dataesr/scientific-paragraphs-categorization huggingface dataset:
Calculate the normalized depth of coverage (NDoC) To evaluate the recombination region detection analysis, several features like Depth of Coverage (DoC) and mapping quality are important. The analysis of DoC is essential to detect events like insertion, deletion and copy number variation (CNV) between and along the chromosomes during recombination. We used the program DepthOfCoverage of the GATK toolkit  [18]  to obtain the base-level coverage of the genome (Fig. 1c ). After that, to correct the technical DoC variation between parent and recombined cells, we calculated a normalized depth of coverage by dividing the base coverage of the recombinant cell by the base coverage of the parent and averaged the DoC by a one Kb sliding window along the genome (A cutoff that the users can adapt to their data).
Discussion In this study, we developed a python-based method called UGDR to detect recombination in different euploid strains and identify new out-performing recombinant yeast. To overcome the limitations of reference specificity and limited ploidy, UGDR is based uniquely on the analysis of allele ratio variation and supported by normalized depth of coverage. The allele ratio is calculated from the data obtained from the variant calling analysis, and the likelihood distribution of the allele frequency is used to detect recombination between two strains (parent and tetrads, mother and daughter or two phylogenetically close strains). The performance of UGDR was evaluated using four meiotic tetrads, two meiotically induced diploids, two triploids and two tetraploids obtained from the literature.
Acknowledgements The author would like to thank  Dr. Alain Nicolas ,  Donald Halstead  and  Alexandre Serero  for helpful comments on a draft of this manuscript,  Sophie Loeillet  for the helpful discussions and technical advices and  Hector Maldonado Perez  for fruitful discussions and critical reading of the manuscript. The author would like to specifically thank  Dr. Martin J. Hessner  for generously offering the open access fee.
PASSI survey is one of the two National Health Interviews (NHIS) active in Italy. 11, 21  Through a continuous sampling of the resident population, it conducts telephone interviews collecting information about health behaviors, use of health services and participation in preventive interventions. 22 23  It also collects data on socioeconomic characteristics of the people interviewed: educational attainment (4 categories: elementary school; middle school; high school; higher education), perceived economic difficulties (3 categories: many economic difficulties; some economic difficulties; no economic difficulties) and citizenship (2 categories: Italians with foreign nationals from high-income countries; foreign nationals from middle or low-income countries -according to the World Bank classification (UNDP, 2007)).
Participation in the survey is free and voluntary, individuals can refuse to be interviewed or can interrupt the interview at any time. The interviewers are specifically trained to safely and correctly process personal data. Individuals selected for the interview are informed by letter about the objectives of the investigation, its methods and the arrangements taken to ensure the confidentiality of the collected information. After receiving the letter, they are contacted by phone; during the phone interview the interviewer presents the information again and asks for the interviewee's consent to conduct the interview. In the present study, the analyzed data were collected by PASSI between 2017 and 2021, from interviews of more than 106,000 people, a representative sample of the Italian population aged 25-69, except for the Lombardy region that suspended the surveillance in 2016.
Ethics and data sharing In the PASSI surveillance system, personal data are processed in compliance with the GDPR 2016. PASSI was approved by the Ethics Committee of the National Institute of Public Health on January 23, 2007. Interviews are transferred anonymously to a national archive via a secure internet connection. Personal Identifiers on paper or computers are subsequently locally destroyed. Although the anonymized dataset is not yet available, the National Institute of Health is working to make it available on request (http://www. epicentro. iss. it/passi/PresPolicy. asp).
Data from the PASSI interviews. Figure  6. Trends of the proportion of the screening target population who declared to have had a test in due time, by geographical macro area. For breast cancer, we considered as being eligible the female population aged 50 to 69 and those who reported as having had a mammogram in the last two years as upto-date with screening; for cervical cancer, we considered as being eligible the female population aged 25 to 64 and those having had a Pap test in the last three years or an HPV-DNA test in the last five years as upto-date with screening; for colorectal cancer, we considered as being eligible males and females aged 50 to 69 and those who reported having had a FOBT in the last two years or a colonoscopy or sigmoidoscopy in the last five years as up-to-date with screening.
