dataesr/scientific-paragraphs-categorization huggingface dataset:
Preprocessing and differential gene expression analysis was performed in R using the DESeq2 package  (64). Next, a combined pathway collection of the COVID-19 Disease Map (21 pathways (76)), WikiPathways (597 pathways  (11) ) and Reactome (1,222 pathways (  12 )) was created. Pathway enrichment analysis was performed using the clusterProfiler R package  (73). Differentially expressed genes (DEGs; p-value < 0. 05 and absolute fold change > 1. 5) were used as input for the over-representation analysis. The analysis was performed separately for NHBE, and A549 cells and the overlap in enriched pathways was analyzed. Selected pathways are visualized in Cytoscape using the WikiPathways app  (77). A pathway-gene network for the shared pathways was created to study pathway crosstalk and overlap. Next, the harmonized bipartite graph was used to create a pathway-gene network for all C19DMap pathways.
Single-cell transcriptomic data analysis in epithelial cell types of COVID-19 patients. In this section, we provided gene expression analysis to explore differential expressed genes (DEG) on scRNAseq in specific epithelial cell populations in the COVID-19 patient group (moderate, severe, and critical cases), comparing with isolated epithelial cells from the lungs of healthy subjects. An exploratory gene expression data was carried out on single-cell RNAseq analysis of bronchoalveolar lavages from nine COVID-19 patients, three moderate cases, one severe case, and five critical cases (GSE145826) from  (13). To obtain high confidence of differential expressions in three different groups, single-cell RNAseq data of isolated epithelial cells (DAPI-, CD45-, CD31-, CD326+) from control lung explant tissue of nine health subjects was chosen as a healthy control specific for epithelial cell types  (14).
All filtered samples were merged in only one filtered gene-barcode matrix and analyzed with R package Seurat v. 3  (78). In parameter settings, the first 50 dimensions of canonical correlation analysis (CCA) and principal component analysis (PCA) were used. Moreover, the filtered gene-barcode matrix was first normalized using 'LogNormalize' methods with default parameters. UMAP was performed on the top 50 PCs for visualizing the cells, while clustering was performed on the PCA-reduced data for clustering analysis with Seurat v. 3. The resolution was set to 0. 5. A UMAP embedding represents the distribution of major cell types in the single-cell RNAseq database (Fig. S6 ). The epithelial cell group (TPPP3, KRT18), directly infected by SARS-CoV-2, was analyzed for every patient group. At first, the classification was provided, following these gene markers, as reported in  (13) : macrophages (CD68), neutrophils (FCGR3B), myeloid dendritic cells (mDCs; CD1C, CLEC9A), plasmacytoid dendritic cells (pDCs; LILRA4), natural killer (NK) cells (KLRD1), T cells (CD3D), B cells (MS4A1), plasma cells (IGHG4) and epithelial cells (TPPP3, KRT18).
For the finest cell annotation of epithelial cell types, specific gene markers were used as reported in the Human Protein Atlas database (https://www. proteinatlas. org/), and markers of health epithelial cells reported by Deprez and colleagues (79) (10. 1164/rccm. 201911-2199OC) and extracted. In particular, ciliated cells (CFAP157, FAM92B; SARS-CoV-2-infected" cells 15. 5%), Secretory cells (BPIFB1, SCGB1A1, SCGB3A1; SARS-CoV-2-infected" cells 6. 4%), Suprabasal cells (KRT5, SERPINB4, KRT19, COVID19 cells 37. 7%), Alveolar Type 1 cells (AGER, CAV1, EMP2, SARS-CoV-2-infected" cells 6%), Basal cells (KRT5, KTR15, COVID19 cells 11. 2%). Alveolar Type 2 cells were not included because of an unbalanced ratio of cell sample size between COVID-19 cases and healthy control (SARS-CoV-2infected" cells <2%; see table S1 for a detailed summary of all cell types).
