dataesr/scientific-paragraphs-categorization huggingface dataset:
A Cockcroft-Walton-type high-voltage supply is used to suppress power consumption, including a circuit to protect the photomultipliers from instantaneous bursts of light, like in the case of lightning or bright fireball phenomena. The FS electronics system records the signals of UV photons generated by EECRs successively in time. A new type of front-end ASIC has been developed for this mission, which has both functions of single photon counting and charge integration in a chip with 64 channels. The FS electronics is configured in three levels corresponding to the hierarchy of the FS detector system: frontend electronics at EC level, PDM electronics common to 9 EC units, and FS electronics to control 137 units of PDM electronics. Anode signals of the MAPMT are digitized and recorded in ring memories for each GTU to wait for a trigger assertion, then, the data are read and sent to control boards.
The study protocol was approved by the local ethics committee (CCP Tours Région Centre Ouest 1) for human research in Tours. All patients signed informed consent forms. Patients were evaluated at baseline (one day before cardioversion) and one month later, as follows: Physical examination and standard 12-lead electrocardiogram (ECG); standard transthoracic echocardiography with a Vivid I digital ultrasound system (GE Medical Systems Ultrasound) and digital cineloop format for off-line analysis using a dedicated software package (EchoPac PC, version 110. 1. 2; GE Healthcare); clinically required blood samples were collected, in addition to a sample for the evaluation of natriuretic peptides (BNP and proANP). LA function was reevaluated one month after cardioversion because that is generally the estimated time necessary for recovery from atrial stunning or left atrial dysfunction  [2].
Statistical analysis Data were expressed as means ± standard deviations. Since normality testing of the different variables showed a non-Gaussian distribution, non-parametric statistical tests were applied. Differences in the means of the various parameters before and after cardioversion were assessed by a Wilcoxon test for paired series. Differences in the means of baseline parameters between patients with AF and patients in SR one month after cardioversion were assessed by a Wilcoxon test for unpaired series. Correlations between LA volume or MPALS and ANP or BNP concentrations were evaluated by the Spearman correlation coefficient. A p-value < 0. 05 was considered to be significant. Statistical analyses were performed using JMP 9. 0. 1 software (SAS Institute, Cary, NC, USA). For the reproducibility of the parameters, results were analyzed using Bland-Altman comparison and coefficient of variability.
S3a † ). Moreover, WAXS experiments were also carried out in transmission geometry with the X-ray beam traveling along the direction perpendicular to the template surface using a Bruker AXS Nanostar X-ray scattering instrument (see ESI, Fig. S3b † ), so that Q was nearly perpendicular to the long axis of NWs. The underlying Al substrate was chemically etched from the AAO templates for transmission measurements. The scattered X-rays were detected using a two dimensional multiwire area detector (Bruker Hi-Star). The data were then converted to one-dimensional scattering proles by radial averaging along the azimuthal direction. The sample to detector distance was 10 cm. Both instruments use Cu Ka radiation (1. 54 Å). 3u-SThM was applied to measure P3HT NWs with different diameters embedded in a porous alumina matrix.
Amplification products were verified by agarose gel electrophoresis (1 %) and their concentrations estimated by comparison with molecular size markers (Smart Ladder, Promega). For analysis of 16S rRNA genes and transcripts in CE-SSCP, samples were diluted and 1 µL of each dilution was mixed with 18. 8 µl of deionized formamide (Hi-Di for-mamideTM; Applied Biosystems) and 0. 2 µl of the internal size standard GeneScan-400HD (Rox) (Applied Biosystems). A denaturation step of 5 min at 94 • C was performed, and samples were immediately placed in a water/ice bath for 10 min. Fragments were separated by CE-SSCP as described previously  (Delbes et al. , 2000)  using an ABI 310 Genetic Analyzer (Applied Biosystems) with electrophoresis at 12 kV and 30 • C for 30 min per sample. The electropherograms were analyzed by the GeneScan software (Applied Biosystems), and the obtained profiles were aligned and normalized using the Rox internal standard with SAFUM  (Zemb et al.
