dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation Using Simulations The inference framework was first tested on independently simulated multiple sequence alignments (MSAs) (see Materials and Methods). With the aim of applying the inference method to empirical data sets, the simulation parameters were chosen so as to match an empirically relevant empirical regime. Thus, the tree topology and the branch lengths were chosen based on a tree estimated on the mammalian data set further considered below. The other aspects of the simulation model (fitness landscape, variation in N e ) were then varied along a gradient of increasing complexity, so as to test the inference framework under increasingly challenging conditions. A first series of simulations was meant to test the soundness of our inference framework, by simulating essentially under the model used for inference, although with an independently developed software.
MBE well adapted to the present case, since the changes in N e associated with the transition to a subterranean environment are likely to correspond to relatively sudden shifts, rather than continuous variation, and the ecological correlate (subterranean vs. terrestrial) is not a quantitative trait. However, the data set considered here contains independent transitions to a subterranean lifestyle, thus offering an opportunity to test for a potential correlation between inferred N e variation and terrestrial versus subterranean lifestyles over the terminal branches. To assess the reproducibility of our inference, we analyzed in total six different concatenated MSA each containing 12 randomly sampled genes. The six different concatenated MSA showed similar trends in the change of l and N e between pairs of replicates (see supplementary, Supplementary Material online).
Moreover, in SimuPoly, the instant value of log-N e can also be modeled as a sum of a geometric Brownian process and an Ornstein-Uhlenbeck process. The geometric Brownian motion accounts for long-term fluctuations, whereas the Ornstein-Uhlenbeck introduces short-term fluctuations. In SimuDiv and SimuPoly, each codon site contributes independently to the fitness depending on the encoded amino acids, through site-specific amino-acid fitness profiles experimentally determined  (Bloom 2017). In SimuFold, the fitness of a sequence is computed as the probability of the protein to be in the folded state. SimuFold is a Cþþ adaptation of a Java code previously published  (Goldstein and Pollock 2016, 2017) , where we also allow for changes in N e and l along a phylogenetic tree. Supplementary materials, Supplementary Material online describe the simulations in more details, with parameters and configurations used to produce alignments, as well as performance of the inference model against them.
Empirical Data For placental mammals, alignments were extracted from OrthoMam database  (Ranwez et al. 2007; Scornavacca et al. 2019). LHTs for longevity, age at maturity and weight were obtained from AnAge database (De  Magalhães and Costa 2009; Tacutu et al. 2012). We focused our analysis on 77 taxa for which information is available for at least one LHT. The list of conserved genes putatively not under positive selection is available in supplementary materials, Supplementary Material online.
Acknowledgments This study was supported by  French National Research Agency  ( ANR-15-CE12-0010-01/DASIRE ). We wish to thank  Tristan Lef  ebure for sharing the isopods phylogeny, alignments, and life-history traits. We also thank  Philippe Veber  for insightful discussion on mutation-selection models and software development. We gratefully also acknowledge the help of  Nicolas Rodrigue ,  Laurent Gueguen ,  Benoit Nahbolz ,  Laurent Duret ,  Richard Goldstein ,  Carina Farah Mugal , and  Julien Yann Dutheil  for their advice and review concerning this manuscript. We also thank the editor and three anonymous reviewers for their constructive comments, which helped us to improve the manuscript and the analysis. This study was performed using the computing facilities of the  CC LBBE/PRABI.
Data Availability The data underlying this article are available in Github, at https://github. com/ThibaultLatrille/MutationSelectionDrift (last accessed June 06, 2021), as well as scripts and instructions necessary to reproduce the simulated and empirical experiments. The Bayesian inference model, written in Cþþ in the component based  (Lanore 2019 ) software BayesCode, is available at https://github. com/ThibaultLatrille/bayescode (last accessed June 06, 2021). The simulators written in Cþþ are available at https://github. com/ThibaultLatrille/SimuEvol (last accessed June 06, 2021). Supplementary materials describing additional analysis are available online.
The final diagonalization is performed using the KSHELL shell-model code  48. To estimate theoretical uncertainties in this framework, we note that in the limit of no IMSRG truncations, results would be independent of the chosen reference state for the ensemble normal ordering procedure. Therefore, we examine the referencestate dependence of the observables discussed above. Normal ordering with respect to either a filled neutron g7/2 or d5/2 orbits, we find approximately 1MeV uncertainty for absolute or one-neutron separation energies. However, for all quantities shown in Fig. 3 , this estimated uncertainty is approximately 0. 1 MeV.
