dataesr/scientific-paragraphs-categorization huggingface dataset:
The fragment size estimation of the resulting libraries was assessed with High-SensitivityTM HS DNA kit runned on 2100 Bioanalyzer (Agilent) and quantified using the QubitTM double stranded DNA (dsDNA) High-Sensitivity HS assay (ThermoFisher Scientific). Libraries were then sequenced by pair with a HighOutput flowcel using an Illumina Nextseq 500 with the following mode: 26base-pairs (bp) (10x Index + UMI), 8 bp (i7 Index), and 57 bp (Read 2). Single-nucleus RNA-seq analysis. A minimum of 50,000 reads per nucleus were sequenced and analyzed with Cell Ranger Single-Cell Software Suite 3. 0. 2 by 10x Genomics. Raw base call files from the Nextseq 500 were demultiplexed with the cellranger mkfastq pipeline into library-specific FASTQ files. The FASTQ files for each library were then processed independently with the cellranger count pipeline with parameter set to default.
The single-nucleus RNA-seq assay captures unspliced pre-mRNA, as well as mature mRNA. To include these intronic reads in our analysis, we created a custom "pre-mRNA" reference package, listing each gene transcript locus as an exon with cellranger mkgtf and cellranger mkref according to 10x genomics instruction (https://support. 10xgenomics. com/single-cell-geneexpression/software/pipelines/latest/advanced/references). We used a reference genome built against mouse mm10, Sequence: GRCm38 Ensembl 93. We used STAR21 to align cDNA reads against our reference genome. Once aligned, barcodes associated with these reads-cell identifiers and Unique Molecular Identifiers (UMIs), underwent filtering and correction. Reads associated with retained barcodes were quantified and used to build a transcript count table.
Post GEM Cleanup using DynaBeads MyOne Silane Beads was followed by library construction (98 °C for 45 s, cycled 12 × 98 °C for 20 s, 67 °C for 30 s, 72 °C for 1 min). The libraries were constructed by adding sample index PCR, and SPRIselect size selection. The fragment size estimation of the resulting libraries was assessed with High-SensitivityTM HS DNA kit runned on 2100 Bioanalyzer (Agilent) and quantified using the QubitTM dsDNA High-Sensitivity HS assay (ThermoFisher Scientific). Libraries were then sequenced by pair with a HighOutput flowcel using an Illumina Nextseq 500. Single-nucleus ATAC-seq analysis. A minimum of 10,000 reads per nucleus were sequenced and analyzed with Cell Ranger Single-Cell Software Suite 3. 0. 2 by 10x Genomics. Raw base call files from the Nextseq 500 were demultiplexed with the cellranger-atac mkfastq pipeline into library-specific FASTQ files.
The FASTQ files for each library were then processed independently with the cellranger count pipeline. This pipeline used STAR21 to align reads to the Mus musculus genome. Once aligned, barcodes associated with these reads-cell identifiers and Unique Molecular Identifiers (UMIs), underwent filtering and correction. The subsequent visualizations, clustering and differential expression tests were performed in R (v 3. 4. 3) using Seurat (v3. 0. 2)  25  , Signac (v0. 2. 4) (https://github. com/timoast/signac) and Chromvar (v1. 1. 1)  27. Quality control on aligned and counted reads was performed keeping cells with peak_region_fragments >3000 reads and <100,000, pct reads in peaks >15, blacklist ratio <0. 025, nucleosome_signal <10, and TSS. enrichment >2. We get 6037 nuclei in total and we detected 132,966 peaks (Supplementary Fig.
1 ). The motif activity score was analyzed by running chromVAR (Fig. 3c-e ). The script used for the Signac analysis is available here: https://github. com/matthieudossantos/Single-nuclei-RNAseq-and-single-nuclei-ATACseqscript-for-Seurat/blob/master/script. FISH with amplification (RNAscope) on isolated fibers. RNAscope® Multiplex Fluorescent Assay V2 was used to visualize fMyh pre-mRNAs and mRNAs, and AldolaseA and Idh2 pre-mRNAs. Twenty different pairs of probes against the first intron of each fMyh, AldolaseA and Idh2 pre-mRNAs were designed by ACDbio. Muscles were dissected and immediately fixed in 4% PFA at +4 °C for 30 min. After fixation muscles were washed three times in PBS for 5 min. Myofibers were dissociated mechanically with small tweezers and fixed onto Superfrost plus slides (Thermo Fischer) coated with Cell-Tak (Corning) by dehydration at +55 °C during 5 min.
