dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank the participants for contributing to this study. JYM holds a Canada Research Chair in Sleep Medicine. JFG holds a  Canada Research Chair in Cognitive Decline in Pathological Aging. WO is Hertie Senior Research Professor , supported by the  Hertie Foundation. EAF holds a Canada Research Chair (Tier 1) in Parkinson disease. GAR holds a  Canada Research Chair in Genetics of the Nervous System  and the  Wilder Pen  field Chair in Neurosciences. JFT holds a Canada Research Chair (Tier 2) in Structural Pharmacology. ZGO is supported by the  Fonds de recherche du Québec-Santé Chercheur-Boursier  award and is a  Parkinson Canada New Investigator awardee. We thank  D. Rochefort ,  H. Catoire , and  V. Zaharieva  for their assistance.
This work was financially supported by the  Michael J. Fox Foundation , the  Canadian Consortium on Neurodegeneration in Aging (CCNA) ,  Parkinson Canada the Canada First Research Excellence Fund (CFREF) , awarded to  McGill University  for the  Healthy Brains for Healthy Lives (HBHL) program. The Montreal cohort was funded by the  Canadian Institutes of Health Research (CIHR)  and the  W. Garfield Weston Foundation. The Oxford Discovery study is funded by the  Monument Trust Discovery Award  from  Parkinson's UK  and supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre  based at  Oxford University Hospitals NHS Trust  and  University of Oxford , the  NIHR Clinical Research Network  and the  Dementias and Neurodegenerative Diseases Research Network (DeNDRoN).
AAO = age at onset; AAS = age at sampling; ACMSD = Aminocarboxymuconate semialdehyde decarboxylase; BST1 = Bone marrow stromal cell antigen 1; CADD = Combined Annotation Dependent Depletion; CCDC62 = Coiled-coil domain containing 62; CI = confidence interval; DOC = depth of coverage; F_A = frequency in affected; F_C = frequency in control; FGF20 = Fibroblast growth factor 20; GAK = Cyclin G associated kinase; GATK = Genome Analysis Toolkit; GnomAD = Genome Aggregation Database; GPNMB = Glycoprotein (transmembrane) nmb; GTEx = Genotype-tissue expression; GWAS = genome-wide association study; hg19 = human genome version 19; HIP1R = Huntingtin interacting protein 1 related; iRBD = isolated rapid-eye-movement sleep behavior disorder; ITGA8 = Integrin subunit alpha 8; LAMP3 = Lysosomal-associated membrane protein 3; LD = linkage disequilibrium; LOF = loss-offunction; MAF = minor allele frequency; MAPT = Microtubule-associated protein tau; MCCC1 = Methylcrotonoyl-CoA carboxylase 1 (α); MIPs = molecular inversion probes; NS = nonsynonymous; OR = odds ratio; PCA = principle component analysis; PM20D1 = Peptidase M20 domain containing 1; QC = quality control; QS = quality score; REM = rapid eye movement; RIT2 = Ras Like without CAAX 2; SCARB2 = Scavenger receptor class B, member 2; SETD1A = SET domain containing 1A, histone lysine methyltransferase ; SIPA1L2 = Signal induced proliferation associated 1 like 2; SKAT = sequence kernel association test; SKAT-O = optimized SKAT; SLC41A1 = Solute carrier family 41 (magnesium transporter), member 1; STK39 = Serine/threonine kinase 39; STX1B = Syntaxin 1B; SYT11 = Synaptotagmin 11; TMEM163 = Transmembrane protein 163; USP25 = Ubiquitin specific peptidase 25; UTR = untranslated region; vPSG = video polysomnography.
