dataesr/scientific-paragraphs-categorization huggingface dataset:
Practical Improvements to Genomic Systematics Reducing dataset size has practical implications for phylogenetic projects. Computation time for alignment estimation and topology testing (AU test) is reduced proportionally to the reduction in dataset size (Table  2 ). Gains in efficiency of phylogenetic inferences are 4-6 fold improvements (Table  2 ). These are significant in practice. For instance, the C100_Full tree only took 275 CPU hours to reach the optimization stop criteria, over a month faster than 265_Full (1025 CPU hours). Reducing heterogeneous loci provides the ability to simplify the evolutionary mode -avoiding over-parameterization  (Sullivan and Joyce 2005)  and further improving computation time. We did not re-estimate optimal partition and model schemes for our dataset so we could consistently compare our results with the 265_Full phylogeny.
This demonstrates that by targeting maximally phylogenetically useful loci, one can drastically reduce the monetary cost and computational resources of projects while maintaining, or even improving, the quality of the results. Thus, in addition to robustly recovering the phylogeny of Blaberoidea, this study provides an efficient workflow to infer robust phylogenomic trees with optimized datasets. The only potential drawback is decreased node support and lessened ability to do further data reduction tests. Table 1   700  Table  1   The ratio of total tip branch lengths to internal branch lengths. b Robinson-Foulds distance to the 265_Full tree c Log-likelihood of the tree given its alignment d how many times the tree was accepted (p > 0. 05) by an AU test (maximum of three). From left to right: a large multi-locus alignment is composed; the alignment is subsampled based on features hypothesized to improve phylogenetic inference (gray); phylogenies are inferred from full and subsampled alignments (dashed arrow); resulting phylogenies are compared and the subsample's phylogeny that has fewest differences (blue branches) from the full alignment's phylogeny (i.
Acknowledgements Thanks to the  1KITE consortium  who supported this research with preliminary data and advice with software. Specifically, appreciation extends to  Karen Meusemann ,  Alexander Donath ,  Bernhard Misof ,  Xin Zhou ,  Shanlin Liu ,  Ralph S. Peters ,  Lars Podsiadlowski ,  Ward Tollenaar ,  Mari Fujita , and  Ryuichiro Machida. Huge  thanks to all breeders ( Nicolas Rousseaux ,  Tristan Shanahan ,  T. J. Ombrelle  and  Piotr 480 Sterna), colleagues ( Mike Picker ), museums (MNHN, MFN, NHMUK and CAS) and curators ( Jurgen Deckert , and  George Beccaloni ) who assisted in providing specimens. Great appreciation to  New England Biolabs, MycroArray (now Arbor Biosciences) ,  Sara Ruane ,  Ciara-Mae Mendoza ,  Melissa Sanchez-Herrera ,  Steven Ramirez  and  Mihaela Glamoclija  for providing assistance in the lab.
The Regulatory Sequence Analysis Tools (RSAT, http://rsat. eu) [47, 26]  has established itself in the last 20 years as a major software suite dedicated to the analysis of regulatory regions, with five public servers supporting more than 500 eukaryote and 9,000 prokaryote genomes. With a major focus on usability and accessibility to users with or without formal bioinformatics training, RSAT provides tools to retrieve sequences, perform motifs analysis, evaluate TF motif quality, compare and cluster motifs, convert file formats, etc. Here we describe Variation-tools, a subset of tools included in RSAT that enable users to analyse regulatory variants and assess their putative impact on TF binding sites.
Dataset: experimentally-determined regulatory variants in red blood cells The regulatory activity of 2,756 red blood cell variants has been systematically measured using Massively Parallel Reporter Assays (MPRA)  [60]. MPRA is a high-throughput assay in which a library of putative regulatory elements, each followed by a unique barcode, is inserted into a plasmid, then transfected into a cell, and transcripts are then quantified through the abundance of barcodes. These variants are known to be in strong linkage disequilibrium (LD) with 75 variants associated with common traits of this cell type. Three sliding windows per variant (left, right, and center) were synthesized, barcoded and used to study the effect of slight changes in their genomic context. Following methods described by Ulirsch, et al.
