil ring | C5 | 3.7 |
| Residue | Residue atom | Ligand moiety | Ligand atom | Distance (Å) |
| Hydrogen bond | ||||
| R155 | NH2 | \(\beta \)-phosphate | O2B | 2.7 |
| K158 | NZ | O2B | 3.2 | |
| G19 | N | O1B | 3.2 | |
| H235 | ND1/N | \(\alpha \)-phosphate | O1A | 2.8/2.8 |
| V236 | N | O2A | 3.3 | |
| R22 | NH1/NE | Ribose ring | O3C | 3.0/4.0 |
| V236 | OE2 | O3C | 2.6 | |
| E239 | OE1 | O2C | 2.9 | |
| N17 | ND2 | Uracil ring | O2 | 2.8 |
| L209 | O/N | N3/O4 | 3.2/3.5 | |
| T214 | OG1 | N3 | 3.3 | |
| hydrophobic interaction | ||||
| V151 | CG1 | Uracil ring | C5 | 3.7 |
| Signature genes | log2FC (one group vs others) | P.Value | adj.P.Val | k=4 groups | k=2 groups | log2FC (group2 vs group1) | P.Value | adj.P.Val |
| LCE3D | 4.292 | 1.54E-12 | 2.95E-10 | Differentiated | Differentiated | -2.419 | 0.00016296 | 0.00252447 |
| CDSN | 3.966 | 7.01E-14 | 1.05E-10 | Differentiated | Differentiated | -2.300 | 4.28E-05 | 0.00084861 |
| KLK5 | 3.884 | 7.18E-13 | 1.79E-10 | Differentiated | Differentiated | -2.316 | 4.81E-05 | 0.0009412 |
| SPRR2G | 3.685 | 1.02E-09 | 4.77E-08 | Differentiated | Differentiated | -1.724 | 0.00628954 | 0.03822871 |
| DSG1 | 3.590 | 2.00E-11 | 2.00E-09 | Differentiated | Differentiated | -1.525 | 0.00717146 | 0.04182777 |
| MS4A1 | 2.591 | 1.56E-12 | 4.04E-11 | Immunogenic | Immunogenic | 2.503 | 5.35E-14 | 1.09E-11 |
| CD79A | 2.558 | 1.16E-19 | 5.82E-17 | Immunogenic | Immunogenic | 2.352 | 2.05E-20 | 7.17E-17 |
| CXCL9 | 2.263 | 8.70E-12 | 1.95E-10 | Immunogenic | Immunogenic | 2.117 | 2.20E-12 | 2.82E-10 |
| MZB1 | 2.262 | 7.31E-22 | 1.10E-18 | Immunogenic | Immunogenic | 2.081 | 1.01E-23 | 2.81E-19 |
| IDO1 | 2.206 | 1.48E-09 | 1.94E-08 | Immunogenic | Immunogenic | 2.444 | 3.15E-14 | 6.79E-12 |
| GSTA1 | 3.956 | 3.37E-09 | 2.10E-07 | Metabolic | - | 0.505 | 0.41609476 | 0.59872715 |
| ADH7 | 3.862 | 6.39E-12 | 4.79E-09 | Metabolic | - | 0.276 | 0.6040843 | 0.75100198 |
| UGT1A10 | 3.797 | 3.52E-07 | 1.04E-05 | Metabolic | - | 0.485 | 0.47654616 | 0.65090706 |
| UGT1A3 | 3.692 | 1.17E-07 | 4.08E-06 | Metabolic | - | 0.577 | 0.36608899 | 0.55313354 |
| ALDH3A1 | 3.574 | 8.64E-11 | 1.62E-08 | Metabolic | - | 0.005 | 0.99301751 | 0.99646932 |
| WFD2 | 2.446 | 7.43E-07 | 1.71E-05 | Stemness | - | 0.579 | 0.20650854 | 0.38417833 |
| PEG10 | 1.791 | 0.00024981 | 0.00148696 | Stemness | - | -0.487 | 0.27289587 | 0.46017491 |
| SFRP1 | 1.781 | 0.00012949 | 0.00087493 | Stemness | - | 0.090 | 0.83101981 | 0.90348777 |
| LGR6 | 1.731 | 4.19E-05 | 0.00035815 | Stemness | - | -0.037 | 0.92251786 | 0.95710801 |
| VWA2 | 1.678 | 1.18E-07 | 4.44E-06 | Stemness | - | 0.001 | 0.99749075 | 0.99871802 |
| Signature genes | log2FC (one group vs others) | P.Value | adj.P.Val | k=4 groups | k=2 groups | log2FC (group2 vs group1) | P.Value | adj.P.Val |
| LCE3D | 4.292 | 1.54E-12 | 2.95E-10 | Differentiated | Differentiated | -2.419 | 0.00016296 | 0.00252447 |
| CDSN | 3.966 | 7.01E-14 | 1.05E-10 | Differentiated | Differentiated | -2.300 | 4.28E-05 | 0.00084861 |
| KLK5 | 3.884 | 7.18E-13 | 1.79E-10 | Differentiated | Differentiated | -2.316 | 4.81E-05 | 0.0009412 |
| SPRR2G | 3.685 | 1.02E-09 | 4.77E-08 | Differentiated | Differentiated | -1.724 | 0.00628954 | 0.03822871 |
| DSG1 | 3.590 | 2.00E-11 | 2.00E-09 | Differentiated | Differentiated | -1.525 | 0.00717146 | 0.04182777 |
| MS4A1 | 2.591 | 1.56E-12 | 4.04E-11 | Immunogenic | Immunogenic | 2.503 | 5.35E-14 | 1.09E-11 |
| CD79A | 2.558 | 1.16E-19 | 5.82E-17 | Immunogenic | Immunogenic | 2.352 | 2.05E-20 | 7.17E-17 |
| CXCL9 | 2.263 | 8.70E-12 | 1.95E-10 | Immunogenic | Immunogenic | 2.117 | 2.20E-12 | 2.82E-10 |
| MZB1 | 2.262 | 7.31E-22 | 1.10E-18 | Immunogenic | Immunogenic | 2.081 | 1.01E-23 | 2.81E-19 |
| IDO1 | 2.206 | 1.48E-09 | 1.94E-08 | Immunogenic | Immunogenic | 2.444 | 3.15E-14 | 6.79E-12 |
| GSTA1 | 3.956 | 3.37E-09 | 2.10E-07 | Metabolic | - | 0.505 | 0.41609476 | 0.59872715 |
| ADH7 | 3.862 | 6.39E-12 | 4.79E-09 | Metabolic | - | 0.276 | 0.6040843 | 0.75100198 |
| UGT1A10 | 3.797 | 3.52E-07 | 1.04E-05 | Metabolic | - | 0.485 | 0.47654616 | 0.65090706 |
| UGT1A3 | 3.692 | 1.17E-07 | 4.08E-06 | Metabolic | - | 0.577 | 0.36608899 | 0.55313354 |
| ALDH3A1 | 3.574 | 8.64E-11 | 1.62E-08 | Metabolic | - | 0.005 | 0.99301751 | 0.99646932 |
| WFD2 | 2.446 | 7.43E-07 | 1.71E-05 | Stemness | - | 0.579 | 0.20650854 | 0.38417833 |
| PEG10 | 1.791 | 0.00024981 | 0.00148696 | Stemness | - | -0.487 | 0.27289587 | 0.46017491 |
| SFRP1 | 1.781 | 0.00012949 | 0.00087493 | Stemness | - | 0.090 | 0.83101981 | 0.90348777 |
| LGR6 | 1.731 | 4.19E-05 | 0.00035815 | Stemness | - | -0.037 | 0.92251786 | 0.95710801 |
| VWA2 | 1.678 | 1.18E-07 | 4.44E-06 | Stemness | - | 0.001 | 0.99749075 | 0.99871802 |
