dataesr/scientific-paragraphs-categorization huggingface dataset:
5 ha. All transects were performed as straight as possible while keeping a constant speed above ground of 0. 3 m•s -1 in average. Final Cut Pro 7 (Apple Inc. ) video editing software was used to remove the time intervals when the underwater vehicle stayed stationary, so multiple counting of the same item was avoided. Those sections with poor image quality, mostly due to sediment resuspension or excessive distance from the seabed, were also identified and subsequently removed from the statistical analyses. During the PROMARES cruise (dives 37-49), an average field of view (FOV) estimated at 3 m was selected for litter quantification (see details in  Tubau et al. , 2015). Dives performed with the manned submersible JAGO (dives 1-9 and 32-36) had an estimated average FOV of 2 m. For the remaining transects, which were performed with smaller ROVs that filmed from a closer distance to the seabed, a 50 cm average FOV was chosen to calculate litter density.
If properly recorded, by-catch data from fishing boat logbooks could bring complementary information to better assess the percentage of coral colonies broken or entirely removed by longlining yearly. It seems also plausible that loose longlines, ropes and net fragments, together with their loose ends and hanging sections, could be swung and moved down by the strong bottom currents measured in the Cap de Creus canyon head and beyond. This might favor secondary entanglement around already entangled colonies and on new colonies, with the associated damage this could cause.
e. the ARPERA dataset (Herrmann and Somot, 2008) , which is a dynamic downscaling of the ERA40 climate model reanalysis and of the ECMWF (European Centre for Medium-Range Weather Forecasts) model reanalysis since 2001. This forcing consists in daily averaged wind stress, solar flux, long wave net heat flux, sensible and latent heat flux and precipitation, leaving the diurnal cycle unresolved. The sea surface temperature was nudged towards the climatological one used in the NEMOMED8 model, ensuring the consistencybetween the two models. The two components of the horizontal current were then extracted from the daily outputs of the model, rotated to be along the WE and NS axis and interpolated on a regular grid. The simulations were run from the beginning of year 2000 to the end of 2013, and the daily currents for the month of February 2012 were averaged to illustrate the strength of bottom currents in the area.
Acknowledgements The authors would like to thank the crew of the research vessels and all the scientists and technicians that helped during the different surveys. We are grateful for the professionality shown by  Gavin Newman  (ROV pilot) and the members of the  JAGO Team Jurgen Schauer  and  Karen Hissman  during the acquisition of the video footage during the DEEP CORAL and  INDEMARES cruises. Generalitat de Catalunya autonomous government funding  to  CRG Marine Geosciences  is also acknowledged (Ref. 2017 SGR 315 ).
Location of the mid-point of the 68 underwater video transects carried out in Cap de Creus mid and outer shelf and submarine canyon to assess the occurrence, abundance and composition of seafloor litter. Dashed lines correspond to the isobaths defining the limits of the inspected domains: (LIFE07/NAT/E/000732) funded the 2009, 2010 & 2012 cruises on board R/V Garcia del Cid and the littoral surveys with Bleeper EVO in 2009 and 2013. Numerical simulations were performed using HPC resources from the CALMIP platform (French region Occitanie) under grant P09115. The SYMPHONIE model is distributed by the SIROCCO group Figure 1. northern shelf and southern shelf (80-150 m), canyon head (250-400 m), (https://sirocco. obs-mip. fr). This work also is a contribution of project IDEM upper canyon (400-1000 m) and middle canyon (1000-1600 m).
Methods data acquisition and normalization RNA-seq data from TCGA were downloaded and processed using TCGA Assembler (V. 2. 0. 3). Gene symbols were converted to official HGNC gene symbols and genes without symbol or gene information were excluded. RNA-seq data from as wide as possible sample set of the total of 33 available cancer types of tissue types primary solid tumor (TP), recurrent solid tumor, additional-new primary, metastatic (TM), additional metastatic and solid tissue normal were used to generate a pan-cancer normalized dataset. Normalization was performed within lanes, to correct for gene-specific effects (including GC-content and gene length) and between lanes, to correct for sample-related differences (including sequencing depth) using R package EDASeq (Exploratory Data Analysis and Normalization for RNA-Seq) (V.
