dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank the  Genom'IC platform of the Cochin Institute  for the sequencing of RNA-Seq experiments. This work was funded through institutional support from  Centre National de la Recherche Scientifique ,  Institut National de la Santé et de la Recherche Médicale ,  ATIP-Avenir  and the  ARC Foundation  ( ARC-RAC16002KSA-R15093KS ), the  SIRIC CARPEM  and the " Who Am I?" Laboratory of Excellence   ANR-11-LABX-0071 , funded by the  French Government  through its  Investissement d'Avenir program , operated by the  French National Research Agency  ( ANR-11-IDEX-0005-02 ) to VB. The project also received funding from the  Uniscientia Foundation  (keyword tumor model), the  Deutsche Forschungsgemeinschaft  (project  HA8297/1-1 ), and the  CRC/Transregio   205/1  (The  Adrenal: Central Relay in Health and Disease ) to CH.
Labeled are transcription factors with a two-sided Wilcoxon signed-rank p-value <0. 05, red dots indicate a q-value <0. 05. The color in the box signify in which subgroup the super-enhancer scores were higher. c, Predicted CRC membership for genes with differentially active super-enhancers identified in (b) in patient samples from the two molecular subgroups. d, Gene expression of transcription factors identified in (b). Gene expression values were taken from the TCGA ACC dataset (N= 90). q-values were derived from edgeR's exact test, **** q-value <0. 0001, ** q-value <0. 01, * q-value <0. 05 e, Copy number aberrations (CNA) of the transcription factor genes identified in (b). Data were taken from the TCGA ACC dataset (N= 90) and averaged across patients of the same molecular subgroup. Copy number profile: -1, genomic loss; 0, no change; 1, genomic gain.
| Data and descriptors The list of CN compounds (containing 82,5 K molecules) was downloaded from https://chembiofrance. cn. cnrs. fr. ChEMBL version 28 (containing more than 2 M compounds) was used as a representation of the biologically relevant chemical space. ZINC version 20 (with reactivity parameter being chosen as «hot» and purchasability -«In-Stock», containing more than 13,8 M compounds) -as a representation of the commercially available chemical space. The confidential collection of commercially available building blocks (BBs) for the synthesability analysis of CN was provided by eMolecules. ChEMBL, CN and ZINC compounds were standardized according to the in-house standardization protocol based on ChemAxon's standardizer tool and then filtered according to NIH rules  [11]  (annotated compounds with problematic functional groups, e.
| Scaffold analysis The concept of scaffold is widely used in medicinal chemistry and drug design to analyze the core structures of active compounds and their analogue series  [19]. Scaffold analysis provides information about the structural diversity of a dataset of compounds, as well as, like in the case of libraries' comparison, might reveal some library-specific chemotypes. In this work, classical Bemis-Murcko (BM) scaffolds  [20]  were generated by RDKit  [15]. BM scaffolds are obtained by removing the side chains while retaining the ring system and their linkers (heteroatoms included).
Finally, the manifold is unbent to its planar form, thus forming a 2-dimensional map. A compound's position on the map is calculated by averaging of nodes' coordinates weighted by the responsibilities. Responsibility vectors support different types of GTM-based landscapes: density, class and property landscapes. A density landscape characterizes distribution of different chemotypes over the map and allows to detect over-or underpopulated areas of the chemical space. It is associated with the cumulated responsibility vector resulting from superposition of individual vectors {R ik }. In each node, it measures an average density of the residing molecules. Density landscapes are used to analyze the data distribution within the given dataset, where a specific chemical motif could be related to a particular region of the map.
Synthons are the parts of the building blocks that will be introduced to the molecule upon chemical reaction. They possess a label determining both the reactivity of the fragment and types of possible reagents that can produce the synthon. Synthons, generated via query molecule fragmentation, can then be matched against a database of available BBs -molecules that possess at least one reactive functional group and thus can be used as reagents for synthesis. The process ends when synthons match available BBs or/and the target molecule cannot be broken down further. Then for each target molecule a parameter called «availability rate» is calculated. Availability rate shows the fraction of the target molecule that can be contributed by available synthons:.
