dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analyses Change scores were computed by subtracting mean task values computed from both tasks, from mean baseline values, thereby returning a delta score indicative of reactivity. A preliminary analysis was conducted in order identify outliers and cardiovascular reactivity scores deviating +/-3. 00 SD from the mean were considered outliers and excluded from analyses; one outlier on SBP reactivity was identified. Further, three participants were missing baseline and/or stress task cardiovascular measures and thus, reactivity scores for these participants could not be computed. In order to determine if the stress protocol was successful in perturbing the cardiovascular system, a series of simple paired-samples t-tests (baseline, task) were conducted on each cardiovascular parameter. Similarly, in order to determine if the stress protocol was psychologically stressful, a paired-samples t-test (pre and post task) was conducted on self-reported stress.
One aspect that has particularly defied progress is the measurement of large-scale fluxes (apart from discharge), and the measurement of subsurface fluxes at any scale. One potential path forward is the use of proxies, replacing few accurate data by many less accurate data, e. g. by using qualitative observations from lay persons or from data mining; however, it is not yet clear exactly what proxies would be of most benefit in a particular situation (Question 17). Similarly, it is not clear under what conditions one can infer past or future trajectories of hydrological systems from contemporary spatial patterns ("space-fortime" substitution). Similar statements apply to the conceptualisation and modelling of coupled human-water systems, which, in the past decade, has been dominated by stylised models using little data, yet a more solid database.
This has included the fusion of quantitative with non-quantitative data, as well as hydrological with other types of data (e. g. socio-economic, land-use; Pan et al. 2018), and seems essential for making further progress (Question 18) (see also  Mount et al. 2016 , Di Baldassarre et al. 2019 , Hall 2019). There are many datasets from local sociohydrological studies throughout the literature. Compiling a database and performing a meta-analysis of these studies would be beneficial. An important element of our ability to reverse the current trend of decline of observation systems will be the ability to convincingly put a value on hydrological observation systems with open data (Question 17), perhaps building on novel developments in crowd-sourcing and Citizen Science, e. g. as reflected by CANDHY (the Citizen AND HYdrology Working Group of IAHS).
2013) , and by providing the data and the model code, preferably in public repositories. Indeed, as datasets used in publications are becoming more extensive and models more complex, it has sometimes become very difficult to assess the validity of a new theory or model prediction on the basis of the published material, and to build on it, because of a lack in reproducibility  (Hutton et al. 2016). Most hydrology journals and research funders have therefore adopted an open data and open model policy, to allow peersat least in principleto repeat any published study (e. g. Data Citation Synthesis Group 2014, Quinn et al. 2018), notwithstanding challenges with proprietary data and models in some countries. Koutsoyiannis et al. (2016)  suggested that a change in culture is needed in linking research studies to each other, e.
Acknowledgements We would like to thank the members of the  IAHS, EGU, AGU  and IAH for supporting this initiative. The LinkedIn group and overall secretariat was hosted by the  IAHS , the Splinter meeting by  EGU  and the  Vienna Catchment Science Symposium  by the  Vienna Doctoral Programme on Water Resource Systems  ( DK W1219-N28 ) funded by the  Austrian Science Funds (FWF).
Data and empirical results Due to data availability for centralized and decentralized bitcoin markets that use the euro, U. S. dollar, and British pound, I formed the following datasets. For the centralized bitcoin exchanges, I used data provided by GDAX/CoinsBank (for the euro, U. S. dollar, and British pound), Kraken (for the euro and U. S. dollar), Rock Trading (for the euro and U. S. dollar), Bitcoin. de (for the euro), Bitstamp (for the U. S. dollar), and the first exchange-backed peer-to-peer marketplace, Coinfloor (for the British pound). LocalBitcoins was used to form a dataset on a decentralized exchange (a P2P market). To calculate returns, I used the daily closing prices for bitcoin in euros, U. S. dollars, and British pounds from April 27, 2015 to August 9, 2018. 4   The estimated kernel densities of the returns for the euro, U.
Conclusion The performed analysis depicts the correlation between the currency pairs across the decentralized (LocalBitcoins) and six centralized bitcoin markets (GDAX, Kraken, Bitcoin. de, Bitstamp, Rock Trading, and the exchange-backed peer-to-peer marketplace, Coinfloor). The estimates derived reasonable results and were consistent with the data provided. The results showed that the decentralized bitcoin exchange has higher volatility than the centralized markets. In the centralized markets, volatility increased as prices jumped, but that was not the case in the decentralized exchanges. The volatility analysis results are contrary to the conventional leverage reason that market drops cause volatility. Thus, bitcoin price increases cause an increase in risk because a greater dispersion of returns around the mean causes a larger drop in the compound return.
