dataesr/scientific-paragraphs-categorization huggingface dataset:
The precursor ions were isolated within a 15 Da window and fragmented by HCD acquired with normalized collision energy 28 and default charge state 3 and with a starting m/z of 200. Center values of isolation windows are reported in Supplementary Notes 2 and 3. Search of DDA data and spectral library generation. The assignment of MS/MS spectra generated from DDA analyses of the Mixed Proteome protein digests was made with Proteome Discoverer 2. 2 software and Sequest HT algorithm using UniProt database filtered for Homo sapiens (downloaded April 2016), Saccharomyces cerevisiae (downloaded May 2016), or Escherichia coli (downloaded February 2016) taxonomies, concatenated with iRT peptide. fasta file (downloaded from the Biognosys webpage). Tolerances on precursors and fragment ions were set at +/-10 ppm and +/-0.
02 Da, respectively. The searches were performed by specifying Trypsin (full) enzyme digest specificity constraints with a maximum of two missed cleavage sites allowed, Oxidation as dynamic modification, and Carbamidomethylation as static modification. The data were also searched against a decoy database, and the results were used to estimate q values using the Percolator algorithm within the Proteome Discoverer version 2. 2 suite. Protein and peptide identifications were filtered at a false discovery rate (FDR) <1% with no threshold on the minimum number of peptides. Proteome discoverer result files were imported into Spectronaut Pulsar 11. 0. 15038. 23. 24843 (Asimov) software for the generation of the spectral libraries (. kit files) for each organism using default settings. Data independent analysis bioinformatics and protein-level roll-up.
A step-bystep procedure for data processing and evaluation with Spectronaut software is reported in Supplementary Note 4. Briefly, raw files (including QC standard and Sample A and Sample B raw data) were imported into Spectronaut software and searched against pertinent spectral libraries. The data generated by the acquisition method combining MS1 scans with interspersed DIA MS2 scans were directly processed by Spectronaut without conversion or pre-definition of the actual method structure as input information in the software. Both MS1 and MS2 data were used for peptide identification while the parameters of the quantification process were solely derived from the MS1 data. The extraction of data used dynamic MS1 and MS2 mass tolerances, dynamic window for extracted ion current extraction window, and a non-linear iRT calibration strategy.
The identification was carried out using a kernel density estimator and FDR cut-off of 0. 01 at precursor and protein levels. The extracted quantitative data benefited from interference correction and a local cross-run normalization strategy. The data processing results were exported using two customized reports for peptide and protein identification and for further quantification processing using R scripts (Supplementary Software). The peptide customized report included EG. Pre-cursorId, PG. ProteinAccessions, PG. ProteinDescriptions, EG. Qvalue, PEP. Quantity, and PG. Quantity fields. The protein customized report included PG. FastaFiles, PG. ProteinGroups, and PG. Quantity fields. The scripts were applied to subsets of analyses, which were grouped together according to day and lab. Through these scripts, additional filtering steps were applied prior to the comparison of quantifications of proteins between samples A and B including the removal of peptides shared by different organisms and the removal of sub-optimal precursor ions of peptides detected under different charge states (i.
e. , the precursor ions not showing the highest number of retained quantitative data across the series of analyses considered). For each retained peptide, the maximum number of inter-sample combinations between the replicated analyses with quantitative data available was determined. This estimation was expanded at the protein level by summing these numbers obtained for all surrogate peptides. The proteins were considered as reliably quantifiable in the experiment and retained for further processing when at least eight combinations were available. The actual relative quantification of retained proteins was performed by calculating the geometric median of the peptide pairwise ratios obtained from all inter-sample combinations. Data independent analyses of ovarian cancer samples. Equivalent amounts of tissue digests were shipped to three analytical laboratories, (Lab 2, Lab 5, and Lab 9).
