dataesr/scientific-paragraphs-categorization huggingface dataset:
For all acquisitions, QCloud was used to control instrument longitudinal performance during the project  (44). All proteomic data were searched against the human proteome (uniprot reviewed sequences downloaded 28 February 2020), enhanced green fluorescent protein (EGFP) sequence, and the SARS-CoV or MERS protein sequences using the default settings for MaxQuant (version 1. 6. 12. 0)  (45). Detected peptides and proteins were filtered to 1% false discovery rate in MaxQuant. All MS raw data and search results files have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset (PXD identifier PXD021588).
High-confidence protein interaction scoring Identified proteins were then subjected to PPI scoring with both SAINTexpress (version 3. 6. 3) and MiST (https://github. com/kroganlab/mist) (6, 7). We applied a two-step filtering strategy to determine the final list of reported interactors, which relied on two different scoring stringency cut-offs. In the first step, we chose all protein interactions that had a MiST score ≥0. 7, a SAINTexpress Bayesian false-discovery rate (BFDR) ≤0. 05, and an average spectral count ≥2. For all proteins that fulfilled these criteria, we extracted information about the stable protein complexes that they participated in from the CORUM (46) database of known protein complexes. In the second step, we then relaxed the stringency and recovered additional interactors that (i) formed complexes with interactors determined in filtering step 1 and (ii) fulfilled the following criteria: MiST score ≥0.
6, SAINTexpress BFDR ≤0. 05, and average spectral counts ≥2. Proteins that fulfilled filtering criteria in either step 1 or step 2 were considered to be high-confidence protein-protein interactions (HC-PPIs). Using this filtering criteria, nearly all of our baits recovered a number of HC-PPIs in close alignment with previous datasets reporting an average of around six PPIs per bait  (47). However, for a subset of baits, we observed a much higher number of PPIs that passed these filtering criteria. For these baits, the MiST scoring was instead performed using a larger in-house database of 87 baits that were prepared and processed in an analogous manner to this SARS-CoV-2 dataset. This was done to provide a more comprehensive col-lection of baits for comparison, to minimize the classification of nonspecifically binding background proteins as HC-PPIs.
This was performed for SARS-CoV-1 baits (M, Nsp12, Nsp13, Nsp8, and ORF7b), MERS-CoV baits (Nsp13, Nsp2, and ORF4a), and SARS-CoV-2 Nsp16. SARS-CoV-2 Nsp16 MiST was scored using the in-house database as well as all previous SARS-CoV-2 data  (5).
Hierarchical clustering of virus-human protein interactions Hierarchical clustering was performed on interactions for (i) viral bait proteins shared across all three viruses and (ii) passed the highconfidence scoring criteria (MiST score ≥0. 6, SAINTexpress BFDR ≤0. 05, and average spectral counts ≥2) in at least one virus. We clustered using a new interaction score (K), which we defined as the average between the MiST and SAINT score for each virus-human interaction. This was done to provide a single score that captured the benefits from each scoring method. Clustering was performed using the ComplexHeatmap package in R, using the "average" clustering method and "euclidean" distance metric. K-means clustering (k = 7) was applied to capture all possible combinations of interaction patterns between viruses.
GO enrichment analysis on clusters Sets of genes found in seven clusters were tested for enrichment of GO terms, which was performed using the enricher function of clusterProfiler package in R  (48). The GO terms were obtained from the C5 collection of Molecular Signature Database (MSigDBv7. 1) and include biological process, cellular component, and molecular function ontologies. Significant GO terms were identified (adjusted P < 0. 05) and further refined to select nonredundant terms. To select nonredundant gene sets, we first constructed a GO term tree based on distances (1 -Jaccard similarity coefficients of shared genes) between the significant terms. The GO term tree was cut at a specific level (h = 0. 99) to identify clusters of nonredundant gene sets. For results with multiple significant terms belonging to the same cluster, we selected the term with the lowest adjusted P value.
Structural modeling and comparison of MERS-CoV ORF4a and SARS-CoV-2 Nsp8 To obtain a sensitive sequence comparison between MERS-CoV ORF4a and SARS-CoV-2 Nsp8, we took into consideration their homologs. We first searched for homologs of these proteins in the UniRef30 database using hhblits (1 iteration, E-value cutoff 1 × 10 -3 ) (49). Subsequently, the resulting alignments were filtered to include only sequences with at least 80% coverage to the corresponding query sequence, and hidden Markov models (HMMs) were created using hhmake. Finally, the HMMs of ORF4a and Nsp8 homologs were locally aligned using hhalign. The structure of ORF4a was predicted de novo using trRosetta  (50). To provide greater coverage than that provided by experimental structures, SARS-CoV-2 Nsp8 was modeled using the structure of its SARS-CoV homolog as template (PDB ID: 2AHM) (51) using SWISS-MODEL  (52).
