dataesr/scientific-paragraphs-categorization huggingface dataset:
Construction of the global spectra We identified the main axes of functional trait variation by performing principal component analyses (PCA) on the log-transformed and scaled functional traits of each group. We used Horn's parallel analysis in the R package paran  (28)  to determine the number of axes retained in these PCA; we will refer to these reduced spaces as functional spaces from now on. We checked the reliability of the functional spaces obtained with imputed functional trait values by comparing them with the spaces that were based only on species with complete functional information. We performed this comparison by estimating the correlation between distance matrices of the species that were common to the two spaces (space with imputed data and space with complete species only) through a Procrustes of each taxonomic group (16), using the 'procuste.
rtest' function from the R package ade4  (62). To assess the significance of the correlation, permutation tests (9,999 randomizations) based on Monte-Carlo simulations were generated. All the Procrustes tests were highly significant (P=0. 0001 in all cases, see table  S2 ), indicating a strong correspondence between the complete and imputed functional spaces; consequently, we used the PCA based on imputed trait data in the rest of analyses. We estimated the probabilistic distribution of the species within the functional spaces by performing multivariate kernel density estimations with the 'TPD' and 'ks' R packages  (20, 29, 63, 64). The kernel for each species was a multivariate normal distribution centered in the coordinates of the species in the life history or functional space and bandwidth chosen using unconstrained bandwidth selectors from the 'Hpi' function in the 'ks' package  (63) (64) (65).
In all vertebrate taxa, the species whose conservation status has been assessed by IUCN formed a relatively random subset of the functional spectra of the different groups (high similarity between the assessed species and the global spectra; table  S3 ). Plants constituted an exception to this pattern, reflecting the bias towards trees in the IUCN Red List (78) (fig. S2 ), as well as the high proportion of species for which conservation status is not known (in our dataset, roughly 80% of the plant species with trait measurements have not been assessed or are classified as data deficient). After examining the overlap of TPD functions, we mapped the conservation status of species within the functional spaces. For this, we considered only species assessed by IUCN. The relationship between conservation status (1: Threatened; 0: Non-threatened) and the position in the corresponding space (PCA axes) was analyzed using a tensor product smoother-based generalized additive model (GAM; 26) with a binomial response (R package 'mgcv'; 78).
This strategy allowed us to ascertain whether losing threatened species affects reduces more or less than expected the functional spectra of the different groups (in terms of lost space and mean absolute quantile changes). explained, eigenvalues and trait loadings of principal components (the number of components depend on the dimensionality of the functional space selected by Horn's parallel analysis) in the PCAs done with the phylogenetically imputed trait dataset (see Methods) and the subset of the dataset that included only species with complete trait information. Results reported in the main text refer to the imputed datasets. The results of the PCA done with the imputed dataset and the complete dataset show very high concordance in all groups (p= 0. 0001 in all cases), according to Procrustes tests with 9,999 replications.
Acknowledgments: The study has been supported by the TRY initiative on plant traits (http://www. try-db. org). The TRY initiative and database is hosted, developed and maintained by  J. Kattge  and  G. Boenisch  ( Max Planck Institute for Biogeochemistry, Jena, Germany ). TRY is currently supported by  Future Earth/bioDISCOVERY  and the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig.
Funding: CPC, AT, MP and RT were supported by the  Estonian Ministry of Education and Research  ( PSG293 ,  IUT20-29 ,  PRG609 , and  PSG505 ) and the  European Regional Development Fund  ( Centre of Excellence EcolChange ). MV-V and RG-M were supported by the  Dora Plus Fellowship Programme (University of Tartu ). SB was supported by  'Investissement d'Avenir ' grants ( CEBA ,  ANR-10-LABX-0025 ;  TULIP ,  ANR-10-LABX-41 ). RS-G was supported by  NERC   IRF NE/M018458/1.
