dataesr/scientific-paragraphs-categorization huggingface dataset:
The electron multiplying charge coupled device chip size was 1600 × 200 pixels, the camera controller was a 16-bit A/D converter operating at 2. 5MHz. The acquisition time of a single spectrum was set to 0. 5 sec. An area of 150 × 150 points per image was recorded leading to the acquisition of 22500 spectra for one image. The advantage of our analysis method using EMCCD (Electron multiplying CCD) is the high speed of scan. Each pixel can be recorded with a very good signal-to-noise ratio at low integration times. For the high spatial resolution of the system (300nm), a complete image of a whole cell is recorded for a better analysis. Data acquisition and processing were performed using Image Plus 5. 2 software (Ulm, Germany) from WITec. The analysis is based on two methods. The first method provides integrated Raman intensities in specific regions in particular C-H stretching modes.
The integrated Raman intensities of C-H stretching mode (2800-3000cm -1 ) represent the lipid-protein distribution in the cells. Data processing is performed using Image Plus software from WITec. Each image regarding these integrated intensities could provide a map of the region. Using a color-coded reference table, bright yellow hues indicate the highest intensities while orange hues the lowest integrated intensities of the chosen region. The second analysis method is the K-mean cluster analysis (KMCA), which separates data into k-mutually exclusive clusters and can run multiple times. KMCA was realized using the WITec Project Plus software. For Raman experiments, 2x105 MCF7 cells were cultivated for 24h onto polished and disinfected calcium fluoride CaF 2 (Crystran Ltd, Dorset, UK) substrates in 35mm petri dishes.
Cytotoxicity Study It is also important to investigate the cytotoxicity of these cobalt nanoparticles against human cells before applying them in nanomedicine. The Co element is known to be well tolerated by the human body that can easily eliminate cobalt as it is a non-accumulating metal. 46 Therefore, the cytotoxicity of PBS solutions containing Co MNPs was investigated on Human embryonic kidney HEK 293 cells. 47 Different concentrations of Co MNPs dispersed in PBS solution were prepared and studied by MTT assay. In Figure  7 , MTT assays were performed on Co MNP solutions of concentration ranging from 0. 5µg to 120mg/L for 24h and the data were analysed using one way ANOVA followed by Dunnett's post hoc test. Differences were considered significant if p<0. 05. Figure  7  shows the mean ± standard error of mean (SEM) of duplicate measurements of a representative sample of three independent experiments.
Acknowledgments PRO-1 NANOSolutions is gratefully acknowledged for providing the cobalt metal nanoparticles used in this study. The authors thank  Dr. Céline Gongora  ( Cancer Research Institute IRCM Montpellier, France ) for providing the colorectal cancer cell line. The  Center for Electron Nanoscopy at the Technical University of Denmark  is acknowledged for access to their transmission electron microscopes. This research was funded by the  European Regional Development Fund project EQUiTANT , grant number  TK134  ( F180175TIBT ). This work was developed in the scope of the project  CICECO-Aveiro Institute of Materials  (ref. no. FCT UID/CTM/50011/2013 ), financed by national funds through  FCT/MEC  and co-financed by  FEDER  under the  PT2020 Partnership Agreement.
The CTWN-A experiment is inspired by AgMIP's Coordinated Climate-Crop Modeling Project (C3MP; see  Ruane et al. , 2014; McDermid et al. , 2015)  and contributes to the AgMIP Coordinated Global and Regional Assessments (CGRA; see  Ruane et al. , 2018; Rosenzweig et al. , 2018). In this paper, we describe the GGCMI Phase 2 model experiments and present initial summary results. In the sections that follow, we describe the experimental goals and protocols; the different process-based models included in the intercomparison; the levels of participation by the individual models. We then provide an assessment of model fidelity based on observed yields at the country level, and show some selected examples of the simulation output dataset to illustrate model responses across the input dimensions. 2 Simulation objectives and protocol.
