TheinﬂuenceofArcticseaicelossonmid-latitude climateinthecoldseason Claude Frankignoul, Amélie Simon [amelie.simon@locean-ipsl.upmc.fr], Guillaume Gastineau LOCEAN-IPSL, Sorbonne Universités/CNRS/IRD/MNHN, Paris, France CONTEXT Since 1979, satellite observations have shown a rapid decline of the Arctic sea ice extent (SIE) and these trends might continue in response to increased green- house gas concentrations. The Arctic sea ice decline has strong local impacts, but its inﬂuence on the lower latitudes remains actively debated. This work aims at linking the anthropogenic sea ice loss to mid-latitudes winter climate by using observations. OUR BASIC ASSUMPTIONS • The direct atmospheric response to the slow Arctic sea ice loss is the same as that to inter- annual pan-Arctic sea ice ﬂuctuations with iden- tical spatial patterns • The response is sufﬁciently linear to be esti- mated by lag regression analysis • Atmospheric response to SIC ﬂuctuations is seen 1 to 3 months later. DATA & METHODS Observational data • Monthly sea ice concentration (SIC) from pas- sive microwave measurements January 1979- February 2017 (NASA-NSIDC) • ERA-Interim, HadISST, snow cover (NOAA/Rutgers University) Method • Determine the main pattern of monthly sea ice loss between 1979 and 2016 from EOF analysis • Represent sea ice loss by quadratic ﬁt; inter- annual ﬂuctuations are the residual • Estimate the atmospheric response to the inter- annual SIC ﬂuctuations • Estimate statistical signiﬁcance by controlling the False Discovery Rate (FDR) • Determine synchronous inter-annual seas sur- face temperature (SST) and snow cover ﬂuctua- tions. If FDR signiﬁcant, use multiple regression for attribution • Extrapolate these results to the quadratic de- crease of the Arctic SIC. NOVEMBER SEA ICE CONCENTRATION (SIC) PATTERN Raw SIC (with trend) - EOF1 (left) and PC1 (right) Interannual SIC (detrended) - EOF1 (left) and PC1 (right) • Anthropogenic and inter-annual SIC ﬂuctuations has identical spatial pattern with different amplitude CONCOMITANT SST & SNOW ENSO (resp. Siberia snow) times series regression on quadratically detrended SST (resp. snow) Weak la Nina and increased Siberian snow cover LAG MULTIPLE REGRESSION OF GEOPOTENTIAL HEIGHT AT 50 HPA (Z50) IN DECEMBER ON NOVEMBER Simple regression on SIC Multiple regression on ENSO, SNOW and SIC • The simple regression suggests a downward negative NAO-like sig- nal propagation from December to February (not shown) • No signiﬁcant inﬂuence of Arctic sea ice loss in stratosphere. • December simple regression on SIC is mostly due to concomitant Siberian snow cover. LAG MULTIPLE REGRESSION OF SEA LEVEL PRESSURE (SLP) IN JANUARY ON NOVEMBER Simple regression on SIC Multiple regression on ENSO, SNOW and SIC • The contours indicate 10% signiﬁ- cance and hatching is based on 10 % FDR signiﬁcance. • In January, negative NAO-like signal is largely due to SIC • Very similar results in February and much weaker signal in March CONCLUSIONS • A negative NAO/AO-like relation with previous SIC ﬂuctuations is found from December to March. • Multiple regressions with SST and snow cover anomalies (concomitant with the SIC ﬂuctuations) indicate that the December negative NAO-like signal is largely due to Siberian snow cover via a stratospheric path- way whereas the January, February, and March signal is attributed to the SIC via a tropospheric pathway. • Scaling suggests that quadratic decrease in the Arctic SIC from 1979-2016 has increased SLP by 4 hPa and Z500 by 80 m above Greenland and increased surface air temperature over northeastern America by 2.5 K. ACKNOWLEDGMENT This research was supported by the Blue-Action project (European Union’s Horizon 2020 research and innovation program, grant number: 727852.


A variance analysis of galaxy spectra Zahra Sharbaf 1, Ignacio Ferreras 1,2,3 1 Instituto de Astrofísica de Canarias, Calle Vía Láctea s/n, E38205, Tenerife, Spain 2 Departamento de Astrofísica, Universidad de La Laguna (ULL), E-38206 La Laguna, Tenerife, Spain 3 Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK 3 Galaxy formation transition through the PCA Figure 1: Galaxy spectra projected on the first three principal components (corresponding to the highest variance). The red, blue and green dots represent the results for the spectra of quiescent, star-forming, and active nuclei galaxies, respectively. The spectra are classified with the standard BPT diagram regarding nebular emission (Baldwin et al. 1981, PASP, 93, 5). The covariance analysis is performed independently for each type. We apply PCA to a homogenous sample of galaxy spectra from the Sloan Digital Sky Survey (SDSS) in the velocity dispersion range 100-150km/s, including all three types of galaxies concerning nebular emission – namely star-forming, AGN, and quiescent – according to the standard BPT classification, to assess the properties of the spectra in a model- independent way. We perform PCA separately in each of the three groups across the 3800–4200Å wavelength range. The galaxy spectra are de-redshifted, continuum subtracted and normalized, and we mask out the intervals containing strong emission lines to focus on the stellar population content. This way of performing PCA allows us to assess in more detail the variations in each group of galaxies. If applied to the whole sample, i.e. from a single covariance matrix, PCA will pick a larger signal between galaxies of different types, whereas our approach allows us to focus on variations within each group, and then among the three classes. The first three principal components carry ~95 percent of information (here understood as variance). We adopt only those first three components and the galaxy spectra are projected to produce a distribution of data points on a 3D space. Fig. 1 shows the projection of the spectra of all galaxies in our sample on components 1, 2, and 3, shown as a 3D plot. Fig.2 shows the equivalnt 2D projections in Cartesian space. The projections of the third component (PC3, whose corresponding parameter in spherical coordinates is roughly the Theta polar angle) show the separation between three groups of galaxies, so we try to study this component and find a physical interpretation of the projection. Neighbourhood galaxies in Theta space Figure 3: Correlation of the galaxy projections (using the PCA-derived parameter Theta) with respect to a few physical observables for the three spectral classes (quiescent: red, star-forming:blue and AGN:green). Each point represents the binned average of 500 galaxies, and error bars give the binned variance. Figure 4: This figure illustrates the features of spectra corresponding to extreme values of the Theta parameter in the three classes, as labelled. The stacked spectra comprise the 500 galaxies with the highest and lowest Theta in each class. Fig. 3 compares the projection of the principal components with a set of physical observables such as redshift, stellar mass, etc. The figure illustrates the transition from the blue cloud to the red sequence, in a model-independent way (i.e. no stellar population observables have been used to define the three classes). It is quite remarkable to find such a trend for continuum-subtracted spectra, where the colour information has been removed, and the interval of emission lines has been omitted, when presenting the spectra to PCA. To put the physics in context of the Theta parameter, we look for the properties of galaxies with high and low Theta within each group of galaxies. What do their spectra look like, and what do they have in common? Fig 4. shows the comparison of the stacked spectra of 500 galaxies with high and low Theta. The most apparent differences are


Extended-body corrections to the inverse-square law for spherically symmetric sources Mradumay Sadh, Lorenzo Gavassino Nicolaus Copernicus Astronomical Center, Warsaw msadh@camk.edu.pl, lorenzo@camk.edu.pl Introduction We attempt to explore the deviations from the standard inverse-square variation of irradiance when one considers the source as an extended-body. The ideal example to study these differences is a planet very close to its host-star. The recent advances in the study of extra-solar planets have shed light on such planets that are very close to their parent stars (Mayor et al. 1995). The irradiation received by these planets plays an important role in understanding the geological and climate properties of such planets. This underscores the importance of deriving a correct model for irradiation in such planets. The inverse-square law has been well established and has been used ubiquitously in previous studies (Kane et al. 2020). However, it doesn’t sufﬁce for extremely close planets. If one simply imagines such a close conﬁguration, it is immediately clear that inverse-square law doesn’t explain irradiation on the night-side of a close exoplanet since it assumes that the star is point-sized. This demands a new model that better explains the irradiation distribution and also incorporates the stellar limb darkening. In our approach we attempt to make a robust model that accurately predicts the irradiation distribution on such planets. Violation of the inverse square law Figure 1: Boundaries of validity for the inverse-square law Considering a star-planet system, the blue region in Fig.1 depicts the set of all those positions from which the star is not entirely vis- ible. An observer (represented in our case by the eye) sitting on an arbitrary blue point would see a partially eclipsed star. Since in this case, some of the photons directed to the observer are screened by the planet, the ﬂux fails to be radial. This opens the door to pos- sible violations of the inverse-square law on the dark green region of the planet. Quantifying the importance of the violations of the inverse-square law in the dark-green region and their dependence on the geometrical parameters of the star-planet system is the main purpose of this work. Geometrical model In our approach, we intend to integrate the irradiance from each surface element from the apparent surface of the star. For a close-in planet, the limits of integration are deﬁned by the region bound be- tween the tangents to the stellar circle from the point of observation on the planet. Additionally, the stellar surface hidden beneath the local horizon is omitted from the total ﬂux received. Figure 2: Star-planet system geometry in spherical coordinates Fig.2 shows the system geometry with λ and δ deﬁning the latitude and longitude of the planet, respectively. ds gives the distance be- tween the surface element on the star to a given point on the planet, and (r + R + d) is the semi-major axis(a). The star is assumed to have a bolometric luminosity Lb. Critical point of symmetry The position at which the light-green region of the planet (where the inverse-square law is valid) meets the dark-green region (where the inverse-square law ceases to hold) is deﬁned as the critical belt of symmetry and each point of it is a critical point of symmetry. The latitude at which the local horizon will be a tangent to the star is given by: λ = cos−1 R + r a  Without loss of generality we can conclude that all critical points on the planet that make an equivalent angle constitute a critical belt of symmetry. Terminator angle The terminator angle, for a given star planet system, determines how far will the terminator extend beyond the day-side of the planet. Mathematically, the terminator angle can be calculated from the slope of the exterior common tangent of the stellar and planetary circles : At = π/2 + sin−1 R −r a  . Analytical formulation The analytical formulation of the new equation for the