Evaluation of variation-scan The variant dataset was used as input for variation-scan; the variants assessed in the red blood cell assay were annotated with the Ensembl GRCh37 human genome release, and given as input to convert-variations followed by retrieve-variation-seq. Since three sliding windows were used for each variant in the MPRA, the corresponding windows were merged before computing a background model using the create-background-model tool. According to the original study  [60] , binding sites for the following TF were enriched in the sequences of interest: GATA1, KLF1, DHS, TAL1, ETS, FLI1 and AP-1. Therefore, a total of 48 PSSMs annotated as related to these TF were retrieved from the nonredundant RSAT motif collection  [8] , and given as input to variation-scan. A negative control set of motifs was created using the RSAT tool permute-matrix  [47] ; five pemuted motifs were created for each of the 48 motifs, generating a collection of 240 control motifs.
2. 6. Case studies 2. 6. 1. Case study 1: Identification of regulatory variants in the ''Platinum" genomes haplotypes The set of high-confidence variants from the two CEU (Northern Europeans from Utah) human Platinum Genomes NA12877 and NA12878  [23]  were downloaded through the Amazon Web Service (AWS) Command Line Interface from the Illumina Platinum Genomes AWS S3 bucket (https://github. com/Illumina/Plat-inumGenomes). The downloaded VCF files contained phasing information of each CEU individual haplotype configuration. The genome version used was GRCh37. We selected SNPs intersecting with the annotated DNAseI-seq clustered peaks V3 from the ENCODE project  [4]. The VCF file with the selected SNPs was processed using convert-variations with the option phased and then the haplotype sequences were reconstructed with retrieve-variation-seq.
8) and a maximal distance of 200 bp. Annotations (chromosomal location, type of genomic region) of the resulting 612 SNPs (48 DA + 564 LD) were collected from Ensembl BioMart  [22, 21]. We then restricted the selection to SNPs in non-coding regions, resulting in a set of 572 SNPs of interest (SOIs) for the detection of regulatory variants. Using SNPs in LD, we determined LD-Block regions. These were then annotated based on overlaps with ChIP-seq peaks collected from the ReMap database  [10]. We also calculated enrichment for disease annotations using the R XGR package  [24]. Finally, we used retrieve-variation-seq to retrieve the sequence variants around each SOI, and predicted the impact of the variation on TF binding for each motif of the JASPAR non-redundant RSAT motif collection  [8]  using variation-scan, with the thresholds of 1e-4 on the p-value and 100 on the p-value ratio.
Availability Variation-tools are available on the web (Metazoa: http://metazoa. rsat. eu/, Plants: http://plants. rsat. eu/, Teaching: http://teaching. rsat. eu/). The tools can be also installed for command-line usage with the RSAT suite (http://download. rsat. eu/). The code and material to reproduce the results presented in the article can be accessed through GitHub (https://github. com/RSATdoc/supp-material-publications. git).
Focusing on the variants reported as positive in the MPRA data set, we observed a weak correlation between the weight difference and the MPRA mRNA/DNA ratio in positive variants. However, this correlation is not significant, as MPRA values do not scale with the variation-scan weight differences. Nevertheless, all variants show a p-value ratio indicative of allele binding effects, showing that variation-scan gives accurate measurements of the impact of regulatory variants (Fig. 2A ). With the proposed thresholds, we can confidently reject 96. 35% of MPRA negative sequences, which could be improved using more restrictive parameter, with a concomitant reduction in true positives. Noteworthy, as any high-throughput assay, MPRA has its limitations  [51]  and sequencing biases could increase the number of false negatives.
B) Receiver Operating Characteristic (ROC) curve comparing the performance when aiming to classify MPRA experimentally analyzed variants using variation-scan (turquoise), DeepSea (purple), deltaSVM (green), and a negative control which consists of permuted motifs scored with variation-scan (red). Fig. 3. Haplotype analysis in high-quality human genomes. A) The number of heterozygous variants (X-axis) within the same putative binding site tend to have a greater impact on the TF binding probability. This is expected as the increase of weight difference observed on the violin plot corresponds to the expected cumulated impact of variations affecting different positions of the same binding site. B) Number of predicted disrupted Transcription Factor Binding Sites (TFBSs) with Cis-Regulatory Modules (CRMs) and TF ChIP-seq peak annotation (blue), with only peak annotation (yellow), and non-annotated predictions (grey).
