dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analyses Post-processing and statistical analysis were carried out by the statistical programming language R (version 4. 0. 4). Proteins with more than 10% missing values within a condition were discarded to reduce data missingness which could interfere with upcoming statistical analysis. The functions from the R-package 'DEP' were used for normalization, missing value imputation and for differential expression analysis. Here, technical variability between samples was corrected with vsn. After normalization, missing values substituted by using a downshift imputation (shift = 1. 5, scale = 0. 5). Differential expression was examined by the R-package 'limma' and Benjamini-Hochberg (BH) was used for false discovery rate (FDR) of the resulting p-values. Enrichment examination was performed with the R-package 'pathfindR' with proteins obtained from differential expression in pathways via active protein interaction networks (PIN).
Immunohistochemistry Immunohistochemical analyses was conducted as previously described  [39]. Primary antibodies were diluted as follows: IDH1 R132H (1:2, clone H09  [12] ), NF1 (1:4, clone NFC  [32] ), p53 (1:50, Novocastra, Leica, Wetzlar, Germany), SMA (1:500, Cell Marque, Sigma Aldrich, St. Louis, USA), H3K27me3 (1:25, monoclonal antibody, Cell signalling, Danvers, USA), YAP1 (1:100, Cell signalling), Caldesmon (CALD1) (1:100, clone E89, zytomed-systems, Berlin, Germany), GFAP (1:2000, clone GA5, Cell signalling, Danvers, USA), OLIG2 (1:50, clone ERP 2673, Abcam, Cambridge, UK). Stained slides were scanned on the Aperio AT2 Scanner (Aperio Technologies, Vista, USA) and digitalized using Aperio ImageScope software v12. 3. 2. 8013.
Statistical analysis Differences in CDKN2A deletion frequencies were tested for significance with chi-square and CNV load between groups were tested for significance with unpaired t-tests. Kaplan-Meier curves were created and log-rank tests calculated using GraphPad Prism 9 for Windows, GraphPad Software, San Diego, California USA, https:// www. graph pad. com. A p value ≤ 0. 05 was considered as significant. The Pearson correlation was calculated on the B-allele Frequencies (BAF) to estimate the genetic relationship between tumors coming from the same and different patients.
3 years, this appeared clearly shortened, especially considering that most Fig. 5  Oligosarcomas have a highly distinct proteome with evidence for smooth muscle differentiation. a Volcano plot of significantly differentially regulated proteins between oligosaroma and conventional grade 3 oligodendroglioma. Using the 500 most variable proteins of the dataset for principal component analysis (b) and unsupervised hierarchical clustering (c) differentiates oligosarcomas from conventional grade 3 oligodendrogliomas. d Heatmap shows upregulation of (smooth) muscle specific proteins in oligosarcomas (left) and immunohistochemistry confirms expression of CALD1 and ACTA2 (SMA) in tumor cells of oligosarcoma (right). Scale bar is 200 µm of the cases in this study were astrocytomas  [24]. To corroborate this, we complied a cohort of patients from Heidelberg with molecularly defined oligodendroglioma in which a second operation took place at first recurrence and the diagnosis of conventional grade 3 oligodendroglioma was verified by histology and DNA-methylation profiling (Supplementary tables, online resource).
Cell internalization studies, fluorescence imaging and quantification Fluorescently labelled-biosensor peptide or CNT-biosensor conjugates were overlaid on living cells grown to subconfluency on glass coverslips for 20 min at the indicated concentrations in H2O/glucose, and cells were then replaced in cell culture medium supplemented with serum for recovery for 30-45 min. Cells were extensively washed with PBS, stained with Hoechst, fixed with formalin and mounted in Mowiol. Cells were imaged with a Zeiss Axioimager Z2 microscope equipped with a CoolSnap HQ2 camera and piloted by MetaMorph software. Image J was then used for image analysis and quantification of signals. Regions of interest (ROI) corresponding to 10-15 cells in which fluorescence appeared homogeneous were designed and the mean grey levels of fluorescence for each channel (Hoechst and rhodamine) were quantified within these ROIs.