Quantitative analysis and scoring of knockdown and knockout library screens Virus readout by qPCR (A549-ACE2, expressed as plaque-forming units per milliliter) and focus-forming assay readouts (Caco-2, focusforming units per milliliter) were processed using the RNAither package (www. bioconductor. org/packages/release/bioc/html/RNAither. html) in the statistical computing environment R. The two datasets were normalized separately, using the following method. The readouts were first log transformed (natural logarithm), and robust z-scores [using median and MAD (median absolute deviation) instead of mean and standard deviation] were then calculated for each 96-well plate separately. z-scores of multiple replicates of the same perturbation were averaged into a final z-score for presentation in Fig.
The cells were then immunostained for the viral NP protein (rabbit antisera produced in the Garcia-Sastre laboratory; 1:10,000) with a DAPI counterstain. Infected cells (488 nm) and total cells (DAPI) were quantified using a Celigo (Nexcelcom) imaging cytometer. Infectivity is measured by the accumulation of viral NP protein in the nucleus of the cells (fluores-cence accumulation). Percent infection was quantified as {[(number of infected cells / total cells)background] × 100}, and the DMSO control was then set to 100% infection for analysis. The IC 50 and IC 90 for each experiment was determined using the Prism (GraphPad Software) software. Cytotoxicity measurements were performed using the MTT assay (Roche), according to the manufacturer's instructions. Cytotoxicity was performed in uninfected Vero E6 cells with same compound dilutions and concurrent with viral replication assay.
Quantification of Tom70 down-regulation in infected Caco-2 cells Caco-2 cells were seeded on glass coverslips in triplicate and infected with SARS-CoV-2 at a MOI of 0. 1 as described above. At 24 hours postinfection, cells were fixed with 4% paraformaldehyde and immunostained with antibodies against Tom70, Tom20, and ORF9b. For signal quantification images of noninfected and neighboring infected cells were acquired using a LSM800 confocal laserscanning microscope (Zeiss) equipped with a 63X, 1. 4 NA oil objective and the Zen blue software (Zeiss). The mean fluorescence intensity of each cell was measured by ImageJ software. Forty-three cells were quantified for each condition-infected or noninfected-from three independent experiments. Coexpression and purification of ORF9b-Tom70 (residues 109 to end) complexes SARS-CoV-2 ORF9b and Tom70 (residues 109 to end) were coexpressed using a pET29-b(+) vector backbone where ORF9b was tag-less and Tom70 had an N-terminal 10XHis-tag and SUMO-tag.
To build the model of Tom70 (109 to end), the crystal structure of Saccharomyces cerevisiae Tom71 (PDB ID: 3fp3; sequence identity 25. 7%) was first fit into the cryo-EM density as a rigid body in UCSF ChimeraX and then relaxed into the final density using Rosetta FastRelax mover in torsion space. This model, along with a BLAST alignment of the two sequences  (69) , was used as a starting point for manual building using COOT  (70). After initial building by hand, the regions with poor density fit or geometry were iteratively rebuilt using Rosetta  (71). ORF9b was built de novo into the final density using COOT, informed and facilitated by the predictions of the TargetP-2. 0, MitoFates, and JPRED servers. The ORF9b-Tom70 complex model was submitted to the Namdinator web server  (72)  and further refined in ISOLDE 1.
RNA extraction, RT, and RT-qPCR for IL-17A treatment studies Total RNA from samples was extracted using the Direct-zol RNA kit (Zymogen, R2060) and quantified using the NanoDrop 2000c (ThermoFisher). cDNA was generated using 500 ng of RNA from infected A549-ACE2 cells with Superscript III reverse transcription (ThermoFisher, 18080-044) and oligo(dT) 12-18 (ThermoFisher, 18418-012) and random hexamer primers (ThermoFisher, S0142). RT-qPCR reactions were performed on a CFX384 (BioRad) and delta cycle threshold (DCt) was determined relative to RPL13A levels. Viral detection levels and target host genes in treated samples were normalized to water-treated controls. The SYBR green qPCR reactions contained 5 ml of 2x Maxima SYBR green/Rox qPCR Master Mix (ThermoFisher; K0221), 2 ml of diluted cDNA, and 1 nmol of both forward and reverse primers, in a total volume of 10 ml.
