dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Doug Dorer ,  Steve Henikoff ,  Julius Brennecke  and the  Bloomington Stock Center  for providing stocks. We thank  Bill Theurkauf  for providing antibodies. We thank flybase. org for providing databases. We thank  Ritha Zamy  for technical assistance. We thank Clement Carre,  Lori Pile ,  Ana Maria Valle  `s and  Jean-Rene  ´Huynh for critical reading of the manuscript. We thank  Christophe Antoniewski  for helpful advices and development of the ARTbio platform (http://artbio. fr/).
Stochastic simulations To check our results and also relax some key assumptions, we ran stochastic simulations. The simulations were run for 10 8 generations (one generation is one time step for the Wright-Fisher life cycle, and N time steps for the Moran life cycles). For each set of parameters and life cycle, we estimated the long-term frequency of altruists by sampling the population every 10 3 generations and computing the average frequency of altruists. All scripts are available at https://flodebarre. github. io/SocEvolSubdivPop/.
Sperm Quality Assessment Functional sperm parameters were evaluated after incubating fresh and cryopreserved sperm in CAP or NC media. Total and progressive motility were assessed with the Sperm Class Analyzer software (SCA ® ) (Microptic; Barcelona, Spain), as has been previously described by García-Álvarez et al. [37]. During apoptosis, mitochondrial activity and reactive oxygen species (ROS) production were measured using a flow cytometer (Flowsight ® controlled with the INSPIRE ® software). Samples were diluted to 5 × 10 6 sperm/mL in different staining solutions prepared with SOF-PVA-HEPES and immediately handled, except those used for assessing mitochondrial activity (15 min of incubation in the dark at 37 • C). The staining solution for apoptosis was 12 µM of PI and 50 nM of YO-PRO-1; for mitochondrial activity, 25 nM of YO-PRO-1 and 100 nM of Mitotracker Deep Red; and for ROS production, 12 µM of PI and 5 µM of CM-H2DCFDA.
Proteomics Data Acquisition and Analysis Protein samples (100 µg/sample) were precipitated following a methanol/chloroform procedure  [77]. After precipitation, protein extracts were resuspended, on-gel concentrated, and trypsin digested, and the resulting peptides were analyzed using RP-LC-MS/MS, as previously described by Villar et al. [78]. The MS/MS raw files were searched against the Uniprot-Ovis aries database  (27, 417  entries in March 2017) (available online: https://www. uniprot. org) using the SEQUEST algorithm (Proteome Discoverer 1. 4, Thermo Fisher Scientific, San Jose, CA, USA). Search parameters included tryptic cleavage after Arg and Lys, up to two missed cleavage sites, with tolerances of 1 Da and 0. 8 Da for precursor ions and MS/MS fragment ions, respectively, and Met oxidation and Cys carbamidomethylation were optionally allowed.
Gene Ontology Analysis Functional annotations of identified proteins were carried out using Blast2GO software (version 3. 0; available online: https://www. blast2go. com) and unannotated proteins were manually searched for in UniProtKB database (available online: http://www. uniprot. org). Sperm proteins were classified according to their gene ontology (GO) annotations for biological process, molecular function, and cellular component. GO annotations of proteins were grouped for each treatment (cryopreserved sperm, fresh sperm, sperm incubated in CAP medium, and sperm incubated in NC medium) to identify changes due to cryopreservation or capacitating conditions. Finally, special attention was paid to those proteins involved in an apoptosis-stress response and other biological processes that are directly or indirectly implicated in reproductive functions.
Statistical Analyses The average number of peptide spectrum matches (PSMs) for each protein was calculated from the two replicates and then normalized against the total number of PSMs in each sample. A paired comparison χ 2 test (p < 0. 05) between different samples (0 F NC, 0 C NC, 1 F CAP, 1 C CAP, 15 F CAP, 15 C CAP, 15 F NC, 15 C NC, 240 F CAP F, 240 C CAP, 240 F NC, 240 C NC) was performed to quantify significant changes in protein abundance. IDEG6 software (available online: http://telethon. bio. unipd. it/bioinfo/IDEG6/) was used for this analysis. The protein concentration of CAPZA2 was compared between fresh and cryopreserved sperm at 15 min using Student's t-test with unequal variance (p < 0. 05) using SPSS v. 23. 0 (IMB Corp. , Chicago, IL, USA). Differences in sperm quality, global tyrosine phosphorylation, and reproductive-processes-distribution between samples were evaluated in the two biological replicates using a general linear model (GLM) with SPSS.
