dataesr/scientific-paragraphs-categorization huggingface dataset:
The region from 8 Mb to 10 Mb containing amh is zoomed in panel (b). c The National Center for Biotechnology Information (NCBI) Accession number NW_020523543. 1 unplaced scaffold exhibits a region around 0. 1 Mb harboring a small region (200 kb) with a high-density of male-specific SNPs. Meanwhile, sequence comparisons demonstrate that 7 malebiased RAD-tags (colored circles) on a total of 32 map with a high identity onto this scaffold. In contrast, few female-specific SNPs were enriched on this scaffold (red area). d The NCBI Accession number NW_020523609. 1 unplaced scaffold is enriched in male-specific SNPs mechanism that subsequently becomes fixed and extended by the suppression of recombination on the sex chromosome in the vicinity of the initial sex locus, which may increase the size of this non recombining sex determination locus  [49].
DNA extraction and genotyping For genotyping, fin clips were lysed with 5% Chelex and 20 mg Proteinase K at 55 °C for 2 h, and subsequently denatured by Proteinase K at 99 °C for 2 min. Supernatant containing genomic DNA (gDNA) was collected to a new tube after a brief centrifugation. Finally, DNA was diluted to half and stored at -20 °C. For genome sequencing, gDNA was extracted with NucleoSpin Kits for Tissue (Macherey-Nagel, Duren, Germany) following the manufacturer's instructions. gDNA concentration and quality were measured with a NanoDrop ND2000 spectrophotometer (Thermo Scientific, Wilmington, DE) and a Qubit3 fluorometer (Invitrogen, Carlsbad, CA). Primers were designed from the sequences of malebiased contigs for sex genotyping and a positive control (Table  S1 ) based on our Illumina male genome assembly (National Center for Biotechnology Information Accession number: WSJC000000000 [https://www.
ncbi. nlm. nih. gov/ nuccore/WSJC000000000]) using Primer3 version 0. 4. 0 (http://primer3. ut. ee). These male-specific primers were found to share some sequence similarity with regions located in two unplaced contigs (National Center for Biotechnology Information Accession numbers: NW_020523543. 1 [https://www. ncbi. nlm. nih. gov/nuccore/NW_020523543. 1], NW_020525535. 1 [https://www. ncbi. nlm. nih. gov/nuccore/ NW_020525535. 1]) and LG8 from the goldfish reference genome. Search for homologies using Blast shows that one primer pair is located in the guanylate-binding protein 1like gene (gbp1-like), while the two others are located in transposons with annotations corresponding to putative transposase element L1Md-A101/L1Md-A102/L1Md-A2 and Retrovirus-related Pol polyprotein LINE-1. PCRs were performed with 0.
KK4824). The library was sequenced on one lane of Hiseq2500 in single read 100 nt mode using the clustering and SBS v3 kit following the manufacturer's instructions. Raw reads were demultiplexed with the program process_radtags. pl of Stacks with default settings. 135, 019,110 (79. 1%) reads were kept after this procedure. Demultiplexed reads were subsequently processed by the RADSex software version 2. 0. 0 (http://github. com/ RomainFeron/RadSex). The distribution of sequences between male and female were calculated with function distrib with all settings to default. This distribution of sequences was visualized with plot_sex_distribution function of radsex-vis (http://github. com/RomainFeron/ RADSex-vis) (Fig. 1a ). Sequences significantly associated with sex were extracted using the function signif, which identifies sex-bias tags.
Libraries were purified using magnetic beads, checked on a Fragment Analyzer (Agilent) using the HS NGS Fragment kit (DNF-474-33) and quantified by qPCR using the KAPA Library quantification kit (Roche, ref. KK4824). Each library was sequenced on half a lane of a rapid v2 flow cell (Illumina) in paired end 2x250nt mode. Reads from the male and female pools were remapped to a genome sequence coming from a gynogenesis-derived female (National Center for Biotechnology Information Accession number: QPKE00000000 [https://www. ncbi. nlm. nih. gov/nuccore/QPKE00000000]) using BWA mem version 0. 7. 17 with default parameters. Then, BAM files were sorted and merged with Picard tools version 2. 18. 2 with default parameters. After that, PCR duplicates were removed with Picard tools. Reads with mapping quality less than 20 and that did not map uniquely were also removed with Samtools version 1.