, 2018; Yermack, 2015). Overall, these findings are significant and potentially useful to researchers, practitioners, and participants in the highly volatile bitcoin market for making better investment and risk management decisions. Future research can extend the datasets to cover other currencies; focus on higher-frequency bitcoin data, such as hourly (because the results can vary from the current ones); and use other dynamic models based on the concept of copulae, such as the generalized autoregressive score (GAS) framework. Note: the ARMA-GARCH models used for residuals calculation are the same as in the EMWA calculations (see Figure  3 ).
De novo assembly and annotation. The RNA-seq libraries read quality was evaluated using FastQC (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc). Reads from 45 D. polymorpha and 34 D. rostriformis bugensis individuals were cleaned, filtered and de novo assembled for each species with DRAP (De novo RNA-seq Assembly Pipeline, version 1. 7)  22  using the Oases assembler  23. Contigs were kept if they had at least one FPKM. Contigs were then aligned with NCBI BLAST (version 2. 2. 26, e-value under 1e-5 parameter) on Refseq, Swissprot and three databases issued from Ensembl (Crassostrea gigas, Lottia gigantea and Lingula anatina sequences) to retrieve corresponding annotations. Contigs were also processed with RNAmmer (version 1. 2, standard parameters)  24  to find ribosomal genes, with RepeatMasker (version open-4-0-3, -engine crossmatch -gccalc -species Crassostrea gigas parameters, http://www.
Data Records Raw reads and assemblies were gathered in the same NCBI BioProject (PRJNA507340) which includes all BioSamples used for transcriptome assembly (Table  1 )  [30] [31] [32]. All datasets were also available online on sequencing platform web interface (Table  2 )  33. Datasets annotations (contigs, KEGG, GO) are available on Figshare (Table  3 )  34.
De novo transcriptome assembly validation. A total of 94,217 contigs were assembled spanning from 200 to 40,000 bp and with an average length of 2,314 bp for D. polymorpha and 1,972 for D. r. bugensis (Table  4 ). Assembly validation aims at verifying the correspondence between contigs and assembled reads, between contigs     29, 33. Biomart and BLAST are implemented for quick database interrogation.
Last, contigs were processed with BUSCO version 3. 0. 2 36 using the metazoa OrthoDB (v. 9) database to check for awaited proteins (Table  7 ) which shows that almost 95% of expected metazoa BUSCO sequences were found as complete in our de novo transcriptome. annotation quality. More than the half of assembled contigs found annotation with an e-value under 1e-5 (59% of 44,538 D. polymorpha contigs and 57% of 49,679 D. rostriformis bugensis contigs). Among top 5 species found as best annotation, Crassostrea gigas represent the best hit species matching with approximately 53% of D. polymorpha and D. r. bugensis sequences (Fig. 2 ). Among the "others" category, some non bivalves sequences can be present thanks to the analyzed tissue (as digestive gland may contain processing foods). Sequence functional annotations successfully attribute GOterms to 35,4% and KEGG to 21.
Biomarker best contig hits and closest species hits. D. polymorpha and D. rostriformis bugensis databases were used to find common ecotoxicological biomarkers. Contig coding sequences were then screened in NCBInr (blastn) and closest species (excluding D. polymorpha and D. rostriformis bugensis) were reported according with E-values and identity percentages. GABARAP: Gamma-aminobutyric acid receptor-associated protein; mTOR: mechanistic target of rapamycin; MRP1: Multidrug resistance-associated protein 1; MDR1: Multidrug resistance protein 1; Cds: coding sequence. By providing Dreissena sequences through a user-friendly interface, we open the way to further explorations of Dreissenids molecular mechanisms by such biomarker assays development, primer design allowing targeted expression analysis or promising proteogenomics studies when coupling with mass spectrometry analysis.
acknowledgements This work was supported by the the  BIOMICS program  ( Biomarkers and Omics in the freshwater Bivalve Dreissena polymorpha ) of the  French Agency for Biodiversity/National Agency for Water and Aquatic Environments (AFB/ONEMA ) Authors would like to thanks the  GeT-PlaGe  for performing sequencing and Genotoul platform for transcriptome assembly, annotation and validation.
