dataesr/scientific-paragraphs-categorization huggingface dataset:
Unselected population group: CNV carriers were identified in the UK Biobank. Controls were defined as individuals who did not carry any of the 11 CNVs selected from this study. Demographic details and coordinates of each of the 11 CNVs are provided in Table  1 , and Table  ST1. Signed consents were obtained by investigators from each cohort for all participants or their legal representatives prior to the investigation. This study, using an aggregate dataset, obtained ethics approval from the CHU Sainte-Justine Hospital.
MRI image acquisition and preprocessing: The data sample included 3D T1-weighted (T1w) volumetric brain images at 0. 8-1 mm isotropic resolution across all sites. MRI parameters for each cohort are detailed in the Supplemental-Material. Subcortical volume and shape segmentation: FreeSurfer 5. 3. 0 was used to segment all scans into seven bilateral subcortical regions of interest: nucleus-accumbens, amygdala, caudate, hippocampus, putamen, pallidum, and thalamus. The ENIGMA subcortical shape analysis pipeline (30) (http://enigma. ini. usc. edu/protocols/imaging-protocols/) was then applied to derive two measures of shape morphometry for each subcortical region: 1) the radial distance, which is the distance from each vertex to the medial curve of each region (referred to as thickness); 2) the logarithm of the Jacobian determinant (LogJacs), which corresponds to the surface dilation ratio between the subject structure and the template (referred to as surface).
Quantifying shared variance across CNVs and NPDs: Principal components analysis (PCA) quantified shared variance across all CNVs and NPDs. For volume, we used CNV and NPD maps (z-scored Cohen's d contrasts adjusted for ICV and nuisance variables); for vertices, we stacked the thickness and surface maps and ran a single PCA (FactoMineR package in R)). As a sensitivity analysis, we ran separate PCAs for CNVs and for NPDs (Figure  SF11 ). See Supplementary-Methods for additional details and methods.
Data and materials availability UK Biobank data was downloaded under the application 40980, and may be accessed via their standard data access procedure (see http://www. ukbiobank. ac. uk/register-apply). UK Biobank CNVs were called using the pipeline developed in the Jacquemont Lab, as described at https://github. com/labjacquemont/MIND-GENESPARALLELCNV. The final CNV calls are available for download from the UK Biobank returned datasets (Return ID: 3104, https://biobank. ndph. ox. ac. uk/ukb/dset. cgi?id=3104). References to the processing pipeline and R package versions used for analysis are listed in methods.
Approved researchers can obtain the Simons Searchlight population dataset described in this study by applying at https://base. sfari. org. We are grateful to all families who participated in the 16p11. 2 European Consortium. stress disorder (PTSD), unspecified disruptive and impulse-control and conduct disorder, social anxiety disorder, social phobia disorder, speech sound disorder, moderate intellectual disability, specific learning disorder, gambling disorder, bipolar disorder, conduct disorder, Sønderby is supported by the Research Council of Norway (#223273), South-Eastern Norway attention deficit/hyperactivity disorder ADHD, Substance abuse disorder, global Regional Health Authority (#2020060), European Union's Horizon2020 Research and developmental delay, motor disorder, obsessive compulsive disorder, sleep disorder, Innovation Programme (CoMorMent project; Grant #847776) and Kristian Gerhard Jebsen Tourette's disorder, mood disorder, eating disorders, transient tic disorder, trichotillomania, pervasive developmental disorder NOS, specific phobia, body dysmorphic disorder, Stiftelsen (SKGJ-MED-021).
Each column represents the averaged value and error bars are the standard deviation (n = 3 biological replicates), *P < 0. 05, **P < 0. 01, ***P < 0. 001; ns not significant upon paired t test. Source data, including the unnormalized data used to make the right panel, are provided as a Source Data file. The PRO. 08 ura4T strain was constructed by integrating the strong ura4 terminator (defined in ref. 43) downstream of the SPBTRNAPRO. 08 loci. The integration removes 452 bp next to the poly-T Pol III termination signal and deletes the TGACGTC CRE motif. Growth was determined based on optical densities measured at 595 nm on a BIOTEK Absorbance microplate reader Epoch2NS according to the instructions of the manufacturer. The yeast strains and primers for qPCR and ChIP described in this study are listed in Supplementary Data 1.
