dataesr/scientific-paragraphs-categorization huggingface dataset:
437 mammal species were assigned using ref. 24. From GBIF, we downloaded all the records belonging to the four classes of animals (Mammals 50 , Aves  51  , Reptiles  52  and Amphibians 53 ). Before using the spatial data, we cleaned the dataset following a cleaning procedure that was similar to but more conservative than other currently available methods (e. g. CoordinatesCleaner, BDCleaner  54  ). First, records were screened, and only those with (1) coordinates; (2) a taxonomic rank of "species" were kept. From this list, we filtered out the records with clearly false locality coordinates (e. g. latitude equal to longitude, both latitude and longitude equal to 0, and longitude/latitude outside the possible range (i. e. -180; 180 for longitude and -90; 90 for latitude)). Those are the most common errors encountered with GBIF occurrence data  55.
In addition, we removed the records from living specimens (i. e. from zoos, botanical gardens), conserved specimens (i. e. museums), and unknown sources. We also excluded the species with less than 50 records within each realm as a low number of records can be due to misidentifications, which might have strong effects on our analyses. We finally refined the dataset by overlaying the occurrences within the six biogeographic realms (see below) and dropping the species that fall outside of the polygons. This spatial overlay process was conducted using the 'sp' library  56  in R. The number of species for which realm was assigned using GBIF was 1 (<0. 02%) for mammals, 442 (3. 4%) for birds, 572 (5. 3%) for reptiles and 25 (3. 2%) for amphibians. We performed additional analyses to evaluate how each database (i.
e. IUCN, WWF and GBIF) influenced the functional space of each group in each realm and the world (see "World and realm functional space" below). For that, we calculated the functional overlap between the functional spaces built with all species and the functional space built with all species except the species retrieved from the IUCN spatial data (Supplementary Fig. 7A ), or the species retrieved from WWF (Supplementary Fig. 7B ), or the species retrieved from GBIF (Supplementary Fig. 7C ). The higher the functional overlap, the less affected was the functional space by the data source (IUCN, WWF or GBIF). The functional overlap between the functional spaces built with all species and functional space built with all but the species retrieved from WWF (or from ref. 24  for mammals) was higher than 95% at a global and realm scales for all groups (Supplementary Fig.
7B ). The functional overlap between the functional spaces built with all species and functional space built with all but the species retrieved from GBIF was higher than 99% at a global and realm scales for all groups (Supplementary Fig. 7C ). This testified that the functional spaces of the realms were already well covered by the species retrieved from the IUCN spatial data. For freshwater fishes, we collected the occurrences database from ref. 25  , which provides the spatial distribution of about 13,000 species (out of the 17,000 described) and currently represents the most detailed spatial information on this taxa. We considered six biogeographic realms for terrestrial and aquatic animals  16, 23, 25, 57, 58  , the Afrotropical, Australian (combining Australasian and Oceania), Nearctic, Neotropical, Indo-Malay and Palearctic (Supplementary Fig.
8  and ref. 58  ). Notice that the Australasian and Oceania realms are considered as one combined realm in our analyses since the number of records was too low in Oceania for some taxonomic groups (e. g. fishes) to consider it as an independent realm. We thus called it the Australian realm, following the terminology of ref. 57. Our final database of spatial occurrences for the six realms encompassed 5927 mammals, 12,863 birds, 13,439 reptiles, 7864 amphibians and 13,008 freshwater fishes occurring in at least one biogeographic realm (Supplementary Table  7 ). Functional traits. We collected information on traits related to ecological functions for the five groups of vertebrates. All the traits have been selected for their ecological relevance and gathered from published studies (see Table  1  and Methods in ref.
