dataesr/scientific-paragraphs-categorization huggingface dataset:
IMD/Relish pathway at least partly controls the expression of microbiota-regulated metabolic genes Relish encodes the Drosophila orthologue of the mammalian NF-kappaB factor p105, which functions downstream of the IMD signaling pathway and controls the induction of most IMD target genes  [30]. Buchon et al. studied the midgut transcriptome of Relish mutants and found that changes in the expression of midgut genes upon Ecc15 infection are largely Relish-dependent  [31]. Strikingly, from our list of genes, the vast majority of the 52 microbiotaregulated genes influenced by Ecc15 infection are also Relishdependent (41/52; Fig. 2 ). Based on the dataset from Buchon et al. , we derived the information of Relish dependence for the basal expression level of each midgut gene in CONV animals in their study, and compared this dataset with our list of microbiotaregulated genes.
Microarray analysis Three biological replicates of 20 females for each condition (GF or poly-associated) were homogenized in 500 ml of Trizol (Invitrogen) and 100 ml Choloroform using 0. 75-1 mm glass beads and the Precellys24 Tissue Homogenizer (Bertin Technol-ogies, France). RNA pools were isolated and purified using NucleoSpin RNA kit (Macherey-Nagel). RNA was quantified on NanoDrop ND-1000 and RNA quality was controlled on Agilent 2100 Bioanalyzer chips. For each sample, 1 mg of total RNA was amplified and labeled using the GeneChip IVT Labeling Kit according to the protocol provided by the supplier. Affymetrix GeneChip Drosophila Genome 2. 0 arrays were hybridized with 7. 5 mg of labeled cRNA, washed, stained and scanned according to the Affymetrix' protocols. Raw data are deposited at NCBI GEO with the accession number: GSE56173.
Raw data analysis was performed using R for Statistical Computing (R core team 2013) with Robust Multi-array Average (RMA) and Significance Analysis of Microarrays (SAM) from the package siggenes (http:// bioconductor. wustl. edu/bioc/html/siggenes. html). Gene selection was made using a value of Delta that correspond to a FDR of 0. 2. Gene Ontology Term analysis and enrichments were made using DAVID  [23].
This work was funded by an  ANR  grant ( ANR 2010 JCJC 1304 01 ) and an  ERC  starting grant ( FP7/2007-2013-Nu309704 ). The lab of  Dr Francois Leulier  is sponsored by the  ATIP/AVENIR program , the FINOVI fundation and the '' Fondation Schlumberger pour l'Education et la Recherche ''. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Design and analysis of microsatellite markers To identify potential microsatellite regions, we compared the HdrR and the HNI genome by using Sputnik  (Katsumura et al. 2009 ) and identified regions with a maximal repeat unit length of 2 and the minimum length of simple sequence repeat set to 20. We designed primers with primer3plus  (Untergasser et al. 2007)  to amplify a region of about 200 bp flanking the microsatellite repeat. We searched for one marker per chromosome but finally settled on nine high-quality microsatellite markers. Alleles were annotated from chromatograms after PCR amplification with fluorescently labeled oligonucleotides using GeneMarker software (Softgenetics). For a complete list of primers, see Table  S1.
e. , originally listed as 'N'). However, when using this patched sequence as reference for calling SNPs in other inbred lines, we noticed that a small number of SNP calls (~0. 14-0. 2% total) were consistent with the base identified from the short-read HdrR sequence at quality scores below this threshold. The use of an unthresholded HdrR dataset as the reference resolved this problem, albeit resulting in a marginal addition of 0. 01-0. 017% SNPs that were consistent with the original reference. On the balance of the false-positive and false-negative rates estimated this way, we opted for using the unthresholded "patched" reference for calling SNPs in all other strains. Furthermore, we supplemented the final thresholded patched sequence with 89,227 bases that were consistent with the other four inbred line sequences and submitted the final sequence as a Third-Party Annotation record to the European Nucleotide Archive (ENA; HF933207-HF933230).
Analysis of protein-coding mutations Exon mappings for all regions mapping to chromosome scaffolds in the medaka 2005 reference assembly were obtained from Ensembl. SNPs were annotated as leading to either nonsense, stop-codon, missense, or synonymous substitutions (note that no stop codon mutations were in fact observed). Ambiguities in this annotation due to overlapping transcripts were resolved in this order of preference (i. e. , if a SNP produced a nonsense mutation in at least one overlapping transcript, it was annotated as such, and so on). Expected relative frequencies of each coding mutation type were estimated on the basis of all theoretically possible single-nucleotide substitutions at each codon and the observed frequencies of each codon in the medaka exome.
