dataesr/scientific-paragraphs-categorization huggingface dataset:
Cohort The cohort included participants from the first visit of the Track-On HD study from all sites (London, Leiden, Paris and Vancouver). 5  From a total Track-On HD cohort of 243, participants were excluded (see Kloppel et al. 5  for detailed inclusion/exclusion criteria) due to; manifest disease (21), left handed or ambidextrous (24), poor quality fMRI data (11) and poor quality diffusionweighted imaging (DWI) data (36; see imaging sections). To age-match the remaining cohort 21 participants <30 and >60 years of age were excluded. This left a cohort of 64 pre-HD individuals and 66 healthy controls described in Table  1. Of the participants included, 31 pre-HD and 29 controls had participated previously in Track-HD. Our recently published structural connectivity analysis was conducted using 2011 Track-HD data thus there is no overlap in imaging data used in this study.
MRI acquisition 3T MRI data were acquired on two different scanner systems (Philips Achieva at Leiden and Vancouver and Siemens TIM Trio at London and Paris). Diffusion-weighted images were acquired with 42 unique gradient directions (b = 1000 sec/mm 2 ). Eight images with no diffusion weighting (b = 0 sec/mm 2 ) and one image with no diffusion weighting (b = 0 sec/mm 2 ) were acquired from the Siemens and Philips scanners, respectively. For resting state fMRI, 165 whole-brain volumes were acquired at a repetition time (TR) of 3 sec using a T2*-weighted echo planar imaging (EPI) sequence. Scanning time was approximately 12 min for T1-weighted, 10 min for diffusion-weighted acquisitions and 15 min for rsfMRI. Scanning times of between 12 and 16 min have previously been shown to increase the reliability of resting state fMRI connectivity estimates.
Data preprocessing Preprocessing was performed using SPM8 and the CONN functional connectivity toolbox version 14 (https://www. nitrc. org/projects/conn/)  14 running under MATLAB v8. 3. Segmented images were used to create an improved anatomical scan for coregistration. The first four EPI images were discarded to allow for steady state equilibrium. Functional images were first realigned, incorporating field maps for inhomogeneity correction whenever available and then coregistered to the new anatomical image. Freesurfer ROIs were also coregistered to the anatomical image using NiftyReg. 12 Using the CONN toolbox regression of noise ROIs (without global signal regression) was carried out using the anatomical Compcorr method,  15  along with 6 movement parameters, followed by band-pass filtering between 0.
009 and 0. 08 Hz, calculation of bivariate correlations and application of a Fisher transform. In addition to the inclusion of motion parameters, our main strategy for guarding against the effects of motion generated artifacts was through the implementation of stringent quality control procedures on the raw data and at each stage of preprocessing and analysis (see Kloppel et al  5  for further details). Our study included only pre-HD participants who have not yet developed the motor manifestations of HD.
Funding Information This study was funded by the  Wellcome Trust (GR, PMC)  ( 091593/Z/10/Z ,  515103 ) and supported by the  National Institute for Health Research [NIHR] University College London Hospitals [UCLH] Biomedical Research Centre [BRC]. TrackOn-HD  is funded by the  CHDI foundation , a not for profit organization dedicated to finding treatments for Huntington's disease.
The Pacific and Gulf of California coasts host distinct oceanographic regions  [21] , and marine habitats and coastal SESs vary at the within-BCS scale  [20]. In this study, we use historical landings data to examine temporal and spatial trends in ecological indicators related to the total biomass and composition of catches reported by smallscale fisheries in BCS, Mexico. Fisheries-dependent data necessarily integrate both ecological and social information, and we use them to investigate how four variables known to be linked with ecological resilience in this and other fisheries-associated SESs (i. e. , magnitude and variance of landed biomass; taxon richness of reported catches; and the proportion of top-trophiclevel taxa in reported catches) vary seasonally at two spatial scales: the state of BCS, Mexico and local fishing offices (LFOs) within BCS.