Pre-perceptual grouping of auditory scenes explains contextual biases in the perception of ambiguous tonal shifts. Vincent Adam1, Claire Chambers2, Claire Peloﬁ2, Daniel Pressnitzer2, Maneesh Sahani1 Gatsby Computational Neuroscience Unit, University College London Ecole Normale Superieure, Cognitive Studies Department Background Shepard Tones Base frequency fb, and octave related Bell-shaped envelope on log-frequency Evoke a non ambiguous pitch Shift Ambiguity Small Physical upward shift fb ↑leads to upward percieved shift Octave shift = Identity Half-Octave Shift → Ambiguous percept Biasing Pitch Shift Perception (Chambers and Pressnitzer) [3] Psychophysical Results Demonstration of half-octave ambiguity. (a,b) A single Shepard tone played shortly prior to the ambiguous pair can induce a strong bias, toward the shift that encompasses that tone. (c,d) Build up of the bias with the number of preceding tones (e,f) A simple adaptation mechanism? Adaptation is usually local tonotopically local adaptation eﬀects falls oﬀto 0 with distance →Here bias is strongest in the middle of the region Psychophysical manifestations of adaptation are usually repulsive Englitz et al [2], showed repulsion in a decoding paradigm repulsion predicts the opposite eﬀect More that Average Spectrum of Context Sparsely dropping individual pure tones removes the bias References [1] R. Shepard, JASA 1964 [2] B. Englitz, et al Basic Aspects of Hearing 2013 [3] C. Chambers, et al submitted 2015 [4] A. Bregman, Auditory scene analysis 1994 [5] Z. Ghahramani, Jordan, M Machine learning 1997 Model Concept: Preperceptual Grouping Inspiration: perceptual grouping in stream segretation [4]. Items of an auditory scene can be segregated in multiple distinct perceptual objects Preperceptual grouping Items of an auditory scene can be grouped in multiple distinct pre-perceptual tracks based on spectro-temporal continuity Helps solving the attribution problem (prevents misbinding) Assumption reﬂects natural sounds statistics Model Presentation Model assumptions sensory noise corrupt frequency representation track spectro-temporal continuity tracks persist in time tracks generate tones close to their mean (std σC) sensory noise ∝ 1 √ duration T (0) t T (0) t+1 T (K) t T (K) t+1 Ct Ct+1 ηt ηt+1 ... Tracks’ means (inferred) Components’ attributions (inferred) Components Formalism: Factorial Hidden Markov Model [5] Probabilistic inference: Online inference of the tracks underlying the auditory scene sensed tones are attributed to tracks (clustered) beliefs on tracks center frequency are updated Variational Approximation: p  T (i) t |Ht  ≈N  T (i) t |µ(i) t , σ(i) t  Tone Attribution: rl k(Y ) = p(ηl = k|Y, H) Tracks update: µ(i) ← µ(i) σ(i)2 + PL l=0 rl kck σ2 C ! / 1 σ(i)2 + PL l=0 rl k σ2 C ! σ(i)2 ← 1 σ(i)2 + PL l=0 rl k σ2 C !−1 From pre-perceptual tracks to perception: Track-local features are extracted and combined to give rise to a percept. Here, track-local shifts are linearily combined to give rise to the global shift percept. Model Fitting Results The model predictions qualitatively and quantitatively match the psychophysical results. It explains perceived shift both in the presence and the absence of context. Discussion Summary Chambers et al. presented a contextual bias in auditory perception We suggest that a simple adaptive mechanism cannot account for the bias We propose a model relying on pre-perceptual tracking of auditory scenes to account for the bias and show it matches psychophysical results Pre-perceptual Grouping, a general mechanism? This work was funded by the Gatsby Charitable Foundation. Contact: vincenta@gatsby.ucl.ac.uk


INTRODUCTION • The concept of “cell type” is loose scientifically • The Human Cell Atlas (HCA) needs a firm conceptual basis to ensure consistency • We dissect the concept of “cell type”, providing solid foundations for organizing our knowledge about cells, both in core Human Cell Atas database and in scientific discourse atomic cell types and species-centric divisions form a solid basis for organizing knowledge RESULTS Sensu stricto types, archetypes, infratypes and technotypes: a framework for cell classiﬁcation Tiago Lubiana (presenting) and Helder Nakaya Computational Systems Biology Laboratory - INOVA USP , University of São Paulo, Brazil Scan for extra methods and references email: tiago.lubiana.alves@usp.br github: @lubianat DISCUSSION ●Experiments generate knowledge at the technotype level ●Scientiﬁc articles work at the infratype level (ex: a single mouse strain) ●Reviews usually talk about the species level (ex: Arendt’s evolutionary types) ●Most general biology textbooks work at the archetype level ●Inference from a lower level to a higher one is not logically guaranteed ●HCA should be aware of the species-centric level of cell types in its products, to avoid false conclusions and inconsistency in every case to avoid false conclusions ATOMIC CELL TYPES AND QUALIFIERS ●Archetypes: cell type ideas applicable across multiple species ●Sensu stricto cell types: cell type ideas restricted to a single species ●Infratypes: cell type ideas restricted to a sub-specific population ●Technotypes: cell type ideas describing the objects of cell-population-centric experiments ●cell class: any concept referring to a set of real-world biological cells ●atomic cell type: the indivisible idea for a cell class given its factorization in qualifying slots in a particular context. ●qualifying slots: fields capturing continuous and discrete and sources of diversity SPECIES-CENTRIC DIVISIONS OF “CELL TYPE” SPECIES-CENTRIC DIVISIONS ●Any idea referring to real-world cell classes can be split in an atomic cell type plus one or more qualiﬁers ●HCA needs to clarify its desired qualifying slots for effective knowledge management ●The Cell Ontology needs to provide IDs for all atomic cell types needed by HCA resources ●To avoid competing annotations, resources should cover species as a slot, and only use Cell Ontologies` archetypes ●By parsing out transient and fortuitous qualiﬁers into slots, atomic cell types may correspond to a set of natural stable property clusters (SPCs) ●Technotypes are atomic cell types capturing all possible qualiﬁers in the context of an experiment. They would be relevant for granular representation of experiments in knowledge graphs ATOMIC CELL TYPES HOW TO CATALOG ALL HUMAN CELL TYPES 1. Pick a set of cell classes of theoretical interest at the species level 2. Select all relevant qualifying slots 3. Factorize cell classes in qualifiers and atomic cell types 4. Select or mint IDs for each term 5. Iterate until no new atomic types are seen 6. To get all relevant classes, set valid combinations and computationally infer them Ex: “bone types” x “osteocyte”x “sex” … → male femur osteocyte, female tibia osteocyte This work is supported by FAPESP GRANT #19/26284-1


Future work Our next steps will inves2gate the breakup of the water ﬁnger structure and further deﬁne this transfer event by carefully op2mizing a 2-‐D free energy surface that simultaneously considers the ion transfer mechanism in both energe2c and spa2al terms. Background The transfer of a charged species across immiscible phases is a fundamental event that is studied and used in many ﬁelds and techniques. Molecular insight into ion transfer leads to a deeper understanding of many processes, including phase transfer catalysis and biological ion transport. The liquid-‐liquid interface is buried, mobile, and constantly ﬂuctua2ng. Notable interfacial phenomena occur within only a few molecular diameters of the interface, making experimental study especially challenging. Techniques involving nonlinear spectroscopy, neutron or x-‐ray scaIering, and scanning electrochemical microscopy con2nue to evolve but computer simula2ons, most notably molecular dynamics, contribute signiﬁcantly to our current theore2cal picture of ion transfer at the molecular scale. Ion transfer across a water-‐nitrobenzene interface John Karnes and Ilan Benjamin Department of Chemistry and Biochemistry, University of California, Santa Cruz Free energy of transfer The water solva2on shell helps to shuIle the ion across the interface. We quan2fy this by calcula2ng the energe2cs of two ion transfer situa2ons: These preliminary results emphasize the importance of the solva2on water’s role in the transfer of a bare chloride ion. When the KillFinger rou2ne eﬀec2vely strips the ion of its solva2on shell, we see the required free energy nearly double! Exploring phase space Combining the energe2c and geometric coordinates deﬁnes a phase space through which we can track the ion transfer event. In the normal model, elonga2on of the water ﬁnger as the ion extends into the organic phase is seen as a subtle slope and break of the ﬁnger that occurs at sH2O ≈ 0.5. In the KillFinger model, the ion spends liIle 2me moving through solvent coordinate space. When the ion crosses the interface, the water ﬁnger breaks before it can ever truly form. This occurs at sH2O ≈ 0.1 when the ion is experiencing the last Coulombic aIrac2on with interfacial water as it moves into the organic phase. Quan2fying the water ﬁnger With these results, we consider the role of the water ﬁnger and may assign a geometric coordinate, w, to characterize.2 We deﬁne this coordinate as the largest step required to “hop” from the ion to the bulk water phase along the water molecules in the water ﬁnger. In the snapshots above, w is the water-‐water distance indicated by the gold arrows. Literature cited 1. Benjamin, I. Science 1993, 261, 1558. 2. Schweighofer, K. J.; Benjamin, I. J. Phys. Chem. 1995, 99, 9974. 3. Kikkawa, N.; Wang, L.; Morita, A. J. Am. Chem. Soc. 2015, 137, 8022. This work is supported by the Na2onal Science Founda2on. Simula2on details Our model consists of two adjacent liquid slabs in a 31.3 Å × 31.3 Å × 300 Å rectangular box. The liquid-‐liquid interface is perpendicular to the long axis, designated z. The aqueous phase contains 986 ﬂexible-‐SPC water molecules; the organic phase is 252 nitrobenzene molecules. For nitrobenzene, we use an empirical 14-‐site all-‐atom model. System poten2als are calculated as the pairwise sum of Lennard-‐Jones and Coulombic terms. The equa2ons of mo2on are integrated using the velocity Verlet algorithm with a 2me step of 0.5 fs. In our model system, we consider the transfer of a single chloride ion from the aqueous phase to an adjacent nitrobenzene phase. Representa2ve snapshots of this event are shown below. From lem to right, the ion is in the aqueous phase (a), at the interface (b), and moving into the organic phase (c). Note that in image (c) the ion carries with it the remnants of its water hydra2on shell and remains tethered to the aqueous phase by a “water ﬁnger.”1 In (d) the water ﬁnger has broken and the ion moves within the organic phas


Sparse Representation of HINODE/SOT/SP Spectra Using Convolutional Neural Networks Serena Flint1, Ivan Milic2,3 1University of Rochester, 2LASP, 3NSO serena.flint (at) rochester.edu Conclusions and future work Correlations between compressed spectra and physical parameters Application to simulated and real data References & Acknowledgements We test the performance of our network by applying it to the spectra calculated from a state-of-the-art MHD photospheric simulation as well as to a spectropolarimetric map of the quiet Sun observed by HINODE/SP. We find the optimal compression for network with “bottleneck” of 7. This means that the spectra is compressed to 7 numbers, down from 112 wavelength points. We assess the quality of the compression by calculating the chi-squared metric between the original spectra and the one obtained from the sparse representation. Average agreement between the original and compressed spectra is of the order of 1%. Introduction A fundamental problem in solar spectropolarimetry is relating observed spectra and their polarization to the physical parameters of the underlying atmosphere. One of the difficulties in this process is the fact that the spectra usually can be represented with a much smaller number of hyperparameters than what is suggested by the number of wavelength points used for sampling (e.g. [1]). Said differently, spectra can usually be compressed or described in a sparser basis. In this work, we use the neural networks to investigate the dimensionality of photospheric spectra, and to compare the compressed spectra with the maps of physical parameters used to generate the said spectra. Spectra Compression and CNN training We train our network on a set of 80000 atmospheres generated by applying random perturbations to a simulated 3D atmosphere. We calculated the spectra of neutral iron lines at 6300 angstrom. These are the lines observed by the HINODE spectropolarimeter and are commonly used to diagnose photospheric temperatures, velocities, and magnetic fields. We spectrally smeared the spectra and added Gaussian noise, to mimic instrumental effects. We then devised an encoder-decoder neural network to compress this data. The network takes spectra as the input and should, in principle, output the same spectra. The layer of smallest width (dense 3 in Fig 1) represents the “most sparse” or “most compressed” version of our spectra. All training was done in Keras [2] using default MSQE loss, and the built-in ADAM optimizer. The network was trained through 1000 epochs for all trials, and each took on average 30-45 minutes. Training was done on a machine with enthusiast hardware, including an i7-10700k CPU at 4.7Ghz, 32GB DDR4 RAM, and an SSD. Since the code was set up to utilize only a CPU, it’s reasonable to assume that training speeds could be improved by reconfiguring it to utilize a recent-generation GPU instead. After applying the encoder part of the network to the data, we end up with the spectra compressed down to 7 hyperparameters. The maps of the hyperparameters from the synthetic spectra can be compared to the maps of the physical parameters at depths relevant to the line formation. We calculated correlation coefficients between the maps of sparse parameters and the maps of temperature and velocity at log optical depths -2, -1, and 0. The logical next step in our research is to create a simple, but robust neural network that will match compressed spectra to atmospheric parameters and thus enable CNN-based inversion. We expect the main advantage of this inversion to be the capability to reproduce input spectra well (this is something CNN inversions are struggling with). Namely, the relationship between compressed spectra and the parameters should be very simple, and can be inverted. Stay tuned! We are grateful to HAO visitor program for supporting Serena Flint’s independent work during the summer of 2020 and to Rebecca Centeno and Andres Asensio Ramos for their comments and advice.


