dataesr/scientific-paragraphs-categorization huggingface dataset:
During AquaVIT-1 the effect of stray light backscattering was observed for both FLASH-B1 and FLASH-B2 instruments. Attempts to estimate the contribution of stray light to the FLASH-B signal were performed for every static experiment by assuming a constant water vapor partial pressure. However, since the partial pressure was actually varying within every static experiment, the estimates of stray light contribution (SLC) turned out to be incorrect. It was found that the SLC varied slightly from one experiment to the other, introducing an additive of 7 ± 2. 5 ppm to the value of water vapor mixing ratios from the fluorescence signal. Errors in estimation of SLC introduced a systematic offset into the FLASH-B1 water vapor data as described below. For experiment 3 (15 October) SLC was overestimated by 20 %, resulting in a constant negative offset of about 1.
4 ppm with respect to APicT data for pressures above 100 hPa. At the lowest mixing ratio of 1. 7 ppm observed during experiment 3 (based on APicT data), this offset accounts for an 82 % deviation from APicT. For experiment 7 (18 October) the 4 % error in estimation of SLC resulted in a constant negative offset of about 0. 35 ppm and consequently a 54 % deviation from APicT at 0. 65 ppm. The stray light effect had a much larger contribution to the FLASH-B2 measurements in a small chamber. The average value of SLC amounted to 230 ppm, and, due to some noisiness in the data, mixing ratios below 2 ppm (i. e. , less than 1 % of the SLC value) could not be properly detected by the FLASH-B2 instrument. Accordingly, the FLASH-B2 data from the experiments with low water vapor mixing ratios were not archived.
The data of both FLASH-B1 and FLASH-B2 were not used for the formal intercomparison due to high relative error introduced by incorrect quantification of the stray light contribution. Figure  A4  shows the summary plot of the static experiment results for FLASH-B1 data in the entire range of water vapor mixing ratios with the data from experiments 3 and 7 recalculated using correct SLC values. The SLC values have been determined by fitting FLASH-B1 data at 200 hPa pressure to APicT data. While such a method does not comply with the rules of the AquaVIT-1 blind intercomparison, measuring a known amount of water vapor is the only way to determine SLC in a closed chamber. The results shown in Fig. A4  were obtained by comparing FLASH-B1 segment averages to the reference water vapor mixing ratios.
Using these test results, an empirical model was constructed to determine the appropriate pressure and flow-dependent correction factor for the data. The large uncertainties associated with the correction, however, increased the uncertainty of the final measurement. At the highest pressures and faster flows, the combined contribution from leaks and outgassing was minimized, amounting to less than ∼ 0. 05 ppmv. However, at lower pressures and/or slower flows these contributions exceeded ∼ 1. 0 ppmv and became a significant fraction of the water vapor signal measured by HWV. A full analysis of the test results, the development of the model, and an analysis of the uncertainties associated with the correction factor is explained in detail in  Smith (2012). A brief description is given in the headers for the AquaVIT-1 data files.
There is an additional 8. 6 cm path through the air between the Herriott cell and the window to the evacuated laser/detector housing, yielding a total optical path of 1047. 9 cm. The virtual sampling volume is a 1. 6 L hyperboloid between the two mirrors (plus the small path to the window). JLH operates in two modes: harmonic wavelength modulation spectroscopy  (May, 1998; May and Webster, 1993)  and direct absorption spectroscopy. In harmonic spectroscopy, the laser is scanned at an 8 Hz repetition rate across the strong water absorption line at 1370. 0 nm (7299. 43 cm -1 ), and modulated at f = 64 kHz for sensitive harmonic detection at 2f. All 10 scans are averaged to report data at 0. 8 Hz (1. 3 s). The modulated 2f peak-to-peak amplitude ("pp2f"), normalized by returned laser power, is used to calculate water mixing ratios as described in  May (1998).
