dataesr/scientific-paragraphs-categorization huggingface dataset:
We also checked for significant pathways in which the top ranking game theoretic centrality genes were enriched for using Reactome Pathway Browser (reactome. org), a database of known pathways and biological processes  [18]. Reactome identified 27 significant pathways for the genes Fig. 1  Common top-ranking genes among the centrality measures. Each element of the matrix represents the number of genes shared at the top 10% (left matrix) and 20% (right matrix) ranking between two centrality measures in comparison. The complete list of genes ranked by the various centrality measures can be found in "Additional File 1" under "Supplementary information" Four of the genes ranked in the first analysis, HLA-A, HLA-B, HLA-G, and HLA-DRB1 belong in the human leukoctye antigen (HLA) complex and have been previously implicated in ASD  [25].
Performing pathway analysis also showed that the top ranking genes are enriched in pathways of biological functions that have been previously linked with ASD, further corroborating the potential effect of these genes. This study is limited to well-annotated protein-coding genes where gene to gene interaction networks like coexpression and protein-protein interaction data are available to build graphs. With mounting evidence for the importance of non-coding region in the genetic etiology of disorders like ASD, it is necessary to incorporate ways to include non-coding sequences in the analysis, allowing the exploration of interactions between the coding and noncoding space. In future works, game theoretic centrality can also be applied to computable networks representing various biological systems apart from protein level interactions and expanded to other curated databases.
These preprocessing steps reduced the total number of genes from 13,853 to 965 genes, leading to final binary matrices with the dimension of 965 genes by 1,965 genomes. We then generated a protein-protein interaction network with the genes included in the binary matrices using STRING database V11 (string-db. org) [35]. STRING is a comprehensive database of known and predicted physical and functional protein-protein interactions obtained through multiple data sources including experimental evidence and text-mining. We filtered for interactions with confidence score ≥ 0. 6, where a confidence score of 0. 4 is considered medium confidence for a true interaction and 0. 7 high confidence, producing a graph N, E with 965 vertices (genes) and 273 edges (protein-protein interactions). A slightly lower threshold of 0.
Acknowledgements. We thank  Hans Peter Grossart  and  Jérôme Mallefet  for providing helpful comments on an earlier version of this review. We gratefully acknowledge support from  CNRS  (project  EC2CO  " HEMERA "). Financial support. This research has been supported by a doctoral grant " Région Sud " and the  TANGRAM Architectes agency. The project leading to this publication has received funding from the  European FEDER Fund  (grant no. 1166-39417 ). Review statement. This paper was edited by  Carol Robinson  and reviewed by two anonymous referees.
2 ). The target is sufficiently rigid to withstand repeated blast loading without damage nor deformation. Five pressure gauges separated by 30° can be installed on the target surface along three vertical lines facing the explosive charge. For this campaign, five pressure transducers were mounted on elastic supports and their signals were recorded at 500 kHz by a Nicolet Genesis data acquisition system. Results presented in this document were acquired for three different d distances (Fig. 3 ): 0. 4, 0. 8 and 1. 6 m (Table  1 ) [5]-  [7]. ISL developed a dedicated outdoor blast pad (Fig. 4 ): explosive charges are detonated in a factor two up-scaled version of IRSN test configuration. Each explosive charge was installed on a dedicated reinforced steel ground plate to generate a hemispherical blast wave.
Based on IRSN previous work, ISL also designed a factor two upscaled hemi-cylindrical obstacle (Fig. 5 ). The results obtained for two target configurations are described in this document:  "long" hemi-cylinder composed of 4 segments (800 mm diameter and 3. 2 m total length),  "short" single instrumented (5 lines of instrumentation ports) hemi-cylinder (800 mm diameter and 0. 8 m length). Presented results were recorded at a distance d of 3. 2 m at ISL scale (Table  1 ). Different types of pressure transducers were mounted on polypropylene supports inserted in the hemicylinder ports each separated by 30°. All data were recorded using a Transcom system running at 2 MHz.