11  for details on the estimation of functional traits) and mostly related to lifehistory characteristics. For mammals, birds and reptiles, we used the AMIOTE database  20  including data for 4953 species of mammals, 9802 species of birds, and 6567 species of reptiles. For mammals, we selected a subset of eight traits for which at least 1000 species were informed  11. These traits were: litter size (number of offspring per litter), number of litters per year, adult body mass (g), longevity (years), gestation length (days), weaning length (d), time to reach female maturity (days), and distance from the tip of the snout to the tail base (cm). For birds, we selected a total of eight traits: clutch size (number of eggs), number of clutches per year, adult body mass (g), incubation time (days), longevity (years), fledging age (days), egg mass (g) and distance from the tip of the beak to the opening of the cloaca (cm).
For reptiles, we selected a total of six traits with sufficient information: clutch size (number of eggs), number of clutches per year, adult body mass (g), incubation time (days), longevity (years) and distance from the tip of the snout to the opening of the cloaca (cm). For amphibians, we used the AmphiBIO database  21  to get data for 6776 species of amphibians. Within this dataset, we selected five traits with enough information: age at maturity (years), the maximum number of reproduction events per year, body size (mm), maximum litter size (number of individuals) and offspring size (mm). Finally, for freshwater fishes, we used the last updated version of the most comprehensive database on morphological traits, available for 10,705 species of freshwater fishes  9, 22. The link between morphological traits and ecological functions is well documented for freshwater fishes  [59] [60] [61]  , 8033 (75.
36%) cs: caudal shape (ratio of the maximum depth of the caudal fin to the minimum depth of the caudal peduncle) 7618 (71. 77%) svl: distance from the tip of the snout to the posterior end of the last vertebra or to the posterior end of the midlateral portion of the hypural plate (cm) 5185 (48. 85%) bm: adult body mass (g) 1281 (11. 97%) Adapted from Carmona et al. 11. The completeness corresponds to the number of species for which the trait values are available, and the percentage shows the proportion of functionally informed species compared to spatially informed species. making morphological traits a relevant proxy to describe the functional diversity of this group. This database encompasses 11 traits describing the size and shape of body parts involved in food acquisition and locomotion.
The fish body shape and weight were described through the size using the standard length (cm) and body mass (g) taken directly from FishBase  62  , body elongation (ratio between body length and body depth) and body lateral shape (ratio between the head depth and body depth). The other traits describing the position and the size of each part of the fish were eye size and position, mouth size and position, pectoral fin size and position, and caudal peduncle throttling and were measured on fish pictures  9, 60. Since none of the trait databases assembled was complete, we completed this information by performing trait-imputation procedures generated using the missForest R package  63. Evolutionary relationships were also considered in the imputation process by including the first ten phylogenetic eigenvectors (see details in ref.
11  ). We obtained published phylogenies for each of the taxonomic groups  [64] [65] [66] [67] [68]. Species that were not present in the phylogeny were added to the root of their genus, using the 'add. species. to. genus' from the R package phytools  69. For birds and mammals, 1000 phylogenetic trees were available representing the phylogenetic uncertainties  64, 65. We considered the phylogenetic uncertainties by calculating the eigenvectors as average the eigenvector obtained for each phylogeny. It is important to note that, compared to traditional imputation procedures, we aimed to characterize the position of species in the corresponding trait space rather than estimating the values of the original traits (see 'World and realm functional spectra' below). Thus, we tested the accuracy of our trait imputation procedure by comparison of the position of the species with complete trait values in the functional space (real position of the species) with the position of the same species for which we had artificially removed traits in the different dimensions of the functional space.
We estimated the performance of the imputation using the normalized root mean square error (NRMSE), which expresses the average distance between real and imputed positions of species as a proportion of the range of values of species in the corresponding dimension. To do it, we artificially removed 10% of trait values from a subset of species with complete information and selected one random species with incomplete information and superimposed its pattern of missing values. Thus, we kept constant the pattern of missing values as the one in the original dataset. We performed the phylogenetically-informed imputation procedure as described above using the entire dataset consisting of all the species with non-complete trait information and the species with complete trait information (i. e. including 90% of species with complete trait information and 10% of species with complete trait information plus artificial missing values).