2. 12. 0) and quantile normalized using preprocessCore (V. 1. 36. 0). After normalization, samples were extracted to obtain a single primary tumor tissue (TP) sample per patient. For skin cutaneous melanoma (SKCM) patients without available TP sample, a metastatic sample (TM) was included. Finally, the pan-cancer normalized dataset was filtered to remove duplicate patients and samples that did not pass assay-specific QCs as defined previously,  7  data were log2 transformed. Clinical data were sourced from the TCGA Pan-Cancer Clinical Data Resource. 25 Mutation rate and predicted neoantigen load were obtained from the recent immunogenomic analysis by Thorsson et al. 7  The dataset published by Ellrott et al was used for mutation data analysis. 26 Hematological cancer types Acute Myeloid Leukemia (LAML) and Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC) were excluded from the analysis.
Raw fastq files of datasets GSE78220  27  and GSE78220 28 were downloaded from National Center for Biotechnology Information (NCBI) storage replication adapter (SRA) servers, quality control and adapter trimming was performed using Trim_Galore (https:// github. com/ FelixKrueger/ TrimGalore). Reads were aligned to hg19 using STAR. 29 GenomicFeatures and GenomicAlignments Bioconductor packages were used to generate raw counts. The raw counts were normalized with EDASeq 30 and log2 transformed. The dataset phs000452. v2. p1  31  was downloaded, already normalized, from Tumor Immune Dysfunction and Exclusion (TIDE) (http:// tide. dfci. harvard. edu). TCGA segmented copy number variation (CNV) files were downloaded from Genomic Data Commons (GDC) (https:// portal. gdc. cancer. gov) to run Genomic Identification of Significant Targets in Cancer (GISTIC).
ICr classification Consensus clustering based on the 20 ICR genes (figure  1A ) was performed for each cancer type separately using the ConsensusClusterPlus (V. 1. 42. 0) R package with the following parameters: 5000 repeats, a maximum of six clusters and agglomerative hierarchical clustering with ward criterion (Ward. D2) inner and complete outer linkage. The optimal number of clusters (≥3) for best segregation of samples based on the ICR signature was determined heuristically using the Calinski-Harabasz criterion  33  (source function available on GitHub repository,  34  see cancer datasheets (https:// figshare. com/ articles/ Cancer_ Datasheets/ 7937246) for plots with local maximum). As we were intending to compare cancer samples with a highly active immune phenotype with those that have not, the cluster with the highest expression of ICR genes was designated as 'ICR High', while the cluster with the lowest ICR gene expression was designated 'ICR Low'.
Pan-cancer clustering was performed as explorative/comparative analysis. t-distributed stochastic neighbor embedding (t-SNE) plot t-SNE 35 was used as dimension reduction technique on the complete RNA expression matrix using R package Rtsne (V. 0. 15) (settings perplexity=15, theta=0. 5). t-SNE plot was annotated for SKCM primary, SKCM metastasis and uveal melanoma (UVM) samples. survival analysis OS from the TCGA Pan-Cancer Clinical Data Resource  25  was used to generate Kaplan-Meier curves using a modified version of the ggkm function. 36 Patients with less than 1 day of follow-up were excluded and survival data were censored after a follow-up duration of 10 years. HRs between ICR low and ICR high groups, including corresponding p values based on X 2 test, and CIs were calculated using the R package survival (V.
2. 41-3). The forest plot (figure  1C ) was generated using the R package forestplot (V. 1. 7. 2). Cancer types, Pheochromocytoma and Paraganglioma (PCPG), Thymoma (THYM), Testicular Germ cell tumors (TGCT), were excluded before generation of the plot, as the number of deaths in the comparison groups was too small for survival estimation. 25 Cancer types with a HR >1 with a p<0. 1 were termed ICR-enabled and cancer types with an HR <1 with a p<0. 1 were termed ICR disabled. The forest plot was annotated manually with indicators for ICR-enabled and ICR-disabled cancer types. Cox proportional hazards regression analysis was performed with the R package survival with the AJCC pathological tumor stage as described in the TCGA Pan-Cancer Clinical Data Resource. 25 Factors added in the multivariate analyzes (overall and stratified according to the ICR-enabling categories) include representative oncogenic pathways (proliferation and transforming growth factor beta (TGF-ß) signaling), mutation rate, aneuploidy, stage or histological grade.
