dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and future work To summarize and to answer the questions we initially asked: this study aimed at extracting and characterizing the 3D structure of eddies in the Arabian Sea with a combined approach using altimetric data and Argo floats. Eddies were detected in the whole Arabian Sea and in adjacent gulfs using AMEDA, but the distributions of Argo floats and surface drifters were heterogeneous. With this dataset, we confirmed previous studies concerning the distribution of the important water masses in the different sub-basins of the region, and discussed the mean stratification one may find in eddies depending on the area (our first question). We then used a composite approach to find the mean 3D structure of mesoscale eddies of the NAS (our second question). We focus only on surface-intensified CE and subsurfaceintensified AE, with a radius R D < R < 3 R D , as these two categories had the greatest sampling/least error.
Acknowledgments This work was funded by the  Direction Générale de l'Armement (DGA)  via a full grant for  Charly de Marez 's PhD. We gratefully acknowledge  B. Le Vu  and  A. Stegner 's help, who provided the eddy detection algorithm AMEDA and its output on the Arabian Sea. AMEDA has been developed under the funding of the  ANR - Astrid  Project  DYNED-Atlas  ( ANR 15 ASMA 0003 01 ). The altimeter products were produced by SSALTO/DUACS and distributed by AVISO, with support from  CNES. This work is also a contribution to PRC 1069 'Meso and submesoscale vortices in the Atlantic and  Indian Ocean ', funded by  CNRS  and  RFBR. The authors thank the five anonymous reviewers whose remarks widely improved the contents of this paper. In order to encourage readers to bring the 3D generic structure of eddies to life, the  3D  field of the different quantities we computed are available with this paper (de  Marez  et al.
INTRODUCTION The p53 tumor suppressor protein is a transcription factor that inhibits cell division or survival in response to various stresses, thus acting as a key fail-safe mechanism of cellular anti-cancer defenses  (Kastenhuber and Lowe, 2017; Vousden and Prives, 2009). Frequent mutation of TP53 in human cancers was initially described by Vogelstein and Minna and colleagues  (Baker et al. , 1989; Nigro et al. , 1989; Takahashi et al. , 1989)  and was further cataloged in TP53 mutation databases  (Bouaoun et al. , 2016; Caron de Fromentel and Soussi, 1992; Hollstein et al. , 1991; Leroy et al. , 2014). These compilations established that the pattern of TP53 mutations in several cancers was linked to carcinogen exposure and provided key insights into key p53 functional domains and the role of p53 in cancer etiology  (Caron de Fromentel and Soussi, 1992; Hollstein et al.
, 2008; Matheu et al. , 2008; Soussi and Kroemer, 2018; Wasylishen and Lozano, 2016). Thus, a more accurate readout of p53 function in human cancers not based strictly on mutation of the TP53 gene might lead to more accurate prognostic predictions. We have capitalized on the integrated The Cancer Genome Atlas Network (TGCA) approach in which many tumors in many cancer types have been simultaneously examined on five independent data platforms, along with extensive clinical annotation, to develop a comprehensive picture of the role of TP53 mutation. We broadly show how TP53 mutation confers an array of genomic, transcriptomic, and proteomic changes in cancers that has a major role in regulating clinical outcomes.
RESULTS TP53 Mutation Profile in TCGA Dataset Is Similar to That of Current TP53 Databases We analyzed TP53 mutations in whole exome sequences of 10,225 TCGA patients across 32 different cancer types. We identified 3,786 patients with TP53 mutations. TP53 mutation frequencies by cancer type were variable, with ovarian cancer and uterine carcinosarcoma showing greater than 90% incidence, and seven cancer types displaying less than 5% incidence (Figure  1A ). The spectrum of TCGA TP53 mutations obtained by whole-exome sequencing was broadly similar to that detected by conventional Sanger sequencing. Notable hotspot mutations at codons 175, 248, and 273 in TCGA dataset were also the most frequently mutated TP53 codons observed in the Sanger dataset from the UMD_TP53 database (36,350 patients with TP53 mutations, 4,300 different variants)  (Leroy et al.
