dataesr/scientific-paragraphs-categorization huggingface dataset:
Methylation age and disease analysis For the Horvath Age predictor, methylation age was determined in all cohorts using the online calculator (https:// labs. genetics. ucla. edu/horvath/dnamage/). For the disease analysis, genes were linked to a disease based on the Clinical Genomic Database  [23]  (http://research. nhgri. nih. gov/CGD/download/, last accessed on Feb 27 2016). Agerelated gene sets were annotated by the Illumina HM450 manifest file (version 1. 2). For asthma analysis, we used the full MeDALL data at age 4/5 years and 8 years (asthma cases and population-based controls) to investigate the associations between CpG methylation and asthma status. The basic characteristics of participants included in the asthma study are listed in Additional file 1: Table  S10. The final robust regression model was as follows: methylation levels (corrected by 5PC of control probes) ~asthma + covariables + cell counts.
Acknowledgements We thank  Dr. Marjan Kerkhof  for scientific input and helpful discussions, and  Kate McIntyre  and  Jackie Senior  for editorial assistance. Funding We are grateful for financial support from  MeDALL. MeDALL  is a collaborative project supported by the  European Union  under the  Health Cooperation Work Program of the 7th Framework program  (grant agreement number  261357 ). BAMSE: BAMSE was supported by the  Swedish Foundation for Strategic Research (SSF ,  RBc08-0027 ,  EpiGene  project), the  Swedish Research Council , the  Swedish Heart-Lung Foundation ,  Stockholm County Council (ALF) , and the  SFO Epidemiology program  at the  Karolinska Institutet. In addition, we would like to thank  Eva Hallner ,  Sara Nilsson , and  André Lauber  at the  BAMSE  secretariat for invaluable support, as well as Mutation Analysis Facility (MAF) at  Karolinska Institutet  for genome-wide methylation analysis, and  Ingrid Delin  for excellent technical assistance.
PIAMA: This was funded by the  Netherlands Organization for Health Research and Development (ZonMw) ; the  Netherlands Organization for Scientific Research (NWO) ; the  Netherlands Asthma Fund ; the  Netherlands Ministry of Spatial Planning, Housing , and the Environment; and the  Netherlands Ministry of Health , Welfare, and Sport. Y. L. received financial support from  NWO  ( VENI grant   863. 13. 011 ).
Availability of data and materials HM450K data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001002169. Further information about EGA can be found on https://ega-archive. org and "The European Genome-phenome Archive of human data consented for biomedical research"(http://www. nature. com/ng/ journal/v47/n7/full/ng. 3312. html).
With these criteria, we found 925 and 764 genes that were downand up-regulated respectively upon NCL depletion (Supplementary Table  2. 1). The list of the 20 most down or up-regulated genes with p-values is shown in Fig. 2B. To validate this microarray analysis, we performed RT-qPCR on a selection of 11 genes. As expected, NCL is the strongest down regulated mRNA in the NCL-siRNA treated cells compared to controls both in microarray data and in the RT-qPCR assay (Fig. 2C ). The RT-qPCR assay reproduced the microarray data with accuracy for the other randomly chosen genes (Fig. 2C ).
miRNA expression analysis in HeLa cells upon NCL depletion. As previous studies have pointed out to a function of NCL in miRNA synthesis  23, 24  we analyzed the effects of NCL depletion in HeLa cells on the steady state level of microRNAs. Triplicates RNA samples extracted from control or NCL-depleted HeLa cells were analyzed on Human microarrays (MRA-1001 LC Sciences). By applying a filter for miRNAs having a fold change expression of ≥1. 5 and a detection P-value less than 0. 05 we found that 77 miRNAs were up regulated and 48 were down regulated (Fig. 7B  and Supplementary Table  4 ). Up-regulated Hsa-miR-1825 and down regulated hsa-miR-1323 -hsa-miR-302f were randomly selected for RT-qPCR validation of the micro arrays data (Fig. 7A ). Several studies have implicated NCL in miRNA synthesis in cancer cells.