Phylogenetic analysis Phylogenetic relationships between the wild Southern population, five inbred strains, and stickleback were assessed using a neighbor-joining algorithm based on Kimura two-parameter distances implemented in PHYLYP, i. e. , the PHYLogeny Inference Package  (Retief 2000). Genotypes for the wild population were called using a "majority vote" across the 48 haplotypes. Differences between medaka samples and stickleback were too large to estimate distances. Hence, the length of the stickleback branch shown in Figure  3  is an underestimate. The tree was rooted using the midpoint method and plotted using the T-REX online tool  (Boc et al. 2012). The relationships between the wild samples and the reference strain (Figure  3B ) were analyzed in R using a hierarchical clustering with Gower distances  (Struyf et al.
Population size history analysis To estimate changes in population size over time, we used the method of  Li and Durbin (2011) , implemented in the software package psmc. The following options were used with the psmc executable: -N25 -t15 -r5 -p "4+25 Ã 2+4+6," ensuring that the predicted number of recombinations occurring in each specified time interval were sufficiently high (. 10) to prevent overfitting. The output was scaled assuming a doubling time of 0. 67 years per generation and a mutation rate of 2. 5 • 10 28. Results for all individuals were combined by binning the results into log 10 time intervals of length 0. 1.
Data submissions Whole-genome sequencing of the 5 medaka inbred strains was submitted to the DNA Data Bank of Japan under accession DRA000588. Whole-genome sequencing of the eight medaka wild catch trios from the Kiyosu population was submitted to the ENA under accession ERP001016. The revised genome sequence was submitted to ENA as accessions HF933207-HF933230. The SNPs discovered are currently being submitted to the Single Nucleotide Polymorphism Database (dbSNP), and identifiers will be available.
Recently these divergent phenotypes have even led to suggestion that the Northern and Southern medaka populations are distinct species  (Asai et al. 2011). To clarify the genetic relationship between the Northern and Southern Japanese medaka clades, we investigated potential wild interbreeding events drawing information from the Kiyosu population and the inbred strains. When interbreeding occurs, genotypes will be exchanged between populations that have separated some time ago, events that are referred to as introgression. Recent studies show that whole-genome data can illuminate ancient introgression events that are not visible by methods typing more limited numbers of loci  (Green et al. 2010; Ishiguro et al. 2012; Meyer et al. 2012). The test for introgression proposed by  Durand et al.
As expected, the sample possesses considerable phenotypic diversity, with the phenotypic differences similar in magnitude to differences between inbred mouse strains. During characterization of the progenitors of the panel, we also determined the sequence of a number of existing isogenic medaka strains sampled from several wild populations. Our additional deep sequencing data provide an estimate of the original reference sequence error rate at around 2 • 10 25 assuming that there has been no sequence divergence in this strain since 2002. This is better than the estimated error rates for draft quality genome sequencing. We are in the process of generating an improved reference assembly using the additional sequence generated in this project and other sources in the future. All the sequence data are freely available, and the differences identified have been submitted to the Single Nucleotide Polymorphism Database (dbSNP) and will be available on the Ensembl browser and other resources in the future.
ACKNOWLEDGMENTS We thank  Stephen Fitzgerald  and  Javier Herrero  for discussions on comparative alignments,  Justin Paschall  and  Nima Pakseresht  for assistance with database submissions,  Hilary Browne  at the  Sanger Institute Sequencing Facility  for sequencing of the inbred trios,  Tetsuaki Kimura  and  Yusuke Takehana  for assistance with fish sampling,  Wei Chen  for advice on TrioCaller, and the  National BioResource Project Core Facility Upgrading Program and Genome Information Upgrading Program  for financial support of the resequencing of medaka inbred lines. R. P. and F. L. thank  Nadeshda Wolf ,  Nadine Eschen ,  Natalja Kusminski , and  Nils Trost  for assistance in inbreeding and morphometric studies.