, 2014)   (Figures 1B, 1C, S1A, and S1B). Most TCGA TP53 mutations occurred in the central DNA-binding domain, encompassing exons 4-8, and aligned with those reported in the Sanger dataset of the UMD_TP53 mutation database. In both groups, missense ($40%), frameshift deletion ($20%), and frameshift insertion ($10%) comprised roughly 70% of all mutations  (Figures S1A and S1B). Less frequent were in-frame deletions and insertions, synonymous mutations, and splice-site mutations (Figure  S1B ). In both datasets, mutation types were enriched in the central domain, although the small in-frame deletions and insertions were surprisingly dispersed (Figure  S1A ). The TCGA mutation dataset also includes infrequent TP53 variants in the two newly discovered alternative TP53 exons, exon beta and gamma  (Figures 1C and S1B).
Whether these variants are true somatic pathogenic variants or rare, benign polymorphisms is unknown because this region of the TP53 gene has not been thoroughly analyzed. We combined TP53 single-nucleotide variants (SNVs) from TCGA and UMD_TP53 datasets to assemble a graph showing the fraction of nine possible codon changes for each of the 393 TP53 triplets encoding the full-length p53 (Figure  1D ). The central domain shows at least six of the nine possible codon mutations in almost every location, whereas sites in the N-and C-terminal domains show an absence of any codon changes. The N-terminal mutation-free region correlates closely with the MDM2-binding domain of p53. Because MDM2 is an E3 ubiquitin ligase that mediates p53 degradation, mutation of this binding site should stabilize p53, rather than inactivate it.
A mutationresistant C-terminal domain was also noted, but the mechanism for mutation avoidance is unclear. The absence of variants at codon 114 in loop L1 confirms a study demonstrating that TP53 variants at this position have little or no functional effect  (Zupnick and Prives, 2006). TCGA effort identified a total of 384 previously unreported variants, most of which were frameshift insertions and deletions, although 69 unreported SNVs were noted (Figures  S1C-S1E ). These SNVs were more frequently observed in exons 2-4 and 10-11 (Figure  1C , bottom panel), likely because historical TP53 mutation studies generally sequenced only exons 5-8 or 5-9. The availability of functional data for most missense TP53 variants is an unusual feature among cancer genes. In one study,  Kato et al. (2003)  measured the transcriptional activity of TP53 variants in yeast assays using eight different TP53 response elements.
A clear correlation between TP53 variant loss of activity and its frequency in human tumors has been shown  (Soussi et al. , 2005). Recently, two large-scale analyses of TP53 variants were performed in mammalian cells using cellular assays measuring variant effects on cell proliferation as a quantitative functional readout  (Giacomelli et al. , 2018; Kotler et al. , 2018). Using the  Kato et al. (2003)  data, we confirmed the clear correlation between TP53 loss of activity and the high-frequency TP53 variants in cancer (Figures  S2A  and S2B ). TP53 variants from TCGA dataset are also clearly associated with a loss of activity for the eight different TP53 response elements (see Figure  S2B  for the CDKN1A/p21/WAF1 and AIP1 promoter results). Similarly, we showed that functional analysis data of  Kotler et al.
(2018)  and  Giacomelli et al. (2018)  correlate strongly with the frequency of TP53 variants in human cancer and that TP53 variants from TCGA dataset are generally associated with deleterious TP53 variants (Figure  S2B , bottom graphs). The previously unreported TCGA TP53 variants showed a relatively high residual p53 activity in all functional assays (Figure  S2A  and red boxes in Figure  S2B ). With respect to the p53 three-dimensional structure, deleterious TP53 variants were clustered in specific regions of the p53 protein, such as loop L2 and L3, and part of a Helix Loop Helix, including sheet S10 and helix H2 (Figure  1E ). These domains are highly enriched for mutations in the UMD_TP53 Mutation Database and TCGA dataset and variants exhibit major functional consequences at virtually every codon (Figure  1E ).
To assess TP53 allele status in TCGA dataset, we performed an integrated analysis of TP53 mutant tumors using two data platforms: exome sequence data and copy number data. First, we examined the 10% of TP53 mutant tumors (381 of 3,786 total tumors with TP53 mutations) with two distinct TP53 mutations. Functional analysis of the TP53 mutations revealed that both mutations were usually inactivating (Figure  2A ). To determine whether the two TP53 mutations were on the same allele (cis) or on different alleles (trans), we examined individual DNA sequencing reads that were in the same exon and 6-75 nucleotides apart. Those tumors with individual reads consistently displaying both mutations were considered cis for TP53 mutations, and those tumors consistently exhibiting only one of the two mutations were considered trans.