Data from the PASSI interviews. The decrease in test coverage is steeper in people with a lower level of educational or with many perceived economic difficulties (Figures  7, 8 ). For cervical cancer, the proportion of women aged 25-64 that declared to have a test in the last year decreased dramatically for the screening program and at a lesser extent for opportunistic tests. For breast and colorectal cancer, the reduction was smaller and all attributable to organized screening (Figure  9 ). Figure  7. Trends of the proportion of the screening target population who declared to have had a test in due time, by perceived economic difficulties. For breast cancer, we considered as being eligible the female population aged 50 to 69 and those who reported as having had a mammogram in the last two years as upto-date with screening; for cervical cancer, we considered as being eligible the female population aged 25 to 64 and those having had a Pap test in the last three years or an HPV-DNA test in the last five years as upto-date with screening; for colorectal cancer, we considered as being eligible males and females aged 50 to 69 and those who reported as having had a FOBT in the last two years or a colonoscopy or sigmoidoscopy in the last five years as up-to-date with screening.
Data from the PASSI interviews. Figure  8. Trends of the proportion of the screening target population who declared to have had a test in due time, by citizenship (Italian plus foreign nationals from high-income countries and foreign nationals from middle or low-income countries, according to the World Bank classification (UNDP, 2007)). For breast cancer, we considered as being eligible the female population aged 50 to 69 and those who reported as having had a mammogram in the last two years as up-to-date with screening; for cervical cancer, we considered as being eligible the female population aged 25 to 64 and those having had a Pap test in the last three years or an HPV-DNA test in the last five years as up-to-date with screening; for colorectal cancer, we considered as being eligible males and females aged 50 to 69 and those who reported as having had a FOBT in the last two years or a colonoscopy or sigmoidoscopy in the last 5 years as up-to-date with screening.
Study Design and Data Collection This retrospective multicenter study was approved by the board of the Société Francophone de Greffe de Moelle et Thérapie Cellulaire (SFGM-TC) and conducted according to the Declaration of Helsinki. We included patients allografted in France between 2007 and 2016. Participating centers were asked to verify the data recorded for each patient and provide additional information. The methodology of the study is presented in the supplementary figure. Inclusion criteria were MDS defined according to the 2008 WHO classification, including refractory anemia with excess blasts in transformation/acute myeloid leukemia (RAEB-T/AML) with marrow blasts between 20% and 30%; high-risk classical International Prognostic Scoring System (IPSS) at diagnosis ( ≥1. 5) 31 regardless of the revised IPSS (R-IPSS); age ≥18 years; Karnosfky ≥ 80% and HCT comorbidity index ≤ 4; and first allo-HSCT with a sibling donor or matched-unrelated donor.
Results & conclusions: We conducted a systematic review of articles published in the Google Scholar, and PubMed databases through October 2021. It turns out that inflammasome activation and its consequences including cytokine storms, apoptosis and pyroptosis could be associated with the reduced sperm count as well as the structural and functional sperm defects recorded in several situations associated with male infertility suggesting that anti-inflammatory therapeutic strategies could be possibly considered to restore male fertility in future research.
Résultats et conclusions: Nous avons réalisé une revue systématique des articles publiés dans les bases de données Google Scholar, et PubMed jusqu'en octobre 2021. Il s'avère que l'activation de l'inflammasome et ses conséquences, y compris les "orages" de cytokines, l'apoptose et la pyroptose, pourraient être associées à la réduction du nombre de spermatozoïdes ainsi qu'aux défauts structurels et fonctionnels des spermatozoïdes enregistrés dans plusieurs situations associées à l'infertilité masculine, ce qui suggère que des stratégies thérapeutiques anti-inflammatoires pourraient être éventuellement envisagées pour restaurer la fertilité masculine dans les recherches futures. and its putative relationship with male infertility and how this could potentially be used to correct it.
Methods We conducted a systematic review of articles published in the Google Scholar, and PubMed databases through October 2021. Using the terms: inflammasome, NLRP3, caspase-1, IL-1ß, IL-18 or ASC with male infertility, varicocele, semen, sperm, testis or epididymis. We also searched for term COVID-19 with male infertility, varicocele, semen, sperm, testis or epididymis "and" aforementioned components of inflammasome pathway (NLRP3, caspase-1, IL-1ß, IL-18 or ASC). In addition, we used high quality review articles (N = 10) that were not related to male infertility to present the inflammatory pathway. We only considered English publications. Approximately 86% of the articles used for the current review paper were articles with high quality (Q1 and Q2) which were accepted in high impact journals.