Goals The guiding scientific rationale of GGCMI Phase 2 is to provide a comprehensive, systematic evaluation of the response of process-based crop models to critical interacting factors, including CO 2 , temperature, water, and applied nitrogen under two contrasting assumptions on growing season adaptation (CTWN-A). The dataset is designed to allow researchers to enhance understanding of models sensitivity to climate and nitrogen drivers; study the interactions between climate variables and nitrogen inputs in driving modeled yield impacts; characterize differences in crop responses to climate change across the Earth's climate regions; provide a dataset that allows statistical emulation of crop model responses for downstream modelers; and explore the potential effects on future yield changes of adaptations in growing season length.
This set of major crops has the advantage of historical yield data globally available at subnational scale  (Ray et al. , 2012; Iizumi et al. , 2014) , and has been frequently used in subsequent analyses (e. g. ,  Müller et al. , 2017; Porwollik et al. , 2017). The Phase 2 protocol involves a suite of uniform perturbations from a historical climate time series. The baseline climate scenario for GGCMI Phase 2 is one of the weather products used in Phase 1, daily climate inputs for 1980-2010 from the 0. 5 • NASA AgMERRA ("agricultural"-modified Modern Era Retrospective analysis for Research and Applications) gridded reanalysis product. AgMERRA is specifically designed for agricultural modeling, with satellitecorrected precipitation  (Ruane et al. , 2015). The experimental protocol consists of 9 levels for water supply perturbations, 7 for temperature, 4 for CO 2 , and 3 for applied nitrogen, for a total of 756 simulations (Table  1 ), 672 for rainfed agriculture and an additional 84 for irrigated (W ∞ ).
Values of climate variable perturbations are selected to represent reasonable ranges for changes over the medium term (to 2100) under business-as-usual emissions. Values for nitrogen application levels are intended to cover a wide range of potentials. The resulting GGCMI Phase 2 dataset captures the distribution of crop model responses over a wide range of potential future climate and management conditions. The protocol samples over all possible permutations of individual perturbations; i. e. , all values are applied across all crops and regions, so that the protocol includes many combinations that are not realistic. For example, we simulate high https://doi. org/10. 5194/gmd-13-2315-2020 Geosci. Model Dev. , 13, 2315-2336, 2020 Figure  1. (a-e) Cultivated areas for maize, rice, and soybean from the MIRCA2000 (Monthly Irrigated and Rainfed Crop Areas around the year 2000) dataset  (Portmann et al.
, 2010) , gap filled with the MIRCA2000 crop calendar  (Portmann et al. , 2010)  and, if no SAGE or MIRCA2000 data are available, with simulated Lund-Potsdam-Jena managed Land (LPJmL) growing seasons  (Waha et al. , 2012)  and are identical to those used in GGCMI Phase 1  (Elliott et al. , 2015). In GGCMI Phase 2, we separately treat spring and winter wheat and so must define different growing seasons for each. As for the other crops, we use the SAGE crop calendar, which separately specifies spring and winter wheat, as the primary source for 69 % of grid cells. In the remaining areas where no SAGE information is available, we turn to, in order of preference, the MIRCA2000 crop calendar  (Portmann et al. , 2010)  and to simulated LPJmL growing seasons  (Waha et al. , 2012). These datasets each provide several options for wheat growing season for each grid cell but do not label them as spring or winter wheat.
For evapotranspiration, most models use Priestley-Taylor, Penman-Monteith or Hargreaves schemes, but JULES and PROMET utilize a land surface model approach instead. Note that models that share a common genealogy may still use different schemes for evapotranspiration: for example, EPIC-TAMU uses Penman-Monteith and EPIC-IIASA uses Hargreaves. To describe soils, most models use either the Harmonized World Soil Database (HWSD) from the FAO (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) or the ISRIC-WISE database  (Batjes, 2005)  or a derivation thereof. Table  S1  in the Supplement provides details on these model characteristics as well as on implementation, including spin-up, calibration other than growing season, residue management, and irrigation rules. Table  3  also describes the simulation output contribution of each model to the GGCMI Phase 2 archive.
Discussion and conclusions The GGCMI Phase 2 experiment provides a database designed to allow detailed study of crop yields in process-based models under climate change. While previous crop model intercomparison projects in the climate change context have focused on simulations along realistic projected climate scenarios (e. g. ,  Rosenzweig et al. , 2014) , the use of systematic input parameter variations in GGCMI Phase 2, with up to 756 scenarios, allows not only comparing yield sensitivities to changing climate and management inputs but also evaluating the complex interactions between important driving factors: CO 2 , temperature, water supply, and applied nitrogen. The global extent of the experiment also allows identifying geographic shifts in high potential yield locations. With 12 participating models and 31 simulation years per scenario, the complete database constitutes over 150 000 years of gridded global yield simulation output for each crop.