These circuit activation values can be between conditions to obtain profiles of differential signaling and differential functional activity. The first version of the C19DMap has been implemented in the CoV-HiPathia version  (81). In addition, extracted SIF files from SBML qual files using CaSQ  (26)  can be imported to HiPathia containing the Activity Flow (AF) structure of the Process Description (PD) diagrams, enabling new disease maps to be modeled as they are built thus permitting their exploration and analysis. In order to test the methodology, a public RNAseq dataset of nasopharyngeal swabs from 430 individuals with SARS-CoV-2 and 54 negative controls (17) (GSE152075) was used. First, the RNA-seq gene expression data were normalized with the Trimmed mean of M values (TMM) normalization method using the edgeR R package  (82).
Then, within the CoV-Hipathia web tool (81) , the HiPathia algorithm requires the expression data to be rescaled between 0 and 1 for the calculation of the signal. Finally, quantile normalization using the preprocessCore R package (80) was carried out. The normalized gene expression values were used to calculate the level of activation of the sub-pathways, and then a case/control contrast with a Wilcoxon test was used to assess differences in signaling activity between the two conditions: SARS-CoV-2-infected and normal control nasopharyngeal tissue.
Dynamical modeling at the molecular, cellular, and multicellular levels Dynamical modeling of type I IFN responses in SARS-CoV-2 infection Type I IFN model development and computational validation: We used the type I IFN molecular map as a scaffold and auto-generated the dynamic model using the CaSQ tool. We utilized seven biological scenarios from the scientific literature to evaluate the model's behavior. Global sensitivity analysis: We simulated the model in Cell Collective (30) using varying activity levels of each input. We determined the input-output association using activity levels of 1000 randomly-generated simulations as previously used by our group  (83). We performed probabilistic global sensitivity analysis based on the partial correlation coefficient (PCC) using the "sensitivity" package (https://cran.
r-project. org/web/packages/sensitivity/sensitivity. pdf ) in R (R Core Team, 2016) on data obtained from Cell Collective. It shows the impact of change in the input variable (independent variable) on the output variable (dependent variable) while considering and removing the linear effect of other input variables on the output variable  (84). The script used in this analysis is available in our shared GitLab repository (https://git-r3lab. uni. lu/computational-modelling-and-simulation/analysis/-/blob/master/IFN1_modelling/Global_Sensitivity_analysis_of_IFN_model. R). Sensitivity analysis against overexpression and knockouts: The sensitivity of biomolecules was calculated against knockout and overexpression perturbations. The sensitivity values were quantified in macro values for each biomolecule.
The bitwise distances were calculated for each biomolecule in the same macro class. The highest sensitivity values were then simulated in Cell Collective. The methodology of the algorithm used to calculate the sensitivities against knockout and over-expression perturbations is described in FairdomHub (https://fairdomhub. org/data_files/4090), and the used script that generates the result is available in our shared GitLab repository (https://git-r3lab. uni. lu/computational-modellingand-simulation/analysis/-/blob/master/IFN1_modelling/IFN1_sensitivity_against_mutations. R). Input propagation for calculating stable states: The IFN model has 55 input components. These input components always maintain their activity level as they have no upstream regulators, and their initial configuration plays a vital role in the potential outcome.
Pharmacogenomic analysis We obtained the list of proteins in the C19DMap as well as lists of proteins targeted by drugs and chemicals from annotations from the AILANI COVID-19 research assistant (https://ailani. ai) based on an NLP pipeline  (88) , INDRA (Integrated Network and Dynamical Reasoning Assembler) (1), and from the Clinical Trials DB. We used information from the crossreferences from DrugBank  (72)  to map ChEBI and PubChem identifiers to DrugBank identifiers. We further enriched the list of drug/chemical targets using the information from DrugBank (accessed June 2022). A list of 16 drugs used for the treatment of COVID-19 was obtained from  (89) , and their targets were obtained from DrugBank. After merging the lists, a final dataset of 1,476 drugs and chemicals (identified by DrugBank IDs) and 1,120 drug targets (identified by NCBI Gene Id) was obtained.
Information on pharmacogenomic variants for the drug targets was retrieved from PharmGKB (90) (accessed on Feb 14, 2021). For each gene that encodes a drug target, the list of variants with pharmacogenomic annotations that are significant and are annotated to a dbSNP identifier was retrieved. We used the cross-references from PharmGKB to map the PharmGKB drug accessions to DrugBank identifiers. Data on the allelic frequency of the pharmacogenomic variants were retrieved from The Genome Aggregation Database (gnomAD) (91) (version 2. 1. 1). gnomAD is a resource developed by an international coalition of investigators with the goal of aggregating and harmonizing both exome and genome sequencing data from a wide variety of large-scale sequencing projects and making summary data available for the broader scientific community.