The MS1 scan rate required ~1. 7 s leading to an overall MS1 to MS1 scan time of ~5. 2 s, which equates to the acquisition of ~8-11 MS1 data points across the median LC peptide elution peak width (17 ± 3 s) in this study. The precursor ions were isolated within a 15 Da window and fragmented by HCD acquired with normalized collision energy 28 and default charge state 3 and with a starting m/z of 200. Center values of isolation windows are reported in Supplementary Notes 2 and 3. QC standards were analyzed in triplicate before and after cancer tissue sample analyses and a single QC standard analysis was performed midway through the overall analysis. Protein abundance was determined for patient sample-specific HRMS1-DIA data collected by each analytical site. The proteins retained to undergo comparative analyses were selected using the peptide filtering strategy described above and by requiring that the sum of observations of all retained peptides for each of these proteins had to satisfy a minimum number of 3 across replicated analyses at the individual patient sample level.
Acknowledgements We would like to thank to  Jenny Ho  and  Joshua J. Nicklay  for evaluating and discussion about the study design, and for acquiring the data sets to establish the quality control criteria for the study;  Oleksandr Boychenko  for discussion about the capillary LC setup; We thank  Steve Bios ,  Ming-yi He , and  Scott M. Peterman  for instrument maintenance and support with the MS measurements. David A. Joyce  to setup ThemoCloud accounts to facilitate the data exchanges across the study;  Sven Klingel  and  Shen Luan  for assis-tance with the project coordination;  Lukas Reiter  for discussion about normalization and data analysis in the Spectronaut;  Christoph Henrich  and  Alexander Tiegel  for discussion about the data visualization;  David Sarracino  for the discussion about the study design.
To discard possible differences related to the erecta mutation known to be present in Ler ecotype, we used Col er105 mutant carrying this mutation in Col. We obtained between 47M and 65M of reads (Additional file 1: Table  S1 ). For each ecotype, the reads were independently mapped to their reference genome (Additional file 2: Fig. S1a ): TAIR10 for Col  [43]  and Ler v7 for Ler  [44]. We selected these two genome versions since they shared the same gene annotation (TAIR10). For these two genomes, we predicted new transcripts by comparing our data to those available in TAIR10 annotations. The homology of newly predicted transcripts between the Col and Ler genomes was determined by mapping them on the other genome (TAIR10 and Ler v7). When the newly transcripts overlapped with pre-existing annotations, fusions between the two transcripts were done to generate the new transcripts.
We retained as new transcripts only RNA molecules of at least 200 nt. The Ler v8 version of the Ler genome lacks the official TAIR10 annotation  [45]. Therefore, for the transcripts mapping on Ler genome, we retained only the ones that also mapped on this version. Newly identified transcripts by this pipeline (Additional file 2: Fig. S1a ) were then compared with the ones already described in different Arabidopsis databases: Araport 11  [46] , RepTas  [8] , CANTATAdb  [47] , miRBase v21  [48]  and with the results of two previous studies concerning lncRNAs  [49, 50]. Finally, we used the COME software  [51]  to determine the potential coding capacity of new identified transcripts (Additional file 2: Fig. S1a ). On the basis of both database information and COME predictions we classified the corresponding genes as coding or non-coding.
8 In total, we identified 5313 and 6408 novel putative genes respectively in Col and Ler ecotypes (Fig. 1a ; Additional file 1: Table  S2 ; Additional file 3). In root apexes, newly discovered genes were predominantly non-coding RNAs: 76% for Col and 77% for Ler of total number of new genes (Fig. 1a ; Additional file 2: Fig. S1b, c ). This suggests that the coding capacity of the Arabidopsis genome is now well documented in TAIR10 and Araport11 databases (Additional file 2: Fig. S1b ). As expected, non-coding genes were globally less expressed than coding genes (Additional file 2: Fig. S2a, b ). Genes specifically detected only in one ecotype belong much more to the non-coding class (>40%) than to the coding one (<8%; Fig. 1b, c ). Moreover, the proportion of ecotypespecific expressed lncRNAs was higher for intergenic lncRNA genes (52%) than for NATs (34%; Fig.
It is made available under The copyright holder for this preprint (which was not this version posted March 16, 2019. ; https://doi. org/10. 1101/579656 doi: bioRxiv preprint 20 the two ecotypes. We think reasonable to assume that certain of these lncRNAs could contribute to Col and Ler root growth specificities. Indeed, in animals, certain complex traits are mainly driven by non-coding variants  [65, 66]. In chicken, domestication traits governing body morphology and behavior are under selection and correspond to lincRNA genes  [67]. Moreover, recently, the human LincSNP 2. 0 database, which contains 809451 unique disease associated SNPs, 11,6 million of linkage disequilibrium SNPs and 244 545 lncRNAs, identified approximately 45% of disease-associated human SNPs that mapped to non-coding regions of the genome  [68].