Emulators based on the dataset can be used to identify hotspots of crop system vulnerability and to conduct rapid assessment of new climate projections. In general, the development of multi-model ensembles involving systematic parameter sweeps has large promise both for increasing understanding of potential future crop responses and for improving process-based crop models. Code and data availability. All input data are available via zenodo at https://doi. org/10. 5281/zenodo. 3773827 (Müller et al. , 2020). For the minimum cropland mask, choose the boolean_cropmask_ggcmi_phase2. nc4 file. Growing period data for wheat are now divided up into winter and spring wheat, whereas all other growing season data (maize, rice, soybean) are the same as in Phase 1. Supplement. The supplement related to this article is available online at: https://doi.
A few articles have developed drug-repurposing strategies for COVID-19 starting from these initial works. Gordon et al. propose candidate drugs,  7  Gysi et al. propose various ways of ranking drugs,  10  and Sadegh et al. share an online tool to explore repurposing options interactively, as well as proposing a few examples of how to search for repurposing candidate drugs. 11 Using expression from lungs of COVID-19 patients, Rian et al. identified specific pathways that are affected by SARS-CoV-2 infection and predicted the effect of 8000 compounds as potential treatments. 12  international effort is currently ongoing to organize and mine all available knowledge and data on this disease, 13 its epidemiology,  14  and to create accessible data repositories (https://github. com/CLAIRE-COVID-T4/ covid-data).
Including symptoms in CovMulNet19 allows us to further characterize the pathology of the disease and to recapitulate many characteristic presentations such as respiratory failure, chest pain, nausea, and several neuronal dysfunctions. We also found high similarity of COVID-19 to SARS as well as to pathologies of the intestine, liver, and neural system, in accordance with some of the identified risk factors. The integration of viral proteins, human proteins, diseases, symptoms, and drugs in an interactive visualization of this unified data set will enable the community to freely explore this disease in its molecular and medical context.
Constructing an integrated COVID-19 interactions network With the aim of summarizing available information on COVID-19 to enable network medicine analyses of this new pathology, we set out to collect information on interactions of the viral proteins with human proteins and the relationships between these proteins with diseases and symptoms. We expanded the set of experimentally validated SARS-CoV-2 interactors with predicted interactions (see Materials and Methods section) and proceeded to reconstruct the human Protein-Protein Interaction (PPI) network that is potentially affected by the virus. To this end we combined functional interactions from STRING database  16  with experimentally detected physical and genetic PPIs from BioGRID. 17 e then explored how these proteins are related to specific diseases as annotated in the DISGENET database, which lists genes associated with diseases mainly through mutations.
We then integrated data from six different drug-protein interaction databases into our network, to provide a set of close to 6000 compounds that could be potential repurposing candidates. Finally, and most importantly, we added interactions between proteins and symptoms, using the Human Phenotype Ontology (HPO  18  ), which allows us to identify specific connections FIG. 1. Linking genotype to phenotype in SARS-CoV-2-Homo sapiens molecular interactions. We build a highly reliable map of the human interactome and focus on the subset of human proteins that were shown to putatively interact with the virus in the literature, both through experimental protein interaction assays, 7 through structure-based predictions,  9  and based on similarity of the proteins to other coronaviruses proteins. 8 he COVID-19 PPI network is enriched by biological information related to each involved protein (GO terms), as well as by an extensive data set of drug-protein interactions obtained by integrating different repositories.
In contrast, we considered that the nodes with a higher degree in CovMulNet19 than in random networks were potentially medically relevant. We generated 2500 mock networks composed of 457 random proteins from the BIOSTR database applying the same method as we used in the creation of CovMulNet19 to find associations with GO terms, diseases, symptoms, and drugs for these sets of random proteins. The mock networks contain between 212 and 654 (average 384. 5) PPIs, compared with 1999 PPIs in CovMulNet19. This is evidence of the coherence of proteins that interact with the virus, including multiple members of the same specific pathway or protein complexes. Degrees were calculated for all nodes as the number of edges to human proteins (either putative SARS-CoV-2 interactors in CovMulNet19 or random proteins in the mock networks).