Funding: This work was supported by the  CNRS  ( Centre National de la Recherche Scientifique ) and a grant from the  French National Research Agency (ANR)  ( NANOMULTISENS  Project Grant  N°ANR-13-BS10-0003 ). C. -M. T. and B. D. were funded by the  ANR. We acknowledge the  MRI imaging facility  (Montpellier, France), a member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 , Investissements d'avenir). The Optimal imaging platform is supported by  France Life Imaging  ( French program  " Investissement d'Avenir " grant; " Infrastructure d'avenir en Biologie Santé ",  ANR-11-INBS-0006 ) and the  IBISA French consortium "Infrastructures en Biologie Santé et Agronomie ". This work was partly supported by the  ANR  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ), and by the  International Center for Frontier Research in Chemistry  (icFRC).
Data collection Clinical data were retrieved from medicals charts using a standardized anonymous form and included age at diagnosis, gender, past medical history, ongoing immunosuppressive therapy, white blood cell count, biological markers of in ammation (i. e brinogen and C-Reactive Protein) and the etiological workup for autoimmune diseases (including serum angiotensin-converting enzyme (ACE), IgG4 levels and testing for antinuclear (ANA), thyroid, and antineutrophil cytoplasmic (ANCA) autoantibodies). Additionally, minor salivary gland biopsy was performed routinely either in case of sicca syndrome or elevated serum ACE levels. Data from magnetic resonance imaging or computerized tomography of the orbit were also retrieved. For each patient, the following locations and their laterality were screened in order to specify the anatomic structures involved in the in ammatory process: lacrimal gland, extra-ocular muscles, orbital fat, globe or sclera, apex and optic or infra-orbital nerve.
The relative IDI is calculated as the ratio of IDI over the discrimination slope of the model without FDG-PET/CT  [19]. Patient characteristics are reported as the number and percentage for categorical variables and as the median (Q1-Q3) for continuous variables. All statistical analyses used the SAS software package version 9. 4 (SAS Institute Inc, Cary, NC, USA). A two-sided P-value <0. 05 was considered statistically signi cant. The standard workup was positive in 8 (36%) of the 22 enrolled patients, yet one of them was nally diagnosed as IOIS. Among the 14 (64%) patients with negative workup, 7 additional patients with low-grade lymphoma (n = 2), CSH (n = 1), or probable IgG4-ROD (n = 4) were not correctly classi ed, leading to an overall 8 (36%) cases of misclassi cation when the diagnostic workup was restricted to the standard workup (Table  2 ).
83, median Z-score in controls = 1. 04, p value = 1, N simulations = 100,000) (Supplementary Fig. 4b ). Interestingly, however, we observed a significantly higher median missense Z-score in genes affected by a missense DNM predicted as pathogenic (median Zscore = 1. 21, n = 63) when compared to genes affected by predicted benign (median Z-score = 0. 98, n = 59) missense DNMs in our cohort (p value = 5. 01 × 10 -4 , Fig. 1b ). It should be noted that the same analysis in controls showed no such significant difference (Supplementary Fig. 4c ). Protein-protein interactions reveal link to mRNA splicing. An analysis using the STRING database  24  , revealed a significant enrichment of protein interactions amongst the 84 genes affected by a protein-altering DNM predicted to be pathogenic (PPI enrichment p value = 2.
We therefore studied all DNM genes for the presence of maternally inherited mutations in the entire cohort and compared this to the presence of paternally inherited mutations in the same genes. A total of 4 maternally inherited variants predicted to be pathogenic were identified in DNM genes (TENM2 (2×), CWC25, and EVC). All of these variants, however, were also observed multiple times in an exome dataset from a cohort of 5784 fertile men suggesting that these maternally inherited variants are not causative of male infertility (Supplementary Data 2). Further analysis in additional cohorts of infertile males. In addition to all systematic analyses described above, we evaluated the function of all DNM genes to give each a final pathogenicity classification (Table  1 , details in Methods). Of all 192 DNMs, 29 affected genes were linked to male reproduction and classified as possibly causative, with a further 50 as unclear.
For replication purposes, only one pilot study including 13 trios was recently published in male infertility  7. None of the DNM genes reported in this study showed DNMs in our cohort. To further study the DNM genes identified in our cohort, we looked for the presence of rare predicted pathogenic mutations in these genes in exome datasets of infertile men (n = 2,506), in collaboration with members of the International Male Infertility Genomics Consortium and the Geisinger-Regeneron DiscovEHR collaboration  39. For comparison, we included an exome dataset from a cohort of 11,587 fertile men and women from Radboudumc. In the additional infertile cohorts, we identified 17 LoF mutations in our DNM LoF-intolerant genes (pLI ≥ 0. 9), although we did not detect a statistical enrichment in the LoF mutations in these genes compared to fertile men (Two-tailed Fisher's Exact test with Bonferroni correction adjusted p values >0.