Zerstörtes Kulturgut Die kontextualisierte Aufbereitung von kulturellen Forschungsdaten 17 XML-Dateien im LIDO-Format zu einzelnen Kulturstätten 1 Webseite mit kontextualisierten, gesammelten Daten 1 GitHub-Repo zur kompletten Verfügbarkeit 5 Kriege geschichtlich aufbereitet Typo3 Umstellung der technischen Infrastruktur Standardisiertes Vokabular für die XML-Dateien Automatisierte Erstellung der XML-Dateien FAIR & Open Science LIDO-Schema Carl Montgomery, CC BY-SA Julia Alili & Vivien Wolter E-Mail: kulturgut@uni-trier.de <lido:titleSet> <lido:appellationValue lido:pref="preferred" xml:lang="de">Buddha-Statuen von Bamiyan</lido:appellationValue> <lido:appellationValue lido:pref="alternate" xml:lang="en">Buddhas of Bamiyan</lido:appellationValue> <lido:appellationValue lido:pref="alternate" xml:lang="fa">نایماب ىاهتب</lido:appellationValue> <lido:sourceAppellation lido:type="http://terminology.lido-schema.org/identifier_type/uri"> https://www.wikidata.org/wiki/Q484458</lido:sourceAppellation> </lido:titleSet> <lido:relatedEvent> <lido:displayEvent>Diktatur der Taliban</lido:displayEvent> <lido:event> <lido:eventID lido:type="http://terminology.lido-schema.org/lido00099"> https://www.wikidata.org/wiki/Q42418</lido:eventID> <lido:eventType>Gewaltsame Machtübernahme</lido:eventType> Status Quo - Offenes Repository - Webseitencode, Bilder mit Lizenzangaben, XML-Dateien downloadbar - Webseitenänderungen einsehbar - Waybackmachine - Verlinkungen mit GND, Wikidata In Zukunft - Langzeitarchivierung - Upload im DARIAH-Repository - Upload der XML-Dateien auf Zenodo - Standardisiertes Vokabular


U.S. Environmental Protection Agency Office of Research and Development • The concentration of both volatile and non-volatile PFAS can be measured in the RBT liver slice assay. • Perfluoroalkyl carboxylic acids (PFBA, PFPeA, PFHxA, PFHpA, PFOA) were not metabolized by RBT liver slices. • The fluorotelomer carboxylic acid, 4:3 FTCA, was not metabolized by RBT liver slices. • There was significant loss of 6:2 FTCA in the presence of slices, suggesting possible metabolism. Further investigation is needed to verify this observation as metabolism. • The fluorotelomer alcohols 4:1 FTOH and 4:2 FTOH were not metabolized by RBT liver slices. • There was significant loss of 6:2 FTOH and 8:2 FTOH in the presence of slices in multiple tests, suggesting possible metabolism. Further investigation is needed to verify this as metabolism. OH F F F F F F F F F F F F F F F F F OH OH OH HO O O O F F F F F F F F F F F F F F F F F OH O F F F F F F F F F F F F F F F F F 8:2 FTOH 8:2 FTOH - Glucuronide O F F F F F F F F F F F F F F F F 8:2 FTUAL 8:2 FTCA OH O F F F F F F F F F F F F F F F F 8:2 FTUCA OH O F F F F F F F F F F F F F F F 7:3 FTCA O O F F F F F F F F F F F F F F F OH O F F F F F F F F F F F F F F F 7:3 beta-keto aldehyde PFOA NH2 O NH S OH F F F F F F F F F F F F F F F O NH OH O OH O 8:2 uFTOH - GS OH O F F F F F F F F F OH O F F F F F F F F F F F OH O F F F F F F F F F F F F F OH O F F F F F F F F F F F F F F F F F PFPeA PFHxA PFHpA PFNA The views expressed in this poster are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA • Per-and Polyfluoroalkyl Substances (PFAS) are contaminants of emerging concern because of • Wide distribution • Resistance to degradation • Potential adverse health outcome • There is a need for a greater understanding of the fate and effects of these chemicals within organisms regarding •Bioaccumulation •Metabolism •Toxicity • Most of the existing PFAS metabolism literature pertains to mammalian species. Little is known regarding the activity of PFAS in non mammalian species, such as fish. • The rainbow trout (RBT) liver slice assay has been successfully used to study chemical metabolism of chemical classes other than PFAS. • Metabolic Pathways Knowledgebase Platform (MetaPath) uses a systematic process for compiling metabolism map information from across the open literature to build metabolism databases. Introduction Parent Chemical Screening Results Acknowledgements Sara Mary Daley l Daley.Sara.M@epa.gov l 218-529-5187 Metabolism of per- and polyfluoroalkyl substances (PFAS) in rainbow trout (Oncorhynchus mykiss) using an ex vivo model. Sara M. Daley1, Jose Serrano2, Richard Kolanczyk2, Mark Tapper2 1Oak Ridge Institute for Science and Education Research Participant, Oak Ridge, TN, USA 2U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, Great Lakes Toxicology and Ecology Division, Duluth, MN, USA References We thank Tylor Lahren for assistance in operating the LC-MS (USEPA/ORD/CCTE), Megan Saley (ORISE) for assistance in preparing samples, and Evan Timmerman for maintenance of fish cultures. This project was supported in part by an appointment to the Research Participation Program at the Office of Research and Development Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA. Conclusions Objectives Han J. et al. A Roadmap to the Structure-Related Metabolism Pathways of Per- and Polyfluoroalkyl Substances in the Early Life Stages of Zebrafish (Danio rerio). Environmental Health Perspectives 2021. Nabb D. et al. In Vitro Metabolism of 8-2 Fluorotelomer Alcohol: Interspecies Comparisons and Metabolic Pathway Refinement. Toxicological Sciences 2007. Kolanczyk R. et al. MetaPath: An electronic knowledge base for collating, excha


The PEPSI ultra-high resolution spectral library of M dwarfs Yutong Shan1,2, Mathias Zechmeister1, Ansgar Reiners1, Ilya Ilyin3, Klaus Strassmeier3 yutong.shan@geo.uio.no [1] Reiners, Mrotzek, Lemke, Hinrichs, & Reinsch, 2016, A&A, 587, A56 [2] Strassmeier, Ilyin, & Weber, 2018, A&A, 612, A45 [3] Adibekyan et al., 2020, A&A, 642, A182 [4] Husser et al., 2013, A&A, 553, A6 [5] Zechmeister, Shan, Reiners, Ilyin, & Strassmeier, in prep. [6] Shan et al. 2021, A&A, 654, A118 [7] Nagel et al., 2023, A&A, 680, A73 [8] Blanco-Cuaresma 2019, MNRAS, 486, 2075 [9] Ryabchikova et al., 2015, Physics Scripta, 90, 5 [10] Pakhomov et al., 2019, Astron. Rep., 63, 1010 References Abstract: We present a forthcoming library of M dwarf spectra taken with the ultra-high resolution spectrograph PEPSI (R = 250 000). Our library consists of 7 nearby inactive M dwarfs spanning spectral types M0 to M7, including several known planet hosts. The wavelength coverage (383 — 906 nm) approximately captures the peak of the M dwarf flux density distribution. In particular, the 700 — 900 nm window is information-rich and suitable for chemical abundance measurements. The PEPSI spectral templates will be an invaluable resource for the community by enabling precise radial velocity measurements for exoplanet searches, as well as detailed tests of cool dwarf atmosphere models and refinements of relevant line lists for chemical analysis. 3. The M dwarf sample Our library consists of high-S/N PEPSI spectra for 7 nearby inactive M dwarfs [5]. 1. Why ultra-high resolution? Ultra-high resolution (UHR) stellar spectroscopy encodes information on important stellar properties, including fundamental parameters, chemical compositions, and physical processes in the atmosphere [1, 2, 3]. They also serve as templates for precise radial velocity measurements for exoplanet searches. Cool dwarfs have complicated spectral features that are poorly explained by state-of-the-art models, hampering their interpretation and application. High-quality, UHR spectra can help to map these features and enable detailed refinement of atmosphere models. 1. Institut für Astrophysik und Geophysik, University of Göttingen, Germany; 2. Centre for Planetary Habitability, University of Oslo, Norway; 3. Leibniz-Institut für Astrophysik Potsdam, Germany Star SpT Teff (K) V mag Vrot (km/s) Planet host? S/N @ 830 nm GJ 548A M0V 3985 9.7 0.28 330 GJ 701 M1V 3706 9.4 0.18 380 GJ 226 M2V 3554 10.4 0.46 278 GJ 581 M3V 3500 10.6 0.12 yes 253 Barnard’s Star M4V 3254 9.5 0.06 ? 645 CD Cet M5V 3214 14.0 0.07 yes 92 Teegarden’s Star M7V 3034 15.1 0.06 yes 49 4. Data access Sneak preview: all data and interactive explorer to be made available on the web, similar to [2]. Stay tuned! Acknowledgement: This work has made use of the VALD database, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna. 5. Example spectra & science applications The hyperfine structure (HFS) of vanadium [6] is exquisitely resolved. It is remarkably well-described by model spectra that includes HFS components. Also shown: CARMENES spectra [7] and PHOENIX- ACES model spectra [4]. iSpec model is generated with the iSpec code (MARCS + Turbospectrum) [8] and VALD3 line lists [9, 10]. Detailed model comparisons Detection of chemical elements Wavelength segment with various atomic lines and nice pseudo-continuua enable measuring chemical elements like chromium (Cr) and nickel (Ni). Mismatches with state-of-art synthetic spectra model (PHOENIX-ACES [4]) highlight inadequacies in the treatment of molecules, esp. in late-Ms. HARPS CARMENES- VIS ESPRESSSO Wavelength (nm) 380 – 690 520 – 960 380 – 788 383 – 906 Resolution 115,000 90,000 190,000 250,000 Telescope diameter 3.6 m 3.5 m 8.2 m x 4 8.4 m x 2 Location North + South North South North PEPSI = Potsdam Echelle Polarimetric and Spectroscopic Instrument is an ultra high-resolution optical to near infrared wavelength spectrograph on the Large Binocular