V272M (rs144197373, found in 10 controls and two patients). Overall, these variants were found in 22 (1. 2%) controls vs. 2 (0. 2%) patients. Of these two patients, one had self-reported age at onset of 25 years old (not Finally, there were no differences in the coverage between patients and controls. A total of 1,189 rare variants were found with coverage of > 30X, and 570 rare variants with > 50X (data available from Dryad, Table e-4). We identified 125 common variants across all genes (data available from Dryad, Table e-5) with a coverage of >15X. Rare heterozygous variants in BST1 and LAMP3 are associated with iRBDTo examine whether rare heterozygous variants in our genes of interest may be associated with iRBD risk, we performed SKAT and SKAT-O tests, repeated twice for variants detected at depths of coverage of >30X and >50X (see methods).
Another statistically significant association was found between rare variants in LAMP3 gene and reduced iRBD risk in SKAT-O analysis. This association is driven by two non-coding variants (one intronic [location -chr3:182858302] and one at the 3' UTR of LAMP3 [rs56682988, c. *415T>C]) found only in controls (15 and nine controls, respectively). In order to further examine whether these variants indeed drive the association in both BST1 and LAMP3, we excluded them and repeated the analysis (SKAT and SKAT-O), which resulted in loss of statistical significance for both genes (data available from Dryad, 7). Rare functional and rare nonsynonymous BST1 variants were nominally associated with iRBD (p=0. 021 and p=0. 049, SKAT), but not after correction for multiple comparisons.
V822M 0 0. 000282 2. 03E-05 2. 69E-05 C: Control; M: Male; F: Female; AAS: Age at sampling; AAO: Age at onset of iRBD; dbSNP: Single nucleotide polymorphism database; *Allele: Reference allele/mutant allele; F_A: Frequency in affected; F_C: Frequency in controls; gnomAD ALL: Exome allele frequency in all populations; gnomAD NFE: Exome allele frequency in non-Finnish European.
Metabolic alterations are a hallmark of the malignant transformation in cancer cells, which is characterized by multiple changes in metabolic pathways that are linked to macromolecule synthesis. This study aimed to explore whether salivary metabolites could help discriminate between breast cancer patients and healthy controls. Saliva samples from 23 breast cancer patients and 35 healthy controls were subjected to untargeted metabolomics using liquid chromatography-quadrupole time-of-flight mass spectrometry and a bioinformatics tool (XCMS Online), which revealed 534 compounds, characterized by their retention time in reverse-phase liquid chromatography and by the m/z ratio detected, that were shared by the two groups. Using the METLIN database, 31 compounds that were upregulated in the breast cancer group (p < 0.
The molecular weights of all known medications used by the subjects were compared with those of each upregulated compound (according to the determined charge state in the MS spectrum), which confirmed that the identified compounds were not prescribed medications or their metabolites (Supplementary Table  S3 ). The proposed metabolite names were also search in the METLIN database, which identified 4 of the 35 compounds as potentially being drugs or phytochemical compounds. These compounds corresponded to dioscin (an antifungal agent) or donepezil (an oral medication used to treat Alzheimer's disease), desglucomusennin (a phytochemical compound), dilazep (a vasodilator), and tetrahydrogambogic acid (a compound isolated from fruits) (Table  3 , lines 1-4). Among the 31 remaining compounds, the METLIN database proposed a putative identification for 13 metabolites (seven oligopeptides and six glycerophospholipids) based on the experimental m/z and charge state (Table  3 , lines  [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17].
The de novo biosynthesis of fatty acids is low in normal adult tissues, although tumorigenesis is associated with a dramatic increase in lipid production  [35] , which has also been confirmed in BC patients  [15]. Phospholipids are an essential component of the cell membrane and are involved in a variety of biological functions, such as division of the cytoplasm, inter-cell adhesion, and protein storage  [36]. Our results identified six glycerophospholipids that might be related to BC (PG14:2, PA32:1, PS28:0, PS40:6, PI31:1, and PI38:7). Based on the Human Metabolome Database, PG14:2, PS28:0, PS40:6, and PI31:1 have extracellular and membrane localizations, while PA32:1 has no cellular localization. The pathways of the identified ions are listed in Table  5. The PG14:2 phosphatidylglycerol has a phosphoglycerol moiety occupying a glycerol substitution site and is related to glycerophospholipid metabolism  [37].