Data source We use landings data from the Mexican National Aquaculture and Fishing Commission  [22]  to assess ecological resilience of BCS' small-scale fisheries. These landings are from 588 smallscale (i. e. , artisanal) fisheries and 10 LFOs within BCS. LFOs are local branches of the Mexican National Aquaculture and Fishing Commission, CONAPESCA, where fishers report their catch on a daily to weekly basis  [20]. Each report contains information on the dates of capture, the office where the report was submitted, the fishery type (artisanal; industrial; or aquaculture), names of taxa captured and total biomass and price paid for each taxon. We selected only those fisheries within the "artisanal" category. All fisheries in the dataset are commercial, and fisheries management occurs at the scale of the state.
Data preparation These data were transcribed, cleaned and aggregated into a database by the Scripps Institution of Oceanography's Gulf of California Marine Program at the University of California San Diego  [23]. Cleaning included standardizing common Spanish and scientific taxon names from the descriptive names given on individual landings reports, and assigning each taxon a trophic level, based on life history characteristics. Trophic levels were assigned according to the following: 1 = primary producer, 2 = herbivore, 3 = carnivore, 4 = piscivore. The coarseness of these trophic levels (in contrast to Fishbase, for example) is driven by the data, as many taxa are not identified to species. The common Spanish names provided the most refined level of taxon identification in this dataset. Where multiple Spanish common names exist for the same taxon, data for all common names were combined so that one complete record existed per taxon.
Spanish common, scientific and English common names of top taxa can be found in Table  1. We accessed the data using the visualization and data analytics software Tableau, and prepared data for analyses in both Tableau and Excel (Tableau 9. 0, Tableau Software Inc. ; Excel 14. 4. 2, Microsoft Corporation 2010). Data were filtered by year, state, LFO and fishery type. We restricted our analyses to BCS small-scale fisheries landings from 2001 to 2013, because this was the time period where reporting was consistent, with all LFOs reporting catch for each month. Ten LFOs were used in analyses (Fig  1 ). Landing reports with missing information on month, year or name of taxa (where genus could not be determined) were excluded. While some data were reported daily, these reports were not consistent enough to enable finer scale temporal analyses; therefore we restricted our analyses to the monthly scale.
Data were disaggregated by month and year for all resilience indicators except variance in landed biomass, for which only annual data were used. Resilience variables were calculated as follows: total biomass as the sum of reported biomass; variance in biomass as the square of the standard deviation of the values of biomass for each month in a given year; taxon richness as the number of distinct species reported; and proportion of top-trophic-level taxa as the total predator biomass (summed biomass of taxa with assigned trophic levels 3 and 4) divided by the total biomass of landings. These were calculated for each LFO and month in the dataset, except for variance in biomass, which was calculated for each LFO and year. Data for total biomass and variance in biomass were normalized with a natural log transformation to reduce skewedness.
Analyses Spatial and temporal trends in resilience indicators. To test the effect of intra-annual trends at the state (Baja California Sur) and LFO spatial scales, we performed an ANOVA with the independent variables LFO and month, and the interaction term LFO Ã month for each resilience indicator except variance in biomass. To assess spatial variability in variance in biomass, we ran an ANOVA with the independent variable LFO. To test the seasonality of each variable at individual LFOs, we ran ANOVAs with month as the ordinal independent variable for each dependent variable: biomass; taxon richness; and proportion of top-trophic-level taxa. In order to provide long-term context for seasonal trends in resilience indicators, we also analyzed long-term trends for each of the four resilience indicators at the spatial scale of Baja California Sur, where data from all ten LFOs were aggregated.
For these analyses, we ran ANOVAs with year as the continuous independent variable for each dependent variable. All statistical analyses were conducted using JMP Pro 12 (SAS Institute Inc. 2015). Correlations among indicators. To test whether these potential indicators of high ecological resilience are correlated with one another, we conducted a Pearson's correlation for each pair of indicators at the spatial scale of the LFO. Taxon-specific trends. For each LFO, the top taxa by biomass and value were determined as those taxa whose biomass or economic value made up the ten highest proportions of total annual biomass or value in an average year. There was considerable overlap between the top biomass and top value taxa. We excluded from temporal analyses those taxa with Spanish common names for which genera were unknown.
