dataesr/scientific-paragraphs-categorization huggingface dataset:
We also carried out two additional behavioral tests to further elaborate N versus J differences. Firstly, we compared the performance of N and J in the Morris water maze test used to assess spatial memory. N male mice showed very significantly reduced performance (higher latency) compared with J male mice (Figure  6 ). Secondly, we examined emotional learning or memory for an aversive event using the cue and contextual fear conditioning tests; however, here we found no significant differences between the two strains (data not shown). Exploration of acoustic startle and pre-pulse inhibition (ESLIM_011) (Figure  1a ) in the two strains identified a variety of parameters that were significantly and consistently different across centers. Acoustic startle magnitude at 110 dB and startle response magnitude to prepulse and pulse (PP1-PP4 + pulse, see EMPReSSslim) were reduced in N compared with J, although this effect was not seen in females in one center.
Clinical chemistry Extensive panels of clinical chemistry tests were performed on plasma samples collected at the end of each phenotyping pipeline. The blood sample at the end of Pipeline 1 (ESLIM_021) was collected after an overnight fast, whereas the sample at the end of Pipeline 2 (ESLIM_015) was from a free-fed animal. Data agreeing from at least three centers showed that urea and the electrolytes sodium, potassium, and chloride were significantly higher in plasma from J mice relative to N mice (Figure  1a ), although there was some clear sex-center interactions. Data for free-fed and fasted plasma glucose levels indicated that for each test, at least two centers found plasma glucose levels to be higher in N than in J mice (Figure  1b ). However, blood glucose levels are known to be affected by animal handling, sample processing, and the use of anesthetics.
Hematology Various hematological parameters were measured at the end of Pipeline 2 (ESLIM_016). Significant changes in a number of parameters were found by two centers but these results were not replicated in the others, including white and red blood cell counts, mean cell volume, and mean corpuscular hemoglobin (Figure  1b ). Contradictory results were obtained for hematocrit and mean cell hemoglobin concentration tests (Figure  2 ). In each case, data from two centers agreed whereas a third center showed the opposite effect. This could potentially be due to the different machine technologies used for hematological measurements in the participating clinics, as recorded in the metadata.
SNP and small indel validation We designed extension and amplification primers for 762 SNPs and 169 small indels using SpectroDESIGNER, which were then synthesized (Metabion, Martinsried, Germany). We used the iPLEX GOLD assay of the Sequenom MassARRAY platform for genotyping these variants in eight DNA samples from three C57BL/6 substrains (replicate of four C57BL/6J and replicate of two C57BL/6NJ and C57Bl/6NTac) and SpectroTYPER for data analysis. The resulting genotypes were then downloaded and checked for consistency in the four replicates. Inconsistent or heterozygous genotypes in either the C57BL/6J or C57BL/6N samples were excluded from further analyses. In addition to Sequenom, we used pyrosequencing and traditional Sanger sequencing for validation. We designed primers for 22 SNPs and 10 small indels.
SV identification Using a combination of four computational methods, as described previously  [16] , we detected a total of 551 genome-wide SVs between C57BL/6J and C57BL/6N. We then visually inspected short-read sequencing data at each of these 551 unique sites using LookSeq  [33] , and found that 470 predicted sites were false, owing to paired-end mapping errors. At the remaining 81 sites, we carried out PCR and Sanger-based sequencing analyses as described below.
Phenotyping platforms in EMPReSSslim All SOPs for phenotyping procedures are described within the EMPReSS database  [37]. All mice were analyzed through the complete phenotyping pipeline (excluding FACS and immunoglobulin analyses, which were not undertaken). Subsequently subsets of mice were selected according to the appropriate metadata considerations to ensure robust comparisons between strains (see section on Phenotype data analysis below). Unless otherwise stated, C57BL/6NTac was used throughout the study. Secondary phenotyping protocols have been published previously  [37].
Data capture by EuroPhenome Data generated from EMPReSSslim by the four centers were stored in their local Laboratory Information Management Systems, backed by diverse database schemas running on different relational database management systems. The data were transferred to EuroPhenome in a common format Phenotype Data Markup Language, an extension of eXtensible Markup Language (XML), defined by XML schema. To assist in data export and improve standardization, and data consistency we provided a Java library  [38]  for data export. The informaticians at the centers used this to represent the data to be exported as an object model. The library then performed the necessary validation against the EMPReSS database  [37]. If this was successful, the data were output to XML, compressed, and placed on a file transfer protocol (FTP) site.