U.S. Environmental Protection Agency Office of Research and Development EPA www.epa.gov  Calculation of Potency Ratio (PR): The lower 5th percentile of concentrations that caused a 50% of maximal response (AC50) in NSR assays was compared to the 5th percentile AC50 in non-NSR assays.  PR < 0 indicated greater potency in NSR assays for 103/238 = 43.3% of neuroactive substances with sufficient data. Summary 88% of neuroactives substances are active in >=1 ToxCast assay endpoint; 72% are active in >=1 NSR assay endpoint. Lack of detection in the remaining 12% of neuroactive substances may be due problematic physicochemical properties for some. The number and lack of testing of the remaining undetected neuroactive substances seems insufficient to reveal a gap in the biological space for the detection of neuroactivity. Most whole-cell neuronal assay endpoints detect a greater percentage of neuroactive than other substances tested. Trends for metals, pyrethroids, and organochlorines suggest that NSR assays may detect activity at lower concentrations than other assays. Conclusions Overall, the majority of neuroactive substances evaluated here were detected by ToxCast assays. NSR assays will likely play a role in detecting neuroactive substances at sufficiently sensitive concentrations. Future directions Use in vivo to in vitro extrapolation to compare concentrations at which neurological effects are seen in vivo to the concentrations at which activity is observed in vitro. Assess the activity of neuroactive substances with a known mechanism of action in appropriate target-based assays. Evaluation of the ToxCast Assay Suite for the Detection of Neuroactivity Carpenter, A. F.1,2*, Carstens, K. E.1,2, Martin, M. M.1, Shafer, T. J.1, Paul Friedman, K.1 1 Center for Computational Toxicology and Exposure, ORD, US EPA, RTP, NC 27711 2 Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37830 Amy F. Carpenter l carpenter.amy@epa.gov This poster does not reflect US EPA policy. A.F.C. was supported by appointment to the Research Participation Program of the U.S. Environmental Protection Agency, Office of Research and Development, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. EPA. Neuroactive substances not detected in ToxCast are largely volatile or have not been extensively tested in NSR assays Potency in NSR versus other assays varies by substance 4023/P721 Computational Toxicology I March 29, 2022 10:45am - 12:30pm Society of Toxicology Annual Meeting & ToxExpo 88% of neuroactive substances are detected in >=1 ToxCast endpoint. 80% of other tested substances are also detected in >= 1 ToxCast endpoint. NSR assay endpoints provide comparable sensitivity with higher specificity for neuroactive substances. Difference in % neuroactive vs. other substances detected increases with higher hit count threshold. 1. Russell S Thomas et al., “The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency,” Toxicological Sciences 169, no. 2 (June 2019): 317–332. https://doi.org/10.1093/toxsci/kfz058. 2. Human Protein Atlas proteinatlas.org 3. Mathias Uhlén et al., “Tissue-based map of the human proteome,” Science 347, no. 6220 (January 2015). https://doi.org/10.1126/science.1260419.  Wilcoxon signed rank test: Tested for shift in distribution of PRs towards greater potency in NSR or non-NSR assays by chemical class.  PR’s for organophosphates shifted ~0.5 log10 µM towards greater potency in non-NSR assays.  Several organochlorines, pyrethroids, and metals have PR < 0, though trend is not statistically significant after Bonferroni adjustment by chemical class. 𝑃𝑃𝑃𝑃= log10 5%𝑖𝑖𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴50 𝑁𝑁𝑁𝑁𝑁𝑁𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 5%𝑖𝑖𝑖𝑖𝑖𝑖𝐴𝐴𝐴𝐴50 𝑛𝑛𝑛𝑛𝑛𝑛𝑁𝑁𝑁𝑁𝑁𝑁𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 Concluding remarks ToxCast detects activity from 88% of neuroactive substances Most whole-cell NSR endpoints appear qualitatively but not quantita


spatial networks impact assessment library Tom Russell, Gordon Glasgow and Raghav Pant Environmental Change Institute, University of Oxford SIAM Conference on Computational Science and Engineering, March 2021 Code: https://github.com/nismod/snail Hazard maps Flood inundation under return periods, current and future climate scenarios, other spatial climate hazards. Infrastructure networks Energy, transport, water systems linked to population and economic activity. Infrastructure services Network models of service provision give knock-on effects and indirect impacts of individual asset failure. Fragility ∩ Exposure = Vulnerability Hazards of varying intensity will lead to direct damage to infrastructure assets. Δ Service Provision = Criticality Calculate impact of the failure of a single asset on overall service provision. Probability × Impact = Risk Introduce options to improve resilience, asset fragility, backups or redundancy, means of reducing the duration of service disruption. Summarise and prioritise options, calculate benefits of avoiding potential losses. With or without direct damage, there may also be a period of service disruption. Software sustainability We began work on this library after having conducted a number of analyses [1,2,3] which have developed both the methodology and the toolkit used to implement it. Scripts written for specific studies allow for narrow reproducibility of specific analysis. The library aims: to specify clear data requirements and possible outputs; to provide robust and efficient methods for spatial network analysis; and to be more readily reusable and teachable for similar use in other contexts. References [1] Oh et al. (2020) Addressing Climate Change in Transport: Volume 2: Pathway to Resilient Transport. World Bank. DOI: 10.1596/32412. [2] Pant et al. (2019) Planning for transport resilience under a changing climate. International Symposium for Next Generation Infrastructure. Buenos Aires. [3] Pant et al. (2020) Resilience study research for National Infrastructure Commission: Systems analysis of interdependent network vulnerabilities. Environmental Change Institute, University of Oxford. Available at: https://www.nic.org.uk/wp-content/uploads/Infrastructure-network-analysis.pdf


A Structural and Spectroscopy Study of the Clusters Surrounding the Orion Nebula: NGC 1977 and NGC 1981 Braz, P.H.F.B.1, Corradi, W.J.B.2,1, Katime Santrich, O.J.3, Santos Jr., J.F.1, Roman-Lopes, A.4, Ferreira, F.A.1, Angelo, M.S.5, Maia, F.F.S.6, Andrés Piatti7 Universidade Federal de Minas Gerais - Brazil1, Laboratório Nacional de Astrofísica - MCTI - Brazil2, Universidade Estadual de Santa Cruz - Brazil3, Universidad de La Serena - Chile4, Centro de Federal de Educação Tecnológica - MG - Brazil5, Universidade Federal do Rio de Janeiro - Brazil6, Institutuo Interdisciplinario de Ciencias Básicas - Argentina7 Introduction The study of young open clusters (OCs) provides constraints to models of star formation and evolution as well as to the properties of the Galactic disc (Cantat-Gaudin et al. 2018; Ferreira et al. 2020; Holanda et al. 2021). The surrounding OCs of the Orion Nebula (ON) stands out as an interesting science cases to investigate star and cluster formation and evolution, because: • NGC 1981 presently inhabits a relatively dust-free field, while NGC 1977, located about 20’ southward of it, remains embedded within its progenitor cloud. • A sequential star formation scenario for the OCs in the ON region was suggested by Maia et al. (2010); • A hypothesis that the feedback from the massive and earlier formed OCs played a crucial role in triggering the subsequent star formation events in the ON and its surrounding younger populations was suggested by Bouy et al. (2014) (Fig. 1). 3.5pc (30’) NGC 1981 NGC 1977 OMC-2/3 M42 NGC 1980 M43 L1641-N V380 Ori iota Ori Figure 1: Optic photography of the region sorrounding the ON, where NGC 1981, NGC 1977, NGC 1980, M42 and other nearby populations are highlighted (Alves & Bouy 2012). Goals • Determine the astrophysical parameters and evaluate the origin and connection of NGC 1977 and NGC 1981; • Deepen the understanding of the star formation history of the clusters surrounding the ON; Methodology In order to characterize NGC 1977 and NGC 1981 we have performed the following steps: Astrophysical Parameters Determination • To separate the members of a clusters from the field stars we have used the data form Gaia DR3 (Gaia Collaboration et al. 2023) together with the decontamination method developed by our group, described in detail in Angelo et al. (2019) e Ferreira et al. (2020). Structural Parameters • We have performed a radial density profiles (RDP) analysis and the King’s profile fit (King 1962) to determine the limit radii (Rlim), the core radius (Rc) and the central density (σ0). Spectroscopy Analysis • The spectroscopic data of the brightest stars of NGC 1981, collected at CASLEO - Argentina (2015) and OPD - Brazil (2022), together with the spectra and the stellar parameters from the APOGEE data (Abdurro’uf et al. 2022) were used to analyze the radial velocity (Vrad) and metallicity ([Fe/H]). Astrophysical and Structural Parameters Table 1: Astrophysical parameters of NGC 1977 and NGC 1981. NGC 1977 NGC 1981 RA (◦) 83.85 83.83 DEC (◦) -4.81 -4.35 µα (mas/yr) 1.40 1.2 µδ (mas/yr) -0.75 0.6 ϖ (mas) 2.56 2.53 Rlim (”) 1000 ± 57 1062 ± 57 E(B-V) (mag) 0.07 ± 0.04 0.03 ± 0.04 (M-m)o (pc) 417 ± 54 381 ± 50 Age (Myrs) 4 ± 1 8 ± 1 [Fe/H] (dex) 0.04 ± 0.40 0.04 ± 0.57 Vrad (km s−1) - 41.6 ± 8.3 0 500 1000 1500 2000 2500 R (arcsec) 5.0•10-5 1.0•10-4 1.5•10-4 2.0•10-4 2.5•10-4 3.0•10-4 σ (stars.arcsec-2) BIN SIZE 225" 300" 375" 450" 525" 600" NGC1977_1_5_flags Sky fit 1-σ uncert. σbg = ( 0.000065 ± 0.000010) ( " )-2 0 500 1000 1500 2000 2500 R (arcsec) 2•10-5 4•10-5 6•10-5 σ (stars.arcsec-2) BIN SIZE 225" 300" 375" 450" 525" 600" NGC1981_1_5_flags Sky fit 1-σ uncert. σbg = ( 0.000031 ± 0.000006) ( " )-2 Figure 2: RDP of NGC 1977 (top panel) and NGC 1981 (bottom panel). The bins used on the fit are shown in arcsec on the panel. The limit radii (Rlim) and its error are represented by the solid and dashed vertical lines, respectively. The RDPs and King’s profile present fluctuations in


ROMAN INVASION (55 BC) GERMANIC MIGRATION (5 C) CHRISTIANITY (6 C) VIKING INVASION 8 - 11 C) NORMAN INVASION (1066) THE GREAT VOWEL SHIFT 14 - 17 C) THE HISTORY OF LANGUAGE Get to know how English evolves The Roman empire invaded Britain for 400 years, they brought Latin language that had influence 30% off all English vocabulary. There were three Germanic tribes that migrated to Britain, there are; Anglo, Saxons, and Jutes. These Germanic tribes brought German language to Britain. In the late 6 century, St Augustine was sent from Rome to Britain in order to bring Christianity to the Anglo-Saxons. After that they established church buildings and monasteries, they also used Latin word, because at time Bible was written in Latin language. Vikings ( Norsemen ) began their invasion to Britain in the late 8 century, they also brought their language called Old Norse. The Norman became the ruling class ( royals, nobles ) after they won a war against Britain. They also brought their own language called Old French, and hundred years later it's merged with Old English and became Middle English. Between 14 - 17 century the great vowel shift occurred, some consonant sounds changed as well, because in 16 century the British began to explore and came back. The Great Vowel Shift brought the Middle English to Early Modern English periods. th th th Aisyah Restu Damayanti (03020321042) Ardi Rusdiyansyah ( 03040321094) Gisela Zahratul Afiah (03040321107) Ike Permatasi (03020321056) Ryzma Arasy Tartilla (03010321033) Group members: 1. 2. 3. 4. 5. 1. 2. 3. 4. 5. 6. th th th th th th th th Julius Caesar Germanic Boats Pastor Viking Soldier Norman Soldier Horatio Nelson