For mathematical details of the KS statistics which is a distribution independent and non-parametric test, refer to Hollander, M. and Wolfe, DA  38. The various specific clusters of these enriched miRNAs along with their fold change associated with the differentially expressed genes is reported (Fig. 8 ). The 5 down and 30 up regulated miRNAs and the corresponding 52 down and 43 up (Supplementary Table  4 ) regulated target genes with the statistically significance FDR < 0. 25 and p-value < 0. 05 from GSEA  35  got enriched. Many of these enriched miRNAs and associated targets differentially expressed genes are also reported to be regulated by these miRNAs in the literature and in an experimentally validated database (Tarbase)  39. In our case, 42 out of 52 (80%) of down regulated and 40 out of 43 (93%) of up regulated genes are matching with these already reported literature and Tarbase data (Supplementary Tables  7.
Cell nuclei were counterstained with DAPI and cover slips were rinsed with distilled water and mounted using fluorescent mounting medium (Invitrogen). Cells were visualized under a confocal microscope (Zeiss LSM510). RNA extraction and Real Time-PCR. Total RNA was extracted with RNeasy kit (Qiagen, France) and treated with DNase I (Promega). After extraction, the integrity of total RNA was examined on a 1. 2% agarose gel containing 1 mg/ml ethidium bromide and quantified. For qPCR analysis and validation of the microarrays data and for the nucleolin depletion, 100 ng of total RNA were reverse-transcribed using random primers and 1 st Strand cDNA Synthesis Kit for RT-PCR (Roche Molecular Biochemicals). Real-time PCRs were performed with a Light Cycler 2. 0 instrument (Roche Molecular Diagnostics) in Light Cycler capillaries using a commercially available master mix containing Taq DNA polymerase and SYBR-Green I deoxyribonucleoside triphosphates.
Transcriptome Data Processing and Analysis. To generate the normalized expression data and to find the differentially regulated (up/down) genes, the transcriptome data from Affymetrix Human genome U133 plus 2. 0 microarray chip having three controls and three NCL depleted (siRNA) (hereafter called as treated samples) of the HeLa cells were used. The Affy package (library "affy" in R -https://www. r-project. org/) was used to read and analyze the raw (non-normalized) expression data. The Robust Multi-array Average (RMA)  47  algorithm, part of affy R package, is used to normalize the expression data of both the control and the treated samples. By RMA, the raw expression data (intensity values) is processed in three steps -background corrected, log2 transformed and then quantile normalized. In limma  42  , using the lmFit, eBayes and topTables functions, we computed the differentially expressed up (764) and down (925) regulated genes from the RMA normalized gene expression data with Fold change greater than 1.
5 and Benjamini-Hochberg (BH) controlled FDR (False Discovery Rate)  48  corrected P < = 0. 05 (Fig. 1A -Block (A) and Supplementary Table  1  and Supplementary Table  2. 1). The pathways and biological processes which might be affected by these differentially expressed genes are analyzed using GSEA tool  35  and the statistical enrichment analysis based on weighted Kolmogorov-Smirnov (KS) test in that tool. The KS-test is used to determine whether two data sets (one data set "Pathway/GOBP -genes"-Supplementary Tables 2. 2/2. 3 and the other differentially expressed mRNAs (up/down)-Supplementary Table  2. 1 in our case) differ significantly in their distribution and accordingly get enriched using the pre-ranking of the differentially expressed mRNA by their fold change. The customized pathway/GOBP database used in the enrichment in GSEA (Supplementary Tables 2.
2 and 2. 3) was built by taking the data from various human pathway resources such as: Reactome, Kegg, Panther, WikiPathways, HumanCys, NCI Nature, Pathway Commons, and the GO BP terms from the Gene ontology consortium (http://www. geneontology. org) (Supplementary Table  5 ). The first top 20 enriched pathways and GOBP terms with statistically significant lowest p-value are shown in Fig. 2B  and the heatmap in Supplementary Figs  1A  and 1B ). miRNA data Processing and mRNA-miRNA integrated analysis. Using t-test (Fig. 1A , Block (B)) the differentially expressed miRNA with FC≥ +/-1. 5 (Up/down regulated) and with p-value ≤ 0. 05 were selected. We got 77 up regulated and 48 down regulated miRNAs (Fig. 7B  and full list in Supplementary Table  4 ). For these deregulated miRNA in NCL depleted cells, in order to identify potential mRNA targets, we first collected the different mRNA targets for each miRNA by a search in several databases as shown in Fig.
