dataesr/scientific-paragraphs-categorization huggingface dataset:
Western Blot. Distal colon segments were lysed in 50 mM Tris, containing 100 mM NaCl, 1% Triton X-100, 1 mM EGTA and protease inhibitors (Complete; Roche, Boulogne-Billancourt, France), pH 7. 4, using the Precellys 24 tissue homogenizer (Bertin Technologies, Montigny-le-Bretonneux, France) and followed by sonication with Vibracell 75186 device (Sonics, Newton CT, USA). Total proteins were quantified using BCA protein assay kit (Thermofisher). Equal amounts of lysate (10 µg of proteins) were separated using the Invitrogen NuPage Novex 4-12% Bis-Tris MidiGels with lithium dodecyl sulfate running buffer and transferred to nitrocellulose membranes. Membranes were incubated overnight with either a rabbit anti-PGP9. 5 antibody diluted 1/5000 (Cedarlane, TEBU-BIO France, Le Perray-en-Yvelines, France), a rabbit anti-MAP2 antibody (1/1000, Millipore, Illkirch, France), a rabbit anti-S100β antibody (1/500, Dako) or a rabbit anti-GFAP antibody (1/5000, Dako), then with a HRPconjugated anti-rabbit antibody (Thermoscientific; 1/5000) and visualized by chemoluminescence (Clarity Western ECL Substrate, Bio-Rad, Marnes-la-Coquette, France) using a Gel-Doc imager and the Image Lab Software (Bio-Rad).
Fecal Microbiota assessment by high-throughput 16S-sequencing DNA extraction and sequencing were carried out at the GeT-TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées, France). Following DNA concentration estimation by nanodrop, the V3-V4 region of the 16S rRNA gene was amplified (V3F bac339F-TACGGRAGGCAGCAG (modified from  Wilson KH, et al. J Clin Microbiol. 1990 ) and V4R bac806R-GGACTACCAGGGTATCTAAT). Illumina MiSeq sequencing was performed on 46 samples. One sample provided too low sequences number (n=365) and was removed from analysis. Total reads were filtered for length (min length=300bp) and quality (min quality =25). A total of 316,668 reads was obtained (average 7,037 reads/samples). High quality reads were pooled, checked for chimeras, and grouped into operational taxonomic units (OTUs) based on a 97% similarity threshold with uclust software from QIIME.
Estimates of phylotypes richness and diversity were calculated using Shannon and Simpson indices on the rarefied OTU table (n=4,000 reads). Singletons were removed and phylogenetic affiliation of each OTU (average 503 OTUs per sample) was done by using ribosomal database project taxonomy  (Cole et al. , 2014)  and performed from phylum to species level. Wilcoxon/Mann-Whitney U tests were performed on raw abundance data to determine the effects of early adversity in each sex. Taxa with uncorrected p values < 0. 05 were considered to be differentially regulated between conditions. The statistical language R was used for data visualization and to perform abundance-based principal component analysis (PCA) and interclass PCA associated with Monte-Carlo rank testing on the bacterial genera (ade4 library).
Dye incorporation and cRNA yield were checked using Dropsense™ 96 UV/VIS droplet reader  (Trinean, Belgium). 600 ng of Cy3-labelled cRNA were hybridized on the microarray slides following the manufacturer's instructions. Immediately after washing, the slides were scanned on Agilent G2505C Microarray Scanner using Agilent Scan Control A. 8. 5. 1 software and fluorescence signal extracted using Agilent Feature Extraction software v10. 10. 1. 1 with default parameters. Raw data (median signal intensity) were filtered, log2 transformed, corrected for batch effects (microarray washing bath and labeling serials) and normalized using quantile method  (Bolstad et al. , 2003). A model was fitted using the limma lmFit function  (Smyth, 2004). Pair-wise comparisons between biological conditions were applied using specific contrasts.
Probes with uncorrected p value < 0. 05 and foldchange > 20% were considered to be differentially expressed between conditions. Microarray data and experimental details are available in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series accession number GSE116416 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE116416). Microarray data were analyzed using R (R Development Core Team, 2008) and Bioconductor packages (www. bioconductor. org, v 3. 0,  (Gentleman et al. , 2004)  as described in GEO accession GSE116416.