MNase-seq and MNase-ChIP The MNase-seq datasets and MNase-ChIP protocol were reported in refs. 25,51. Shortly, mononucleosomal DNA for sequencing was isolated  25, 51. The amount of Zymolyase 20T used to prepare spheroplasts was optimized experimentally for each S. pombe strain and for the different physiological conditions to generate a 80:20 ratio of mononucleosomes to dinucleosomes. Mononucleosomal DNA isolated as described above was sequenced in an Illumina Genome Analyzer IIx. 16588557 to 35703552 single reads 36 or 40 nucleotides long, depending on the sample, representing an average genome coverage ranging from 46-to 177-fold were aligned to the S. pombe reference genome. The alignment of reads generated two peaks (one on each strand) corresponding to the boundaries of each nucleosome.
We used the smoothed signal generated by using the multilevel 1-D biorthogonal wavelet decomposition/reconstruction tool implemented in the Matlab "Wavelet Toolbox" to calculate the average spacing between boundary peaks for individual nucleosomes. This parameter (which was estimated for every independent experiment) defined the distance that the individual profiles from each DNA strand had to be shifted to converge and define the midpoint position of each nucleosome. The resulting combined profile was wavelet-smoothed to generate the final nucleosome positioning profile. The sequencing coverage for every nucleotide was divided by the average genomic coverage to normalize the different experiments. We defined nucleosome-depleted regions (NDRs) as regions spanning at least 150 nucleotides (corresponding to the eviction of at least one nucleosome) with a normalized sequence coverage lower than 0.
RT-qPCR In all, 0. 5 μg of RNeasy-purified total RNA (Qiagen 74104) was retrotranscribed with the High-Capacity cDNA Reverse Transcription Kit (Thermo 4368813) by following the manufacturer's instructions. The real-time PCR amplification was performed with SYBR Green Supermix (Bio-Rad 172-5124) in a Bio-Rad CFX96TM Real-Time machine. The PCR program was 3 min at 95 °C and 40 cycles of (15 s at 95 °C and 30 s at 60 °C). Relative RNA quantification relied on the ΔΔCT method. Primer sequences can be found in Supplementary Data 1.
Statistics and reproducibility No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment. For all experiments with statistical analyses, the averaged value from three biological replicates is shown and error bars are the standard deviation. Upon paired t test, the obtained P value is indicated in the Source data file and represented in the figure as: *P < 0. 05, **P < 0. 01, ***P < 0. 001; ns not significant. When normalized data are shown (For CTD S2P and H3K14ac level), the independent data and error bars are provided in the Source data file and the normalized data is shown in the figures.
Acknowledgements We are grateful to  Elena Hidalgo ,  Marc Buhler ,  Sigurd Braun ,  Vincent Vanoosthuyse  and  Dom Helmlinger  for reagents. We thank  Odil Porrua , and  Domenico Libri  for comments on the manuscript. High-throughput sequencing was performed by the  NGS platform of Institut Curie , supported by the grants  ANR-10-EQPX-03  and  ANR-10-INBS-09-08  from the  Agence Nationale de la Recherche  (investissements d'avenir) and by the  Canceropôle Ile-de-France. This work was also supported by  ERC regulncRNA and ANR DNAlife  to A. M. ,  PDR T. 0012. 14 , CDR J. 0066. 16,PDR T. 0112. 21  to D. H. C. Y. -S. is a FNRS Postdoctoral Researcher. D. H. is a FNRS Directors of Research.
Data availability MNase-seq data  25  are available under the NCBI Gene Expression Omnibus (GEO) accession number GSE59768. NET-seq data  27  are available under the GEO accession number GSE72382. Pol III ChIP-seq and RNA-seq data in WT and rpb1 CTD S2A (this study) are available on GEO under the accession number GSE193552. The data and materials including strains presented in this study are available from the corresponding author upon request. Source data are provided with this paper.
To investigate whether seed progeny and recruits (new F1 generation) were obtained from crosses between transplants of different source origins, genotypic data were analyzed using the software STRUCTURE version 2. 3. 4  (Pritchard et al. , 2000) , a Bayesian clustering method. Analyses were performed after downloading the genotypic data on Galaxy web platform, using the public server at UseGalaxy. be (The Galaxy  Community, 2022). In a former study  (Van Rossum et al. , 2022) , STRUCTURE identified K = 6 clusters of gene pools differing by a set of allele frequencies at each locus, allowing distinguishing of the five source populations used for plant translocations (FON transplants were assigned to two clusters). Each transplant was (probabilistically) assigned to the cluster of its source population with a high membership (Q) value (> 80 %).