| (j, J) | population | (j, J) | population |
| 0, 0 | 0.00561 | 0, 8 | 0.03482 |
| 0, 1 | 0.01637 | 1, 8 | 0.0423 |
| 1, 1 | 0.0198 | 2, 8 | 0.0105 |
| 0, 2 | 0.0258 | 0, 9 | 0.03023 |
| 1, 2 | 0.03122 | 1, 9 | 0.03681 |
| 0, 3 | 0.0332 | 2, 9 | 0.00916 |
| 1, 3 | 0.04019 | 0, 10 | 0.02528 |
| 2, 3 | 0.00984 | 1, 10 | 0.03064 |
| 0, 4 | 0.03816 | 0, 11 | 0.01972 |
| 1, 4 | 0.04622 | 1, 11 | 0.02465 |
| 2, 4 | 0.01134 | 0, 12 | 0.01539 |
| 0, 5 | 0.04055 | 1, 12 | 0.01766 |
| 1, 5 | 0.04914 | ||
| 2, 5 | 0.01208 | ||
| 0, 6 | 0.04051 | ||
| 1, 6 | 0.04913 | ||
| 2, 6 | 0.01212 | ||
| 0, 7 | 0.03843 | ||
| 1, 7 | 0.04664 | ||
| 2, 7 | 0.01154 |
| Moments of Inertia (amu Ų) | Polarizability (ų) |
| Ia = 225.54 | αaa = 3.71 |
| Ib = 8.47 | αbb = 3.32 |
| Ic = 234.02 | αcc = 3.04 |
| (j, J) | population | (j, J) | population |
| 0, 0 | 0.00561 | 0, 8 | 0.03482 |
| 0, 1 | 0.01637 | 1, 8 | 0.0423 |
| 1, 1 | 0.0198 | 2, 8 | 0.0105 |
| 0, 2 | 0.0258 | 0, 9 | 0.03023 |
| 1, 2 | 0.03122 | 1, 9 | 0.03681 |
| 0, 3 | 0.0332 | 2, 9 | 0.00916 |
| 1, 3 | 0.04019 | 0, 10 | 0.02528 |
| 2, 3 | 0.00984 | 1, 10 | 0.03064 |
| 0, 4 | 0.03816 | 0, 11 | 0.01972 |
| 1, 4 | 0.04622 | 1, 11 | 0.02465 |
| 2, 4 | 0.01134 | 0, 12 | 0.01539 |
| 0, 5 | 0.04055 | 1, 12 | 0.01766 |
| 1, 5 | 0.04914 | ||
| 2, 5 | 0.01208 | ||
| 0, 6 | 0.04051 | ||
| 1, 6 | 0.04913 | ||
| 2, 6 | 0.01212 | ||
| 0, 7 | 0.03843 | ||
| 1, 7 | 0.04664 | ||
| 2, 7 | 0.01154 |
| Moments of Inertia (amu Ų) | Polarizability (ų) |
| Ia = 225.54 | αaa = 3.71 |
| Ib = 8.47 | αbb = 3.32 |
| Ic = 234.02 | αcc = 3.04 |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t(Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr(a.u.) | D (Angstrom) | t(Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Sample | Temperature (°C) | Original mass | Final mass | Ratio of gel |
| Time (h) | (mg) | (mg) | ||
| TPEIu | 80 °C & 24 h | 31.50 | 0.00 | 0.00% |
| TPEI | 80 °C & 24 h | 33.80 | 27.80 | 82.25% |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Sample | Temperature (℃) Time (h) | Original mass (mg) | Final mass (mg) | Ratio of gel |
| TPEIu | 80℃ & 24 h | 31.50 | 0.00 | 0.00% |
| TPEI | 80℃ & 24 h | 33.80 | 27.80 | 82.25% |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t(Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr(a.u.) | D (Angstrom) | t(Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Sample | Temperature (°C) | Original mass | Final mass | Ratio of gel |
| Time (h) | (mg) | (mg) | ||
| TPEIu | 80 °C & 24 h | 31.50 | 0.00 | 0.00% |
| TPEI | 80 °C & 24 h | 33.80 | 27.80 | 82.25% |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.51649 | 1.153 | -1.023 | LE |
| 2nd | 0.52036 | 0.694 | -0.917 | LE |
| 3rd | 0.52298 | 0.767 | -0.465 | LE |
| 4th | 0.64899 | 1.607 | -0.408 | LE |
| 5th | 0.52174 | 1.001 | -0.476 | LE |
| 6th | 0.53471 | 0.598 | -1.032 | LE |
| 7th | 0.74181 | 0.638 | -1.635 | LE |
| 8th | 0.84146 | 0.855 | -1.015 | LE |
| 9th | 0.78380 | 0.575 | -1.994 | LE |
| 10th | 0.23816 | 3.993 | 2.515 | CT |
| 11th | 0.63355 | 3.822 | 0.872 | CT |
| 12th | 0.87103 | 0.603 | -2.815 | LE |
| 13th | 0.83576 | 0.723 | -3.464 | LE |
| 14th | 0.95267 | 0.153 | -1.674 | LE |
| 15th | 0.75283 | 2.822 | -1.292 | LE |
| 16th | 0.83269 | 0.752 | -3.195 | LE |
| 17th | 0.83051 | 1.319 | -3.491 | LE |
| 18th | 0.82484 | 0.219 | -3.130 | LE |
| 19th | 0.75768 | 1.761 | -4.497 | LE |
| 20th | 0.35695 | 12.015 | 7.386 | CT |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.53409 | 1.085 | -0.526 | LE |
| 2nd | 0.51952 | 0.718 | -0.651 | LE |
| 3rd | 0.61849 | 0.086 | -2.234 | LE |
| 4th | 0.64611 | 1.613 | -0.026 | LE |
| 5th | 0.17538 | 4.13 | 2.698 | CT |
| 6th | 0.53187 | 0.908 | -0.691 | LE |
| 7th | 0.53747 | 0.617 | -0.819 | LE |
| 8th | 0.61462 | 0.904 | -1.786 | LE |
| 9th | 0.84262 | 0.84 | -0.66 | LE |
| 10th | 0.74751 | 0.342 | -1.875 | LE |
| 11th | 0.58401 | 4.257 | 1.888 | CT |
| 12th | 0.9338 | 0.35 | -1.402 | LE |
| 13th | 0.80877 | 0.759 | -4.169 | LE |
| 14th | 0.77719 | 1.585 | -2.361 | LE |
| 15th | 0.91283 | 0.367 | -2.635 | LE |
| 16th | 0.80011 | 0.65 | -3.56 | LE |
| 17th | 0.01403 | 20.79 | 19.54 | CT |
| 18th | 0.77738 | 0.493 | -4.332 | LE |
| 19th | 0.8224 | 0.474 | -4.523 | LE |
| 20th | 0.71744 | 3.939 | -1.345 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.5364 | 1.108 | -0.511 | LE |
| 2nd | 0.50919 | 0.911 | -0.148 | LE |
| 3rd | 0.54436 | 0.999 | -0.33 | LE |
| 4th | 0.51058 | 1.051 | -0.318 | LE |
| 5th | 0.63477 | 0.799 | -1.478 | LE |
| 6th | 0.64299 | 1.193 | -1.665 | LE |
| 7th | 0.64078 | 1.193 | -1.193 | LE |
| 8th | 0.63042 | 1.358 | -0.617 | LE |
| 9th | 0.22768 | 3.825 | 2.101 | CT |
| 10th | 0.52303 | 0.913 | -0.428 | LE |