C) University of California Santa Cruz (UCSC) browser  [48]  screen shot, showing a locus encompassing two SNPs that compose an heterozygous haplotype in one of the Northern Europeans from Utah (CEU) individuals. The figure shows the reference genome haplotype. The variants are located in the FUT10 promoter (top). variation-scan predicts an effect in three motifs that represent binding sites for GABPA, ETS1 and ELF2, factors that have been proven to have binding sites in this region by the ENCODE project. The variant rs2732317 has been associated with effects in gene expression by the GTEx project. Interestingly, the 572 SNPs of interest (SOIs, see methods) show a significant enrichment for diseases related to respiratory functions (lung carcinoma, respiratory neoplasm, squamous cell carcinoma, lung disease), as well as for schizophrenia (Fig.
We identified five and twelve GWAS reported variants falling within the reported coordinates of ePromoters corresponding to the human cell lines HELA and K562, respectively. Using variation-scan with the motifs of TF reported as enriched in ePromoters, we were able to detect two variants (rs3771180, rs3822259) affecting two TF binding motifs in HELA (MAF::NFE2, FLI1/FEV/ETS2/ELK4/ELK4/GABP1/Gabpa), and two variants (rs147997200, rs62229372) affecting three binding motifs in K562 (Creb3l2, Atf3/MAFG::NFE2L1/MAFG/NF2L1, FOS::JUN). Noteworthy, in HELA ePromoters, we found the SNP rs3771180, which is also described as an eQTL in the whole blood dataset by the GTEx project. This variant has been associated with asthma and is upstream the Interleukin Receptor 1 gene, which is consistent with ePromoters related to inflammatory response in HELA  [17].
Depending on the biological question, users should decide to use more or less restrictive thresholds. As shown in Case Studies 3 and 4, when the biological hypothesis is well defined, lower thresholds return manageable numbers of predictions with interesting biological insights. For more general biological questions, as in Case Studies 1 and 2, using a larger number of data, we recommend to select more stringent thresholds to reduce the number of false positives, and thereby focus the analysis on the best predictions. Regarding the selection of a particular set of motifs, there are multiple databases installed within RSAT, which provide easy access to several reference collections (i. e. JASPAR, HOCOMOCO, etc. ). The selection will depend on the biological question. For some TF and TF families there are structural descriptions of the protein-DNA interfaces.
Conclusions Variation-tools enables the prediction of the effect of sequence variants on TF binding. In addition to reasonable computing time, the focus is put on usability and high flexibility: annotated variants can be retrieved from specific genomic loci, as well as from personal collections of variants, motifs (provided as PSSMs) can be chosen from the collections available in RSAT (JASPAR, HOCO-MOCO, CisBP, etc. ), as well as from user-provided PSSM sets. The tools supports various organisms in selected RSAT servers: currently Metazoa, Plants and Teaching. In addition to the web interface, Variation-tools can also be used on the command line to facilitate analysis of custom data sets. Variation-tools can be used in combination with external databases, as exemplified with the study of GWAS data.
Acknowledgements We thank the persons contributing to the maintenance of the RSAT servers, in particular  Laboratorio Nacional de Visualización Científica Avanzada (Mexico)  specially  Luis Alberto Aguilar Bautista  and  Jair Garcia Sotelo , the  ABims platform in Roscoff, France ,  Pierre Vin -cens at the  ENS, Paris and Aurora Martín Cotaina from EEAD-CSIC  for her help on managing the Plants server. We acknowledge  Salvatore Spicuglia  for useful comments during the development of the tools. We thank  Lambert Moyon  and  Swann Floc 'hlay for providing feedback on the use of Variation-tools. We thank  Alejandra Castillo  and  Carina Uribe  for technical assistance. We thank  Mauricio Guzman  for styling the figures.
edu/ for reference and alternative profiles for 160 different static. update atSNP BayesPI-BAR 26202972 26092860 GWAS4D 29771388 sTRAP 20127973 job/analysis/ create/ https:// github. com/ keleslab/atSNP http://folk. uio. no/ junbaiw/BayesPI-BAR/ http://mulinlab. tmu. edu. cn/ gwas4d/gwas4d/ gwas4d/gwas4d_ server http://trap. molgen. mpg. de/cgi-bin/ home. cgi Importance sampling algorithm for p-value calculation, first-order Markov Model to generate random background sequences. Biophysical modeling of protein-DNA interaction, estimation of TF chemical potential (through a bayesian nonlinear regression model) and differential binding affinity. Variant prioritization method, followed by an integrative analysis of genome-wide association. Prediction of local binding affinity followed by a normalization of binding affinities to determine Any organism whose genome is included in the Bioconductor BSGenome package.