This way, the ratio between missing and complete data was higher in the simulations than in the original dataset, therefore ensuring a conservative test of the quality of our imputation procedure. In addition, we repeated this examination 100 times for each taxonomic group and measured the standard error of the NRMSE, which was always <0. 5% demonstrating that these repetitions gave similar results (Supplementary Table  2 ). Finally, we imputed traits to project species onto the functional space based on the full dataset. Conservation status of the species. We collected the conservation status of species from the IUCN Red List (version 2020-3  15  ) using the R package 'rredlist'  70. For each taxonomic group, we used the IUCN classes: CR: critically endangered; EN: Endangered; VU: Vulnerable; NT: Near Threatened; LC: Least Concern and DD: Data Deficient.
In total, we retrieved the status for 6388 mammals, 11,158 birds, 8295 reptiles, 7167 amphibians and 20,295 fish species (including marine species). Matching occurrences, functional traits and IUCN databases. Taxonomies from all the used sources (trait databases, spatial occurrences, phylogenies and IUCN Red List 15 ), were standardized using the R packages 'taxize'  71. All names were resolved against the GBIF Backbone Taxonomy. Among taxonomic groups, the proportion of species described by spatial and functional trait databases varied between 53% for reptiles (5689 functionally described species out of 10,783 species with geographic distributions) and 74% for mammals (4408 species with trait information out of 5926 species). At the realm scale, the proportion of species with trait information within each taxonomic group was congruent with the proportion of species observed using only spatial data (Supplementary Table  1 , Supplementary Data 1, Supplementary Table  3 , and Supplementary Fig.
1 ). Matching species occurrences, functional traits and IUCN status revealed that vertebrates had relatively high information coverage, ranging from 44% for reptiles and freshwater fishes to 74% for mammals (Supplementary Table  1 ). To assess the relevance of the subset of species used in the analyses compared to all species, we quantified how much the functional traits of threatened species differ from the functional traits of non-threatened species, both in terms of (1) realm coverage and (2) trait distortion. (1) We used chi-squared tests between the occurrence dataset (considered as the most complete database), the functional dataset and the IUCN Red List dataset (Supplementary Table  3 ). We compared the proportion of species informed by IUCN in each realm to the expected proportion of species based on the species spatially informed (chi-squared test, see Supplementary Table  3 ).
For each realm, we quantified the standard deviation from the expected number of species with spatial occurrences. (2) We measured to what extent these differences might affect the shape of the functional space for each realm. For that, we calculated the overlap between the functional space built with all species spatially informed in each realm and the functional space built with the subset of species evaluated by IUCN Red List (Supplementary Table  5 ). We used the IUCN Red List database only to assess the loss of functional diversity. Otherwise, to describe the taxonomic and functional diversity patterns, we used species that are both spatially and functionally informed, regardless of the information about their conservation status. World and realm functional spectra. The construction of the spectra of each taxonomic group was similar to the procedure followed in ref.
Then, the value of the TPD function is estimated for each cell. The value of the TPD function in a given point of the space reflects the density of species in that particular area of the space (i. e. species with similar functional traits). The kernel for each species was a multivariate normal distribution centred in the coordinates of the species in the functional space and bandwidth chosen using unconstrained bandwidth selectors from the 'Hpi' function in the 'ks' 72 package (see details in ref. 11  ). To facilitate comparisons, the kernel for each species was kept constant for generating the functional space of each biogeographic realm. Extinctions of threatened species. To test how functional diversity can be affected by species loss at different biogeographic realms, we simulated the loss of threatened species in a progressive framework according to the IUCN status of the species  15.
e. -DD: CR, EN, VU, NT and DD). This scenario considers the eventuality that all data deficient species are threatened and therefore represents an extreme scenario compensating for potential incompleteness in species threat evaluation by the IUCN. We estimated in each biogeographic realm how functional diversity will change in case of extinction of threatened species. For that, in each functional space, we estimated a TPD function considering all the species assessed by IUCN  15  , and another TPD function after removing the species classified as threatened. Whereas the TPD functions of all species assessed by IUCN reflect the current spectra, the TPD functions after removing threatened species reflect the potential spectra if threatened species go extinct. TPD functions are probability density functions so that they integrate to 1 across the whole functional space, a property that permits the comparison of different TPD functions  18.