Acknowledgements: We thank the  ISOLDE technical group  and the  ISOLDE Collaboration  for their support. We acknowledge the support of the  Max Planck Society , the  French Institut National de Physique Nucléaire et de Physique des Particules  ( IN2P3 ), the  European Research Council (ERC)  through the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No. 682841  " ASTRUm " and  654002 "ENSAR2 "), and the  Bundesministerium für Bildung und Forschung  (grants Nos. 05P15ODCIA ,  05P15HGCIA ,  05P18HGCIA , and  05P18RDFN1 ). J. K. acknowledges the support of a Wolfgang Gentner Ph. D. scholarship of the BMBF ( 05E12CHA ). This work was supported by the  U. S. Department of Energy, Office of Science, Office of Nuclear Physics , under Awards No. DE-FG02-96ER40963.
Code Availibity: The analysis codes used for the ToF-ICR and MRToF-MS data are available from the corresponding author upon reasonable request. A second MRToF-MS analysis code used in this study is available at this address: https://github. com/jonas-ka/mr-tof-analysis. The PI-ICR analysis code used in this study is available at this address: https://github. com/jonas-ka/pi-icr-analysis. The code used for the VS-IMSRG calculations is available at https://github. com/ragnarstroberg/imsrg. The source code of KSHELL is available in Ref. 48. References:.
KMT5B loss drives transcriptional diversity, and confers poor prognosis in paediatric high grade glioma Finally, we further carried out SmartSeq2 single cell RNA sequencing in order to assess transcriptional heterogeneity by examining full-length transcripts. These data, although derived from fewer cells than 10X (n=746, median=131 per library), were able to discriminate the various cell model libraries to a similar extent (Supplementary Figure  S6A ), and showed similar patterns of inferred DNA copy number change (Supplementary Figure  S6B ). Applying the GINI index to the expression data reinforced the transcriptional heterogeneity in this sample compared to the clones, but also demonstrated a shift towards a more diverse transcriptional profile in KMT5B/C-deficient cells compared to F8 (Figure  6A ).
Primary antibodies used were H3. 3K27M at 1/1000 (#ABE419 Merck Milipore), CXorf67 (EZHIP) at 1/1000 (HPA004003, Atlas Antibodies), H4K20me3 at 1/2000 (#ab9053, Abcam), H4K20me2 at 1/2000 (#ab9052, Abcam), H4K20me1 at 1/2000 (#ab9051, Abcam), H4 at 1/2000 (#ab70701, Abcam), KMT5B at 1/1000 (#LS-C161629, LSBio), KMT5C at 1/1000 (#PA5-13436, ThermoFischer), GAPDH at 1/1000 (#2118, Cell Signaling), H3K27me3 at 1/500 (#9735, Cell Signaling), H3 at 1/2000 (#9715, Cell Signaling).
RNA-seq RNA was isolated from cell pellets using the RNeasy Mini Kit protocol (Qiagen,74104), and measured using a Nanodrop spectrophotometer. RNA integrity was analysed and quantified using 2200-Tapestation (Agilent). A minimum of 150 ng of RNA was sequenced at Eurofins Genomics after generation of strand-specific cDNA libraries by purification of poly-A containing mRNA molecules, mRNA fragmentation, random primed cDNA synthesis (strand specific), adapter ligation and adapter specific PCR amplification. Library was then run on an Illumina Hiseq or Illumina Novaseq sequencer at 2x150 bp read length generating at least 30 million reads. Gene expression from bulk RNASeq was quantitated in 14918 known protein-coding genes using HTSeq based upon an Ensembl gene model. Gene Set Enrichment Analysis (software.
Primary antibodies directed against 53BP1 (#CS-4937; Cell signaling) were added to the cells and incubated overnight at 4°C; H4K20me3 at 1/500 (#ab9053; Abcam), H4K20me2 at 1/1000 (#ab9052; Abcam), H4K20me1 at 1/400 (#ab9051; Abcam) were added to the neurospheres for 48h at 4C. Samples were then washed in PBS three times and incubated with Alexa Fluor488-conjugated secondary antibodies for 1 hour at room temperature for the cells or 48h at 4C for the neurospheres. Nuclei were counterstained with DAPI for all samples. The neurospheres were transferred into 8-well chamber slides. All samples were mounted with Vectashield (Vector Laboratories) and examined using a Zeiss LSM700 confocal microscope. The number of 53BP1 foci were quantified using the function Tissue Studio IF from the software Definiens Software XD64.