Moreover, glider observations for May 2013 Published by Copernicus Publications on behalf of the European Geosciences Union. Y. Drillet et al. :  Forecasting the mixed-layer depth in the Northeast Atlantic are available in the Coriolis database (available through My-Ocean service) sampling over the whole month with at least one profile per day in a small 1/2 • × 1/2 • box centred on 16. 25 • W and 48. 55 • N. The physical variable chosen for this study is the mixed layer depth because this ocean variable is crucial in the forecast context for several reasons. (i) The mixed layer integrates a lot of physical ocean processes such as the horizontal and vertical advection and diffusion. Measuring the accuracy of the mixed-layer depth is an essential diagnostic for quantifying limitations in the model schemes or parameterizations  (Giordani et al.
Using this scenario, we can build a four-member ensemble for each forecast length differing mostly in their initial states, and the mean and median of this ensemble can be considered as two other forecasts. We obtain for each date thirty 3-D ocean states which are not independent estimates of the ocean. A reference experiment, hereafter called Atl12 free, was also carried out using the Atl12 system without data assimilation. This reference was needed in order to assess the oceanic initial conditions errors (with respect to atmospheric forcing errors), and to discuss their impact on the mixed-layer depth forecast errors  (Sect. 4. 3. 4). This simulation was initialized in March 2013 with the analysis provided by Atl12 system, and forced using the atmospheric forecast analysis to the end of May 2013.
This study focuses on May 2013, when all four ocean forecasting systems described above were providing forecasts and when the Coriolis in situ database contains repetitive in situ profiles obtained from glider observations in a 1/2 • area centred on 16. 25 • W and 48. 55 • N (Fig. 2 ). These Figure  1. Operational scheme for producing daily forecasts with all the Mercator Ocean systems. The ocean initial state is produced once a week on Wednesdays. Then, starting from this state, a hindcast (H) is produced each day using analysed atmospheric forcing. Then the forecast for the current day (F0) up to 4-day forecasts (F4) are performed daily, forced by the atmospheric forecasts. gliders were deployed under the OSMOSIS (Ocean Surface Mixing, Ocean Sub-mesoscale Interaction Study, http:// www. bodc.
ac. uk/projects/uk/osmosis/introduction/) project, in which care is taken to apply a near-real-time quality control, disseminating the observations to the Coriolis data centre in real time. The observations used in this study do not represent the full data set but a subsampled one as carried out in all Mercator Ocean forecasting systems for all types of in situ observations. For each instrument (in this case gliders) only one profile per day is retained to avoid oversampling (in time and space) of the observations since the global model cannot represent processes at such high resolution. This selection is made at random so that no particular time of the day is favoured. In our subsample database, 74 vertical profiles are available for May 2013 with at least one profile per day. This data set allows a good representation of the day-to-day temporal evolution of the temperature profile, and then of the mixed-layer depth during the entire month of  May 2013.
Acknowledgements. This research was supported by the My-Ocean2 European project and is based on MyOcean products. The authors wish to thank collaborators contributing to the development of the ocean forecasting systems under the  MyOcean  project, the  NEMO consortium  and the data centres at CORIOLIS and BODC which disseminate the in situ glider observations collected under the  OSMOSIS  project. Edited by:  A. Schiller.
(i) Àx þ 2; Ày; Àz þ 1; (ii) Àx þ 1; Ày þ 1; Àz; (iii) Àx þ 1; Ày þ 2; Àz; (iv) Àx þ 2; Ày þ 1; Àz þ 1; (v) x þ 1; y; z; (vi) x; y þ 1; z; (vii) x À 1; y; z; (viii) x; y À 1; z. Data collection: APEX2  (Bruker, 2006) ; cell refinement: SAINT  (Bruker, 2006) ; data reduction: SAINT; program(s) used to solve structure: SIR97  (Altomare et al. , 1999) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Putz, 2005) ; software used to prepare material for publication: WinGX  (Farrugia, 2012)  and CRYSCAL (T. Roisnel, local program). Supporting information for this paper is available from the IUCr electronic archives (Reference: BG2526). Acta Crystallographica Section E.
In the past decade, more than 30 satellite-tracking studies of leatherback turtles in the Atlantic Ocean have been published (see electronic supplementary material, table  S1 ) and each of these studies has given an essential, yet partial description of habitat use. Here, we present the first integrated analysis of the spatiotemporal distribution and habitat use of leatherback turtles between reproductive seasons at the scale of the Atlantic Ocean. This information is combined with data on the distribution of pelagic longline fishing effort obtained from the International Commission for the Conservation of Atlantic Tunas (ICCAT) across the same temporal period. This study presents a unique opportunity to identify the areas and seasons of highest susceptibility to turtle bycatch, and provides much-needed preliminary guidance on the design and implementation of potential bycatch mitigation measures at an oceanic scale.