To aggregate the data on the pharmacogenomic impact and allelic frequency of the variants, we computed a modified version of the Cumulative Allele Probability (CAP) and the "Drug Risk Probability" (DRP) score  (47). The CAP score considers the number of pharmacogenomic variants and their frequency in the population for a specific gene. The DRP score combines the CAP scores for all drug target genes for a specific drug. The code to compute the CAP and DRP scores is available at https://github. com/jpinero/pharmacogenomics_covid19_minerva_map/.
The results from our centrality calculations can also be explored and put in context using the software LMME-DM (https://github. com/LSI-UniKonstanz/lmme-dm) that was developed as part of the C19DMap project. It follows an overview and detail approach showing an overview graph containing one node per pathway, and a detailed pathway view, including the detailed crosstalks. The centrality values can now be mapped on both the size and the color of the nodes (see Fig. 10 ).
Orthoinference process for converting from SARS-CoV-1 to SARS-CoV-2 diagrams The standard orthoinference process is used to infer reactions electronically in fifteen evolutionarily divergent eukaryotic species for which high-quality whole-genome sequence data are available. Eligible reactions are checked to determine whether each involved protein has at least one homologous protein in the reaction's input, output, and (if present) catalyst in the organism undergoing inference. If a human reaction involves a complex, at least 75% of the accessioned protein components of the human complex must have homologous proteins in the model organism. The first (V74) draft of this SARS-CoV-2 pathway consists of 101 reactions involving 489 molecular entities (279 proteins, 12 RNAs, and 198 others) and is supported by citations from 227 publications.
Acknowledgements The authors would like to acknowledge all members of the  COVID-19 Disease Map community  for fruitful exchanges and discussions, the LCSB and  Elixir Luxembourg  for the hosting of the C19DMap resource and repository, the  FAIRDOMHub community  for supporting our project, and members of the  SysMod, CoLoMoTo, BioModels and COMBINE communities  for fruitful exchanges and feedback.
Data availability statement All data, code and files are available in our GitLab repository https://gitlab. lcsb. uni. lu/computational-modelling-and-simulation Pharmacogenomics analysis: https://github. com/jpinero/pharmacogenomics_covid19_minerva_map/blob/main/drp_scor e_drugs. tsv https://github. com/jpinero/pharmacogenomics_covid19_minerva_map/blob/main/cap_scor e_genes. tsv Topological analysis: https://github. com/LSI-UniKonstanz/lmme-dm Fairdomhub: https://fairdomhub. org/projects/190.
a). Then, the user can calculate iG-Apex index in order to interpret the observation (Figure  1. c). ApeX-Vigne application records the date and position for each observation. The position is determined using the smartphone's GNSS receiver. The collected data is automatically synchronised with a central database when the smartphone has a 3G signal (or better) or a Wi-Fi connection. In the event that an observation is collected in an area without network coverage, it is stored on the smartphone and then synchronised when a network connection is available. When users download the application, they explicitly give their consent for their observations to be used in research projects. The data analysed in this article correspond to observations from the centralised database that were collected during the three seasons 2019, 2020 and 2021.
According to  Rechenberger et al. (2015) , a simple data collection process favours the collection of a large number of observations. The iG-Apex approach was chosen in this study for this purpose, as it is simple to collect and to interpret. For the communication about ApeX-Vigne mobile application, emphasis was placed on the interest of this tool to answer to the individual challenges of wine sector's stakeholders. Information on the application was disseminated during technical days and in the specialised press.
2019 was a particularly dry year. CWB was closed to -800 mm for the majority of the LRS (Figure  3  LRS and SRS study zones are of interest because they are located in regions with many vineyards and winegrowers. They encompass a wide diversity of soils and socio-economic contexts. Their Mediterranean climate corresponds to that of many other vineyards in the world (California, South Africa, Southern Australia, etc. ). Finally, low CWB values indicate that water resources are limited and therefore that monitoring vine water status in time and space is a major issue in these regions both at LRS and SRS. The ability of crowdsourcing data to characterise the temporal dynamics of vine water status at the scale of LRS study zone was assessed by studying the evolution of the daily mean value of iG-Apex.