Computational docking of PGES-2 and Nsp7 A model for human PGES-2 dimer was constructed by homology using MODELER  (78)  from the crystal structure of Macaca fascularis mPGES-2 [PDB ID: 1Z9H (79); 98% sequence identity] bound to indomethacin. Indomethacin was removed from the structure utilized for docking. The structure of SARS-CoV-2 Nsp7 was extracted from PDB ID 7BV2  (80). Docking models were produced using ClusPro  (81) , ZDock  (82) , HDock (83), Gramm-X (84), SwarmDock  (85) , and PatchDock  (86)  with SOAP-PP score  (87). For each protocol, up to 100 top scoring models were extracted (fewer for those that do not report >100 models); for PatchDock, models with SOAP-PP z-scores >3. 0 were used (fig. S23A ). The 420 models were clustered at 4. 0-Å RMSD, resulting in 127 clusters. The two largest clusters, composed of 192 models, are related by dimer symmetry.
Assessment of positive selection signatures in SIGMAR1 SIGMAR1 protein alignments were generated from whole-genome sequences of 359 mammals curated by the Zoonomia consortium. Protein alignments were generated with TOGA (https://github. com/hillerlab/TOGA), and missing sequence gaps were refined with CACTUS  (88, 89). Branches undergoing positive selection were detected with the branch-site test aBSREL  (90)  implemented in the HyPhy package  (90, 91). PhyloP was used to detect codons undergoing accelerated evolution along branches detected as undergoing positive selection by aBSREL relative to the neutral evolution rate in mammals, determined using phyloFit on third nucleotide positions of codons which are assumed to evolve neutrally. P values from phyloP were corrected for multiple tests using the Benjamini-Hochberg method  (92).
PhyloFit and phyloP are both part of the PHAST package version 1. 4  (93, 94). Comparative SARS-CoV-1 inhibition by amiodarone SARS-CoV-1 (Urbani) drug screens were performed with Vero E6 cells (ATCC #1568, Manassas, VA) cultured in DMEM (Quality Biological), supplemented with 10% (v/v) heat-inactivated FBS (Sigma), 1% (v/v) penicillin-streptomycin (Gemini Bio-products), and 1% (v/v) L-glutamine (2 mM final concentration, Gibco). Cells were plated in opaque 96-well plates 1 day before infection. Drugs were diluted from stock to 50 mM and an 8-point 1:2 dilution series prepared in duplicate in Vero Media. Every compound dilution and control were normalized to contain the same concentration of drug vehicle (e. g. , DMSO). Cells were pretreated with drug for 2 hours at 37°C (5% CO2) before infection with SARS-CoV-1 at MOI 0.
01. In addition to plates that were infected, parallel plates were left uninfected to monitor cytotoxicity of drug alone. All plates were incubated at 37°C (5% CO2) for 3 days before performing CellTiter-Glo (CTG) assays as per the manufacturer's instruction (Promega, Madison, WI). Luminescence was read on a BioTek Synergy HTX plate reader (BioTek Instruments Inc. , Winooski, VT) using the Gen5 software (version 7. 07, Biotek Instruments Inc. , Winooski, VT).
Real-world data source and analysis This study used deidentified patient-level records from HealthVerity's Marketplace dataset, a nationally representative dataset covering >300 million patients with medical and pharmacy records from >60 health care data sources in the United States. The current study used data from 738,933 patients with documented COVID-19 infection between 1 March 2020 and 17 August 2020, defined as a positive or presumptive positive viral laboratory test result or an International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis code of U07. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]. For this population, we analyzed medical claims, pharmacy claims, laboratory data, and hospital chargemaster data containing diagnoses, procedures, medications, and COVID-19 laboratory results from both inpatient and outpatient settings.
Observation of hospitalization outcomes in outpatient new users of indomethacin (treatment arm) versus celecoxib (active comparator) using real-world data We used an incident (new) user, active comparator design  (95, 96)  to assess the risk of hospitalization among newly diagnosed COVID-19 patients who were subsequently treated with indomethacin or the comparator agent, celecoxib. Patients were required to have COVID-19 infection recorded in an outpatient setting during the study period of 1 March 2020 to 17 August 2020 and occurring in the 21 days before (and including) the date of indomethacin or celecoxib treatment initiation. Prevalent users of prescription-only NSAIDs (any prescription fill for indomethacin, celecoxib, ketoprofen, meloxicam, sulindac, or piroxicam 60 days prior) and patients hospitalized in the 21 days before and including the date of treatment initiation were excluded from this analysis.