92), LysoPC22:6 (AUC: 0. 92), and MG 0:0/14:0/0:0 (AUC: 0. 929). These higher AUC values were clearly superior to the values for the lipids that we identified in the present study. However, the present study compared salivary compounds before and after treatment in 10 patients with good treatment response and found that four compounds (three peptides and PG14:2) were upregulated before treatment but subsequently normalized after treatment. Thus, even if this sub-analysis was clearly limited by the small sample size, ours is the first report to examine the effects of systemic cancer treatment on salivary metabolite profiles, which may suggest that salivary metabolites could be useful for monitoring treatment response. In conclusion, the present study identified 31 upregulated compounds in saliva of BC patients, and we were able to identify 13 potentially relevant compounds via the METLIN database (seven peptides and six lipids).
The ion transfer method used two different settings: a couple collision RF/transfer time equal to (i) 100 Vpp/23 µs during 30% of the acquisition time (300 µs) and (ii) 400 Vpp/100 µs during 70% of the acquisition time (700 µs) (i. e. , an acquisition time of 1 s for each MS spectrum). The collision energy and the pre-pulse storage were maintained at 5 eV and 5 µs, respectively. All data acquisitions were controlled using TOF Control software (version 3. 4, BrukerDaltonics, Wissembourg, France), and Hystar software (version 3. 2, Bruker Daltonics, Wissembourg, France) was used to interface the HPLC and MS systems.
Metabolic Pathways The metabolites identified using XCMS Online were searched for in the Human Metabolome Database (www. hmdb. ca), KEGG network (www. genome. jp), PubChem (www. pubchem. ncbi. nlm. nih. gov), Small Molecule Pathway Database (www. smpdb. ca), and LIPID MAPS Lipidomics Gateway (www. lipidmaps. org).
All statistical analyses were performed using SAS software (version 9. 4). The LC/MS profiles generated via LC-Q-TOF/MS were converted into mzML files using Hystar software (Bruker Technology), which were then uploaded to XCMS Online (www. xcmsonline. scripps. edu) [28]. All LC/MS profiles were processed using the "center-wave" algorithm with an allowance of 10 ppm on the experimental m/z and a minimum S/N ratio of 3 to extract the molecular features. The algorithm was used for the retention time correction, with a step size of 0. 5 m/z and a maximum of 5 s allowed for deviation of the retention time.
Pathway H-Arg-Arg-Ser-OH N/S H-His-Lys-(Ala-Ser)-OH or (Gly-Thr)-OH N/S H-Ala-Lys-Phe-Trp-OH Or H-Gly-Lys-Thr-Ser-OH or H-Arg-Arg-Ser-Ser-OH N/S H-Phe-Ile-Gln-Arg-OH N/S H-Glu-Phe-Gln-Arg-OH or H-Ile-Lys-Gln-Trp-OH N/S H-Phe-Lys-Lys-Trp-OH or H-Phe-Gln-Arg-Tyr-OH N: not searched, N/F: not found in the Human Metabolome Database (HMDB), PA: phosphatidic acid; PS: phosphatidylserine; PI: phosphatidylinositol, PG: phosphatidylglycerol.
Provenance trials, species and phenotypic data We used tree height recorded in common garden networks for three pine species: Pinus nigra Arn. , P. pinaster Aiton and P. pinea L. (see Figure  S1  in Supporting Information). For Pinus nigra, we used 192 221 measurements of individual tree height recorded in 15 trials distributed across three countries (France, Germany and Spain) where 78 populations (provenances) from origins covering the entire range of the species were planted. Trials were planted between years 1968 and 2009 and tree heights were measured between 2 and 18 year-old. For P. pinaster we used 123 801 measurements of individual tree height recorded in 14 trials established across three countries (France, Morocco and Spain) and 182 populations covering the entire range of the species.
