dataesr/scientific-paragraphs-categorization huggingface dataset:
Control of selection effects To take into account selection effects due the voluntary participation, we draw a 'control cohort' from a random sample of 400 000 non-participants, that is, persons who were invited but did not participate, for whom we prospectively collect social and occupational, health and healthcare usage data from the same administrative databases as for the participants. Auxiliary data are known for the whole sample, and data from the other sources are only known for respondents. Statistical analysis was conducted in two steps: (1) determination of the variables to be used to model the probability of response to the survey from the auxiliary data using logistic regression with the respondent as the dependent variable; (2) estimations of the prevalence of variables of interest after correction for the non-response by a reweighting technique where the final weight is equal to the product of the inverse probability of participation (original sampling weight) and a correction factor for non-participation.
HOW HAVE PEOPLE BEEN FOLLOWED? After enrolment, participants are followed-up by an annual selfadministered questionnaire sent to their homes ( paper or webbased) and they are invited for a new health examination every 5 years. The annual participation rate to the follow-up questionnaire of participants who were included from 2012 to 2014 was higher than 80%. They are also followed up by annual linkage with national social and health administrative databases that permanently record data: the retirement pension database collects social and occupational data from employers' annual reports and from social welfare organisations; 32 the 'Système national d'information interrégimes de l'Assurance Maladie' (SNIIRAM), contains medical data about reimbursements, serious chronic diseases and hospital discharge records;  33  the National Death Registry-CepiDc records the cause of death.
WHAT HAS BEEN MEASURED? The main data regularly collected include social and demographic characteristics, health-related behaviour, disease history, self-reported health scales, incident and prevalent diseases, sick leaves, impairments, limitations, disabilities, injuries and healthcare usage and cause of death. In the HSC examination weight, height, waist-hip ratio, blood pressure, electrocardiogram, vision, hearing, lung function and laboratory tests are measured. For those aged 45 years and older cognitive and physical functioning tests are conducted by neuropsychologists. The collection of biological samples (blood and urine) from half of the cohort will start in 2017. We placed particular focus on a multifaceted view on working conditions and occupational exposure, as well as environmental factors, using different sources.
From the questionnaires we collect at inclusion and during follow-up data on current and lifelong occupational exposure to chemical, physical and biological agents, postural, mechanical and organisational constraints and stress at work (http://www. constances. fr/ espace-scientifique/questionnaires. php). Table  2  lists the main data on occupational exposures collected at inclusion, and table 3 presents the number of participants exposed or having been exposed in their professional career among the participants already included for which data are currently available. Full job histories are collected at inclusion from a specific questionnaire: for each work episode of 6 months or more, participants are asked to detail starting and end dates, occupation, type of contract. These data are needed for the coding of jobs according to the international ISCO 1968, 36 and ISIC rev.
40 Coding of occupational histories began in early 2016, and it is expected that linkage with the Matgene JEMs will be performed by the end of 2016 and later with CANJEM. From the retirement pension database full job histories since the first job are also collected at enrolment and are prospectively updated each year during the follow-up. The following data are extracted for each job episode: employer, work periods, interruptions (for disease or accident, disability, maternity, military service, unemployment, retirement), salary, type of contract. Regarding environmental factors, residential addresses are collected and x/y geocoded. This is carried out prospectively from the enrolment of the participants; we are also currently collecting and geocoding all the addresses of the 10 years before enrolment.
DATA ACCESS Every group in France or in other countries is entitled to apply to develop a nested project within CONSTANCES and to access to its database. Projects are evaluated twice a year by the CONSTANCES Scientific Committee on feasibility and scientific quality criteria. A Charter describes the rules that have been established for using the CONSTANCES infrastructure. The material needed for applying can be downloaded from the CONSTANCES website: http://www. constances. fr/index_EN. php#propose. A call for proposals is open since 2014. More than 60 projects covering a wide range of topics were already proposed and approved by the Scientific Committee; the full list is available at: http://www. constances. fr/espace-scientifique/projets-valides. php.
