dataesr/scientific-paragraphs-categorization huggingface dataset:
statistical analysis Proportions were calculated for categorical data, whereas median and interquartile ranges were calculated for continuous data. Statistical significance for categorical data was assessed using a χ 2 test or a Fisher's test, as appropriate. Continuous variables were compared using the Mann-Whitney U-test. Univariate and multivariable linear regression analyses were used to identify factors associated with APA. The following patient characteristics were investigated: period of admission, sex, age, body mass index, comorbidities, FEV, oral steroid at admission, PA in respiratory sample, health care-associated infection, SAPS II (Simplified Acute Physiology Score II), and ventilation at ICU admission. Then, univariate and multivariate analyses were used to identify factors associated with poor outcomes.
Using nano liquid chromatography-tandem mass spectrometry to analyse 387 variant and non-variant protein spots, 114 different proteins were identified that were classified into 16 functional classes. We noted that some proteins involved in the regulation of transcription, like HMG1/2-like protein and histone deacetylase HDAC2, were most abundant before the phase transition from cellularization to grain-filling, suggesting that major transcriptional changes occur during this key developmental phase. The maturation period was characterized by high relative abundance of proteins involved in ribosome biogenesis. Data are available via ProteomeXchange with identifier PXD002999.
Image and Statistical Analyses Images (300 dpi, 16-bit greyscale pixel depth) of two-dimensional gels stained with CBB were acquired with a GS-800 (Biorad) scanner and analyzed using SameSpots v4. 5 (TotalLab) 2D gel image analysis software. Statistical analyses were performed on normalized protein spot volume values. Differences in normalized protein spot volume due to grain development were analyzed using One-way ANOVA. P-values and adjusted Pvalues (q-value) were calculated using SameSpots procedures. The abundance of a protein spot was considered to have changed during grain development when its P-value and q-value were both <0. 05. In this case, the protein spot was considered as "variant" and others as "non variant. " Principal component analysis was performed using the FactoMineR  (Husson et al.
Protein identity was sought by using Mascot v2. 3 (Matrix Science) software against a custom database containing 249,032 sequences from Aegilops tauschii, A. thaliana, Brachypodium distachyon, H. vulgare, O. sativa, T. aestivum, and T. urartu and sequences from the wheat transcription factor database wDBFT  (Romeuf et al. , 2010). Proteins were considered to be identified if at least two non-redundant peptides were found to match a single reference in the databases. A cut-off was applied for individual peptide ion scores according to the significance threshold of the MASCOT program (P < 0. 05). Curated protein sequences which had no functional information were submitted as BLASTP searches against the National Center for Biotechnology Information non-redundant database (http://blast. ncbi. nlm.
nih. gov). Proteins were then classified in functional classes according to the KEGG PATHWAY database  (Kanehisa et al. , 2014)  and gene ontology  (Ashburner et al. , 2000). Subcellular localization of identified proteins was predicted by in silico analysis using Multiloc2  (Blum et al. , 2009) , WolfPSort  (Horton et al. , 2007) , Y loc  (Briesemeister et al. , 2010) , and LocTree  (Goldberg et al. , 2014)  programs. The top two hits were considered for Multiloc and Y loc. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  (Vizcaíno et al. , 2014)  via the PRIDE partner repository with the dataset identifier PXD002999.
Histone deacetylase HDAC2 (C) and 60S acidic ribosomal protein P0 (D) were each found in two spots including one which varied significantly (open circles) and one which did not vary significantly during grain development (closed circles). HMG1/2-like protein was identified in eight spots including six that varied, presented graphically in (E). The 40S ribosomal protein S12 (F) was found in three spots whose volumes varied significantly during grain development. Data are means ± 1 s. d. for n = 4 independent replicates. grain development are discussed, and could indeed be the object of further studies.
ACKNOWLEDGMENTS The authors thank  Didier Viala  ( INRA, Clermont-Ferrand ) for help with MS analyses and protein identification,  Joelle Henry-Berger  and  Christelle Damon  ( Blaise Pascal University, Clermont-Ferrand ) for help with microscopic observation, and  Dr. Philippe Leroy  ( INRA, Clermont-Ferrand ) for compiling the database for functional annotation of proteins. This work was supported by a Ph. D. grant from the  French Ministry for Higher Education and Research to TB  and funding from the  French Government  managed by the  Research National Agency (ANR)  in the framework of Investments for the  Future  ( ANR-10-BTBR-03 ),  France AgriMer  and the  French Fund  to support Plant Breeding (FSOV).