The accuracy of the model was evaluated in a 10-fold cross-validation setting computing the correlation between the model prediction and the true ICR scores, finally obtaining a Spearman correlation of 0. 669±0. 012 (p<10 -400 ). The R package ComplexHeatmap (V. 1. 17. 1) was used to plot ICR score ratios between mutated versus wild-type (WT) groups. For cancer type/gene combinations with a number of samples of <3 in the mutated group, ratios were not calculated (NA; gray color in plot). A ratio >1 implies that the ICR score is higher in the mutated group compared with WT, while a ratio <1 implies that the ICR score is higher in subset of tumors without mutation. CNV analysis was performed for selected genes (PTEN and CTNNB1). For both genes, CNVs were categorized as deep deletions, hemideletions, WT, gain and amplification (GISTIC scores of -2,-1, 0, 1 and 2, respectively).
32 or PTEN deletion, CNV-low versus CNV-high categories were also defined using a Log R ratio cut-off of -0. 4, previously optimized in melanoma. 44 Additionally, a focused CTNNB1 mutation analysis was performed by only selecting mutations in exon 3 (amino acids positions 29 to 49), for which strong functional data exists. 45 Differences in ICR scores between PTEN/CTNNB1 somatic mutations and CNVs were assessed by t-test.
Associations between ICR and survival were defined for each pathway 'high' and pathway 'low' group separately using the survival analysis methodology as described above. Pathways for which a significant association between ICR and survival was present in one group, but not in the other one, were selected (online supplementary table  2 ). Similarly, these pathways were used to categorize samples per individual cancer type in pathwayhigh (ES >cancer specific median ES) and pathway-low (ES <cancer-specific median ES). Differences between HRs of groups in individual cancer types were calculated and plotted using ComplexHeatmap. Predictive value ICr score in immune checkpoint datasets ICR scores, or the mean expression of ICR genes, were compared between responders and non-responders to immune checkpoint therapy.
For the Chen et al dataset, performed on Nanostring platform, ICR score was calculated using the 17 ICR genes available in the Nanostring panel. Difference in mean ICR score between groups was tested using two-side t-test (cut-off p<0. 05). For datasets, GSE78220, 28 GSE78220 27 and Prat et al,  48  the response category includes both partial and complete clinical responders according to respective publications. For the Chen et al dataset, clinical responders also included stable disease, as described by Chen et al. 49  In the van Allen et al dataset, response was defined as patients with clinical response or long-term survival after treatment. 31 Samples of the van Allen dataset were dichotomized based on median ES of (1) genes of the proliferation metagene and (2) TGF-ß signaling signature.
Prognostic impact of ICr classification is different between cancer types RNA-seq data of samples from a total of 9282 patients across 31 distinct solid cancer types were obtained from TCGA. To classify cancer samples based on their immune orientation, we performed unsupervised consensus clustering for each cancer type separately based on the expression of the ICR immune gene signature. This signature consists of 20 genes that reflect activation of Th-1-signaling (IFNG, TXB21, CD8B, CD8A, IL12B, STAT1, and IRF1), CXCR3/CCR5 chemokine ligands (CXCL9, CXCL10, and CCL5), cytotoxic effector molecules (GNLY, PRF1, GZMA, GZMB, and GZMH) and compensatory immune regulators (CD274/PD-L1, PDCD1, CTLA4, FOXP3 and IDO1) (figure  1A ). 3 19 22 23  Expression of these genes showed a positive correlation with each other across all cancer types (online supplementary figure  2 ).
ICr reflects antitumor immune activity and is inversely correlated with tumor-related pathways associated with immune escape To further explore differences between cancer types, we aimed to compare the density of leucocyte subpopulations between ICR high and low samples across cancers. Gene expression signatures specific to 24 cell types  13  were used to deconvolute the abundance of immune cells in tumor samples by performing ssGSEA. 37 Cellspecific ESs for each patient demonstrated a clear enrichment of transcripts specific to T and B cells in ICR high patients (figure  2A ). More specifically, ICR high samples showed increased expression of transcripts associated with cytotoxic T cells, T-regulatory (T-reg) cells, Th-1 cells, NK CD56dim cells, activated dendritic cells (DC) and macrophages, compared with ICR medium and ICR low samples.