| 11th | 0.54583 | 0.887 | -0.757 | LE |
| 12th | 0.25143 | 3.083 | 0.953 | CT |
| 13th | 0.48012 | 1.376 | -1.181 | LE |
| 14th | 0.54666 | 0.896 | -1.015 | LE |
| 15th | 0.53344 | 0.849 | -0.928 | LE |
| 16th | 0.77915 | 0.533 | -1.178 | LE |
| 17th | 0.15882 | 3.78 | 2.163 | CT |
| 18th | 0.81774 | 0.881 | -0.8 | LE |
| 19th | 0.83441 | 0.888 | -1.09 | LE |
| 20th | 0.71296 | 0.983 | -0.841 | LE |
| Excited state | Sr (a.u.) | D (Angstrom) | t (Angstrom) | classification |
| 1st | 0.52078 | 1.255 | -0.31 | LE |
| 2nd | 0.56718 | 0.976 | -0.682 | LE |
| 3rd | 0.51741 | 0.767 | -0.54 | LE |
| 4th | 0.53732 | 0.75 | -0.645 | LE |
| 5th | 0.29739 | 3.416 | 1.444 | CT |
| 6th | 0.6108 | 0.305 | -1.334 | LE |
| 7th | 0.6442 | 1.426 | -2.574 | LE |
| 8th | 0.61037 | 0.785 | -3.221 | LE |
| 9th | 0.64196 | 1.433 | -0.14 | LE |
| 10th | 0.20475 | 4.073 | 2.527 | CT |
| 11th | 0.44172 | 2.252 | 0.571 | CT |
| 12th | 0.47932 | 2.099 | 0.122 | CT |
| 13th | 0.53519 | 0.662 | -0.608 | LE |
| 14th | 0.53837 | 0.923 | -0.684 | LE |
| 15th | 0.60424 | 1.022 | -1.048 | LE |
| 16th | 0.53506 | 0.804 | -0.671 | LE |
| 17th | 0.32551 | 4.75 | 2.392 | CT |
| 18th | 0.54738 | 2.637 | -0.073 | LE |
| 19th | 0.47927 | 3.142 | 0.761 | CT |
| 20th | 0.82487 | 1.009 | -0.582 | LE |
| Sample | Temperature (℃) Time (h) | Original mass (mg) | Final mass (mg) | Ratio of gel |
| TPEIu | 80℃ & 24 h | 31.50 | 0.00 | 0.00% |
| TPEI | 80℃ & 24 h | 33.80 | 27.80 | 82.25% |
| Catalyst | Conversion (%) | SCO2 | SCO | SCH4 |
| CdTiO3 | 0 | 0 | 0 | 0 |
| CdTiO3/TiO2 | 0.5 | 84.5 | 15.5 | 0 |
| CdO | 0.1 | 0 | 100 | 0 |
| Cd | 0 | 0 | 0 | 0 |
| Pretre atment | Temperature /Time | WA /% | WR /% | crystal /nm | size CNmax | N (A) /1015 g-1 | N (R) /1015 g-1 | N (A) / N (R) | / Amax /m2 g-1 |
| A R | |||||||||
| Air a | 600 °C/2h | 90.1 | 9.9 | 31.6 | 41.7 | 10.4 | 7.5 | 0.3 | 25.0 |
| 700 °C/2h | 80.2 | 19.8 | 44.2 | 60.8 | 11.4 | 2.4 | 0.2 | 12.0 | |
| 800 °C/2h | 57.4 | 42.6 | 49.1 | 77.2 | 14.8 | 1.3 | 0.2 | 6.5 | |
| N2 b | 500 °C/2h | 79.6 | 20.4 | 19.2 | 36.7 | 21.9 | 29.6 | 0.9 | 30.1 |
| 600 °C/2h | 69.9 | 30.1 | 21.7 | 36.9 | 17.3 | 18.0 | 1.4 | 12.6 | |
| 700 °C/2h | 29.6 | 70.4 | 33.0 | 48.4 | 12.9 | 2.2 | 1.5 | 1.5 | |
| \(H_{2}^{c}\) | 500℃/2h | 73.6 | 26.4 | 19.6 | 21.6 | 7.3 | 25.7 | 6.2 | 4.1 | 9.9 |
| 500℃/4h | 68.4 | 31.6 | 17.9 | 24.8 | 11.5 | 31.4 | 4.9 | 6.4 | 10.1 | |
| 500℃/8h | 51.3 | 48.7 | 21.5 | 22.5 | 6.6 | 13.6 | 10.2 | 1.3 | 6.3 |
| *CH2OOH | *CHOOH | *CHOO | |
| Cd1/A (101) | 2.49 eV | 3.16 eV | 1.8 eV |
| Cd1/R (110) | 2.50 eV | 2.75 eV | 2.82 eV |
| Cd1/A (101)-R (110) | 0.96 eV | 0.60 eV | 0.19 eV |
| Catalyst | Conversion (%) | \(S_{CO2}\) | \(S_{CO}\) | \(S_{CH4}\) |
| CdTiO₃ | 0 | 0 | 0 | 0 |
| CdTiO₃/TiO₂ | 0.5 | 84.5 | 15.5 | 0 |
| CdO | 0.1 | 0 | 100 | 0 |
| Cd | 0 | 0 | 0 | 0 |
| Catalyst | Conversion (%) | SCO2 | SCO | SCH4 |
| CdTiO3 | 0 | 0 | 0 | 0 |
| CdTiO3/TiO2 | 0.5 | 84.5 | 15.5 | 0 |
| CdO | 0.1 | 0 | 100 | 0 |
| Cd | 0 | 0 | 0 | 0 |
| Pretre atment | Temperature /Time | WA /% | WR /% | crystal /nm | size CNmax | N (A) /1015 g-1 | N (R) /1015 g-1 | N (A) / N (R) | / Amax /m2 g-1 |
| A R | |||||||||
| Air a | 600 °C/2h | 90.1 | 9.9 | 31.6 | 41.7 | 10.4 | 7.5 | 0.3 | 25.0 |
| 700 °C/2h | 80.2 | 19.8 | 44.2 | 60.8 | 11.4 | 2.4 | 0.2 | 12.0 | |
| 800 °C/2h | 57.4 | 42.6 | 49.1 | 77.2 | 14.8 | 1.3 | 0.2 | 6.5 | |
| N2 b | 500 °C/2h | 79.6 | 20.4 | 19.2 | 36.7 | 21.9 | 29.6 | 0.9 | 30.1 |
| 600 °C/2h | 69.9 | 30.1 | 21.7 | 36.9 | 17.3 | 18.0 | 1.4 | 12.6 | |
| 700 °C/2h | 29.6 | 70.4 | 33.0 | 48.4 | 12.9 | 2.2 | 1.5 | 1.5 | |
| \(H_{2}^{c}\) | 500℃/2h | 73.6 | 26.4 | 19.6 | 21.6 | 7.3 | 25.7 | 6.2 | 4.1 | 9.9 |
| 500℃/4h | 68.4 | 31.6 | 17.9 | 24.8 | 11.5 | 31.4 | 4.9 | 6.4 | 10.1 | |
| 500℃/8h | 51.3 | 48.7 | 21.5 | 22.5 | 6.6 | 13.6 | 10.2 | 1.3 | 6.3 |
| *CH2OOH | *CHOOH | *CHOO | |
| Cd1/A (101) | 2.49 eV | 3.16 eV | 1.8 eV |
| Cd1/R (110) | 2.50 eV | 2.75 eV | 2.82 eV |
| Cd1/A (101)-R (110) | 0.96 eV | 0.60 eV | 0.19 eV |
| Catalyst | Conversion (%) | \(S_{CO2}\) | \(S_{CO}\) | \(S_{CH4}\) |
| CdTiO₃ | 0 | 0 | 0 | 0 |
| CdTiO₃/TiO₂ | 0.5 | 84.5 | 15.5 | 0 |
| CdO | 0.1 | 0 | 100 | 0 |
| Cd | 0 | 0 | 0 | 0 |
| Figure | Data |
| 3a-c | prCLK-DN1 and prCLK-OE phototaxis, ERG, trangenial cross-sections. |
| S. 1b, d-f, 1 | Tim01 circadian transcriptome analyses. |
| S. 4b | prCLK-DN1 and prCLK-OE ERG traces. |
| S. 7c | arr1 expression from nCLK-DN1 RNA-Seq. |
| S. 8a-b | Cone-cell specific ATPalpha-RNAi knockdown phototaxis and lifespan. |
| S. Data 12 | Rationale and strengths/weakness of lines used in study. |
| Figure | Data |
| 4f | prCLK-DN1 lifespan (N=3). |
| S. 7d | Eye-specific arr1 knockdown phototaxis. |