Cell purity was confirmed by microscopy and flow cytometry (Figures  1I  and S1H ; Data S1). Flow cytometry C57BL/6 BMDMs or BMDCs were challenged as indicated and stained with anti-CD11c, CD11b, MHCII I-A/I-E, CD86, CD115, IRF4, Ly6C, IL-12p40 or isotype control antibodies and live/dead far red stain. For intracellular staining cells were fixed (2% PFA) and then permeabilized in either Tween-20 (1%) in FACS buffer (PBS/0,5% FBS/1mM EDTA; IL-12p40) or Triton X-100 (1%) in PBS/5% BSA (IRF4/isotype) supplemented with anti-CD16/CD32 antibody prior to staining. For IL-12p40 staining cells were cultured in the presence of Brefeldin A (eBioscience) for the last 4h of challenge. Extracellular staining was performed on fixed (2% PFA) or live cells, blocked with anti-CD16/CD32 antibody, in FACS buffer.
Chemotaxis BMDCs, BMDMs or human macrophages were challenged as indicated, washed, resuspended in CM with 1 mg/mL bovine collagen type I (Sigma) and seeded into gelatin (1%)-coated or uncoated ibiTreat m-slide chemotaxis chambers (Ibidi, Martinsried, Germany). Collagen was allowed to polymerize for 45 minutes and media, inhibitors and 1,25 mg/mL murine (BMDCs, BMDMs) or human recombinant CCL19 (human macrophages) were added as indicated conform the manufacturer's instructions (application note 23). Cells were then imaged every 2,5 (BMDCs) or 5 (BMDMs, human macrophages) min for 4h (BMDCs) or 8h (BMDMs, human macrophages; Zeiss Observer Z. 1). Motility tracks for 35-60 cells per condition were analyzed using ImageJ software for each experiment.
Handling of publicly available datasets ChIP-seq data available from NCBI GEO series GSE40727 (IRF4), GSE66899 (H3K27ac), GSE64767 (H3K4me3), which are described elsewhere, was visualized in the USCS genome browser  (Glasmacher et al. , 2012; Grajales-Reyes et al. , 2015; Lin et al. , 2015). Changes in gene expression by 1 or 2 day treatment of sorted monocytes with GM-CSF and IL-4 were determined from RNA-seq read counts in NCBI GEO serie GSE99837  (Boulet et al. , 2019). Microarray data from NCBI GEO series GSE27972 was analyzed with R packages oligo and limma to derive differentially expressed genes upon 6h T. gondii (RH) challenge of BMDMs  (Morgado et al. , 2011).
The completed libraries were quantified by Agilent 2100 Bioanalyzer. The libraries were denatured with 0. 1 M NaOH to generate single-stranded DNA molecules, captured on Illumina flow cell, amplified in situ. The libraries were then sequenced on the Illumina NovaSeq 6000 following the NovaSeq 6000 S4 Reagent Kit (300 cycles) protocol. After the sequencing platform generated the sequencing images, the stages of image analysis and base calling were performed using Off-Line Basecaller software (OLB V1. 8). Sequence quality was examined using the FastQC software. After passing Solexa CHASTITY quality filter, the clean reads were aligned to human genome (UCSC HG19) using BOWTIE software (V2. 2. 7). Aligned reads were used for peak calling of the ChIP regions using MACS V1. 4. 2. Statistically significant ChIP-enriched regions (peaks) were identified by IP, using a p-value threshold of 10-4.
The peaks in samples were annotated by the nearest gene using the newest UCSC RefSeq database. The annotation of the peaks which were located within -2Kb to +2Kb around the corresponding gene TSS in samples can be found from the Peaks_ Promoter_Annotation. xls file. The signal profile (at 10 bp resolution) with UCSC WIG file format was generated from ChIP-seq data, which can be visualized on UCSC genome browser or IGB browser (Integrated Genome Browser, Java Runtime Enviroment needed, http://www. bioviz. org/igb/). All wig flies and a brief guide of visualizing the profiles can be found in Data_Visualization folder.