Trials were installed between years 1966 and 1992 and tree heights were measured between 2 and 34 yearold. For P. pinea, we used 56 624 measurements of individual tree height recorded in 9 trials established in France and Spain, where a total 55 populations covering the entire range of the species were planted. Trials were established between years 1993 and 1997, and tree heights were measured between 2 and 22 year-old. Further description of these databases can be found in  Vizcaíno-Palomar et al. , (2019). To analyse the effect of age on phenotypic plasticity we defined three developmental classes (DC. 1, DC. 2 and DC. 3) covering the range of ages of each species. In all species, DC. 1 included information for 4 year-old trees, DC. 2 included trees of 8, 13, and 9 year-old, and DC. 3 included information for 14, 24, and 22 year-old trees, for P.
Climate data We used the EuMedClim dataset that provides annual measurements between 1901 and 2014, at 30 arc-seconds (~ 1km) of resolution (http://gentree. data. inra. fr/climate/datasets/; Fréjaville & Benito Garzón, (2018) ). We used a total of 21 climatic variables related with either annual or seasonal parameters of climate in terms of precipitation and temperature (Table  S1 ). From this database, we computed the following climate-related variables and indices: i) Long-term climate effect on trees' height population (clim p ) was calculated as the average climate at the population origin between the beginning of the 20 th century (1901) and the year before the trees were planted in the trials. This 'long-term' effect reflects the climate that occurred when the planted seeds were generated, and it can be related to population effects.
To do this, we calculated the Pearson coefficient, ρ, between a model fitted with the 2/3 parts of the data and independently validated with the remaining 1/3 part of the data. Additionally, to detect collinearity between explanatory variables in the best-supported model we used the variance inflation factor (VIFs) and set maximum value of VIF to 5 which is considered acceptable  (Belsley, 1991). Appropriateness of the best model was assessed by plots of predicted vs. observed values. We used the R version 3. 2. 3 (R Core Team, 2016) run in linux-gnu operating system to perform all the analyses, and the ''lme4'' package  (Bates et al. , 2015).
7|. ‫ܫܲ‬ ൌ ∑ ߙ ୀଵ ܺ ߝ ௦ Eq. 4 where PI j is the phenotypic plasticity index at the developmental class j (j= 1, 2 or 3), ߙ is the set of p parameters associated with the effects of ܺ (sd bio i ) and ߝ ௦ is the residual error. Models were fitted with a Gaussian distribution of errors and identity link function. A step-wise procedure (direction= "backward") was implemented to choose the best-supported model. Appropriateness of the models were assessed by plots of residuals vs. fitted values, qq-plots and the Cook's distance that identify outliers in the data that could over-influence the model fitting, if necessary they were removed from the analysis.
All phenotypic data used in this study are available onZENODO with DOIs    10. 5281/zenodo. 3250704,10. 5281/zenodo. 3250698and 10. 5281/zenodo. 3250707for P. nigra, P. pinaster and P. pinea, respectively (Vizcaíno-Palomar et al. , 2019). Random and fixed effects selection of tree height model in response to age, clim t and clim p using the Akaike Information Criterion (AIC) for 623 the three pine species. 624 AIC is the Akaike Information Criterion value, ∆AIC is the difference between the full model and the models tested in 626 each round. In random effects selection, the smaller the AIC is, the better supported the random structure part is. In fixed effects selection, if ∆AIC.
For each colored curvecorresponding to each concentric square-a temporal maximum was detected and associated to the distance vis-à-vis the initial ROI. After plotting mean delays to maximum fluorescence for each concentric square, a linear regression performed with GraphPad Prism 6. 0 software enabled for the calculation of a slope and thus the speed of fluorescence wave, following the equation indicated. Alternatively, fluorescence values at the time of maximal fluorescence, observed in each concentric square and normalized to that observed in the first square, gave rise to a plot that could be fitted with an exponential decay using GraphPad Prism 6. 0 software. The k parameter of the plot was estimated, and thus a decay rate, following the equation indicated.