SEASONALITY OF RESPIRATORY SYNCYTIAL VIRUS (RSV) INFECTION IN INFANTS AND YOUNG CHILDREN IN LUSAKA, ZAMBIA Ruth Nakazwe1, William MacLeod2, Leah S. Forman2, Lawrence Mwananyanda3, Magdalena Mwale3, Benard Ngoma3, Charles Chimoga3, Gertrude Munanjala3 , Zacharia Mupila3, Geoffrey Kwenda4, Caitriona Murphy3, Rachel C. Pieciak2, Donald M. Thea2, Christopher J. Gill2 1University Teaching Hospital, Lusaka, Zambia; 2Department of Global Health, Boston University School of Public Health, Boston, MA, USA; 3Right to Care Zambia, Lusaka, Zambia; 4Department of Biomedical Sciences, University of Zambia, Lusaka, Zambia Results Conclusions Introduction Research Question • Does RSV infection in infants in Lusaka, Zambia have a predictable seasonality? Rationale • By understanding the seasonality of RSV outbreaks we can create and implement targeted intervention strategies and improve the outcomes of Respiratory Syncytial Virus (RSV) infections among infants in Lusaka, Zambia. Longitudinal RSV Surveillance in Zambia • We have observed and tested infants for RSV across three cohort studies: the Pneumonia Etiology Research for Child Health study (PERCH); the Southern African Mothers Infants Pertussis study (SAMIPS) and the Zambia Pertussis/RSV Infant Mortality Estimation study (ZPRIME). Together these studies give us a clue into the patterns of RSV infection over several years. • PCR-confirmed RSV infection in infants is cyclical across PERCH, SAMIPS and ZPRIME (Fig 1). • Counts of PCR-confirmed RSV in the ZPRIME cohort peaked from February-April (Fig 2). • RSV is a significant cause of seasonal deaths among Zambian infants. • The number of PCR confirmed RSV infections is highest from January-April. • More data collection is needed to understand the drivers behind RSV seasonality in this location. Methods SAMIPS Figure 1. Longitudinal Surveillance of RSV Positivity in Infants across 3 cohort studies in Lusaka, Zambia Enrollment • Infants were enrolled at the University Teaching (PERCH, SAMIPS, ZPRIME) and Level 1 hospitals in Lusaka Zambia (ZPRIME) Sampling and Testing • Trained hospital staff collected Copan nasopharyngeal (NP) swab samples, and RSV (RSV-A, RSV-B) was identified using real-time PCR. • All samples were run with positive and negative controls and RNase P as a housekeeping enzyme control Study Duration Study Populations Infant Age Range PERCH Oct 2011 – Oct 2013 1,154 children hospitalized with severe pneumonia, sampled once 1-59 months SAMIPS Mar 2015 – Jan 2016 939 healthy infants sampled fortnightly 1-13 weeks old ZPRIME Sep 2017 – June 2019 (ongoing) 1,306 deceased infants sampled within 48 hours of death 4 days to <6 months Table 1. Summary table of cohort studies conducted in Lusaka, Zambia. We would like to recognize the families in Zambia who have suffered great loss and were willing to participate in these cohort studies. We hope that this research will help to prevent future deaths. The authors declare no financial conflicts of interest. Funding for the ZPRIME research is provided by the Bill and Melinda Gates Foundation, grant number OPP1163027. Figure 2. Percent PCR-confirmed RSV-positive NP swabs of total NP swabs tested by month in ZPRIME cohort. 2017 2018 2019 Gap between studies Gap between studies PERCH ZPRIME


ACKNOWLEDGEMENTS Research personnel were funded both by ORD and by the American Chemistry Council Long-range Research Initiative (ACC-LRI). The views expressed in this poster are those of the authors and do not necessarily reflect the views of the U.S. Environmental Protection Agency. Applying a Tiered Risk-based Approach to Prioritizing Thousands of Chemicals for Further Evaluation: A Comparison of Current High Throughput Computational Approaches Chantel I. Nicolas1, Kamel Mansouri1, Patrick D. McMullen2, Rebecca A. Clewell1, Miyoung Yoon1, Martin B. Phillips1, Grace Patlewicz2, John F. Wambaugh2, and Harvey J. Clewell III1* 1ScitoVation, Research Triangle Park, NC and 2National Center for Computational Toxicology, U.S. Environmental Protection Agency, RTP, NC INTRODUCTION There is a need to prioritize thousands of environmental chemicals for further testing with the help of current computational technologies. Here we present a case study on applying a risk-based approach to chemical prioritization based on an initial triage by ranking the ratios between ExpoCast1 high-throughput exposure estimates and Thresholds of Toxicological Concern (TTCs)2. To demonstrate the applicability of TTCs for the initial triage, 358 ToxCast3 chemicals were processed as follows: 1) oral equivalent doses (OEDs)4 were calculated based on ToxCast bioactivity measurements and available metabolism data for estimating in vivo clearance, 2) TTC values were determined using the Cramer1 classification system, 3) OEDs and TTCs were then compared with available ExpoCast exposure estimates to determine their respective activity:exposure ratios (AERs). TTCs were lower than OEDs for 349 (97%) of the evaluated ToxCast compounds, implying that TTCs can serve as a conservative estimate of hazard in the absence of chemical- specific data. The TTC approach was then applied to a curated dataset of ~45,000 chemicals. In order to ground-truth the results, we curated an ensemble of compounds with established points of departure (e.g., no observed adverse effect levels (NOAELs)). TTC values were lower than NOAELs for all 128 compounds that overlap with the chemical database and have published NOAEL values based on a daily oral exposure. This study demonstrated the utility of exploiting computational approaches as part of a tiered risk-based approach to prioritize thousands of chemicals. This abstract does not necessarily reflect U.S. EPA policy. REFERENCES 1. Investigate whether TTC-based chemical prioritization approach is relevant for environmental chemicals of interest. 2. Compare TTC-based chemical prioritization to ToxCast OED-based chemical prioritization. 3. Ground-truth prioritization results by comparing available regulatory points of departure to TTC and OEDs OBJECTIVES Figure 9. Margin of exposures (MoEs) for top 50 ranking CERAPP compounds in grey dots (Cramer TTC Class III). 24 of these are ToxCast compounds, 3 have data necessary for HT-IVIVE, and 0 are ToxCast active compounds that also have data for employing HT-IVIVE. MoEs were calculated by dividing the Class 3 TTC (0.0015 mg/kg/d) by the upper 95% bounds on the ExpoCast SEEM2 exposure estimates in mg/kg/d. Some compound names were truncated for display. The breakdown of CERAPP compounds per Cramer class was roughly 82% (36773), 3% (1410), and 15% (6847), for Class III, Class II, and Class I, respectively (Figure 7a). Median margin of exposures for the three classes was 51.2, 392.1, and 1409.1 respectively (Figure 7b). Roughly 1000 chemicals have TTC-derived MoE of less than 10, while 164 have a margin of exposure of less than 1. These results are consistent with the a previous study10 comparing Cramer classes across compounds with biomonitoring data from the National Health and Nutrition Examination Survey (NHANES)11; 66% of NHANES compounds are in Cramer Class III. Furthermore, this study also illustrated that MoEs of less than 1 are feasible (58 compounds). Overall, TTC’s were more protective than


FOR FURTHER INFORMATION REFERENCES [1] T. Lebo, S. Sahoo, D.L. McGuinness, PROV-O: The PROV Ontology, (2013). <http://www.w3.org/TR/prov-o/> [2] <https://github.com/CSIRO-LW-LD/prov/tree/master/real-things> [3] S J D Cox, Ontology for observations and sampling features, with alignments to existing models, Sem. Web J. (in press) [4] S J D Cox & N J Car, PROV and Real Things. MODSIM 2015 <https://www.researchgate.net/publication/284180374_PROV_and_Real_Things>. ACKNOWLEDGEMENTS Thanks to Keith Sircombe of Geoscience Australia for providing the story upon which this example is based. A second example based on identification of an insect specimen was provided by Catherine Car of the Western Australian Museum, is documented in [4] and is also available from GitHub [2]. Provenance of things Flexibility required While laboratory notebooks are used for private record-keeping, and laboratory information systems (LIMS) on a facility basis, this data is not generally published. Workflows are often scoped to a lab, or an instrument. New procedures and workflows are frequent, so formalization of the description of sample preparation and observations must be both rigorous and flexible. Geochemistry observations typically follow a complex specimen preparation process after field sampling. Details of this are required to support assessment of the reliability of the data produced, and to ensure reproducibility. We have applied W3C PROV [1] in describing complex retrieval, processing and observation processes associated with physical specimens. Describing sample preparation and observation workflows using PROV-O Simon J D Cox, Nicholas J Car LAND AND WATER Simon J D Cox e simon.cox@csiro.au w www.csiro.au/research/lwf Summary PROV-O provides a rigorous yet flexible tool for recording the workflow associated with sample preparation, with intermediate and final specimens used in observations, and the observation results themselves, to be linked back to the sample retrieved in the field. This complements the IGSN system, which focuses on registration of field samples. Core PROV model The core W3C PROV model [1] (shown above) distinguishes entities, agents and activities. Its design was primarily conditioned by stories around information resources (datasets, papers, reports). Here we extend its application to non-information assets. Application to geochemistry A provenance trace for a geochemistry workflow provided by Geoscience Australia is shown graphically (shown at left), with the RDF representation available from GitHub [2]. Note that both people and machines are modelled as ‘agents’ – this follows the precedent of W3C PROV which has a specialization for ‘Software agents’. Registers of agents should be established, allowing them to be referred to using stable URIs. In an OWL implementation of the O&M Standard that has recently been developed [3], the Specimen class is explicitly sub-classed from prov:Entity in order to utilize the capability illustrated here (see above). PROV supports a comprehensive trace of predecessor entities and transformations at any level of detail. Tony Jones Maham Aziz Amelia Davis Michelle Rouse Hammer: b45 SieveStack :80-200 CPV:2015-07-23 Viz:2015-08-02 Seiving:2015-07-18 Grinding:2015-07- 18/6 Splitting:2015-04- 18/9 FieldSampling:2015- 03-25/47 LF-09/2-gt- 80 81.6% CO3 75% CO3 LF-09/2- pulp LF-09 IGSN:CSIWALF09 Leederville formation Visual analysis protocol CC Entities: specimens, data, reports, protocols Agents: people, machines Activities: prep-phases, observations Activity Person Machine Physical entity Physical entity (destroyed) Record (data or report) Plan or protocol Key wasAssociatedWith wasAssociatedWith wasAssociatedWith wasAssociatedWith wasAssociatedWith Saw:s4 Mill:m7 wasAssociatedWith used used used used used used used used wasGeneratedBy wasGeneratedBy wasGeneratedBy wasDerivedFrom wasDerivedFrom wasDerivedFrom wasDerivedFrom LF-09/2-lt- 80 wasGeneratedBy CPV protocol LF-09/2 LF-09/1


ECCS # 2203625: CDS&E: Machine-Learning-Driven Methods for Multiobjective and Inverse Design of van-der-Waals-Material-Based Devices PI: Jing Guo (email:guoj@ufl.edu) Department of ECE, University of Florida, Gainesville, FL, 32611 This work was supported by National Science Foundation Award # 2203625 Introduction Simulation of vdW-Material-Based FTJ Simulate Ferroelectric Tunnel Junction for Memory Simulation Tool Development & Deployment Conclusion Multi-Objective Design Results Background: Why van der Waals (vdW) Semiconductors for Electronic Devices? • Ultrathin thickness ductors preferred for excellent device scalability • A wide range of material choices with desired properties for device applications • Vertical vdW stacking to flexibly form heterojunctions (HJs) • Good carrier transport, optoelectronic, and mechanical properties • New device functionalities derived from unique material and HJ properties Acknowledgement • Machine learning methods explored for nanoscale transistor and memory device simulations. • ML assisted approach speeds up quantum transpot simulation of ferroelectric tunnel junction devices (for memory applications) • vdW heterojunction based FTJ can promise extradinarily large ON-OFF ratio • Tellurium NW array FETs promises significantly reduced leakage compared to CNT array FETs, which suffers from ambipolar transport • Multi-objective design optimizes multiple design targets, including speed, power, and device variations simultaneously and automatically. • Online simulation tools on nanoscale transistors based on 2D semiconductors deployed. ML TB Simlauton of Transistors based on vdW Materials Multi-Objective Design of vdW 2D FETs Device Performance Assessment https://nanohub.org/resources/2dfets 2DFET Tool - 2DFET is an online toll simulating the device characteristics of field-effect transistors based on 2D materials. - Model nanoscale transistors based on a wide variety of vdW 2D semiconductors by using a semi-analytical model. - Supporting >1,100 users since its depolymnet on nanoHub . Nanosheet FET Tool https://nanohub.org/resources/nanosheetfet This tool performs rigorous self-consistent quantum transport device simulations of vdW nanosheet FETs Desai et al., Science, 354, 99 (2016) 1nm-Gate-Length Transistor Memory Device: Ultrahigh On-OFF Ratio Wu et al. Nature Electronics, 3, 466 (2020) Objective 1 Objective 2 Optimal Pareto Front Design Target Objectiv e Domain Pareto front Parameter 1 Parameter 2 Design Parameter Space Two methods explored: • Non-dominated Sorting Genetic Algorithm (NSGA) • Pareto Active Learning (PAL) Simulation/evaluation Generation of new designs by using ML algorithms: • ML-Guided method automatically identifies the Pareto surface for three design targets (delay, leakage power, and variation • The method renders the distribution of the optimal device design parameters. • Pareto active learning (PAL) and non-dominated sorting genetic algorithms (NSGA) can achieve comparable results to identify the Pareto front. • PAL significantly faster than NSGA. Two design objectives: Delay and Dynamic Power: Three design objectives: Leakage, Variability, and Delay: T. Wu and J. Guo, IEEE Trans. Electron Devices, 68, 5476 (2021) Devlelop Machine Learning Methods for 2D-Material-Based Device, to: • Speed up demanding quantum transport device simulations • Develop multi-objective device design capacbilites • Delop an inverse device design framework Design goals of ferroelectric tunnel junction (FTJ) for non-volatile memory - Large tunneling resistance ratio between two states. - Large on-state current Metal 2 HZO or other FE Metal 1 Conventional FTJ: - limited barrier modulation - Scalability of tunnel layer 2D-material FTJ: - Graphene contact - 2D FE tunnel layer N. Yang, …, H. Wang, J. Guo, IEDM, p. 55 (2020) § FTJ devices show I-V characteristics in agreement with experiment § Unprecedently large tunnel electroresistance (TER) ratio >6×107 1 2010 2015 2020 10 0 10 1 10 2 10 3 10 4