The advantages of harmonic spectroscopy are fast response and high precision. Every 24 s, JLH switches to its second operational mode, namely direct absorption spectroscopy. The advantages of direct absorption measurements are high accuracy, detection of background water, signal specific to water, and more highly constrained results (i. e. , the laser linewidth is constrained by fitting the absorption line). The volume mixing ratio of water vapor is calculated from the Beer-Lambert law using the known pathlength and spectroscopic parameters from the HI-TRAN 2004 database  (Rothman et al. , 2005) , which did not change in the HITRAN 2006 update. One can tie the direct absorption calculation of water either to (1) the spectroscopic parameters in HITRAN and the measurements that were used to make the line list in the database or to (2) in situ calibration with a reference source, while varying the spectroscopic parameters accordingly.
Acknowledgements. In ordering the authors, the first eight include the referees ( D. W. Fahey ,  R. -S. Gao ,  O. Möhler ), the organizers ( H. Saathoff ,  C. Schiller  4, V. Ebert), and those who helped conceive and initiate the intercomparison ( M. Krämer ,  T. Peter ). The remaining authors from individual participating teams are in alphabetical order. The success of the AquaVIT-1 campaign derived significantly from the excellent support of the staff scientists and technicians at the  AIDA  facility led by  O. Möhler  and  H. Saathoff. R. Bauer  of  Forschungszentrum Jülich, Jülich, Germany , provided support for the AquaVIT-1 wiki page. Travel support for the referees ( D. W. Fahey  and  R. S. Gao ) and some investigator groups was provided by the  EU  project  EUROCHAMP ; by  SPARC  -a core project of the world climate research program; and by the  Institute for Meteorology and Climate Research of Karlsruhe, Germany.
To make inferences from a case-cohort study, it is necessary to account for the over-representation of cases in the sample. Cox proportional hazards (PH) regression models need to be weighted, with cases outside the subcohort only included in the risk set at the time of their event  [1]. Different weighting methods have been described in detail  [2]  and compared by simulation  [3]. The usual standard error estimates from the Cox PH model are not valid in the weighted versions, and should be replaced by alternatives such as a robust jack-knife estimator  [4]. Weighted Cox regression models can be fit using standard statistical software packages, including Stata  [5]  and R  [6]. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) Statement is a checklist of 22 items  [7]  which provides guidance to authors on the reporting of three types of observational study design: cohort, case-control and crosssectional studies.
Search Strategy We used the electronic search engines PubMed, Scopus and Web of Knowledge to identify papers reporting analyses of casecohort studies published between January 2010 and March 2013. We restricted the search to 24 major general medical and epidemiological journals/databases [Appendix S1]. We searched paper titles and abstracts for the keywords ''case-cohort'' and ''case cohort''. For each paper, we identified the original cohort from which the case-cohort study was constructed, and recorded the number of individuals in the following groups: original cohort, subcohort, total cases, subcohort cases and subcohort non-cases. Where the information was available, we recorded these numbers both before and after exclusion of individuals due to application of specific eligibility criteria for the analysis (e.
g. exclusion of individuals with missing values of particular covariates). We recorded whether or not the subcohort was selected by stratified sampling and the stratification factors if it was. For papers where the information was available, we calculated the subcohort sampling fraction as the ratio of the reported size of the subcohort to that of the original cohort, using values before any exclusion criteria were applied. We noted which of the groups of individuals (as defined above) were described using summarized baseline characteristics. We recorded the statistical methods used (and choice of weights, if applicable), whether statistical modelling assumptions were tested, and how missing data were handled in the analysis. Initial assessment of all papers was carried out by one assessor (MP) and a random selection of 20% of the papers was appraised independently by a second assessor (AW).
Papers included in review We identified 47 published papers using our search strategy. Fifteen papers were excluded from the review for the following reasons: used the term ''case-cohort'' incorrectly to describe the study they were reporting (N = 9 papers), reported a meta-analysis of published case-cohort and case-control studies (N = 1), used case-cohort analysis methods even though the data included were not from a case-cohort study (N = 1), discussed specific methods for the design and analysis of case-cohort studies (N = 3), described the protocol for a planned case-cohort study (N = 1). The remaining 32 papers (list of references in Appendix S2) were published in eight of the 24 journals/databases considered, with 15 papers published in PLOS ONE. Within the journals covered by this review, the number of published papers reporting case-cohort studies increased between 2010 and 2012 (2010:5, 2011:9, 2012:13), with five papers already published in the first three months of 2013.