Cryostat sections were washed three times 5 min with PBS and then incubated with blocking solution (PBS and 10% goat serum) 30 min at room temperature. Sections were incubated overnight with primary antibody solution at +4 °C, then washed three times for 5 min with PBS and incubated with secondary antibody solution 1 h at room temperature. Sections were further washed three times for 5 min and mounted with Mowiol solution and a glass coverslip. Images were collected with an Olympus BX63F microscope and a Hamamatsu ORCA-Flash 4. 0 camera. Images were analyzed with ImageJ program. The references of the antibodies used are listed in Supplementary Table  1. Statistical analysis. The graphs represent mean values ± SEM. Significant differences between mean values were evaluated using two-way ANOVA with Graphpad 6 software and student t test for Fig.
Acknowledgements We thank  P. -A. Defossez ,  E. Bloch-Gallego ,  P. Mourikis ,  V. Hakim ,  D. Duprez , and  S. Gautron  for helpful critical reading of the manuscript. We thank  T. Guilbert  at the Cochin IMAG'IC platform for helpful advice. We thank  S. Joshi  for helpful advice for nuclei purification, and  S. Schiaffino ,  P. Mourikis  and  L. Machado  for helpful discussions. M. D. S was successively supported by a  Ph. D fellowship  from the  Association Française contre les Myopathies (AFM) , by a post doc fellowship from  EUR G. E. N. E. (reference # ANR-17-EURE-0013 ) and is part of the  Université de Paris IdEx  # ANR-18-IDEX-0001  funded by the  French Government  through its " Investments for the Future" program , from the  Agence Nationale pour la Recherche (Myolinc ,  ANR R17062KK ).
Comparative Assessment of Phylogenetic Data Classes Performance measures for phylogenetic data classes consisted of number of taxa sampled, alignment length, GC content (see  Arabi et al. 2010) , Robinson-Foulds (RF) distance, weighted Robinson-Foulds (wRF) distance, and evolutionary rate (mean percent pairwise sequence identity; MPSI). The mean number of taxa per locus was highest for the mitochondrial and nuclear ribosomal genes (68. 2 and 85. 7, respectively) and comparable for targeted exons, albeit with high variance (mean ¼ 50. 9, r 2 ¼ 20. 9). Ultraconserved element (UCE) loci bore the most missing data, with an average of 22. 2 terminals per locus. In addition, after trimming, the targeted exons and UCEs had comparable alignment lengths and were shorter on average than mitochondrial and nuclear ribosomal genes.
Inference of tree topologies based on each of the four data partitions was performed in IQ-TREE with identical heuristics as for the species tree analyses. Comparison of tree topologies reflected the performance metrics measured above; mitochondrial genes and targeted exons retained many of the higher-level relationships recovered by the species tree, such as the monophyly of various families, and the sister group relationships of Callipallenidae þ Nymphonidae and Pallenopsidae þ Ammotheidae (supplementary fig. S3 , Supplementary Material online). However, by themselves, none of these partitions could resolve basal relationships with support. Reflecting the high proportion of missing data, the UCE tree topology was largely discordant with the species tree and other partitions, although some higher-level relationships were consistently recovered (e.
2008). Future work should prioritize the gap segmentation genes of sea spiders, especially those functionally linked to gap segmentation phenotypes in chelicerate model species  (Schwager et al. 2009; Pechmann et al. 2011; Setton and Sharma 2018). Surprisingly, the position of the putative ascorhynchid N. tapetis was unstable to analytical treatment of the data set, a result that cannot be attributed to missing data alone (N. tapetis was represented in 47-59% of loci across Matrices 1-4). Nymphonella is clearly distinguished from other sea spiders by anterolaterally projecting chelifores, annulated distal podomeres of the first walking leg, and a bulbous proboscis (fig. 1F ). Previous efforts to infer the placement of Nymphonella have recovered limited support for its placement or for the monophyly of Ascorhynchidae, though Nymphonella has only been represented by 18S rRNA in such studies  (Miyazaki et al.
Ancestral state reconstruction is based on equally weighted parsimony. Note the omission of functional adult chelifores in colossendeids with supernumerary segments (a derived state within the genus Colossendeis). Specimens in counterclockwise order from top left: male Austrodecus glaciale, male Endeis spinosa, female Phoxichilidium femoratum, egg-bearing male Ascorhynchus ramipes, male Stylopallene cheilorhynchus, male Nymphon gracile, female Ammothella longipes, female Colossendeis angusta, female Pycnogonum litorale, female Rhynchothorax australis. Phylogenomic Resolution of Sea Spider Diversification. doi:10. 1093/molbev/msaa228 MBE exactly within this desired range (supplementary fig. S2 , Supplementary Material online). Problematically for the UCE data set, capture efficiency of UCE probes was much lower than other data partitions (supplementary fig.