021 -0. 140 0. 327 -0. 583 -0. 110 0. 422 -0. 271 Complete cases 0. 044 -0. 228 0. 339 -0. 165 -0. 100 0. 000 -0. 157 0. 283 -0. 490 -0. 067 Number of species Snout-vent length (svl) Body length (svl) 38,764 -0. 373 0. 413 0. 471 -0. 022 0. 087 -0. 173 0. 343 0. 548 1,734 -0. 260 0. 216 0. 058 -0. 032 Body mass (bm) PC1 0. 470 -0. 354 PC2 0. 013 -0. 231 PC1 0. 593 -0. 289 PC2 0. 181 -0. 159 Variation explained (%) d) Reptiles Proportion of variance 54. 1 30. 0 47. 1 27. 1 Eigenvalue Procrustes test Trait loadings Leaf area (la) Number of species Leaf nitrogen content (ln) Plant height (ph) Variation explained (%) Specific leaf area (sla) Eigenvalue Procrustes test Specific stem density Trait loadings (ssd) Seed mass (sm) Litter size (ls) Litters per year (ly) 3. 26 Imputed dataset (main 1.
81 P=0. 0001 analyses) 0. 341 0. 461 0. 148 3. 89 Complete cases 2. 2. 24 6,219 186 0. 525 -0. 170 0. 624 -0. 336 PC1 PC2 PC1 PC2 0. 568 0. 516 0. 151 0. 503 54. 2 17. 3 55. 5 19. 6 0. 281 -0. 300 0. 568 -0. 460 3. 25 1. 03 5. 04 1. 78 0. 447 P=0. 0001 0. 510 -0. 112 0. 457 0. 059 0. 488 0. 202 0. 438 0. 463 0. 000 0. 354 0. 374 0. 345 -0. 416 -0. 263 -0. 367 -0. 491 Body mass (bm) b) Mammals Incubation time (inc) 0. 518 0. 061 -0. 105 -0. 956 0. 522 0. 521 0. 124 -0. 773 Longevity (long) Snout-vent length (svl) Imputed dataset (main analyses) 0. 277 -0. 067 0. 513 -0. 029 0. 237 Complete cases -0. 005 0. 375 0. 071 Number of species 4,718 1,238 e) Amphibians PC1 PC2 PC1 PC2 Variation explained (%) Eigenvalue Procrustes test Number of species Trait loadings Litter size (ls) Litters per year (ly) Variation explained (%) Body mass (bm) Eigenvalue Longevity (long) Procrustes test Gestation length (gest) Trait loadings Snout-vent length (svl) Age at maturity (am) Weaning length (wea) Body size (bs) Litter size (ls) Female maturity time (fmat) Offspring size (os) 64.
1 5. 12 Imputed dataset (main analyses) 16. 4 1. 31 6,776 P=0. 0001 73. 8 7. 81 PC1 PC2 PC1 -0. 300 0. 456 -0. 302 Complete cases 8. 5 0. 90 236 PC2 0. 348 -0. 353 0. 276 -0. 355 48. 2 35. 9 52. 6 25. 7 0. 281 0. 341 0. 531 0. 369 1. 92 1. 44 3. 62 1. 77 0. 560 0. 352 0. 038 0. 285 P=0. 0001 -0. 045 0. 368 -0. 261 0. 364 -0. 046 0. 313 0. 592 0. 350 0. 608 0. 100 0. 702 0. 551 0. 610 0. 388 -0. 010 0. 391 0. 637 0. 108 0. 279 0. 242 -0. 233 0. 181 0. 775 -0. 401 0. 737 0. 401 -0. 115 0. 397 -0. 237 0. 438 -0. 615 0. 518 -0. 308 f) Freshwater fishes c) Birds Imputed dataset (main analyses) Complete cases Number of species PC1 Imputed dataset (main analyses) 10,614 PC2 PC3 PC4 597 Complete cases PC1 PC2 PC3 PC4 Number of species Variation explained (%) 23. 2 9,723 20. 7 16. 5 13. 7 26. 8 348 18.
Results Linear trends in CA. We assessed 12 chilling models coded as C 1 -C 12 (see "Methods" for a full description). The linear trends in CA during winter to early spring (previous November to April) from 1951 to 2018 were calculated for each station based on all models and E-OBS gridded temperature data set. We expected that the accumulated chilling in Central Europe has progressively decreased as the winter temperature (Previous November to February) increased significantly by 0. 25 °C/decade from 1951 to 2018. The trends in CA and its spatial pattern, however, varied among the models (Fig. 2 ). CA for Models C 1 , C 2 , C 4 , C 5 , and C 12 decreased significantly at >88% of the stations (p < 0. 05) because the winter temperature exceeded the maximum effective temperature under climate warming (e.