| S. Data 1 | AL and DR circadian transcriptome analyses in wild-type and circadian mutant flies |
| S. Data 7 | Survival analyses. |
| S. Data 9 | Positive phototaxis responses and statistics. |
| S Data 10 | Electroretinogram analyses and statistics. |
| S. Table 2 | Drosophila strains used in this study. |
| TermID | Term | Circadian_Enrichment | Non- Circadian_Enrichment |
| GO:0009416 | response to light stimulus | 3.32E-06 | 0.7968651 |
| GO:0009642 | response to light intensity | 1.27E-05 | 0.5084995 |
| GO:0071482 | cellular response to light stimulus | 2.60E-05 | 0.91390436 |
| GO:0016059 | deactivation of rhodopsin mediated signaling | 2.64E-05 | 0.71738834 |
| GO:0022400 | regulation of rhodopsin mediated signaling pathway | 2.64E-05 | 0.71738834 |
| GO:0007602 | phototransduction | 5.98E-05 | 0.43081234 |
| GO:0009583 | detection of light stimulus | 8.65E-05 | 0.46534368 |
| GO:0007603 | phototransduction, visible light | 0.000111058 | 0.79408137 |
| GO:0006874 | cellular calcium ion homeostasis | 0.000561518 | 0.13304433 |
| GO:0009584 | detection of visible light | 0.000996215 | 0.65752068 |
| GO:0050953 | sensory perception of light stimulus | 0.001365551 | 0.48052537 |
| GO:0001895 | retina homeostasis | 0.0015157 | 1 |
| GO:0016056 | rhodopsin mediated signaling pathway | 0.0015157 | 0.64171047 |
| GO:0002032 | desensitization of G protein-coupled receptor signaling pathway by arrestin | 0.001704044 | 1 |
| GO:0050962 | detection of light stimulus involved in sensory perception | 0.002159627 | 1 |
| GO:0007601 | visual perception | 0.003498833 | 0.34515896 |
| GO:0071484 | cellular response to light intensity | 0.004972445 | 1 |
| GO:0045494 | photoreceptor cell maintenance | 0.009003586 | 1 |
| GO:0016062 | adaptation of rhodopsin mediated signaling | 0.009674064 | 1 |
| GO:0036367 | light adaption | 0.009674064 | 1 |
| GO:0050908 | detection of light stimulus involved in visual perception | 0.009674064 | 1 |
| GO:0046154 | rhodopsin metabolic process | 0.011643387 | 1 |
| GO:0042052 | rhabdomee development | 0.013623166 | 0.75601232 |
| Figure | Data |
| 3a-c | prCLK-DN1 and prCLK-OE phototaxis, ERG, trangenial cross-sections. |
| S. 1b, d-f, 1 | Tim01 circadian transcriptome analyses. |
| S. 4b | prCLK-DN1 and prCLK-OE ERG traces. |
| S. 7c | arr1 expression from nCLK-DN1 RNA-Seq. |
| S. 8a-b | Cone-cell specific ATPalpha-RNAi knockdown phototaxis and lifespan. |
| S. Data 12 | Rationale and strengths/weakness of lines used in study. |
| Figure | Data |
| 4f | prCLK-DN1 lifespan (N=3). |
| S. 7d | Eye-specific arr1 knockdown phototaxis. |
| S. Data 1 | AL and DR circadian transcriptome analyses in wild-type and circadian mutant flies |
| S. Data 7 | Survival analyses. |
| S. Data 9 | Positive phototaxis responses and statistics. |
| S Data 10 | Electroretinogram analyses and statistics. |
| S. Table 2 | Drosophila strains used in this study. |
| TermID | Term | Circadian_Enrichment | Non- Circadian_Enrichment |
| GO:0009416 | response to light stimulus | 3.32E-06 | 0.7968651 |
| GO:0009642 | response to light intensity | 1.27E-05 | 0.5084995 |
| GO:0071482 | cellular response to light stimulus | 2.60E-05 | 0.91390436 |
| GO:0016059 | deactivation of rhodopsin mediated signaling | 2.64E-05 | 0.71738834 |
| GO:0022400 | regulation of rhodopsin mediated signaling pathway | 2.64E-05 | 0.71738834 |
| GO:0007602 | phototransduction | 5.98E-05 | 0.43081234 |
| GO:0009583 | detection of light stimulus | 8.65E-05 | 0.46534368 |
| GO:0007603 | phototransduction, visible light | 0.000111058 | 0.79408137 |
| GO:0006874 | cellular calcium ion homeostasis | 0.000561518 | 0.13304433 |
| GO:0009584 | detection of visible light | 0.000996215 | 0.65752068 |
| GO:0050953 | sensory perception of light stimulus | 0.001365551 | 0.48052537 |
| GO:0001895 | retina homeostasis | 0.0015157 | 1 |
| GO:0016056 | rhodopsin mediated signaling pathway | 0.0015157 | 0.64171047 |
| GO:0002032 | desensitization of G protein-coupled receptor signaling pathway by arrestin | 0.001704044 | 1 |
| GO:0050962 | detection of light stimulus involved in sensory perception | 0.002159627 | 1 |
| GO:0007601 | visual perception | 0.003498833 | 0.34515896 |
| GO:0071484 | cellular response to light intensity | 0.004972445 | 1 |
| GO:0045494 | photoreceptor cell maintenance | 0.009003586 | 1 |
| GO:0016062 | adaptation of rhodopsin mediated signaling | 0.009674064 | 1 |
| GO:0036367 | light adaption | 0.009674064 | 1 |
| GO:0050908 | detection of light stimulus involved in visual perception | 0.009674064 | 1 |
| GO:0046154 | rhodopsin metabolic process | 0.011643387 | 1 |
| GO:0042052 | rhabdomee development | 0.013623166 | 0.75601232 |
| TMPRSS2_ERG | AR_amp | Freq |
| FALSE | FALSE | 209 |
| TRUE | FALSE | 217 |