As a result, of an initial 230 UCEs amplified, we discarded 56% of alignments where fewer than six sea spider terminals were obtained. The remaining 101 UCE loci bore high proportions of missing data, in contrast to the targeted exons. Similar outcomes have been reported for palpimanoid spiders, a group that had the advantage of a well-annotated theridiid spider genome for validation of probe design  (Wood et al. 2018). Nevertheless, we observed high informativeness of the UCE loci despite missing data, as inferred from distributions of RF and weighted RF distances. The concatenated UCE tree, while incongruent with the species tree, recovered such higher-level groupings as Nymphonidae nested within Callipallenidae, and Colossendeidae sister group to Rhynchothoracidae þ Pycnogonidae. Given the promise of this phylogenetic data class, efforts to improve the recovery of UCE data sets in sea spiders should target the generation of high-quality sea spider genomes, with downstream improvements in the design of sea spider-specific UCE probes.
Species Sampling and Locus Selection Specimens of all sea spider families were obtained from field expeditions and museum collections, as well as material collected during multiple deep sea cruises. A list of taxa sampled is provided in supplementary table S1, Supplementary Material online. DNA was extracted using the Qiagen DNeasy Blood and Tissue Kit (Valencia, CA) from one to five specimens, prioritizing tissue from the propodus and main claw (gut-free leg podomeres) where possible. Taxonomic sampling consisted of 89 sea spiders; outgroups consisted of 14 Arachnida (including one Xiphosura, which has recently been shown to be possibly nested within the arachnids;  Ballesteros and Sharma 2019; Ballesteros et al. 2019) , 3 Myriapoda, 3 Pancrustacea, and 1 Onychophora. Probes were designed using available references and databases, given the absence of a published sea spider genome, following the pipeline established by  Breinholt et al.
Probes design was guided by conservation of intron-exon boundaries in the genomes of D. melanogaster, Strigamia maritima, and Parasteatoda tepidariorum. For UCE sequencing, we deployed probes from the UCE Arachnida 1. 1Kv1 bait set  (Starrett et al. 2017). Probe design, synthesis, automated library preparation, and paired-end sequencing (2 Â 150 bp) on the Illumina Hi-Seq 2500 platform were performed through RAPiD Genomics (Gainesville, FL). Sequencing efficiency is provided in supplementary table S2, Supplementary Material online; details of locus selection and orthology validation are provided in supplementary text S1, Supplementary Material online. All probe sequences are made available in the Dryad Digital Repository.
Bioinformatic and Phylogenomic Operations Raw reads were assembled using Trinity v. 2. 7  (Grabherr et al. 2011; Haas et al. 2013 ) using a path reinforcement distance of Phylogenomic Resolution of Sea Spider Diversification. doi:10. 1093/molbev/msaa228 MBE 75. Reference sequences of sea spiders (mitogenomes and nuclear exons) and/or euchelicerates (UCEs) were used to separate loci into alignments. We included the nuclear ribosomal genes 5. 8S rRNA, 18S rRNA, and 28S rRNA, which were obtained as bycatch; on-target amplification of these bycatch loci was verified using BlastN against available sea spider ribosomal sequences in GenBank. Outgroup taxa were subsequently added into the alignments using available transcriptomes from our previous works (for nuclear exons;  Sharma, Kaluziak, et al.
2014) , from GenBank-accessioned data (mitochondrial genes/genomes and nuclear ribosomal genes), and from a subset of the taxa in chelicerate UCE alignments  (Starrett et al. 2017). Due to the unavailability of the same phylogenetic data classes among the genomic resources of some data-poor outgroups, we created chimeric terminals for such terminals as Eremobates and Ricinoides. The nature of these congeneric chimeras is that sequence data for nuclear exons and ribosomal genes are drawn from the transcriptomes of one species (e. g. , Eremobates cf. transfugoides; Ricinoides atewa), but the mitochondrial genome is drawn from a congener (e. g. , Eremobates cf. palpisetulosus; Ricinoides karschii). The list of chimeric outgroup terminals is detailed in supplementary table  S3 , Supplementary Material online.