Biomarkers Contig database name Closest blastn hit E-value % Identity D. polymorpha D. r. bugensis Species D. p. D. r. b. D. p. D. r. b. Acetylcholinesterase Dp_LOC105324424. 2. 2 Db_LOC105324424. 3. 3 Crassostrea virginica 2e-8 4e-12 68% 67% Beta Actine Dp_ACT. 1. 3 Db_ACT. 2. 4 Crassostrea virginica 0. 0 0. 0 90% 90% Catalase Dp_LOC105339902 Db_LOC105339902. 2. 4 Corbicula fluminae 0. 0 0. 0 77% 76% Superoxide dismutase (Cu-Zn) Dp_SODC Db_SODC Ostrea edulis 4e-55 1e-48 73% 72% Superoxide dismutase (Mn) Dp_LOC101852344 Db_LOC101852344 Haliotis fulgens 3e-78 3e-77 72% 72% Estrogen receptor Dp_LOC105318922. 2. 2 Db_LOC105318922. 1. 2 Ruditapes philippinarum 0. 0 0. 0 75% 74% GABARAP Dp_LOC105335545. 1. 2 Db_LOC105335545. 1. 6 Meretrix meretrix 2e-103 1e-98 84% 83% Glutathione peroxidase 1 Dp_GPX1.
Large Eddy Simulation The Large Eddy Simulation (LES) methodology is used in this paper on a very fine mesh in order to ensure highly resolved simulations together with a subgrid model. This allows to accurately characterizing the unsteadiness and transition phenomena encountered in this kind of flow. This approach, by means of a filtering operation applied to the Navier-Stokes equations, resolves explicitly the dynamics of the unsteady large scales of turbulence while modeling the small scale motions. Three-dimensional CFD calculations are carried out using the finite-volumebased CFD commercial code Starccm+ 10. 02. In the present LES simulations, the Wall-Adapting Local Eddy-viscosity (WALE)  [23]  model is used for the modeling of the subgrid scale viscosity and a constant turbulent Prandtl number (P r t = 0.
The following assumptions are made: • the working fluid is air with thermo-physical properties constant except for ρ (ideal gas law) • natural convection and radiation are neglected The thermo-physical properties of the air and the aluminum which are used for the heat exchanger fin are tabulated in Table  1. Regarding the data provided by the simulations, before carrying out the data analysis, the achievement of statistical steady state was checked by monitoring the volume-averaged turbulent kinetic energy (time-averaged), and the pressure drop (temporal evolution). After waiting for the LES simulations to become statistically converged, the LES results are then statistically averaged for an additional time period of more than six flow-through times, while achieving a converged solution at each time-step  [34].
RNA Extraction and High Throughput Sequencing Total RNA was isolated with the Plant RNAeasy Mini kit (Qiagen) and quantified using a NanoDrop. The paired-end sequencing was performed using an Illumina HiSeq2000 sequencer at the Next Generation Sequencing (NGS) platform of GeT: http://get. genotoul. fr/. About 900 million raw reads generated from the 24 samples were deposited to the National Center for Biotechnology Information (NCBI) in the SRA (Short Read Archive) database (BioProject #PRJNA543764).
RNA-Sequencing Analysis, Assembly, and Annotation The quality of the raw reads was checked using FastQC v0. 11. 4  [31]. After removing adapter sequences with Cutadapt v1. 0  [32] , reads were cleaned using Prinseq v0. 20. 3  [33]. The clean reads of the twenty-four samples were de novo assembled using Trinity v20140717  [34]. The contigs obtained by Trinity were filtered using TransRate v1. 03  [35] , which detects chimeric sequences, structural errors, incomplete assembly, and base errors. Then, Quast v2. 3  [36]  was used to assess the quality of the assembly. The transcriptome was annotated with Blastx searches  [37]  against A. thaliana (TAIR 10) protein databases. Gene ontology (GO) (http://geneontology. org/ Gene Ontology Consortium and http://pantherdb. org/), and Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.