Acknowledgements. This work is a contribution of the DUNE experiment (a DUst experiment in a low Nutrient, low chlorophyll Ecosystem, http://www. obs-vlfr. fr/LOV/DUNE/index. html)and was funded by the  LEFE, UPMC  and  ANR-DUNE  under the contract  ANR-07-BLAN-0126-01. DUNE was endorsed by the international  SOLAS (Surface Ocean -Lower Atmosphere) program  in February 2009, (http://www. solas-int. org/activities/project-endorsement. html). We would like to sincerely thank the  Réserve naturelle de Scandola, Parc naturel régional de Corse , in particular  J. M. Dominici  and collaborators, for professionalism and cooperation in the implementation of the field work in the bay of Elbo. We thank all of the participants of the DUNE-2 experiment for their involvement. The service charges for this open access publication have been covered by  CNRS-INSU  and the  Laboratoire d'Océanographie de Villefranche (LOV) ,  CNRS UMR7093.
MS/MS Database MASCOT Search and Protein Quantification by Spectral Count Peak lists were generated with the MASCOT DAEMON software (v2. 3. 2, Matrix Science) from the LC-MS/MS raw data using default parameters. The MASCOT 2. 3. 02 search engine (Matrix Science) was used to search all MS/MS spectra against a database corresponding to a complete list of annotated Coding Domain Sequences (CDS) from Phaeobacter strain DSM 17395 (NCBI RefSeq: NC_018290. 1, NC_018291. 1, NC_018288. 1, and NC_018287. 1). Bovine trypsin and the 22 most common keratin contaminants were included as a follow-up. Searches for peptides were first performed with the following parameters: mass tolerance of 5 ppm on the parent ion and 0. 5 Da on the MS/MS (FT/IT procedure), static modifications of carboxamidomethylated Cys (+ 57.
0215), and dynamic modification of oxidized Met (+15. 9949). High-resolution MS/MS (FT/FT procedure) searches were performed with mass tolerance of 0. 1 Da on the MS/MS. To process the data resulting from trypsin proteolysis, the maximum number of missed cleavage was set at 2. All peptide matches with a score above its peptidic identity threshold (set at p,0. 01) with the ORF database and rank 1 were filtered with the IRMa 1. 28. 0 software  [30]. A protein was only validated when at least two peptides had been assigned. In a second stage, we performed additional searches for both ''semi-trypsin'' and ''no-enzyme'' protease specificities. Protein abundance was evaluated in the shotgun analysis by MS/MS spectral counts as established by Liu and co-workers  [31]  and previously described  [32].
De novo Assignment of MS/MS Spectra Peak lists were submitted to Peaks Studio 5. 3 software (Bioinformatics Solutions Inc). At first, a data refinement step was performed with a quality threshold set at 0. 65. De novo sequencing was then carried out with the following parameters: a parent mass error tolerance of 5. 0 ppm, a fragment mass error tolerance of 0. 1 Da, cysteine carbamidomethylation (+57. 02) as fixed modification, and methionine oxidation (+15. 99) as variable modification. More stringent criteria were applied with a parent mass error tolerance of 2 ppm with high resolution mode for both MS and MS/MS. Successively, trypsin, semi-trypsin and noenzyme were chosen as enzyme specificities. The list of assigned spectra obtained with MASCOT using Phaeobacter strain DSM 17395 CDS database was compared with Peaks Studio results for each samples.
After trypsinization, the peptide mixture was injected into a C18 reversed phase chromatography column coupled to a high resolution LTQ-Orbitrap XL hybrid mass spectrometer. The acquired tandem mass spectra were assigned with the Mascot software using as database the complete list of annotated CDS from Phaeobacter strain DSM 17395. The dataset comprised 2321 MS/MS spectra, from which 1112 could be assigned to specific peptides from Phaeobacter strain DSM 17395 (Table  S1  in File S1). These spectra corresponded to 478 different peptides which validated the presence of 75 proteins with at least two different peptides (Table  S2  in File S1). In terms of spectral counts, the first 15 proteins accounted for half of the total number of assigned spectra. The most detected proteins were an outer membrane porin (ZP_02147263), a flagellin-like protein (COG1344, ZP_02145485), a hemolysin-type calcium-binding protein (ZP_02144235), and the periplasmic phosphate-binding subunit of a phosphate ABC transporter (ZP_02143869) with 86, 55, 52, and 41 MS/MS spectra, respectively (Table  S2  in File S1).