ACKNOWLEDGMENTS We thank  Eric Denarier  and  Sami Chaaban  for fruitful discussions and critical review of the manuscript and the imaging facility platform ( PIC-GIN ) for help with image acquisitions. This work was supported by grants from the  Institut National de la Santé et de la Recherche Médicale (INSERM) , from the  Fondation Ducoin  to M. S. , and from the  Association Française contre les Myopathies (AFM-Téléthon ) to M. S. , P. A. , and I. M.
Library construction A tissue sample was obtained from a spleen of a 2. 5-year-old female harvested on Revillagigedo Island, Alaska, USA and frozen promptly after harvest. High quality molecular DNA was extracted using the phenol chlorophorm method  (Sambrook and Russell 2006). Two TruSeq Nano genomic library preparations with an insert size of 500 bp were constructed and sequenced on an Illumina HiSeqX platform with 150 bp paired-end reads. A total of 214 Gbp of raw short-read data were generated. We trimmed the adapters and the low quality bases from the reads with BBDuk (part of BBTools)  (Bushnell 2018)  to a minimum base quality 20 and a minimum read length 50 bp after a quality assessment with FastQC  (Andrews 2010 ). To avoid any potential contamination of the genome with bacterial or viral sequences, we screened the trimmed reads with Kraken (Wood and Salzberg 2014) using the full standard database (Oct.
2017 release). A total of 0. 95% of the reads were classified as belonging to a known bacterial or viral taxon and were removed. The final short-read dataset comprised 176 Gbp. Three Chicago libraries were prepared (Dovetail Genomics) following the approach in  Putnam et al. (2016). Briefly, Chicago library preparation involves in vitro chromatin reconstitution to generate libraries with separations between reads up to maximum fragment size of the input DNA. These libraries were sequenced on one lane of the Illumina HiSeqX instrument generating a total of 104 Gbp of sequence data.
Genome assembly A k-mer size of 101 was selected for the short-read dataset with KmerGenie  (Chikhi and Medvedev 2014). The draft de novo assembly was completed with the Meraculous assembler v. 2. 0. 4  (Chapman et al. 2011 ) using the following options: 'diploid_mode 1', 'min_depth_cutoff 0' to allow for auto-detection of k-mer frequency cutoff by the assembler, 'no_read_validation 0' to decrease the runtime as the reads were trimmed and screened prior to the assembly, and 'gap_close_aggressive 1' to remove the uniqueness requirement and use the most common sequence obtained from potential gap-closing reads. For comparison purposes, the short-read data were also assembled with MEGAHIT v. 1. 1. 1 (Li et al. 2015)  using 'k-list 101' option. We improved the short-read assembly by scaffolding with the Chicago library data using the HiRise pipeline software  (Putnam et al.
Genome annotation We identified and classified the repeat regions of the assembled genome using RepeatMasker v. 4. 0. 8 (Smit et al. 2013). We configured RepeatMasker with RMBlast v. 2. 6. 0 sequence search engine, Tandem Repeat Finder v. 4. 0. 9 (Benson 1999) , Dfam_Consensus database (20181026 release) and RepBase RepeatMasker edition (20181026 release)  (Bao et al. 2015)  and used '-species artiodactyl' parameter for the analysis. We then annotated scaffolds that were greater than 2500 bp (n = 482) using both homology-based and de novo predictions. Proteins from Homo sapiens, Equus caballus, Bos taurus, and Ovis aries (all Ensembl 89 release  (Hunt et al. 2018 )) were aligned to genome using blastx v. 2. 7. 1. We also used NCBI mammalian RefSeq transcipt database v. 1. 0 and BLAT v. 1. 0 to help identify exon structure.