Map of the locations of the main sampling sites (handpicking localities are not included) Ecology of the ground beetles (Insecta: Coleoptera: Carabidae) from the Sarnena Sredna Gora Mts. Ecology of the ground beetles (Insecta: Coleoptera: Carabidae) from the Sarnena Sredna Gora Mts. Teodora Teofilova*, Nikolay Kodzhabashev *oberon_zoo@abv.bg Teodora Teofilova*, Nikolay Kodzhabashev *oberon_zoo@abv.bg Category Dominance structure No eudominant Laemostenus cimmerius 1 dominant Aptinus bombarda, Carabus montivagus, C. convexus, Molops piceus, Pterostichus oblongopunctatus 5 subdominant Abax carinatus, Calathus distinguendus, C. fuscipes, Carabus hortensis, [C. intricatus], Harpalus tardus, [Limodromus assimilis], Myas chalybaeus, [Xenion ignitum] 6 recedent Abax ovalis, A. parallelus, Brachinus crepitans, Br. explodens, Calosoma inquisitor, Carabus intricatus, C. coriaceus, Limodromus assimilis, Notiophilus rufipes, Pterostichus niger, Trechus quadristriatus, Xenion ignitum 12 Subrecedent all the rest 82 F Frequency of occurrence No > 50% constant 91%: Laemostenus cimmerius; 82%: Carabus convexus, Myas chalybaeus; 68%: Abax carinatus, Carabus montivagus; 64%: Carabus coriaceus, Trechus quadristriatus; 55%: Calathus fuscipes, Calosoma inquisitor, Harpalus tardus, Pterostichus oblongopunctatus 11 25–50% accompanying 45%: Carabus intricatus, Molops piceus, Notiophilus rufipes; 36%: Calathus distinguendus, Carabus hortensis, Xenion ignitum; 32%: Cychrus semigranosus, Pterostichus niger; 27%: Abax ovalis, Aptinus bombarda, Brachinus explodens, Carabus scabrosus, Laemostenus venustus, Molops alpestris 14 < 25% all other 81 Life forms No sp. % Class: Zoophagous Life form subclass: 1.1 – Phytobios 1.1.2 Stem-dwelling hortobionts 1 0.6 1.1.3 Leaf-dwelling dendrohortobionts 3 1.7 Life form subclass: 1.2 – Epigeobios 1.2.2 Large walking epigeobionts 12 6.9 1.2.2(1) Dendroepigeobionts 1 0.6 1.2.3 Running epigeobionts 3 1.7 1.2.4 Flying epigeobionts 3 1.7 Life form subclass: 1.3 – Stratobios Series: 1.3(1) – crevice-dwelling stratobionts 1.3(1).1 Surface & litter-dwelling 19 10.6 1.3(1).2 Litter-dwelling 16 9.1 1.3(1).3 Litter & crevice-dwelling 18 10.3 1.3(1).4 Endogeobionts 2 1.1 1.3(1).5 Litter & bark-dwelling 2 1.1 1.3(1).6 Bothrobionts 3 1.7 Series: 1.3(2) – digging stratobionts 1.3(2).1 Litter & soil-dwelling 21 12.0 1.3(2).2 Litter & crevice-dwelling 1 0.6 1.3(2).3 Bothrobionts 1 0.6 Life form subclass: 1.4 – Geobionts 1.4.1(1) Narrow-headed running & digging geobionts 1 0.6 Zoophagous total 107 61.0 Class Mixophytophagous Life form subclass: 2.1 – Stratobios 2.1.1 Crevice-dwelling stratobionts 9 5.1 Life form subclass: 2.2 – Stratohortobios 2.2.1 Stratohortobionts 16 9.1 Life form subclass: 2.3 – Geohortobios 2.3.1 Harpaloid geohortobionts 33 18.9 2.3.1(1) Crevice-dwelling harpaloid geohortobionts 2 1.1 2.3.2 Zabroid geohortobionts 5 2.6 2.3.3 Dytomeoid geohortobionts 3 1.7 Mixophytophagous total 68 39.0 This is the second part of the first purposive study on the ground beetles (Coleoptera: Carabidae) from Sarnena Gora Mts. So far, 175 species (23% of all Bulgarian Carabidae species) are to be found there. The present study aimed at analyzing the ecological structure of the carabid coenoses in relation to main ecological parameters, e.g. dominance structure, life form categorization, wing development, humidity and habitat preferences, with a subsequent assessment of the environmental trends and anthropogenic impact in the studied area. The species list is completed on the basis of the available bibliographic data and material collected during field trips carried out in 2017–2020. Ground beetles were collected with pitfall traps, hand picking and light attraction, and different types of habitats were sampled. Humidity preferences Humidity preferences H – hygrophilous MH – mesohygrophilous M – mesophilous MX – mesoxerophilous X – xerobiont, E – eurybiont Wing morphology Wing morphology m – macropterous D – wing di(poly)morphic b – brach


U.S. Environmental Protection Agency Office of Research and Development CELL AGENT-BASED MODEL (ABM): multicellular and signaling dynamics were modeled in CompuCell3D (www.compucell3d.org/); the small working prototype simulated mouse hindlimb-bud development between Theiler stages 16-19 (~42h) in ~55,000 Monte Carlo Steps (MCS). ABM Gradients for FGFs, SHH, BMPS,GREM1, Retinoic acid (RA) and (RAD gradient: RA with aldrin and dieldrin). We mined the ToxCast high-throughput screening (HTS) database [1] for in vitro chemical-bioactivity profiles that significantly correlated with skeletal defects in ToxRefDB for prenatal developmental toxicity studies [2]. 734 of 1060 chemicals had in vivo developmental toxicity data; 44 of them (4-5%) produced limb defects. In addition, we identified 132 ToxCast chemicals not in ToxRefDB that according to published articles produced limb defects. We hypothesized that significant in vitro – in vivo correlations would reveal meaningful mechanistic relationships for predictive modeling of limb/paw defects. Introduction References Workflow for Data-mining DISCLAIMER: this work does not reflect EPA policy Conclusion and Future Directions Computational Toxicology and Computational Modeling of Limb Development using ToxCastTM High-Throughput Screening Data Ahir1 BK, Spencer2 RM, Baker3 NC, Judson, RS4 and Knudsen4 TB U.S. Environmental Protection Agency, Office of Research and Development 1 University of Illinois College of Medicine at Chicago, IL, USA, 2Applied Research Associations, Inc., 3Leidos, 4National Center for Computational Toxicology, Research Triangle Park, NC, USA Bhavesh Ahir l bhavesh.med@gmail.com l 312-989-6683 Hits to assay targets are based on if an AC50 (50% effect on the assay readout) was recorded (active) or not (inactive); the list was filtered for those assays with at least 3 true positive calls (p ≤0.05, TP ≥3). Assays were grouped by biological function; endpoint categories indicate limb or paw defects. Retinoic acid receptor (RAR) assays emerged as the top correlated target and were disrupted by 23 ToxCast chemicals. Vitamin A (retinol) and its biologically active derivatives (e.g. retinoic acid) are essential for embryonic development [4]. Endpoint Class Limb-bud Outgrowth Control Network ToxPi Ranking of in vitro Bioactivity Assay (Univariate Features) Chemicals with effects on limb/paw development were ranked by ToxCast bioactivity profile. Highlighted are aldrin, dieldrin, endrin, all-trans retinoic acid, and retinol. Retinoic acid (RA) signaling play an essential role in limb-bud outgrowth and patterning as a ligand for nuclear RA receptors (α, β, or γ) [4]. Aldrin and dieldrin (the major metabolite of aldrin) bind to RARs (α, β and γ) [5], and can activate these RARs. This is consistent with interference with RA- signaling. Chemical Disruption in the Agent-Based model (ABM) How might local effects predicted by HTS data propagate through the pivotal GREM1 cell lineage in silico to predict, therefore, a key event? We selected aldrin and dieldrin as known limb defect teratogens [5]. Our signature prediction suggest these two compounds are very potent (e.g. Dieldrin (concentration at 50% of maximum activity values (AC50) AC50 = 1.679 µM on RARγ, and 0.770 µM on RARα) and Aldrin (AC50 = 0.911 µM on RARγ, and 1000 μM on RARα) to RARs and activate the RARs assay (see ToxPi Results)). The agonistic activity of these two compounds activate endogenous RA levels, and excessive RA levels, downregulate GREM1 expression. Electronic knock down of GREM1 expression subsequently affects other important signaling networks (e.g. BMPs, FGFs and SHH), and the resultant outcome or ‘cybermorphs’ predict limb defects. The current results support the use of ABM as a predictive model for disruptive embryonic limb development, potentially leading to defects. The ABM correctly predicted that aldrin and dieldrin disrupt embryonic limb development [6] and predicts down-regulation of GR


Sustainable biogas purification system in landfills and municipal solid waste treatment plants Promoting the use of biogas as a sustainable energy source A new cost-efficient and environmental friendly technology for biogas desulphurisation LIFE BIOGASNET system will present potential environmental advantages minimising the environmental footprint up to 55% by reducing the carbon dioxide emission and the waste generation More information: www.biogasnet.eu info@biogasnet.eu The project has received funding from the LIFE Programme of the European Commission under contract number LIFE18 ENV/ES/000426 Consortium: LIFE BIOGASNET European project demonstrates an innovative, sustainable and low-carbon footprint technology for biogas purification based on biological processes. The project aims to boost the use of biogas as a sustainable energy source and to promote renewable energies production through the circular economy concept.


