dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by grants from  Human Frontiers Science Program (HFSP)  and  John Simon Guggenheim Foundation  (to F. G. D. F. ),  National Institute for Translational Neuroscience (INNT/Brazil) , the Brazilian funding agencies  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  and  Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)  (to S. T. F. and F. G. D. F). H. M. M, G. S. S. S, B. C. M, J. R. C, V. S. M. C. , G. C. K. , J. T. S. F and J. H. L were supported by  CNPq  and  FAPERJ  predoctoral and postdoctoral fellowships. This work has been partially developed and supported through the  LABEX (excellence laboratory, program  investment for the future) DISTALZ ( Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease ) and the  Hospital University Federation (FHU) OncoAge (FC).
Searching medical guidelines Because published guidelines were not present inKore-an databases, other databases such as Ovid-MEDLINE, Ovid-EMBASE, and the National Guideline Clearinghouse were used to search for medical guidelines. The words, "trauma, severe trauma, initial management, guideline, resuscitation, hypovolemic shock, initial management" were selected as search terms for finding medical guidelines. The results were reviewed by the development committee and evaluated by the working committee.
Evidence summary To determine the use of MTP, the NICE guideline utilized nine retrospective studies comparing clinical risk scores, ABC scores, TASH scores, PWH scores, McLaughlin scores, emergency transfusion scores, the shock index, ATLS, and categories of shock. However, much is left to be desired in the study, as the proportion of missing data was very high in many studies, and the confidence interval was wide. One meta-analysis was conducted with ABC scores, but the evidence level was not significantly high. In the studies of Brockamp et al. [30] , Cotton et al. [31, 32] , Krumrei et al. [33] , and Mitra et al. [34, 35] , ABC scores, systolic blood pressure, the presence of a penetration injury, blood pressure lower than 90 mmHg and heart rate over 120 beats per minute at the arrival of the emergency ward, and FAST were measured to determine the use of MTP.
ACKNOWLEDGEMENTS We thank to members of clinical research group for this study ( Kang Hyun Lee ,  Seungje Go ,  Oh Snag Wown ,  Kim Seongyup ,  Dae Sung Ma ,  Jeong-Ho Yoon ,  Kyung-Hag Lee ,  Woo Jin Jung ,  Young Goun Jo ,  Cho Jae-Woo ,  Kang Kook Choi ,  Mi Young Choi ,  Jeong-Joo Hwang ). We are very grateful to ' Baek seung in ' for translating this research.
Code Description We designed 3D thermo-mechanical numerical models of a convergence zone, in which oceanic subduction is followed by continental subduction and collision. Models were performed using the finite element code CITCOM  (Moresi & Gurnis, 1996; van Hunen et al. , 2005; Zhong et al. , 2000). This code solves for conservation of mass, composition, momentum, and energy. As we consider an incompressible viscous medium and ignore density variations everywhere but in the driving force of the momentum equation (Boussinesq approximation), the conservation of mass is therefore described with a divergence-free velocity field: ∂V x ∂x þ ∂V y ∂y þ ∂V z ∂z ¼ 0; (1) where V x , V y , and V z correspond to the three components of the material velocity vector in the 3D (x, y, z) Cartesian coordinate system.
Bioinformatics BCL2FastQ2 Conversion Software (version 2. 20, Illumina Inc. ) was used to demultiplex data and convert the sequencing files to reads in FASTQ format. Paired-end reads were trimmed for adaptors with Trimmomatic  (42)  and filtered by quality (phred ≥ 30) and length (≥ 35). Filtered paired-end reads were then aligned to the reference human genome (hg38) using Bowtie2  (43). The Bowtie2 output was used as input for Telescope software  (44)  to define and quantify retrotransposon elements (HERV and LINE-1) using annotations, previously described and available at https://github. com/mlbendall/telescope_annotation_ db/tree/master/builds. In addition, we mapped all reads to the human genome (hg38) with STAR  (45). The output was then used for gene quantification using Htseq-count  (46)  with Gencode version 31.