The summary of CN compounds' synthonization and its availability rate calculation is shown in Figure  11b. As it was found, only 29,7 % of CN compounds (18 425 molecules) can be fully synthesized using commercially available BBs. 1 016 CN molecules (1,6 %) were synthonized, but no corresponding PBBs were found in eMolecules collection. Overall, 10 700 CN generated synthons are not commercially available. In general, the analysis revealed that only around 30 % of CN compounds can be fully synthesized using commercially available BBs. Even though the number of reactions that encode the rules of fragmentation is  limited, this conclusion was not surprising, since the CN compounds were expected to be much more sophisticated in terms of chemical synthesis than, for example, ZINC compounds, and require much more difficult multi-step synthesis.
| CONCLUSIONS The chemical space of Chimiothèque Nationale was investigated by means of physicochemical property distribution analysis, main level Bemis-Murcko scaffolds analysis, comparative analysis with ChEMBL and ZINC collection using Generative Topographic Mapping and retrosynthetic analysis of compounds via comparison with the collection of commercially available building blocks provided by eMolecules. It was shown that CN compounds distribution is shifted towards fragment-like molecules. The BM scaffold analysis revealed more than 5 K CN-specific scaffolds comparing to ChEMBL and ZINC collections. 2D maps generated using GTM method were used to compare the populations of the CN-ChEMBL and CN-ZINC databases. It was proven that CN covers well the biologically relevant space represented by ChEMBL.
Introduction The COVID-19 pandemic, which simultaneously deepened political divisions in the United States (US) and shined a light on the role of the political system in population health, sparked increased scientific interest in the associations between political context and health outcomes  1- 5. In contrast to assessments of individual political ideology and health, as measured by individual-level survey questions and/or by aggregated voting data (at the precinct or county level) on voter political lean, several members of our team recently published a novel study on the relationship between political representatives' voting behavior and constituents' health  5. Specifically, this study analyzed associations between congressional representative voting behavior (plus other political metrics at the state level), and congressional district (CD) level COVID-19 outcomes reaggregated from county-level data.
Moreover, while only 13% of counties were intersected by more than one CD, these counties contained 60% of the US population. For this study, atoms are created by intersecting county and CD boundaries (Figure  2 ). Thus, each atom overlaps a single county and a single CD. Atoms containing zero population are omitted from the analyses (1. 7% of atoms). Our analytic dataset contains 3,104 counties, 432 CDs, and 3,728 atoms (Table  1 ). Note that our exclusion of Alaska (1 CD) and Hawaii (2 CDs) accounts for our use of 432 out of the total 435 US CDs. 72% of the atoms are countyequivalent atoms, i. e. , atoms exactly equivalent to counties, a result of the fact that most counties are fully contained within one CD. The remaining atoms are sub-county atoms. All data used in our analyses are collected at either the county level, CD level, or the census block level.
Outcomes Our outcome of interest is COVID-19 mortality during April 2021-March 2022, a time frame during which the COVID-19 vaccine was publicly available in the US. Counties are the smallest units for which COVID-19 mortality data are made publicly available nationwide. As in Krieger et al  5  , we collect county-level COVID-19 death counts for April 2021-March 2022 from the CDC Wonder Database (Provisional Mortality Statistics). Death counts <10 are suppressed in this data source, thus we impute values for counties with suppressed counts as described in Krieger et al  5. Because our interest lies in COVID-19 age-adjusted death rates rather than death counts, we also obtain population count data to serve as denominators. Because our model is built at the atom level, we prefer atom-level data where available.
Thus, we compute total population counts by aggregating census block-level counts from the 2020 Decennial Census redistricting files to the atom level. At the time of writing, age distribution data from the 2020 census have not yet been released, so we obtain age distribution information at the county level from the 2016-2020 5-year American Community Survey (ACS) data. We assume that age distributions are constant across atoms within each county and use these age distributions, alongside atomlevel total population counts, to create atom-level expected COVID-19 mortality counts. These serve as the denominators in our models of age-standardized rates.