Invasion 50-100µm diameter neurospheres were harvested after three days culture and invasion assays performed essentially as described  (Vinci et al. , 2015; Vinci et al. , 2013; Vinci et al. , 2018). Briefly, a total of 100μl medium was removed from ULA 96-well round-bottomed plates and 100μl of matrigel gently added to each well (6 replicates). Plates were incubated at 37°C, 5% CO2, 95% humidity for 1hr. Once the matrigel solidified, 100μl/well of culture medium was added on top. Starting from time zero, and at 3h intervals up to 6 days, automated image analysis was carried out on the live-cell imaging system IncucyteS3 (Essen Bioscience) using the Tumor Spheroid Assays application. Cell spread was measured after segmentation using MATLAB to perform threshold-based image segmentation on pixel intensity values followed by filtering by size, a minimum bounding circle algorithm was used to quantify cell spread from the resulting segmentation masks (n>=3).
7 Da in quadrupole. The top 5 MS2 fragment ions were SPS selected with the isolation width at 0. 7 Da, and fragmented in HCD at 65% collision energy, and detected in the Orbitrap to get the report ions' intensities at a better accuracy. The resolution was set at 50,000, and the AGC at 50,000 with maximum injection time at 86 ms. The dynamic exclusion was set 40 s with ± 10 ppm exclusion window. The raw files were processed with Proteome Discoverer 2. 3 or 2. 4 (Thermo Fisher) using the Sequest HT search engine. Spectra were searched against fasta files of reviewed Uniprot homo Sapiens entries (April 2019 for or February 2020) and an in-house contaminate database. Search parameters were: trypsin with 2 maximum miss-cleavage sites, mass tolerances at 20 ppm for Precursor, and 0. 5 Da for fragment ions, dynamic modifications of Deamidated (N, Q), Oxidation (M) and Acetyl (Protein N-terminius), static modifications of Carbamidomethyl (C) and TMT6plex (Peptide N-terminus and K).
Single Cells 3' Gene Expression libraries were sequenced on the NextSeq 500 and processed with Cell Ranger analysis pipeline. For SmartSeq2, Single cell suspensions were sorted for viable cells (calcein positive,TO-PRO-3 negative) into 96-well plates for whole transcriptome amplification, library preparation and pooling for sequencing on a NextSeq 500 as previously described  (Picelli et al. , 2014). Raw sequencing reads were aligned to hg19 genome using Bowtie, and gene expression was quantified as transcripts per million (TPM) with RSEM. Analysis was performed using Seurat version 3. 0  (Butler et al. , 2018) ) and visualised using UMAP projection. Copy number profiles from single cell RNA sequencing were generated with infercnv  (Patel et al. , 2014).
1 manual. Targeting a 5,000 nuclei recovery, samples were added to the tagmentation reaction, loaded into the Chromium Controller for nuclei barcoding, and prepared for library construction following manufacturer's protocol (10X Genomics PN-1000175). Resulting libraries were quantified using the KAPA Library Quantification Kit for Illumina platforms (KAPA Biosystems), and sequenced with PE34 sequencing on the NextSeq 500/550 sequencer (Illumina). Sequenced data were processed with the Cell Ranger ATAC software, with alignment to the human (hg38) genome. The Cell Ranger output files were used as input to Active Motif's proprietary analysis program, which creates Excel tables containing detailed information on cluster-specific peak locations, gene annotations, and motif enrichment. The alignment files generated by Cell Ranger were also processed as pseudo-bulk ATAC-Seq samples.
Duplicate reads were removed, only reads mapping as matched pairs and only uniquely mapped reads (mapping quality >= 1) were used for further analysis. Alignments were extended in silico at their 3'-ends to a length of 200 bp and assigned to 32-nt bins along the genome. The resulting histograms (genomic "signal maps") were stored in bigWig files. Peaks were identified using the MACS 2. 1. 0 algorithm at a cutoff of p-value 1x10-7, without control file, and with thenomodel option. Peaks that were on the ENCODE blacklist of known false ChIP-Seq peaks were removed. Signal maps and peak locations were used as input data to Active Motif's proprietary analysis program, which creates Excel tables containing detailed information on sample comparison, peak metrics, peak locations and gene annotations.