Maisch). The MS and MS/MS data were acquired by Xcalibur (Thermo Fisher Scientific). Peptides and proteins were identified by Mascot (version 2. 6. 0, Matrix Science) through concomitant searches against the T. gondii database (ME49 taxonomy, v. 30, downloaded from ToxoDB), the Uniprot database (Mus musculus taxonomy, August 2019 version), and a homemade database containing the sequences of classical contaminant proteins found in proteomic analyses (bovine albumin, keratins, trypsin, etc. ). Trypsin/P was chosen as the enzyme and two missed cleavages were allowed. Precursor and fragment mass error tolerances were set at respectively at 10 ppm and 25 mmu. Peptide modifications allowed during the search were: Carbamidomethyl (C, fixed), Acetyl (Protein N-term, variable) and Oxidation (M, variable).
The Proline software was used for the compilation, grouping, and filtering of the results (conservation of rank 1 peptides, peptide length R 7, PSM score R 25, false discovery rate of peptide-spectrum-match identifications < 1% as calculated on peptide-spectrum-match scores by employing the reverse database strategy, and a minimum of 1 specific peptide per identified protein group). Proline was then used to perform a compilation, grouping and MS1 quantification, based on razor and specific peptides, of the identified protein groups. Proteins from the contaminant database were discarded from the final list of identified proteins. Proteins in each sample were further quantified through calculation of their corresponding intensity-based absolute quantification values (iBAQ). The MS proteomics data have been deposited to the ProteomeXchange Consortium through the PRIDE partner repository with the dataset identifier PXD032360.
QUANTIFICATION AND STATISTICAL ANALYSIS Western blot analysis was performed by band intensity quantification with ImageJ. qPCR results were analyzed using the DCq and DDCq methods from Cq values provided by QuantStudio Design and Analysis Desktop Software. Cell tracks were obtained by manual tracking with ImageJ from image sequence files. Speeds and endpoints from individual cells were calculated and derived from these tracks with the Chemotaxis and Migration Tool and combined with track lengths calculated with R and RStudio. Flow cytometry data in FCS files was analyzed with FlowJo X. Quantification of RNA-seq and proteomics data was performed as described in the method details. Details on the software used can be found in the key resources table.
(F) Motility plot shows displacement of BMDMs challenged with DTgWIP tachyzoites in a CCL19 gradient and analysis as in (A). All datasets are from 3 to 5 independent experiments and displayed as mean + SE. *p % 0. 05, **p % 0. 01, ***p % 0. 001, ns p > 0. 05 (ANOVA and Dunnett's [B-D] or Holm-Bonferroni [E] post-hoc tests). See also Figure S3.
(I) Flow cytometric analyses of anti-CD86 staining on BMDMs challenged as in (B). (J) qPCR analysis of Egr1, Zbtb46, and Irf4 in BMDMs challenged with type I wild-type (Tg), Dgra24 or Dgra28, or Dgra28+GRA28 tachyzoites, or left unchallenged as in (G). Relative expression (2 ÀDDCq ) related to unchallenged (=1; n R 3). Datasets are from at least 3 independent experiments and displayed as mean + SE. *p % 0. 05, **p % 0. 01, ***p % 0. 001, ns p > 0. 05 (ANOVA and Dunnett's [G, H, J] or Holm-Bonferroni [I] post-hoc test). See also Figure S4.
(D) qPCR analysis of Irf4, Zbtb46, Batf3 and Ccr7 cDNA of BMDMs treated with TPCA-1 (STAT3/IKKi), AS1517499 (STAT6i) or DMSO (Vehicle) for 1h and then challenged for 6h with freshly egressed T. gondii type I wild-type tachyzoites (MOI 3) or left unchallenged, displayed as in (B). (E) qPCR analysis of Ifng, Il2, Il4 and Mki67 in syngeneic mixed leukocytes reactions (MLR) of BMDMs challenged RH or RHΔrop16 tachyzoites (MOI 2) or left unchallenged for 4h and mixed with splenocytes for 18h, displayed as in (B). (F) qPCR analysis of Il12p40, Zbtb46 and Irf4 in BMDM challenged with type I wild-type (Tg), TgΔmyr1, TgΔrop17, TgΔgra45 or TgΔTgwip tachyzoites and displayed as in (B). All datasets are from 3-4 independent experiments. Statistical comparisons were made with ANOVA and Dunnett's (A-E) or Holm-Bonferroni (F) post-hoc tests (* p ≤ 0,05, * p ≤ 0,01, *** p ≤ 0,001, ns p > 0,05).