Validation of transcriptomic data using Microfluidics. Validation of results from microarrays by qRT-PCR was performed using the BioMark system (Fluidigm). Detailed information and sequences of primers used are provided in the Supplemental Table  S1. The specificity of the PCR reaction was validated according to MIQE (Minimum Information for publication of Quantitative real time PCR Experiments) guidelines. Each cDNA was diluted (5 ng/µL) and used for target amplification. Primer pairs targeting all the genes of interest were pooled to a final concentration of 200nM for each primer pair. A multiplexed preamplification process was performed for the primers pool on every 1. 25 µL of cDNA using 14 cycles of cDNA preamplification step (at 95°C for 15 s and at 60°C 4 min) and PreAmp Master Mix (Fluidigm) in a standard PCR thermocycler.
Acknowledgements This work was supported by the  University of Bordeaux ,  INRA, AVIESAN Immunology, Hematology and Pneumology. M. R. was supported by a stipend of the  French Ministry of Research. We warmly thank  Céline Monot & Karine Le Roux  for technical support and logistics with the microbiological analyses. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources. We also thank the  Genotoul Get-PlaGe  sequencing platform, and  Thierry Lesté-Lasserre  for qPCR primer design and validation. Authors are grateful to  Aline Foury  and  Dr. Marie-Pierre Moisan  for their valuable help with the microarray analysis.
TDG is expressed in melanoma, and its knockdown induces morphological changes in melanoma cell lines Examination of the Oncomine database (http://www. oncomine. org) revealed that the median expression levels of TDG mRNA are similar in melanoma samples and melanocytic nevi (levels are higher in normal skin, in which, however, the melanocytes are a minority) (Fig. 1a ). In the Human Protein Atlas database, nuclear expression of TDG protein is also maintained at high-to-medium levels in melanomas; and high expression is associated with unfavorable prognosis (Suppl. Figure  1a ; http://www. proteinatla s. org). Interestingly, in TCGA-SKCM (skin cutaneous melanoma) cases, there is a positive correlation between TDG and TET1-3 mRNA expression levels (Suppl. Figure  1b ). These observations were consistent with the possibility that TDG is a melanoma target and prompted us to examine the consequences of its knockdown.
TDG knockdown affects genes involved in cell cycle and senescence The above results show that TDG knockdown causes cell cycle arrest, senescence, and cell death. Given the role of TDG in transcription  [2] , to gain insight into the changes in gene expression associated with TDG knockdown, we compared the transcriptomes between control and C8-infected SK28 cells 5 days after infection by RNA-seq. Using a log2 fold change and adjusted p value, TDG knockdown had an important effect on gene expression with more than 1100 significantly downregulated and 1000 upregulated genes (see Supplemental Dataset). Gene set enrichment analysis (GSEA) revealed marked downregulation of E2F target genes involved in cell cycle progression and DNA replication and repair in C8-infected SK28 cells compared with control pLKO.
RNA sequencing RNA (biological triplicates) was prepared with the RNeasy Mini Kit (Qiagen), and quality control was performed on a Bioanalyzer. Messenger-RNA-seq was performed essentially as described  [51]. Sequence reads were mapped to reference genome mm9/NCBI37 using Tophat v2. 0. 10  [52]  and the bowtie2 v2. 1. 0 aligner  [53]. Only uniquely aligned reads were retained for further analyses. Data normalization and quantification of gene expression was performed using the DESeq2 Bioconductor package. Significantly deregulated genes were selected using a log2 fold change >1 and <1 and adjusted p value cutoff of 0. 05. GO analyses were performed using DAVID functional annotation clustering tool (https://david. ncifcrf. gov/summary. jsp). For GSEA, we used the mean of the log2 fold changes of the biological replicates as metric for the H Hallmark gene sets of the BROAD javaGSEA tool with 1000 permutations and the canonical pathway subcollection of the C2-curated BROAD molecular signature gene set collection.
Comparisons of interest were performed  [54]  as implemented in the DESeq2 Bioconductor library (v1. 0. 18). Resulting p values were adjusted for multiple testing by the Benjamini and Hochberg method  [55]. Heatmaps were made using the log2 of RPKM values with R package "pheatmap" v1. 0. 10 using options scale = 'none' and cluster_rows = TRUE. For heatmaps of deregulated genes, only the 300 most upregulated or downregulated genes were represented.
We used a Python script to map aligned sequencing reads to target CCCGGG sites in the human genome and enumerate signatures corresponding to methylated (starting with CCGGG) and unmethylated (starting with GGG) CpG for each target site. We calculated methylation levels at each SmaI/XmaI site based on the percentage of methylated signatures. Background-corrected GO analyses were performed using ConsensusPathDB (http://cpdb. molgen. mpg. de).