Overpressure evolutions along the IRSN hemi-cylinder central line were determined for three distances to the explosive charge. Results are compared to a reflected face simplified model (TM5-1300  [9]  + Kinney and Graham  [8] ) and free-field side on overpressure (Kinney and Graham  [8] ). A good agreement is observed between experimental data and the simplified model on the directly exposed face of the obstacle. To predict the blast load on the expansion side in a similar way, transmission coefficients are represented as functions of an innovative parameter: the reduced shifted direct distance (RSDD). By taking into account the geometry of the obstacle and the 3D free-field pressure decay, this physical conjecture (Model S) allows the prediction of the peak overpressure evolution on the expansion side of the ISL hemi-cylinder at double scale.
2a) FITC, CD5 (53-7. 3) PE-Cy7, CD21/35 (4E3) PE, GL7 (GL7) eFluor 660, CD11c (N418) PE, CD62L (MEL-14) FITC, CD25 (PC61. 5) APC, CD8 (53-6. 7) APC-eFluor 780, CD103 (2E7) FITC, CD279/PD-1 (J43) FITC, and Foxp3 (FJK-16s) PE-Cy7 were purchased from eBioscience (San Diego, CA, USA). mAbs against mouse I-Ad FITC (AMS-32. 1), were purchased from BD Pharmingen (San Diego, CA, USA). Intracellular staining for Foxp3 and Bcl-6 was performed after cell surface staining using the Foxp3/Transcription Factor Staining Buffer Set (eBioscience) following the manufacturer's instructions. Flow cytometry was conducted on a FACSCanto II flow cytometer (BD Biosciences) or FACSAria III (BD Biosciences) and data analysis was performed using the FlowJo software version 8 (Tree Star, Inc. , Ashland, OR, USA). For t-SNE, data was acquired in a FACS Aria III (BD Biosciences) and the analysis was performed using the Rtsne package in R software.
After washing the plates five times with PBS, 3-amino-9-ethyl carbazole (AEC) substrate was added and incubated at 37 • C for 30 min. The plates were washed with bidistilled water and dry uncovered for 3 h at 37 • C. Plates were read using an ELISpot reader AELVIS and the software Eli. Analyse ELISPot Analysis Software V6. 0.
Libraries were quantified using the KAPA Library Quantification Kit (Kappa Biosystem) using the Eco PCR system (Illumina), following manufacturer's protocol. Libraries were sequenced on a Miseq platform (Illumina) using a v3 150 kit with 2 × 75 bp paired-end. Samples were subsequently analyzed using R/Bioconductor, and the DESeq2 procedure was used to normalize the data. Differentially expressed genes were identified using an adjusted p-value cut-off of 0. 05 and a fold change of at least 1. 5. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE147359 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE147359).
e driving alerting system is an algorithm able to considerate external variables for updating the estimated autonomy value. is kind of algorithms requires the same set of data and dynamic models as other Advanced Driver Assistance Systems (ADAS) already implemented in the market  [3]. e dynamic models required are the longitudinal vehicle data and battery model. According to the external current data about weather, road, traffic, and so forth, the acceleration profile is estimated based on the historical data set of acceleration profiles in the same conditions. e acceleration profile is translated to a power demand and it is sent to the SoC (state of charge) estimator. e SoC estimator uses the power demand profile and the battery dynamic model to determine if there is enough energy to complete the travel or not.
ose electrical characteristics and the travel characteristics of the road such as wind speed, wind direction, or slope will determine the amount of power required for tracking a speed profile described by a drive cycle. It is important to note that the different properties of the electric vehicle such as the weight, the front area, or the power devices capabilities will be required to do the translation between the speed reference (drive cycle) and the power demand profile. ere are two kinds of drive cycles: In all cases, the external conditions such as wind speed and slope profile are the same to keep the results comparable. In the case of slope profile, realistic data obtained from Google Maps in a traject from Paris to Brussels were used.
This confirms that under our experimental conditions (in particular our λ exn digestion conditions), putative G4, G-rich and GC-rich sequences are digested almost as efficiently as randomised DNA sequences, and that the background generated by regions resistant to digestion can be accounted for by using a suitable experimental background sample. Summits and orientation of origins. Summits of origins were defined by calculating the highest number of SNS-seq reads in bins of 50 bp from 25 bp sliding windows, using bam files from all samples with a custom-made script (see code availability). Middle point of the bin with highest number of reads was considered the summit of the IS. Origins were assigned a plus or a minus strand based on the G-content of the regions flanking the IS summit, such that the G-rich flanking region was oriented upstream (left) of the IS summit.