Material and methods (a) Turtle-tracking dataset Between June 1995 and February 2010, 106 platform transmitter terminals (PTTs) were deployed on leatherback turtles in the Atlantic Ocean and in the southwestern Indian Ocean (see electronic supplementary material, table  S1 ). Our study involves an integrative synthesis of these data, which were all previously published in scientific peer-reviewed literature, except for two tracks (see electronic supplementary material, table  S1 ). PTTs were predominantly attached to females (n ¼ 101), with four on males and one on a juvenile (sex unknown). The majority of females (n ¼ 93) were equipped while nesting at 13 sites fringing the Atlantic Ocean and at one site in the southwestern Indian Ocean (see electronic supplementary material, table  S1 ), while the remaining turtles (n ¼ 4 males, 8 females and 1 juvenile) were equipped at sea.
Warehousing and standardization of satellite-tracking data from the research groups, which spanned 10 countries and four continents, were achieved using the Satellite Tracking and Analysis Tool (STAT)  [21]. Transmissions were collected and relayed via the Argos System (https://argos-system. cls. fr). Only locations with LC (Location Classes) 3, 2, 1, A and B were used. The locations were filtered using the maximum rate of travel of 10 km h 21 and the maximum azimuth of 358 between successive locations  [22]. The location with the greatest spatial accuracy received in each 24 h period (00. 00-23. 59 UTC) was then selected to minimize spatio-temporal autocorrelation in the dataset. For each turtle, when no location was received during a 24 h period, a linear interpolation was used to interpolate the route, but only for up to 5 days following the last received valid location.
Unequal tracking durations No leatherback turtle has been tracked throughout a complete interbreeding migration, which is estimated to be between 1095 and 1460 days for Atlantic leatherback turtles (maximum tracking duration ¼ 713. 1 days). In order to account for (i) tracks of different durations and (ii) tracks that end near the release location, a weighting factor was applied to the tracking dataset following the method developed by Block et al. [6]. All tracks were normalized by weighting each location estimate by the inverse of the number of individuals that had location estimates for the same relative day of their track. We imposed a threshold relative day of tracking (85th percentile of the frequency distribution of the track lengths, i. e. 337th day) above which locations received the same weight as on the threshold day.
Unequal sample sizes among tagging sites The number of deployed satellite tags differed among the nesting sites and tagging effort was not proportional to the estimated number of females nesting at each site (Spearman's rank correlation, p ¼ 0. 086). In order to account for these unequal and unbalanced sample sizes, a second weighting factor was applied to the tracking dataset. Each rookery was assigned a weight between 0 and 1, proportional to the size of its nesting population (estimated by previous studies  [23] [24] [25] ) relative to the estimated total number of nesting females in the Atlantic Ocean (i. e. approx. 16 600 adult females)  [23]. The rspb. royalsocietypublishing. org Proc. R. Soc. B 281: 20133065 Central Africa nesting assemblage was given a weight of 0. 3, because the number of nesting females is estimated to be about 5000 (i.
(b) Fisheries dataset All spatio-temporally relevant pelagic longline fishing effort data from the T2CE (Task II Catch and Effort) database from the ICCAT were utilized (1995-2009; northern and southern Atlantic). Fisheries data were prepared at monthly intervals to a spatial resolution of 5 Â 58. Only the records of fishing-effort reporting the number of hooks deployed were considered, as effort reported using other units was estimated to account for less than 2% of the total effort. Three fishing-effort classes were defined: low (less than 25th percentile, i. e. less than 7375 hooks km 22 ), medium (more than or equal to 25th and less than 75th percentile, i. e. 7375 medium , 58 748 hooks km 22 ) and high (more than or equal to 75th percentile, i. e. 58 848 high , 415 757 hooks km 22 ). Three classes representing the consistency in fishing effort were also defined: constant (less than 25th percentile), moderate (more than or equal to 25th and less than 75th percentile) and variable effort (more than or equal to 75th percentile).