8. Subsequently, the two sex BAM files were used to generate a pileup file using samtools mpileup with per-base alignment quality disabled (-B). A sync file was created using popoolation mpileup2sync version 1. 201 (parameters: --min-qual 20), which contains the nucleotide composition of each sex for each position in the reference. Finally, with this sync file, SNPs and coverage between the two sexes of all reference positions were overall calculated with PSASS (version 2. 0. 0, doi:https://doi. org/10. 5281/zenodo. 2615936). We used a 100 kb sliding window with an output point every 500 bp to identify sex-specific SNPs enriched regions with PSASS. The PSASS parameters were as follows: minimum depth set to 10 (--min-depth 10), range of heterozygous SNP frequency for a sex-linked locus 0. 5 ± 0.
2 (--freq-het 0. 5, --range-het 0. 2), homologous SNP frequency for a sex-linked locus > 0. 98 (--freqhom 1, --range-hom 0. 02), overlapped sliding window (--window-size 100,000, --output-resolution 500). Data visualization was implemented with an R package (http:// github. com/RomainFeron/PSASS-vis). Sequencing and de novo assembly of a goldfish male genome One genetic male was selected for de novo assembly using the Y-specific primers described above. Library was generated using the Truseq nano DNA sample prep kit (Illumina, ref. FC-121-4001) following the manufacturer's instructions. Briefly, DNA from a single male individual was sonicated using a Bioruptor (Diagenode). The sonicated DNA was repaired, size selected on magnetic beads aiming for a 550 pb insert size and adenylated on its 3′ ends.
Adenylated DNA was ligated to Illumina's specific adapters and, after purification on magnetic beads, was amplified in an 8 cycles PCR. Library was purified using magnetic beads, checked on a Fragment Analyzer (Agilent) using the HS NGS Fragment kit (DNF-474-33) and quantified by qPCR using the KAPA Library quantification kit (Roche, ref. KK4824). The library was sequenced on one lane of a rapid v2 flow cell (Illumina) in paired end 2*250 nt mode. Illumina pairedend reads were assembled using DiscovarDeNovo (reference https://software. broadinstitute. org/software/discovar/ blog/) with standard parameters.
Funding This project was supported by funds from the  "Agence Nationale de la Recherche" and the "Deutsche Forschungsgemeinschaft" (ANR/DFG ,  PhyloSex  project,  2014-2016 ) to MS and  YG. Montpellier Genomics  (MGX) facility was supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). This work was also supported by  Grant-in-Aid for Scientific Research  ( 19 K22426 ) to YO, and grants  R01OD011116  and  5R01GM085318  from the  USA National Institutes of Health  to JHP. INRAE-U3E was supported by a  PEARL INRAE   1036 U3E  funding from the  ANAEE-France National infrastructure.
Availability of data and materials This Whole Genome Shotgun project has been deposited in the National Center for Biotechnology Information DDBJ/ENA/GenBank databases under the accession number WSJC000000000 [https://www. ncbi. nlm. nih. gov/ nuccore/WSJC000000000], The version described in this paper is version WSJC010000000. Genome sequencing reads of the male genome, the male and female pool-sequencing reads and the RAD-seq demultiplexed sequences have been deposited in the National Center for Biotechnology Information Sequence Read Archive (SRA) database, are publicly available under the BioProject accession number PRJNA592334. A gynogenesisderived female assembly containing two unplaced (NW_020523543. 1 = https://www. ncbi. nlm. nih. gov/nuccore/NW_020523543. 1/, NW_020525535. 1 = https://www.
Directed protein networks with only a few thousand of nodes are rather complex and do not allow to extract easily the effective influence of one protein to another taking into account all indirect pathways via the global network. Furthermore, the different types of activation and inhibition actions between proteins provide a considerable challenge in the frame work of network analysis. At the same time these protein interactions are of crucial importance and at the heart of cellular functioning. We develop the Google matrix analysis of the protein-protein network from the open public database SIGNOR. The developed approach takes into account the bi-functional activation or inhibition nature of interactions between each pair of proteins describing it in the frame work of Ising-spin matrix transitions.