For sub-county atoms Y = 1, … , Z [ nested within county \, the latent + ] values can be sampled conditional on their corresponding county totals, + [ , as ^+A , … , + _ `|+ [ a ~Zbcd!efY!gc h+ [ , ^iA , … , i _ `aj, i ] = 8 M k ∑ 8 M k _ ] lA (3) Note that the latent ) -values for sub-county atoms can be sampled analogously if desired. All model components are fit jointly via Markov Chain Monte Carlo (MCMC) sampling, enabling uncertainties to be fully propagated through all components. Code to implement the model was written in Nimble  22  and executed through R. We report rate ratio estimates as exponentiated posterior means for the coefficient parameters and corresponding 95% credible intervals (CIs) from the models.
Results The final analytic dataset contains 3,104 counties in the continental US, 432 congressional districts, and 3,728 atoms (Figure  2 , Table  1 ). There are 2,686 county-equivalent atoms and 1,042 sub-county atoms. Sub-county atoms tend to be concentrated in densely populated counties, which can contain multiple CDs. Illustrating this, county-equivalent atoms have an average population size of 48,810, while sub-county atoms (atoms falling in counties containing 2+ atoms) have an average population size of 187,378 (Table  1 ). The COVID-19 mortality rate ratio (MRR) estimates and 95% CIs from the ABRM, CD-level model, and county-level model are shown in Figure  3 , where all models use age-standardized outcomes and are adjusted for the additional covariates described in Section 2. 3. In each of the models, all covariates besides poverty were standardized prior to modeling, so that 100*(MRR-1) can be interpreted in the percent change in COVID-19 mortality rate corresponding to a 1standard deviation increase in the covariate.
Given today's vast amount of administrative data collected over incompatible geographies, the need for accessible methods that address spatial misalignment in a statistically-principled manner is greater than ever. ABRM are a powerful tool for assessing associations in misaligned data, but are often overlooked in practical applications, likely due to the absence of software or code examples to assist with implementation. Our work and publicly available code on Github (https://github. com/rachelnethery/atom_model) provide a prototype for implementation of ABRM leveraging modern optimized software for MCMC sampling, which can serve as a template to enable these methods to be more widely adopted in scientific research. Figure  1. Example of two types of spatial misalignment in congressional district and county boundaries in New Hampshire and Vermont (using 2010 CD redistricting boundaries).
To demonstrate the impact of ships on relatively large scales, the potential shipping impacts on various air pollutants can be simulated with chemistry transport models. To determine formation, transport, chemical transformation and fate of PM2. 5 in the Mediterranean Sea in 2015, five different regional chemistry transport models (CAMx -Comprehensive Air Quality Model with Extensions, CHIMERE, CMAQ -Community Multiscale Air Quality model, EMEP -European Monitoring and Evaluation Programme model, LOTOS-EUROS) were applied. Furthermore, PM2. 5 precursors (NH3, SO2, HNO3) and inorganic particle species (SO4 2-, NH4 + , NO3 -) were studied, as they are important for explaining differences among the models. STEAM version 3. 3. 0 was used to compute shipping emissions, and the CAMS-REG v2. 2. 1 dataset was used to calculate land-based emissions for an area encompassing the Mediterranean Sea at a resolution of 12 × 12 km 2 (or 0.
Based on a normalized profile of precipitating hydrometeors, CMAQ operates a simple algorithm to assign precipitation amounts to individual layers  (Foley et al. , 2010). In EMEP, wet scavenging is treated with simple scavenging ratios, taking into account in-cloud and sub-cloud processes. The EMEP model's parameterization of wet deposition processes covers both the in-cloud and sub-cloud scavenging of gases and particles. The parameterization of wet deposition is described in  Berge and Jakobsen (1998). There are two types of wet deposition in LOTOS-EUROS: below-cloud scavenging and in-cloud scavenging. The technique is described in  Seinfeld and Pandis (2006), and Banzhaf et al. (2012)  served as the foundation for the in-cloud scavenging module.
Shipping Emissions The shipping emission dataset produced with the STEAM model has a spatial resolution of 12 × 12 km² and a temporal resolution of one hour. The STEAM v3. 3. 0 emissions are divided into two vertical layers (0 m to 36 m; 36 m to 1000 m) and are provided for mineral ash, carbon monoxide (CO), carbon dioxide (CO2), elemental carbon (EC), NOx, organic carbon (OC), PM2. 5, particle number count (PNC), sulfate (SO4), SOx (containing SO2 and SO3) and VOC. To reduce the number of generated emission maps and the computational resources needed to run the STEAM model, VOC emissions were divided into four categories according to their properties as a function of the engine load. Emission factors for VOC are based on the average values taken from various publications  (Agrawal et al. , 2008; Agrawal et al.