1A  Block (C2). To avoid the false positives and having more reliable association of miRNA-mRNA genes, for each miRNA, the target genes considered were either experimentally validated or were present in at least 5 different databases (called as Hit score-5). Several hundred putative targets were identified this way (Supplementary Table  6 , "miRNA -target genes" database). The enrichment analysis and the association of the differentially expressed genes as statistically significant targets of the differentially expressed miRNAs of the Fig. 7B /Supplementary Table  4  is conducted with the Gene Set Enrichment Analysis (GSEA) software-http://software. broadinstitute. org/gsea/index. jsp (Fig. 1  Block C1). As mentioned earlier in transcriptome Data analysis section, GSEA, a computational framework, basically uses weighted Kolmogorov-Smirnov statistics to detect the statistically significant and concordant differences in a priori defined gene sets such as list of pathways or the list of miRNAs (in our case) between two biological states (Phenotypes like up and down regulated genes or miRNAs using pre-ranked analysis).
To extend our efforts to understand and elucidate the complex regulation of the miRNA-mRNA and the functional association with the pathways in which these miRNAs may be involved, we constructed the miRNA-mRNA network using the fold changes as edge weights of the network and Cytoscape as visualizer  49. The enriched miRNA-mRNA as mentioned in Supplementary Fig. 2  and the pathways/GOBP in Supplementary Tables 2. 2 and 2. 3 were used to construct the network. The data of Supplementary Fig. 2  were parsed appropriately using various in-house written code in Perl/C/HTML to construct and visualize the networks. The flow of the functional annotation of these enriched miRNAs i. e. the association with related pathways/GOBP is shown in the Fig. 1B. Out of all differentially expressed mRNAs, first the genes which are in the enriched pathways Supplementary Tables 2.
Acknowledgements We thank  Nathalie Bernoud-Hubac  and the Lyon lipidomic plateform for help in the lipidomic analysis. This project was funded thanks to the  Ligue contre le Cancer (Allier and Saone et Loire, France) , the  CNRS and Ecole Normale Supérieure de Lyon  and the  Agence Nationale de la Recherche , project  ANR   Theranuc ,  ANR-16-CE17-0023  and  Hubert Curien  program  RILA 2017 , project  N°   38647PM. At BioCOS Life Sciences , the work was funded internally.
, 2016b). The time series used in the present work start in January 2005 for the Gironde, and in January 2007 for the Loire. They include data until July 2014 in both systems. The rates of correct operating during these periods were: 71% (Bordeaux), 70% (Portets), 70% (Libourne) and 57% (Pauillac) for the Gironde; 94. 5% (Donges), 80% (Paimboeuf), 80% (Cordomais), 80. 5% (Le Pellerin), 84% (Trentemoult) and 90% (Bellevue) for the Loire. Daily river flows of the Loire, Garonne and Dordogne Rivers are provided by the French national discharge database (www. hydro. eaufrance. fr).
Data treatment: spectral analysis Turbidity time series were analyzed using the Singular Spectrum Analysis (SSA) combined with the Lomb-Scargle periodogram (LSP) for the multiannual analysis, and by the Continuous Wavelet Transform (CWT) for the seasonal analysis. This strategy is based on a recent work (Jal on-Rojas et al. , 2016a) that tested and compared the performance of the different spectral techniques mostly used in coastal research for the analysis of high-frequency, long-term time series of physico-chemical parameters. Using turbidity time series as an example, this work evaluated the limits of each method for different rates and two distributions of missing data, and their capacity to quantify the contribution of environmental forcings to the variability of the studied parameter, among others.
Lomb-Scargle periodogram (LSP) The LSP  (Lomb, 1976; Scargle, 1982)  decomposes time series into periodicities and identifies the most energetic ones. Based on harmonic regression, it consists on a least-squares power spectrum that is applicable to irregularly sampled data. LSP was widely promoted by  Press et al. (1992) , who developed an optimized algorithm. We use the Press's algorithm implemented in MATLAB by Brett Shoelson, which provides significance levels to select the main variability time scales. The LSP method is able to detect periodical forcings, even with 70% missing data, but is less suitable for evaluating non-periodic factors and long-term trends (Jal on-Rojas et al. , 2016a). In addition, it does not allow time-varying analysis. LSP appears then less efficient compared to SSA, but can give complementary information.
