dataesr/scientific-paragraphs-categorization huggingface dataset:
p13), defined as [-750, +250] around the transcription start site. To generate the PPI network, the interactions between all TFs in the prior network were obtained and weighted based on interaction scores from StringDB v10  (Szklarczyk et al. , 2015). We applied PANDA to estimate one aggregate regulatory network including auto-and allosomes for the complete dataset, which included 9,435 gene expression samples. We then applied LIONESS to infer individual networks for each sample in the population. LIONESS uses an iterative process that leaves out each individual in a population, estimates the network with and without that individual, and then interpolates edge weights to derive an estimate for the network active in that single individual. We applied LIONESS to compare the aggregate PANDA network to PANDA networks that iteratively left out each of the 8,279 samples in the 29 tissues retained for analysis in this study.
05 were considered differentially expressed. We performed Gene Ontology (GO) enrichment analysis using pre-ranked Gene Set Enrichment Analysis (GSEA) (Java command line version 2-2. 2. 4)  (Subramanian et al. , 2005) , and GO term assignments available in the Molecular Signatures Database (MSigDB) (https://www. gsea-msigdb. org/gsea/msigdb/collections. jsp#C5) (''c5. all. v6. 0. symbols. gmt''). For each tissue, we performed a GSEA on the list of genes ranked by the voom t-statistics. Figure  2D  shows heatmaps indicating GO term enrichment. We selected the GO terms with a positive Normalized Enrichment Score (NES) across all tissues, which indicates GO enrichment in males, then we calculated the average NES, and selected the ten GO terms with the highest average NES value (first panel). We repeated this for negative NES to select the top ten GO terms enriched in females (second panel).
Analysis of differential network targeting To compare the weight of each edge between male and female networks, we used a linear regression model (limma package in Bioconductor  (Ritchie et al. , 2015) ) similar to the differential gene expression analysis described above, and adjusting for the same covariates. We used the following linear regression model to detect sex-biased edges in each tissue, where Z is the edge weight and the covariates as defined previously: Z $ b0 + b1Batch + b2Race + b3Age + b4BMI + b5RIN + b6Sex + ε The p values were adjusted for FDR using the BH method. Edges with FDR < 0. 05 were considered sex-biased. Each gene is connected to 644 TFs as the generated networks are complete graphs. Genes with more than 5% of their edges significantly different between males and females (FDR < 0.
To systematically characterize the biological processes associated with each TF's sex-biased targeting patterns, we ran preranked GSEA. In each tissue, we performed one enrichment analysis for each TF with expression-targeting correlation greater than 0. 3. Genes were ranked based on their level of differential targeting (t-statistics from the limma analysis) by the corresponding TF in the tissue. Knowing that the differential targeting of the Y chromosome genes is an extreme outlier when comparing male and female networks, and no additional biological processes would be found enriched in males when all the Y chromosome genes are ranked at the top of the differentially targeted genes list, we removed the Y chromosome genes for this enrichment analysis. We performed two separate analyses, one using GO annotations (as described previously), and the other one using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database  (Kanehisa et al.
ACKNOWLEDGMENTS This work was supported by grants from the  U. S. National Institutes of Health , including grants from the  National Heart, Lung, and Blood Institute  ( 5P01HL105339 ,  5R01HL111759 ,  5P01HL114501 ,  K25HL133599 , and  K25HL140186 ), the  National Cancer Institute  ( 1R35CA220523 ,  5P50CA127003 ,  1R35CA197449 ,  1U01CA190234 , and  5P30CA006516 ), and the  National Institute of Allergy and Infectious Diseases  ( 5R01AI099204 ). Additional funding was provided through a grant from the  NVIDIA Foundation  and grants from the  Norwegian Research Council ,  Helse Sør-Øst , and  University of Oslo  through the  Centre for Molecular Medicine Norway  (NCMM). This work.