, 2010; Sippula et al. , 2014; Reichle et al. , 2015). All shipping emissions are included in the lowest layer of CAMx. In CAMx, all gridded emissions are at the ground level except punctual and linear emissions. For CHIMERE, 88 % of the emissions below 36 m and all shipping emissions above 36 m were added to the second layer. Only 12 % of the emissions below 36 m were allocated to the model's lowest layer. The STEAM emission dataset, which included stack heights, was used for this procedure. In CMAQ, shipping emissions were split between the two lowest levels; those below 36 m were ascribed to the lowest layer, while those above 36 m were positioned in the second layer. The height of the lowest layer in CMAQ are 42 m for each. The STEAM emissions were summed from hourly to daily emissions and attributed to the lowest layer (up to 90 m) in the EMEP simulations.
Discussion Various reasons for underestimations in PM2. 5 in regional CTM systems might be traced back to model-specific calculations. For CAMx, Pepe et al. (  2019 ) linked these underestimations to meteorological parameters and to the overestimation of the vertical mixing in the lower atmosphere. Tuccella et al. (2019)  found underestimations of PM2. 5 in the CHIMERE model and explained these by an excess of wet scavenging in the model. An excess of wet scavenging in CHIMERE compared to the other CTM systems is not found in the present study, thus it cannot be used as explanation for deviations here. In EMEP, differing from the other CTM systems, the MARS module was used to calculate the equilibrium between the gas and aerosol phases; this model does not treat sea salt or dust, leading to underestimations of PM2.
Furthermore, this leads to lower NO3 -concentration in CHIMERE output (Figure  S19 ). Also for SO2 and SO4 2-concentration cloud water and amount of cloud droplets plays an important role. Regarding the thermodynamic equilibrium within the models, ISORROPIA and ISORROPIA II mechanisms are used in all CTM systems except EMEP, meaning similar results can be assumed to be obtained from this mechanism. Despite this similarity, differences in concentrations may be a result of differences in available cloud water, vertical mixing, the spatiotemporal distribution of emissions or aerosol size distributions. EMEP uses the MARS module to calculate the equilibrium between the gas and the aerosol phase. Although four of five models use the ISORROPIA or ISORROPIA II mechanisms for inorganic secondary aerosol formation, many factors within these models still cause significant differences among the model outputs.
3. 1. In addition, the calculations how to determine PM2. 5 vary among CTM systems or even within one CTM. As an example, there are two possibilities for calculating PM2. 5 within CMAQ: either online during the model run with the PM2. 5 module or subsequently by calculating the value as the sum of two modes. These different options lead to different results (as shown by  Jiang et al. , 2006)  and will also affect the particle composition. In the present study, the sum of two modes is used in CMAQ. Model simulations with relatively high PM2. 5 concentrations display higher absolute shipping impacts on PM2. 5, as presented in Sect. 3. 2. Consequently, relatively low variability in the relative potential ship impacts among the models compared to that of the absolute values could be expected. For a more quantitative evaluation, relative potential ship impact is plotted against absolute potential impact.
Acknowledgements This was work supported by  SCIPPER  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement Nr. 814893. AtmoSud acknowledges the continuous support of CAMx by  RAMBOLL  and CHIMERE by LMD. https://doi. org/10. 5194/egusphere-2023-406 Preprint. Discussion started: 2 May 2023 c Author(s) 2023. CC  BY 4. 0 License. The  Community Multiscale Air Quality Modelling System (CMAQ ) is developed and maintained by the  USEPA. Its use is gratefully acknowledged. Ronny Petrik  from  Helmholtz-Zentrum Hereon  (now at  Marinekommando Deutsche Marine, Rostock ) is acknowledged for providing meteorology and boundary conditions for CMAQ runs. The computations for the regional modeling using the EMEP-model was enabled by resources provided by the  Swedish National Infrastructure for Computing (SNIC) , partially funded by the  Swedish Research Council  through grant agreement no.