The continuous wavelet transform (CWT) Wavelet spectra are time-varying periodograms that provide both the dominant variability time scales and their energetic variability over time. In this work we apply the continuous wavelet transform (CWT) with the Morlet function, which is appropriate for coastal time series  (Daubechies, 1992; Farge, 1992). The CWT is used to decompose the signal into a weighted sum of small waves that are obtained by translation and dilation of a reference wavelet (the Morlet function in this case). A complete guide on Morlet wavelets and their applications is provided by  Torrence and Compo (1998). The main limitation of CWT is the need of regularly sampled data. Therefore it can only be applied to short continuous datasets from the original discontinuous time series.
Seasonal changes in forcings influence RCs time series highlight that the energy of signal oscillation varies over time (e. g. Fig. 4 ). For example at Le Pellerin, the modes associated to tidal cycles show seasonal variation of energy. This implies that longer timescales can also force variability on the tidal time scale and therefore the relative influence of forcings can vary over time. To investigate more in detail such seasonal changes, we applied the Continuous Wavelet Transform (CWT) combined with SSA to raw datasets of almost continuous turbidity data containing  a period of low discharge (period LD ) followed by a period of high discharge (period HD ). Figs. 8 and 9 illustrate this analysis for datasets of almost 3 months at the lower and upper estuarine regions of the Gironde and the Loire respectively.
Acknowledgements I. Jal on-Rojas thanks the  Agence de l'Eau Adour-Garonne (AEAG)  and the  Aquitaine Region  for the financial support of her PhD grant. Authors gratefully acknowledge the  MAGEST Consortium  and the  Groupement d'int erêt publique (GIP) Loire Estuary  for providing turbidity time series. We address our sincere thanks to the three reviewers and to the guest editor who helped us to improve this article.
Different molecular typing methods have been used to compare Staphylococcus aureus strains in outbreak investigations, including MLVA, spa-typing and pulsed-field gel electrophoresis (PFGE)  [21]. Until now, PFGE remains the Gold Standard for typing of CPS although new molecular techniques in outbreak investigation and strain typing such as next generation sequencing (NGS) become more popular and seem promising for future surveillance  [22] [23] [24] [25]. Currently, PFGE is less expensive as compared to NGS and does not require exhaustive bio-informaticians or software. However, NGS and newly developed software tools to analyze whole genome sequence data might allow rapid detection of toxin genes and determination of MLST-, Spa-, MLVA-and maybe even pulsotypes. [14] [15] [16] [17] [18]. Over these 5 years, Staphylococcal enterotoxins (SEs) were involved in half of the reported bacterial toxins outbreaks, whilst the remaining involved the emetic toxin of Bacillus cereus (B.
cereus) or enterotoxins produced by Clostridium perfringens or B. cereus. In 2015, 16 EU Member States reported 434 SFP, representing 51% of food-borne outbreaks caused by bacterial toxins and 9. 9% of all outbreaks  [18]. Here we report on three food poisoning outbreaks due to staphylococcal enterotoxins which occurred in 2013 in Belgium. Staphylococcal enterotoxins were characterized and quantified in the food leftovers in order to determine the toxin dose causing disease. The obtained quantitative data could help risk managers to set food safety criteria for SEs in food products different from those currently available in the legislation, and thus prevent SFP. Molecular typing of human and food isolates was used to assess the link between patients and the consumption of a suspected foodstuff and to investigate food handler involvement.
The radio signals produced by extensive air showers initiated in the atmosphere by high energy cosmic rays are routinely observed and registered by the various instruments of the CODALEMA experiment located at the Nançay radio observatory and notably by the large array of self-triggering stations equipped with wide band and dual polarization antennas. Precise comparisons between observed radio signals and simulations performed with the SELFAS code allow most of the main features of the primary cosmic ray to be determined : arrival direction, energy and Xmax estimates from which the composition in the energy range covered by CODALEMA may be derived. After a presentation of the analysis methods, its sensitivity will be discussed and the results obtained over a significant set of experimental events will be detailed.