We were able to phase 29% (n = 59) of all our DNMs using a combination of short-read WES and targeted long-read sequencing (Supplementary Table  1 ). In agreement Fig. 2  Protein-protein interactions predicted for proteins affected by pathogenic de novo mutations. Significantly larger number of interactions were observed in proteins affected by de novo mutations than expected for a similar sized dataset of randomly selected proteins (PPI enrichment p value = 2. 35 × 10 -2 ). The central module of the main interaction network (blue dashed circle) contains 5 proteins involved in mRNA splicing (Supplementary Fig. 6 ). with literature  [40] [41] [42] [43]  , 72% of all DNMs occurred on the paternal allele. Interestingly, phasing of 8 likely causative DNMs showed that 6 of these were of paternal origin (75%).
091. 14 version 5. 0, Newcastle: REC ref. 18/NE/0089) and written informed consent from all patients and their parents was obtained prior to enrollment in the study. We used residual genomic DNA extracted from a blood sample taken at the time of evaluation and treatment at the fertility center. DNA from all proband's parents was obtained from saliva by using the Oragene OG-500 kit (DNA Genotek, Ottawa, Canada). Immunofluorescence staining of human testis biopsies. Tissue sections were cut from formalin fixed paraffin embedded (FFPE) testicular biopsies. As staining controls testicular biopsies obtained from fertile men after a previous vasectomy was used. FFPE sections were prepared for staining following standard protocols. To detect RBM5, antibody HPA018011 from Atlas Antibodies was used in a 1:500 dilution.
For detection, a donkey anti-rabbit Alexa488 antibody was used (A-21206, 1:1000 dilution), which was applied in combination with lectin coupled to Alexa568 (L32458, 1:1500 dilution) to detect the acrosome (both Thermo Scientific). Slides were counterstained with DAPI. Images were obtained with a Zeiss Axio Imager ZI fluorescence microscope equipped with the Zen software package. Cohort of verified fertile Dutch parents. We used an anonymized exome dataset derived from 5784 Dutch men and 5803 Dutch women who had conceived at least one child as a control cohort for the frequency of rare variants in fertile men and fertile women. These men and women received routine exome sequencing at the Radboud diagnostics center as the healthy parent of a child with a severe illness. Although these men fathered a child with intellectual disability, their fertility is expected to be similar to an unselected sample of the male population.
Variants with recalculated GQs ≥ 20 and the same AC/AF criterion were classified as high confidence DNMs. Afterwards, tagged variants with coverage <10, variant read percentage <15% and GATK quality scores <400 were removed to ensure only the most reliable variants were considered. Sanger sequencing was then used to validate DNMs calls. Ensembl's Variant Effect Predictor (VEP)  56  was used to fully annotate all de novo variants. Variant filtration and interpretation. The primary stages in filtering of variants included removing all variants with an allele frequency of >0. 1% in the gnomAD database to only include rare variants in our analysis. All variants then with <10 reads in the exome data and/or less than 15% of these reads containing the mutation were then removed. At this stage, any remaining variants lying outside the exonic regions were then removed.
This provided the initial list of 192 rare de novo variants. All synonymous and non-protein-altering spice site variants were then removed, leaving a total of 145-protein-altering rare DNMs. Pathogenicity prediction was then based on SIFT  20  , MutationTaster  21  , and PolyPhen2 22 and all variants were classified according to the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) 2015 guidelines  57. All protein-altering variants predicted to be pathogenic by at least 2 out of 3 prediction models, absent from the fertile male cohort, present in <5 males in the gnomAD database were considered for further functional analysis (n = 84). Maternally inherited mutations present in genes identified as having a proteinaltering DNM were identified in all patients and submitted to the exact same method of filtration and interpretation as described above.
(Also see Supplementary Data 5). protein function in relation to spermatogenesis and finally whether the given gene interacts with any known fertility genes. For expression levels retrieved for each gene of interest from the GTEx database (https://www. gtexportal. org/), an expression of medium (≥10 < 100 TPM) or high (>100 TPM) gave a score of 1 with low (>2 < 10 TPM) and no expression (<2 TPM) giving a score of 0. RNA enrichment was based on elevated expression (tissue enriched, group enriched, or tissue enhanced) in the Human Protein Atlas 58 or being among the genes up-or downregulated during spermatogenesis as found in a recent single cell RNA sequencing study  59. Protein expression was retrieved from the Human Protein Atlas  58  and interaction with known infertility genes 3 was calculated using STRING version 11  24.