| FALSE | TRUE | 70 |
| TRUE | TRUE | 64 |
| cancer_type | n_test | n_train |
| Breast | 100 | 896 |
| Colorectal | 71 | 639 |
| Lung_NonSmallCell | 58 | 530 |
| Prostate | 56 | 504 |
| Skin_Melanoma | 40 | 366 |
| Pancreas | 35 | 312 |
| Liver | 34 | 306 |
| Gastroesophageal | 33 | 295 |
| Ovarian | 28 | 256 |
| Kidney_ClearCell | 24 | 213 |
| Lymphoid | 21 | 197 |
| Urothelial | 20 | 180 |
| CNS_Medullo | 15 | 123 |
| CNS_Glioma | 13 | 118 |
| Sarcoma_Other | 13 | 118 |
| NET_Pancreas | 12 | 107 |
| Biliary | 12 | 102 |
| HeadAndNeck_Other | 9 | 82 |
| Uterus | 7 | 68 |
| Thyroid | 6 | 61 |
| Sarcoma_GIST | 7 | 60 |
| Sarcoma_Leiomyo | 7 | 57 |
| Cervix | 6 | 53 |
| CNS_PiloAstro | 6 | 52 |
| NET_Gastrointestinal | 5 | 49 |
| Lung_SmallCell | 5 | 48 |
| Sarcoma_Osteo | 4 | 40 |
| Kidney_Chromophobe | 4 | 39 |
| Sarcoma_Lipo | 4 | 38 |
| Mesothelioma | 4 | 37 |
| HeadAndNeck_SalivaryGland | 3 | 31 |
| Myeloid | 4 | 30 |
| Kidney_Papillary | 3 | 29 |
| NET_Lung | 3 | 23 |
| Skin_Carcinoma | 2 | 23 |
| TMPRSS2_ERG | AR_amp | Freq |
| FALSE | FALSE | 209 |
| TRUE | FALSE | 217 |
| FALSE | TRUE | 70 |
| TRUE | TRUE | 64 |
| cancer_type | n_test | n_train |
| Breast | 100 | 896 |
| Colorectal | 71 | 639 |
| Lung_NonSmallCell | 58 | 530 |
| Prostate | 56 | 504 |
| Skin_Melanoma | 40 | 366 |
| Pancreas | 35 | 312 |
| Liver | 34 | 306 |
| Gastroesophageal | 33 | 295 |
| Ovarian | 28 | 256 |
| Kidney_ClearCell | 24 | 213 |
| Lymphoid | 21 | 197 |
| Urothelial | 20 | 180 |
| CNS_Medullo | 15 | 123 |
| CNS_Glioma | 13 | 118 |
| Sarcoma_Other | 13 | 118 |
| NET_Pancreas | 12 | 107 |
| Biliary | 12 | 102 |
| HeadAndNeck_Other | 9 | 82 |
| Uterus | 7 | 68 |
| Thyroid | 6 | 61 |
| Sarcoma_GIST | 7 | 60 |
| Sarcoma_Leiomyo | 7 | 57 |
| Cervix | 6 | 53 |
| CNS_PiloAstro | 6 | 52 |
| NET_Gastrointestinal | 5 | 49 |
| Lung_SmallCell | 5 | 48 |
| Sarcoma_Osteo | 4 | 40 |
| Kidney_Chromophobe | 4 | 39 |
| Sarcoma_Lipo | 4 | 38 |
| Mesothelioma | 4 | 37 |
| HeadAndNeck_SalivaryGland | 3 | 31 |
| Myeloid | 4 | 30 |
| Kidney_Papillary | 3 | 29 |
| NET_Lung | 3 | 23 |
| Skin_Carcinoma | 2 | 23 |
| CAF Meg3 | CAF Wif1 | Mac Mrc1 | Mono-cyte | Mela- nocyte | CAF Lrrc15 | NC Aqp1 | CAF Fbln1 | Mac Arg1 | BEC | Adven-titial | |
| Apoe | T | T | F | F | T | T | T | T | F | F | T |
| Hmgb1 | T | F | F | F | F | F | T | T | F | F | F |
| Ntrk2 | T | T | F | F | F | F | T | F | F | F | F |
| Ccl11 | T | T | F | F | F | F | F | F | F | F | |
| Tnfsf9 | T | F | F | F | F | F | F | F | F | F | |
| Thbs1 | T | F | F | T | F | F | F | F | T | F | F |
| Cebpd | T | T | F | F | F | F | F | F | F | F | |
| Tac1 | T | F | F | F | F | F | F | F | F | F | |
| Ccl2 | T | T | F | F | F | F | F | F | F | F | |
| Cp | F | T | T | F | F | T | F | T | F | F | F |
| Serping1 | T | T | F | F | F | F | F | T | F | F | F |
| Cxcl1 | T | T | F | T | F | T | F | T | T | T | F |
| Il6 | T | F | F | F | F | F | F | F | F | F | |
| Fosl2 | T | F | F | F | F | F | F | F | F | F | |
| Kitl | T | F | F | F | F | F | F | F | F | F | |
| Agtr1a | F | F | F | F | F | F | F | T | F | F | F |
| Cfh | T | T | F | F | F | F | F | T | F | F | F |
| Dock10 | T | T | F | F | F | F | F | T | F | F | F |
| Tslp | F | F | T | F | F | F | F | F | F | F | |
| Apod | F | T | F | F | F | F | F | F | F | F | |
| Il6st | T | F | F | F | F | F | F | F | F | F |
| CAF Meg3 | CAF Wif1 | Mac Mrc1 | Mono-cyte | Mela- nocyte | CAF Lrrc15 | NC Aqp1 | CAF Fbln1 | Mac Arg1 | BEC | Adven-titial | |
| Apoe | T | T | F | F | T | T | T | T | F | F | T |
| Hmgb1 | T | F | F | F | F | F | T | T | F | F | F |
| Ntrk2 | T | T | F | F | F | F | T | F | F | F | F |
| Ccl11 | T | T | F | F | F | F | F | F | F | F | |
| Tnfsf9 | T | F | F | F | F | F | F | F | F | F | |
| Thbs1 | T | F | F | T | F | F | F | F | T | F | F |
| Cebpd | T | T | F | F | F | F | F | F | F | F | |
| Tac1 | T | F | F | F | F | F | F | F | F | F | |
| Ccl2 | T | T | F | F | F | F | F | F | F | F | |
| Cp | F | T | T | F | F | T | F | T | F | F | F |
| Serping1 | T | T | F | F | F | F | F | T | F | F | F |
| Cxcl1 | T | T | F | T | F | T | F | T | T | T | F |
| Il6 | T | F | F | F | F | F | F | F | F | F | |
| Fosl2 | T | F | F | F | F | F | F | F | F | F | |
| Kitl | T | F | F | F | F | F | F | F | F | F | |
| Agtr1a | F | F | F | F | F | F | F | T | F | F | F |
| Cfh | T | T | F | F | F | F | F | T | F | F | F |
| Dock10 | T | T | F | F | F | F | F | T | F | F | F |
| Tslp | F | F | T | F | F | F | F | F | F | F | |
| Apod | F | T | F | F | F | F | F | F | F | F | |
| Il6st | T | F | F | F | F | F | F | F | F | F |
| REVIEWERS' COMMENTS | Answer |