GTEx dataset We downloaded the Genotype-Tissue Expression (GTEx) version 6. 0 RNA-Seq dataset (phs000424v6. p1, 2015-10-05 released) from dbGaP (approved protocol #9112). Using YARN in Bioconductor (https://bioconductor. org/packages/release/bioc/html/yarn. html), we performed quality control by removing sex-misannotated samples, gene filtering, tissue-aware normalization using qsmooth  (Hicks et al. , 2018) , and grouped related body regions based on gene expression similarity (further details described in  Paulson et al. , 2017). ''Skin'' includes samples from the lower leg (sun exposed) and from the suprapubic region (sun unexposed); brain (basal ganglia) category includes caudate, nucleus accumbens, and putamen; brain (cerebellum) category includes cerebellar hemisphere and cerebellum; brain (other) category includes amygdala, anterior cingulate cortex (BA24), cortex, frontal cortex (BA9), hippocampus, hypothalamus, spinal cord (cervical c-1), and substantia nigra.
This YARN-filtered dataset included gene expression profiles from 9,435 samples across 38 tissues. We removed sex-specific tissues (prostate, testis, uterus, vagina, and ovary), cell lines (EBV-transformed lymphocytes and transformed fibroblasts), and those with fewer than 30 samples in either of the sexes (kidney cortex and minor salivary gland). The final dataset contained 8,279 samples from 29 tissues (28 solid organ tissues and whole blood) and 548 research subjects (188 females and 360 males), and included expression information for 30,243 genes (including 1,074 genes encoded on the sex chromosomes). Network inference using PANDA and LIONESS Sample-specific networks were reconstructed using PANDA  (Glass et al. , 2013)  and LIONESS  (Kuijjer et al. , 2019)  as implemented in netZooM 0.
Materials To compare different tools, possibly used for creativity, we selected four of them. For the two extreme ones, we chose pen & paper and CAD. The pen & paper is a tool that everyone knows how to use to draw freely and that does not present any constraint of use. For CAD software, we chose Solidworks that allows 3D concept modelling through standardised interactions, with a mouse and keyboard, which enable the export of files to other platforms. Moreover our panel used to work with Solidworks. We selected two intermediate tools in order to validate the benefits and impacts of virtual reality compared to the aforementioned traditional tools. We used the VR application Time2Sketch (see figure  1 ), which works with an HTC Vive and computer with Windows 10. This application allows the user to draw in a 3D virtual environment.
Procedure As figure  3  shows, the experiment has two parts: (i) the first measures the impact the selected tools have on creativity and (ii) the second assesses the generated ideas communicability for each tool. This study   follows a between-subject experimental design, with four groups of participants, one per tool. Even if the drawing task was done on their own, the communicability part was done in pairs. Participants had to describe the idea of their partner. At the beginning of the experiment, participants ware invited to complete the first questionnaire to collect demographic data (gender, age), their ability to use virtual reality and CAD software. The first phase of experimentation was dedicated to the task of creativity. A training of around five minutes was proposed to participants to help them to get familiar with the tool they will use regarding their group.
Mineral dust is the most abundant aerosol specie by mass in the atmosphere, and it impacts global climate, biogeochemistry, and human health. Understanding these varied impacts on the Earth system requires accurate knowledge of dust abundance, size, and optical properties, and how they vary in space and time. However, current global models show substantial biases against measurements of these dust properties. For instance, recent studies 15 suggest that atmospheric dust is substantially coarser and more aspherical than accounted for in models, leading to persistent biases in modelled impacts of dust on the Earth system. Here, we facilitate more accurate constraints on dust impacts by developing a new dataset: Dust Constraints from joint Observational-Modelling-experiMental analysis (DustCOMM).
This dataset leverages observational and experimental constraints on dust size distribution and shape to obtain more accurate constraints on three-dimensional (3-D) atmospheric dust properties than is possible 20 from global model simulations alone. Specifically, we present annual and seasonal climatologies of the 3-D dust size distribution, 3-D dust mass extinction efficiency at 550 nm, and two-dimensional atmospheric dust loading. Comparisons with independent measurements taken over several locations, heights, and seasons show that DustCOMM estimates consistently outperform conventional global model simulations. In particular, DustCOMM achieves a substantial reduction in the bias relative to measured dust size distributions in the 0. 5-20 µm diameter 25 range. Furthermore, DustCOMM reproduces measurements of dust mass extinction efficiency to almost within the experimental uncertainties, whereas global models generally overestimate the mass extinction efficiency.
We therefore process the corrected size distributions over a given location as follows: (1) we correct and scale each model's lower and upper diameter limits to the common diameter range of 0. 2 -20 µm (see section 2. 1. 1); and (2) we estimate the sub-bin distribution for each model's biascorrected size distribution by fitting a generalized analytical function, extending the  Kok et al, (2017)  theoretical 25 expression of dust size distribution to the 3-D dataset (see section 2. 1. 2).