ACKNOWLEDGMENTS The authors acknowledge their partners and families for support in childcare and other matters during this time. The views, opinions, and findings contained in this study are those of the authors and do not represent the official views, policies, or endorsement of the  Department of Defense  or the  U. S. Government. Imaging at the University of Sheffield  was performed in the  Wolfson Light Microscopy facility. A549 cells stably expressing  ACE2  ( A549-ACE2 ) were provided by  O. Schwartz. We thank  H. Malik  for helpful discussions. We thank  M. Akdel  and  J. Durairaj  for input on analysis,  R. Albrecht  for support with the BSL3 facility and procedures at the ISMMS, and  R. Cadagan  for technical assistance. We thank the  High Containment Core at Georgia State University  for supporting the BSL3 facility and procedures.
B. F. ; by  Howard Hughes Medical Institute  to K. S. ; by  Damon Runyon Cancer Research Foundation   DRG-2402-20  to C. P. ; by  Burroughs Wellcome Fund   1019894  to  N. He. ; by the  Chan Zuckerberg Initiative  to O. S. R. and T. Ko. ; by Cytoscape: A modeling platform for biomolecular networks ( NHGRI R01 HG009979 ) to T. I. ; by the generous support of the  JPB Foundation , the  Open Philanthropy Project  [research grant  2020-215611 (5384 )] and by anonymous donors to A. G. -S. ; a  Laboratoire d'Excellence grant   ANR-10-LABX-62-IBEID  and the  URGENCE COVID-19 Institut Pasteur fundraising campaign  to M. V and  N. Jo. ; by grants from the  BBSRC  ( BB/S009566/1  and  BB/ L002841/1 ) to A. A. P. and D. W. and by  BBSRC White Rose DTP  ( BB/J014443/1 ) to A. S. S. -S. ; by The  Augusta University-Georgia State University Seed Grant program  to C.
40 PFig. 7. 407 Fig. 7. Cryo-EM structure of ORF9b-Tom70 complex reveals ORF9b adopting a helical fold and binding at the substrate recognition site of Tom70. (A) Surface representation of the ORF9b-Tom70 structure. Tom70 is depicted as molecular surface in green, ORF9b is depicted as ribbon in orange. Region in charcoal indicates Hsp70 or Hsp90 binding site on Tom70. (B) Magnified view of ORF9b-Tom70 interactions with interacting hydrophobic residues on Tom70 indicated and shown in spheres. The two phosphorylation sites on ORF9b, S50 and S53, are shown in yellow. (C) Ionic interactions between Tom70 and ORF9b are depicted as sticks. Highly conserved residues on Tom70 making hydrophobic interactions with ORF9b are depicted as spheres. (D) Diagram depicting secondary structure comparison of ORF9b as predicted by JPred server-as visualized in our structure-or as visualized in the previously crystallized dimer structure (PDB ID: 6Z4U) (16).
Aims. The nearby Orion-Eridanus superbubble, which was blown by multiple supernovae several million years ago, has likely produced cosmic rays. Its turbulent medium is still energised by massive stellar winds and it can impact cosmic-ray transport locally. The γ radiation produced in interactions between cosmic rays and interstellar gas can be used to compare the cosmic-ray spectrum in the superbubble and in other regions near the Sun. It can reveal spectral changes induced in GeV to TeV cosmic rays by the past and present stellar activity in the superbubble. Methods. We used ten years of data from the Fermi Large Area Telescope (LAT) in the 0. 25-63 GeV energy range to study the closer (Eridanus) end of the superbubble at low Galactic latitudes. We modelled the spatial and spectral distributions of the γ rays produced in the different gas phases (atomic, molecular, dark, and ionised) of the clouds found in this direction.
Such criteria reduce the contamination by residual cosmic rays and by Earth atmospheric γ rays in the photon data. The instrument functions, the exposure map, the γ-ray emissivity spectrum of the local interstellar gas, q LIS  (Casandjian 2015) , and the spectrum of the isotropic background were evaluated in 12 energy bins, 0. 2 dex in width and centred from 10 2. 5 to 10 4. 7 MeV. We took the energy resolution of the LAT into account when modelling the data. To ensure photon statistics robust enough to follow details in the spatial distributions of the different interstellar components, we analysed the data in seven broad and independent energy bands, bounded by 10 2. 4 , 10 2. 6 , 10 2. 8 , 10 3. 0 , 10 3. 2 , 10 3. 6 , 10 4. 0 , and 10 4. 8 MeV. 1 https://fermi. gsfc. nasa. gov/ssc/data/access/lat/BackgroundModels.