Each center's FTP site was regularly checked by the EuroPhenome data capture system, and any new files were uploaded. The data were again verified against the schema and EMPReSS, and further checked for consistency against existing data within EuroPhenome. The results of the upload and validation were provided to the sites in the form of XML log files and a web interface, the EuroPhenome Tracker. If validation was successful the data were loaded into the EuroPhenome database. Data can be removed from the database by placing the files in the delete directory of the FTP site. The same process was used to capture and validate the data before removal. Phenotype data may be downloaded from MRC Harwell  [39].
Phenotype data analysis Statistical analysis was carried out in a manner consistent with the EuroPhenome data repository  [19]. In order to compare phenotype data from N and J, groups of comparable measurements from each center were extracted from the EuroPhenome database. In a few instances there was more than one dataset from each center that differed according to a crucial metadata difference. Where there was more than one comparable group for a parameter, the largest one was used. An example was the simplified IPGTT procedure at the Helmholtz Institute, where the 'type of strip' was changed from 'accu-check aviva' to 'roche'. This resulted in two groups of data, one of sizes  (20, 22)  and one of  (20, 13) , for C57BL/6N and C57BL/6J respectively. The group with the largest minimum value within the two groups (in this case 20, 22), was used.
Once comparable groups of C57BL/6J and C57BL/6N were identified, statistical tests were applied separately to the male and female groups for each center. A dataset of each group's sizes, means, standard deviations, effect sizes, and the resultant P-values from the statistical test was then created for subsequent hypothesis testing during the creation of the heat maps. Fisher's exact test (for 2 × 2 contingency tables) or a χ 2 test were used for categorical data to produce a P-value. The Mann-Whitney U-test was applied to numerical data, as this is a non-parametric test suitable for all types of unimodal distributions. Two-dimensional data (such as parameters where a measurement is taken over a time course) were averaged into a single mean, which caused loss of information but still gave an overall comparable value for that parameter.
Acknowledgements The project was funded by the  European Commission  contract numbers  LSHG-CT-2006-037188 EUMODIC  and  LSHG-CT-2007-037445 , and also supported by the  Wellcome Trust  (grant number  098051 ). We gratefully acknowledge the contribution of all contributors to the  EUMODIC  project; a list can be found in the paper by Morgan et al. [19]. We thank the  Genomics Services group at the Wellcome Trust Centre for Human Genetics, University of Oxford , for running the Sequenom assays. Binnaz Yalcin  is supported by an  EMBO Long-Term Fellowship  and the  Agence Nationale de la Recherche. The ICS was also supported by the  French  state aid managed by the  National Agency for Research  under the program of future investments ( PHENOMIN ,  ANR-10-INBS-07 ). 11  Infection and Immunity Division, Roslin Institute, University of Edinburgh, Easter Bush Veterinary Campus, Midlothian ,  EH25 9RG, UK.
Flow cytometry Cells were harvested using cell dissociation solution and washed twice in phosphate-buffered saline (PBS, Lonza, Verviers, Belgium) supplemented with 1% BSA. 200,000 cells were treated with the appropriate amount of Fc blocking reagent (Miltenyi, Utrecht, The Netherland) and were then stained with specific antibodies or the corresponding isotype control in 50 mL PBS-1% BSA for 30 min at 4uC. After washing, cells were fixed using the commercial BD FACS lysis solution according to manufacturer's instructions. All antibodies were purchased from Immunotools, except CD184-APC (BD Biosciences, Erembodegem, Belgium). To eliminate dead cells from analysis, LIVE/DEADH Fixable Near-IR Dead Cell Stain Kit was used according to the manufacturer's instructions (Invitrogen). 20,000 to 40,000 events were acquired on a BD FACSCanto TM flow cytometer and analysis was performed with the FACSDiva TM software (BD Biosciences).