Peyer's patches (PPs) are primary inductive sites of mucosal immunity. The PP mononuclear phagocyte system, which encompasses both dendritic cells (DCs) and macrophages, is essential for the initiation of the mucosal immune response. We recently developed a method to isolate each mononuclear phagocyte subset of PP  (Bonnardel et al. , 2015). We performed a transcriptional analysis of three of these subsets: the CD11b + conventional DC, the lysozyme-expressing monocyte-derived DC termed LysoDC and the CD11c hi lysozyme-expressing macrophages. Here, we provide details of the gating strategy we used to isolate each phagocyte subset and show the quality controls and analysis associated with our gene array data deposited into Gene Expression Omnibus (GEO) under GSE65514.
Genomics Data j o u r n a l h o m e p a g e : h t t p : / / w w w. j o u r n a l s. e l s e v i e r. c o m / g e n o m i c s -d a t a / to block Fc receptors before staining for the following surface markers: CD8α (Brilliant Violet 785; clone 53-6. 7; BD Biosciences), BST2 (FITC; clone 927; Biolegend), CD11b (APC-Cy7; clone M1/70; Biolegend), CD11c (PE-Cy7; clone N418; Biolegend), CD4 (PE-Cy5. 5; clone RM4-5; eBioscience), SIRPα (APC; clone P84; eBioscience) and MHCII (Alexa Fluor 700; clone M5/114. 15. 2; eBioscience). Cell viability was evaluated using Fixable Viability Dye eFluor 506 (eBiosciences). Multiparameter fluorescence-activated cell sorting was performed using a FACSAria III (BD Biosciences). Data were analyzed with the BD FACSDiva software (BD Biosciences). First, cells were gated according to their CD11c and MHCII expression (Fig.
The chips were washed and stained in the GeneChip® Fluidics Station 450 (Affymetrix) and scanned with the GeneChip® Scanner 3000 7G (Affymetrix) at a resolution of 0. 7 μm. Raw data (. CEL intensity files) were extracted from the scanned images using the Affymetrix GeneChip® Command Console (AGCC) version 3. 2. CEL files were further processed with Affymetrix Expression Console software version 1. 1 to calculate probe set signal intensities using Robust Multi-array Average (RMA) algorithms with default settings or using RMA via the oligo package, through Bioconductor (release 2. 13) in the R statistical environment (version 3. 0. 2).
Quality control and data analysis Raw data were quality assessed using Relative Log Expression (RLE) and Normalized Unscaled Standard Error (NUSE) boxplots. RLE plot showed boxes centered to 0 with similar spread for all arrays (Fig. 2A ) and NUSE plot was centered close to 1 for all arrays (Fig. 2B ). Thus, both RLE and NUSE indicated that all arrays were of good quality. Boxplot and density plot of unprocessed and RMA processed probe intensities across all arrays shown in Fig. 2C  indicated similar distribution of signal intensities between arrays. Hierarchical clustering (HC) by Pearson correlation distance and Ward's aggregation (Fig. 2D ) and principal component analysis (PCA, Fig. 2E ) were performed after selection of probes with a differential expression in any combination of array N 1.
Acknowledgements We thank  M. Barad  for cell sorting experiments;  Violaine Alunni  and  Christelle Thibault  from the  "Plateforme Biopuces et sequençage de l'IGBMC" (Strasbourg, France ) for microarray experiments;  R. Bonifay  and  G. Bouget  for technical help; and  M. Dalod ,  C. Jones ,  T. Soos  and  C. Arendt  for helpful discussions. This work was supported by institutional funding from  Centre National de la Recherche Scientifique ,  Institut National de la Santé et de la Recherche Médicale  and by the I2HD collaborative project  10756A10-106072  developed jointly by CIML and SANOFI.