Numerous data are prospectively and repeatedly collected. CONSTANCES has also some limitations. First it covers only salaried workers excluding agricultural and self-employed workers; however a collaboration was established with the COSET project managed by the Occupational Health Department of the National Institute for Health Surveillance, which is currently setting up two complementary cohorts, one of agricultural workers and one of self-employed persons which were designed in a way that data sharing with CONSTANCES will be possible (http://www. invs. sante. fr/Dossiers-thematiques/ Travail-et-sante/Coset-Cohortes-pour-la-surveillance-epidemiologique-en-lien-avec-le-travail). Another limitation is the voluntary participation of the cohort members and the low participation rate (7. 3%), the same order of magnitude as other similar cohorts, like UK Biobank in which the participation rate is 5.
47% (http://www. jpidataproject. eu/Home/Database/56?topicId=1). However, the use of reweighting techniques relying on the 'non-participants cohort' allows for correcting for selection effects. Contributors MG and MZ planned the study. MC, AD, AL and YR planned the collection of the occupational factors. GS designed the reweighting methods. MG drafted the manuscript, and MG and MZ are responsible for the overall content of the manuscript. Funding The CONSTANCES cohort is supported by the Caisse Nationale d'Assurance Maladie des travailleurs salariés-CNAMTS, and was funded in its pilot phase by the Direction générale de la santé" of the Ministry of Health (CPO 2007-2009), and by the Institut de Recherche en Santé Publique-Institut Thématique Santé Publique, and the following sponsors : Ministère de la santé et des sports, Ministère délégué à la recherche, Institut national de la santé et de la recherche médicale, Institut national du cancer et Caisse nationale de solidarité pour l'autonomie (AMC10003LSA).
CONSTANCES is accredited as a 'National Infrastructure for Biology and health' by the governmental Investissements d'avenir programme and was funded by the Agence nationale de la recherche (ANR-11-INBS-0002 grant). CONSTANCES also receives funding from MSD, AstraZeneca and Lundbeck managed by INSERM-Transfert. CONSTANCES is conducted in partnership with the National Health Insurance Fund administered by CNAMTS, and with the National Retirement Insurance Fund administered by the Caisse nationale d'assurance vieillesse-CNAV. Quality control procedures are taken in charge by ClinSearch for the data collected in the HSCs, and by Asqualab and EuroCell for the biological data. The authors also gratefully acknowledge the major contribution to the protocol of numerous colleagues, in France and abroad, who helped in the general design of the cohort, and of the participating HSCs.
The authors express also their thanks to Dominique Polton from the CNAMTS for her help, and to Christophe Albert and Joël Brulard for the drawing of eligible persons and the access to the CNAV database. ( presentation of CONSTANCES, type of data to be collected, ability to refuse to participate, informed consent, etc). Concrete procedures for setting up the two cohorts ( participants and non-participants) ensure the confidentiality of the data at every point in its circulation as well as the anonymity of the cohort of nonparticipants. In addition, CONSTANCES was approved by the National Council for Statistical Information (Conseil national de l'information statistique-CNIS), the National Medical Council (Conseil national de l'Ordre des médecins-CNOM), the Institutional Review Board of the National Institute for Medical Research-INSERM and our local Committee for Persons Protection (Comité de protection des personnes).
Methods Study population. The data set was drawn from cancer GWAS of solid tumours performed at the National Cancer Institute Division of Cancer Epidemiology and Genetics and the Cancer Genome Research Laboratory. In total, peripheral leukocyte or buccal epithelial DNA was available for 20,878 solid tumour cancer cases and 17,425 cancer-free controls. DNA was genotyped on one or more commercially available Illumina Infinium Human SNP array (Hap300, Hap240, Hap550, Hap610, Hap660, Hap 1, Omni Express, Omni 1, Omni 2. 5 and Omni 5). Quality control procedures were applied after genotyping and samples were clustered in batches to optimize accuracy and minimize batch effects. All GWAS studies were reviewed by the Institutional Review Board of the National Cancer Institute and those of the participating study centers.
The LightCycler software (Release 1. 5. 0) was used for initial analysis of the raw data, utilizing the absolute quantification analysis with the second derivative maximum method and high-confidence detection algorithm, to yield a crossing threshold (Ct) for all replicates. The Ct for each assay was used to interpolate concentration of target and reference sequences using the standard curves. The ratio of target to reference was multiplied by 2 to determine the diploid amount of X chromosome in that region. The ratios of the 12 assays were then averaged to yield an overall X-chromosome signal ratio. Seventy-five normal copy-number controls were used to estimate normal probe ratio means and s. d. A value of 3 s. d. above the normal mean ratio was used as the threshold to call gains and a value of 3 s.