Real-time quantitative PCR For assessment of messenger RNA expression, total RNA was isolated using TriReagentH and the RNeasyH mini kit (Qiagen) according to the manufacturer's instructions. Potential contaminating genomic DNA was digested by DNase I treatment (Qiagen). One mg of total RNA was reverse-transcribed using the SuperscriptH II Reverse Transcriptase (Invitrogen). PCR primers were designed using the Beacon Designer software (Premier Biosoft, Palo Alto, USA) and were chosen to encompass an intron. Primer details are shown in Table  1. PCR was performed using the iCyclerH and the IQTM SYBRH Green Supermix (Bio-Rad, Nazareth Eke, Belgium). 1/10 dilution of cDNA was used. PCR conditions were as follows: 3 min at 95uC, 30 sec at 95uC and 1 min annealing (40-fold). Optimal annealing temperature was determined for each primer pair.
Melting point analysis was obtained after 80 cycles for 10 sec from 55uC up to 95uC. Each run included negative reaction controls. b-actin was chosen as housekeeping gene for normalization. Expression levels were calculated by the relative quantification method (DDCt) using the Genex software (Bio-Rad) which takes into account primer pair efficiency. For assessment of miRNA expression, total RNA was extracted using the miRVana isolation kit (Applied Biosystems,). Five hundred ng of total RNA were used for reverse transcription using miScript reverse transcription kit (Qiagen). PCR was performed using the miScript SYBR-green PCR kit and the Homo sapiens_miR-150miScript Primer Assay (MS00003577) according to manufacturer's instructions (Qiagen). U6 (Homo sapiens_RNU6-2_1 miScript Primer Assay, MS00033740, Qiagen) was used for normalisation.
Microarray Cellular mRNA was analyzed by microarray as described previously  [24]. Briefly, total RNA was analyzed for quantity (Nanodrop, Nanodrop products, Wilmington, USA) and purity (Bio Analyser, Agilent Technologies, Santa-Clara, USA) before amplification using the Amino Allyl Message AmpH kit (Ambion, Austin, USA). cDNA obtained after retrotranscription was labelled with Cy3 or Cy5, and hybridized onto human microarrays covering 25,000 genes  [25]. Microarrays were scanned using an Axon 3000 B scanner (Molecular Devices, Sunnyvale, USA) and raw data were acquired with Genepix software (Molecular Devices). Data are available at the Gene Expression Omnibus database, accession number GSE26744. Expression levels were compared using Significance Analysis of Microarrays software and heat maps were drown using TreeView software.
Alexa FluorH488-coupled donkey anti-mouse antibody, Alexa FluorH568-coupled donkey anti-goat antibody and Alexa FluorH688-coupled donkey anti-rabbit antibody (Invitrogen) were used as secondary antibodies. Staining specificity was ensured by omission of primary antibodies. DAPI (blue) was used to stain nuclei. Hematoxylin and eosin staining was performed to assess vascularization in heart sections. The number of vessels was counted in the whole section of each animal. Images were recorded with a confocal microscope (Zeiss Laser Scanning Microscope LSM 510) and the LSM 510 META software.
Statistical analysis Results are presented as mean 6 SD. Comparisons between two groups of continuous data were performed with two-tailed ttest for data with a normal distribution and Mann-Whitney test for non-normally distributed data. Normality was determined using the Shapiro-Wilk test. Comparisons between multiple groups were performed with one-way ANOVA for normal data and Kruskal-Wallis one-way ANOVA on ranks for non-normally distributed data. In case of significance, post-hoc tests (Dunn's or Holm-Sidak method) were run to isolate groups that differ from the others. Comparisons between multiple groups with two parameters were performed with two-way ANOVA. Statistical tests were performed with the SigmaPlot v11. 0 software. A P value ,0. 05 was considered significant.
However, while miR-150 appears to slow EPC migration in our study, a previous study reported that miR-150, secreted by monocytes, enhances the migration of human microvascular endothelial cells  [42]. Interrogation of web-accessible databases identified over 400 predicted targets of miR-150. Additional studies are required to test whether miR-150 affects the expression of other genes regulating the functionality of EPC. Ultimately, the usefulness of miR-150 as a target for cardiac repair will have to be determined in animal models. In conclusion, we have shown that Ado stimulates the migration of EPC. In vitro, the mechanism involves A 2B receptor activation, up-regulation of CXCR4 expression and down-regulation of miR-150. This effect is associated with enhanced angiogenesis in the ischemic heart.