Acknowledgements This study was funded by the  European Union LIFE+ Nature and Biodiversity Program  (project no. LIFE11 NAT/BE/001060 ). We thank the  Département de la Nature et des Forêts  ( Service Public de Wallonie ) and Natagora for giving access to the study sites and authorization to collect plant material,  S. Bocca ,  M. Dubois ,  S. Godefroid ,  F. Hanssens ,  X. Janssens ,  L. Jubert ,  M. Laureys ,  G. Mager ,  J. -L. Mairesse ,  K. Perard ,  M. Schotte ,  J. Smyers ,  E. Tonglet ,  E. Van der Straeten ,  G. Van Manen ,  M. Verswyvel ,  P. Verté , and  M. Wagemans  for plug planting and fencing;  A. Van de Vyver  and  F. Vandelook  for help with the germination experiment;  D. J. Parmentier  for the map; and two anonymous reviewers for constructive comments on the manuscript.
Data and Methods There are two main criteria for SsPp data selection on Earth. First, the epicentral distance should be larger than 30° to avoid mantle triplications generated by the 410-km and 660-km discontinuities  (Kang et al. , 2016). On Mars, the ideal epicentral distance to detect SsPp should be smaller than 60°, since the phase transformation of olivine to its higher-pressure polymorphs occurs at around 1,000 km depth  (Huang et al. , 2022). Second, the source wavelet should be simple. Deep earthquakes are therefore usually preferred (e. g. ,  Tseng et al. , 2009)  since their source time function is often simple, and the depth phases from deep earthquakes, which LI ET AL.
We first calculated synthetic seismograms for the radial (R) and vertical (Z) components using a MATLAB package  (Yu et al. , 2017)  based on the propagator method  (Kennett, 2009)  with a planar incident SV-wave (with a delta source wavelet, filtered between 1. 5 and 5 s). Using one of the models from the receiver function study  (Knapmeyer-Endrun et al. , 2021)  with a Layer 1 thickness of 8 km and an average P-wave velocity of 3. 0 km/s, we found that the simulated SsPp phase arrives about 4 s after the direct SV phase for marsquakes with an epicentral distance of 30° (i. e. , a ray parameter of 13. 3 s/deg). This SsPp phase is observable on both the radial and vertical components (Figure  1c ). Particle motion analysis of the radial and vertical components of the synthetics shows that the first signal (at -1 to 2 s) and the second signal (at 3-5 s) have distinctive particle motions.
Acknowledgments J. Li  thank  Tiezhao Bao  and  Weiliang Yin  for sharing codes for the particle motion analysis,  Ross Maguire  for providing the Marsquakes downloading script, and  Brigitte Knapmeyer-Endrun  for discussing the models from the receiver function study. The authors thank three anonymous reviewers and editor  David Baratoux  for their constructive comments. The misfit maps are plotted with the Scientific color maps (Crameri, 2018). J. Li  and  C. Beghein  were supported by  NASA   InSight PSP  grant # 80NSSC18K1679  and  S. M. McLennan  by  NASA   InSight PSP  grant # 80NSSC18K1622. P. Lognonné ,  E. Stutzmann , and  M. Wieczorek  are supported by  Agence Nationale de la Recherche  ( MAGIS ,  ANR-19-CE31-0008-08 ;  IdEx Université Paris Cité ,  ANR-18-IDEX-0001 ) and by  CNES  for SEIS science support.
2D ). To determine how this toxin participates in severity in the case of strains also producing PVL, specific gene deletions were performed in the ST80 strain either individually in the genes encoding PVL, HlgC, and Hla or simultaneously at all genes encoding toxins (triple mutant). The results reveal that in this context, only the triple mutant strain had its virulence considerably attenuated, suggesting the existence of compensation between the different toxins (Fig. S1 ). These data strongly demonstrate that, at least in PVL-negative strains, high levels of HlgCB are correlated with severe outcomes and mortality in a manner similar to that of PVL-positive strains like USA300 and ST80. Analysis of the hlgCB operon reveals a processed hlgB transcript. To compare the proteomic profiles of the USA300, ST80, and PEN strains with RNA levels, Northern blot analysis was performed on the three strains using specific probes complementary to hlgA, hlgC, and hlgB mRNAs.
A clear toeprint at position 116 of the hlgB ribosome binding site was observed for all transcripts (Fig. S6 ). These data showed that the hlgB ribosome binding site is accessible for the formation of a translation initiation complex, whatever the mRNA context. Next, in vitro translation assays were performed using S. aureus 70S ribosomes and hlgCB mRNAs of the three strains. The level of the HlgC protein synthesized was much lower from USA300 hlgCB mRNA than from the PEN and ST80 transcripts (Fig. 5C ). Hence, the HlgB and HlgC levels appeared almost equimolar among ST80 and PEN hlgCB mRNAs but not USA300 hlgCB mRNA. These results confirm the impact of the 59-UTR SNP on the translation efficiency of hlgC. The experiments also clearly showed that hlgB can be translated from both hlgCB and hlgB mRNAs in vitro (Fig.