De novo Sequencing with High Resolution Data Revealed the Identity of the Abundantly Secreted Protein We carried out a search to manually assign spectra that had not been automatically assigned to a tryptic peptide comprised in the Phaeobacter strain DSM 17395 CDS database. For this de novo sequencing strategy, we reanalyzed the peptides from the 55 kDa protein band by nanoLC-MS/MS with a FT/FT procedure which enables getting higher resolution MS/MS spectra and better mass accuracy (15,000 in the orbitrap analyzer instead of the 3,000 previously done with the LTQ analyzer). From this new dataset acquisition, 246 MS/MS spectra were assigned directly with the Mascot software using the complete list of annotated CDSs from Phaeobacter strain DSM 17395 as a database. The same dataset was also searched with the de novo sequencing software Peaks Studio 5.
High Resolution Data Allowed a High Coverage of ZP_02147451 with Non-tryptic Peptides To confirm the identity of the protein and because de novo sequencing of some MS/MS spectra had shown the presence of abundant non-tryptic peptides, we re-analyzed the FT/FT experimental data with the Mascot software using ''no-enzyme'' as the enzyme parameter. Table  S4  in File S1 lists the resulting 228 MS/MS spectra assigned to 128 distinct peptides belonging to proteins of the Phaeobacter strain DSM 17395 bacterium. As expected, less MS/MS spectra are assigned with the no-enzyme search mode than with the trypsin search mode, due to the loss of discrimination introduced by the larger search space. This time the hemolysin-type calcium binding protein ZP_02147451 was clearly the most abundant protein identified accounting 67 MS/ MS spectra assigned (30% of the spectral counts).
The present study shows that usual proteomic parameters for the interpretation of MS/MS data are misleading when exoproteomes are being analyzed. Here, trypsinspecific searches for MS/MS assignment did not identify correctly the ZP_02147451 RTX-toxin and led to false quantitative results regarding the main exoproteome components. Therefore, we propose for further exoproteome studies to analyze by tandem  mass spectrometry the proteins cleaved with a combination of different proteases (e. g. trypsin, chymotrypsin or EndoGluC proteases) in order to not underestimate, or simply miss, abundantly secreted proteins of interest. As an example, here the experimental proteolysis of the 55 kDa band with chymotrypsin allowed the identification of the ZP_02147451 RTX-toxin with 701 MS/MS spectra assigned to the protein out of 817 MS/MS spectra recorded, i.
e. 85% % (Table  S5  in File S1). Proteolysis with endo glu-C led to the identification of the same protein with 662 assigned MS/MS spectra out of 747 MS/MS spectra recorded, i. e. 89 (Table  S6  in File S1). In this paper we outlined a strategy that could be systematically used to identify secreted proteins in environmental environments. It would be worth reanalyzing previous exoproteome datasets to check whether the lack of identification of an intense protein was common to several of these studies. However, the lack of the corresponding raw data (SDS-PAGE and MS/MS datasets) prevented such analysis. While RTX-proteins are found numerous in marine bacteria with more than a thousand members, some have been found involved in virulence of uropathogenic E. coli revealing strong medical interest  [42].
In light of the recommendations made in recent guidelines for using BMI national reference data on the management of obesity in youth  14, 15  , this study evaluates the performance of the last age-and sex-specific BMI reference values proposed for Brazilian children and adolescents (Brazil-2006)  16  , against excess body fatness in a representative sample of schoolchildren from Florianópolis. In addition to the comparison of sensitivity and specificity of the Brazil-2006 with that of the WHO-2007, a LR analysis was performed to select the optimal cut-offs for classifying children as overweight (including obesity) according to WHO-2007 and Brazil-2006 BMI reference curves, allowing the calculation of the post-test probabilities of excess body fatness.
SubjectS Data were extracted from the database of a cross-sectional epidemiological study  17  which reported the prevalence of overweight and obesity in Florianopolis' schoolchildren in 2002. Florianopolis is the capital of the province of Santa Catarina, in the south of Brazil. The city has a total area of 433 km 2 ; its demographic density is 760 hab/km 2. At the time this study was conducted, the population was 369,102 inhabitants (urban 94. 1%), life expectancy at birth was 72. 8 years, and infant mortality rate was 18. 5/1000 live births  18, 19. Sample size was calculated considering 10% overweight prevalence  20  and 95% confidence limits. The sampling error was 2. 0 and the design effect 2%. The reference population consisted of students enrolled in public and private elementary schools.