Terms preceded with a (*) or (**) were significantly overrepresented in CovMulNet19 compared with observed appearance in the mock random networks ( p-value < 0. 1 and 0. 05, respectively). The complete list of nodes with their associated z-scores and p-values can be accessed in the bootstrap results tables in supplementary data (Supplementary Tables  S1  and S2 ). intestinal, liver, and neurological diseases or conditions, as well as a blood cancer, consistent with COVID-19 pathology and risk factors highlighted by recent meta-analyses. 24 Interestingly, symptoms that are over-represented in CovMulNet19 compared to the mock networks according to their z-scores, also include respiratory failure, nausea, and other neurological conditions. From the start, the list of COVID-19 symptoms included respiratory issues and nausea, but there are increasing reports of neurological symptoms that had been overlooked in the first few weeks of the epidemic that can be typical of other virus infections or quite specific.
7  and we have included the corresponding results in Supplementary Tables S1-S4. As can be seen in the Supplementary Tables  S3  and S4 , most of the results remain unchanged, indicating that the further inclusion of the 125 proteins from predicted interactions does not substantially alter our findings, and might even increase their specificity toward SARS-CoV-2 pathology, since, for example, ''Severe Acute Respiratory Syndrome'' appears to be the second most over-represented disease only after adding these predicted interactions and is only found at position 1198 of the ranking with a negative z-score of À0. 14682 in the analysis using exclusively experimental interactions. Moreover, we must also consider that inaccuracies generally plague large-scale databases of proteins/drugs/diseases interactions, both due to the data being inaccurate and to issues in the merging of different identifiers and simple human errors.
Overall, the bootstrap approach presented in this study and the recapitulation of most of our results with a data set considering only experimentally validated interactions, should ensure that our findings are robust and do not rely on just a few specific network edges (which could represent false positives in the network's interactions). CovMulNet19 should only be viewed as a tool for hypothesis generation and any suggestion for biologically relevant associations between COVID-19 and genes, drugs, diseases, or symptoms should be experimentally verified before being considered further.
Materials and Methods Building the human interactome: BIOSTR In this section, we provide details about the procedure used to reconstruct the interaction network of human proteins by cross-linking different publicly available databases. Since databases do not use the same format for protein names, as a first step we used the NCBI gene database to map all protein names and aliases to a common nomenclature of official symbols. Specifically, we used the data made publicly available from NCBI at the URL ftp://ftp. ncbi. nlm. nih. gov/gene/DATA/GENE_ INFO/Mammalia/(Accessed March 28, 2020). 32 n a second step, we downloaded two PPI networks for Homo sapiens. More precisely, we considered Bio-GRID v3. 5. 182  17, 33  (publicly available at URL: https:// downloads. thebiogrid. org/BioGRID/Release-Archive/ BIOGRID-3.
5. 182/) and the STRING v11. 0  16  functional interactions network (publicly available at URL: https://string-db. org/cgi/download. pl). In the BioGRID data, we filtered by official (common) symbols for proteins, identifying a total of 429,232 PPIs. A total of 30,959 interactions (7. 21% of the data set) contained at least one protein with noncommon symbol. After discarding the later interactions, a total of 18,053 proteins (nodes) and 398,273 interactions (edges) were identified. The resulting Bio-GRID network of interactions exhibits a multilayer structure,  34, 35  including different biologically relevant layers  [36] [37] [38]  : (1) direct interaction, (2) physical associa-tion, (3) suppressive genetic interaction defined by inequality, (4) association, (5) colocalization, (6) additive genetic interaction defined by inequality, and (7) synthetic genetic interaction defined by inequality.