Bioinformatics analysis Oncomine 4. 5 database (http://www. oncomine. org) was used to investigate the expression of TDG in normal skin, nevi, and cutaneous melanoma samples in the Talantov melanoma dataset. The Human Protein Atlas pathology database (http://www. proteinatlas. org) was used to assess TDG protein expression in human melanoma and to investigate the correlation between TDG mRNA expression level and patient survival by Kaplan-Meier analysis. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, permits use, sharing, adaptation, distribution reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Acknowledgements We thank  Dr. P. Abbosh  and  Dr. I. Astsaturov  for critical reading of the manuscript;  Dr. R. Zaidi  for comments and advice;  L. Cathay  for secretarial assistance; and  John  and  Robin Spurlino  and the  Edwards Family  for their generous support of this research. We would like to thank  Alice M. Hungerford  for her philanthropic funding through the  David A. Hungerford Endowed Fund in Basic Chromosome Research , honoring her late husband's legacy as a renowned scientist and the co-discoverer of the  Philadelphia Chromosome. We thank the following core services at  Fox Chase Cancer Center: Genomics, Cell Culture, Cell Sorting, Biological Imaging, High-Throughput Screening, Biostatistics and Bioinformatics, and Laboratory Animal Facilities. This study was supported by  NIH  grants  CA78412  (to  A.
Bellacosa ),  CA191956  (to  A. Bellacosa  and  T. Yen ), and  CA06927 ; the intramural research program of the  National Center for Advancing Translational Sciences  (NCATS),  National Institutes of Health (NIH) ,  PA Department of Health C. U. R. E. and an appropriation from the  Commonwealth of Pennsylvania  to the  Fox Chase Cancer Center. PM was supported in part by  INCa  Project  PLBIO11-027. RT was supported in part by a  William J. Avery Endowed Postdoctoral Fellowship. RWS was supported by grants from the  National Institutes of Health  ( CA148629  and  ES025138 ) and is an Abraham A. Mitchell Distinguished Investigator. LL was supported by a grant from  Ligue Nationale Contre le Cancer ,  INCa , ITMO cancer, and is under the program " Investissements d'Avenir " launched by the  French Government  and implemented by  ANR Labex CelTisPhyBio  ( ANR-11-LBX-0038  and  ANR-10-IDEX-0001-02 PSL ).
Work in the laboratory of ID was supported by grants from the  INCa , the Ligue National contre le cancer, and the  Labex INRT  ( ANR-10-IDEX-0001-02 ). RNA-seq was performed at the IGBMC high-throughput sequencing facility, a member of " France Génomique " consortium ( ANR10-INBS-09-08 ). LL and ID are "equipes labélisées of the Ligue National Contre le cancer. "AB would like to dedicate this article to the memory of his mother Maria, who showed that strength and tenderness, elegance and simplicity, resilience and joyfulness can coexist, and who will be missed tremendously.
Calibrations Frequent calibration and blank samples ensured optimum and stable performance of the GC-FID systems. A multicomponent reference gas mixture (National Physical Laboratory, UK, 2015) was diluted with synthetic air (6. 0, Westfalen AG, Germany) and passed through both the ozone scrubber and the Nafion drier to provide sampling conditions similar to ambient air. In total, 79 calibration and 20 blank samples were obtained throughout the campaign. Prior to ambient samples, a six-step calibration curve (6 × 3) was obtained for 450 and 900 mL sampling volumes, confirming the high sensitivity and excellent linearity of FID detectors as the R 2 of the linear fit for all species was always higher than 0. 98. During the campaign, single calibration points and blanks were frequently obtained to monitor the sensitivity while minimizing data loss.
Analysis of the initial calibrations revealed volume-dependent sensitivity for ethane, propane, ethene, and propene. Therefore, another set of linear calibrations with 1350 mL sampling volume was performed towards the end of the campaign. For the aforementioned species, the volume-dependent calibration curve was applied for deriving the ambient mixing ratios. Due to varying baseline noise, dependent on the ship motion, condition-specific detection limits (DLs) and total uncertainties have been calculated (Table  1 ). The limit of detection was calculated from the baseline noise, as this was determined by IAU Chrom integration software  (Sala et al. , 2014). Peaks that were higher than 3 times the noise signal were considered to be above DL. With this approach DLs for each calibrated chromatograph were derived.