Distribution data for An. gambiae and An. coluzzii were gathered from various published and unpublished sources reporting about their occurrence and abundance. The data set consisted of 1140 georeferenced localities across the study area sampled during 2001-2011 and included a total of 45 834 An. gambiae sensu lato (i. e. An. gambiae and An. coluzzii) that were molecularly identified to species using rDNA-based PCR assays  (Scott et al. 1993; Fanello et al. 2002; Santolamazza et al. 2004 ) (Table  1 , Fig. 1A ).
Statistical analysis of dose-mortality response to salinity To assess the dose-response and calculate the median lethal concentration (LC 50 ) for different habitats (rural versus urban), locations (coastal versus inland) or species (An. coluzzii versus An. gambiae), we fitted binomial generalized linear mixed models (GLMM) with a logit link function using the lme4 package in R v. 2. 15. 3. We fitted 'salinity concentration', 'habitat', 'location' and 'species' as fixed effects in the GLMMs; the random effects were 'salinity concentration' included as a linear model term grouped by 'locality' and 'breeding site', with 'breeding site' nested within 'locality'. In this way, a separate logistic regression with random intercept and slope was fitted to each individual breeding site to account for heterogeneities and nonindependence of the dose-response among breeding sites from the same locality, as well as in the average doseresponse among different localities.
We are especially grateful to Jean-Pierre Agbor, Serge Donfack, Sylvie Kemleu and Jean-Claude Toto for technical support and to Drs Xavier Garde, Richard Oslisly, Philippe Le Gall, Thierry Baldet, Isabelle Morlais for their help during the course of this work. Funding was provided by the Institut de Recherche pour le D eveloppement, the National Institutes of Health (Projects R01 AI63508, and R01 AI76584 to NJB) and the Wellcome Trust (Intermediate Fellowship in Public Health and Tropical Medicine N°WT086423MA to CAN). PA was funded by the Spanish Ministerio de Econom ıa y Competitividad (MINECO) and Universidad de Castilla-La Mancha (UCLM) through a 'Ram on y Cajal' contract  (RYC-2012-11970). INAM was supported by a fellowship from the Universidad Nacional de Guinea Ecuatorial.
Figure1(A) Map of the study area showing major eco-regions (Olson 2001 ) and the sampled localities included in the data set (dark grey dots). Light grey area falls outside the limits of distribution of An. gambiae (Sinka et al. 2010)  and was not included in species distribution modelling. The outer dashed line delineates the approximate limits of the area of sympatry of An. gambiae and An. coluzzii. The inner dashed line delimits the study area in Central Africa where the two species were tested for salinity tolerance. (B) The study area in Central Africa showing localities from which larvae were collected for salinity tolerance testing. The pies show the relative frequency of the two species in larval samples from each locality (blue: An. coluzzii; red: An. gambiae). The size of the pie is proportional to the size of the sample.
g. , at the community level, meetings were organized with community members to establish indicator thresholds and evaluate the indicator status), and secondary data from government, NGO, and international agency reports. This was collated by the paper authors and further clarified with literature reviews and communication with Project COBRA participants. Once indicators were scored, an average value was calculated for each system property at each scale, and these final average values are illustrated in Figure  3. Full details of the methods and analysis at each level are given in  Berardi et al. (2012 Berardi et al. ( , 2013a, b) , b)  and participatory videos and photostories can be accessed at the Project COBRA web site.
Acknowledgments: We would like to thank all the communities of the  North Rupununi, Guyana and Tumucumaque, Brazil , for their participation in this research. A special thanks to  Cloude de Souza  for his System Viability analysis of the Tumucumaque region. Thank you also to the anonymous reviewers for their very useful comments. This research has been funded by the  Environment Programme, Management of Natural Resources, DG Research and Innovation ,  European Commission 7th Framework.
(The MatLab code used for the calculations is given in the SI. ) To verify that the method is valid in the regime of interest for pharmaceutical excipients, we first use a water-based suspension of EC nanoparticles (Aquacoat ECD) where the particle size has been evaluated from light scattering measurements (zaverage, diameter = 169 nm, intensity average, diameter = 180 nm). Figure  S2  shows measured R-PRE enhancements and fits using the model described above, which leads to the determined domain sizes for EC given in Table  1. In the fits for all samples T 1,surface was set to 13 and 19 ms for the experiments with 30 and 15 mM AMUpol, respectively. This is the measured solvent T 1 , which depends on radical concentration. We note that being able to measure the relaxation rate at the surface of the domain is a big advantage over previously published 23 methods to determine particle sizes using local T 1 differences.