ACKNOWLEDGEMENTS This work was supported by  Cancer Research UK  ( C13468/A23536 ),  Children with Cancer UK  ( 16-212  and  16-234 ),  Billie Butterfly Fund  and the  Brain Tumour Charity , the  CRIS Cancer Foundation  and the  DIPG Collaborative. A. S. is supported by the  Wellcome Trust  ( 202778/B/16/Z ) and  Cancer Research UK  ( A22909 ). We acknowledge funding from the  National Institute of Health  ( NCI U54 CA217376 ) to A. S. This work was also supported a  Wellcome Trust  award to the  Centre for Evolution and Cancer  ( 105104/Z/14/Z ).
Objective: To test the efficacy of the Athletics Injury Prevention Programme (AIPP) to reduce the percentage of athletes presenting at least one injury complaint leading to participation restriction (ICPR) over an athletics season. Methods: During the 2017-2018 athletics season, we included in this cluster randomised controlled trial (ClinicalTrials. gov Identifier: NCT03307434) 840 athletes randomly assigned (randomisation unit: athletic clubs) to a control group (regular training) or to an intervention group (regular training plus the AIPP 2/week). Using a weekly online questionnaire, athletes reported the ICPR, training and competition exposures, and, for the intervention group, the compliance with the AIPP. The primary outcome was the percentage of athletes presenting at least one ICPR over the study follow-up.
Study Design and Overall Procedure From October 2017 to July 2018 (40 weeks of athletics season), we conducted a researcher-blinded unsupervised cluster RCT (called "PREVATHLE") including competitive athletes who were randomly assigned (allocation ratio 1:1) to a control group where they continued their regular training or to an intervention group where we added the AIPP to their regular training. The study protocol was reviewed and approved by the Committee for the Protection of Persons (CPP Ouest II-Angers, number: 2017-A01980-53) and by the French Federation of Athletics (FFA, http://www. athle. fr; accessed on 19 October 2021) prior to recruitment, and was registered on ClinicalTrials. gov (Identifier: NCT03307434). To produce the present manuscript, we used the Consolidated Standards of Reporting Trials (CONSORT)  [12].
Randomisation After the inclusion, we randomised each athlete to the intervention or the control group at a club level to minimise the risk of contamination bias between athletes from the same club. An independent statistician performed this cluster randomisation a priori using SAS 9. 4 (SAS Institute, Inc. , Cary, NC, USA) in a 1:1 ratio that assigned a unique randomisation number to each included athlete. The randomisation was stratified according to the number of athletes and the national ranking of each club.
Data Collection At the start of the season, we collected baseline information on each included athlete using a survey developed in Google Forms (Google ® ): sex, age, height, body mass, discipline, usual week hours of athletics practice, and history of ICPR during the preceding season (yes or no). During the season, we automatically sent an e-mail every Monday with a secured link to the weekly online questionnaire to all included athletes. It aimed to collect information on the preceding week: number of hours of training and competition (i. e. , athletics exposure), number of complete AIPP sessions performed, and possible injury complaints. We sent two automatic reminders 3 and 5 days after the first e-mail to non-responders. The data were collected using a secured website called "Prevathle" (Windows Server 2013 R2 64 bits-SP2; IBM DOMINO 9.
Blinding The independent statistician who performed the cluster randomisation was blinded to the study protocol. The assessment and the delivery of the intervention were blinded as all data were collected via an online questionnaire, and the intervention was delivered online through e-mail and the website, ensuring no influence or bias of healthcare providers or assessors of outcomes. The principal investigator (PE) and the statistician (DB) were also blinded, and outcome measures were not available to any party until all data had been collected. However, it was not possible to blind athletes and coaches.
Study Outcomes The primary outcome was the percentage of athletes who presented at least one ICPR during the 40 weeks of the athletics season. The secondary outcomes are the ICPR burden over an athletics season, defined as the number of days lost due to ICPR per 1000 h of exposure  [11] , and the time (in weeks) between the first week of the study and the week of the first ICPR. All these variables were calculated using data from the weekly online questionnaire.