Therefore, a total of ten focus stocks were considered in OSMOSE-PNCIMA, i. e. Herring-PRD, Herring-HG, Herring-CC, Cod-HS, Cod-QCS, Lingcod-HS, Lingcod-QCS, Walleye Pollock, Arrowtooth Flounder, and Euphausiids. OSMOSE-PNCIMA simulated the life cycles of the ten focus stocks, from the egg stage to the terminal age, with a time-step of 3 months. At the first time-step after the production of eggs, the total number of eggs of each population was split into 120 super individuals called "schools," which were distributed spatially according to input distribution maps. The distribution maps (10 Â 10 km 2 ) were density-based and obtained from georeferenced data of both commercial fisheries and research surveys (data archives are maintained by Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, British Columbia).
, 2018) , data availability  (Forrest et al. , 2015a; Holt et al. , 2016) , and sometimes environmental variability  (Froese et al. , 2011; Little et al. , 2011). Thus, further investigation of system-and stock-specific HCRs for Pacific Cod, and especially Lingcod was warranted in future research. Moreover, evaluation of the performance of alternative HCRs for stocks in a feedback simulation context was also important for evaluating acceptable outcomes and trade-offs  (Haltuch et al. , 2009; Cleary et al. , 2010; Forrest et al. , 2018). More advanced simulations using the OSMOSE platform were underway for obtaining stock-specific thresholds of HCRs for these predatory stocks. Such ecosystem simulations can further explore alternative decisions on harvest rates and cutoff thresholds that explicitly consider the trade-offs among multiple ecosystem objectives, such as maintaining socioeconomic outcomes, biodiversity and ecosystem health  (Fulton et al.
Acknowledgements We thank  Jackie King  and  Jennifer Boldt  for providing valuable comments on the earlier version of the manuscript. We are grateful to the following colleagues (listed alphabetically) who have generously provided different types of data:  Leslie Barton ,  Jaclyn Cleary ,  Kristen Daniel ,  John Ford ,  Moira Galbraith ,  Chris Grandin ,  Kendra Holt ,  Jim Irvine ,  Sheena Majewski ,  John Morris ,  R. Ian Perry ,  Ian Stewart ,  Matt Thompson , and  Marc Trudel.
EwE has three main modules: (i) Ecopath, a static mass-balanced snapshot of the system; (ii) Ecosim, a dynamic simulation module; and (iii) Ecospace, a spatial and temporal dynamic module. EwE provides valuable information about ecosystem functioning; however, it considers only extraction of organisms from the ecosystem by fisheries and overlooks relevant aspects related to the performance of fishing. For instance, fuel consumption was found to be an important driver behind environmental impact of fisheries  (Thrane 2004; Tyedmers et al. 2005). The model does not consider effects of the energy and material used to construct and maintain fishing vessels and gear  (Hayman et al. 2000; Ziegler et al. 2003)  and the use of paint and antifouling paint  (Hospido and Tyedmers 2005). LCA considers these aspects, which are crucial to establish a robust and complete impact assessment of fishing.
The main objective of the LCA was to analyze the contribution of each production stage to environmental impacts and to understand drivers of the impacts associated with demersal trawling. Thus, impacts were not allocated between coproducts. The LCI was based on data collected from official DGPA records (e. g. , landings, length, tonnage, engine type, engine power, lightship weight). Additional data were obtained from surveys of demersal trawler skippers and fishermen in the port of Sfax, the main fishing port in the Gulf of Gabes, and of demersal trawler builders in the shipyard. Gathered data included trawlers' operational aspects (e. g. , fuel consumption, number of fishing trips, and number of days at sea) and information about vessel construction (e. g. , material used for construction, paint and antifouling paint quantities, dimensions of vessels, life span).
Collected data supplement and validate official statistics. Mean lifespan of wooden vessels in the Gulf of Gabes is estimated at 40 years and maintenance and repair of vessels are taken into account by adding 25% of the total amount of wood used for vessel construction  (Tyedmers 2000). The composition of paint and antifouling paint was obtained from material safety data sheets and validated by managers of the principal paint-producing company in Tunisia, and their emissions to water were estimated based on the assumption that two-thirds of the paint and antifouling paint used is released into the water  (Hospido and Tyedmers 2005). Required background data were extracted from the ecoinvent 3. 0 database  (Weidema et al. 2013). See  Abdou et al. (2018a)  for further details about the method.