7% according to WHO-2007 BMI reference (Table  1 ), an expected value of 90% for sensitivity/specificity as the mid-point of their range between 85 and 95% in Brazil-2006 BMI reference (Table  2 ), and 5% error for minimal acceptable lower confidence limit  21. This method estimated 474 and 1614 children with and without overweight, respectively, producing the total sample size of 2088. Rev BRas epidemiol apR-jun 2014; 517-530 Prior to the data collection the informed consent form was delivered to the school, along with a letter addressed to parents with information about the survey, data to be collected, and assurance that it would not present any risks. Information included anthropometric data (height, weight, skinfolds and arm, waist and hip circumferences) and sociodemographic status of the family.
10 times (using WHO-2007 or Brazil-2006 BMI curves) as likely to be classified as overweight. Table  4  presents the post-test probability of excess body fatness estimated using the optimal BMI cut-offs and the existing BMI Z-values for diagnosis of overweight from the WHO-2007  and Brazil-2006  Brazil-2006 optimal BMI cut-offs), the child's probability of having excess body fatness increased to 50. 0% for boys and 46. 0% for girls. The negative and positive post-test probabilities of excess body fatness in schoolchildren diagnosed as non-overweight or overweight according to the existing BMI Z-values from the WHO-2007 and Brazil-2006 references were lower than using the optimal BMI cut-offs. When data from non-overweight children were assessed, both BMI reference values produced similar results to the optimal BMI cut-offs for the negative post-test probability of excess body fatness.
The comparison of the obtained results with those of other studies is limited because of differences in age of the children, methods to measure body fat content, the reference standard and the definition of cut-off values for the evaluation of excess body fatness, as well as the analytical approaches for data analysis. By way of comparison, we screened the literature to identify studies reporting sensitivity and specificity values of BMI which used the same two references as those used here, and conducted in populations with similar age ranges to our study. In general, the sensitivity obtained for WHO-2007 (98. 6% for boys and 92. 5% for girls) and Brazil-2006 (94. 5% for boys and 94. 8% for girls) classifications in this study were higher than those reported in previous studies. In Brazil, the performance of three BMI-based classification systems was analyzed (IOTF, WHO-2007, and Brazil-2006) in 1570 schoolchildren aged 7 to 12 years from the city of Paraíba.
Data Sources and Analyses The precision of the thermal functions was evaluated using data from three different sources: 1   (2002). In short, three Pioneer maize hybrids (CRM 106, 111, and 115) were grown at three planting dates, ranging from 22 April to 17 June, each at four locations in Ohio and Indiana from 1991 to 1994. Note that Pioneer uses CRM (comparative relative maturity) to designate hybrid RM and that hybrid RM ratings may vary somewhat among different seed companies  (Lauer, 1998). Dates of 50% silking and 50% black layer were recorded. 3. Argentina: This data set consists of information collected on various maize hybrids ranging from RM 92 and 127 that were grown between 1988 and 2013 near Balcarce, Argentina. Data included the results of a published study  (Cirilo and Andrade, 1994a, 1994b)  and several unpublished studies.
Some of the studies had multiple planting dates that ranged from September to January. The experimental area was irrigated when deemed necessary. Dates of 50% silking and 50% black layer were recorded. Weather data were collected for each location-year in the three data sets. Records of daily maximum and minimum air temperatures were obtained either from the National Climate Data Center (U. S. weather stations, http://www. ncdc. noaa. gov/cdo-web/search) or the Government of Canada Climate Service (Canadian weather stations, http://climate. weather. gc. ca/index_e. html#access) for the location-years in the North American and Indiana-Ohio data sets. All weather service stations were, on average, within 20 km of the experimental site. Field data were analyzed for the pre-flowering and postflowering periods.