Anna Peterson*1, Brenton Gilbertson*2, Terri Jicha3 Examination of the data from the nutrient uptake assays found no significant difference in the initial nutrient concentrations between sites. Additionally, neither the differences in uptake rates between sites nor within each site were found to be significant. The regression slopes calculated for each nutrient at each sample location were also found not to be statistically significant. Pre-remediation Nutrient Cycling in the Pickle Pond AOC A comparison of the nutrient cycling between pre-remediation Pickle Pond and a nearby control site within the SLRE. Examine initial nutrient concentrations between sites as well as nutrient uptake rates between sites and within each site. Nutrient cycling is expected to be higher at the control site if contamination has had a negative impact on the functional indicators. 5L biotainer of water was collected from each site Water temperature and weather conditions noted Assay is done on all biotainers simultaneously Nutrients (KNO3, NH4H2PO4) were added to each biotainer at 10:00am Initial 200ml sample collected directly after nutrients were mixed in Biotainers are incubated in water (Fig 4A) for four hours with a sample point every hour Half of each sample is filtered with 60ml syringe and attachable 0.45µm acetate filter (figure 4B) Remaining portion is labeled as unfiltered Samples put on ice until frozen back at the lab. Study Sites Pickle Pond is a 9-acre pond separated from the bay by a railroad line with only two small openings connecting it to the bay. Historically, sewage outfalls were in Pickle Pond. Today, stormwater outfalls are present. Both have contributed to the accumulation of contaminates and gradual filling in of the pond. Loon's Foot Landing is located ~1.4 miles from Pickle Pond. The landing is near a retired taconite dock and now serves as a boat launch. Selected as the control site due to the proximity to Pickle Pond and natural connection to the estuary. Additionally, it does not have the direct contamination impacts that Pickle Pond has. Sites are displayed in Figure 3. The data presented here represents a small snapshot of how nutrients cycle through an aquatic ecosystem. Other data can be used in conjunction with the results from the nutrient uptake assays discussed. Sediment samples were collected from each site along with the water. The data from the 2020 sediment is currently being analyzed and could offer insight into how nutrients are processed at each location. Of particular interest is the enzyme activity and the role it plays in the nutrient cycle. The same suite of samples were collected from Pickle Pond and Loons Foot Landing in 2021. Upon completion of analysis, they will add another valuable year of pre-remediation data for a more robust comparison. The data collected pre-remediation will be used to compare to post-restoration values. This will allow us to determine if there were significant changes after the completion of the restoration at Pickle Pond. In addition to the samples collected and ran in the previous years, a new focus will be given to identifying Cyanobacteria through phytoplankton taxonomy, and phycocyanin fluorescence. The presence of Cyanobacteria could be useful in explaining the nitrogen fixation seen at some sites. Remediation is scheduled to begin in 2022 and sampling will continue. The proposed remediation (Fig 7) will feature the removal of contaminated sediment and invasive species, additional openings to the bay, as well as improving fish and wildlife habitat. Discussion  Filtered samples were analyzed for dissolved NO3-N, NH4-N, and PO4-P concentrations using flow-injected colorimetry (QuickChem 8000, Lachat Instruments, Loveland, CO, USA). NO3 -N and PO4-P were analyzed simultaneously by the cadmium reduction method (QuikChem Method 10-107-04-1-B 1996) and the ascorbic acid method (QuikChem Method 10-115-01-1-B 1996), respectively. NH4-N was analyzed using the sali


„Nötig ist ein Zusammenspiel von Finanzabteilung, Controlling, Bibliothek und Vorstand.“ „Für das Informationsbudget braucht es den politischen Willen der Leitungsebene.“ Informationsbudget: Navigation auf den Wegen zur Open-Access-Transformation Erfahrungsaustausch auf dem Workshop-Ring: Fahren Sie zu neuen Impulsen und Entwicklungen. Auf dieser Strecke können Sie von anderen lernen, gemeinsam Lösungen entwickeln und über vielfältige Praxis-Erfahrungen diskutieren. Wege zum Informationsbudget: In dem Streckennetz sind Rahmenbedingungen und Bausteine als Haltestellen verzeichnet, die Ihnen bei der Einführung und Etablierung eines Informationsbudgets begegnen und helfen können. Welche Route wählen Sie? Welche Strategien und Potenziale passen zu Ihrer Einrichtung? https://doi.org/10.5281/zenodo.11617334 https://creativecommons.org/licenses/by/4.0/deed.de Rhein Modul: Ausgaben Informationsver- sorgung Modul: Ausgaben Produktion von Informationen Modul: Ausgaben nationale/internationale Mitgliedschaften oder Services Modul: Ausgaben Infrastruktur und Betrieb Modul: Ausgaben Personal Modul: Weitere Ausgaben Modul: Finanzierungs- quellen Kostenelemente und -vorgänge Rollen und Verantwortlichkeiten Virtuelle und reale Informationsbudgets Identifikation von Institutionen und Untereinheiten Beratungs- und Schulungsbedarf Nachhaltigkeit und Transparenz Reporting, Bereitstellung von Informationen, Steuerung Open-Access-Tage Köln, 10.-12.09.2024 Katja Dammann,a,b Lea Maria Ferguson,c,d Marcel Meistring,c,d & Paul Schultze-Motela,d a BMBF-Projekt open-access.network; bUB der Universität Bielefeld; cDFG-Projekt Transform2Open; dHelmholtz Open Science Office, Helmholtz-Gemeinschaft Die Workshopreihe „Finanzielle Gestaltung der Open-Access-Transformation an Hochschulen und Wissenschaftseinrichtungen“ ist ein Informations- und Diskussionsforum zu praktischen Fragen bei der Einführung eines Informationsbudgets. Die Reihe ist Teil des BMBF-geförderten Projekts open-access.network Das DFG-geförderte Projekt Transform2Open unterstützt das Zusammenspiel von Bibliotheksetats, Drittmitteln und weiteren Finanzmitteln an wissenschaftlichen Einrichtungen zur Schaffung von übergreifenden Informationsbudgets; in diesem Rahmen wird ein modulares Konzept zur Einführung von Informationsbudgets erarbeitet. Module zur Einführung eines Informations- budgets sind flexible Bau- steine, die sich an die spezifischen Gegebenheiten in den Einrichtungen anpassen lassen. Rahmenbedingungen und Erfolgsfaktoren sind für die Ein- führung eines Informationsbudgets entscheidend, um nachhaltigen Nutzen für die Open-Access- Transformation sicherzustellen. „Das Informationsbudget ist ein Thema, aber wir haben noch nicht damit angefangen.“ Start Informationsbudget (Köln Hbf) Beschäftigte in Wissenschaftseinrichtungen aus Bibliotheken, Finanzabteilungen, Forschungsabteilungen, Leitung Wissenschaftseinrichtungen erstellen ihr Informationsbudget, das alle Publikationskosten enthält (Erwerb, Bereitstellung, Finanzierung) und erstellen planbare Budgets für das Publikationswesen. Ziel open-access.network Workshops open-access.network Workshops open-access.network Workshops open-access.network Workshops open-access.network Workshops open-access.network Workshops


Integrated Pest Management: Holistic Approaches to Managing Pests in Herbarium Collections Ms Erin Berkowitz1, Administrative Curator Mare Nazaire1, Volunteer Deb Woo1 1California Botanic Garden, Claremont, United States The Herbarium at California Botanic Garden (RSA) holds over 1.25 million vascular plant specimens, making it the 3rd largest herbarium in California. With over 850 cabinets, a collection of this size creates an often- overwhelming challenge to prevent, mitigate, and manage pest infestations. Further, pest management at this scale requires dedicated personnel and time, which was significantly hindered due to the COVID-19 pandemic. Prior to 2018, the RSA Herbarium’s Integrated Pest Management (IPM) practices were primarily word of mouth protocols. Additionally, the database used at that time for monitoring pest activity was inefficient and difficult to interpret, which presented challenges with data analyses. In 2018 the Herbarium experienced a surge in Lasioderma serricorne (cigarette beetle) populations within the collection, which served as the catalyst to our IPM reform. Staff reviewed current IPM literature and techniques and implemented several best practice methods in conjunction with the already established quarterly check method to better track and manage pests within the collection. With the help of a skilled volunteer, we developed a database to track pest activity more accurately and to better inform our pest management strategies. We also produced a detailed written protocol and implemented this as part of our routine training for all curatorial staff. Other pest management measures included installing additional pheromone traps in all areas of the building containing herbarium cabinets, eliminating environments hospitable to pests, and implementing a routine freezing treatment on specimen pressing supplies and cabinets in areas with inadequate climate control. The overhaul of RSA’s integrated pest management program over the past four years has helped us to holistically and effectively manage pests in our collection. Keywords: IPM, database, protocols, cigarette beetles, monitoring


Can we investigate microplastic pollution with school students? Experiences from eight years of citizen science research Janto Schönberg* 1, Sinja Dittmann1, Tim Kiessling1, Linda Mederake 2, Mandy Hinzmann 2, Doris Knoblauch2, Katrin Knickmeier1, Martin Thiel3, 4, 5 4Millennium Nucleus Ecology and Sustainable Management of Oceanic Islands (ESMOI), Coquimbo, Chile 5Centro de Estudios Avanzados en Zonas Áridas (CEAZA), Coquimbo, Chile 1Kiel Science Factory, Leibniz Institute for Science and Mathematics Education (IPN) and Kiel University, Germany 2Ecologic Institute, Berlin, Germany 3MarineGEO Program, Smithsonian Environmental Research Center,Edgewater, Maryland, USA • Since 2016, schoolchildren and youth groups investigate litter pollution in and on rivers and since 2024 at coasts. • Main objectives are achieving a large scale overview on the riverine and marine litter pollution and increasing young peoples scientific literacy. • More than 25,000 people have participated from 11 European countries, generating over 1,700 datasets. • Data on plastic litter and the Plastic Pirates project was published in six peer-reviewed studies. Plastic Pirates Brief To get citizen science plastic litter data published, you need good and robust data. To get good and robust data, the project has to be carefully designed and closely supported at all stages. The successful publishing of our data relied on four well structured components of this citizen science project. Communication is key! Adapted from Dittmann et al. 2022 From Dittmann et al. 2023 • The purpose of the individual communication was to coordinate the shipment of sampling materials, answering questions regarding the organization and methodology of the sampling, and getting in contact to obtain missing data and samples and clarify questions regarding the citizen science data quality. • 12,767 mails were registered (7,237 sent and 5,530 received) • This represents more than 1,000 hours of work (assuming 5 minutes of attention per Mail), enough for 37 weeks of employment time (given a typical contract of 30 hours per week). Plastic Pirates Brief • A carefully designed project is advantageous in all phases of your initiative. • Implement a rigorous validation scheme to ensure best data quality. • Detailed and transparent communication with participants will help in obtaining scientifically valuable data. • Make sure to have the extensive personnel ressources needed for efficient communication Take-aways We are grateful to all schoolchildren and teachers who participated in the Plastic Pirates, without their effort the project could not have been realized!