Using a combination of multi-source RS data-including high-resolution optical imagery and three-dimensional (3D) topographic models obtained from LiDAR (Light Detection and Ranging)-we aimed to derive robust correlates of P, A and B at landscape scales. By combining these with a comprehensive spatial database capturing the occurrence of invasive plant species in coastal dune ecosystems of central Italy, we then tested the effectiveness of the PAB framework for predicting the distribution of IAS across these ecosystems and identifying potential invasion hotspots in the region. In addition, we quantified and ranked the relative impact of each PAB component affecting invasion outcome.
Invasive Species Data Field records detailing the location of invasive plant species along the coastlines of Lazio and Molise were derived from an existing database of vegetation plots "Ranvegdunes"  [27]. Plots were 8 × 8 m in size (64 m 2 ) and were established at the beginning of the growing season (April-May) between 2002 and 2011. The location of each plot was assigned randomly and subsequently recorded using a GPS to within 3 m accuracy. The presence of all vascular plant species (including IAS) was recorded in each plot. Following Acosta et al. [28]  and Celesti-Grapow et al. [29] , a total of five IAS were identified across the plot network. Of these, three were found along the Thyrrenian coast in Lazio (Agave americana, Carpobrotus acinaciformis, Carpobrotus edulis) and two along the Adriatic coast in Molise (Erigeron canadensis L.
Species Distribution Modelling We modeled the occurrence of the five IAS using a binomial generalized linear model (GLM) with logit link function using the PAB variables described above (and listed in Table  1 ) as model predictors. In particular, PAB values for presences were extrapolated from plots were at least one of the IAS was recorded, while, at the same time, PAB values for absences from plots where any IAS was recorded. As we expected different predictors to shape the distribution of the IAS along the coasts of Lazio and Molise (due to inherent ecological differences among the species, as well as differences in the environmental and anthropogenic context of the two coastlines), we grouped the species from each of the two regions and analyzed them separately. In a first step, a visual inspection of the data was used to explore how the presence/absence of IAS varied in response to each of the predictor variables.
The predicted growth rate versus frequency relation in Figure  2n  shows a peak near 0. 8 ω pe , which agrees with the observed fundamental peak in Figure  2m. The harmonics that appear in panel (m) are not reproduced by the linear analysis of PDRK because they result from nonlinear effects (e. g. ,  Klimas, 1983; Yoon et al. , 2003). The results of the dispersion analysis for location 2 are shown in the bottom half of Figure  2. The measured 1-D DFs averaged over the black and red sectors in Figures  1k  and 1m  are plotted by the red traces in Figures  2e  and 2g. For this location there is a positive df/dv both parallel and perpendicular to B. As is the case for WHAMP  (Rönnmark, 1983)  and other linear dispersion solvers, the PDRK code only handles parallel (to B) beams or unmagnetized plasmas.
Therefore, size selection was performed on the products from PCR1, retaining only products over 200 bp using Pippin Prep (www. sagescience. com). A second polymerase chain reaction amplification (PCR2) was executed on these products. Quality control was performed after Pippin size selection and before library submission for sequencing using an Agilent BioAnalyzer (www. agilent. com). Details about the procedure and the Pippin Prep are available at https://github. com/BenoitCastandet/ Terminome Seq. The final cDNA libraries were purified using magnetic AMPure beads (www. beckman. com) following the manufacturer's protocol. Multiple steps in the above protocol, including fragmentation, ethanol precipitation, Pippin size selection (5 libraries only) and AMPure purification of cDNA libraries, resulted in a bias toward the retention, and therefore sequencing, of fragments >67 nt.
As a consequence, ends of small RNAs (smRNAs) and tR-NAs would be expected to be underrepresented in the results. Additionally, a minor bias was introduced because the RT primer is fully complementary to the 3 adapter, ending in a sequence complementary to TGG. Illegitimate priming by the adapter resulted in an estimated 52 additional 3 ends terminating in TGG which are included in our data as they cannot be distinguished from legitimate 3 termini ending in T(U)GG. While this bias was inevitable, the overall interpretation of the results was not affected. Libraries were pooled and sequenced on a NextSeq500 Sequencer (www. illumina. com) using the v3 kit, with paired-end reads generating 40 bp long R1 reads and 35 bp long R2 reads for all libraries. R1 reads are only of use for libraries generated to obtain 5 related data, while R2 reads contain data related to 3 ends and therefore are only relevant for libraries generated to obtain 3 data.
Raw sequences have been deposited on the SRA database with the number PRJNA533962 and can be accessed here https://www. ncbi. nlm. nih. gov/sra/PRJNA533962. The detailed pipeline used to analyze the relevant reads is available at https://github. com/BenoitCastandet/Terminome Seq. Briefly, the quality of relevant reads was checked using fastq-mcf (https://github. com/ExpressionAnalysis/ ea-utils/blob/wiki/FastqMcf. md) followed by align-ment to the chloroplast reference genome, Arabidopsis TAIR10 version modified to add the first exon of the chloroplast gene ycf3, using tophat2 (https://ccb. jhu. edu/software/tophat/index. shtml). Two customized scripts allowed us to extract the positions of the 5 and 3 termini and the results were normalized according to the numbers of reads aligned to the chloroplast genome.