The percentage of stations with significantly negative Pearson's r between CA and HR was larger for the valid than the invalid models, regardless of the forcing model chosen (Supplementary Fig. 7 ). Thus, the performance of the chilling models was not affected by the choice of forcing models. Although some of the invalid models were calibrated specifically for fruit and nut trees (e. g. , Model C 7 and C 9 ), they still did not perform better than the valid models for Prunus avium (a fruit tree supplying edible cherry) and Corylus avellana (widely grown hazelnut plants for commercial nut production) (Supplementary Fig. 7 ). In order to assess the scale effect caused by large grid size of E-OBS data set (about 10 km) and the non-uniform cover of plants and temperature in the grid cells, we compared the HR and CA based on E-OBS data set and a station-based Global Historical Climatology Network (GHCN) data set.
Methods Phenological and climatic data. We used data from the Pan European Phenology Project (PEP725)  29  , an open-access database with long-term plant phenological observations across 25 European countries (http://www. pep725. eu/). The regional/ national network partners of PEP725 are following a consistent guideline for phenological observations  30  and prepare the data for submission to the PEP725 database curators  29. We selected 30 species for which sufficient observational data were available: 21 deciduous broadleaved trees or shrubs, 6 herbaceous perennials, 2 evergreen coniferous trees, and 1 deciduous coniferous tree (Supplementary Table  3 ). Particularly, our data set included one fruit tree (Prunus avium) and one nut tree (Corylus avellana) since some of the chilling models are specifically developed for fruit and nut trees.
A total of 2,493,644 individual records from 15,533 phenological stations were used. The stations were mainly distributed in moderate climates in Central Europe (Supplementary Fig. 3 ). Four spring events based on the BBCH code were investigated: BBCH 10, 11, 60, and 69, representing first leaves separated, first leaves unfolded, first flowers open, and end of flowering, respectively  31. We used the E-OBS v19. 0eHOM data set  32  with a spatial resolution of 0. 1 × 0. 1°f or 1950-2018 for calculating CA and HR of the in situ phenological records. This data set is provided by the European Climate Assessment & Data set project and includes homogenized series of daily mean, minimum, and maximum temperatures. We also use the daily maximum and minimum temperature data from the GHCN data set  33  to assess the scale effect.
The GHCN data set contains station-based measurements from over 90,000 land-based stations worldwide, but only parts of PEP725 stations match with the GHCN stations. For future climatic data (2019-2099), we used daily minimum and maximum temperatures simulated by the HADGEM2-ES model (with a spatial resolution of 0. 5 × 0. 5°) under two climatic scenarios (RCP 4. 5 and RCP 8. 5). These data have been bias-corrected by applying the method used in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP)  34  , which were available on the ISIMIP server (https://esg. pik-potsdam. de/projects/isimip2b/). Chilling models. We used 12 chilling models to measure the amount of chilling. One type of chilling model is based on several specific temperature thresholds. The most commonly used model, developed in the 1930s and 1940s for peach  35  , calculates the number of hours or days with temperatures <7.
CA was calculated as the sum of CU i from 1 November in the previous year to the date of onset of spring events. We chose 1 November as the start date for CA because the endodormancy of temperate trees began around 1 November 52. We only tested the linear relationship because the data were better fitted by the linear regression than the exponential model (Fig. 3 ), even though CA was linearly or nonlinearly negatively correlated with HR 17. Pearson's r between CA and HR for all records was calculated for each species, with a significantly negative Pearson's r (p < 0. 05) indicating that the chilling model met the physiological assumption. We also analyzed the relationship between CA and HR at stations with at least 15-year records to determine if the results were robust at the station level.
The above analysis is based on the E-OBS data set. Given the large grid size of the E-OBS data set (about 10 km) and the non-uniform cover of plants and temperature in the grid cells, we assessed the scale effect by comparing the HR and CA based on E-OBS data set with GHCN data set. We only retained the phenological station where a corresponding meteorological station (distance and altitude difference should be less than 5 km and 100 m, respectively) existed in the GHCN data set and compared the HR, CA, and their relationship. We developed an empirical model based on the linear regression function between CA and HR (e. g. , the linear fitted line in Fig. 3 ) to simulate the past and future spring phenological change. We calculated CA (from the previous 1 November to the current date) and heat accumulation (from 1 January to the current date) for each species in each year using a daily step.