Representative read files for cis and trans tumors are shown in Figure  2B. For those 35 bi-allelic (A) p53 functional analyses in some TCGA tumors with two TP53 mutations. Heatmaps show relative transcriptional activity of TP53 variants compared with wild-type TP53 based on data in  Kato et al. (2003). Each column shows a p53 transcriptional target, and each row shows a TP53 variant. W, WAF1 (CDKN1A); M, MDM2; B, BAX; 14, 14-3-3 sigma (SFN); A, AIP (TP53AIP1); G, GADD45A; N, NOXA (PMAIP1); P, p53R2 (RRM2B TP53 mutation tumors with tightly linked mutations, 28 (80%) were trans and 7 (20%) were cis (Figure  2C ). Assessment of these 35 tumors for copy number status revealed that 6 of the 7 cis tumors exhibited TP53 copy number loss, while 27 of the 28 trans tumors retained a diploid TP53 status (Figure  2C ).
Searches in the Pathway Commons database identified known pathway interactions between TP53 and a fraction of the mutually exclusive genes (Figure  6B ). MDM2, KRAS, and ARID1A are mutually exclusive with TP53 in more than one cancer type and their functional relation to TP53 has been characterized. In addition, a group of genes demonstrated mutual exclusivity with TP53 in at least one cancer type but searches in signaling databases did not capture a known functional relationship to TP53. Those genes are grouped without a connection to  With respect to TP53 co-occurring mutations, we noted that IDH1 and ATRX were highly significant in LGG and GBM (Figure  S6A ), as has been previously reported  (Kannan et al. , 2012; Liu et al. , 2012). In UCEC, TP53 and PPP2R1A, a negative growth regulatory subunit of protein phosphatase 2, have highly significant co-occurring mutations, as previously noted  (McConechy et al.
DISCUSSION TCGA-sponsored, large-scale analyses of 32 different cancer types using five high-throughput data platforms integrated with relevant clinical data has provided a rich dataset from which to derive important insights  (Weinstein et al. , 2013). Despite more than 90,000 papers that have been published on p53, previous studies have generally used tumor samples of one cancer type and perhaps one or two experimental methodologies to examine p53 effects. TCGA large-scale, integrated, multi-data platform approach facilitates extraction of statistically significant patterns that might be obscured by background noise associated with smaller, lessdiverse datasets. The integration of two data platforms, exome sequencing and DNA copy number, was particularly useful in the analysis of individual TP53 alleles in both wild-type and mutant TP53 tumors.
MUT TP53 cancers showed enhanced expression of cell-cycle progression genes and S-phase-promoting E2F target genes. The E2F results are consistent with the findings that wild-type p53 may indirectly suppress E2F1/2 through CDKN1A (p21 CIP1 ) activation, which, in turn, results in suppression of RB1 phosphorylation and cell cycle inhibition  (Polager and Ginsberg, 2009). p53 may also have suppressive effects on a number of E2F target genes, including FOXM1, one of the highest differentially upregulated proteins in the MUT TP53 RPPA dataset  (Barsotti and Prives, 2009). Studies on TP53 mutations as a prognostic marker have been historically mixed, and many variables may contribute to that  (Robles and Harris, 2010). One problem in these clinical studies is that there are mutationally independent mechanisms to inactivate the p53 signaling pathway.
Other mutations not considered bona fide mutations were non-exonic mutations in the 5 0 UTR, the 3 0 UTR, and introns. In using the UMD_TP53 database for comparison purposes, minimal genomic information such as genomic coordinates and genetic events were extracted from each dataset to define a correct annotation using HGVS recommendations. In a second step the variant annotation were validated using the Name Checker tool developed by Mutalyzer (https://mutalyzer. nl/) (Wildeman et al. , 2008). Mutalyzer handles all types of variations that can target the TP53 gene, such as substitutions, insertions, duplications, deletions, or more complex insertion or deletion. The current version of Mutalyzer (Mutalyzer 2. 0. 26) uses the stable NCBI sequence NG_017013. 2 as a reference for TP53. Assessment of TP53 Allele Status through integration of TP53 mutation and copy number data For six cancer types with high numbers of TP53 mutations (UCEC, LGG, OV, LUAD, LUSC, HNSC) we downloaded all TP53 mutations for each TP53 locus and stratified each tumor into one of two categories: (a) tumors with two or more distinct TP53 mutations, or (b) tumors with one TP53 mutation (tumors with no TP53 mutations were not further analyzed).