These labeled peptides were added to the digested supernatant at a final concentration of 2. 1 pmol/mL. Next, the digest was desalted on a C 18 ZipTip (MacroSpin Harvard) before drying by vacuum centrifugation and storage at 220°C. The dried peptide digest was solubilized in 40 mL of a solution containing 2% acetonitrile and 0. 1% formic acid, and 6 mL of this solution was analyzed via targeted proteomics. Targeted proteomic analyses were performed on a 6500 QTrap mass spectrometer (AB Sciex) operating in the selected reaction mode (SRM). Liquid chromatography (LC) separation was performed on an Ultimate 3000 system (Thermo Scientific). The parameters for LC separation and scheduled SRM acquisitions are presented in the supplemental material. LC-SRM data analysis was performed using Skyline software.
ACKNOWLEDGMENTS We thank  Sylvère Bastien  for bioinformatics support;  Xanthe Adams  for technical help;  Christiane Wolz ,  Peter Redder , and  Paul Verhoven  for providing strains;  Iuilia Macavei  and  Jérôme Lemoine  for proteomics analysis; and  Véréna Landel  from the  Hospices Civils de Lyon (France ) for help in manuscript preparation. This work was funded by the  ANR  (grant  ANR-16-CE11-0007-01 , Ribostaph) and the  RHU IDBIORIV. We declare no conflicts of interest.
Histological analysis For histological analysis, tissues were sliced at 5 μm thickness sections. Modified Masson's Trichrome Stain Kit (Scy Teklaboratories, USA) was used to determine interstitial fibrosis, which can distinguish blue fibrosis and red muscle. All sections were scanned with digital microscopy (Olympus, Japan) equipped with a 20X objective lens for 15 to 20 images per sample. All images were acquired under same conditions. The automatic exposure and white balance were turned off. Images were analyzed with Image J software (version 1. 48v; National Institutes of Health). The area of fibers in deconvoluted color images was measured with 'Threshold' tool. Blood vessels, perivascular tissue and epicardium were excluded when measuring fibrotic area for fibrosis quantification. For transmission electron microscopy observation, rat hearts were fixed in 2% glutaraldehyde and immersed in 2% osmium tetroxide and 1% aqueous uranyl acetate for 1 h.
Time-lapse live-cell imaging NRCMs were incubated with Hoechst 33342 (Beyotime, China) and Lyso-Tracker Green (Beyotime, China) at the concentrations of 1:2000 and 1:20000 respectively for 30 min and washed by HSBB solution for 3 min. Then NRCMs were incubated with completed culture medium and imaged using Nikon confocal microscope. The moving distance of lysosome was measured by Image J software.
Live-cell lysosomal acidification NRCM lysosome acidification was detected by fluorescence intensity using Lyso-Sensor Green (Yeasen, China) with 30 min incubation. This dye is concentrated in acid cell component, of which fluorescence intensity is higher in lower pH component. Each group was captured for 10 to 15 fields using Nikon confocal microscope. All the images were analyzed by Image J software to measure lysosome acidification. The average pH value in knock-down group was normalized to the average value of control group.
Single-cell RNA sequencing database analysis The scRNA-seq data were downloaded from the Gene Expression Omnibus database (GEO; GSE128908). Seurat's (version 3. 1. 1 in R  [8] ) standard process was conducted to analyze the sequencing results. In brief, genes expressed in less than 10 cells and cells with fewer than 200 unique molecular identifiers (UMIs) or mitochondrial gene expression exceeding 60% were excluded. Clustering of filtered cell data set was identified by highly variable genes on their mean expression from 0. 05 to 10 and dispersion between 1. 5 and 20. The clusters were presented by t-distributed stochastic neighbor embedding (t-SNE) using dimensionality reduction. The Shared Nearest Neighbor (SNN) graph was constructed with 50 nearest neighbors and 20 dimensions of PCs as input.