Persistence homology, on the other hand, performs computation of topological invariants in the dimension of the original dataset and is, unlike MDS, more stable under perturbation. To assess global kinetic dissimilarities across different CoV infections, we calculated topological differences between MERS-CoV SA 1, MERS-CoV Eng 1 and icSARS-CoV, each grouped throughout all time points and with data set-matched mock samples. The kinetics of the host response refers to the spread of infected samples relative to mock samples over time. There were large topological differences between MERS-CoV Eng 1 and icSARS-CoV (score: 2. 42), and between MERS-CoV SA 1 and icSARS-CoV (score: 2. 18), and moderate differences between MERS-CoV Eng 1 and MERS-CoV SA 1 (score: 0. 29) (Methods and Table  1 ). To further assess the magnitude impact of MERS-CoV infection in Calu-3 2B4 cells, we integrated viral genomic RNA levels into a topological assessment of host response differences.
MERS-CoV genomic RNA was measured by qRT-PCR using primer (KL200F) located in the leader region (nt 9-28) and primer (KL201) located in the ORF1 region of the CoV genome (Figure  1A  and Additional file 1: Table  S5 ). Additionally, we measured viral titers by plaque assay and found MERS-CoV SA 1 had more robust infectious viral particle production than MERS-CoV Eng 1, with significantly different viral titers at 18 and 24 hpi, despite similar viral genomic RNA levels at these late time points (Additional file 2: Table  S4 ). In measuring viral genomic RNA and infectious viral particle production, we assessed two different stages in the viral life cycle and the data support differences in the kinetics of replication for the two MERS-CoV strains. Since our analysis focuses on the host response to infection and its origin in pathogen recognition and activation of innate immune responses, we computationally integrated viral genomic RNA with host mRNA to better assess the differences in host response kinetics.
For comparison of topological differences in the host response, we calculated persistence homology bar codes for data set-matched mock and infected samples. The differences between bar codes were calculated using a maximal bipartite graph matching algorithm. For icSARS-CoV we also included 30 and 36 hpi samples. S5 ). The error bars represent the standard deviation among triplicate samples. B. Clustering graph for samples according to their Euclidean distances in gene expression obtained by an integration of viral genomic RNA levels. Samples with similar viral genomic RNA levels were grouped together. Using statistical criteria on the single linkage heights between all samples, we extracted the highly interconnected part (with smaller linkage heights). These samples were compared to highly interconnected samples of a second group of samples with overlapping viral genomic RNA levels of the first group and so forth.
Cells and viruses Calu-3 2B4 cells were cultured in minimal essential media (MEM; Gibco) supplemented with 20% fetal bovine serum (HyClone) and 1% antibiotic antimycotic (Gibco). Human coronavirus MERS-CoV SA 1 (GenBank: JX869059. 2) was received from Bart L. Haagmans (Erasmus Medical Center, Rotterdam, Netherlands). MERS-CoV SA 1 was isolated from the sputum of a 60-year-old man in Saudi Arabia who died after developing acute respiratory distress syndrome (ARDS) in June 2012  [1]. Human coronavirus MERS-CoV Eng 1 (GenBank: KC667074. 1) was received from the United Kingdom Health Protection Agency (HPA) Imported Fever Service. MERS-CoV SA 1 and MERS-CoV Eng 1 were propagated in VeroE6 cells and supernatants clarified. For questions of written informed consent for participation in the study, we refer to  [1]  and  [2] , where isolation of viruses from clinical samples is described.
In order to merge MERS-CoV Eng 1 and MERS-CoV SA1 data sets, we considered only probes that were mapped to gene names (Refseq IDs) in both arrays. To remove batch effects from the merged data sets we applied the R function ComBat  [39]. The resulting data set was then quantile normalized. For each sample, a log2 fold change value was calculated as a difference between log2 normalized data for each sample and the average of log2 normalized data for time-and data set-matched mock-infected samples. The same procedure, but handled as separate data sets, was applied to the cytokine treatment microarray data sets.