The full coverage for Terminome-seq at single nucleotide resolution for 5 (±TAP) and 3 ends generated for this manuscript can be accessed in Supplementary Table  S1. It is important to note that transcripts with post-transcriptional modifications, such as tRNAs with a post-transcriptionally added CCA tail, or RNA degradation intermediates bearing polynucleotide tails, would not fully align to the genome and are therefore excluded from our curated dataset. A genome-wide view of end distribution is depicted in Figure  1. Clusters of 5 and 3 ends, covering a remarkable 13 and 16% of the genome, respectively, are clearly discernable, but are largely absent from areas antisense to known transcripts. In contrast, the full chloroplast genome is covered by reads from total RNA-seq experiments  (2, 19, 20, 23).
The case of the psbB operon The psbB operon is particularly well studied, containing five genes on the plus strand (psbB-psbT-psbH-petB-petD, the last two containing introns) and one on the minus strand (psbN). Altogether, over 20 accumulating transcripts generated from this operon have been characterized (5,10,57), making it an attractive testbed for the abil- ity of Terminome-seq to reflect such a complex landscape accurately. Figure  4A  shows the positions of the eight TSS (numbered in red), along with at least 12 processed 5 ends (numbered in blue) and 17 3 ends (numbered in green), which are annotated in Table  1. A consistent feature of 5 ends is their clustered organization around a dominant peak, whereas the 3 termini are more discrete. Heterodisperse 5 ends are reminiscent of degradation intermediates, perhaps created by an enzyme such as RNase J that would progressively stall as it encountered secondary structures or bound proteins.
In agreement with these results, Terminome-seq data analysis identified a single major 5 end for petB correlating with the 5 end of a smRNA established as the footprint protected by HCF152 binding  (9, 66, 71). The Terminome-seq psbH 3 end also correlates with HCF152 binding, with an additional cluster of 3 ends found ∼10 nt downstream, possibly reflecting the different stalling characteristics of the exoribonucleases PNPase and RNase II (Figure  4C ). The correlation between RBPs, their footprints found in smRNAs and Terminome-seq ends can be extended beyond HCF152 to at least 13 RBPs that have been described as involved in RNA maturation (Supplementary Figure  S6 ). These include PPR10, which protects the two adjacent transcripts atpI and atpH and assists in the processing of both their 5 and 3 ends (66,70,72); and HCF107 and CRP1 which, like HCF152, target the psbB operon, in particular the psbH -44 5 end and the petB +67 3 end.
The chloroplast TSS landscape In this work, we systematically sequenced Arabidopsis thaliana chloroplast RNA 3 termini as well as primary and processed 5 ends, using TAP treatment to discriminate between them. Such strategies are under continual improvement, as transcriptome analyses generally endeavor to be as comprehensive and quantitative as possible. In our case, transcript chemistry, size and secondary structure will all introduce biases to the dataset ultimately used to draw the main conclusions. In addition, factors such as ligation bias--the preferential ligation of adapters to certain sequences  (81, 82) , undoubtedly impacted the selection and ratios of the transcript ends we have reported. Therefore, our conclusions--particularly quantitative ones -should be evaluated with those caveats in mind.
Some known TSS such as PatpE -430, PpsbN -32 and PpsbD -948  (87) , have a ±TAP ratio <10 and were therefore not formally considered to be TSS (Supplementary Figure  S3B ). Perhaps the biggest surprise from this Terminome-seq dataset is the evidence for massive transcription initiation activity in the 3 part of the ndhF gene, antisense to ndhF and within the first intron of clpP. The former two areas represent a staggering ∼20% of the reads derived from primary transcripts, and could have functional implications in the chloroplast.
Although Terminome-seq could identify the expected atpH 5 end, only a minor 3 end mapped to the PPR10-dependent atpI 3 end (Supplementary Figure  S6 ). The major Terminome-seq 3 end is located further downstream, inside the atpH coding region and does not correlate with a smRNA. Systematic mapping of the atp operon termini already revealed that transcripts with overlapping ends are a minority (52) and coimmunoprecipitation showed that PPR10 is preferentially associated with processed atpH mRNA rather than processed atpI mRNA  (66). At a genome-wide level, only ∼10 footprints (including PPR38 and HCF152) can be linked to simultaneous stabilization of 5 and 3 ends, suggesting that this is the exception rather than the rule. Secondary structures, on the other hand, can stabilize the 3 ends of two adjacent transcripts, for example in the cases of psbT-psbN, petD-rpoA and petA-psbJ (Figure  4B  and Supplementary Figure  S5 ).