To estimate impacts, data were aggregated into 11 midpoint impact categories that reflect the environmental issues associated to fisheries and seafood production system. The selection was based on previous guidelines in fishery LCA studies  (Avadí and Fréon 2013). Impacts were calculated per ton of seafood produced using SimaPro® 8. 0 software  (Goedkoop et al. 2008)  A full description of the LCI and impact categories is found in  Abdou et al. (2018a). All impact categories were calculated for an average demersal trawler in the Gulf of Gabes.
Ecopath with Ecosim modeling Principles, basic concepts, and assumptions of the EwE modeling approach are described in detail in  Christensen et al. (2008) ;  Christensen and Walters (2004) ;  Walters et al. (1997). EwE has three main components: & Ecopath: a static snapshot of interactions among functional groups in an ecosystem  (Christensen and Walters 2004). Ecopath is built from two linear equations: one for mass balance and one for energy balance. & Ecosim: a dynamic simulation model based on balanced Ecopath parameters. It enables exploring effects of fishing options and changes in ecosystem functioning  (Christensen and Walters 2004). & Ecospace: a spatially explicit dynamic module of EwE (www. ecopath. org; Christensen and Walters 2004; Walters et al. 1999). It integrates trophic and temporal dynamics of Ecopath and Ecosim in a two-dimensional space.
It includes the main fleets operating in the area: demersal trawling, small seines, tuna purse seines, purse seines using lights, coastal motorized fishing, and sponge fishing. Landing statistics were obtained from D G PA. F u r t h e r d e t a i l s o f d a t a r e s o u r c e s a n d parameterization are found in  Hattab et al. (2013). The Ecosim approach was used to carry out dynamic simulation using the parameters from the balanced Ecopath model. Landing data, time series of fishing effort by fishing gear, and stock assessment estimates were collected by the Tunisian National Institute of Sciences and Technologies of the Sea. Ecosim was fitted to landings for the period 1995-2008. Primary production in the study area for the period 1997-2007 (data from SeaWIFS project, http://oceancolor.
This study developed a framework to supplement the LCA of seafood production with ecosystem indicators from Ecopath with Ecosim. The Gulf of Gabes was used as a case study to conduct the LCA, which was combined with the EwE model to provide a complete assessment of the environmental performance and ecosystem characteristics associated with production of seafood landed by demersal trawlers. LCA results showed that fuel consumption by fishing vessels, fuel production, and paint and antifouling production were the main contributors to environmental impacts. Ecosystem indicators from the EwE model provide valuable information to conduct environmental analysis using LCA and place it in the context of an ecosystem approach to fisheries. The Ecospace module of EwE was used to simulate management scenarios.
Funding information The authors would like to acknowledge valuable financial support from the "Institut de Recherche pour le Développement" (JEAI GAMBAS project). This study was partially funded by the "ICV pêche" project and the "LabexMer" (ANR-10-LABX-19). Open Access This article is distributed under the terms of the Creative Comm ons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
On the other hand, a non-observation of 0ν2β would imply an upper bound on m 5 , see Fig. 4(b), (c ) (e) or (f), and therefore one would expect to detect the second sterile neutrino in a low energy experiment. An interesting case of the latter situation would be when the second sterile neutrino has a mass m 5 lying also in the KeV regime, since KATRIN could potentially observe it as a second kink, as shown in Fig. 5(a). Finally, we explore the case where both sterile neutrino masses are below 18. 5 KeV, thus no signal in 0ν2β is expected whatever is the ordering of the light neutrino mass spectrum. As already said, KATRIN could then signal the presence of one kink or two kinks, depending on the remaining physical parameters. This is exemplified in Fig. 5(b) , where the scatter plot shows viable solutions imposing the lightest sterile neutrino within KATRIN sensitivity and complying with neutrino data (for NO in this case) and the relevant constraints (blue points).
Acknowledgments The authors would like to thank  S. Mertens  for fruitful discussions. They are also thankful to  C. Garcia-Garcia  for her help and suggestions. We acknowledge partial support from the  European Union   Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie :  RISE InvisiblesPlus  (grant agreement No  690575 ) and the  ITN Elusives  (grant agreement No  674896 ).