It requires that 5 scintillators out of 13 see a signal above 15 mV corresponding to a detection energy threshold of the order of 10 15 eV (considering the array extension). This trigger is also used to record signals from dedicated cabled antenna arrays and to provide software triggers sent over the network to distant antennas  [4]. CODALEMA is taking data in this mode steadily since several months. On a typical period of a month, the scintillator array is producing of the order of 40 000 events while the central daq system retains of the order of 1 200 000 events from the SA array. This figure reduces to 60 events per month when requiring for a time coincidence between both arrays.
Event reconstruction in the autonomous station array Event reconstruction using the data from the autonomous stations is rather straightforward. Once recorded on disk the data are transferred into a Firebird database for an easy data mining and data selection. As a matter of fact, retrieving coincidences consists simply in an SQL request requiring to group database entries by identical event time. Once aggregated, reconstructed data from the different stations are combined and treated. Similarly to the scintillator data, the arrival direction of the coincidences is deduced using a plane front approximation and using only the timing information (the time jitter induced by the station self triggering system is negligible). Analysis of the transients (Figure  3 ) gives further information on the air shower features and its unambiguous identification.
Conclusions This preliminary analysis on a limited data set shows quite good agreements between the autonomous station signals compared to SELFAS simulations. First comparisons with the scintillator array are encouraging but still needs detailed work to improve the reconstruction in the particle detectors. These results suggest that the radio detection technique has the intrinsic capabilities to infer the main properties of high energy cosmic rays. This ongoing work clearly needs improvement on several aspects : a larger set of data can be quickly processed, careful analysis of the various source of error must be done in order to determine the resolution for the core location, Xmax and energy values. Probabilities must be computed for a particle identification estimate to study its variations over the energy range covered by CODALEMA.
Target events are coronary disease, stroke, congestive heart failure, major vascular amputations, and deaths. The definitions of the suspect, likely, and certain cases of each target CVD have been specified. Demographic and lifestyle data, histories of CVD, of hypertension, and of diabetes are collected at baseline. Anthropometrics parameters, ankle brachial index (ABI), and capillary blood glucose are measured. The electrocardiogram recording is planned. The same evaluations are done at two follow-up visits. Events surveillance outside visits is active and continuous through a network of health centres and community sources. The activities are supervised monthly by a physician member of the investigation committee. The scheme of the surveillance is presented in Figure  1. Data are entered in an online secured database by a research assistant using a digital tablet.
Baseline data collection For the present study, data were collected either in households or in the public health centre of the study area in February 2015. Written informed consent was obtained from each participant. A standardized questionnaire adapted from the World Health Organization (WHO) STEPS instrument [29] was used. It included demographic, socioeconomic, and behavioural data, non-communicable disease histories, and screening questionnaires for angina pectoris  [30] , stroke  [31] , claudication  [32] , anxiety and depression (during the previous 12 months)  [33] , and congestive heart failure symptoms  [34]. A French version was provided and translated into 'Fon', the main local language, by two professional translators. At 1 STEP, individual interviews were conducted either in French or in the local language by trained interviewers.
Definitions Stroke was defined according to WHO criteria  [36]. Congestive heart failure was defined by the presence of typical symptoms confirmed by a physician. CHD was defined by a positive WHO Rose questionnaire or a history of myocardial infarction based on medical data (the presence of typical Q waves on an electrocardiogram or acute coronary syndrome ST+ or ST-with a specific enzymatic rise). Lower limb amputation of vascular origin was defined by nontraumatic amputation of leg or thigh related to PAD based on medical data; PAD was defined by an ankle brachial index ≤ 0. 9 or ≥ 1. 4 or vascular stenosis at echography. Presence of claudication was suspected by a positive Edinburgh questionnaire. CVD deaths were defined by deaths arising within 28 days after a first appearance, a recurrence, or a worsening of CVD, or sudden deaths or deaths of unknown origin without other evident cause than cardiovascular; in the last case, traumatic, infectious, or tumoral causes must be excluded.
Data analysis The analysis for this paper were limited to validated data by the time the paper was written. It concerns baseline data and the first 8 months of follow-up. We used Epi Info 7 to analyze data for this report. Continuous variables were described using the median and the interquartile range. Qualitative variables were described using frequencies and percentages. Chi-square tests or Fisher's exact test were used to compare proportions between groups. The crude death rate was calculated.