Discussion For the 13 years of landings data used in this study, we found that the magnitude and variance of landed biomass, taxon richness of reported catches, and the proportion of top-trophic-level taxa in reported catches all varied spatially within BCS, supporting our hypothesis that the four variables exhibit spatial variation. Moreover, in support of the second hypothesis, we also found significant seasonal variation in magnitude of landed biomass, taxon richness of reported catches, and the proportion of landed biomass made up of top-trophic-level taxa. These trends were not evident from state-level analyses, contrary to our expectations (see S2 Appendix for details). Local fishing office (LFO)-specific trends in the four resilience indicators we assessed varied considerably. Baja California Sur encompasses several diverse social-ecological system regions, defined by the oceanographically and ecologically-distinct Pacific and Gulf of California coasts, as well as diverse fisher and institutional characteristics  [20, 25].
BCS fisheries are currently managed at the level of the state, ignoring significant spatial variability in fishing offices with differing ecological conditions and fished assemblages. Although Mexico's national fisheries law permits greater decentralization of fisheries governance that would allow for place-specific management, this law is not yet fully functional in BCS  [20]. We find that ecological indicators relevant to resilience vary spatially among fishing offices, and temporally within years, but are relatively stable over the 13 years of the dataset. Our findings highlight the value of managing fisheries at a finer spatial scale, recognizing that matching the scales of human activity and environmental dynamics is vital for sustaining marine fisheries  [20, 40].
Acknowledgments We thank  Octavio Aburto-Oropeza  and the entire team at the  Center for Marine Biodiversity & Conservation  for their acquisition and cleaning of the dataset prior to analysis. We are especially grateful to  Alfredo Giro  ´n for dataset assistance, and to  Andrew Johnson ,  Jon Witman  and  Stephen Porder  for feedback on earlier versions of the manuscript. We would also like to thank three anonymous reviewers, whose thorough and constructive comments greatly improved the manuscript. We also acknowledge the Mexican government and the fishermen of BCS, without whom these data would not exist.
This list includes top taxa by biomass and value for each office, as well as taxa important at the level of BCS. It is not comprehensive of all taxa in the dataset. Please see Methods for details. Where multiple Spanish names exist for the same taxon, they are listed under "Common Spanish name" and separated by a forward slash. English common names are capitalized where they are species-specific; nonspecific English common names are lowercase. https://doi. org/10. 1371/journal. pone. 0182200. t001.
Microplates were kept static for 24 h at 4 • C for cold-stressed (precultured at 37 • C) and cold-adapted (precultured at 4 • C) conditions and at 37 • C for the positive control. After incubation, wells were covered with a few drops of Liquid Contrast (inert a FCS: food contact surface b FPE: food processing environment The contaminated food sources of certain epidemic and sporadic cases are specified. and non-toxic oil), and the plates were placed on a magnetic block with 96 mini-magnets centered under the 96 wells of the microplate for 1 min to apply magnetic fields that attracted mobile beads, creating a quantifiable spot above each minimagnet. The bottoms of the plates were scanned with a plate reader and analyzed using BFC elements 3 R software (BioFilm Control, France) to obtain a numerical value termed the BioFilm Index (BFI) for each well, which ranged from 0 to 20 depending on the size and intensity of the spot.
To stain the bacterial biomass, a 0. 1% w/v CV (Merck KGaA, Germany) solution was filtered (0. 22-µm filter, Millipore, France), and 220 µl was added to each well. After static incubation for 30 min, CV solution was removed by sharply flicking the plates while upside down. Wells were washed three times with 300 µl of saline, followed by tapping them upside down on paper towels. Plates were air-dried for 3-4 h, filled with 150 µl of 33% v/v acetic acid Data are presented as the mean log CFU/ml ± standard deviation at an OD 600 of 1. (Sigma-Aldrich, France) and placed on a plate shaker with slight agitation for 10 min to completely destain CV and obtain a homogenized solution. Destained CV levels were determined using a microplate reader at OD 600. At least four experiments were performed, with triplicate wells for each condition.