To determine the percentage of SNS-seq signal that falls in Core origins in each cell type, the total normalised (background subtracted and normalised) SNS-seq signal and the fraction that belongs to Q1, Q2 and stochastic origins (Q3-Q10) were calculated. Differential origin activity was calculated using the R libraries Diffbind (v3. 9, TMM_minus) and DeSeq2 consecutively (see code availability for code). Total initiation from early and late replicating domains. The early and late replicating domains were defined based on early and late replication domains common to H9 and CD34+ hematopoietic progenitors (Supplementary Data 3). The origin coordinates (±2 kb) were removed (masked) from the domains. The SNS-seq signal was then quantified in these domains in both sample and background samples and normalised by RPKM.
SNS-seq read density plots and heatmaps were generated using deeptools (plotProfile, plotHeatmap). When required, genome coordinates of different genome assemblies were converted using UCSC LiftOver (UCSC Toolkit). A full list of the genomic regions downloaded from external sources can be found in Supplementary Data 3. ReMap and putative enhancers. Origins were mapped onto the ReMap atlas  56  (http://remap. cisreg. eu). ReMap results from an integrative analysis of transcriptional regulator ChIP-seq experiments from both Public and Encode datasets. The ReMap catalogue includes 80 million peaks from 485 transcription factors, transcription coactivators and chromatin-remodelling factors. Overlaps were assessed with bedtools (v. 2. 25), counting only regions with a minimum of 10 ChIP-seq peak overlap.
RNA-seq and analysis. RNA-seq profiling was performed on all HC samples in order to determine whether origin positions (SNS-Seq) are adapted with transcription programs (RNA-seq). To do so, ≥2 µg RNA was extracted and purified from an aliquot of 200,000 cells using TRIzol reagent (Sigma-Aldrich), followed by RNA purification using the RNEasy MiniKit (Qiagen 74104). RNA quality and quantity were analysed using a Fragment Analyzer (Advanced Analytical). cDNA libraries were prepared by the Montpellier GenomiX facility using the TrueSeq Chip Library Preparation Kit (Illumina). After quality control (using FastQC v0. 11. 5), the TopHat software (version 2. 1. 1) was used for splice junction mapping through Bowtie2 (version 2. 2. 8) for mapping reads. Reads count on genes was performed using HTSeq-count (version 0.
6. 1p1). Gene annotations were downloaded from GENCODE, release 25 (GRCh38. p7, 23 September 2016). Data were normalised by the relative log expression implemented in edgeR (version 3. 8. 6), and pairwise comparative statistical analysis to identify differential genes was performed using DeSeq2 (version 1. 18. 0 in R 3. 2; results were confirmed with edgeR version 3. 8. 6) using a generalised linear model. Definition of G-rich regions (G4, CpGi, G-rich). Two methods were used to define G4 elements in the human genome based on (i) identification of mismatches induced by K + and pyridostatin (PDS) treatment  28  (in vitro G4) (ii) predictions by G4Hunter  29  (in silico G4). Both datasets were generated in hg19, therefore we have converted our origin coordinates to hg19 in order to examine overlaps.
7 Kb). This set of regions was used to define predictability of origins in Fig. 3f, g. For the mouse genome, the same algorithm was run with exactly the same parameters, which retains 689,285 window pairs out of the (27 × 2 million possible pairs from mm10). Similarly, these regions were merged (bedtools merge) to generate 230,052 nonoverlapping regions and intersected with mouse origins using bedtools (bedtools intersect -wa -u) to generate Fig. 3h. Machine learning and hyper-motif analysis. Predicted variable for our algorithm is the membership to the "origins" class defined by intersection of the nonoverlapping coordinates with an origin (maximising the predictive power on core origins in particular). In all, 30 million pairs of 500 bp windows were randomly split into two equally sized datasets.
One of the datasets was reserved for the final validation at the end of the model development (test set). The other set was used for training and internal validation of the prediction model. Next, the training set was randomly split into 10 non-intersecting subsets and 10-fold internal crossvalidation was performed (i. e. used nine of these subsets for internal training and the remaining one for internal validation of the models, this was repeated 10 times, each time with a different validation subset). Initially, the Genome Scan algorithm was run on each one of those 10 internal training datasets. On the set of 1,041,594 regions generated by the GS algorithm (window pairs, see above), we constructed a set of 22 parameters/predictors (see Supplementary Data 2) using domain knowledge. Then, machine learning procedures were applied to the output of the Genome Scan, thereby constructing a hierarchical classifier.