| REVIEWERS' COMMENTS Reviewer #1 (Remarks to the Author): The authors have addressed my concerns. The additional experiments have strengthened the manuscript. | We wish to thank the Reviewer for his valuable remarks. We also believe that the additions made following his remarks strengthened the manuscript. |
| REVIEWERS' COMMENTS Reviewer #2 (Remarks to the Author): The paper by Achdout et al. studies the effect of SARS-CoV-2/influenza superinfection in a transgenic mouse model expressing human ACE2 under the K18 promoter. The data obtained in this model is compared with that obtained in C57BL/6 mice in which ACE2 is delivery via Adenovirus infection. The study indicates that superinfection results in severe respiratory disease. This is precluded by previous immunity to flu but not SARS-CoV-2, and this immunity is antibody dependent. This is a great study, well conducted and with conclusions of obvious public health relevance. I only have some minor suggestions as indicated below. Well done. | We wish to thank the Reviewer for his comments and remarks. Our replay for his suggestions appears below. The line numbers specified are for the track changes version. |
| 1- In the abstract (Line 27), I think antibody-dependent is more adequate than humoral-dependent. Alternatively 'dependent on humoral immunity'. | As suggested, changed to antibody-dependent. |
| 2- Why do the authors conclude that 'in the human population, coinfection is most likely to occur during the asymptomatic period'? This for sure does not apply for mild cases of COVID-19 which are the vast majority. | We are sorry for the misunderstanding. We added clarification for the meaning of asymptomatic period of influenza (lines 112-113). |
| 3- I think that throughout the paper, superinfection is more appropriate than coinfection | We address the terms coinfection and superinfection in lines 81-84. ("While the terms coinfection and superinfection are often used interchangeably, the use of 'coinfection' here refers to a sequential infection with 2 viruses within a very short time, with the second infection occurring prior to the elimination of the first virus."). |
| 4- Did the authors sequenced their SARS-CoV-2 stocks? This is important in view of the furing cleavage deletions appearing as a consequence of passage in Vero's. This should be indicated | Yes. GISAID submission: EPI_ISL_3838266. No deletion in the furin cleavage site is noted. Data was added to the text, lines 262-264. |
| 5- it would be easier for the reader if the color codes of the graphs were maintained in all the figures | The color codes of the groups are maintain in all figures when possible. |
| REVIEWERS' COMMENTS | Answer |
| REVIEWERS' COMMENTS Reviewer #1 (Remarks to the Author): The authors have addressed my concerns. The additional experiments have strengthened the manuscript. | We wish to thank the Reviewer for his valuable remarks. We also believe that the additions made following his remarks strengthened the manuscript. |
| REVIEWERS' COMMENTS Reviewer #2 (Remarks to the Author): The paper by Achdout et al. studies the effect of SARS-CoV-2/influenza superinfection in a transgenic mouse model expressing human ACE2 under the K18 promoter. The data obtained in this model is compared with that obtained in C57BL/6 mice in which ACE2 is delivery via Adenovirus infection. The study indicates that superinfection results in severe respiratory disease. This is precluded by previous immunity to flu but not SARS-CoV-2, and this immunity is antibody dependent. This is a great study, well conducted and with conclusions of obvious public health relevance. I only have some minor suggestions as indicated below. Well done. | We wish to thank the Reviewer for his comments and remarks. Our replay for his suggestions appears below. The line numbers specified are for the track changes version. |
| 1- In the abstract (Line 27), I think antibody-dependent is more adequate than humoral-dependent. Alternatively 'dependent on humoral immunity'. | As suggested, changed to antibody-dependent. |
| 2- Why do the authors conclude that 'in the human population, coinfection is most likely to occur during the asymptomatic period'? This for sure does not apply for mild cases of COVID-19 which are the vast majority. | We are sorry for the misunderstanding. We added clarification for the meaning of asymptomatic period of influenza (lines 112-113). |
| 3- I think that throughout the paper, superinfection is more appropriate than coinfection | We address the terms coinfection and superinfection in lines 81-84. ("While the terms coinfection and superinfection are often used interchangeably, the use of 'coinfection' here refers to a sequential infection with 2 viruses within a very short time, with the second infection occurring prior to the elimination of the first virus."). |