Code and data availability CAMx source code and documentation can be downloaded from https://camx-wp. azurewebsites. net/download/source/ (last access: 19 January 2023; Ramboll, 2023) and the Chimere website (https://www. lmd. polytechnique. fr/chimere/2020_getcode. php, last access: 19 January 2023). CMAQ version 5. 2, which was used here, is available at https://doi. org/10. 5281/zenodo. 1167892 (US EPA Office of Research and Development, 2017). EMEP is available at https://doi. org/10. 5281/zenodo. 3647990 (EMEP MSC-W, 2020), LOTOS-EUROS is available at https://lotos-euros. tno. nl/open-source-version/ (last access: 19 January 2023; TNO, 2023), and WPS/WRF is available from WPS (2022; https://github. com/wrf-model/WPS, last access: 19 January 2023) and WRF Community (2000, https://doi. org/10.
5065/D6MK6B4K). The COSMO software is available at https://www. cosmomodel. org/content/support/software/default. htm#models (last access: 24 January 2023; COSMO, 2023) and ecmwf-ifs/ifsscripts at https://github. com/ecmwf-ifs (last access: 19 January 2023; ECMWF, 2023). Data on measurement stations from EEA can be downloaded at https://discomap. eea. europa. eu/map/fme/AirQualityExport. htm (last access: 20 January 2023). CTM model results are available upon request.
Existing methods for 3D tracking from monocular RGB videos predominantly consider articulated and rigid objects (e. g. , two hands or humans interacting with rigid environments). Modelling dense non-rigid object deformations in this setting (e. g. when hands are interacting with a face), remained largely unaddressed so far, although such effects can improve the realism of the downstream applications such as AR/VR, 3D virtual avatar communications, and character animations. This is due to the severe ill-posedness of the monocular view setting and the associated challenges (e. g. , in acquiring a dataset for training and evaluation or obtaining the reasonable non-uniform stiffness of the deformable object). While it is possible to naïvely track multiple non-rigid objects independently using 3D templates or parametric 3D models, such an approach would suffer from multiple artefacts in the resulting 3D estimates such as depth ambiguity, unnatural intra-object collisions and missing or implausible deformations.
Hence, this paper introduces the first method that addresses the fundamental challenges depicted above and that allows tracking human hands interacting with human faces in 3D from single monocular RGB videos. We model hands as articulated objects inducing non-rigid face deformations during an active interaction. Our method relies on a new hand-face motion and interaction capture dataset with realistic face deformations acquired with a markerless multi-view camera system. As a pivotal step in its creation, we process the reconstructed raw 3D shapes with position-based dynamics and an approach for non-uniform stiffness estimation of the head tissues, which results in plausible annotations of the surface deformations, handface contact regions and head-hand positions. At the core of our neural approach are a variational auto-encoder supplying the hand-face depth prior and modules that guide the 3D tracking by estimating the contacts and the deformations.
Despite several studies on the reconstruction of face and hand motions, the capture of interactions between them and the corresponding deformations from a monocular RGB video remains unaddressed  [Tretschk et al. 2023 ]. On the other hand, naïvely using existing template-based hand and face reconstruction methods leads to artefacts such as collisions, and missing interactions and deformations due to the inherent depth ambiguity in the monocular setting and the lack of deformation modelling in the reconstruction pipeline. Several key challenges are associated with this problem setting. One (I) is the lack of an available markerless RGB capture dataset for face and hand interaction with non-rigid deformations for model training and method evaluation. Capturing such a dataset is highly challenging due to the constant presence of occlusions caused by hand and head motions, particularly at the interaction region where non-rigid deformation occurs.
This approach provides non-uniform stiffness to the mesh, which significantly improves the qualitative plausibility of the reconstructed geometry compared with uniform stiffness values. To address the challenge (II), we train the networks to obtain the 3D surface deformations, contact regions on the head and hand surfaces, and the interaction depth prior from single-view RGB images utilising our new dataset. During the final optimisation stage, we utilise this information from different modalities to obtain plausible 3D hand and face interactions with non-rigid surface deformations, which helps disambiguate the depth ambiguity of the single-view setup. Our approach results in much more plausible hands-face interactions compared to the existing works; see Fig. 1  for representative results.