Methane measurements The CH 4 molar mixing ratio was measured using a cavity ring-down spectroscopy analyser (Picarro G2401). Four different calibration gases traceable to WMO X2004A scale and bracketing typical ambient concentrations were injected into the analyser at ports and every 15 days for calibration purposes. The injection sequence consists of four 15 min injections of each of the four gases. An additional target gas was injected daily to assess measurement accuracy. The data have been documented and processed following ICOS (Integrated Carbon Observing System) standard procedure  (Hazan et al. , 2016) , including the propagation of the calibration and threshold-based filters. The mean drift of measured concentrations from calibration cylinders between two sequences is 0. 5 nmol mol -1 , significantly below the drifts typically observed at fixed observatories  (Hazan et al.
Considering the complete propane dataset, the aforementioned regions displayed only moderate correlations with the longer-lived ethane (0. 47 < R 2 < 0. 72) and poor correlations with the rest of the alkanes, alkenes, and aromatics. The three regions that showed a high correlation between propane and NMHCs were the SC, RSN, and AG (Fig. 8 , Table  3 ). Among them, the propane-NMHC relationship was markedly different in the SC as propane mixing ratios were correlating only with butanes, alkenes, and aromatic hydrocarbons. N-butane in particular was the only hydrocarbon that was emitted more strongly than propane (ER = 1. 1 ppb ppb -1 , R 2 = 0. 91), while the known tight relationship with its isomer i-butane resulted in an enhancement mixing ratio slope of 0. 43 ppb ppb -1 (R 2 = 0. 89) for i-butane.
By calculating the excess mole fraction of NMHCs, we showed that the ethane-propane interrelationship is different in hydrocarbon-rich associated gas compared to the methane-rich non-associated gas which contained higher fractions of propane over ethane, in line with previous observations that demonstrated the propane dominance in gas leak emissions in the region  (Salameh et al. , 2016). The atmospheric abundance of ethane and propane over the Arabian Gulf was inconsistent with methane mixing ratios when considering the complete dataset of this region since the different emission ratios are dependent on the source type and characteristics along with the O&G processing method and techniques. By utilizing the variability-lifetime relationship, we confirmed the extent of remoteness from NMHC sources.
Acknowledgements. We acknowledge the fruitful collaborations with the  Cyprus Institute (CyI) , the  King Abdullah University of Science and Technology (KAUST) , and the  Kuwait Institute for Scientific Research (KISR). We are grateful to  Hays Ships Ltd , the ship's captain  Pavel Kirzner , and the ship's crew for providing the best possible working conditions onboard Kommandor Iona. We thank all the participants of the AQABA ship campaign and in particular  Harwig Harder  for the fruitful discussions and day-today organization of the campaign, and  Marcel Dorf ,  Claus Koeppel ,  Thomas Klüpfel , and  Rolf Hoffmann  for logistics organization and assistance during the setup phase. Finally, we acknowledge  Ivan Tadic  and  Philipp Eger  for the use of a preliminary KI contamination flag. Marc Delmotte ,  Laurence Vialettes , and  Olivier Laurent  helped with setting up the methane measurements.
Data availability. The data are available upon request to all scientists agreeing to the AQABA protocol from August 2019 (https://doi. org/10. 5281/zenodo. 3050041), Bourtsoukidis et al. , 2019. Atmos. Chem. Phys. , 19, 7209-7232, 2019 www. atmos-chem-phys. net/19/7209/2019/.
| Laboratory analysis The majority of individuals were identified under 10-60× magnification to family level using  Merritt, Cummins, and Berg (2008)  as immature stages and small body sizes prevented consistency in further identification. Acari and Gastropoda were identified to subclass and class, respectively. Oligochaetes were too damaged and fragmented by the disturbances to count accurately, so were excluded from the dataset. Fragments indicated that oligochaetes were broadly distributed across treatments but occurred in low abundance, so it is unlikely their exclusion from our dataset would have altered the outcome of our analyses. After separating invertebrates from the sample, we used a series of nested sieves to retain fine particulate organic matter (FPOM, 63 µm -1,000 µm). FPOM was oven-dried (60°C, >72 hr), weighed, ashed in a muffle furnace for 2 hr at 550°C, and then reweighed to determine ash-free dry mass.
| Data analysis Two baskets were sampled from each channel on 29th August and 7th September. The first sample (29th August) was only analyzed for number of chironomids and total FPOM, not family-level community composition, due to time constraints. The community data from both baskets were pooled, as was the total FPOM. All abundances and FPOM ash-free dry mass data therefore reflect the contents of two baskets, and not the entire stream channel. We analyzed the effects of disturbance frequency on taxon richness, evenness, and total abundance using linear mixed effects models (package nlme in R). We regressed the response variables against the number of disturbances a channel experienced over the duration of the experiment (August 1, 2014, -September 7, 2014) and used experimental block as a random term.