Background Dengue is a major emerging public health concern in tropical and subtropical countries. Severe dengue can lead to hospitalisation and death. This study was performed to assess the economic burden of hospitalisations for dengue from 2007 to 2011 in three French territories of the Americas where dengue is endemic (French Guiana, Martinique and Guadeloupe). Methods Data on dengue-associated hospitalisations were extracted from the French national hospital administrative database, Programme de Medicalisation des Systemes d& Information (PMSI). The numbers of stays and the corresponding number of hospitalised patients were determined using disease-specific ICD-10 codes. Associated hospital costs were estimated from the payer perspective, using French official tariffs. Results Overall, 4183 patients (mean age 32 years; 51 % male) were hospitalised for dengue, corresponding to 4574 hospital stays.
Severe dengue causes an estimated 500,000 hospitalisations every year  [2]  and approximately 20,000 deaths annually  [6]. Dengue is also an increasing public health concern in the Americas where there has been a 4. 6-fold increase in the number of cases over the last three decades (from approximately 1 million cases during the 1980s to 4. 7 million during 2000-2007)  [7]. With an ever increasing number of cases, the economic burden inflicted on those populations in endemic areas is substantial  [8] [9] [10]. In the Americas, the economic burden associated with dengue was estimated to be US$2. 1 billion  [11]. French Guiana, Martinique and Guadeloupe (three French territories in the Americas) are dengue endemic areas with cyclical epidemics every 3-5 years. The availability of an exhaustive official French hospital database represents a unique opportunity to assess the burden of dengue in these territories where the population is approximately 1 million (around 400,000 in Guadeloupe, 380,000 in Martinique and 250,000 in French Guiana)  [12].
Data sources The French Medical Information System (Programme de Medicalisation des Systemes d& Information, PMSI) is an exhaustive medico-administrative hospital discharge database that covers all public and private hospitals in France as well as those in the French Territories  [13] [14] [15]. Diagnoses identified during admission are coded using the International Classification of Diseases, 10th revision (ICD-10) by the physician. PMSI includes a compilation of standard discharge summaries (''ResumeStandard de Sortie'', RSS) for every admission. Anonymised data (''ResumeStandardiseAnonymise'', RSA) with limited socio-demographic information (gender, age, residence code) and medical information on the main diagnosis that led to hospital admission, the nature of treatments received and examinations carried out, underlying comorbidities and possible complications, are made available for epidemiologic studies.
Each patient& s stay is classified by Diagnosis Related Group (DRG) (Groupe Homogene de Sejours) according to information documented by the physician. The economic burden of hospitalised cases would be expected to be well documented within the PMSI database because since the introduction of a DRG-based prospective payment system (the ''Tarification al'Activite'') in 2005, the PMSI database has been used as the basis for the funding of services in all hospitals, with each hospital receiving DRG-based payments according to the national tariff. Thus, data extracted from this database is exhaustive (all public and private hospitals are included and no sampling is done) and of high quality, with limited coding errors. In addition, the FICHCOMP (''FiICHier COMPlementaire'') database contains a restricted list of ''expensive drugs'' that are fully reimbursed, and has been available since 2008 for public hospitals only.
Data collection of dengue stays All hospital stays in the French territories of French Guiana, Martinique and Guadeloupe from 2007 to 2011 (data from more recent years were not available at the time of analysis) with a primary or associated denguespecific code were selected from the PMSI database using the ICD-10 codes A90* or A91* [''Dengue fever (classical dengue)'' or ''Dengue haemorrhagic fever (severe dengue)'', respectively]. In this first data collection, we gathered hospital stays for confirmed dengue or suspected dengue. In a second step we assessed a medical interpretation in order to select the confirmed dengue cases completed by PMSI guidelines. In this second step, we used the PMSI guidelines to firstly select confirmed dengue cases based on ICD-10 diagnosis code A90* or A91* in position of primary diagnosis.