Introduction Protein-protein interactions (PPI) are at the heart of information processing and signaling in cellular functions. It is natural to present and analyze these PPI by presenting them as a directed network of actions between proteins (or network nodes). The simplest case of action is activation or inhibition so that such networks can be considered as bi-functional. The development of related academic databases of PPS networks with an open public access is a challenging task with various groups working in this direction (see e. g. [1] ,  [2] ,  [3] ,  [4] ,  [5] ). A typical example is the SIGNOR directed network of PPI links for about 4000 proteins of mammals and 12000 bi-functional directed links as reported by  [2]. URL: http://www. quantware. ups-tlse. fr/dima (Dima L. Shepelyansky) On the scale of the past twenty years, modern society has created a variety of complex communication and social networks including the World Wide Web (WWW), Facebook, Twitter, Wikipedia.
The efficiency of these algorithms has been demonstrated for Wikipedia networks of politicians  [10]  and world uni-versities  [11] ,  [12]  and multi product world trade of UN COMTRADE database  [13]. Thus it is rather natural to apply these algorithms to PPI networks which have a typical size being significantly smaller than Wikipedia and WWW. From a physical view-point the LIRGOMAX approach corresponds to a small probability pumping at a certain network node (or group of nodes) and absorbing probability at another specific node (or group of nodes). This algorithm allows first to determine the most sensitive group of nodes involved in this pumping-absorption process tracing a pathway connecting two selected proteins. In a second stage one can then apply the REGO-MAX algorithm and obtain an effective reduced Google matrix, and in particular effective interactions, for the found subset of most sensitive nodes.
INTRODUCTION Seventy-nine percent of internet users use Facebook, and on average they access Facebook eight times a day  (Greenwood et al. , 2016). To put these numbers into perspective, according to  Clement (2019) , around 30% of the world's population uses this Online Social Network (OSN) site. Despite the constantly growing body of academic research on Facebook  (Chou et al. , 2009; Back et al. , 2010; Kaplan and Haenlein, 2010; McAndrew and Jeong, 2012; Wilson et al. , 2012; Krasnova et al. , 2017) , there remains limited research regarding the motivation behind Facebook use across different cultures. Our main goal was to collect data from a large cross-cultural sample of Facebook users to examine the roles of sex, age, and, most importantly, cultural differences underlying Facebook use.
g. ,  Conroy-Beam et al. , 2019a,b). (  1 ) The instructions for completing the Facebook scale were as follows: "Do you use Facebook? Yes/No (if no, please continue to the next scale); (2) Please use the scale below (ranging from 1-very rarely, to 5-very often) to assess how often you use Facebook for the following purposes" (see Measures below). Participants completed questionnaires in ∼30 min. Data were collected simultaneously across all locations, and then coded, standardized, and merged into one dataset.
Strengths and Limitations The present dataset has several strengths, which distinguish it from other studies: (1) it was conducted on a large number of participants (N = 11,765); (2) we considered six different regions of the world (Africa, Asia, Australia, Europe, Latin America, and North America), some of which have only been included in a handful of previous studies (e. g. , Australia & Asia:  Hsu et al. , 2015; Asia: Kim et al. , 2011; Makashvili et al. , 2013)  ; (3) all participants filled in the same questionnaires; (4) all persons took part in the study in the same years; (5) we measured additional variables, which may be useful in further analyses, replications and extensions of the current research (i. e. , sex, age, education level, years of study, economic situation, and religious affiliation).
Despite these contributions, the present dataset has several limitations that hinder drawing general conclusions. First, the study samples from each of the study sites are not representative for the whole of each country, as half of the participants were students, and the other half originated from the local community; some sub-populations or cultural groups may have been excluded. Second, our questionnaire did not include any items related to entertainment (for instance, games), which is a significant omission, as playing games on Facebook has been shown to be a popular activity  (Wohn and Lee, 2013). Third, as our questionnaire consisted of one-item questions regarding various reasons for using Facebook, a reliability analysis could not be performed. Last, but not least, we have not collected data on participants' frequency of Facebook use, Facebook membership duration, and their number of Facebook friends.
Possible Research Paths Based on the presented dataset, scholars can conduct numerous analyses concerning a broad range of research questions. They can explore reasons for Facebook usage in various cultures, with regard to, for instance, age, sex, country of origin, culture, and numerous other variables. Preliminary analyses (see: https://figshare. com/s/68f306f31958d9a5c0c0) presented statistically significant differences in the range of all identified, individual reasons for Facebook use across continents (p < 0. 001), but the effect sizes for the given differences were rather small (ranging from 0. 01 to 0. 10). The highest effect size was observed in presenting opinions (d = 0. 102). Furthermore, the analysis indicated that there are significant sex differences in all reported reasons for using Facebook, except for presenting opinions and writing private messages.