Acknowledgements This work was funded by the  National Key R&D Program of China  (Grant No. 2018YFA0606102 ),  National Natural Science Foundation of China  (Grant No. 41871032 ),  Youth Innovation Promotion Association, CAS  (Grant No. 2018070 ), and  Program for 'Kezhen' Excellent Talents in IGSNRR, CAS  (Grant No. 2018RC101 ). PC and JP were supported by  ERC Synergy  grant  ERC-SyG-2013-610028 IMBALANCE-P.
Data availability Phenological data can be accessed from http://www. pep725. eu/. E-OBS data set can be accessed from https://www. ecad. eu/download/ensembles/download. php. GHCH data set can be accessed from https://www. ncdc. noaa. gov/data-access/land-based-station-data/ land-based-datasets/global-historical-climatology-network-ghcn. Future climate data (2019-2099) were available on https://esg. pik-potsdam. de/projects/isimip2b/.
[60]  using 16S universal primers  [61]. The obtained electropherograms were assembled and edited using the ChromasPro1. 7. 7 software (Technelysium Pty Ltd. , Tewantin, Australia), and then the sequences obtained were compared with those available in the GenBank database by NCBI BLAST (http://blast. ncbi. nlm. nih. gov/Blast. cgi).
Statistical Analysis The Epi Info version 7 program (http://www. cdc. gov/epiinfo/index. html), (Addinsoft 2019) and the XLSTAT statistical and data analysis solution (https://www. xlstat. com) were used to compare mortality rates recorded at 72 h after the administration of 6 mg/L of the supernatant fractions. The Kruskal-Wallis test, comparison of k proportions and pairwise comparison were realized. A difference was statistically significant when p-values were ≤0. 05. The Dunn procedure (Bilateral test) was performed to separate groups of strains according to their efficiencies.
The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications.
This has resulted in enormous challenges for the research scientist when attempting to find and select information relevant to specific areas of viral biology and pathology. In order to aid the scientific community and expedite drug and vaccine development, multiple data curation efforts have been undertaken to perform a critical assessment of the literature and represent different aspects of the virus and the disease in a structured and computationally accessible manner. One recent example of such efforts is the COVID-19 Disease Map (3), a community effort to capture the intricate aspects of SARS-CoV-2 biology as reusable and interoperable pathway maps, so they can be used in systems biology and modeling pipelines. Identification of virus-host interactions and the analysis of the topological structure of a relevant molecular interaction network are the necessary steps to understand the cellular mechanisms involved in a biological process such as viral infection of a cell.
These data need to be supplied in a standardized and computer-accessible format, which allows for scoring, filtering and selection. The International Molecular Exchange (IMEx) Consortium (4) has been providing such data for over 15 years, supplying experimental details using controlled vocabulary terms, captured using a detailed curation model. Important aspects of an interaction experiment are described, including host organism, interaction detection and participant identification methodologies as well as details of the constructs such as binding domains and the effects of site-directed mutations. Current membership of the IMEx Consortium includes the IntAct (5), MINT  (6) , DIP  (7) , UniProt (8), MatrixDB  (9)  and IID  (10)  data resources, who collaborate to provide the users with a single, consistent viral-host dataset to work with.
When a novel virus emerges, as was the case with SARS-CoV-2 in 2019, the study of closely related species, such as SARS-CoV-1 and other coronaviruses, may help with this process, giving the scientific community time to produce species-relevant data. For this reason, the network includes data on all coronaviruses available in the scientific literature. While primarily consisting of protein-protein interaction data, the network also contains interactions with lipids, glycosaminoglycans and RNAs, again curated to IMEx standards. The data are open under a CC-BY 4. 0 license and downloadable from the IntAct website and FTP in PSI-MI standard tabdelimited and XML-based formats. A brief description can be found at http://www. ebi. ac. uk/intact/resources/datasets, and a collection of interactive network representations of the dataset at the time of writing is available at http://www.
Analysis software and packages Analyses were done using R 4. 0. 0  (11)  and the R data. table package  (12). Venn diagrams were created using ggVennDiagram 0. 3  (13). All other plots were created using ggplot2 3. 3. 0 (13) and wesanderson  (14)  packages. The network in Figure  1  was created using NAViGaTOR 3. 0. 13 (15) ; the data used to create the network are provided in Supplementary Table  5F. The rest of the interactive networks were created using Cytoscape 3. 8. 0 (16) , and all networks were uploaded to NDEx using Cytoscape.