Daily thermal accumulation was estimated using the daily mean temperature (GTI), minimum and maximum temperatures (GDD 10,30 and CHU), 1-h temperatures (CERES-Maize, TLU, MAIZSIM, and EnzymResp), and 3-h temperatures (APSIM). The 1-h temperatures were estimated using a sine wave between the minimum and the maximum daily temperature (e. g. ,  Tollenaar et al. , 1979). The 3-h temperatures in the APSIM function were estimated using range fractions for each of the eight time periods within a 24-h day that describe a sine wave. The CV (coefficient of variability) was used to quantify the precision of the thermal functions. The mean thermal accumulation (and days), standard deviation, and CV were calculated from all observations (i. e. , hybrid-location-year) within an RM class for the North American data set and within a planting date for the Indiana-Ohio and Argentinean data sets for the planting to anthesis (or silking) interval and the silking to physiological maturity interval.
The optimization procedure of the b function was performed by varying the values of T opt and T ceil (keeping T base unchanged at 0°C) and selection based on the lowest CV. The optimization of the b function was done using the optimum function of the R statistical language (R Development Core Team, 2013) with the Nelder-Mead algorithm for both entries in each of the five RM classes of the 43-location North American data set and for all RM classes combined (b OPT ). This is a general purpose routine for function error minimization. Several of the minimization calculations were run for two different starting values, and the results were identical in the two cases. This exercise confirmed that the algorithm was converging to a global rather than to a local optimum. In addition, for each of the five RM class in the 43-location North America data set, the hybrid-location-years of the data set were subdivided equally into three groups representing 33% low, 33% medium, and 33% high means of daily minimum temperature and of daily maximum temperature, resulting in 15 mean-minimum and 15 mean-maximum temperature groups (i.
Impact of Data Source on Thermal Function Evaluation Data source is an important variable when evaluating the precision of thermal functions. The data sets evaluated in this study each had limitations. The Indiana-Ohio data set was comprised of a multilocation, multiyear study with orthogonal treatments, but contained only a relatively small number of hybrids and environments (i. e. , location-years). In contrast, the North American data sets consisted of a large number of hybrids and environments, but the treatments were not orthogonal. Different hybrids were grown in each location-year, resulting in a different number of observations for each RM class in the 43-location data set (Tables  2  and 3 ). In addition, the silking to black layer interval was evaluated only at a single geographic location near DeKalb, IL, for multiple years (Table  3 ).
The Argentinean data set was not orthogonal and contained a relatively small number of environments (years). Empirical nonlinear functions were more precise than either empirical linear or process-based functions in the North American and Indiana-Ohio data sets, but the precision of the thermal functions did not differ in the Argentinean data set (Tables  2 3 4 5 6 ). Differences among thermal functions and calendar days were generally smaller in the Indiana-Ohio than the North American data set. The precision of the thermal functions for the planting to anthesis phase in the North American data set were calendar days < empirical linear < process based < empirical nonlinear, whereas in the Indiana-Ohio data set they were calendar days < empirical linear = process based < empirical nonlinear.
3 to 10. 7°C for the early and late planting dates, respectively. Table  4. Coefficients of variation of the number of days and thermal accumulation during the planting to silking and silking to black layer intervals determined by eight thermal functions for three maize hybrids planted at three dates from April to June during 1990 to 1994 at four locations in Ohio and Indiana  (Nielsen et al. , 2002) ; the total number of observations is 108. Data were analyzed both separately for the early, medium, and late planting dates and together for all planting dates. -------------------------% -------------------------- -------------------------% --------------------------.
Thermal function Empirical linear Process-based Empirical nonlinear -------------------% -------------------Planting to anthesis or silking North America 10. 1 d † 7. 7 c 6. 9 b 5. 9 a Indiana-Ohio 8. 1), while precision of the three functions in the North American data sets was in the order CHU > TLU > GDD 10,30 (Tables  2  and 3 ). Thermal functions with higher normalized developmental rates across a wide range of temperatures (e. g. , nonlinear empirical functions) have lower temperature sensitivity. The precision of the thermal functions varied with RM class in the North American data set, and the variation in precision was associated with air temperature. The precision of the thermal functions increased from low-to high-RM classes during the planting to anthesis interval (Table  7 ) in the 43-location North America data set.
Acknowledgments We would like to thank the support of projects  CAPES/COFECUB 707/11 ,  PNPD 2484/2009 ,  FAPERGS-INRIA 1706-2551/13-7 ,  CNPq   312184/2012-3 ,  551964/2011-1, 482520/2012-4  and  312077/2012-2.