Acknowledgements We thank the  National Natural Science Foundation of China  (Grants No. 51372203  and No. 51332004 ), the  Foreign Talents Introduction and Academic Exchange Program  (Grant No. B08040 ),  DARPA  (Grants No. W31P4Q1310005  and No. W31P4Q1210008 ),  Poitiers University , the  CNRS, and the Government of the Russian Federation  (Grant No. 14. A12. 31. 0003 ) for financial support. The authors also acknowledge the  High Performance Computing Center of NWPU  for allocation of computing time on their machines.
Characteristics of the raw dataset A qPCR assay of the epiRIL12 line that we used in this study suggested the presence of newly transposed copies of the retrotransposon EVD (see Methods). This material was therefore perfectly suitable to tentatively detect these insertions using ONT sequencing platform. Three flow cells were used to sequence the genomic DNA of epiRIL12 line with two MinIon devices : one of the first available version and the other two of the MkI release type. The first MinIon run (FLO-MAP003) generated 49,061 sequence reads totalling 232 Mb. The ONT platform produces three types of reads : the template reads are produced from the forward strand sequencing. The complement reads are produced from the reverse strand sequencing. This distinction is made possible by the linking of the two strands of the molecule to be sequenced.
Because the reads obtained from the 3 MinIon sequencing runs corresponded to the same DNA sample of epiRIL12, all sequence data were concatenated into a final multifasta file containing 118,554 sequences. The read length varied between 6 and 691,915 nucleotides, although 72% of the reads had a length comprised between 500 and 15,000 nucleotides with a median size of 4. 6 kb (Additional file 1: Figure  S2 ). All reads were then mapped onto the Arabidopsis reference genome TAIR10 (The Arabidopsis Information Resource, http://www. arabidopsis. org) using blastN programme (see material and methods) with allowed gaps. 47,765 reads (40. 3%) produced a significant alignment and were kept for further analyses. These represent ~80 Mbp of sequences, i. e. ~0. 8X genome coverage. In order to validate this estimation, we searched for the presence of 10 selected unique Arabidopsis genes in our dataset and found that 8 produced a significant alignment with at least one of the 47,765 reads (Additional file 2: Figure  S3 ), which confirms the first estimation.
Detection of EVD insertions sites The first step of our analysis was to search for all EVD insertions in the genome of the epiRIL12 line. For this, a simple similarity search of the EVD reference sequence used as a query against a database composed of 47,765 reads dataset, using the blastN programme was performed. Sixteen reads ranging from 4. 2 to 8. 8 kb showed similarity with EVD. Four of these reads entirely spanned the element (i. e. 100% of the reads corresponded to the element). Eleven reads showed partial similarity to EVD, with one part of the sequence corresponding to the element and the other to another locus in the Arabidopsis genome, which corresponded to nine distinct insertion sites. One among these nine insertion sites corresponded to the native copy of EVD present on the chromosome 5.
Detection of transposition events ab initio The long reads generated by the ONT platform should theoretically allow a reliable detection of TEASVs using the SRM method. Our method is based on blastN similarity search rather than on Burrows-Wheeler based algorithms usually used with Illumina data. BW-based algorithms are designed for the mapping of a high number of short reads onto a reference genome. Moreover, BW mapping is usually achieved at high stringency, given the short read length. The Nanopore reads are, on the contrary, very long and exhibit a lot of mismatchs and indels, due to the high error rate. We therefore found the BLAST algorithm more appropriate for the analyses. Thirty-six MinIon reads were identified as spanning a similarity breakpoint and homologous to one sequence of the Arabidopsis TE database.