We performed ssGSEA to identify enrichment of transcripts of common tumor-related pathways. 22 39 40 Not surprisingly, immune-related pathways including TNFR1 Signaling and ICD showed a strong positive correlation with expression of ICR genes (figure  2B ). This implies that our immune signature captures the antitumoral immunological processes well across a wide range of cancer types. Interestingly, a few pathways were identified that inversely correlated with ICR gene expression, potentially representing mechanisms by which immune silent tumors develop their phenotype. These pathways include WNT -β catenin,  54 55  barrier genes,  40  AMPK (5' AMP-activated protein kinase) signaling,  56  mismatch Open access repair, telomerase extension by telomerase, Notch signaling and Hedgehog signaling pathways.
Interestingly, a common characteristic of these three cancer types is frequent hypermutation as a consequence of microsatellite instability (MSI). 58 This hypermutator phenotype could be responsible for the observed increased ICR score in the mutated group, as the genes with negative coefficient could be mutated in the context of hypermutation. We indeed observed an increased ICR score in the MSI-high group compared with MSI-low and microsatellite stable groups in COAD and STAD datasets for which sufficient data on MSI status were available  58  (online supplementary figure  10A, B ). Mutated genes were frequently part of multiple pathways, suggesting impact on various tumor biological systems (online supplementary figure  11 ). Open access PTEN and CTNNB1 somatic alterations (either mutations or CNVs) have been associated with differential response to immunotherapy (PTEN)  44 59-62  and intratumoral immune response (PTEN  44  and CTNNB1).
Open access whether tumor proliferation independently contributes to the prognostic value of ICR. Therefore, we segregated pan-cancer samples in four categories based on both mutation rate and proliferation (online supplementary figure  15 ). Interestingly, in the proliferation high group, ICR high was associated with significantly improved survival independent of mutation rate. A similar observation is made for the mutation rate high group, ICR high is associated with better survival independent of proliferation. These finding suggest that mutation rate and enrichment of proliferation-related transcripts provide additive information to define the prognostic value of ICR. Furthermore, in a multivariate Cox proportional hazards model including ICR classification, proliferation, TGF-ß signaling, tumor mutation rate, stage/grade and aneuploidy, all parameters but aneuploidy remain statistically significant (online supplementary table 4; ICR: p=1.
Predictive value of ICr score in immune checkpoint therapy is dependent on proliferation and tgF-ß signaling To define the clinical relevance of classification of ICR immune phenotypes, in the setting of immune checkpoint treatment, we first evaluated the predictive value of ICR score across multiple public datasets of anti-CTLA4 and anti-PD1 treatment. A significantly increased expression of ICR in responders compared with non-responders was observed across most of the datasets (figure  7A ). 27 28 31 48 49 he conditional activation of the prognostic impact of the ICR was tested in the Van Allen dataset, which was the only one for which survival information was available. Strikingly, in the proliferation-high subgroup, ICR score was significantly higher in pretreatment samples of patients with longsurvival or response (p=0.
data availability statement Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as online supplementary information. Scripts for analysis are shared on https:// github. com/ Sidra-TBI-FCO/ ISPC. git  (34). Data generated or analyzed during this study are included in this article. Data sheets for results of individual cancer types are available as cancer data sheets at figshare (https:// figshare. com/ articles/ Cancer_ Datasheets/ 7937246).
Illumina shotgun libraries were prepared using the Nextera XT DNA sample preparation kit and subsequently sequenced on a MiSeq system with the reagent kit version 3 with 600 cycles (Illumina, San Diego, CA, USA) as recommended by the manufacturer. Quality filtering using Trimmomatic version 0. 36 (3) resulted in 3,804,436 paired-end reads with an average read length of 232 bp. The assembly was performed with the SPAdes genome assembler software version 3. 13. 0 (4). The assembly resulted in 48 contigs (Ͼ500 bp) with an N 50 value of 212,499 bp and an average coverage of 334-fold. The assembly was validated and the read coverage determined with QualiMap version 2. 2. 1 (5). Throughout, default parameters were used for all bioinformatics. The draft genome of Staphylococcus sp. 170179 has a size of 2.
yet-undescribed strains were similar to the genome of strain 170179. The highest ANI (99. 6%) was found with strain 51-48 (GenBank accession no. CUEE00000000), a blood culture isolate from Norway; an ANI of 97. 8% was found with two strains, SNUC 119 (QYJM00000000) and SNUC 1342 (PZIO00000000), both isolated from Bos taurus in Canada  (7). Genomic differences with S. haemolyticus exist, such as the presence of a urease gene cluster in the Staphylococcus sp. 170179 genome. The genome sequence of this new staphylococcal species may contribute to our understanding of the evolution of human-associated Staphylococcus spp.