Data availability. Original study data has been posted in dbGaP (http://www. ncbi. nlm. nih. gov/gap) under accession numbers phs000093. v2. p2, phs000336. v1. p1, phs000351. v1. p1, phs000361. v1. p1, phs000652. v1. p1, phs000716. v1. p1, phs000734. v1. p1, phs000396. v1. p1, phs000147. v2. p1, phs000346. v2. p1, phs000863. v1. p1 and phs000206. v5. p3. Data on called event features, location and individual characteristics are available in Supplementary Table  1. Methylation array beta values for events are presented in Supplementary Table  6  and raw data is posted in dbGaP under accession number phs001112. v1. p1. The methylation data has been deposited in dbGaP under accession code phs001112. v1. p1.
Data Analysis-Fasta files for the E. coli strain K12 (reference proteome, version from May 2016, 4306 entries) and S. islandicus LAL14/1 (December 2014, 2591 entry) were downloaded from Uniprot, common contaminants database was furnished with MaxQuant. Andromeda search engine in MaxQuant 1. 5. 2. 8  (28)  was used for protein identification and label-free relative quantitation. Briefly, Acetyl (protein N terminus) and Oxidation (M) and Methylation (K) were used as variable modifications, trypsin/P was used as an enzyme with maximum of 2 missed cleavages allowed, precursor and fragment tolerances were 5 ppm and 18 ppm, respectively, peptide and protein identification FDRs were set to 1%, for detailed settings see file ESI 1. For the stagewise data sets with HCD and ETD data, the FDRs were calculated jointly for HCD and ETD files of a biological replicate.
For gel band identifications, Acetyl (protein N terminus), Acetyl (K), Oxidation (M) and Methylation (K) were set as variable modifications. Methylation sites obtained with Andromeda database search were filtered using home-made scripts. A K-methylation site was considered confident if it had a continuous fragment ion series localizing the site, i. e. having the fragment ion series continuing on both sides of the lysine (or at one, if it is a C-terminal lysine). Homemade scripts were written on Python 3. 3. 2 using Pyteomics package  (29). Unless stated otherwise, we use the term methylation interchangeably with the term monomethylation throughout this article. For label-free quantitation, the averaging of technical replicates, intensity normalization and retention time alignment (2 min window) were performed automatically in MaxQuant 1.
Intensities were normalized on the median in each sample, then log2-transformed. The missing values were imputed from a normal distribution (shift 1. 8, width 0. 3). Two-sided Student t-tests were performed on different groups, permutationbased FDR was calculated (with S0 parameter of 2). ProSight PC 3 SP 1 (Thermo Scientific) was used for top-down database search. For detailed search parameters see in ESI 1. Briefly, raw files were deconvoluted using Xtract and subjected to a fourstage database search consisted of "Absolute Mass" search with 4. 08 Da precursor tolerance, then 3 kDa precursor tolerance, then "Biomarker" search with 10 ppm precursor tolerance and "Absolute Mass" search with 250 kDa precursor tolerance, fragment ion tolerance was set to 3 or 4 ppm. S. islandicus LAL14/1 TrEMBL database of 2,591 entries was downloaded in Uniprot flatfile format and imported with full PTM annotation, N-terminal acetylation and methionine excision, yielding a database of 10,442 entries.
According to the article from Catherman et al. (31)  FDR cutoff of 0. 01 corresponded to a ProSightPC p-score cutoff of 4. 7 ϫ 10 Ϫ9 , which gives an E-value cutoff of 5 ϫ 10 Ϫ5 on the database of 10,442 entries. ProSightPC E-value cutoff of 5 ϫ 10 Ϫ5 was thus used to filter the confident identification results. Annotated top-down spectra from ProSightPC search output can be viewed in MS-Viewer  (32)  on the Protein Prospector public website (prospector. ucsf. edu): search key dk458i4qtb for SAX ETD data, rvs63gz69o for SAX HCD data, vmhsk5c8pb for GELFrEE ETD data and wgvkcvlyeg for GELFrEE HCD data. To quantify the relative abundancies of Alba1 and Cren7 proteoforms, for each biological replicate in two technical repeats, extracted ion chromatograms (XIC) were calculated in Xcalibur 2. 2 (Thermo Scientific) with 10 ppm accuracy for 4 -5 charge states per proteoform.