We obtain ¡ 7 :Q= from  Kok et al. (2017) , which constrained the dust extinction efficiency by combining measurements of the dust index of refraction and probability distribution of dust particle shape with the single-scattering database of  Meng et al. , (2010). Specifically,  Kok et al. (2017)  estimated the globally-averaged values of the real and imaginary dust index of refraction as » = 1. 53 ± 0. 03 and log(-k) = -2. 5 ± 0. 3  (Sokolik et al. , 1993; Patterson et al. , 1977; Dubovik et al. , 2002; K. Kandler et al. , 2009; Kim et al. , 2011; Denjean et al. , 2016) , and both are assumed to be 5 normally distributed. Dust particle shapes were represented by the dust aspect ratio -the ratio of the major and minor axes of an ellipsoid best fit to the irregularly-shaped 2-D image of a dust particle -and the height-to-width ratio.
333 (see details in the supplementary document of  Kok et al. , 2017). By combining these constraints on the optical properties and shape of 15 the ensemble of dust particles in Earth's atmosphere with the single-scattering database of  Meng et al. (2010) ,  Kok et al. (2017)  obtained a constraint on the globally-averaged size-resolved extinction efficiency ¡ 7 :Q= (#), which explicitly accounts for the enhancement of extinction by the asphericity of dust. Specifically, they found that accounting for dust asphericity enhances the extinction produced by a unit mass loading of dust by 29 ± 5% over the extinction calculated from Mie theory for spherical dust particles, which is used in most climate models. We use this constrained globally-20 averaged ¡ 7 :Q= to constrain º̂ae (Eqn. 8) for every location because measurements of dust shapes and index of refraction are currently insufficient to constrain º̂ae on a regional basis.
25 We now combine the above-estimated mass extinction efficiency at 550 nm (section 2. 2) with dust aerosol optical depth at the same wavelength, to constrain the atmospheric dust loading (… 7 ). That is:  )  where º̂v(Z, [) is the mass-weighted vertically-integrated 2-D mass extinction efficiency calculated from º̂ae, and ,(Z, [) is obtained from an ensemble of reanalysis dust aerosol optical depth products. 30 … 7 (Z, [) = ,(Z, [) ºv(Z, [) (9 The ensemble dust aerosol optical depth (AOD) climatology is obtained from the average of four different reanalysis products (see section 3. 2 for details). This individual reanalysis dataset assimilate several satellite and ground-based measurements from multiple platforms, including MODIS (Terra and Aqua), AVHRR and MISR satellites, as well as from the ground-based AERONET stations  (Lynch et al.
, 2016; Mccarty et al. , 2016; Flemming et al. , 2017;  https://doi. org/10. 5194/acp-2019-484 Preprint. Discussion started: 18 June 2019 c Author(s) 2019. CC BY 4. 0 License. observations and model simulations, thus producing column-integrated dust AOD that is largely representative of what is observed, based on validation studies (e. g. Buchard et al. , 2017). Despite the advantage of assimilating observational datasets, estimating a realistic overall error in the dust AOD across the reanalysis datasets is difficult, yet important. Here, we estimate the total error ( † , ) by considering both the 5 systematic error ( † ù√ù ) and random error ( † ÉwØ, ) inherent in the reanalysis-derived dust AOD. As such, we estimate the uncertainty in dust AOD as: † , (Z, [) = Ã † ù√ù ± (Z, [) + † ÉwØ, ± (Z, [).
We define the † ÉwØ, as the standard error between the four datasets, which represents that part of the total uncertainty that does not correlate across the four reanalysis dust AOD data sets. For instance, † ÉwØ, may be associated with differences in the assimilating systems for the different reanalysis products. In contrast, the † ù√ù is expected to correlate between the four data sets since most of 10 the reanalysis datasets use similar observational datasets. Hence, we assume that the † ù√ù will be proportional to the mean dust AOD, such that Õ OEoeOE (x,√) ae -(x,√) = í ,. We estimate the proportionality constant, í , , by requiring that the relative error is the same as the relative error obtained from annually-averaged climatology of dust AOD from  Ridley et al. (2016) , which leveraged observational datasets similar to those used for the reanalysis dataset, but propagated many of the relevant uncertainties.