For de novo prediction we first mapped RNAseq data (SRX1947618) from Capra hircus, the most closely related species available on NCBI Sequence Read Archive, to the genome using HISAT2 v. 2. 10 (  Kim et al. 2015)  and used these data for prediction with Augustus v. 3. 1. 1 (Stanke et al. 2006 ). EVidenceModeler v. 1. 1. 1 (Haas et al. 2008 ) was used to generate a consensus gene set with the following default weighting scheme  (Humann et al. 2018 ): gene prediction via Augustus (1x weight); protein alignments via blastx (5x weight); transcript alignment via blat (10x weight). Lastly, we used PASA v. 2. 3. 3  (Haas et al. 2008 ) to refine our gene identifications. To assign function to the newly annotated genes we aligned them to the NCBI mammalian RefSeq protein database using blastp v. 2.
7. 1 with a maximum HSP distance of 30,000 and e-value of 1e -8. The Interpro v 5. 29-68 and KEGG databases were screened to annotate domains and identify pathways based on the top blast hit. We used Infernal v. 1. 1. 1 (Nawrocki and Eddy 2013) and the Rfam database release 14. 1 to annotate miRNA, rRNA, and snRNA genes; tRNAs were detected using tRNAscan-SE version 2. 0. 1  (Lowe and Eddy 1997).
Data availability The raw sequence data have been deposited in the Short Read Archive (SRA) under accession number PRJNA510081. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession WJNR00000000. The version described in this paper is version WJNR01000000. Supplemental material available at figshare: https:// doi. org/10. 25387/g3. 9884003.
2009) , suggesting this individual should have low levels of heterozygosity. A total of 4,104 single-copy orthologs were screened against the assembled mountain goat genome with 3,817 (93%) being complete, and 287 missing or fragmented BUSCOS (7%); this is in the same range as the BUSCO scores reported for other ungulate species  (Li et al. 2017; Zhang et al. 2018). Similarly, we detected 471 of 481 ultraconserved elements (UCEs)  (Bejerano et al. 2004 ) using scripts available in PHYLUCE v. 1. 5. 0  (Faircloth 2016)  which is comparable to high quality mammalian genomes  (Seemann et al. 2016). n■ Table  1  Genome assembly statistics for the North American mountain goat for the short-read data and subsequent scaffolding Short-read assembly Scaffolded + Chicago assembly.
Genome annotation Long interspersed nuclear elements (LINEs) comprised 27. 2% of the assembled genome, short interspersed nuclear elements (SINEs) comprised 11. 4%, and in total repeats represented 46. 7% of the assembly (Table  2 ). The repeat content is consistent with other ungulate genomes  (Dong et al. 2013; Li et al. 2017; Zhang et al. 2018). Our annotation pipeline of the longest scaffolds resulted in 22,292 total genes with 16,012 being protein coding. Of these, the Interpro v 5. 29-68 databases identified 13,874 (74%) genes with information, and 9,460 (51%) having a gene ontology assignment. Similarly, RefSeq protein database detected a a total of 14,470 genes (78%) with NCBI RefSeq match. Lastly, the Interpro v 5. 29-68 databases annotated 13,874 (74%) genes with information, with 9,460 (51%) having a gene ontology assignment.
ACKNOWLEDGMENTS This research was supported by  NSERC   Discovery Grant ,  Alaska Department of Fish and Game Federal Aid, Compute Canada  award to ABAS and  INCEPTION grant  ( PIA/ANR-16-CONV-0005 ) to RC. We thank  Maël Kerbiriou  and  Dr. Antoine Limasset  from  Université de Lille  who provided valuable insights for this study. DM is supported by an  Ontario Graduate Scholarship ; the  Globalink Travel Award  that made this collaboration possible was provided by  Mitacs and Inria. We thank two anonymous reviewers for comments that improved the manuscript.