In this analysis, we only included athletes with 100% of weekly response proportion. For the secondary outcomes, we first adjusted a linear regression model where the dependent variable was ICPR burden and followed the same process described above, only considering athletes with 100% response proportion. Finally, we used a survival analysis to evaluate the time to the first ICPR. For athletes who reported no ICPR during athletics practice, we censored them (1) at the time of their last questionnaire submission in case they stopped completing the weekly questionnaire, (2) when they had a ICPR that occurred outside of athletics practice, or (3) at the end of the study (40 weeks) if they submitted all questionnaires and had no ICPR outside of athletics. First, we carried out a competing risk analysis (using R package "cmprsk") to determine whether there were significant differences between the control and intervention groups in terms of the cumulative incidence of ICPR occurred during athletics and outside of athletics  [18].
Deviations from the Protocol We did not conduct all the secondary analyses planned in the Clinicaltrials. gov (gov Identifier: NCT03307434; accessed on 19 October 2021). In particular, no analyses at 12 weeks of follow-up have been performed. Due to an informatic problem which did not allow the last weekly automatic email to send, the entire period of follow-up was 39 weeks instead of the planned 40 weeks. We included more athletes than calculated a priori to allow us enough power for subgroup analyses.
Challenge of Compliance with the Intervention The first lesson that we learnt was the low compliance of athletes with the unsupervised exercises-based injury prevention programmes: only 8. 8% were fully compliant. This was consistent with those from previously published RCT  [24, 25] , and a recent RCT analysing an unsupervised tennis-specific prevention programme (8% of participants with high compliance)  [22]. This issue can be explained by the nature of individual sports, which makes implementing an intervention and conducting RCTs difficult  [22, 24, 26]  in comparison with team sports  [20, 27]. In this context of low compliance, it is recommended to use appropriate statistical approaches, such as instrumental variable analysis  [24, 28]. However, as previously discussed  [28] , instrumental variable analysis cannot be applied to the present dataset.
Acknowledgments: The authors highly appreciate the cooperation of the athletes who participated in this study. They would like to thank  Pedro Branco ,  Florian Celli ,  Yann Celli ,  Joris Chapon ,  Emilien Chave ,  Emmanuelle Chazal ,  Emmanuelle Cugy ,  Benoit Delattre ,  Romain Delattre ,  Frédéric Depiesse ,  Florent Derail ,  Romain Dolin ,  Anthony Leclair ,  Nicolas Morel ,  Damien Oliveras ,  Agathe Salque ,  Romain Slotala ,  Romain Vernede , for their help in developing the  Athletics Injury Prevention Programme ,  Laurie Sahuc  for her help in writing the document for the Committee for the protection of persons,  Jean-Michel Serra  ex-team physician of the  French Athletics Federation  for his support in the project, the  French Athletics Federation  for sharing the questionnaire through their email lists, and  Rasmus Oestergaard Nielsen  ( Department of Public Health, Aarhus University, Denmark and Research Unit for General Practice, Aarhus, Denmark ) for his help in the statistical plan for analyses.
use spatially explicit global datasets to visualize histories of human use in areas identified as biodiversity-rich and high-priority for conservation, including those specifically labeled as more "natural" or "wild," and test the degree to which global patterns of land use and population at different times are associated statistically with contemporary global patterns of high biodiversity value and vertebrate species richness and threat within areas prioritized for conservation. Through this examination, we assess the early and sustained global significance of cultural landscapes as a basis for better understanding and conserving terrestrial nature. Anthropological, archaeological, and paleoecological evidence indicate that, at least since the start of the current interglacial interval 11,600 y ago, all human societies were interacting with biota and environments in ways that shaped evolutionary dynamics, ecosystems, and landscapes  (25) (26) (27) (28).
The AIC indicates the relative association between anthrome maps at each time step and each contemporary pattern (AIC is only comparable for models fit to the same dataset, so rescaled values are shown here). Red dashed line marks transition from 1,000 y to 100 y time steps. Exchange and European colonial expansion. Either way, the current extinction crisis is better explained by the displacement of species-rich cultural natures sustained by past societies than the recent conversion and use of uninhabited Wildlands  (17, 22).