Conclusion/Discussion In order to demonstrate the suitability of Nanopore technology for TEASV detection, we sequenced an epiRIL from A. thaliana. We used three runs of MinIon that in our hands produced from 38 to 232 Mbp, reaching a total of 336 Mb. This variation in yield was mostly attributed to differences in flow cell quality, according to the manufacturer's technical support. None of the flowcells provided subsequently indeed showed such discrepancies (personnal communication). After checking the quality of the sequences through homology searches against the Arabidopsis reference sequence, only 80 Mb, i. en 23% of the initial dataset could be used for further analyses. We do not provide any explanation for such large difference. Whether the non-Arabidopsis sequences originate from artefacts or from contaminants should be further tested.
Sequencing Three Oxford Nanopore flow cells: 1 FLO-MAP003 and 2 FLO-MAP103 were used in this study. The number of available pores was first recorded to evaluate the flow cell's quality with the MinKNOW™ software. The version v0. 51. 1. 62 of MinKNOW™ software was used with the flow cell FLO-MAP003 and version v0. 51. 3. 40 with the two other flow cells FLO-MAP103. Before starting the analyses, the Minknown application was first used to make the platform QC before starting the analyses and a map/NC-48-h sequencing run flow (Map 103) protocol was chosen and initiated. 500 μl of a priming mix (26. 6 μl of Fuel mix (FMX), 500 μl of Running buffer (RNB), 473. 4 μl of Nuclease-free water) were introduced two times in the MinIon flow cell to primed the sensory array. Thirty-six ng of the prepared library was diluted into a mix composed of 75 μl of RNB, 4 μl of FMX and Nuclease-free water was added to reach a final volume of 150 μl.
Characterisation of sequencing raw data Raw reads were first extracted from the native HDF5 format MinIon reads using poretools  [24]   (Loman and Quinlan, 2014). Similarities between each read and Arabidopsis genome were estimated using a BlastN search using standard parameters, the read as query and the TAIR10 version of genome assembly as database. Reads producing alignment on TAIR10 assembly on at least 20% of their length were kept for further analyses. The estimation of the percentage of identity between the reads and the Arabidopsis genome was achieved by averaging that of all HSPs.
Detection of EVD insertions A BLAST multifasta database of all the MinIon reads was created with formatdb (version 2. 2. 26). Then the EVD sequence (ATCOPIA93,  [11] ) was aligned against the multifasta database with blastall (version 2. 2. 26) with the default parameters. For each blast hit, a dotter (version 4. 1) with the Nanopore read against the EVD sequence was produced. In parallel, the same Nanopore read was aligned against the Arabidopsis thaliana genome, using ncbi BLAST with default parameters in order to identify the position of the flanking region. The start and end position of the alignement were kept and a sequence with plus or minus 1 kb was extracted from the Arabidopsis thaliana genome for each position. The extracted sequence and the EVD sequence were concatenated and a dotter with the 2D fasta MinIon read and the concatenated sequence was performed.
ab initio detection The ab initio detection method that we developed is conceptualy close to the Split Read Method developped for Illumina data, except that the long reads allow a systematic use of Blast algorithm. First, each read was aligned against TAIR10 genome assembly using BlastN using the -G 5 -E 2 -r 2 parameters in order to take into account the error rate generated by the technology. Those for which a maximum of 75% and a minimum of 400 bp contiguous sequences could be aligned were kept as putative representative of similarity breakpoints and then aligned against the Arabidopsis TE database (www. arabidopsis. org). Only the reads for which at least 200 bp of the remaining portion could be aligned on a well characterized TE were selected. After these filtering steps, each positive read was again compared to the putative region of the TE insertion (obtained by performing an extractseq of a 2 kb region spanning the insertion site) using the blast2seq software.
All statistical analyses were performed using R software. 28 alitative data analysis Focus groups were audio-recorded and transcribed. The principle of thematic encoding developed by Flick  29  for the analysis of interviews was applied as for other studies with focus groups. 30 Therefore, thematic and pragmatic dimensions were combined into a dialogical unit to highlight patterns specific to the different groups. Lexical analysis was carried out using both Alceste and MAXQDA software. The different dimensions were cross-classified to generate new insights about the organisation of the data (ie, how common themes are linked together, specific interactions intra and intergroups, and so on).
We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the "normal" metabolome and its main sources of variation.