This procedure was repeated 100 times for two different machine-learning algorithms (i) logistic regression with greedy incremental feature and (ii) support vector machines with lasso regularisation. Greedy feature selection was performed by means of a modified version of statistical R-package CARRoT (Predicting Categorical and Continuous Outcomes Using One in Ten Rule, R CRAN package, 2018, Alina Bazarova and Marko Raseta, v1. 0). The software was modified in such a way that would allow to incorporate merging of the output into non-intersecting genome regions by means of bedtools and then assessing the predictive power of the model given these regions. The support vector machine prediction was performed using Rpackage sparseSVM 66 and additional scripting described above. We chose the models aiming at maximising their balanced (average class-wise) accuracy defined as 0.
For logistic regression, the best model emerged with the highest frequency of the predictors constituted by the features: UP_C_fraction, UP_G_fraction, Down_T_fraction, G_content_2kb, rampG, AAA, GG and TTT (Supplementary Data 2). Once the training was complete, the chosen models based on 10-fold cross-validation were fitted with the whole original training set of 15 million pairs of 500 bp windows. The resulting trained models were then tested on the final hold-out test set (isolated from the training one in the very beginning and never touched throughout the model construction phase). Please note that each algorithm reported non-duplicate window pairs (i. e. if a window pair is retained with both forward and reverse scanning procedure by the genome scan algorithm, this window pair is reported once as positive by either machine-learning algorithm).
3f , normalised to average fragment length). Calculation of origin density and total initiation signal across TAD domains. To calculate the origin density across TAD domains, each TAD was divided into 100 bins (bedtools makewindows -n 100). As the bin size in each TAD was a fraction of the TAD size, the number of origins in each bin of the TAD was normalised to the bin size. To determine whether origin density across the TAD was significantly different in different cell types, the origin density across TADs for each bin was normalised to the 20 bins in the middle of each TAD (bin numbers 40-60). These values represent the differential origin density between the TAD middle and borders, rather than the overall origin density across the TAD. We have calculated the sum of normalised (background subtracted) signal from origin regions that fall onto TAD borders or TAD centres (dataset on Table  3  and Fig.
Acknowledgements We warmly thank  Harold Riethman ,  Benoit Miotto ,  Chunlong Chen ,  Oliver Hyrien  and  David Gilbert  for providing details on their data. I. A. is supported by  Diabetes UK RD Lawrence fellowship. This research received funding from the  Association Française contre les Myopathies (AFM) , the  ARC foundation , the  ANR14-CE10-0019 , and by the  MSD AVENIR Fund GENE-IGH. This work also benefited from support by the  Labex EpiGenMed , an  Investissements d'Avenir programme  (reference  ANR-10-LABX-12-0 ), and from a Pre-maturation support from the  Region Occitanie.
Results Freshening of the eastern subpolar region in 2012-2016. Integrated across the SPNA, the freshwater content of the upper 1000 m increased rapidly by ~6600 km 3 during 2012-2016 (Fig. 1 ). However, the change in salinity was not distributed evenly over the region. The evolution of the salinity field in two layers, 0-200 m and 200-1000 m (anomalies from a long-term mean derived from the EN4 data sets, see Methods) is shown in Figs. 2  and   The EN4 profile data sets inclusive of all available Argo float data provide good annual information over a wide region. However, to resolve the location and timing of subsurface salinity anomalies associated with this freshening event with greater confidence, we turn to high resolution, high quality observations at fixed locations and transects. First we consider the magnitude of this freshening event in a multidecadal context.
Methods Salinity and freshwater. Salinity is reported on practical salinity scale throughout the paper. The subpolar North Atlantic (SPNA) is defined as the region 47-65°N, 0-60°W. The annual mean ocean freshwater content anomaly (m) has been derived using the EN4 hydrographic data set following the formulation of ref. 66   The net freshwater gain maps and time series are produced by adding evaporation and total precipitation based on monthly means of daily forecast accumulation of the first 12 h from ERA interim reanalysis  67. Consistent with earlier analysis, the annual mean net freshwater gain anomaly maps are calculated with respect to the 2005-2016 climatology. Time series of net precipitation are ERA5 monthly anomalies for 45-65°N, 30-10°W. The ERA5 data was obtained from ECMWF (https://www.