CHARACTERISTIC LENGTHS CALIBRATION The procedure of calibration of the characteristic lengths has been demonstrated for a Ti6Al4V Titanium alloy with the followign material properties: Both the elastic and the plastic characteristic lengths have been calibrated by confronting the load-displacement graphs and the distribution of the generated temperature at the shear band: ASSESSING TEMPERATURE AND STRAIN RATE EFFECT The ability of the model of capturing the effect of the strain rate and of the temperature inducing softening has been tested: DEFINING THE THERMOMECHANICAL MODEL The mathematical model of the thermodynamically-consistent visco-plastic Cosserat medium for small deformation was the first step upon which then the Finite Element could be created [3]. The degrees of freedom of the system are: where, besides the usual displacement, there is also the rotational degree of freedom, meant to describe the microstructure rotation. The structure of the model is built with the following items: This model has been successively discretized according to a Finite Element formulation, such that it was possible to incorporate it inside the software Z-Set. MOTIVATIONS Recent experimental observations at the micrometre or nanometre scale with newly probes such as nano-indenters and ato- mic force microscopes have suggested that classical continuum theories are no longer sufficient for a precise and detailed description of the corresponding deformation phenomena. These drawbacks have led to the recent development of theories that attempt to capture such phenomena via dependencies on plastic strain gradients. The purpose of strain gradient model- ling is twofold. First, it provides a continuum model that can account for size effects observed during deformation, machining processes or material processing. Second, it is well-known that severe deformation leads to strain localization, e.g. adiabatic shear banding. Finite element simulations of localization phenomena usually exhibit spurious mesh-dependence (dependence of the results on finite element size and orientation) that can be regularized by means of strain gradient models. Coupling with thermal diffusion can lead to regularization but associated length scales are generally too small. Material length scales, related to microstructural features like grain size or dislocation structures, then play an essential role for the understanding and simu- lation of localisation processes. The purpose of strain gradient modelling is twofold. First, it provides a continuum model that can account for size effects observed during deformation, machining processes or material processing. Second, it is well-known that severe deformation leads to strain localization, e.g. adiabatic shear banding. Finite element simulations of localization phenomena usually exhibit spurious mesh-dependence (dependence of the results on finite element size and orientation) that can be regularized by means of strain gradient models. Coupling with thermal diffusion can lead to regularization but associa- ted length scales are generally too small. Material length scales, related to microstructural features like grain size or dislocation structures, then play an essential role for the understanding and simulation of localisation processes. PROVING THE MESH INDEPENDENCY OF THE COSSERAT MODEL Before adopting the Cosserat model, it was our concern to prove that under purely strain-driven softening behavior the chosen method was able to deliver a mesh-independent solution. Classic CM Cosserat CHOICE OF A TEST TO REPRODUCE MANUFACTUING SIMULATION Manufacturing simulations are characterized by severe localization of the plastic strain in a narrow zone called Shear Band. The development of plastic strain involves rises in temperature, which might be locally retained if the production rate of heat due to plastic strain development is higher than the rates with which the temperature is diffused inside the bulk metal. In order to repr


Simultaneous Measurements of Solubilities and Diﬀusion Coeﬃcients of Volatile Species in Liquids Bernd J¨ahne, Institut f¨ur Umweltphysik and HCI, Universit¨at Heidelberg Bernd.Jaehne@iwr.uni-heidelberg.de 7th SOPRAN Annual Meeting, March 25/26, 2014, Universit¨at Bremen   State of the Art • For many volatile species of environmental importance, no measured diﬀusion coeﬃcients in fresh water and sea water are available, but only theoretical estimates [6, 7] according to the Wilke and Chang relation [5]. • These estimates are associated with high uncertainties. • Most measurements do not cover full range of environmental tempera- tures, 0–40 ◦C. Solubility (partition coeﬃcient α in cm3/cm3), diﬀusion coeﬃcient D, and Schmidt number Sc in water for some gases and volatile species useful for gas exchange studies and/or of environmental importance. All values at 25 ◦C. Values marked in grey: no measurements available, only estimates. Species MG g/Mol α D, (10−5cm2/s) Sc Long-lived species 3He 3.02 0.0089 8.30 108 4He 4.00 0.0095 7.22 124 H2 2.02 0.0191 5.13 174 Ne 20, 22 0.0110 4,16 215 Kr 80–86 0.061 1.84 486 Xe 128–136 0.107 1.47 608 N2 28.01 0.0161 2.3 388 O2 32.00 0.031 2.6 344 N2O 44.01 0.591 1.92 468 CO2 44.01 0.86 1.92 468 methane (CH4) 16.04 0.0346 1.84 486 acetylene (C2H2) 26.04 0.97 1.68 532 n-hexane (C6H14) 86.18 0.0188 0.79 1131 cyclohexane (C6H12) 84.16 0.124 0.94 947 cyclohexene (C6H10) 82.15 0.54 0.92 971 benzene (C6H6) 78.11 4.4 1.10 812 Fluorocarbons CF4 88.00 0.0052 1.42 629 SF6 146.06 0.0060 1.20 745 pentaﬂuorethane 120.02 0.184 1.12 798 diﬂuoromethane (CH2F2) 52.02 2.1 1.64 545 CFC-12 (CCl2F2) 120.91 0.052 1.08 825 CFC-13 (CClF3) 104.46 0.025 1.02 880 1,4-diﬂuorobenzene (C6H4F2) 114.09 3.2 0.94 950 hexaﬂuorobenzene (C6F6) 186.05 0.1 0.86 1039 Halocarbons chloromethane (CH3Cl) 50.49 2.55 1.40 638 bromomethane (CH3Br) 94.94 4.0 1.35 662 iodomethane (CH3I) 173.84 4.6 1.29 693 dichloromethane (CH2Cl2) 84.90 8.6 1.26 709 dibromomethane (CH2Br2) 173.84 27.2 1.16 770 diiodomethane (CH2I2) 267.84 66 1.03 867 chloroform (CHCl3) 119.38 6.3 1.08 827 bromoform (CHBr3) 252.73 44 0.98 912 Oxygenated volatile organic carbons (OVOCs) methanol (CH40) 32.04 5380 1.66 538 acetonitrile (C2H3N) 41.05 1270 1.38 647 acetaldehyde (C2H40) 44.05 342 1.39 643 dimethyl ether (C2H60) 46.07 25 1.30 687 acetone (C3H60) 58.08 690 1.28 698 Sulfur containing volatiles hydrogen sulﬁde (H2S) 34.08 2.51 1.35 662 carbonyl sulﬁde (COS) 60.08 0.48 1.48 604 carbon disulﬁde (CS2) 76.14 1.41 1.31 682 dimethyl sulﬁde (C2H6S) 62.13 12.7 1.35 662 Nitrogen containing volatiles ammonia (NH3) 17.03 670 1.72 519 dimethylamine 45.08 1410 1.45 715 trimethylamine 59.11 363 1.04 519 Previous Experimental Approaches Diaphragm Method Diﬀusion through a water diaphragm gel (0.5% agarose) with thickness d supported by a 0.5 mm silicon rubber membran [1]. Evaluation by modifed Barrer method [1] Diﬀusion constant from delay: Tl = d2 6D Solubility from linear concentration increase (slope m): m = αD dheﬀ6D Works very well for inert gases with low solubility, not suitable for volatile liquids because of high surface to volume ratio of apparatus [4]. Jet Method From Petermann [3] • Short contact times • Invasion ﬂow proportional to α √ D • Evasion ﬂow proportional to √ D • Diﬃcult to handle, not suitable for automated operation. Proposed Novel Experimental Approach • Simultaneous determination of diﬀusion coeﬃcients and solubility by gas transfer measurements in gas-tight tank • Partly ﬁlled with water (Vw) and argon atmosphere (V a) • Built out of seawater-resistant aluminum, total volume 15 L; suitable for corrosive gases and liquids • Gas phase stirred by wind paddle • Temperature control by Peltier cooling/heating • Automatic measurements using computer-controlled mass ﬂow control- lers and magnetic values • Use of reference tracers with well known diﬀusivity and solubility for absolute measurements of diﬀusion coeﬃcients; candidates marked red in table on left. Tran


M A I N A C H I E V E M E N T S : 1. Deﬁnition of the architecture, based on the concept of Reusable Functional Blocks (RFBs). 2. Pro-active monitoring and feedback allowing services to react on demand 3. Lightweight virtualization: fast (milliseconds) boot times, ultra-efﬁcient packet processing in modular software routers 4. Modelling of network functions through semantic description allowing simulations and veriﬁcations for increased security 5. Prototype of a Cloud-RAN that is decomposed into RFBs. It includes front-haul and core 6. Prototype of a Mobile Edge Computing (MEC) that is decomposed into RFBs; it runs a video delivery application T W O T E S T B E D S : • Nokia France premises: a hardware and wireless platform allowing to demonstrate innovations (e.g. Cloud RAN, RFB decomposition,...) conducted in the project and made accessible to all partners • BT UK premises: hardware platform consisting of 5 servers and a switch (made accessible to all partners) which allows ﬂexible virtualisation experiments and demonstrations to be run by all partners T W O D E M O N S T R AT I O N S : 1) Rapid deployment/reconﬁguration of a software-deﬁned wireless network (C-RAN), integrated with Mobile Edge Computing, for efﬁcient video delivery from the edge. 2) Demand-driven orchestration for 5G deployment The project has received funding from European Union’s HORIZON 2020 Research and Innovation Programme under Grant Agreement n°671566 W H O W H E N W H E R E W H Y W H AT H O W Provisioning network functions and services on-the-ﬂy, running them anywhere in the network, moving them transparently to different locations, and making them portable across multiple hardware platforms. Beneﬁts: - location-independence: network services deployable in heterogeneous networks - time-independence: near instantaneous deployment and migration of services - scale-independence: transparent service scalability - hardware-independence: development and deployment irrespective of the underlying hardware. End results: A cloud-native 5G concept that will enable innovative use cases at the mobile edge, empower new business models, and reduce investments and operational costs. To tackle crucial today’s shortcomings: The increase of world population and proliferation of new or improved applications and services that need network connectivity pose more and more demanding requirements to networks; but revenue growth for telecom operators is expected to halve from now to 2020. Demand cannot be satisﬁed by simply increasing network capacity, especially in networks that are becoming always more diverse, dense, mobile and changing unpredictably. Speciﬁc technical issues: • long provisioning times • wasteful over-positioning used to meet variable demand • reliance on rigid and cost-ineffective hardware devices • complexity, emerging from heterogeneity of trafﬁc and sources, services and needs, and access technologies. • decomposition of network components and services into elementary and reusable primitives • native, converged cloud-based architecture • virtualization of radio and network processing tasks • platform-independent abstractions, permitting reuse of network functions across heterogeneous hardware platforms while catering to the vendors’ need for closed platforms/implementations • high performance software optimizations along with leveraging of hardware accelerators March 2018 S U P E R F L U I D I T Y


Scaling MetPy to Big Data Workflows in Meteorology and Climate Science Presenter: Ryan M. May rmay@ucar.edu What is MetPy? MetPy is an open-source package that provides various tools for meteorology and atmospheric science implemented in Python. This community-driven package leverages the broad base of functionality in the scientific Python ecosystem. For more information, see MetPy’s documentation at: https://unidata.github.io/MetPy To request a feature, report a bug, or see the current plans, visit: https://github.com/Unidata/MetPy/issues @metpy ●Reading data: NEXRAD, GEMPAK, GINI, METAR ●Calculations ○Severe weather indices ○Thermodynamic and kinematic quantities ○Isentropic interpolation and gridding ○Many more... ●Meteorology-specific plotting ○Skew-T ○Station Plots We are using scientific Python tools to make MetPy scale to Big Data, like climate and ensemble model datasets. Migrating bottlenecks within the code from Python to Cython allows us to increase performance, while maintaining much of the development benefits of Python. Bottlenecks Cython Many of the calculations within MetPy are straightforward and readily run in a vectorized fashion on large datasets. A few, especially those requiring vertical profiles of varying size, are difficult to write as array operations and instead require looping, which results in poor performance in pure Python code. These types of operations are important for calculations like CAPE, which is an important variable related to atmospheric instability, and used in climate modelling to relate to a variety of atmospheric hazards, like heavy rain and severe storms. Cython is a tool for writing code using a Python-like syntax that is compiled to C, and then machine native binaries; this allows code to run much faster than pure Python allows. Cython also interfaces very well with NumPy, the predominant array library within scientific Python. Together, this allows us to refactor calculations like CAPE to loop over data at the C-level, and in the end allow running the calculation with much more reasonable run time on large, gridded datasets. Authors: Ryan M. May1, M. Drew Camon1, Kevin H. Goebbert2, Jon Thielen3 1 UCAR/Unidata, Boulder, CO, USA 2 Valparaiso University, Valparaiso, IN, USA 3 Colorado State University, Fort Collins, CO, USA Packaging While using some compiled code addresses performance challenges, it makes the overall distribution and support of MetPy more complicated. This is because now the C code must be individually compiled for all supported platforms. This also means that individual problems have a greater chance of being particular to a specific platform. To address the problem of compilation and distribution, we are relying on a tool called cibuildwheel. This is a tool created by the open source community that greatly streamlines the process of running package builds across a variety of platforms available as workers on public continuous integration services, like GitHub Actions. This allows us to keep MetPy as a package that is relatively easy to release on a frequent basis. award OAC-2103682 Award OAC-2103682