3. Material and method The material and method part is divided in three parts. In the first part, we present both hydrological and hydrodynamics models, their input and how we will calibrate and validate these models. The second part details all the data required by both models and for the analysis: i) water level time series, ii) rainfall and meteorological data, iii) Bottom friction map , iv) topography and bathymetry data, v) flood extent from ALOS/PALSAR imagery, vi)velocity data. We describe where the data can be accessed and the preprocessing phases. The third part details the methodological approach for the analysis of water circulation pattern and inundation frequency 4. Results In the first part of the results, we verify that the accuracy of the 2D-hydrodynamics model in terms of water level, flood extent and velocity.
, 2017; Paiva et al. , 2013; Yamazaki et al. , 2011) , medium scale  (Wilson et al. , 2007) , and local scale in the lowland Amazon  (Bonnet et al. , 2008 (Bonnet et al. , , 2017;; Lesack & Melack, 1995; Rudorff et al. , 2014a). These studies essentially focused on capturing flood extents (FE), WL, exchanged fluxes with the main channel and stored water volume throughout HY. Wilson et al. (2007)  applied this model to describe the flooding of a medium-size reach (240 km) of the Solimões and highlighted the necessity to improve topographic data to succeed realistic simulations, especially during the flood receding period. Baugh et al. (2013)  showed that vegetation removal from the available digital elevation model (DEM) SRTM V3 was promising to improve simulations. Going further,  Yamazaki et al.
In this study, we combine in situ information, remote sensing products and a hydrologichydrodynamic modelling to simulate flooding dynamic in a medium-size Amazonian FP and investigate spatial velocity patterns and flood frequency. We firstly present i) the methodology and the necessary datasets; ii) the hydrologic-hydrodynamic model performance in reproducing WL, FE and velocity. Then we employed this model to i) show spatial velocity patterns and flood frequency at seasonal scale and ii) examine the influence of extreme drought or flood events on velocity patterns and flood duration.
Hydrodynamics model The TELEMAC-MASCARET system is freely available at www. opentelemac. org. This modelling system was designed for computational fluid dynamics  (Hervouet, 2000)  by the Laboratoire National d'Hydraulique et Environnement (LNHE). It is an hydrodynamics suite for massively parallel architectures  (Moulinec et al. , 2011)  that uses an unstructured mesh allowing representing complex bathymetry. We specifically used its two dimensional (2D) hydrodynamic module (TELEMAC-2D v7p3) that simulates free-surface flows in the two dimensions of horizontal space. For more details, readers are referred to the TELEMAC-2D user manual  (Lang et al. , 2014). The main equations are reported in appendices material. In the Amazon region, this model was successfully applied to simulate estuarine regions hydrodynamics  (Oliveros et al.
, 2016). Bottom friction was based on the Manning coefficient map. We used the Blue Kenue software (Canadian Hydraulics Centre, 2012) to generate a mesh of 224 527 triangular elements with sides ranging from 23 m to 77 m. Cobby et al. (2003)  recommended using meshes with approximately equilateral elements to minimize mass balance error, variable element size to concentrate computational resource, and gradual transition between element size to maintain model stability. The model was run from 11/01/2006 to 12/31/2015 using a 30 s time-step on a multi-core cluster environment: the Altix-XE 340 cluster platform, located at LNCC (Laboratório Nacional de Computação Científica, Brazil).
Calibration and validation assessment As LUMP-FP model was fully validated for the study site  (Bonnet et al. , 2017) , and as we used same settings and the same simulation period to analyze the results, further calibration and validation were not necessary for the hydrological model. However, we calibrated the hydrodynamic model (TELEMAC component) against WL measured at RL1 and RL2 from April 2007 (rising water) to January 2008 (low water) adjusting Manning coefficients by a trialand-error method. The model was validated over the whole simulation period in terms of vertical and horizontal accuracy. The reference datasets for the vertical validation were WL measured at RL1, RL2, VS1_564, VS2_564, and VS1_149. The horizontal validation consisted in comparing modeled and ALOS-1/PALSAR-deduced FEs.