H i and CO data, as well as cloud separation In order to trace the atomic gas, we used the 16. 2 resolution HI4PI survey  (Ben Bekhti et al. 2016) , with a velocity resolution of 1. 49 km/s in the local standard of rest (LSR). We selected velocities between -90 and +50 km s -1 to exclude the H i emission coming from the high-velocity clouds that lie in the hot Galactic corona, far behind the local medium we are interested in  (Wakker et al. 2008). We checked that these high-velocity clouds were not detected in γ rays and we removed them from our model. In order to trace the molecular gas, we used the 8. 5 resolution 12 CO (J=1-0) observations at 115 GHz from the momentmasked CfA CO survey  (Dame et al. 2001; Dame & Thaddeus 2004). We completed this dataset with the CO observations of the MBM 20 cloud that were obtained with the Swedish-ESO Submillimetre Telescope (SEST).
Analysis iteration In order to extract the DNM gas present in both the dust and γray data in addition to the gas seen in H i and CO emission, we iteratively coupled the γ-ray and dust models. We built residual maps between the data (dust or γ rays) and the best-fit contributions from the N H ii , N H i , W CO and other non-gaseous components. We kept only the positive residuals with high significance above the noise (a simple cut at zero would induce an offset bias in the next model). To do so, we de-noised the residual maps using the multi-resolution support method implemented in the MR filter software  (Starck & Pierre 1998). We used a multiresolution thresholding filter, with six scales, Gaussian noise and a 2-σ threshold to filter the dust residuals. For the γ-ray ones, we used seven scales, Gaussian and Poisson noise, and a 3-σ threshold.
Acknowledgements. We acknowledge the financial support from the  ANR-DFG  grant  CRiBs  ( ANR-15-CE31-0019-01  and  Schl 201/34-1 ) for this work. We thank  Alexei Ivlev  and  Andrei Bykov  for stimulating discussions and the referee for his/her suggestions about the turbulence. The  Fermi LAT Collaboration  acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the  National Aeronautics and Space Administration  and the  Department of Energy in the United States , the  Commissariat à l'Energie Atomique  and the  Centre National de la Recherche Scientifique / Institut National de Physique Nucléaire et de Physique des Particules in France , the  Agenzia Spaziale Italiana  and the  Istituto Nazionale di Fisica Nucleare in Italy , the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) ,  High Energy Accelerator Research Organisation (KEK)  and  Japan Aerospace Exploration Agency (JAXA) in Japan , and the  K.
This paper aims to highlight the potential of solar water heater installations in Morocco. The project involves the comparison of active and passive solutions for energy efficiency in buildings. To this end, a numerical simulation model of solar water heater installations is created under TRNsys. Three hot water demand scenarios (Low, Standard, and High) were taken into account for the six climatic zones defined in the Moroccan thermal regulation of constructions. The same software (TRNsys) is used to model a pilot building consisting of 16 flats. Energy efficiency actions have been applied to the building envelope (insulation and glazing) and simulations are made for the six areas. The simulation results comparing energy and financial savings show the influence of subsidized gas prices on solar water heaters' relevance despite significant energy savings.
Building Envelope (Construction Materials) The composition and construction materials of the building are shown in Table  2. Figure  3  shows the simulation model flowchart on TRNsys studio software. The building model was created using TRNBUILD connected to TRNsys by the construction model (type 56). It is a non-geometric scale model with one air node per zone, representing the air volume's zone heat capacity and the capacities that are closely related to the air node (furniture, for example). Thus, the node's capacity is a separate entry in addition to the volume of the zone.
Simulation Building Model and Hypothesis Figure  3  shows the simulation model flowchart on TRNsys studio software. The building model was created using TRNBUILD connected to TRNsys by the construction model (type 56). It is a non-geometric scale model with one air node per zone, representing the air volume's zone heat capacity and the capacities that are closely related to the air node (furniture, for example). Thus, the node's capacity is a separate entry in addition to the volume of the zone. The thermal zones: the selected model was considered with 36 thermal zones, 16 heated and air-conditioned thermal zones. Each zone includes the living rooms and rooms of each apartment and 20 thermal buffer zones, which are the bathrooms, kitchens, and halls of all the apartments. Figure  4.