25 The above procedure propagates various uncertainties in the estimation of each product. These include the measurement uncertainties and the uncertainties in model simulations. First, the measurement uncertainties are associated with the globally-averaged size distribution and the globally-averaged extinction efficiency (Fig. 1 ), and these are propagated equally to every location. In addition, we estimated the correlated systematic error in the dust AOD (section 2. 3), associated with the assimilated observational dataset, and this is also propagated. Second, the 30 uncertainty in model simulations is associated with the spread of the model dust size distribution which is different for every location. This model uncertainty is, in turn, a result of many processes, such as dust emission, deposition, and transport processes in the models  (Ginoux et al.
Data and Models The methodology described above highlighted measurements and model simulations, and reanalysis datasets used as inputs for the DustCOMM products. Here we will describe these datasets, the model simulations, as well as other 20 independent datasets we use to evaluate our products. To create the DustCOMM products (Fig. 1 ), we use three sets of input datasets: (1) the constrained globally-averaged data from  Kok et al. , (2017) ; (2) model simulation datasets of size-resolved dust mass concentrations, from which we estimate the modelled dust size distribution; and (3) reanalysis datasets of the dust aerosol optical depth. We focus here only on describing the model simulations (section 3. 1) and the reanalysis products (section 3. 2), as details of the in-situ measurements used to constrain the globally-averaged 25 datasets are described in  Kok et al.
20 Although the total AOD is constrained, errors in each reanalysis model's treatment of emission, transport, and deposition of mineral dust introduces uncertainties. Dust emission and deposition in the assimilation procedure are either modelled or sometimes constrained by observations. For example, the dust emission for NAAPS is constrained by using a regional source tuning that is, in turn, constrained by space-based and ground-based AOD observations  (Lynch et al. , 2016). Other reanalysis products use dust emissions that are parameterized and model dependent (e. g. 25 Yumimoto et al. , 2017). In general, wet deposition is partially constrained by the assimilated global satellite-based precipitation information, such as from the NOAA Climate Prediction Center MORPHing technique data (CMORPH).
In addition, ensemble model simulations of dust emission and transport are driven by different meteorological datasets 20 (Table  1 ), which represent the actual historical meteorology with various degrees of accuracy (e. g. Evan, 2018). Dust transport is also influenced by model resolution and sub-grid parameterizations of wind and turbulence, which differ between models (e. g. ,  Zender et al. , 2003; Cakmur et al. , 2004). Although averaging over multiple models and over long time periods reduces random errors, systematic errors that affect different models similarly would affect the model ensemble (e. g. Ridley et al. , 2012) , and would impact the spatial distribution of dust mass (e. g. Johnson et al. 25 2012; Ridley et al. , 2016). In addition, uncertainties in the vertical distribution of size-resolved dust mass fractions directly affect DustCOMM dust size distributions.
Data availability 30 Annually-averaged and seasonally-averaged DustCOMM dust size distribution, dust mass extinction efficiency and dust loading are publicly available at http://doi. org/10. 5281/zenodo. 2620475. The model dust mass concentration,  Kok et al. 2017  datasets, as well as other input dataset used to generate the DustCOMM dataset presented here can be found at http://doi. org/10. 5281/zenodo. 2620547 (Adebiyi et al. , 2019b).
https://doi. org/10. 5194/acp-2019-484 Preprint. Discussion started: 18 June 2019 c Author(s) 2019. CC BY 4. 0 License. 6 Summary and Conclusions In this study, we presented a new dataset of atmospheric dust aerosol properties called the 'Dust Constraints from Joint Observational-Modelling-experiMental Analysis' -DustCOMM. DustCOMM combines observational and experimental constraints on dust properties and abundance with an ensemble of global model simulations of dust 5 spatial distribution to obtain more accurate 3-D annual and seasonal climatologies of dust properties and abundance than possible with global model simulations alone. Here, we presented three DustCOMM products: the threedimensional (3-D) dust size distribution, 3-D dust mass extinction efficiency, and two-dimensional dust loading.