Telescope and Htseq-count table outputs were merged and genes or retrotransposon elements which were present in less than two samples were removed. The processed output was then used to calculate HERVs and genes differentially expressed genes in breast cancer from HIV-positive vs. HIV-negative patients using DESeq2, Wald-test  (47). Volcano plots were drawn with the Bioconductor EnhancedVolcano (https://github. com/kevinblighe/ EnhancedVolcano). HERVs and host genes with adjusted p-value < 0. 05 and absolute(log2FoldChange) > 1. 0 were considered differentially expressed genes (DEGs) and results were shown using pheatmap and ggplot R packages. HERV localization in the human genome was visualized with integrative genomics viewer (IGV) software (Broad Institute, Cambridge, MA) using Gencode version 31 and HERV annotations as previously described  (41).
We also analyzed nearby HERVs to genes associated with breast cancer development (Supplementary Table  1 )  (48) (49) (50). The HERV localization was defined and HERV and nearby gene expression results were shown in graphs using ggplot R packages. Gene set enrichment analysis (GSEA) was performed with WebGestal (available at http://webgestalt. org/) using Reactome as functional pathway database  (51). We also analyzed the differentially immune gene expression, including CD4 and CD8 (CD8A and CD8B) mRNA in the samples to check T-cell-derived material in transcriptome data. The graphs were constructed using the ggplot R and the adjusted p-value was calculated from DEseq2 analysis performed using the Wald-test. All data generated in the study has been deposited in GEO under the accession number GSE149156.
RESULTS We analyzed RNA-seq data of breast tissue from women with IDCA HIV-positive and -negative breast cancer. Samples from both HIV-positive and -negative groups were predominantly of luminal subtype and of advanced stage at diagnosis (Table  1 ). The one case of triple-negative IDCA was not of medullary subtype, and there was no morphologically apparent increase in tumor-infiltrating lymphocytes in any of the samples. No patient received neo-adjuvant therapy before resection. It is important to note that, although gene expression data was available, IDCA subtyping was performed using immunohistochemical analysis (IHC) of estrogen receptor and progesterone receptor and a combination of IHC and in situ hybridization analysis, as necessary. The transcriptomic analyses showed no significant difference in expression of ESR1, ESR2, PGR, or ERBB2 by RNA-Seq analysis.
FUNDING The work was supported in part by  US National Institutes of Health  grants:  CA206488  (DN) and AI076059 (DN), and by the  Brazilian Research Council  grant # 305765/2015-9  (MS). Additionally, this work was partially funded by the  American Society of Tropical Medicine & Hygiene  through the  Benjamin H. Kean Travel Fellowship in Tropical Medicine (JM). GC is the recipient of a Ph. D. fellowship by the  Brazilian National Cancer Institute (INCA). MM supported in part by the  Department of Medicine, Fund  for the  Future program  at  Weill Cornell Medicine , sponsored by the  Kellen Foundation.
One of the most unusual aspects of the TP53 gene is the high frequency of somatic and germline missense mutations that occur in it, which is particularly unusual for a tumor suppressor gene 4. This specific selection is believed to be linked to the antimorphic and/or neomorphic activities of the variants that transform the tumorsuppressive wild-type TP53 into a mutant oncogene. An analysis of the most recent release of the UMD_TP53 database in the context of the TCGA showed that nearly any of the 393 residues of the TP53 protein can be found mutated in a human tumor, albeit at very different frequencies 5. Determining the impact of all these mutations on protein functions will thus be essential. Multiple prediction methods are currently available, using information related to phylogenetic sequence conservation, amino acid physicochemical properties, functional domains and structural attributes 6.
between the loss of TP53 activity and the frequency of TP53 variants in human cancer  10. That information was quickly included in a number of databases and is currently the most informative and useful parameter to infer TP53 variant pathogenicity. More recently, this functional analysis approach was extended to a larger panel of TP53 variants in two new studies, each employing different read-outs to infer TP53 variant activities  11, 12. In both of them, TP53 variants were expressed in mammalian cells and activities such as growth arrest or apoptosis were analyzed. Whether data from these three functional analyses, each using different read-outs, are in agreement for each TP53 variant has not yet been determined. In the present study, we performed an in-depth correlation analysis using these three large datasets of TP53 activities.