New transcript identification According to their ecotype of origin, mRNA cleaned reads were aligned on TAIR10  [43]  or Ler v7  [44]  genome using tophat2 (version 2. 0. 13, [88] ) with the following arguments: --max-multihits 1 --num-threads 8 -i 20 --min-segment- intron 20 --min-coverage-intron 20 --library-type fr-firststrand -microexon-search -I 1000 --max-segment-intron 1000 --max-coverage-intron 1000 --b2-very-sensitive. Independently of ecotype, new transcripts were predicted using GFFprof included in RNAprof  [89]  The transcripts predicted on each genome, TAIR10 and Ler v7, were positioned on the other genome, respectively Ler v7 and TAIR10, and on Ler v8 [45] using blastn (from BLAST suite 2. 2. 29+) using a maximum e-value of 10 -4. For each transcript, the different blast hits fragments were fused together if the distance between two fragments was less than 5000 nucleotides and placed on the same strand of the chromosome.
Only hits with at least 90 % of sequence identity and where the length was conserved (at least 90 % and less than 110 % of length outside of insertion) were kept. For each transcript only the best hit was conserved according first to the conservation of the sequence length and then identity. In case of hits of the same strength, a higher priority was given when the chromosome and then the strand were conserved. Each transcript was therefore placed on each of the three genomes. For Ler-predicted transcripts only those positioned on Ler v8 were kept. On each genome independently, transcripts coming from the same ecotype (GFFprof prediction) or the other one (blastn positioning) were fused using cuffmerge (version 1. 0. 0) with. CC-BY-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under The copyright holder for this preprint (which was not this version posted March 16, 2019. ; https://doi. org/10. 1101/579656 doi: bioRxiv preprint 27 default parameters. Only transcripts longer than 200nt in either ecotype were kept for further processing. Based on the position of the transcripts on the TAIR10 genome, new transcripts were annotated according to already known transcripts in the following databases: Araport 11  [46] , RepTas  [8] , CANTATAdb  [47] , miRBase v21  [48]  lncRNAs predicted from Ben Amor et al. [49]  and root predicted lncRNAs from Li et al. [50]. GffCompare (version 0. 10. 4, https://ccb. jhu. edu/software/stringtie/gffcompare. shtml) was used for the comparison. In case of overlap with a known transcript (=, c, k, j, e, o codes of GffCompare), the closest transcript was used to determine the identification and the coding potential of the transcript.
Library saturation analysis The saturation of libraries was computed using the RPKM_saturation. py script included in RSeQC v3. 0. 0  [91, 92]. Raw counts were quantified at gene level from resampled bam for each additional 2% of the reads from 2% to 100% of the reads (-l 2 -u 100 -s 2). The saturation was computed 100 times. For each library and each sampling the number of detected gene (known or new and coding or non-coding) was defined as the number of genes having at least one read.
Measurement of the primary root length Pictures of the plates were taken with a flat scanner and root lengths measured using RootNav software  [96]. Each measure corresponds to a different plant. Quantitative RT-PCR Total RNA was extracted from whole roots using the Quick-RNA MiniPrep™ kit (Zymo Research, USA) and treated with the included DNAse treatment according to the manufacturer's instructions. Reverse transcription was performed on 500Sng total RNA using the Maxima Reverse Transcriptase (Thermo Scientific™). Quantitative PCR (qRT-PCR) was performed on a 480 LightCycler thermocycler (Roche) using the manufacturer's instructions with Light cycler 480sybr green I master (Roche) and with primers listed in Additional file 1: Table  S4. We used PP2A subunit PD (AT1G13320) as a reference gene for normalization.
Funding This work was supported by grants from  Agence Nationale pour la Recherche (ANR) RNAdapt  (grant no. ANR-12-ADAP-0019 ), SPLISIL (grant no. ANR-16-CE12-0032 ) and grants of  The King Abdulla University of Science and Technology (KAUST). CC-BY-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted March 16, 2019. ; https://doi. org/10. 1101/579656doi: bioRxiv  preprint 31 International Program   OCRF-2014-CRG4  and  'Laboratoire d'Excellence (LABEX) '  Saclay Plant Sciences (SPS ;  ANR-10-LABX-40 ).