Materials and Methods For the purposes of large-scale analysis we have considered the entire North Atlantic basin deeper than 800 m (Figure  1A ) north of a line between Natal, Brazil and Bolama, Guinea-Bissau using 30-second gridded global bathymetry data  [23] (corresponding to 926 m resolution in latitude). For detailed studies we sampled an area between the Azores and Iceland centred on the Charlie-Gibbs Fracture Zone (Figure  1A, B ). In order to characterise the sea floor habitat, sonar swath bathymetry data were collected during voyages of the FRV GO Sars in 2004 (Kongsberg Simrad EM 300)  [24] , and RRS James Cook in 2007 (Kongsberg Simrad EM120). Data were viewed in real time on board the vessels using OLEX software but were post processed on shore with MB system V5 (www. mbari. org/data/mbsystem) or equivalent software for seven segments traversing the ridge (Figure  1B ).
A total of 24 km were sampled in this way with 500 m long transects placed at random within the three slope categories. A mooring was placed at each station, with sediment traps 100 m and 1000 m above bottom, plus other instrumentation measured downward flux of organic matter and water flow over the ridge from July 2007 to June 2010. Voyages of the RRS James Cook serviced the moorings, surveyed and sampled in 2007, 2009 and 2010. RRS Discovery serviced the moorings in 2008 (see File S1). Cruise reports can be downloaded at http://www. oceanlab. abdn. ac. uk/ecomar/cruises. php. All data from these cruises and cruise reports are also archived and can be accessed from the British Oceanographic Data Centre: http://www. bodc. ac. uk. Since all the sampling locations were in high seas areas beyond any national jurisdiction no specific permits were required for the described field studies.
However the work followed the ''Code of conduct for responsible marine research in the deep seas and high seas of the OSPAR maritime area''  [25]. Satellite remote sensing data were used to evaluate frequency of fronts and primary production over the MAR and adjacent regions. Thermal fronts were investigated using 8-day composite front maps  [26]  derived from daily merged microwave and infrared sea surface temperature data from 2006 to 2011, and then aggregated to indicate regions where strong fronts are most frequently observed. Primary production was estimated using a wavelength resolving model  [27, 28]  using mean monthly 9 km NASA SeaWiFS OC4v4 Chla and Pathfinder v2009 AVHRR SST data to generate mean monthly satellite maps of PP from 1997 to 2010. The satellite estimates of primary production are accurate to 20% in the Atlantic Ocean  [29]  and were verified by in situ incubation of samples on board ship.
Advection of surface particles to the sediment traps was calculated based on altimeterestimated surface currents and settlement velocities covering phytoplankton size range of 0. 2-20 mm. Particles were found to advect from a 70 km radius over the NW and NE moorings and a 700 6400 km ellipse over the SW and SE moorings. Primary and new production values were calculated from cloud free pixels over these areas. Missing data (28%) were estimated from interpolation using a Gaussian fit on the production climatology (r 2 = 0. 99) and annual production was calculated from the March-September mean for 2007-2010. Mass and organic carbon fluxes from the time-series sediment traps (McLane research Laboratories, Inc. MA, USA) at the 4 MAR stations were estimated using JGOFS (Joint Global Ocean Flux Study) protocols.
http://usjgofs. whoi. edu/protocols_rpt_19. html Two proxies were used to investigate patterns of pelagic biomass distribution above the MAR, acoustic surveys and bioluminescence. The Reykjanes Ridge section of the MAR and the adjacent Irminger Sea have been surveyed in June and July of each year  (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009)  down to a depth of 850 m by an international fisheries acoustic survey for the pelagic redfish (Sebastes mentella). All non-redfish features on the echograms were designated as deepscattering layer. Composition was verified by pelagic net tows. For depths from 500 m to the sea floor, data from vertical profiles of counts of bioluminescent organisms impacting on a fast-descending mesh screen were abstracted from surveys over the MAR  [30] , Porcupine Abyssal Plain (PAP) and Porcupine Seabight (PSB) (Figure  1B )  [31].
Quantitative sampling of benthic fauna was done using a multiple corer (10 cm diameter core tubes) for macrofauna (see File S1[S2]) and an otter trawl (see File S1 [S4]) for megafauna and fishes. In addition, samples were collected by baited traps and in 2010 by ROV Isis equipped with manipulators, cores, grabs, suction samplers and a suite of cameras. Sampling was focussed on areas around the four instrumented stations (Fig  1B ). Sampling over wider latitudinal (41u229N to 60u189N) and depth (607 to 3465 m) ranges during the voyage of the RV GO Sars in 2004 provided further data for assessment of species occurrences on the MAR  [24].