Although the first saturation mutagenesis of TP53 performed by Kato et al. included only SNS, the two recent studies performed by Kotler et al. and Giacomelli et al. included SNS, DNS and TNS in either the core domain of TP53  11  or the entire TP53 ORF  12. Variants in the core domain of TP53, whether they result from SNS or DNS and TNS, lead to a loss of function of the TP53 protein (Fig. 1A ). The absence of DNS and TNS variants in the UMD_TP53 database (or any other cancer database) is coherent with their rarity in cancer genomes. Relation between TP53 loss of activity and frequency in the UMD_TP53 database. The UMD_ TP53 database has been steadily updated since its creation in 1991. It is furthermore regularly and carefully curated to remove artifactual data. UMD_TP53 was the first database to integrate the functional data from Kato et al.
13, 14. A full analysis of the 2018 release of the database was recently published in collaboration with the TCGA and will not be reiterated here. The new release of the database (100K_UMD TP53 database, October 2019) includes more than 100,000 TP53 mutations and a novel feature developed for the analysis of TP53 variants, the cancer shared dataset (CSD), which comprises only variants highly likely to be pathogenic  15  (see "Methods"). The CSD includes 258 TP53 SNS common to four different large independent sequencing datasets (see "Methods"). In addition to its hotspot variants found highly frequently in various types of cancer, the CSD also includes less common variants that would have been missed in a selection based solely on mutation frequency. The 100K_UMD TP53 database now includes the data of the two recent saturation mutagenesis studies performed by  Kotler et al.
and Giacomelli et al. 11, 12. The database thus comprises 12 different read-outs for each TP53 variant, with 8, 3 and 1 read-outs resulting respectively from the works of Kato et al. , Giacomelli et al. , and Kotler et al. (see "Methods"). Analysis of the loss of activity of TP53 variants in relation to their frequency in the database is shown in Fig. 1B , Supplementary Fig. 2. First, for the 12 different readouts, we confirmed a clear correlation between the TP53 variants' losses of activity and their frequency in human tumors. Second, TP53 single nucleotide variants that are never found in human cancer displayed a strong propensity for keeping their wild-type activity and had profiles similar to those of uncommon cancer variants. Third, the profiles of TP53 variants included in the CSD were the same as those of frequent TP53 variants, despite different frequency distributions in human tumors (Fig.
) showed strong correlation, with Pearson R (R) values between 0. 52 and 0. 72 (Fig. 2 ) and a highly significant p value (p < 0. 001 for all binary comparison, Supplementary Fig. 3 ). Depending on the promoter used to report TP53 loss of activity, the correlation between the yeast-derived data from Kato et  = 0. 54, 0. 66 and 0. 68 respectively for the three readouts). In binary comparisons, data tended to be distributed in two groups (Fig. 3  and Supplementary Fig. 3 ). To clarify the presentation, variants markedly present in the CSD datasets were colored red and those found infrequently in the database green (Fig. 4A , Supplementary Fig. 3 , Supplementary Video 1). Most CSD variants clustered in the lower left corner of the graph, confirming minimal remaining activity. In contrast, On the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
The region around codon 106 was shown to contain a SF2/ASF motif that recruits the serine/arginine-rich splicing factor 1 (SRSF1) involved in pre-mRNA splicing (Supplementary Fig. 6 ). The C > G mutation associated with p. S106R was strongly predicted to inactivate this site and therefore to impair TP53 splicing (Supplementary Fig. 6 ). Two other CSD outlier TP53 variants, p. R181H and p. R181C, were located at the same position. Recent structural studies have shown that the TP53 molecules Table  1. Alternative loss of function of the 7 CSD TP53 variants. 1 The sequence nomenclature used for TP53 variants in this work is in accordance with the Human Genome Variation Society's guidelines using the NM_000546. 5 transcript sequence and the full-length protein NP_000537. 3. 2 TP53 functional data were scaled in the range of 0 to 1 with 0 corresponding to the lowest activity of TP53 variants.