Several cancer types had less than five patients with TP53 mutations and these were excluded from analysis. The 500 most differentially upregulated genes (determined by lowest t test p values) in wild-type and mutant TP53 tumors of each cancer type were then ranked (Table  S3 ). Genes highly upregulated in wild-type TP53 tumors were enriched for known p53 target genes (Figure  4A ; Tables  S2  and S3 ). These were identified from a manually curated list of known p53 target genes from the literature (see Table  S3 ) This p53 target gene list contained genes identified from individual gene experimental studies as well as genes from global expression genomic studies shown to be consistently upregulated in multiple datasets  (Fischer, 2017). Pathway analysis was performed on both the 500 most upregulated genes in wild-type and mutant TP53 tumors of each cancer type by Gene Set Enrichment Analysis (GSEA)  (Mootha et al.
Development and Testing of a Mutant p53 RNA Expression Signature The mutant p53 RNA expression signature was based on the aggregated expression of four genes, CDC20, PLK1, CENPA, and KIF2C, in the TCGA PanCancer dataset. These four genes were almost invariably significantly overexpressed in all cancer types with mutant TP53. Moreover, these genes have been established as (a) targets of wild-type p53 repression, (b) promoters of cell cycle progression, (c) components of the G2/M checkpoint, and (d) established E2F targets (with the exception of CENPA). For each individual cancer type (with sufficient numbers of TP53 mutations) each of the four signature genes was ranked by RNA expression levels. These rankings were then added together to give a combined ranking of the four genes across all tumors of a given cancer type to give relative mutant p53 signature values.
, 2018)  by both top and bottom quartile splits and by median splits (comparison of tumors with signature values above and below the median signature values) (Table  S7B , last column). Values from these analyses correlated well with our earlier analyses. Likewise, we performed Kaplan-Meier plotter analyses on non-TCGA tumor RNA expression datasets, including breast, ovarian, lung, gastric, and two liver carcinoma datasets and found that these survival analyses closely matched our results with TCGA RNA expression datasets. The mutant p53 signature studies were developed from TCGA datasets subjected to fairly uniform data collection methods for each cancer type. So, application of the signature analysis to any newly accrued patient cancers might be subject to data collection methods and conditions quite different from the original TCGA data collection and analysis methods.
Red bars indicate SNVs observed for each codon, whereas green bars indicate SNVs not yet reported. Unmutated residues at codon 114 and hotspot residues at codon 175, 248, and 273 are shown with asterisks. TAD, transactivation domain; Pro, proline-rich domain; OLI, tetramerization domain; I to V, evolutionarily conserved domains. (E) TP53 variants in functional domains of p53. The DNA-binding surface of p53 protein is composed of two large loops (L2 and L3) stabilized by a zinc ion. Sheet S10 and Helix H2 are components of the LSH (Loop Sheet Helix) domain that includes loop L1. These various domains are essential for DNA recognition. Variants are depicted for each structural domain. Sanger, number of TP53 variants found in the Sanger dataset of the UMD_TP53 database; TCGA, number of TP53 variants in TCGA dataset.
ACKNOWLEDGMENTS We thank  Tajhal Dayaram  for editorial assistance on the manuscript. We are also grateful for funding from multiple sources:  NIH/NCI   U24 CA210950 ,  U24 CA210949 ,  U24 CA199461 ,  UM1HG008898 , and  P30 CA016672 ; and DoD/CDMRP  W81XWH-16-1-0237. Personal grants for T. S. to support the development of the UMD_TP53 database were received from  Radiumhemmets Forskningsfonder.
For each codon, nine SNVs are possible. Red bars indicate SNVs observed for each codon, whereas green bars indicate SNVs not yet reported. Unmutated residues at codon 114 and hotspot residues at codon 175, 248, and 273 are shown with asterisks. TAD, transactivation domain; Pro, proline-rich domain; OLI, tetramerization domain; I to V, evolutionarily conserved domains. (E) TP53 variants in functional domains of p53. The DNA-binding surface of p53 protein is composed of two large loops (L2 and L3) stabilized by a zinc ion. Sheet S10 and Helix H2 are components of the LSH (Loop Sheet Helix) domain that includes loop L1. These various domains are essential for DNA recognition. Variants are depicted for each structural domain. Sanger, number of TP53 variants found in the Sanger dataset of the UMD_TP53 database; TCGA, number of TP53 variants in TCGA dataset.