ecmwf. int/en/forecasts/datasets/reanalysis-datasets/ era5). The empirical orthogonal function analysis of the salinity fields was carried out following ref. 68. Statistical significance of the correlation analysis followed ref. 69. Wind stress curl. The wind stress curl (Nm -3 ) was calculated as follows (Eq. 2): ∇ τ ¼ ∂τ y ∂x À ∂τ x ∂y ð2Þ where τ x and τ y are the zonal and meridonal wind stress, respectively, based on the monthly means of daily means from the ERA-interim reanalysis  70. The 2004-2016 climatology was removed from the monthly data before calculating the December-Mars winter averages. DJFM wind-stress curl anomalies are generally considered to best represent the atmospheric circulation. The grid resolution of the data set is 0. 75°× 0. 75°. Velocity. The geostrophic velocities in Fig.
Acknowledgements The idea for this analysis emerged from the  Iceland-Faroe-Scotland -Ridge Exchanges Workshop organised by  Henrik Søiland ,  Jan Even Nilsen ,  Tom Rossby ,  Bee Berx  and  Karin Margretha H. Larsen , and held at the  Bjerknes Centre, University of Bergen  in September 2017. N. P. H. was supported by  UK NERC National Capability programmes ACSIS  ( NE/N018044/1 ), the  Extended Ellett Line  and  CLASS  ( NE/R015953/1 ), and  NERC  Large Grant  UK OSNAP (NE/K010875/1 ). N. P. H. , B. B. , H. H. , M. O. and K. M. H. L. are supported by the  Blue-Action  project (European Union's  Horizon 2020 research and innovation program , grant:  727852 ). G. R. was supported by  CNRS  for his contribution to SNO SSS (http://www. legos. obs-mip. fr/observations/sss/datadelivery/products).
W. J. was supported through  NSF   OSNAP grants   OCE1259398  and  OCE1756231. L. C. acknowledges support through the  Swedish National Space Agency (SNSA ;  Dnr 133/17 ) and the  FiNNESS  project. M. O. was supported by the  AtlantOS  project (European Union's  Horizon 2020 research and innovation program , grant:  633211 ) and the project  RACE-Synthesis-Regional Atlantic Circulation and global Change  ( 03F0824C ) from the  German Federal Ministry of Education and Research (BMBF). S. M. has been funded by the  European Copernicus Marine Environment Monitoring Service. V. T. was supported by  Ifremer , and the  GEOVIDE  ( ANR-13-BS06-0014-02 ) and  BOCATS  ( CTM2013-41048-P ) project co-funded by the  Fondo Europeo de Desarrollo Regional   2014-2020  (FEDER).
Data availability All data used in this analysis are available as follows. The EN4 data set (ref. 74 version 4. 2. 0) with the bias correction of ref. 75  was obtained from https://www. metoffice. gov. uk/ hadobs/en4/, downloaded on the 1 November, 2017. The time series of salinity of the upper ocean (Faroe-Shetland Channel, Rockall Trough, Faroe Bank Channel, Iceland Basin, shown in Fig. 4a ) were obtained from ICES (https://ocean. ices. dk/iroc/, downloaded November 2018). The surface salinity data (Fig. 4c ) are made freely available by the French Sea Surface Salinity Observation Service (http://www. legos. obs-mip. fr/observations/sss/) (downloaded November 2018), https://doi. org/10. 6096/SSS-BIN-NASG. Time series of annual salinity anomalies of the upper ocean (0-500 m) in the Labrador Sea (Fig.
Statistical analysis. Software R version 3. 5. 3 (R core team 2019; https ://www. R-proje ct. org/)  30  was used for all statistical analyses. For correlation analysis, because the distribution of synthetic variables was not Gaussian, the Spearman correlation test was used. To study the effect of fixed variables of the model, the likelihood ratio test (ANOVA function in R) was used. When significant effects of weeks were found, a post hoc comparison test with FDR correction was applied (emeans package  31  ). The function nls of package stat was used to estimate the parameters of the quadratic function (Model 1. 2). For each of the synthetic variables, the Gaussian distribution was checked with a Shapiro-Wilk test  32. In the absence of normality, a box-cox transformation was applied to determine the adequate power transformation, using the boxcox function of R.