), as at the LRS scale, where the collectives are larger and less formally structured in the wine industry (e. g. administrative region). Considering crowdsourced iG-Apex values are relevant to be monitored at this scale in relation to vine water restriction, future research should be conducted on the implementation of this lever and its capacity to reinforce the quantity and quality of crowdsourcing data collected. Secondly, results of this study show that observations collected by crowdsourcing using ApeX-Vigne application allow to characterize the cessation of growth trend at the LRS study zone scale. The number of observations collected over the study period seems to be large enough to characterise this trend despite variations in the density and location of observations over time. Considering a similar contribution and even if the location changes from year to year, this result shows that relevant maps at this spatial scale can be obtained from one year to another.
A limitation to be considered, however, is the spatial dispersion of the contributions. Indeed, in the (extreme) case where all contributions are concentrated in a small area of the LRS, the resulting map would be of poor relevance over zones where no contributions are made. The approach therefore assumes that the contributions are distributed in a regular or random manner over the study area. Crowdsourcing observations from ApeX-Vigne application also allow to collect spatially structured information and to produce interpolated iG-Apex maps at this scale. Observations collected in the SRS study zone can also be interpolated but are less spatially structured. At this scale, the quality of collected data seems to be too limited to describe satisfactorily vine water status. This issue of quality of crowdsourcing data has already been widely addressed in the literature  (Goodchild and Li, 2012).
In the case of this study, the quality of the data could be influenced by several factors with a different impact depending on the spatial scale considered:  Other factors than water constraint may have influenced the vine shoot growth. This is the case, for example, for cultivar and rootstock  (Bota et al. , 2016) , cover cropping  (Delpuech and Metay, 2018)  or farming practices  (Reynolds, 2010). In the ApeX-Vigne data, this information about the field was unknown. This uncertainty may potentially limit the ability of iG-Apex to correctly describe vine water status. However, the results of this study showed that despite this limitation, collected observations allowed the mapping and the identification of major trends in the evolution of vine water status at LRS. At SRS, the potential of these data is also important but the spatially structured part of the variance in iG-Apex observations is lower.
CONCLUSION ApeX-Vigne mobile application has been developed to collect data on vine water status by making observations on vine shoot growth. This application allows the gathering, geolocation and pooling of these observations. It makes it possible to consider a collaborative collection of observations (crowdsourcing) at a regional scale. This study explored the potential of crowdsourcing observations collected by ApeX-Vigne application for temporal and spatial monitoring of vine water status at two regional scales. It also tested the hypothesis that an egoist motivation of participants is sufficient to ensure this monitoring. According to the results, the amount of collected data was large enough to describe the trend of cessation of vine shoot growth at a large regional scale during the three studied years.
ACKNOWLEDGEMENTS The authors would like to thank the " Institut Français de la Vigne et du Vin"  for their involvement throughout the project  ApeX-Vigne. We also thank the  Occitanie region  who financially supported this work in the framework of the crowd-viti project (repere project). The  ApeX-Vigne  project was part of the  DATI  project, supported by the  French National Research Agency  under the  Horizon 2020 PRIMA Program  ( ANR-21-PRIM-0001 ).
The ANNRI model uses GEANT4 Monte Carlo simulation and the spectrometer data to describe the γ-ray spectrum from neutron captures on Gd  (Hagiwara et al. 2019). The models assume an isotropic distribution for the angles of γ-ray emission. Both are included in this analysis to provide systematic uncertainties for the pre-supernova neutrino sensitivity results in SK. To reproduce IBD coincidence events, simulation of prompt positrons and delayed neutrons were performed and put in coincidence in time and distance to each other. The SK Monte Carlo software was employed to simulate positron events and γ-ray events from the neutron captured on Gd, using the GLG4sim and ANNRI models. These coincidence events were simulated in different positions of the ID, with positron energies ranging between 0.