FUNDING Division of Chemical Sciences, Geosciences, and Biosciences, Office of Basic Energy Sciences , of the  U. S. Department of Energy  [ DE-FG02-10ER20015 , in part];  LabEx Saclay Plant Sciences-SPS  [ ANR-10-LABX-0040-SPS  to B. C. ]. Funding for open access charge: Internal Funds (to D. B. S).
Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation.
Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7•8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1•4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data.
We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0•0035 for intracranial volume, p=0•024 for putamen volume), smoking status (p=0•024), and educational attainment (p=0•038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8•00 × 10 -⁷). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biologi cal context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data.
Study design The work flow and rationale of our study is shown in figure  1. Three sources of data were used for dis covery analyses, these include three previ ously published GWAS studies,  4, 6  13 new datasets (figure  1 ), and proxycase data from the UK Biobank. Previous studies comprised sum mary statistics published in Nalls and col leagues,  6  GWAS sum mary statistics from the 23andMe WebBased Study of Parkinson's Disease by Chang and colleagues,  4  and the pub licly available NeuroX dataset from the International Parkinson's Disease Genomics Consortium previously used as a replication sample. 6 These cohorts have been reported in detail, but in brief represent all European ancestry Parkinson's disease casecontrol GWAS studies available for collaboration. 4, 6 We included 13 new case control sample series for metaanalyses through either publicly available data or collabora tions (appendix pp 1-3, 22).
Research in context Evidence before this study Previous studies have used genome-wide association study (GWAS) methods to discover 42 independent risk loci associated with Parkinson's disease. Some of these loci harbouring common risk variants also include rare variants implicated in familial Parkinson's disease risk such as SNCA, LRRK2, or GBA. Earlier studies have attempted to quantify how much heritable risk is captured by common variation that can be easily imputed using commercial genotyping arrays and estimate the amount of risk explained by GWAS. Since 2011, GWAS of Parkinson's disease have integrated expression and methylation datasets to evaluate possible candidate genes for follow-up at Parkinson's disease loci. Many epidemiological and observational studies have attempted to assess risk of Parkinson's disease and various exposures like smoking, caffeine, or occupational hazards, with a mixed track record of success at validating presumed associations.
Added value of this study This study increased the count of independent common genetic risk factors for Parkinson's disease to 90. We added 38 novel risk variants not previously identified as genomewide significant. We refined heritability estimates and genetic risk predictions suggesting that common genetic variants account for approximately 22% of Parkinson's disease risk on the liability scale, with a range of 16-36% of that risk being explained by GWAS loci in this study. These updated risk predictions also suggested that polygenic risk scoring can be used to achieve an area under the curve of near 70%, although this prediction uses many more variants than just the 90 independent risk factors identified in this report. Of the 90 risk variants we have characterised here, we have nominated at least one possible candidate gene for follow-up functional studies in 70 of these genomic regions by mining recently available expression and methylation reference datasets on a scale not possible just a few years ago.
We have additionally mined single cell RNA sequencing data from mice to identify tissue-specific signatures of enrichment relating to Parkinson's disease genetic risk, showing a major focus on neuronal cell types. We also used the massive amount of publicly available GWAS results to survey genetic correlations between Parkinson's disease and other phenotypes showing significant correlations with smoking, education, and brain morphology. Subsequent analyses using Mendelian randomisation methods showed probable causal links between increased cognitive performance and Parkinson's disease risk on a genetic level.
data from 23andMe in further conditional analy ses. If a variant nominated during the conditional and joint analy sis strategy phase of analysis was greater than 1 Mb from any of the genomewide significant loci nominated in Chang and colleagues,  4  we considered this to be a novel risk variant. We defined nominated risk variants as from a single locus if they were within 250 kb of each other. We instituted two filters after fixedeffects and conditional and joint analyses, exclud ing variants that had a randomeffects p value across all datasets more than 4•67 × 10 -⁴ and a conditional analysis p value more than 4•67 × 10 -⁴ using participant level 23andMe genotype data. Please see appendix (pp 4-5, 22) summarising all variants nominated.
Heritability estimates and extant genetic risk 1] [12]  In addition, PRSice incorporates permutation testing, in which case and control labels are swapped in the withheld samples to generate an empirical p value. This workflow identi fies the best p value thresholds for variant inclusion while doing LD pruning. In many cases this best p value threshold for PRS construction does not meet what is commonly regarded as genomewide significance. A twostage design was also used, training on the largest single array study (NeuroXdbGaP  13  ) and then tested on the second largest study (Harvard Biomarker Study) using the same array. These two targeted array studies were chosen for three reasons: precedent in the previous pub lications in which the NeuroXdbGaP dataset was used in PRS; direct genotyping of larger effect rare variants in GBA and LRRK2; and participant level genotypes for these datasets are publicly available.