The TP53 gene is the most frequently mutated gene in human cancer but it is also the gene that sustains the largest diversity of single nucleotide variants, with more than 3000 different missense variants identified so far. Although hotspot variants have been shown to be truly pathogenic, the situation is more ambiguous for rare variants. The UMD database includes 4500 TP53 missense variants that have been described only once or twice and whether they are true pathogenic variants, rare passenger mutations or sequencing artifacts is currently unknown. The greatest advantage for TP53 is that the read-out of its functions can be easily monitored. In 2003, C. Ishioka's group published a seminal paper wherein they described the first large-scale analysis of TP53 using a transactivation assay developed in yeast  9.
Their functional data, unique for a cancer gene, have been widely used to increase the prediction of TP53 variants  10. Nevertheless, multiple studies have shown that the relation between the transcriptional activity of TP53 and the effects on biological functions, such as growth arrest or apoptosis, is not straightforward  27. The two recent large-scale analyses performed in mammalian cells have greatly advanced the identification of TP53 variants that sustain a loss of function  11, 12. For the present analysis, we developed upon the CSD, a specific TP53 benchmark dataset that includes only highly likely pathogenic variants  15. As variant recurrence is one of the strongest indicators of pathogenicity, using four independent sources of TP53 variants, each deploying a different analysis methodology, should negate methodological issues for the specific selection of pathogenic variants.
We found excellent correlation between the three saturation studies used as a base for the present study, i. e. , those of Kato et al. , Kotler et al. , and Giacomelli et al. We showed that the yeast assay using the NOXA promoter was highly correlated with the two studies performed in mammalian cells, thus confirming the importance of this gene in the tumor suppression function of TP53  28. Furthermore, our analysis showed that the vast majority of the variants in the CSD sustain a loss of function in the TP53 protein. The inclusion of data derived from Figure  6. TP53 RNA expression differs in tumors depending on TP53 mutation status. Box-and-whisker plots show the interquartile ranges (boxes), median values (horizontal lines inside the boxes), and full-range distributions (whisker lines) for TP53 RNA content according to the type of TP53 mutation.
RNA expression and TP53 mutational status values for multiple tumor types were extracted from cBioPortal (see "Methods"). mammalian cells increased the accuracy of the analysis with the identification of leaky pathogenic variants that were not identified in the yeast assay. In addition, several pathogenic variants found at high frequency in the various databases did not display any prominent losses of function. Of course, gene mutations can impair mRNA, and RNA splicing and/or stability can be damaged by multiple synonymous or nonsynonymous mutations located in exons or introns  19, 29. Several motifs localized in exons, such as ESEs or ESSs, are known to modulate gene splicing. Most computational predictive algorithms rely on the properties of the protein, and functional assays force protein expression using unspliced cDNA constructs.
It is therefore possible that some TP53 loss of function is due to RNA expression and not a potential inactivation of protein. Using RNA data expression from tumors with matched TP53 status, we showed that some CSD TP53 variants without obvious loss of protein activity are in fact spliced variants associated with a loss of TP53 RNA expression. The number of variants altering TP53 RNA is likely underestimated; although functional assays may report inactivity for the protein, it is possible that this latter was never expressed due to the lack of RNA expression. Increasing the number of tumors with matched genomic and transcriptomic data will be the only way to improve the detection of this type of variant and define more accurately whether the ultimate target of the mutation is the mRNA or the protein.
This will be essential for clinical studies targeting the TP53 protein as TP53 null tumors, whether at the DNA or RNA level, will not benefit from them. Benchmark variants are required for developing, optimizing and assessing the performance of sequencing and bioinformatics methods. For TP53, benign variants are generally identified in ClinVar, dbSNP or gnomAD whereas their pathogenic counterparts usually emanate from cancer mutation databases. Unfortunately, both sets of data are generally not accurate. We have previously shown that gnomAD is heavily contaminated by pathogenic TP53 variants due to the high frequency of de novo mutations in the human population  15. Similarly, as shown in previous studies, cancer mutation databases can include passenger mutations, unidentified low frequency SNP and artifactual data  30, 31.