Acknowledgements. We acknowledge support from  National Science Foundation (NSF)  grant  1552519  (J. F. K. ). Chun Zhao  is supported 10 by the  National Natural Science Foundation of China NSFC  (Grant No. 41775146 ), and  Akinori Ito  is supported by the  Japan Society for the Promotion of Science KAKENHI  Grant Number  JP16K00530  and  Integrated Research Program for Advancing Climate Models (MEXT). The authors thank  Claire L. Ryder  for providing access to the Fennec and AER-D data which are both available at http://www. met. reading. ac. uk/~jp902366/research/. We also thank  Bruce Albrecht  and  Eunsil Jung  for providing access to the size distribution collected during the  Barbados 15 Aerosol Cloud Experiment (BACEX). The MERRA-2 reanalysis datasets are acquired from the  NASA Goddard Earth Sciences (GES).
Code availability The codes used to generate DustCOMM size distribution, dust mass extinction efficiency and the atmospheric loading are available at http://doi. org/10. 5281/zenodo. 2620556. Data and Information Services Center (DISC) and available at https://gmao. gsfc. nasa. gov/reanalysis/MERRA-2/. NAAPS data was obtained through the USGODAE Data Catalog at https://usgodae. org/cgi-bin/datalist. pl?generate=summary and CAMSiRA data was obtained through the ECMWF's Meteorological Archiving and Retrieval System at http://apps. ecmwf. int/data-catalogues/cams-reanalysis. Finally, we 20 thank Yumimoto, K. for providing access to the Japanese Reanalysis dataset through their website at https://www. riam. kyushu-u. ac. jp/taikai/JRAero/.
In practice, the CoLoMoTo notebook environment  (Naldi et al. , 2018b)  provides a Python API for several software FIGURE 1 | Description of the proposed workflow for the development and analysis of dynamical logical models. The novel methods described in this article are emphasized with blue fonts. Starting with the delineation of a molecular map integrating the available scientific knowledge, we derive a regulatory graph and logical rules to generate a logical model, and induce dynamical specifications serving as test cases to verify the model. Moreover, when the available knowledge is specific to a smaller part of the regulatory graph, a sub-model us extracted to perform local tests. We further implemented an analysis and visualization method, called Value propagation, to assess the impact of environmental and genetic perturbations.
(Naldi et al. , 2018b). This framework is available inside the CoLoMoTo Docker image together with packaged libraries for the analysis of dynamical logical models of biological networks. tools, enabling the definition of a wide range of dynamical analyses for the computation of observed values. Individual test cases can be assembled into a library, also called testing suite. Existing tools and packages enabling software testing can then be applied to automatically assess whether a model satisfies (or not) a series of specifications. In this study, we used the python package "unittest, " taking advantage of its seamless integration into the CoLoMoTo interactive notebook. This unit-testing package is integrated by default into the recent versions of the Python standard library (http://python.
Comprehensive Molecular Mapping of T Cell Activation Network Prior to mathematical modeling, knowledge about biological entities involved in T Cell activation was collected from available pathway databases, including Reactome  (Fabregat et al. , 2016) , PantherDB  (Mi et al. , 2013) , ACSN  (Kuperstein et al. , 2015) , and WikiPathways  (Slenter et al. , 2018). Moreover, the scientific literature indexed in the PubMed database was further explored and carefully curated. Using the software CellDesigner (version 4. 3. 1) (Funahashi et al. , 2008) , this knowledge was encoded in a molecular map describing reactions between biological entities (either proteins, RNAs, genes, complexes, or metabolites). Each biological entity included in the map was annotated with a series of standard identifiers, including UniProtKB accession number, recommended and alternative names, gene name and synonyms, and cross-references to unique HGNC identifiers and approved symbols.
Logical Modeling of T Cell Activation Using the logical modeling software GINsim (version 3. 0. 0b) (Naldi et al. , 2018a) , we then manually derived a regulatory graph encompassing 216 nodes and 451 arcs (Figure  4 ) from the content of the molecular map. One by one, biological entities represented in the molecular map were re-created as components of the logical model. In most of the cases, the representation of entities having different states was further compressed into a single component summarizing their activity in the TCR signaling cascade. Furthermore, to obtain a dynamical logical model, a specific logical rule must be assigned to each node. In many cases, this can be achieved rather easily based on published data. For more complex situations, a default generic logical rule was initially considered, where all activators are needed for the activation of a component (using the AND operator) and where only one inhibitor is sufficient to repress it (using the OR and NOT operators), which served as a basis for further rule refinement.