Acknowledgements A. T. , C. G. B. , M. P. , R. T. and C. P. C. were supported by the  Estonian Ministry of Education and Research  ( PSG293 ,  PRG609  and  PSG505 ) and the  European Regional Development Fund  ( Centre of Excellence EcolChange ). S. B. is supported by  French Laboratory of Excellence  projects  ANR-10-LABX-41 ,  ANR-10-LABX-25-01.
Data availability The data used in this study are available in the database under accession code in Figshare (https://figshare. com/s/f076a046963c6f782f8d). AmphiBIO are available at https:// doi. org/10. 1038/sdata. 2017. 123. IUCN Red List data are available at http:// www. iucnredlist. org. IUCN spatial database available at https://www. iucnredlist. org/ resources/spatial-data-download. Mammal data are available at https:// www. mammaldiversity. org/. AMIOTE data are available at https://doi. org/10. 1890/15-0846R. 1. Fishbase is available at http://www. fishbase. org. WWF data used are available at www. worldwildlife. org/WildFinder. GBIF data are available at https://doi. org/10. 15468/ dl. xabdgp; https://doi. org/10. 15468/dl. ixznsa; https://doi. org/10. 15468/dl. pdvdl4; https:// doi.
Oncology Biomarker Panel The HTG EdgeSeq Oncology Biomarker Panel (OBP) direct-transcriptomic assay quantitatively measures the expression of 2549 human RNA transcripts (https:// www. htgmolecular. com/assays/obp) associated with tumor biology. Among these RNA transcripts, 118 are associated with DDR mechanisms and only these genes were considered for the purpose of this study. For direct transcriptomics, all the samples used in the assay had to have at least 70% of tumor area; samples with less than 70% tumor or with more than 20% of necrotic tissue underwent macro-dissection. All the samples and controls were quantified in triplicate, and no template control was included in each run. The RNA-Seq libraries were synthesized through HTG EdgeSeq chemistry. In summary, this method consists of a first step that lysed and permeabilized the sample, which allows the release of the RNA.
Then, nuclease protection probes were added to the lysed samples to hybridize with the exposed mRNA and target-captured the transcripts. The S1 nuclease was added to the mix, allowing the production of a stoichiometric amount of target mRNA/nuclease protection probes duplexes. The reaction was blocked by denaturing S1 enzyme by heat. In order to reduce potential biases in the run, the samples were randomized before the inclusion in the HTG EdgeSeq system. PCRs were set up using each hybridized sample as template and specially designed tags that shared common sequences complementary to 5 0 -end and 3 0sequences of the probes, as well as with common adaptors that are required for cluster generation on an Illumina sequencing platform. Additionally, each tag contains a unique barcode useful for sample identification and multiplexing.
Agencourt AMPure XP (Beckman Coulter, Beverly, MA, USA) was used to clean the PCR amplification product. Moreover, the libraries were quantified by quantitative PCR, using the KAPA Library Quantification (Roche, Basel, Switzerland) kit, according to the manufacturer's instructions. All samples and controls were quantified in triplicate, and no template control was included in every run. Library denaturation was performed by adding first 2 N NaOH to the library, followed by the addition of 2 N HCl. The PhiX was spiked in at a 5% (concentration of 12. 5 pM). One demultiplexed FASTQ file per sample was retrieved from the sequencer for data processing. The HTG EdgeSeq host software performed the alignment of the FASTQ files with the probe list, the results were parsed, and the output was obtained as a read counts matrix.
Data filtering and normalization Negative control probes were used as quality control to evaluate baseline performance, as previously described by Chadly et al. [17]. Briefly, the mean of negative probes for each sample was calculated, and the difference between negative control average and the mean of all negative control probes was obtained (Δ mean ). Those samples with a Δ mean outside the bounds of AE2 standard deviation were excluded from the study. Of 140 sample initially included in the study, seven were removed from the analysis. Data normalization was performed by applying trimmed mean of M-values method (TMM) using EDGER package from R/BIOCONDUCTOR, adjusting for the total reads within a sample  [18].