6-1. 6, and then performed 10 cycles of PASEF MS/MS with a target intensity of 1. 5 k and a threshold of 2500. Active exclusion was enabled with a release time of 0. 4 min. MaxQuant 1. 6. 14. 0 software is used for identification and quantitation analysis of MS raw data. The original data files were available on the iProX database (https://www. iprox. cn/page/home. html; IPX0005144000). The studied proteins were blasted against the online Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://geneontology. org/) to retrieve their KEGG orthology identifications and were subsequently mapped to pathways in KEGG. Enrichment analysis were performed by the Fisher' exact test and p-value was adjusted by Benjamini-Hochberg correction for multiple testing. Both functional categories and pathways with p-values below 0.
Statistical analysis Data were analyzed using the package for social sciences (SPSS) for Windows 10. Numerical variables with a normal distribution are presented as the mean ± SEM. For intergroup comparisons of numerical variables, an independent sample t-test and Mann-Whitney U test were used. Bonferroni corrections were used to adjust the p-value to reduce type I errors in multiple comparisons. A two-tailed p < 0. 05 was considered to be statistically significant.
1C ). The levels of certain autophagyrelated proteins MAP1A, CTSB, MAP1S and RAB1B were significantly decreased (Fig. 1D ). We re-analyzed the single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO; GSE128908) that recorded scRNA-seq results of MYL4 ± and MYL4-/-hESC-atrial cells as well as MYL4 +/+ controls  [9]. After filtration and quality control, 14,861 digested single cells were subjected to subsequent analysis. We then defined 11 cell clusters by cluster analysis and evaluated gene expression differences among MYH6+ hESC-atrial cells (Fig. 2A-C ). The mutant cell lines were significantly enriched for the dilated cardiomyopathy pathway and the autophagy-related pathway according to scoring of individual cells for pathway activities  [10]  (Fig. 2D-F ). Fig.
Study population We screened 106,621 breast-cancer cases and control subjects with available pheno-and genotype data deposited in the database of the Breast Cancer Association Consortium (BCAC)  [19, 20]  at the University of Cambridge. Exclusion criteria at the study and individual level are specified in Fig. 1. Studies were not included if reference age (age at diagnosis for cases, age at interview for controls) was missing in > 30% of the study participants, relevant epidemiological variables were not recorded, or controls were not population-based or a case-only design was used. All subjects had to be women as well as of European descent, and cases were required to have a diagnosis of primary breast cancer. Based on these criteria, 44,405 eligible women (22,992 cases and 21,413 controls) from 14 populationbased case-control studies were included in the analysis.
Polymorphisms and genotype data We focused on 97 single nucleotide polymorphisms (SNPs) at 18 genes including melatonin biosynthesis (e. g. TPH1 and TPH2), melatonin receptors (MTNR1A and MTNR1B) as well as various MAP kinases (e. g. MAP2K1, MAP2K2, MAPK1, MAPK8). All 97 SNPs are listed in Supplementary table  S2  together with their characteristics in the study population. Corresponding genotypes were retrieved from the BCAC database Cambridge. They were previously generated within the framework of the Collaborative Oncological Gene-environment Study (COGS) using a custom Illumina iSelect array with 211,155 SNPs as described elsewhere  [19]. For the SNP selection, all available SNPs on the array at the aforementioned genes were considered.
In the first stage of our procedure, we selected interactions for the logic tree by using the logic Feature Selection (logicFS) algorithm to avoid overfitting  [21]. Here, we used logicFS to fit 100 logic regression models from bootstrap samples and to calculate a variable importance measure for the multiple tree approach based on the number of correctly classified out-of-bag observations for each bootstrap sample for every interaction consisting of up to six terms included in these models  [21]. We ran the algorithm three times with a different random seed and selected the 20 most important Fig. 1  Flow chart of BCAC data set for the inclusion of case-control studies in the analysis, detailed information on individual studies is provided in Suppl. table  S1  interactions each. In the second stage, we fitted individual adjusted logistic regression models with the selected terms.
To account for multiple testing and an increased type I error rate, we calculated the Bayesian false-discovery probability (BFDP) for SNPs/interaction terms with a p value < 0. 05 in the adjusted models, assuming a four-fold cost of a false non-discovery compared to a false discovery as suggested by Wakefield  [22]. Effects with BFDP < 0. 8 are termed noteworthy. We calculated the BFDP for three different prior probabilities (0. 1, 0. 05, 0. 01) for a true association and the OR corresponding to the 97. 5% quantile of the prior OR was set to 1. 2 for positive associations and to 0. 83 for negative associations. Linkage disequilibrium was checked for noteworthy SNPs. The statistical software R, version 3. 4. 2, was used for all calculations  [23]. The R-packages 'logicFS' and 'LogicReg' were used for the interaction analysis  [24, 25].