The use of the cancer shared dataset (CSD) in the present study alleviated some of these problems and did not bias the choice of pathogenic variants toward hotspot variants. Moreover, this approach can capture pathogenic TP53 variants that would be missed by functional analysis. The robustness of the CSD and the availability of multiple independent cancer datasets such as TCGA, MSKSCC, ICGC or the Locus-Specific Database (LSDB) could be easily mined to define multiple, gene-specific CSDs. Such resources would contribute to pinpointing rare pathogenic variants and also curating fast-growing population databases such as gnomAD that are essential for genetic analyses.
Methods Database and datasets used for the analysis. The most recent issue of the UMD database (100K_ UMD TP53 database) was released in November 2019 with 125,130 TP53 mutations retrieved from the literature. Of them, 40,624 were identified in studies using conventional Sanger sequencing and 75,339 in studies using NGS. Variants from publications using other methodologies or combining conventional sequencing and NGS were not used in the present study. Functional data used in the present study. Functional data from the works of Kato et al. 9  , Kotler et al. 11  , and Giacomelli et al. 12  were used for the present study. Transcriptional activity from the work of Kato et al. in yeast transformants containing a p53 cDNA and a green fluorescent protein reporter plasmid was assessed with eight different promoters for 2315 TP53 variants.
These yeast data are currently the most widely used criteria to define the pathogenicity of TP53 variants  10. Kotler et al. generated a library of TP53 variants from the core domain of TP53 (residues 100 to 300) that were analyzed using a range of assays both in mammalian cell lines and in mice. Only data obtained from the over-expression of 9833 TP53 variants in H1299 cells was used in the present study as only a subset of their TP53 variants was used for other read-outs. Data from TP53 variants with two or more mutations in different codons were discarded as were nonsense and frameshift variants to focus our analysis on missense variants with one, two or three substitutions in a single codon. Data from Giacomelli et al. included 7469 TP53 variants observed across the whole TP53 protein.
The cancer shared dataset (CSD). The CSD concept was developed to optimize the representativeness of pathogenic variants in a dataset  15. Previously, training sets used for defining TP53 pathogenicity were based on either the whole set of mutations found in various databases or a selection of the most frequent TP53 variants with an arbitrary cut off. Both methods showed bias: The first one included the potential passenger and artifactual variants that plague the various cancer databases and the second did not account for infrequent pathogenic variants resulting from rare genetic events such as T > A transversions. To circumvent these issues, we developed a novel strategy involving the extraction of TP53 mutation data from four non-overlapping databases: (i) TP53 variants from the UMD database that were harvested from studies using exclusively conventional Sanger sequencing for diagnostics; (ii and iii) data from the TCGA and MSKCC studies and downloaded from cBioPortal (https ://www.
cbiop ortal. org/, October 2019); and (iv) data from the ICGC portal and downloaded from the ICGC website (https ://dcc. icgc. org/, data release 26 December 2017). Only missense variants found at least once in each dataset were included in the CSD. Compared to the CSD described in our previous study, we added another parameter in this selection, i. e. , the choice of tumors carrying only a single TP53 mutation. That additional parameter removed the passenger mutations frequently associated with hotspot variants. The CSD used for the present study thus included a core of 258 recurrent missense TP53 variants found at least once in each database. As the four datasets were derived from independent studies using different patients and different methodologies, it was highly likely that the 258 shared variants were true recurrent pathogenic variants.
This dataset was validated in a previously-published report  15. It includes both hotspot and less frequent variants and is more representative of the heterogeneous frequency of TP53 mutation in human cancer. In silico analysis of TP53 splice variants. The Human Splicing Finder (HSF; https ://www. umd. be/ HSF/) system combines 12 different algorithms to identify and predict the effects of mutations on splicing motifs including the acceptor and donor splice sites, the branch point, and auxiliary sequences known to either enhance or repress splicing: exonic splicing enhancers (ESE) and exonic splicing silencers (ESS). These algorithms are based on position weight matrices, the maximum entropy principle or a motif comparison method. For each of them, we defined a consensus value threshold and a score variation threshold, based on literature datasets.