Benthic Biomass on the MAR The mean macrofaunal biomass in sediment core samples from the four MAR stations was 56. 10 (SD = 41. 26, n = 11) mg C m 22. These values are comparable to samples from similar depths on the North Atlantic continental margins  [36, 37]  and straddle the trend line of the global predictive equation from the Census of Marine Life (CoML) Fresh Biomass Database for macrofauna  [38]  (Figure  7 ). The trend line predicts an expected biomass of 57. 88 mg C m 22 at 2500 m depth, very close to our observed value. The mean benthic megafaunal biomass in demersal otter trawl (OTSB) catches at the NW, NE and SE MAR stations was  Demersal fish biomass from the trawl samples at 2500 m depth was 0. 90 kg 1000 m 22 (SD = 0. 84, n = 11) on the MAR compared with 1. 09 kg 1000 m 22 (SD = 0.
Acknowledgments We thank, the ships' companies of  FRV GO Sars, RRS Discovery, RRS James Cook, NOAA Ship Henry B. Bigelow  and other research vessels. Also thanks to  PINRO, Russia and Federal Research Centre for Fisheries, Hamburg, Germany  for access to DSL data from redfish surveys (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009). Satellite data were provided to cruises by  NERC  Earth Observation Data Acquisition and  Analysis Service  (www. neodaas. ac. uk).
Specifically, for each sample, a first amplification round was performed with external primers, followed by a re-amplification round of the initial PCR product using nested primers. Different primer pairs were used for most species. The primer pairs, the primer sequences, and the length of the fragment amplified are detailed in supplementary table  S3 , Supplementary Material online. For P. gangelion, we failed to amplify a clean COI sequence with the degenerate primers used for the other species. Specific primers were thus designed based on the SOAPdenovo-Trans contig (the Pyura contig was assigned in preliminary analysis using as bait the COI sequence of Pyura dura FJ528619 available in public databases). The COI sequence of P. gangelion was then amplified in a single PCR reaction using the genomic DNA as a template.
Bioinformatics Analysis of the Raw Data A total of 201,057,100 paired-end reads, 100-bp long, were produced by the HiSeq 2000 sequencing platform. We then discarded all reads that were marked to have failed chastity and purity quality filtering by the Consensus Assessment of Sequence and Variation (CASAVA) pipeline used by the Illumina sequencing platform. As a result, 10,052,019 pairs of reads were removed (~5%). We additionally used the FASTX-Toolkit (http://cancan. cshl. edu/labmembers/gordon/ fastq_illumina_filter/, last accessed June 14, 2013) to remove reads for which more than 10% of the bases had a phred quality score below 20. This resulted in the removal of an additional 17,783,809 pairs of reads, leaving 173,220,272 read pairs for all subsequent analyses (~86% of the initial read count).
Coverage Analysis Coverage statistics of the mt genomes were computed with the software Geneious Pro version 5. 4 (http://www. geneious. com, last accessed June 14, 2013), by mapping single-mate reads (i. e. , the reads were considered individually and not as pairs) on the mitochondrial contigs obtained with SOAPdenovo-Trans. The following mapping parameters were used: a minimum of 24 consecutive read bases that must perfectly match the reference sequence, a maximum 10% of single mismatches over the read length, a minimum of 80% of base identity in the overlapping region, and a maximum of 10% of gaps with a maximum gap size of 3 nucleotides.
Mitochondrial Genome Annotation Mitochondrial genes were annotated by similarity to orthologous genes of metazoans, taking advantage of the BlastN/ BlastP service of the MitoZoa database (D'Onorio de  Meo et al. 2012). The start codon of a protein-coding gene was defined as the first ATG or the first nonstandard initiation codon  (Wolstenholme 1992 ) that does not cause overlap with the upstream gene and maximizes the similarity to orthologous ascidian proteins. According to the punctuation model of mt transcript maturation  (Ojala et al. 1981 ), incomplete T or TA stop codons were hypothesized only if immediately adjacent to a downstream tRNA gene and are assumed to be completed by transcript polyadenylation  (Gissi and Pesole 2003). Transfer RNA genes were identified by their potential cloverleaf secondary structure using the programs tRNAscan-SE  (Lowe and Eddy 1997)  and ARWEN  (Laslett and Canback 2008).