Gene trees were inferred using IQ-TREE v. 1. 6 (Nguyen et al. 2015)  with the best-fitting model selected by ModelFinder (-m MFP)  (Kalyaanamoorthy et al. 2017). Gene tree topologies were visually inspected for potential paralogs, which were summarily discarded from alignments (principally found in the UCE alignments previously generated for arachnids). Orthology was validated using BLAST-based annotation; the annotations for all sequenced loci are provided in supplementary table S6, Supplementary Material online. Four matrices were constructed for our main analyses and all data sets were treated as nucleotide alignments. Matrices 1-3 were constructed using taxon occupancy thresholds of 50%, 33%, and 25%, respectively. Matrix 4 consisted of loci stipulated to sample at least one Austrodecidae (the putative sister group to the remaining sea spiders).
The final alignments were analyzed as concatenated supermatrices using IQ-TREE v. 1. 6 with best-fitting models per locus. Due to ongoing debate over the benefits of model fitting, we additionally analyzed our data sets using a unique GTR þ C 4 model for each locus as well. Nodal support was estimated using bootstrap resampling frequency with 1,000 ultrafast bootstrap replicates in IQ-TREE. In addition, approximately unbiased tests of monophyly were performed using in-built tools in IQ-TREE for selected phylogenetic hypotheses using Matrices 1 and 4. For assessment of data set performance, we inferred the normalized RF and wRF distances between the species tree inferred using the most complete matrix (Matrix 1) and each gene tree. To overcome missing data, the species tree was recursively pruned of tips to match the terminals of each gene tree prior to calculation of RF and wRF distances, and the distances were normalized using the maximal possible distance between each gene tree and the pruned species tree.
(2019). All samples were removed from RNAlater and transferred to TRIZOL TriReagent for RNA extraction, following our previous procedures  (Sharma, Kaluziak, et al. 2014). Library preparation, paired-end Illumina sequencing, and transcriptome assembly with Trinity also followed our previous approach  (Sharma, Kaluziak, et al. 2014). We used the conserved homeodomain sequences of all Hox genes of the spider Parasteatoda tepidariorum to retrieve Hox sequences from new libraries, using tBLASTn searches. Putative A. hentzi and sea spider Hox homologs were added to multiple sequence alignments recently generated by us  (Gainett and Sharma 2020) , in addition to all available sea spider Hox sequences previously generated using RACE PCR  (Manuel et al. 2006 ). Gene tree inference was performed using IQ-TREE, treating sequence data as peptides and model fitting using ModelFinder.
MBE Leica EM CPD300 and glued with the posterior body pole on plastic welding rods (dry scan). Scans were performed with an Xradia. Scan settings were individually optimized for each specimen, including objective choice (0. 39Â, 4Â, 10Â) according to size. Scans were performed under 40 kV/200 mA/8W, exposure times ranged from 0. 4 to 1. 5 s. Tomography projections were reconstructed using the XMReconstructor software (Carl Zeiss Microscopy GmbH) with TIFF format image stacks as output. All scans were performed with Binning 2 to reduce noise and subsequently reconstructed with Binning 1 (¼full resolution) to avoid information loss. Processing and 3D visualization of the image stacks (including highlighting of cephalic appendages and removal of nontarget structures) were performed with Imaris (version 7.
Acknowledgments This work is dedicated to the memory of  Roger Norman Bamber. We are indebted to  Katsumi Miyazaki ,  Anna Soler-Membrives , and  Julia D. Sigwart  for providing specimens for study. We thank the crews of many research vessels for assistance with fieldwork, and the  Australian  and  Paris Museums  for loans of materials for study. Sequencing and assembly of the Pycnogonum litorale mitogenome were performed by  Anke Braband  and  Jakob Machner. Chronograms  of selected marine invertebrate taxa were kindly provided by  Heather D. Bracken-Grissom ,  Simon Y. W. Ho ,  Tim D. O'Hara ,  Greg W. Rouse , and  Jo M. Wolfe. Access  to computing nodes for intensive tasks was facilitated by the  Bioinformatics Resource Center (BRC) and Christina Koch at the Center for High Throughput Computing (CHTC) of the University of Wisconsin-Madison.
Comments from three anonymous reviewers and the Associate Editor improved a previous draft of this work. J. A. B. was supported by the M. Guyer postdoctoral fellowship. E. V. W. S. was supported by a  National Science Foundation Graduate Research Fellowship. C. E. S. L. was supported by postdoctoral  CONACYT (Grant Reg. 207146/ 454834 ). Morphological data collection was supported by  Deutsche Forschungsgemeinschaft  (Grant No. BR5039/3-1  to G. B. ). Fieldwork in Antarctica was supported by  National Science Foundation  (Grant Nos. ANT-0551969  to A. L. M. and  ANT-0440577  to H. A. W. ). Collection under projects  POL2006-06399/CGL (CLIMANT)  and  CTM2012-39350-C02-01  (ECOWED) were funded by the  Spanish Comisi on Interministerial de Ciencia y Tecnolog ıa and the Ministerio de Econom ıa y Competitividad.