However, the effect sizes for sex differences were small (from dating, d = 0. 211, higher among men; to reducing the sense of boredom, d = -0. 147; higher among women). Additionally, there were statistically significant but generally weak relationships between age and several of the reported reasons behind Facebook usage. Nevertheless, age explained <1% of the variation in any declared purpose for Facebook usage. In future analyses, the present dataset and aforementioned differences could also be investigated on a cross-cultural level. In addition, scientists may apply these data to identify other country-level predictors of Facebook usage. For example, the degree of Facebook usage: degree of individualistic/collectivistic values preferred within a given culture, which can be easily obtained from other online sources (e.
Alternative branchline weighting designs To identify alternative designs that enable placing pelagic longline branchline weights immediately adjacent to the hook during setting, the Western and Central Pacific Fisheries Commission's Bycatch Management Information System online database of references, https:// www. bmis-bycatch. org/ references, was searched by filtering for species group: seabirds; fishing gear: longline; and mitigation technique: 'line weighting and bait sink rate. ' The Consortium for Wildlife Bycatch Reduction's online database of references (https:// www. bycatch. org//) was searched for relevant studies and filtered for hook-and-line fishing gear and seabird species, for both field and non-field studies. A Boolean search string using various combinations of the terms longline, seabird, weight, weighting, branchline, line, sink rate, pelagic and bycatch, as well as various operators, was conducted in Google Scholar.
Specifically, we used the catch and effort from the 15 longline sets sampled in this fishing experiment for each of the 15 captured species. We then used those data to derive the speciesspecific log relative risk ratio and variance using the 'escalc()' function in the 'metafor' package for R  (Viechtbauer 2010). We fitted a Bayesian regression model with Gaussian likelihood to those standardized effects sizes to estimate the overall log relative risk of capture on an experimental branchline, with species as the random effect -this is also known as a normal-normal hierarchical model and is especially useful for modeling studies such as ours with small sample sizes  (Günhan et al. 2020). This model was fit using the Stan computation back-end (Carpenter et al. 2017) via the 'brms' interface for R (Bürkner 2017).
We applied this model separately to the assemblage of retained species (n = 12) and then to the assemblage of discarded species (n = 3). We then summarized the results separately for the retained and discarded species in forest plots of the species-specific posterior densities to display the model-predicted mean posterior estimates and 95% credible intervals (see  Schild & Voracek 2015  for an overview of various forms of forest plot). We used the back-transformed posterior samples to calculate the percent risk reduction in catch risk for experimental branchlines. Here we used the highest posterior density interval (HDI) as our measure of uncertainty (Kruschke & Liddell 2018). The HDIs were summarized from the posterior samples for each meta-analytic model fit using the 'tidybayes' package for R (Kay 2019).
Acknowledgements. We are extremely grateful for the participation of  Captain Michael Wild , first mate  Aga Oliveros  and the crew of the  FV 'Kilaeua ,' vessel manager  Darleen Dela Cruz  and owner Michael Ostendorp. We are thankful for the contributions of the following Hawaii longline fishermen to the survey:  Scott Barrows ,  Jim Cook ,  Micah DeLong ,  John Haddad ,  Sean Martin ,  Richard McQuade ,  John Myking ,  Joe Viernes ,  Craig Yeackel , and 3 fishermen who chose to remain anonymous. We thank the journal editor and anonymous reviewers for their insightful and constructive comments. This study was made possible through a grant from the  Bycatch Reduction Engineer Program ,  National Marine Fisheries Service , award number  NA19 NMF 4720221. LITERATURE CITED ACAP (Agreement on the Conservation of Albatrosses and Petrels)  (2014) Report of the  Seabird Bycatch Working Group.