The final classification of the genes was then split into Not causative, Unlikely causative, Unclear and Possibly causative. These classifications were given based on the variant scores out of 6 with: [0 points + not expressed/not detected/ not present on several occasions = Unlikely causative], [0 points + "Unknown" on several occasions = Unclear], [1-2 points = Unclear] and [3-6 points = possibly causative]. CNV analysis. CNV calling was performed on our trio-based exome data with a custom GATK4-based pipeline. This workflow exploits the GATK4 sequence read depth normalization  60  and a custom R based segmentation and visualization  61. Parental samples from the trios under examination were used as controls for the normalization step. The CNVs detected were annotated using AnnotSV (https:// lbgi.
fr/AnnotSV/)  62. CNVs present in more than 1% of the samples of the Database of Genomic Variants present in more than 10% of the patients were excluded from the analysis. The remaining rare deletions and duplications were individually inspected through the genomic profiles and detailed Log2Ratio plots generated by the workflow. Only CNVs involving more than 2 exons were further considered to minimize the inclusion of false positives, and we selected 2 CNVs present in the probands but absent in their parents for further validation. Variant validation. Validation of low-quality DNMs was performed using standard Sanger sequencing approach on an Applied Biosystems SeqStudio Genetic Analyzer (ThermoFisher, MA, USA) to confirm the presence of the mutation in probands and its absence in the parents.
Primers for each SNV were designed using PrimerZ  63  (Supplementary Data 8) and PCR reactions were performed using AmpliTaq 360 DNA Polymerase (ThermoFisher, MA, USA) according to the manufacturer's protocol. Validation of CNVs was performed with the whole genome Illumina Infinium CytoSNP-850K v1. 1 microarray platform for the larger deletion on chromosome 11 and a gene-specific TaqMan Copy-Number assay designed for NXT2 was exploited to validate the smaller CNV using the Applied Biosystems QuantStudio 7 Flex Real-Time PCR System (ThermoFisher, MA, USA). Functional enrichment. To evaluate the intolerance of each gene for loss-offunction (LoF) mutations, we used the probability of LoF intolerance (pLI) score, based on data from the Genome Aggregation Database (gnomAD)  8  containing genetic data from 141,456 individuals.
To predict the affected protein function and the potential role in disease, we evaluated the interactions between the genes with a DNM using STRING version 11  24. Additional cohorts of infertile men. The strongest candidate genes with DNMs were further investigated in exome data from four additional cohorts of infertile men. For the Italian cohort of 48 patients with NOA, exome sequencing was carried out as a service by Macrogen Inc. (Republic of Korea) utilizing the Agilent SureSelect_V6 enrichment and a NovaSeq 6000. The German Male Reproductive Genomics (MERGE) study comprised exome data of 887 men with azoo-, crypto-, or severe oligozoospermia. Known causes for male infertility like chromosomal aberrations and microdeletions of the AZF region were excluded in advance. WES was performed as previously described  65.
The 88 patients diagnosed with male infertility participating in the Geisinger-Regeneron DiscovEHR collaboration were selected from deidentified EHR information using the ICD-10CM code N46 which refers to "Male Infertility" including oligospermia, azoospermia, other male infertility and male infertility unspecified. All patients were sequenced at the Regeneron Genetics Center (RGC) as previously described  39. In brief, 1ug of genomic DNA per sample was used for targeted exome capture using the Nim-bleGen VCRome 2. 1 or the IDT XGen reagents. Captured libraries were sequenced on the Illumina HiSeq 2500 platform with v4 chemistry using paired-end 75 bp reads. Exome sequencing was performed such that >85% of the bases were covered at 20× or greater. Raw sequence reads were mapped and aligned to the GRCh38/ hg38 human genome reference assembly using BWA-mem  51  , single nucleotide and indel variants were called using GATK's HaplotypeCaller  53.