| 4- Did the authors sequenced their SARS-CoV-2 stocks? This is important in view of the furing cleavage deletions appearing as a consequence of passage in Vero's. This should be indicated | Yes. GISAID submission: EPI_ISL_3838266. No deletion in the furin cleavage site is noted. Data was added to the text, lines 262-264. |
| 5- it would be easier for the reader if the color codes of the graphs were maintained in all the figures | The color codes of the groups are maintain in all figures when possible. |
| Ca2+ oscillation | This work | Similarity |
| IP3 | H2O | Start the oscillation |
| Ca2+ | H+ | Oscillated signal |
| 1st pool | -COOH | In positive feedback(s) |
| 2nd pool | -SO3- | In negative feedback(s) |
| Ca2+ oscillation | This work | Similarity |
| IP3 | H2O | Start the oscillation |
| Ca2+ | H+ | Oscillated signal |
| 1st pool | -COOH | In positive feedback(s) |
| 2nd pool | -SO3- | In negative feedback(s) |
| Year | Technology platform | Type of devices | Number of functions | Type of functions | Tuning range (GHz) | Performance enhancement (dB) | Gain | Noise figure (dB) | SFDR (dB·Hz2/3) |
| 2017 [10] | InP | Las., Mod., RR, PD | 1 | LPF | 0-6 | No | -20 | N/A | 81.4 |
| 2018 [33] | SOI | MZI mesh | 20 | BPF, notch | N/A | No | N/A | N/A | N/A |
| 2017 [36] | Si3N4 | RR | 1 | Notch | 0-12 | NF:LB | 8 | 15.6 | 116 |
| 2018 [48] | SOI | Mod., RR, PD | 1 | BPF | 3-10 | No | -39 | N/A | 92.4 |
| 2018 [49] | Si | MEMS | 1 | BPF | 20-40 | No | -1.1 | N/A | N/A |
| 2018 [50] | Si | MEMS | 1 | BPF | 5.5-15 | No | -3 | N/A | N/A |
| 2019 [51] | As2S3 | RR, SBS | 1 | Notch | 0-15 | NF:LB | -10 | 27.1 | 96.5 |
| 2019 [52] | InP | Las., Mod., MMI | 7 | BPF, notch IFM, RFG | 8-15 | No | N/A | N/A | N/A |
| 2020 [53] | SOI | SBS | 1 | BPF | 4-10 | No | -17 | 56.7 | 90.3 |
| 2020 [54] | Si3N4 | RR | 1 | BPF | 2-7 | NF:CS | -10 | 27 | N/A |
| 2021 [39] | Si3N4 | RR | 1 | Notch | 3-10 | NF:CS | 3 | 31 | 100 |
| 2021 [40] | SOI | Mod., MT, RR, PD | 2 | Notch, BPF | 5-25 | No | N/A | N/A | N/A |
| 2021 [12] | InP+SOI | Las., Mod., RR, PD | 2 | Notch, BPF | 3-25 | No | -28 | 51 | 99.7 |
| 2021 [55] | Si3N4+As2S3 | RR, SBS | 1 | Notch | 2-12 | No | N/A | N/A | 92.2 |
| 2021 [56] | Si3N4+LiNbO3 | Mod., RR MT, DI-RR | 1 | Downconversion | 4-20 | No | -10 | 45 | 105 |
| (this work) | Si3N4 | MT, DI-RR | 6 | Notch | 5-20 | NF:LB | 10 | 15 | 116 |
| (this work) | Si3N4 | MT, DI-RR | 1 | BPF | 5-20 | NF:LB | 1.2 | 21.8 | 112 |
| Notch | 6-18 | SFDR:Lin. | -26 | 35 | 123 |
| Year | Technology platform | Type of devices | Number of functions | Type of functions | Tuning range (GHz) | Performance enhancement | Gain (dB) | Noise figure (dB) | SFDR (dB·Hz2/3) |
| 2017 [10] | InP | Las., Mod., RR, PD | 1 | LPF | 0-6 | No | -20 | N/A | 81.4 |
| 2018 [33] | SOI | MZI mesh | 20 | BPF, notch | N/A | No | N/A | N/A | N/A |
| 2017 [36] | Si3N4 | RR | 1 | Notch | 0-12 | NF:LB | 8 | 15.6 | 116 |
| 2018 [48] | SOI | Mod., RR, PD | 1 | BPF | 3-10 | No | -39 | N/A | 92.4 |
| 2018 [49] | Si | MEMS | 1 | BPF | 20-40 | No | -1.1 | N/A | N/A |
| 2018 [50] | Si | MEMS | 1 | BPF | 5.5-15 | No | -3 | N/A | N/A |
| 2019 [51] | As2S3 | RR, SBS | 1 | Notch | 0-15 | NF:LB | -10 | 27.1 | 96.5 |
| 2019 [52] | InP | Las., Mod., MMI | 7 | BPF, notch | 8-15 | No | N/A | N/A | N/A |
| 2020 [53] | SOI | SBS | 1 | IFM, RFG | |||||
| 2020 [54] | Si3N4 | RR | 1 | BPF | 4-10 | No | -17 | 56.7 | 90.3 |
| 2021 [39] | Si3N4 | RR | 1 | BPF | 2-7 | NF:CS | -10 | 27 | N/A |
| 2021 [40] | SOI | Mod., MT, RR, PD | 2 | Notch, BPF | 3-10 | NF:CS | 3 | 31 | 100 |
| 2021 [12] | InP+SOI | Las., Mod., RR, PD | 2 | Notch, BPF | 5-25 | No | N/A | N/A | N/A |
| 2021 [55] | Si3N4+As2S3 | RR, SBS | 1 | Notch | 3-25 | No | -28 | 51 | 99.7 |
| 2021 [56] | Si3N4+LiNbO3 (this work) | Mod., RR MT, DI-RR | 1 | Downconversion | 2-12 | No | N/A | N/A | 92.2 |
| 6 | Notch | 4-20 | No | -10 | 45 | 105 | |||
| 1 | BPF | 5-20 | NF:LB | 10 | 15 | 116 | |||
| 1 | Notch | 6-18 | SFDR.Lin. | 1.2 | 21.8 | 112 | |||
| 1 | Notch | 6-18 | SFDR.Lin. | -26 | 35 | 123 |
| Freq. (GHz) | RF gain (dB) | Freq. (GHz) | RF gain (dB) | Freq. (GHz) | RF gain (dB) | Freq. (GHz) | Freq. (dB) |
| 5 | 4.23 | 9 | 2.60 | 13 | 2.71 | 17 | 1.61 |
| 6 | 4.01 | 10 | 2.39 | 14 | 2.05 | 18 | 1.19 |
| 7 | 3.62 | 11 | 2.16 | 15 | 2.60 | 19 | 1.14 |
| 8 | 2.87 | 12 | 2.72 | 16 | 2.11 | 20 | 1.71 |
| Freq. (GHz) | RF Gain (dB) | Freq. (GHz) | RF Gain (dB) | Freq. | RF Gain (GHz) | Freq. | RF Gain (dB) |
| 5 | 4.23 | 9 | 2.60 | 13 | 2.71 | 17 | 1.61 |
| 6 | 4.01 | 10 | 2.39 | 14 | 2.05 | 18 | 1.19 |
| 7 | 3.62 | 11 | 2.16 | 15 | 2.60 | 19 | 1.14 |
| 8 | 2.87 | 12 | 2.72 | 16 | 2.11 | 20 | 1.71 |