The application uses the IAEA's database Power Reactor Information System (IAEA 1989) to account for each reactor's activity during specific periods. To give the best possible estimations of the current reactor and geoneutrino backgrounds, when applying this tool to the pre-supernova alarm the most up-todate information from geoneutrinos. org is used and further corrected with the most recently updated information regarding reactor activity in Japan. Backgrounds due to radioactive contamination of the Gd loading come mainly from 18 O(α,n) 21 Ne * and 17 O(α,n) 20 Ne * processes, mainly a consequence of contamination with 235 U series isotopes that are α emitters. The decay of neon isotopes results in pairs of neutrons and the signal of one of the neutrons can be mistaken for a positron, thereby becoming a background for coincidence IBD events.
The hits passing this procedure proceed to another filter called STORE (Software Triggered Online Reconstruction of Events). STORE looks for PMT hits originating from a single vertex by considering the Cherenkov emission of low energy electrons a point source, a reasonable assumption since these electrons can only travel a few centimeters in water  (Carminati 2015) , which is much smaller than SK's vertex resolution. STORE selects the largest set of hits that are consistent with being from a point source. Another trigger condition applied requires that for any two PMT hits the time difference between them must be less than or equal to the time required to travel between them at the speed of light in water. From these selected hits, STORE gathers four-hit combinations, using their arrival times to calculate vertex position, in which the time residuals ((time of the PMT hit) -(time of flight) -(time of the test vertex)) of the four hits must be zero.
We gratefully acknowledge the cooperation of the  Kamioka Mining and Smelting Company. The SK experiment has been built and operated with funding by the  Japanese Ministry of Education, Culture, Sports, Science and Technology ; the  U. S. Department of Energy ; and the  U. S. National Science Foundation. Some of us have been supported by funds from the  National Research Foundation of Korea   NRF-2009-0083526  (KNRC) funded by the  Ministry of Science, ICT, and Future Planning  and the  Ministry of Education  ( 2018R1D1A1B07049158 ,  2021R1I1A1A01059559 ); the  Japan Society for the Promotion of Science ; the  National Natural Science Foundation of China  under grants No. 11620101004 ; the  Spanish Ministry of Science, Universities and Innovation  (grant  PGC2018-099388-B-I00 ); the  Natural Sciences and Engineering Research Council (NSERC) of Canada ; the  Scinet and Westgrid consortia of Compute Canada ; the  National Science Centre  ( UMO-2018/30/E/ ST2/00441 ) and the  Ministry of Education and Science  ( DIR/WK/2017/05 ), Poland; the  Science and Technology Facilities Council (STFC)  and  GridPPP, UK ; the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie  grant agreement no.
This paper is an extension of our previous work  [17]  and the major contributions of this paper are as follows: • We designed a microgrid-cloud architecture with a two ways energy exchange where each MG can buy and sell energy from/to the SG and the other MGs. • We enhanced our previous work  [17]  by formulating a two-stage optimization approach using game theory and linear and non-linear programming. • We designed our game payoff function as a nonlinear optimization problem and determined the optimal solution. • Considering the assigned power to data center, we formulated the MG energy cost minimization problem as a linear optimization problem and solved it using Matlab optimization tool  [18]. • We compare our two-stage optimization scheme with three other schemes, and show that our proposal is more efficient in terms of energy cost.
C. ENERGY COST PROBLEM FORMULATION In our system, each MG aims to minimize its total energy cost by purchasing the optimal quantity of power and selling its renewable stored energy. So, the total energy cost of a MG m j is composed of two parts: the power buying cost part and the power selling profit part. We denote by C t j the total energy cost of a MG m j at a time slot t,∀t ∈ T , and we define it as follows: C t j =C_Buy t j -C_Sell t j = P g t j × yg t j + J k=1 P m t jk × ym t jk × x jk -P s t j × ys t j × x ′ j × ω j , ∀j, k ∈ J, j ̸ = k, ∀t ∈ T ( 20 ) We model the energy cost minimization problem as a constrained linear optimization problem. Convex optimization problems can be solved using a variety of methods, we choose to solve it using Matlab optimization tool. We give the formulation of a mirogrid m j energy cost minimization problem as follows:.