When the ICD-10 diagnosis code A90* or A91* is observed in position of associated diagnosis, the medical interpretation is absolutely necessary. Based on the medical interpretation of PMSI database stays and particularly the primary diagnosis, the dengue was confirmed or not. A conservative approach was used by selecting only those hospital stays that had a primary diagnosis of dengue or had a direct link with dengue. One of the authors (LT) manually assessed all hospital stay cases that had an associated link to dengue (i. e. where dengue was a secondary diagnosis) to exclude those where dengue was considered doubtful. For patients hospitalised for dengue, gender, age and comorbidities, type of management received (medical, surgical or exploratory), type of stay (conventional inpatient stays or short outpatient stays), and length of stay, as well as whether stays occurred partly, or completely, in the emergency or an intensive care unit, and occurrence of related deaths were collected.
Results A total number of 6273 hospital stays for dengue were identified in the PMSI database between 2007 and 2011, including 4750 stays (76 %) that occurred in French Guiana, Martinique or Guadeloupe (Fig. 1 ). Of the hospital stays in the three French territories, 176 (4 %) were considered to be doubtful for dengue and were excluded. The remaining 4574 hospital stays constituted the base case of the present analysis. Overall, 4183 patients were hospitalised for dengue during the study period: 622 (15 %) patients in French Guiana; 2231 (53 %) in Martinique; and 1354 (32 %) in Guadeloupe. A patient may have several dengue hospitalisations in different territories, resulting in a non-arithmetic total of patients over all the three territories. Three peaks in the overall number of hospital stays were observed during the study period (Fig.
Emergency departments allow both triage of patients who do not require hospitalisation and for those who do in severe cases. Unfortunately, the PMSI database does not record information on patients who attend the emergency department but who do not require subsequent hospitalisation. It would be interesting to analyse the number of patients who attend the emergency department for dengue but who do not require subsequent hospitalisation and their related associated costs, especially during an epidemic. Epidemics represent significant challenges for health-care providers with regard to delivering care to a large number of patients in a short period of time, as well as identifying those at risk of developing severe dengue requiring hospitalisation  [1]. For instance, in Martinique in the 2010 outbreak, it was observed that dengue represented more than 10 % of the total emergency  admissions, while this usually represents less than %, which could lead to a possible saturation of the emergency department  [16].
Although outbreaks were associated with qualitative changes in the management and in the profile of hospitalised patients, this was not reflected in the cost per dengue case. For instance, the use of expensive drugs was predominant during the 2010 outbreak, which accounted for 94 % of the overall costs for these expenses. In addition, the proportion of patients hospitalised in intensive care units was also increased during the 2007 and 2010 epidemics. Nonetheless, these variations in costs had a negligible impact on the average annual costs per case (Table  1 ). However, as the PMSI database is based on the collection of coding added by various health professionals, it is possible that coding bias may have contributed to this observation. At the opposite end, the cost per stay was highest in 2011, a dengue non-epidemic year.
Limitations of this study In addition to the direct costs associated with dengue, which escalate during epidemics, dengue prevention measures such as vector control through use of larvicides and fumigation as well as education, media, and community campaigns, incur additional costs that are not captured within the PMSI database. The additional costs incurred for prevention measures have been reported for both the 2007 epidemic in Guadeloupe and the 2010 epidemic in Martinique. During the epidemic of 2010 in Martinique, €60,000 and €120,000 were spent on insecticides and communication, respectively, and extra funding of €100,000, given by the government, was used mainly for educational campaigns  [16]. In Guadeloupe, the cost of a communication campaign for the 2007 epidemic was reported as €130,424, of which, €98,542 was paid by the national government with the remainder paid by various external partners, private organisations or municipalities  [17].
Mice Female NMRI mice (Charles River WIGA GmbH, Sulzfeld, Germany) or C57BL/6N mice from bred in the internal facility were housed at ambient temperature with 12-h light-dark cycle on chow (Kliba Nafag #3437, Provimi Kliba, Kaiseraugst, Switzerland). Stromal-vascular fraction (SVF) FACS profiles were not significantly different and beige/brite differentiation capacity was comparable between the two strains across numerousindependent experiments (data not shown). The RNA expression profiling data were obtained from NMRI cells. Animal handling and experimentation were performed in accordance with the European Union directives and the German animal welfare act (Tierschutzgesetz) and approved by local authorities (Regierungspräsidium Karlsruhe).