Differential Expression Analysis and Statistical Tools For the differential expression analysis, Illumina-cleaned reads from the 24 samples were pseudo-aligned on the de novo transcriptome assembly using Kallisto v0. 43. 1  [50]. Count data were analyzed using a multifactorial design (line (Ch and Te), treatment (LT and N), and time (T0, T1, and T2)) with the R package DESeq2  [51]. The multiple steps of statistical and clustering analyses led to three sets of distinct DEGs (Differentially Expressed Genes) (Figure  S1 ). Firstly, only unigenes fulfilling the criterion TRUE (when the number of reads is sufficient to perform the statistical test) in DESeq2 were conserved for the next analyses. Then, unigenes represented by less than or equal to 48 normalized counts among the 24 samples were discarded.
Differential Expression Analysis and Clustering The threshold applied on DESeq2 results (adjusted p-value ≤ 0. 02) retained 11,076 unigenes, presenting a modulation of expression within the 24 different samples. Among them, 9676 unigenes were annotated using A. thaliana proteins. A two-way ANOVA performed on the 9676 annotated unigenes permitted us to detect 4981 genes, in which expression was significantly modulated according to the line and/or to the low temperature treatment (Figure  S1 ). Among them, 33 were compared with data previously obtained by qPCR. We observed that RNA-seq and qPCR data are correlated (R = 0. 71) (Figure  2 ). Plants 2019, 8, x FOR PEER REVIEW 5 of 24 the N50 value of the assembly was 1904 (Table  S1 ). Blastx searches against A. thaliana protein sequences allowed to annotate 34% (24,854/73,225) of the unigenes (E-value ≤ 9 × 10 -4 ).
Differential Expression Analysis and Clustering The threshold applied on DESeq2 results (adjusted p-value ≤ 0. 02) retained 11,076 unigenes, presenting a modulation of expression within the 24 different samples. Among them, 9676 unigenes were annotated using A. thaliana proteins. A two-way ANOVA performed on the 9676 annotated unigenes permitted us to detect 4,981 genes, in which expression was significantly modulated according to the line and/or to the low temperature treatment (Figure  S1 ). Among them, 33 were compared with data previously obtained by qPCR. We observed that RNA-seq and qPCR data are correlated (R = 0. 71) (Figure  2 ). Scatter plot between the two sets of transcripts issued from the present RNA-seq and qPCR data from Reference  [28]. The x-axis corresponds to log2 of qPCR ratios and the y-axis shows log2 of RNA-seq ratios for 33 transcripts.
Acknowledgments: We thank  C. Devaux  for her technical expertise and  G. Aubert  and  J. Kreplak  for their advices in computing tools. We also thank the high performance computing service of  Bilille, University of Lille , for providing computing resources. This work was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr),and was supported by the  France Génomique national infrastructure , funded as part of an " Investissement d'avenir" program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
S7 ). The colocalization results for each gene are markedly tissue specific (Fig. 2  and fig. S8 ). In ex vivo samples, the LEPR/LEPROT variant was in high LD with the top eQTLs of LEPR and LEPROT genes in omental fat, subcutaneous fat, and whole blood (table  S9 ). Direct lookup of LEPR/LEPROT variant in eQTL data indicated that the G allele of this variant that raised BMI-AP in our GWAS up-regulated the NM017526 transcript of LEPROT and down-regulated the AK023598 transcript from the same gene in adult tissues (table  S10 ). This observation was consistent across two different eQTL studies and four tissues, suggesting the involvement of alternative splicing of a cassette exon. The LEPR/LEPROT variant overlapped DNA binding motifs of transcription factors and regulatory regions, as well as enhancer and promoter histone marks in multiple tissues (fig.
LEPROT and the LEPR genes share the same promoter but encode distinct transcripts  (13). LEPROT is cotranscribed with the LEPR, and both are expressed in multiple tissues with different functionalities. LEPR is widely distributed in peripheral tissues, shows signaling capability, and is thought to transport leptin across the blood-brain barrier  (25). Some LEPR isoforms may function in leptin clearance or buffering (soluble LEPR). In our eQTL data, the G allele that raises BMI-AP up-regulates the NM017526 transcript of LEPROT but down-regulates AK023598 transcript from the same gene in adult tissues. This observation was consistent across the different eQTL studies and tissues, suggesting that this variant may regulate the alternative splicing of a cassette exon in adult blood and subcutaneous and omental adipose tissue.