Globally, wider availability of bycatch rates, in combination with increased transparency and stricter rules for the reporting of bycatch and fishing effort by all fisheries, would greatly help in the assessment of bycatch risks and the design of effective mitigation for species of conservation concern. Besides pelagic longline fisheries, other fisheries employing different gear, such as gillnet and trawl fisheries, can also have high leatherback turtle bycatch rates  [3]. Fishing-effort datasets at the scale of the Atlantic and over the 15-year period considered in this study are, however, only available for pelagic longline fisheries. The primary goal of our analysis was to identify the areas and seasons of highest susceptibility to turtle bycatch. We therefore chose to focus our analysis on the pelagic longline fishery owing to its ubiquity throughout the Atlantic, its known potential to affect leatherback populations  [16]  and its uniqueness regarding data availability.
While our study was successful in describing specific areas and seasons where bycatch susceptibility is high, a finer temporal and spatial resolution of fishing-effort data could undoubtedly enhance our findings. It should also be noted that the existence of illegal, unreported and unregulated (IUU) fishing is another important factor, which has not yet been reliably assessed (e. g. [46] ). Additionally, the impact of coastal fisheries, even though difficult to quantitatively assess, must not be overlooked, particularly in the Atlantic, where leatherbacks use coastal and near-coastal areas (e. g. [47] ). This study offers clear pathways forward to improve the conservation status of this iconic species. The collaboration of many data providers, facilitated by the use of the online data warehouse STAT  [21] , has allowed the assembly of this tracking dataset for Atlantic leatherbacks to unprecedented magnitude.
Acknowledgements. This study results from the collaborative effort of 10 data providers, which have satellite-tracked leatherback turtles in the Atlantic Ocean since 1995, through their voluntary participation in the  Trans-Atlantic Leatherback Conservation Initiative (TALCIN) , a WWF-led initiative. We thank  C. Drews  ( WWF-International ) and  Jean-Yves Georges  ( IPHC-CNRS ) for having initiated this project. Significant contributions were made by  A. Fonseca  and  M. L. Felix  and the  WWF  Guianas office in fostering this project to secure its continuation. We thank those involved in the sea turtle restoration plan in French Guiana (DEAL,  ONCFS, Kulalasi NGO, Kwata , the  Reserve Naturelle de l'Amana, Chiefs of Awala and Yalimapo) ,  Yvon Le Maho  ( IPHC-CNRS ) for having initiated the leatherback tracking programme in French Guiana, colleagues from the  Regional Program for Sea Turtles Research and Conservation of Argentina-PRICTMA, Aquamarina and Fundacion Mundo Marino , the onboard scientific observers from  PNOFA-DINARA , the crew and owner of the  F/V Torres del Paine , the artisanal fishermen from Kiyu, San Jose,  Uruguay ,  D.
del Bene  (PROFAUMA),  Z. Di Rienzo  and colleagues from  Karumbe , the  University of Pisa  for initiating the satellite tagging programmes in South Africa, and the  South African Department of Environmental Affairs  for continuing the work in cooperation with  Dr Nel  from the  Nelson Mandela Metropolitan University ,  Port Elizabeth  and  Ezemvelo KZN Wildlife. We thank  M. L. Felix  for her efforts in the deployment of satellite tags in Suriname and the  Nature Conservation Division Suriname  for facilitating these research efforts. P. M. thanks  C. Palma  for his help in dealing with ICCAT's database,  C. Ere , as well as the GIS training and support received from  SCGIS  and the  ESRI Conservation Program , which allowed processing of fishing-effort data. We thank  J. Parezo  for her careful reading of the manuscript.
Data accessibility. Tracking data are stored on www. seaturtle. org. Requests for materials should be addressed to sabrina. fossette@ googlemail. com or m. j. witt@exeter. ac. uk. Data are also available from the Dryad Digital Repository: http://doi. org/10. 5061/dryad. h21d3. Funding statement. Work in Gabon was financially supported by the Large Pelagics Research Center through National Oceanographic and Atmospheric Agency award no. NA04NMF4550391, the UK Defra Darwin Initiative, the Shellshock Campaign (European Association for Zoos and Aquaria) and the UK Natural Environment Research Council. Sea turtle monitoring programmes in Gabon were financially supported by the Wildlife Conservation Society and by the Gabon Sea Turtle Partnership with funding from the Marine Turtle Conservation Fund (United States Fish and Wildlife Service, US Department of the Interior).