Fold change (FC) values were obtained, along with P-values and adjusted P-values (false discovery rate; FDR) for the 118 genes evaluated. The batch effect was taken into account by adding batch information to the model as a covariable. For data visualization and later analyses, normalized log-TMM values were obtained and the variability due to batch effect was removed using the removeBatchEffect method implemented in LIMMA R package. All analyses were performed with R/BIOCONDUCTOR (3. 10) running on R version 3. 6. 0, (https://www. r-pro ject. org/).
Construction of a predictive signature based on gene expression Raw counts were normalized to transcripts per million (TPM), as it is a more appropriate unit to compare expression across samples than normalized counts from other methods such as DESEQ2 and EDGER. Of 140 cases included, only one patient with PFS = 0 was removed from the predictive gene signature analysis. Univariate Cox regression analysis using the survival package was performed to find individual genes whose expression was significantly associated (P < 0. 05) with PFS. Additionally, patients were divided, for each gene, into two groups, using an optimal cutoff from the survminer package which maximized the separation of Kaplan-Meier curves of every group, and only those genes with significantly different curves (P < 0. 05) were further retained.
In line with these observations, the combination of olaparib with trabectedin has been tested in a phase 1b clinical trial in patients with advanced and non-resectable bone and STS (ClinicalTrials. gov, number NCT02398058). Of 50 patients recruited, 7 had a PR, suggesting that this combination has antitumor activity  [40] ; however, patient selection based on PARP1 and/or PARP3 expression levels should be considered for further clinical trials with this combination. In the study presented here, only BRCA1 high expression was significantly associated with shorter PFS and OS, while ERCC1 and ERCC5 expression, which were previously associated with good prognosis to trabectedin  [13, 15, 16] , did not had a significant impact in survival. The different methodology used for gene expression analysis could justify the distinct results obtained in terms of survival observed for these two genes; however, it is important to mention that HTG quantitative nuclease protection assays for gene detections had been reported to have similar sensitivity as qPCR  [41, 42].
Acknowledgements This study was funded by the  Spanish Group for Research on Sarcoma (GEIS)  and partially by Phar-maMar. The authors would like to thank the  GEIS data center  for data management. The authors also thank the donors and the  Hospital Universitario Virgen del Rocıó-Instituto de Biomedicina de Sevilla Biobank (Andalusian Public Health System Biobank and ISCIII-Red de Biobancos   PT17/0015/0041 ) for part of the human specimens used in this study. David S. Moura  is recipient of a  Sara Borrell postdoctoral fellowship  funded by the  National Institute of Health Carlos III (ISCIII)  ( CD20/00155 ).
Visualizing and characterizing microvascular abnormalities with optical coherence tomography angiography (OCTA) has deepened our understanding of ocular diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Two types of microvascular defects can be detected by OCTA: focal decrease because of localized absence and collapse of retinal capillaries, which is referred to as the non-perfusion area in OCTA, and diffuse perfusion decrease usually detected by comparing with healthy case-control groups. Wider OCTA allows for insights into peripheral retinal vascularity, but the heterogeneous perfusion distribution from the macula, parapapillary area to periphery hurdles the quantitative assessment. A normative database for OCTA could estimate how much individual's data deviate from the normal range, and where the deviations locate.
Here, we acquired OCTA images using a swept-source OCT system and a 12×12 mm protocol in healthy subjects. We automatically segmented the large blood vessels with U-Net, corrected for anatomical factors such as the relative position of fovea and disc, and segmented the capillaries by a moving window scheme. A total of 195 eyes were included and divided into 4 age groups: < 30 (n=24) years old, 30-49 (n=28) years old, 50-69 (n=109) years old and >69 (n=34) years old. This provides an age-dependent normative database for characterizing retinal perfusion abnormalities in 12×12 mm OCTA images. The usefulness of the normative database was tested on two pathological groups: one with diabetic retinopathy; the other with glaucoma.