Hence, the regulatory variant identified is involved in the regulation of adult LEPR through a mechanism that does not alter BMI after later childhood (age, >4 years). More work is necessary to identify the impact of LEPROT mutations in weight gain and growth, as well as in the identification of the tissues and regulatory elements of the different LEPR isoforms. Our study has limitations that should be taken into consideration when interpreting the data. First, dense longitudinal growth and GWAS data are only available in a few population studies worldwide, so we had limited power to detect genetic variants with smaller effects and/or low allele frequencies. Nevertheless, a post hoc power analysis showed that we are well powered to detect the reported effect sizes in the discovery sample ( = 0.
Derivation of Age-AP, Age-AR, BMI-AP, and BMI-AR The methods used for growth modeling of age and BMI have been previously described in detail by Sovio et al. (26). Because of the specificity of longitudinal changes in BMI, i. e. , succession of peak and nadir as described in fig. S1 , the data were divided into two age windows for modeling: (i) growth in infancy using height and weight data from 2 weeks to 18 months of age and (ii) growth in childhood using growth and weight data from 18 months to 13 years of age. Each contributed most data available within any of these two age windows. In studies where the data available consisted of both height and weight data within a given window, the data point nearest to the mid time points of that window was used as a proxy for the BMI measurement. Before model fitting, age was centered using the median age of the relevant age window.
Stage 1 GWASs, genotyping, and imputation Stage 1 genome-wide association analyses included up to 7215 children of European descent from five studies (four studies for each early growth trait) that had growth data and genome-wide data. These included the Helsinki Birth Cohort Study (Finland), Northern Finland Birth Cohort 1966 (NFBC1966; Finland), Lifestyle-Immune System-Allergy Study (LISA; Germany), The Western Australian Pregnancy Cohort Study (Raine, Australia), and Generation R (The Netherlands) (figs. S2  and S3 ). Informed consent was obtained from all study participants (or parental consent, as appropriate), and the local ethics committees as appropriate approved all study protocols. Study characteristics, genotyping platform, imputation and association test software used, as well as sample and genotyping and imputation quality control steps in each stage 1 study are given in table  S1.
Stage 1 genome-wide association analyses and meta-analyses According to the availability of dense enough data for growth modeling, a total of up to 7215, 6222, 6219, and 6051 children were used to analyze PHV/PWV, Age-AP, BMI-AP, and Age-AR/BMI-AR, respectively (fig. S2 ). We only included children who were born between 37 and 41 completed weeks of gestation (i. e. , term born) from singleton pregnancies and children who had more than three growth measurements available within the age range in question. Gestational age (GA) was either defined from the date of the last menstrual period or ultrasound scans depending on the study. All six early growth traits except for Age-AP and Age-AR were naturally log-transformed to reduce skewness, and all traits were converted to z-scores before association testing to facilitate the comparison of results across the studies.
The risk of introducing collider bias was dismissed because gestational effects occur before birth, and the loci found did not overlap with GA signals in the GWAS catalog or PhenoScanner. The genome-wide association analyses (i. e. , stage 1) were performed using either SNPTEST or MACH2QTL in each cohort, and data exchange facilities were provided by the AIMS server  (40). All stage 1 study beta estimates and their SEs were meta-analyzed using the inverse-variance fixed-effects method in the METAL software  (41). SNPs with poor imputation quality (e. g. , r 2 < 0. 3 for MACH and "proper_info" score < 0. 4 for IMPUTE) and/or an HWE P < 1 × 10 -4 were excluded before the meta-analyses. Double genomic control  (42)  was applied: first, to adjust the statistics generated within each cohort and, second, to adjust the overall meta-analysis statistics.
Analysis of the phenotypic effects of the lead GWAS SNPs in published genetic studies The phenotypic implication of the lead GWAS SNP on 778 phenotypes was obtained from the Gene ATLAS (9) PheWAS conducted on 452,264 white British individuals from UK Biobank available at http://geneatlas. roslin. ed. ac. uk. In addition, we looked up the lead GWAS SNP or a proxy on published large-scale GWAS datasets using PhenoScanner  (10)  available at www. phenoscanner. medschl. cam. ac. uk/phenoscanner. Only proxy SNPs from the 1000 genomes panel in high LD (R 2 > 0. 8) with the lead GWAS SNP were considered. We then searched for all SNPs with phenotypic associations in the same chromosomal region of our lead GWAS signals using the GWAS catalog (48) available at www. ebi. ac. uk/gwas/. We reported associations obtained from these analyses with significance cutoff P < 5 × 10 -8.