Topology analysis For normalized intensity data, the Euclidean distance matrix between samples was clustered with respect to sample-matched viral genomic RNA measurements using an algorithm described in  [17]. We used a freely available MATLAB implementation of this algorithm (http://appliedtopology. org/) with the parameter setting filterSamples = 5, pverlapPct = 65, mFudge = 5. The topology induced by a distance matrix can be described by a filter function, which assigns to every sample a value (phenotypic outcome, viral load, etc. ). First, the topological space is decomposed into overlapping subsets using level sets of the filter function. The set of samples falling into a particular subset are clustered, and the cluster tree is partitioned into two parts using statistical criteria of the single linkage length.
Only the part of the cluster tree with small linkage lengths (dense part) is retained. If resulting filtered clusters of different subsets have at least one sample in common, then they are defined as adjacent in a global discrete cluster graph. Topological differences between MERS-CoV SA 1 and MERS-CoV Eng 1 gene expression during the course of infection was furthermore assessed using homology persistence barcodes (only 0-and 1-homology were taken into account) as described in  [15]. For calculations, we used the freely available R package phom  [40]  and their maximum weighted bipartite graph matching  [41]  as a measure of difference (see R script implementation in Additional file 5). Additionally, multi-dimensional scaling (MDS) was performed on the Euclidean distance matrix between samples after differential gene expression statistical analysis  [42].
Differential gene expression analysis Differential expression was determined by comparing MERS-CoV SA 1-and MERS-CoV Eng 1-infected replicates to time-and data set-matched mock-infected controls, based on a linear model fit for each probe using the R package Limma  [43]. The same method was applied to determine differential expression between strains using time-matched MERS-CoV SA 1-and MERS-CoV Eng 1-infected samples. Criteria for differential expression were an absolute log2 fold change of 1 and a q value of <0. 05 calculated using a moderated t test with Benjamini-Hochberg correction. The cytokine Calu-3 treatment data sets were analyzed separately using the same methods and cut-offs as the virus-infected Calu-3 cells. Differentially expressed genes were determined to be statistically significant in at least on time point and for at least one treatment.
In addition to IPA Upstream Regulator Analysis, we also used EnrichR  [46] , a collaborative gene list enrichment tool, to determine possible upstream regulators via overrepresented transcription factor binding site motifs  [47]. Causal Network Analysis in IPA was used to understand gene expression changes and causal relationships between genes and networks of upstream regulators in the experimental dataset. The genes within the causal network represent nodes and the edge that defines the biological relationship between two nodes is represented as an arrow signifying regulation. Dashed arrows represent indirect relationships and solid arrows represent direct relationships. All edges are supported by at least one published reference or from canonical information stored in IKB.
Acknowledgements This project was funded in part by federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under contract  HHSN272200800060C  and grant  U54AI081680. The findings and conclusions in this report are those of the authors and do not necessarily reflect the views of the funding agency. We would like to thank  Victoria Carter  and  Chris Williams  at the  University of Washington  for their technical assistance related to the MERS-CoV SA 1 microarray and Calu-3 IFN treatment experiments, respectively. We would like to thank  Laurence Josset  for her helpful advice toward the analysis.
Availability of supporting data Raw microarray data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO accession GSE33267 (icSARS-CoV), GSE45042 (MERS-CoV SA 1), GSE56677 (MERS-CoV Eng 1), GSE33264 (IFN-α, and IFN-γ, Calu-3 treatments), and GSE56678 (IL-1α, and TNF Calu-3 treatments). All data sets and associated metadata have been submitted to Virus Pathogen Resource (ViPR, http://www. viprbrc. org). Study details can be accessed through the Systems Virology website (http:// www. systemsvirology. org).