We used the R package piecewiseSEM  (Lefcheck, 2016)  to calculate R 2 values from mixed effects models (following  Nakagawa & Schielzeth, 2013). In practice, because undisturbed channels were subject to the standardized disturbance that began the period, the frequency of disturbance varied from 1 to 8 among the five treatments. Response variables were transformed in a manner appropriate to meet assumptions of normality, and a significance threshold of α = 0. 05 was employed. Rarefied familial richness was calculated with the function "rarefy" in R package vegan using the minimum per-channel abundance across all channels  (Oksanen et al. , 2016).
, 1988; Overbaugh and Bangham, 2001; Zhuang et al. , 2002; Santoro and Perno, 2013). The surveillance of HIV diversity assists to monitor the emergence of new subtypes and the presence of novel strains in a given geographic location  (Hemelaar, 2013). The great diversity of HIV-1 group M, which disseminated on a global scale and dominates the current AIDS pandemic, allowed the phylogenetic classification in nine pure subtypes (A-D, F-H, J, and K), sub-subtypes (A1-A5, F1-F2), circulating recombinant forms (CRFs) and unique recombinant forms (URFs)  (Robertson et al. , 2000). Currently, more than 90 CRFs have been reported in the HIV Sequence Database of the United States Los Alamos National Laboratory 1. Recombinant viruses are the result of simultaneous infection by multiple viruses during a single transmission event (co-infection) or from sequential infection at multiple transmission events (superinfection)  (Yerly et al.
Study Population and Sample Collection A total of 84 convenience samples were used for generating the experimental data presented in this study. These were from HIV-1-seropositive patients recruited between February 2016 and December 2017 during the routine services conducted at Sexually Transmitted Diseases/HIV ambulatory at Hospital Federal de Ipanema (HFI) and at Hospital Universitário Clementino Fraga Filho (HU-UFRJ), both located in Rio de Janeiro, southeastern Brazil, and at Hospital Universitário Dr. Miguel Riet Corrêa Jr. (HU-FURG), located in Rio Grande, southern Brazil. Clinical and epidemiological data were obtained through a questionnaire and 10 ml of whole peripheral blood were collected. This research was approved by the Ethics Committees in Research of the Brazilian National Cancer Institute -INCA and of HFI (CAAE 52862016.
9. 0000. 5274), HUCFF-UFRJ (CAAE 56604816. 2. 0000. 5257), and HU-FURG  (CAAE 52862016. 9. 3001. 5324). The inclusion criteria were being 18 years or greater, being under first antiretroviral scheme and being upon virological success (undetectable HIV viral load) for the last 12 months. A fraction of this casuistic, 32 patients from HFI, has been previously described  (Alves et al. , 2017). We pooled these data with all Brazilian HIV-1 data comprising NFLG and partial sequences determined by NGS and publically available  (Pessoa et al. , 2015 (Pessoa et al. , , 2016)). All studies included in the present report (either experimentally determined herein or retrieved from the literature) used a similar methodology to amplify the HIV NFLG and sequence them in an Illumina MiSeq platform. Multiple infection analyses were also made by de novo assembly as described below.
Data Analysis The analysis of the obtained files was performed in Geneious v. 9. 1. 3 using the same alignment parameters for a referencebased genome assembly described by  Dudley et al. (2014). Reads were assembled using an annotated HIV-1 HXB2 reference sequence and 10 iterations to obtain each viral genome sequence. The presence of multiple infections was investigated by de novo assembly performed with IVA (Iterative Virus Assembler) with default parameters  (Hunt et al. , 2015). This assembler is based on seed sequences that are iteratively and conservatively extended into a contig using reads that have a perfect match. After this process, the program uses those contig as reference in a reference genome aligner to extend the initial contigs obtained  (Hunt et al. , 2015). The contig sequences obtained were submitted to a BLAST nucleotide analysis to discard those of human origin and confirmed with the Los Alamos National Laboratories HIV BLAST Tool 2.