2. 1. The reads were further trimmed using Sickle version 1. 200 with a minimum window quality score of 20. Reads shorter than 10 bp after trimming were removed. Raw sequence reads are available in the NCBI Sequence Read Archive under BioProject PRJNA517619. Data analysis. QIIME2 version 2019. 1. 0 was used for analysis of the Illumina data  (52). Amplicon sequence variant (ASV) assignment was completed using the dada2 plug-in  (53)  and an ASV table produced in biological observation matrix (BIOM) format  (54). The resulting ASV table was divided into three tables, one containing all samples, including transplant samples for use in diversity analyses, and one each for samples from the pilot and repeat experiments to identify differentially abundant ASVs between control and treatment groups.
Taxonomy was assigned using the q2-feature-classifier plug-in  (55)  with a pretrained NaiveBayes classifier based on the SILVA 132 database of the 515F/R806 region of the 16S rRNA gene  (56) , available for download at https://docs. qiime2. org/2018. 11/data-resources/. Alpha and beta diversity analyses were performed at a sampling depth of 5,000 using the alignment (57), phylogeny  (58) , and diversity (https://github. com/qiime2/q2-diversity) plug-ins. Alpha diversity, a metric used to assess species richness and evenness, was measured using a Shannon diversity index. Taxa plots were produced using the q2-taxa plug-in (https://github. com/qiime2/q2-taxa). Beta diversity, a metric used to compare species diversity and abundance between samples, was calculated with unweighted and weighted UniFrac metrics.
Based on their presence in intersections, ASVs were classified as successfully transplanted, possibly transplanted, or environmental. ASVs were classified as successfully transplanted if they were present in the transplant and in treated chicks at least one time point before control chicks. ASVs were classified as possibly transplanted if they were present in the transplant and in both treated and control chicks at the same time point. ASVs were classified as environmental if they were present in the transplant and present in only control chicks or present in control chicks before treated chicks. Any ASV not present in the transplant was classified as environmental. A chi-square test of independence was performed to examine the relationship between ASVs identified as differentially abundant between treatment groups and those defined as successfully transplanted, possibly transplanted, or environmental using Python's SciPy module.
The taxonomy of ASVs classified as successfully transplanted, possibly transplanted, and environmental was compared to that of ASVs identified as more abundant in treated chicks, more abundant in control chicks, and not differentially abundant with a hybrid Sankey diagram created using Sankeyview version 1. 7. 7  (60). Quantitative PCR. Taxa were selected for further testing using quantitative PCR based on results from Gneiss analysis. A literature search was conducted to find suitable primers. Where suitable primers were not available, the sequences retrieved from Illumina sequencing were used to produce taxa-specific primers. The sequence was input into Primer-BLAST, and a suitable primer pair was chosen. To test the specificity of primers, each primer pair was input into TestPrime for comparison against the SILVA database SSU-r132.
Further testing of primers was conducted using PCR. The primers were tested against known positive and negative samples to check for the correct amplicon size and nonspecific amplification. A gradient PCR was conducted to establish the correct annealing temperature for quantitative PCR. The primers used are displayed in Table  5. The real-time quantitative PCR assay was conducted on a 1:10 solution of extracted DNA using a Rotor-Gene Q PCR machine (Qiagen) and PrecisionPLUS qPCR master mix (Primer Design, UK). The V4 region of the 16S rRNA gene was used as a reference gene. Rotor-Gene Q software version 2. 3. 1. 49 was used to produce melting curves and identify the cycle threshold (C T ), the point at which fluorescence above the background level is detectable. Each sample was run in triplicate with an averaged C T used in further analysis.