Anim Conserv 22: 376-385 Kay M (2019) tidybayes: tidy data and geoms for Bayesian models. R package version 1. 1. 0. http: // mjskay. github. io/ tidybayes/ Kazama K,  Harada T ,  Deguchi T ,  Suzuki H, Watanuki  Y (2019) Foraging behavior of black-footed albatross Phoebastria nigripes rearing chicks on the Ogasawara Islands. Ornitholog Sci 18: 27-37 Kruschke JK, Liddell TM (2018) The Bayesian new statistics: hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychon Bull Rev 25: 178-206 Lajeunesse MJ (2011) On the meta-analysis of response ratios for studies with correlated and multi-group designs. Ecology 92: 2049-2055 Løkkeborg S (2011) Best practices to mitigate seabird bycatch in longline, trawl and gillnet fisheries -efficiency and practical applicability.
) Heynh (Brassicaceae) is a self-fertilizing and annual species with a worldwide distribution. Several researches about the natural genetic and phenotypic variation of A. thaliana along altitudinal gradients have been published  [27] [28] [29] [30] [31] [32] [33] [34]. These studies have highlighted the importance to combine multi-level datasets (e. g. , environmental, genetics, morphological). Taken together, the results show strong links between the spatial distribution of plants and the genetic diversity, potentially reflecting adaptation to low or high altitude. The phenotypic differentiation may result from interactions between adaptation to local microhabitat and the demographic history influenced by glaciation cycles or by the recent seed dispersal  [35]. A. thaliana capacity to adapt to diverse environmental conditions make it a very appropriate model for phenotypic plasticity studies  [16, 36, 37].
To tackle the complex mechanisms of adaptation to sub-optimal temperatures, a systems biology approach combining climate parameters, phenomics, metabolomics, proteomics and transcriptomics has been developed. The statistical integrative analysis of the various heterogeneous omic datasets using the mixOmics package  [39, 40]  allowed identifying new genes/proteins possibly involved in the phenotypic plasticity responses of the four Pyrenean populations and the Columbia ecotype (Col) as a reference to temperature changes, with a specific focus on the CW.
Macrophenotyping Rosettes of plants grown at 22 and 15 • C were analyzed at the time of sampling. We have measured their diameter and fresh mass. The number of leaves was counted. Before freezing, pictures were taken to measure the rosette areas with the ImageJ software  [42]. Regarding the floral stems, the length, the number of cauline leaves, the mass and the diameter at the base of the floral stem were measured before freezing. Data are described in  [43].
Identification of Proteins by LC-MS/MS The identification of proteins extracted from CWs was performed by LC-MS/MS at the PAPPSO proteomic platform (pappso. inrae. fr) after tryptic digestion in solution as described  [48]. Parameters for MS data processing in the X!Tandem software (JACKHAMMER, 2013. 6. 15, www. thegpm. org/tandem/) and the X!Tandem Pipeline 3. 3. 4 [49]  are detailed in  [48]. Tryptic digestion was declared with no possible miscleavage. Only proteins identified with at least two different specific peptides in the same sample and found in at least two biological replicates were validated. Furthermore, quantification was performed on peptides with standard deviation retention times lower than 20 s and peak width lower or equal to 100 s. Raw data are described in  [50].
Bioinformatics Annotation of Proteins and Quantification The prediction of sub-cellular localization and functional domains of proteins was performed with the ProtAnnDB tool (www. polebio. lrsv. ups-tlse. fr/ProtAnnDB/) [51]. A protein was considered as a CWP if two bioinformatics programs predicted it as secreted, no intracellular retention signal was found and no more than one trans-membrane domain was found as described  [52]. We have checked the prediction of the cell wall localization of many of the identified CWP families by doing a literature search as well as a mining of the WallProtDB database dedicated to cell wall proteomes (www. polebio. lrsv. ups-tlse. fr/WallProtDB). Quantification was only operated for CWPs using the MassChroQ software  [53]  as in  [54]. The functional annotation of CWPs is given according to WallProtDB.
Statistical Analysis and Data Integration Data analysis and methodology were achieved as described  [40]. We have performed multi-block supervised analyses with the sparse version Multi-Block Partial Least Squares Discriminant Analysis (MB-PLS-DA) implemented in the mixOmics R package version 6. 1. 1. (http://www. bioconductor. org/ packages/release/bioc/html/mixOmics. html) [39]. They have taken into account all phenomics and metabolomics variables associated to a restrictive pool of 40 CWPs and 40 genes having modified transcript levels and encoding CWPs identified in the proteomic studies. To improve the visualization, the clustered image map was done with the high scoring 20 proteins and 20 genes and all the variables of the phenomics and metabolomics blocks.