Data Availability The complete data set, including sequence alignments, annotated tree files, chronograms, and embryonic transcriptomic assemblies, have been deposited at Dryad Digital Repository under URL https://datadryad. org/stash/share/HsFKzSdD00rw a5BQSEf9km6dXuURuiAtOwv0w0QVDUg. Raw read data were deposited at NCBI Sequence Read Archive. Loci from targeted sequencing were deposited in NCBI GenBank.
1  down: Readout electronics). Amplifier output pulse was shaping with a time constant at around 1 us. The energy histograms were monitored with the home-made AXINTDETECT labview software. Signal measurements, using the gamma sensor system, were exploited as an energy histogram distributed along 4096 channels. This energy histogram was reprocessed using a moving average on 10 points (still equivalent to a range DE~2. 2 keV). This provides a smoother and legible signal without changing interpretation given the fact that energy resolution of the sensor is superior to 2. 2 keV. Differ to classical scintillation system design, the novelty from our sensor is that it offered the flexibilities to test the various SiPMs array that were connected. As illustrated in the following figure (Fig. 2  top), various SiPMs were possible to be connected between SiPM bias and system power (þ30V could be in connection with BIASX  (1, 2, 3, 4) ), between SIG and ARRAYX (1,2,3,4).
After optimization of SiPMs configurations, a minimum gamma energy detection level has been identified using 241 Am sources as low energy gamma emitters. As SiPM is sensible to temperature variations, at this stage we measured the photoelectric peak position in response of different measurements conditions this including measurements at room temperature, in an isothermal bath milieu, in a refrigerator. The temperature was lectured by a digital temperature sensor (AD7814) which is direct connected to readout electronics. After that we established a gain correction formula based on fitting these measurements data. Finally, this formula has been integrated into AXINTDETECT software and for a user defined time base, this new correction gain of SiPM value has been taken into account. This new embedded functionality has been verified as well lastly.
Sociodemographic and injury-related factors Seventeen questions about sociodemographic and injury related factors are included in the survey. The phrasing of the questions has been adapted from that used in the International Spinal Cord Injury (InSCI) Community Survey  [20]. Adverse events are reported at all three time-points of data collection. Based on feedback from stakeholders and on personal experience of the researchers, we developed a list of adverse events that comprises a list of 9 options. Table  1  provides more details for these variables.
Sample size The G* Power software package was used to prospectively calculate the adequate sample size for this study (G*Power Team, Germany downloaded from http:// www. gpower. hhu. de/en. html). Assuming a two-sided test with alpha = 0. 05 and beta = 0. 05 (power 0. 95), a small effect size of 0. 4 and expecting a 20% attrition rate, we would need 101 cases in each country. Effect size of 0. 5 for SCIM-III (10% change in total score) has been shown to be a meaningful change  [31]. Inter-PEER aims to find changes that have the same or slightly less sensitivity than SCIM-III. We are not interested in finding changes in primary outcomes that have far less sensitivity than SCIM-III, that is an effect size of less than 0. 4, for two reasons. First, capturing even smaller effect sizes in self-reported surveys may not be meaningful for community organisations.
Lessons learnt from the pilot To ensure the feasibility of the research protocol, we conducted a pilot that included seven participants with diverse spinal cord lesion and sociodemographic characteristics in Sweden. In addition to having the participants complete the evaluation survey and wheelchair skills test, we collected feedback about study procedures through interviews with participants, peer-mentors and programme organisers, as well as through field observations. We identified several problems with the initial version of the protocol: i) the survey was perceived as very lengthy and taking a long time to complete; ii) the use of paper and electronic means to complete the survey was perceived as inconvenient; iii) the data collection was not sufficiently integrated into the schedule of the AR programme, making the participants feel as if they were missing out on other programme activities; iv) some outcome measures (e.
Dissemination Findings of the Inter-PEER will be shared with academics and the wider scientific community through peer-reviewed publications, seminars and conference presentations. There will be a series of articles specific for each country and articles based on pooled data. Findings will also be communicated to people with SCI through lay language articles in consumer-oriented magazines and newspapers, as well as on the websites of the community organisations. Procedures and findings will be further disseminated to community organisations through workshops and technical reports.