Here, we developed predictive models for the prevalence of hantavirus infections in small mammal assemblages by using ecological assemblage correlates and accounting for the phylogenetic relationships between potential host species. To calibrate the models with prior information, we analyzed data derived from the scientific literature. We hypothesized that small mammal assemblages with more phylogenetically diverse species would have fewer hantavirus antibody positive individuals, where this relationship may be mediated by the differences in rodent host species abundance between low and high species-richness assemblages. Furthermore, we expected that assemblages with a higher phylogenetic lineage divergence would contribute to a decrease in hantavirus antibody prevalence, as interspecific transmission is less probable.
Site Data Collection Species abundance distributions for rodent assemblages worldwide were gathered from peer-reviewed publications by using a combination of terms such as "Hantavirus, rodents, Hantavirus Cardiopulmonary Syndrome (HCPS), Hantavirus hemorrhagic fever with renal syndrome (HFRS), nephropathia epidemica (NE), hosts, and reservoirs" for articles published from 1971 to 2014 using Google, Google Scholar, Web of Science, and the US Centers for Disease Control and Prevention (CDC) homepage (www. cdc. gov). The CDC website provided relevant articles and related links including PubMed (www. pubmed. gov) and the U. S. National Library of Medicine (www. nlm. nih. gov). References listed in journal articles were also utilized to source the initial reports of hantavirus hosts, their related serotypes, and any human disease associations.
Site data were categorically grouped by continent (i. e. , Asia, Europe, North America, and South America), as reports of hantavirus surveys are often followed by human disease outbreaks and are monitored differently according to geographic and political regions  [20]. For inclusion in the study, all individuals within each assemblage must have been trapped in Sherman live traps or snap traps and tested for hantavirus antibodies, revealing at least one positive individual reported at each site. Additionally, for model analyses, we included only sites reporting a raw abundance equal to or greater than 20 individuals and no greater than 200 individuals in the sampling effort (20 ≤ n ≤ 200; statistical rationale described below). The following data were collected for each site: 1) the geographic identity (e.
Variable 4) was the Shannon diversity index (H'), which was calculated using EcoSim 700  [52]. A benefit of using H' is that, collectively among sites, it generally follows a normal distribution  [53]. Variable 5) was the Hurlbert's Probability of Interspecific Encounter (PIE) index, which is the probability that two randomly sampled individuals from an assemblage pertain to different species  [54]. This evenness index, or measure of heterogeneity  [55] , was used to combine species richness and dominance characteristics of the assemblage  [56]. We used EcoSim 700 to calculate PIE  [52]. Variable 6) consisted of the mean nearest taxon distance and mean pairwise distance, which were phylogenetic hypotheses created in R (package "Picante", http://artax. karlin. mff. cuni. cz/r-help/library/spacodiR/html/00Index.
html/) using a cytochrome-b (Cyt-b) phylogeny created with data obtained from GenBank (http://www. ncbi. nlm. nih. gov/genbank/) comprising all species included in the generated dataset ("MrBayes"; http://nbisweden. github. io/MrBayes/). This phylogeny was used to generate predictor variables (mean nearest taxon distance, MNTD; mean pairwise distance, MPD) from the relatedness of each species within an assemblage using the "bladj" algorithm of phylocom (http://phylodiversity. net/phylocom/)  [57] [58] [59]. The MNTD and variable 7)-the MPD values-were compared with a Student's t-test to determine if any differences exist for use in the analysis. Variable 8) consisted of the phylogenetic diversity totals (PD), which relate to the minimum total length of all phylogenetic branches required to span a given set of taxa on the phylogenetic tree  [60].
Statistical Analysis The relationship(s) between the response variables (seroprevalence calculated as TAS or MDHS) of the assemblage to the predictor variable(s) mentioned above were analyzed through stepwise linear regression models in R version 3. 5. 2 and the "leaps" package  [64]. Because of the way the predictor variables are derived, correlations between predictor variables were analyzed using the Pearson product-moment correlation (Pearson's r). Where correlations were present, principal component analysis (PCA) (e. g. , dimensionality reduction) and the R package "FactoMineR"  [65]  were used to unify, or compress, correlated variables into a single index (i. e. , eigenvectors), thus reducing the statistical issues of multicollinearity  [66, 67]. Additionally, all response and predictor variables were scrutinized, and violations in homoscedasticity were determined and corrected to fit a normal distribution.