Materials availability New plasmids and materials generated in this study are available from the lead contact. Data and code availability d The MS proteomics data have been deposited to the ProteomeXchange Consortium through the PRIDE partner repository with the dataset identifier PXD032360. This paper analyzes existing, publicly available data. The NCBI GEO accession numbers for these datasets are GSE40727, GSE66899, GSE64767, GSE99837 and GSE27972. All data reported in this paper will be shared by the lead contact upon request. d This paper does not report original code. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Human cell culture Human CD14 + monocytes were isolated from peripheral blood mononuclear cells (PBMC) after density gradient centrifugation on Lymphoprep with CD14 MicroBeads from buffy coats obtained from healthy donors at the Karolinska University Hospital Blood Center and cultured in complete medium.
Background: There is considerable evidence for the importance of the DNA methylome in metabolic health, for example, a robust methylation signature has been associated with body mass index (BMI). However, visceral fat (VF) mass accumulation is a greater risk factor for metabolic disease than BMI alone. In this study, we dissect the subcutaneous adipose tissue (SAT) methylome signature relevant to metabolic health by focusing on VF as the major risk factor of metabolic disease. We integrate results with genetic, blood methylation, SAT gene expression, blood metabolomic, dietary intake and metabolic phenotype data to assess and quantify genetic and environmental drivers of the identified signals, as well as their potential functional roles. Methods: Epigenome-wide association analyses were carried out to determine visceral fat mass-associated differentially methylated positions (VF-DMPs) in SAT samples from 538 TwinsUK participants.
Replication of components of the analysis was pursued in three independent datasets (104 individuals from the LEAP cohort in the New England Family Study  [8] , 28 T2D cases and 28 controls from the Danish Twin Registry  [22] , and 199 severely obese individuals undergoing bariatric surgery at the Quebec Heart and Lung institute  [20] ). To consider how our results relate to the clinical consequences of elevated VF, we showed that a subset of the identified signals have strong associations with insulin resistance and can serve as potential predictors of IR.
Cohort sample datasets The primary dataset in this study included 538 White British female twins (mean age 58. 9±9. 5 years; Table  1 ) from the TwinsUK cohort  [23]  who were free from major diseases including cancer, and for whom SAT biopsy DNA methylation levels were profiled. Three subsets of the 538 primary TwinsUK dataset were included in downstream follow-up analyses where participants had complete relevant data (including 397 of 538 for diet analyses, 347 of 538 for metabolomic analyses, and 528 of 538 for lipid analyses). Details of data collection for the diet, metabolomic and lipid data have been previously described  [23]  and are described further below (see TwinsUK phenotype data). A second set of TwinsUK participants included 901 individuals (mean age 57. 8±10. 1 years 97% female; Table  1 ) for whom whole blood DNA methylation was profiled.
These data are described further below (see TwinsUK study participants). The final TwinsUK dataset included in this study contained 720 participants from the TwinsUK cohort for whom adipose gene expression profiles were available, and these data were used in the analysis of the association between gene expression and visceral fat described previously  [24] , and below (see Gene expression profiles and analyses). Validation and replication were carried out in three independent cohort datasets that are described in more detail below (see 'Validation and replication'). For validation using AGR as a surrogate adiposity phenotype, we analysed a dataset of 104 individuals from the LEAP cohort in the New England Family Study, as previously described  [8]. For replication of metabolic health impacts, two further datasets were used.
TwinsUK study participants The primary dataset of 538 participants consisted of 107 monozygotic (MZ) twin pairs and 163 dizygotic (DZ) twin pairs from whom SAT biopsy samples were obtained, as previously described  [21]. Briefly, subcutaneous tissue was dissected from the abdomen, near the umbilicus. Fat was immediately stored in liquid nitrogen, after separation from the skin layer. The second set of 901 study participants consisted of 422 MZ twin pairs and 73 DZ twin pairs, and 222 individuals overlapped with the set of 538 twins with SAT biopsies. Descriptions of DNA extraction in blood samples and SAT have been described previously  [21, 25]. All study participants provided informed consent. Ethical approval was granted by the National Research Ethics Service London-Westminster, the St Thomas' Hospital Research Ethics Committee.