Label-Free Relative Quantitation of Protein Expression Between Different Growth Stages by Bottom-Up Proteomics- Cultures of S. islandicus cells were harvested at three different time points corresponding to the early-exponential (throughout the text referred to as "Early"), mid-exponential ("Mid"), and stationary ("Late") growth phases (supplemental Fig. S1 ) in three biological replicates. Cytosolic fraction of each cell lysate was digested in solution and analyzed using a 2 h chromatography gradient and an Orbitrap Fusion in data-dependent mode with HCD fragmentation. This single-shot approach allowed us to identify 1529 proteins at FDR 0. 01 (out of 2591 sequences in the S. islandicus LAL14/1 protein database) and to quantify 1335 proteins with nonzero intensities for all three biological replicates at least at one time point.
To assess the suitability of the data for the label-free quantitation, coefficients of variability (CVs) were calculated for technical replicates on the nonlogarithmized, nonnormalized peptide intensities. The determined median (11-20%) and mean (16 -23%) CV levels for different biological samples were consid-ered acceptable for label-free quantification. The details of the calculation are provided in ESI 2. Fig. 1  represents the volcano plots corresponding to the relative changes in protein abundances between Early and Mid (Fig. 1A ), Mid and Late (Fig. 1B ) as well as Early and Late (Fig. 1C ) time points. An FDR of 0. 01 and S 0 parameter of 1 were chosen as the threshold values to filter statistically significant changes in Perseus. Spreadsheet containing the intensity data for all the proteins, as well as lists of differentially expressed proteins, can be found in the ESI 3 file.
1%) were identified in 2 fractions. However, SCX prefractionation increased the number of identified proteins only to 1,618 (FDR 0. 01), i. e. by 5% compared with singleshot LC-MS. Additionally, we reused the files from the LFQ data set (HCD fragmentation) and acquired a supplementary shotgun run with ETD fragmentation for each biological replicate on the unfractionated samples of each growth stage. The resulting HCDϩETD shotgun data sets for each biological replicate were then subjected to database search with PTMs of interest set as variable modifications. Global identification of protein lysine N 6 -acetylation is notoriously difficult because of its dynamic property and rather low abundance  (38 -40). Indeed, searches for lysine side chain acetylation yielded no occurrences in our data set, suggesting that special enrichment steps might be required for detection of this modification.
In 32 other cases the number of methylation sites increased at the Late stage without a noticeable increase of the LFQ abundance of the corresponding protein. Peptide intensity values from the reprocessed LFQ data set were normalized according to the median value of the sample and t-tests were performed to compare the peptide abundances across growth stages. Because of variability in peptide intensity and high number of peptides, which inevitably result in the elevated multiple testing error probability, only 4 K-monomethylated peptides passed the FDR threshold of 0. 01. After relaxing the FDR threshold to 0. 05 (S 0 ϭ 2), 625 peptides out of 18,369 were found to significantly change in abundance from Early to Late stage (see Fig. 2E  and the ESI file 7). Among them, 15 K-methylated peptides significantly decrease in their abundance, including 4 that do not show the corresponding change at the protein level.
Top-down proteomics has already proven to be a useful tool to study archaeal proteins  (52, 53). However, thus far, it has not been applied to Sulfolobus species. Strong anion exchange (SAX) chromatography has been reported by  Bunger et al. (54)  to be an efficient prefractionation method that can be coupled with subsequent RPLC-MS top-down analysis. Using an LTQ XL ion trap mass spectrometer, Bunger et al. (54)  identified 322 proteoforms of 174 proteins of E. coli. We therefore performed a SAX separation of 1 mg of one of the S. islandicus cytosolic preparations (Late stage replicate 1) into 11 fractions, including flow-through and 10 eluted fractions. All fractions were further analyzed on the Orbitrap Tribrid Fusion, using data-dependent methods with HCD or ETD fragmentation. ProSightPC was used for the database search, allowing the characterization of intact unmodified proteoforms, modified proteoforms and proteolytic fragments.