| Year | Technology platform | Type of devices | Number of functions | Type of functions | Tuning range (GHz) | Performance enhancement (dB) | Gain | Noise figure (dB) | SFDR (dB·Hz2/3) |
| 2017 [10] | InP | Las., Mod., RR, PD | 1 | LPF | 0-6 | No | -20 | N/A | 81.4 |
| 2018 [33] | SOI | MZI mesh | 20 | BPF, notch | N/A | No | N/A | N/A | N/A |
| 2017 [36] | Si3N4 | RR | 1 | Notch | 0-12 | NF:LB | 8 | 15.6 | 116 |
| 2018 [48] | SOI | Mod., RR, PD | 1 | BPF | 3-10 | No | -39 | N/A | 92.4 |
| 2018 [49] | Si | MEMS | 1 | BPF | 20-40 | No | -1.1 | N/A | N/A |
| 2018 [50] | Si | MEMS | 1 | BPF | 5.5-15 | No | -3 | N/A | N/A |
| 2019 [51] | As2S3 | RR, SBS | 1 | Notch | 0-15 | NF:LB | -10 | 27.1 | 96.5 |
| 2019 [52] | InP | Las., Mod., MMI | 7 | BPF, notch IFM, RFG | 8-15 | No | N/A | N/A | N/A |
| 2020 [53] | SOI | SBS | 1 | BPF | 4-10 | No | -17 | 56.7 | 90.3 |
| 2020 [54] | Si3N4 | RR | 1 | BPF | 2-7 | NF:CS | -10 | 27 | N/A |
| 2021 [39] | Si3N4 | RR | 1 | Notch | 3-10 | NF:CS | 3 | 31 | 100 |
| 2021 [40] | SOI | Mod., MT, RR, PD | 2 | Notch, BPF | 5-25 | No | N/A | N/A | N/A |
| 2021 [12] | InP+SOI | Las., Mod., RR, PD | 2 | Notch, BPF | 3-25 | No | -28 | 51 | 99.7 |
| 2021 [55] | Si3N4+As2S3 | RR, SBS | 1 | Notch | 2-12 | No | N/A | N/A | 92.2 |
| 2021 [56] | Si3N4+LiNbO3 | Mod., RR MT, DI-RR | 1 | Downconversion | 4-20 | No | -10 | 45 | 105 |
| (this work) | Si3N4 | MT, DI-RR | 6 | Notch | 5-20 | NF:LB | 10 | 15 | 116 |
| (this work) | Si3N4 | MT, DI-RR | 1 | BPF | 5-20 | NF:LB | 1.2 | 21.8 | 112 |
| Notch | 6-18 | SFDR:Lin. | -26 | 35 | 123 |
| Year | Technology platform | Type of devices | Number of functions | Type of functions | Tuning range (GHz) | Performance enhancement | Gain (dB) | Noise figure (dB) | SFDR (dB·Hz2/3) |
| 2017 [10] | InP | Las., Mod., RR, PD | 1 | LPF | 0-6 | No | -20 | N/A | 81.4 |
| 2018 [33] | SOI | MZI mesh | 20 | BPF, notch | N/A | No | N/A | N/A | N/A |
| 2017 [36] | Si3N4 | RR | 1 | Notch | 0-12 | NF:LB | 8 | 15.6 | 116 |
| 2018 [48] | SOI | Mod., RR, PD | 1 | BPF | 3-10 | No | -39 | N/A | 92.4 |
| 2018 [49] | Si | MEMS | 1 | BPF | 20-40 | No | -1.1 | N/A | N/A |
| 2018 [50] | Si | MEMS | 1 | BPF | 5.5-15 | No | -3 | N/A | N/A |
| 2019 [51] | As2S3 | RR, SBS | 1 | Notch | 0-15 | NF:LB | -10 | 27.1 | 96.5 |
| 2019 [52] | InP | Las., Mod., MMI | 7 | BPF, notch | 8-15 | No | N/A | N/A | N/A |
| 2020 [53] | SOI | SBS | 1 | IFM, RFG | |||||
| 2020 [54] | Si3N4 | RR | 1 | BPF | 4-10 | No | -17 | 56.7 | 90.3 |
| 2021 [39] | Si3N4 | RR | 1 | BPF | 2-7 | NF:CS | -10 | 27 | N/A |
| 2021 [40] | SOI | Mod., MT, RR, PD | 2 | Notch, BPF | 3-10 | NF:CS | 3 | 31 | 100 |
| 2021 [12] | InP+SOI | Las., Mod., RR, PD | 2 | Notch, BPF | 5-25 | No | N/A | N/A | N/A |
| 2021 [55] | Si3N4+As2S3 | RR, SBS | 1 | Notch | 3-25 | No | -28 | 51 | 99.7 |
| 2021 [56] | Si3N4+LiNbO3 (this work) | Mod., RR MT, DI-RR | 1 | Downconversion | 2-12 | No | N/A | N/A | 92.2 |
| 6 | Notch | 4-20 | No | -10 | 45 | 105 | |||
| 1 | BPF | 5-20 | NF:LB | 10 | 15 | 116 | |||
| 1 | Notch | 6-18 | SFDR.Lin. | 1.2 | 21.8 | 112 | |||
| 1 | Notch | 6-18 | SFDR.Lin. | -26 | 35 | 123 |
| Freq. (GHz) | RF gain (dB) | Freq. (GHz) | RF gain (dB) | Freq. (GHz) | RF gain (dB) | Freq. (GHz) | Freq. (dB) |
| 5 | 4.23 | 9 | 2.60 | 13 | 2.71 | 17 | 1.61 |
| 6 | 4.01 | 10 | 2.39 | 14 | 2.05 | 18 | 1.19 |
| 7 | 3.62 | 11 | 2.16 | 15 | 2.60 | 19 | 1.14 |
| 8 | 2.87 | 12 | 2.72 | 16 | 2.11 | 20 | 1.71 |
| Freq. (GHz) | RF Gain (dB) | Freq. (GHz) | RF Gain (dB) | Freq. | RF Gain (GHz) | Freq. | RF Gain (dB) |
| 5 | 4.23 | 9 | 2.60 | 13 | 2.71 | 17 | 1.61 |
| 6 | 4.01 | 10 | 2.39 | 14 | 2.05 | 18 | 1.19 |
| 7 | 3.62 | 11 | 2.16 | 15 | 2.60 | 19 | 1.14 |
| 8 | 2.87 | 12 | 2.72 | 16 | 2.11 | 20 | 1.71 |
| Name | MW (g/mol) | Surface Tension (mN/m) | Density (g/cm³) | Viscosity (cP) |
| [EMIM][DCA] | 177.21 | 47.3 | 1.11 | 21 |
| [EMIM]BF4 | 197.97 | 49 | 1.294 | 45 |
| [BMIM]PF6 | 284.18 | 38 | 1.37 | 284.18 |
| [EMIM][NTf2] | 391.31 | 36 | 1.53 | 32 |
| Name | PECVD SiO2 | TOX SiO2 | 10 nm Au on SiO2 |
| [EMIM][DCA] | 71.04 ± 6.4 | 40.1 ± 3.8 | 47.0 ± 4.5 |
| [EMIM]BF4 | 58.9 ± 3.4 | 35.7 ± 4.8 | 55.1 ± 4.1 |
| [BMIM]PF6 | 82.6 ± 6.3 | 62.9 ± 2.1 | 68.2 ± 8.9 |
| [EMIM][NTf2] | 59.2 ± 5.7 | 36.2 ± 3.9 | 37.8 ± 4.1 |
| Name | MW (g/mol) | Surface Tension (mN/m) | Density (g/cm³) | Viscosity (cP) |
| [EMIM][DCA] | 177.21 | 47.3 | 1.11 | 21 |
| [EMIM]BF4 | 197.97 | 49 | 1.294 | 45 |
| [BMIM]PF6 | 284.18 | 38 | 1.37 | 284.18 |
| [EMIM][NTf2] | 391.31 | 36 | 1.53 | 32 |
| Name | PECVD SiO2 | TOX SiO2 | 10 nm Au on SiO2 |
| [EMIM][DCA] | 71.04 ± 6.4 | 40.1 ± 3.8 | 47.0 ± 4.5 |
| [EMIM]BF4 | 58.9 ± 3.4 | 35.7 ± 4.8 | 55.1 ± 4.1 |
| [BMIM]PF6 | 82.6 ± 6.3 | 62.9 ± 2.1 | 68.2 ± 8.9 |
| [EMIM][NTf2] | 59.2 ± 5.7 | 36.2 ± 3.9 | 37.8 ± 4.1 |