Surveillance of laboratory-confirmed dengue cases is based on the weekly follow-up of laboratory-confirmed dengue cases (positive NS1 antigen detection and/or positive anti-DENV immunoglobulin M [IgM] detection and/or reverse transcription-polymerase chain reaction and/or viral isolation) performed by hospitals and private laboratories. Samples are sent by GPs from the sentinel network during inter-epidemic periods and by hospitals during epidemic periods. The information is then collated in a computerized database by the National Reference Center of arboviruses of the Institut Pasteur in French Guiana (CNR)  [15]. Virological surveillance of circulating serotypes is performed by the CNR and the hospital of Fort-de-France in Martinique  [16]. In Martinique, French Guiana and Guadeloupe, suspected dengue deaths are submitted to clinical and biological experts and infectious disease specialists to determine whether there is a link with DENV infection  [13, 17].
Methods A Literature Review Group developed the protocol for conducting this literature review based on the preferred reporting items of systematic reviews and meta-analyses (PRISMA) guidelines  [24]. The protocol was registered on PROSPERO, an international database of prospectively registered systematic reviews in health and social care managed by the Centre for Reviews and Dissemination, University of York (CRD42012002341: http://www. crd. york. ac. uk/prospero/display_record. asp?ID= CRD42012002341) on 30 April 2012. The Literature Review Group was actively involved in defining the inclusion/exclusion criteria and guided the search and selection process described below. Relevant articles were expected to be heterogeneous with respect to data selection, numbers and classification of cases.
Search strategy and selection criteria Specific search strings were devised for each database to be searched, with reference to the expanded Medical Subject Headings thesaurus, broadly encompassing the terms 'dengue', 'epidemiology' and 'Martinique and/or Guadeloupe and/or Saint Martin and/or Saint Barthelemy and/or French Guiana'. Search terms were tailored for each database with the aim of increasing the query's sensitivity and specificity. Only studies published in French or English between 1 January 2000 and 29 March 2012 were included. For databases that did not allow language and/or date limitations, references not meeting these criteria were deleted manually at the first review stage. No limits by sex, age or ethnicity of study participants or by study type were imposed. Single-case reports (except reports of deaths), incomplete surveillance reports and studies that only reported data for the period before 1 January 2000 were excluded.
As duplicate publication of data (e. g. , in meta-analyses and other reviews) could lead to oversampling and overestimates of the incidence of dengue, editorials involving previously published peer-reviewed data were also excluded. Additional publications and unpublished data sources meeting the inclusion criteria were eligible if recommended by a consensus of the Literature Review Group. Between 29 March 2012 and 11 April 2012, we searched nine databases, 14 on-line sources that included Institute Pasteur and surveillance system websites and grey literature (Table  S1 ) for epidemiological studies of dengue in the FTA published between 1 January 2000 and 29 March 2012. Literature from key infectious disease, tropical medicine and paediatric conferences were also searched. Duplicate citations were removed.
The Literature Review Group first reviewed the titles and abstracts of the identified articles and a further selection was made based on review of the full text in accordance with the review objectives. We chose not to exclude articles and other data sources or formally rank them on the basis of the quality of evidence. Although we recognize that assessment of study quality can add value to a literature review, the consensus of the Literature Review Group was that given that a high proportion of available data were expected to come from surveillance data, such quality assessment would not add value in this case due to the nature of surveillance data (passive reporting of clinically suspected dengue). As our primary objective was to describe the recent evolution of dengue, rather than to quantify disease in absolute terms, we therefore retained all available data sources.
Data from the selected sources were collated and summarized using a data extraction instrument developed as a series of Excel (Microsoft Corp. , Redmond, WA, USA) spreadsheets. Data from literature reviews of previously published peer-reviewed studies and pre-2000 data published within the search period were not extracted. Incidence rates were calculated using the number of cases from the selected publications and the population of the associated year issued by the Institut National de la Statistique et des E ´tudes E ´conomiques (http://insee. fr/fr/default. asp), with the exception of the incidence rates in Saint Martin and Saint Barthelemy prior to 2006, for which population data were not available. For these territories, incidence rates prior to 2006 were estimated based on the 2006 population.