The Genetics of Male Infertility INitiative (GEMINI) is a multicenter study funded by the United States NIH. The GEMINI project performed whole-exome sequencing on 1,011 unrelated men diagnosed with spermatogenic failure, the vast majority with unexplained NOA. Sequencing of genomic DNA was performed at the McDonnell Genome Institute of Washington University in St. Louis, MO, USA, using an in-house exome targeting reagent capturing 39. 1 Mb of exome and 2 × 150 bp paired-end sequencing on Illumina HiSeq 4000. Following sample QC, a final cohort of 924 men were analysed as part of the current study. Genetic variants identified within the 152 candidate genes were extracted from each exome dataset. Consistent with our filtering method described above variants with <10 reads and/or <15% reads containing the mutation were discarded.
To minimize discrepancies between genomic positions and annotation, genomic coordinates were recalculated to the GCRh37/hg19 where necessary and fully reannotated with VEP  56. Following annotation variants from each of the additional case and control cohorts were filtered and processed in an identical manner as previously described. Shortly, variants with allele frequency >1% in gnomAD were discarded to focus only on rare variants: Pathogenicity predictions based on SIFT  20  , MutationTaster  21  and PolyPhen  22  were then used to exclude benign variants, all remaining variants were classified according to ACMG guidelines  56. Like before, all protein-altering variants were considered pathogenic if predicted to be so by at least 2 out of 3 prediction models, absent from the fertile cohorts and present in <5 males in the gnomAD database.
Similarly, a gene-based burden test was performed to compare fertile fathers with fertile mothers from the control cohort of verified fertile parents to investigate whether any of the sexes predominantly carried a greater number of rare pathogenic mutations. Phasing analysis to determine parent-of-origin. The origin of DNMs identified in the exomes of patients was first investigated in the short-read exome data by performing phasing analysis on those variants that contained a parental informative SNP (iSNP) within 150 bp from the DNM. As a next step, all DNMs were target-enriched with long-range PCR and sequenced using the Oxford Nanopore's MinION sequencer (Oxford Nanopore technologies, Oxford Science Park, UK). Target regions were designed to encapsulate both the DNM and a parentally informative SNP, from which parent-of-origin, and allele frequencies (percentage read counts associated to a given allele) could be ascertained and DNM pre-/postzygosity could be determined.
The long-range PCR target enrichments of >20 ng were prepared for sequencing with the ONT ligation sequencing kit (SQK-LSK109) following the manufacturer's protocol, with adjustments for sample type and yield. Individual sample libraries were concentrated where necessary at given bead clean-up steps and pooled based on fragment size. Fragment size-based pools were combined prior to flowcell loading. Prepared samples were sequenced on the MinION using the FLO-MIN106 version 9. 4 flowcell platform. Flowcells were run until complete pore exhaustion, with minimal refuel of flowcells performed whenever active pore percentages dropped below 70%, achieving an average of 30 billion basecall yields per flowcell and coverage depth per sample of >5000×. The sequence signal data in multi-fast5 format were basecalled using Guppy 67 (version 3.
Acknowledgements We are grateful for the participation of all patients and their parents in this study. We thank  Laurens van de Wiel (Radboudumc) ,  Sebastian Judd-Mole  ( Monash University ),  Arron Scott  and  Bryan Hepworth  ( Newcastle University ) for technical support, and  Margot J Wyrwoll  ( University of Münster ) for help with handling MERGE samples and data. This project was funded by The  Netherlands Organization for Scientific Research  ( 918-15-667 ) to J. A. V. as well as an  Investigator Award in Science  from the  Wellcome Trust  (  209451 ) to J. A. V. a grant from the  Catherine van Tussenbroek Foundation  to M. S. O. a grant from  MERCK  to R. S. a  UUKi Rutherford Fund Fellowship  awarded to B. J. H. and the  German Research Foundation Clinical Research Unit "Male Germ Cells" (DFG ,  CRU326 ) to C.
The sample introduction was performed by utilizing the Bruker Daltonics direct inlet probe (DIP), coupled with an atmospheric pressure chemical ionization source (APCI), operated in positive ion mode. Source parameters were chosen to ensure a robust, sensitive, and reproducible analysis of the biomass samples. The nitrogen nebulizer gas was set at a pressure of 3 bar and 400 °C and a nitrogen drying gas flow of 2 L/min at 200 °C. The corona current was set to 3 µA (Table  A. 1a for source and acquisition parameters). A mass range of m/z 122. 8 to m/z 1,000 was chosen for this study. The analog image current, or transient, was digitized with 2 million data points resulting in the recording of a 0. 7 s time-domain signal, which was transformed into the corresponding frequency domain by Fourier transform (after one zero-fill and full-sine apodization).