g. different proteolytic fragments, and in other cases-by insufficient separation. The 681 detected unique proteins correspond to more than 40% of the proteins identified in the same S. islandicus sample by bottom-up approach and to 26% of the total number of encoded proteins. For comparison, 563 unique IDs (12% of the encoded proteins) and Ͼ1,600 proteoforms have been identified in the recent study by  Ansong et al. (20)  for another prokaryotic organism, enterobacterium Salmonella typhimurium. However, in that study the authors utilized 5% FDR cutoff and a different software (MS-Alignϩ) for database search, whereas the present study assumes an FDR cut-off of 1%. Abundant cytosolic proteins are represented by a large number of different proteoforms. For instance, there are 270 forms for peroxiredoxin (M9UAA7, see Fig.
Indeed, the latter protein is conserved in organisms of the phylum Crenarcheota and has been shown to have a very relaxed specificity  (62). According to our LFQ data set, this methyltransferase (M9U9Y9) is abundant in S. islandicus and its abundance is stable throughout the growth phases. Notably both top-down and bottom-up analyses showed that the aKMT4 methyltransferase is itself methylated (K-15, K-25, and K-74), consistent with the previous report  (51). Although the bulk methylation is likely to represent a specific adaptation of hyperthermophiles to high temperatures, some of the methylations might also play other, sometimes regulatory, roles, like in the case of eukaryotic histones. By analogy, this might be the case for chromatin proteins. Highthroughput top-down analysis of S. islandicus cytosolic proteins enabled the comprehensive characterization of 85 methylated forms of 54 proteins of up to 19.
Bottom-up and top-down proteomics approaches are therefore highly complementary, and as shown in this study, can be performed on the same mass spectrometer, in a routine fashion: all the consumables used here are commercially available and all the methods can be directly transferred to another instrument without much effort. Associated Content-Supplementary file ESI 1: supplementary experimental procedures, supplementary figures and tables. File ESI 2: technical variability between the technical replicates in the bottom-up LFQ data set. File ESI 3: output tables for the bottom-up LFQ (protein level data). File ESI 4: N-terminal acetylation sites. File ESI 5: lysine monomethylation sites identified from the bottom-up data for S. islandicus. File ESI 6: lysine monomethylation sites identified from the bottom-up data for E.
coli. File ESI 7: peptide-level abundance changes between the Early and Late growth stages, including the K-Me peptides. File ESI 8: peptide-level abundance changes between the Early and Late growth stages for the 5 chromatin proteins. File ESI 9: the proteoforms identified in the SAX-fractionated sample using top-down approach. File ESI 10: summary of the Alba1 and Cren7 proteoforms relative quantitation by top-down. The raw data files have been deposited to the ProteomeXchange with identifiers PXD003074 and PXD004179. Annotated top-down spectra from ProSightPC search output can be viewed in MS-Viewer  (32)  on the Protein Prospector public website (prospector. ucsf. edu): search key dk458i4qtb for SAX ETD data, rvs63gz69o for SAX HCD data, vmhsk5c8pb for GELFrEE ETD data and wgvkcvlyeg for GELFrEE HCD data.
In contrast, the chimeric NS5 proteins that contained residues 406-900 of DENV1 NS5, namely D2 1-273 D1 274-900 , D2 1-319 D1 320-900 and D2 1-405 D1 406-900 (corresponding to construct 6, 7 and 8, respectively, in Fig 2A ) were predominantly localized in the cytoplasm  (Fig 2B). Taken together, these results suggest that the region responsible for the differential subcellular localization of DENV1 and 2 NS5 is located within the C-terminal end of RdRp domain, from residues 406-900. In order to further delineate the sequence responsible for differential subcellular localization within residues 406-900, we focused on exchanging gene segments that satisfy both requirements (i) presence of sequence variation and (ii) absence of structural motif (PDB: 4V0Q  [34]  and 4K6M  [35]  4V0Q,  [34]  was used as the input file for secondary structure depiction.