Images were viewed and measurements taken using Leica Application Suite X software. Sections were assessed for suitability based on orientation of tissue samples. Villus height, villus width, and epithelial cell height were recorded in transverse ileal sections, where entire villi could be visualized to the lamina propria. In such sections, the height and width of five villi with an intact lamina propria were measured. Villus height was defined as the distance from the villus tip to the villus-crypt junction. Villus width was measured at the widest section of the villus. Epithelial cell height was measured at the villus tip and was defined as the distance from the distal point of the microvilli to the basement membrane. Measurements were expressed as a mean for each bird. Mitotic figure counts in the ileum and cecal tonsil were used as an indication of intestinal villus development  (61, 62).
All orientations of tissue were included for mitotic counts where crypts were visible adjacent to the lamina propria and muscular layers. Mitotic figures in crypts within high-power field (400ϫ) of the lamina propria were counted. Cells were identified as mitotic if their nuclei were strongly basophilic and homogenous, with care taken to count cells in the late stages of division as a single mitotic figure. The length of lamina propria over which mitotic figures were counted was measured, and results were expressed as the number of mitotic figures per 100 m. Results were expressed as a mean for each bird. Results were compared between treatment groups using Student's t test implemented in the SciPy version 1. 1. 0 Python module  (63). A Benjamini-Hochberg false discovery rate correction implemented in the Statsmodels version 0.
Acceptability, Adherence, and Engagement Descriptive statistics reported website registration and participant characteristics. We conducted statistical analysis using nonparametric methods in IBM SPSS version 26 (IBM Corporation) to explore baseline characteristics of each trial arm. Adherence was measured by completion of outcome measure at baseline and postintervention. Qualitative feedback, collected at the end of the program via a structured feedback form, interview, and focus group discussion, was audio recorded and transcribed verbatim. Data were analyzed using inductive thematic analysis  [68].
Of the 23 participants (22. 3%) who enrolled in Eat Healthily, only 6 (26%) engaged, 1 until week 6. User data indicated poor fruit and vegetable consumption, with few meeting recommended guidelines and some never consuming the recommended 5-a-day portion  (5/23). Almost all who enrolled in Weight Optimization (37/39, 95%) engaged and provided an initial weight (mean 75. 77, SD 15. 80 kg). A small subsample (10/39, 26%) provided a second weight (mean 75, SD 10. 89 kg). Of the 40 participants who enrolled in Regular Exercise, 33% (13/40) actively engaged. This module saw the longest sustained engagement of the modules, with activity recorded until week 9. Only 2 (2/103, 1. 9%) participants enrolled in Quit Smoking and neither entered nor tracked any data. Well-being had the highest enrollment (41/96, 42%).
Qualitative Data To fully explore acceptability, we collected a range of qualitative data. We facilitated 2 one-to-one interviews (25-minute duration) and 2 focus groups (77-and 73-minute durations, 4 participants each). A total of 15 participants contacted the principal researcher via email and 8 completed the feedback survey. Qualitative feedback was positive (Table  3 ), and it was clear that staff welcomed the inclusion of the well-being intervention; indeed, people who had used the prior releases requested its inclusion. Analysis identified recommendations for future development. Table  3. Sample quotes from participants' feedback and their recommendations.
No automated data were recorded. Feedback and interview data suggested that participants engaged at many additional time points, which was not captured in our analysis. This should be addressed. Equally, the use of rewards and feedback did not support sustained engagement. Future developments should consider use of additional or different gamification features, for example, avatars or social interaction  [75] , or guided support and structured feedback, which have been associated with better adherence. Fourth, due to an error in the database, which has been amended, we were not able to examine health care worker role in relation to self-reported absenteeism.
Evidence for such events in combination with resonances in water-filled cavities is reported in  Helmstetter et al. (2015) , who analyzed the recordings of an accelerometer deployed on ice. In the case of high englacial water pressures exceeding the ice overburden pressure, hydraulic jacking of the ice can occur. Jacking accompanied by seismicity is reported during rapid drainage events of supraglacial lakes  (Das et al. , 2008; Doyle et al. , 2013). We also note that high occurrence rates of overlapping fracturing icequakes may result in sustained tremor-like fracturing events  (Podolskiy et al. , 2018; MacAyeal et al. , 2019). In this study, we analyze data from on-ice seismic stations deployed during the 2016 melt season on Glacier de la Plaine Morte, Switzerland (Sect. 2). We show that both tremors and icequake activity are linked to glacial discharge which includes the outburst flood of a glacier-dammed lake (Sect.