Tissue enrichment analysis Analysis was performed using TissueEnrich (17) 1. 8. 0 R package on human protein targets using Protein Atlas (18) expression data. Background was the entire proteome as listed in the UniProtKB website (https: //www. uniprot. org/proteomes/UP000005640). Only hits with a fold change above zero are shown. Log10 P-value was zero for all tissues. Full data are available in Supplementary Table  3.
Pathway enrichment analysis Pathway enrichment analysis was performed using pathDIP 4 (19) API in R, with literature curated set. Only pathways with false discovery rate (FDR) < 0. 01 (Benjamini Hochberg (BH)-method) were considered. As the majority of enriched pathways (75% for SARS-CoV-1, 80% for SARS-CoV-2, 72% for Gordon_LT, 76% for Li_LT and 55% for Stukalov_LT) were from Reactome database  (20) , we further organized them using level-one Reactome ontology to create the figure. All enriched pathways are listed in Supplementary Table  2 , and pathways present in multiple sets are highlighted in the 'overlap' tab. For network annotation, each protein was linked to the pathway it is part of with the lowest FDR (BH-method) and annotated with level-one Reactome ontology according to such pathway.
Results and discussion As of August 2020, the dataset contains 3735 unique interacting molecule pairs, represented in 4479 binarized interactions extracted from 151 publications, 8 of which are preprints from bioRxiv. The entire 'coronavirus dataset' can be downloaded from the FTP site in PSI-MI standard XML-based formats: PSI-MI XML 2. 5 and 3. 0 (format documentation available at https://psicquic. github. io/PSIMIXML. html). Each file corresponds to one publication in the coronavirus dataset. In addition to the XML format, the data can also be browsed on the IntAct webpage (www. ebi. ac. uk/intact/query/annot:"data set:coronavirus"), where it is available for download in additional formats, such as the tab-delimited PSI-MI-TAB 2. 7 (format documentation available at https://psic quic. github.
Other access options Using the same search strings as on the IntAct website, the data can also be accessed via the PSICQUIC (21) API, the IMEx Consortium webpage (www. imexconsorti um. org) and the VirusMentha  (22)  browser at https:// virusmentha. uniroma2. it/. As with all data hosted by IntAct, the dataset is available under the CC BY 4. 0 license The complete dataset includes all the interactions that have been found in publications that fulfill the criteria reported in the next paragraph. The data refers mostly to protein-protein interactions (3568 interactions), plus some interactions involving different types of RNA (84 interactions) or small molecules (57 interactions). While data on 81 organisms are included (Supplementary Table  1A ), most interactions refer to SARS-CoV-2human and SARS-CoV-1-human interactions (2019 and 870 unique interactions, respectively).
The dataset of only human-Coronaviridae interactions is highlighted in Supplementary Table  1D. Figure  1  shows SARS-CoV-2-and SARS-CoV-1-human interactions, separating human targets that are specific for one or the other coronavirus or in common between the two, also highlighting affected pathways. IMEx Consortium curators have collated interaction evidence from scientific articles and preprints using the following selection criteria: (i) The publication contains interactions involving proteins from any virus member of the Coronaviridae family (NCBI taxon ID 11118). This includes not only SARS-CoV-1 and SARS-CoV-2, but also Middle East Respiratory Syndrome (MERS)-CoV and members of the family that infect other mammals. (ii) The publication contains interactions of human proteins with established relevance for SARS-CoV-2 life cycle (e.
g. ACE2 interactions have been included in the dataset). (iii) Every interaction described in these publications is curated and included in the dataset, even if their relevance to COVID-19 might seem limited. This results in the inclusion of data from apparently irrelevant species (e. g. yeast) that is of interest from the phylogenetic and evolutionary point of view. (iv) Preprints are considered and, if deemed appropriate, curated when containing SARS-CoV-2 data, an exception to IMEx practice of representing only peer-reviewed research. This reflects the interest these data have engendered during the pandemic. These datasets are clearly marked as prepublication and will be re-curated, if necessary, when published. The IMEx curation model captures the details of specific constructs used for the detection of interactions.