For the following analysis, we will consider the aggregated representation of this multilayer functional PPIs. In the STRING data, we filtered high-confidence interactions with any type of evidence (score > 0. 7), identifying a total of 17,161 proteins and 839,522 PPIs out of the original data-including low-confidence interactions-consisting of 11,759,454 PPIs among 19,566 proteins. No biological layer classification is performed on this data set. The merging of the two distinct networks was performed by applying the union of the corresponding sets of PPIs and the final result is named BIOSTR. Overall, the merged interactome-after removing duplicated PPIs-consists of 19,945 proteins and 737,668 highconfidence and undirected PPIs. Therefore, BIOSTR is more complete than BIOGRID and STRING separately, complementing them with 10.
Building the human genotype-phenotype interactome We gathered information about gene-disease interactions from DISGENET v6. 0  39  (publicly available database at the URL: https://www. disgenet. org/downloads) and filtered genes by the ones in our BIOSTR interactome, thus excluding associations involving proteins not in our PPI network. All types of sources were included: curated (UniProt, PsyGeNET, Orphanet, the Cancer Genome Interpreter, Comparative Toxicogenomics Database (CTD) (human data), ClinGen, and the Genomics England PanelApp), from animal models (Rat Genome Database, Mouse Genome Database, and CTD [mouse and rat data]) and inferred (HPO, and GDAs inferred from Variant-Disease Associations reported by Clinvar, the GWAS catalog and GWAS database). We considered all gene-disease associations with no further filtering based on scores.
See https://www. disgenet. org/dbinfo# score for more details. 39 Each disease found in the filtered DISGENET database was associated to symptoms found in the HPO (accessed on March 2020)  18  publicly available at the URL: https://hpo. jax. org/app/. Note that even if DISGENET provides a mapping to other databases, including the HPO and the Disease Ontology (DO), cross-linking with the DO data is very restrictive and we opted for the HPO. The main issue of this choice is to link DISGENET diseases identifiers to symptoms in the HPO: we used Unified Medical Language System identifiers available in DISGENET to link cross-references in the HPO. The final network consists of 15,228 HPO symptoms (nodes) and 628,686 gene-disease associations (edges) in DISGENET among which we found 598,556 matching symbols in our BIOSTR.
Building the drug-target interactions The interactions between a chemical compound (or a drug) and its protein targets were collected from six publicly available data sources. The definition of interaction is heterogeneous across different sources, and thus, for each database, we explicitly list hereafter the corresponding definition. Note that some drug nodes are reported in terms of their combination with other drugs, for example, ''G3139 + DEXAMETHASONE. '' DrugBank v. 5. 1. 5  42 (https://www. drugbank. ca/): A target is defined as a protein, macromolecule, small molecule, and so on to which a given drug binds or otherwise interacts with, resulting in an alteration of the normal function of the bound molecule and desirable therapeutic effects or unwanted adverse effects. DGIdb v. 3. 0.
2  43  (http://www. dgidb. org/): Here a drug-gene interaction is defined by the database curators as a known interaction (e. g. , inhibition) between a known drug compound (e. g. , lapatinib) and a target gene (e. g. , EGFR). Therapeutic Target Database v. 11 November 2019 44 (http://db. idrblab. net/ttd/): Interactions are defined as connections between known and explored therapeutic protein targets and the corresponding drugs directed at each of these targets. * Note that some drugs in the data set are reported in terms of their combination with other drugs. Drug Target Commons  45  (http://drugtargetcommons. fimm. fi/): Interactions are defined as annotated or unannotated bioactivity between drug and target. chEMBL v. 26  46  (https://www. ebi. ac. uk/chembl/): Interactions are known pharmaceutical associations as declared by drug producers.
chEMBL also provides annotated experimental drug-target interactions that were not included in CovMulNet19. {  Tabei et al. 47  (http://labo. bio. kyutech. ac. jp/~yamani/ drugprotein/): The links are a subset of 78,692 drugprotein interactions extracted from older versions of ChEMBL, 48 KEGG,  49  DrugBank,  50  PDSP Ki,  51  and Matador. 52 he original sources adopt the following nomenclature for the drug ID (as reported from the corresponding official information): DrugBank-Standard name of drug as provided by drug manufacturer Tabei DB-Drugbank ID DGIdb-the primary drug name Therapeutic Target Database-Drug Name Drug Target Commons-Compound name ChEMBL-Compound name and synonyms. *TTD was updated on June, the 1st 2020 while drafting the current manuscript. New interactions have not been added to CovMulNet19.