dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, the model does not yet take into account the effects of cultivar mixtures, whereas some authors have described a reduction of eyespot by cultivar mixtures  [70] [71] [72]. However, this cropping practices is not currently widespread, data are sparse and there is no consensus in the literature on this matter. IPSIM-Wheat-Eyespot requires the provision of qualitative basic attributes. This is a benefit for the ex-ante design of innovative cropping systems. However, this requires translating nominative or quantitative variables used to describe cropping practices and the production situation into ad hoc qualitative variables. In order to avoid subjectivity when translating these variables, some reference values have to be used. Such values were gathered for several French regions (data not shown) in order to design an algorithm that translates nominative or quantitative variables describing cropping practices and the production situation into relevant basic attributes of the model.
Acknowledgments We thank  Alain Cavelier  ( INRA, Rennes ),  Xavier Coquil  ( INRA, Nancy), Claire Thierry (INRA, Nancy ),  Michel Bertrand  ( INRA, Versailles-Grignon ) for providing data. We also acknowledge  Marc Delos  ( French Ministry of Agriculture ),  David Gouache  ( ARVALIS-Institut du Vegetal), Claude Maumene(ARVALIS-Institut du Vegetal) ,  Sabrina Gaba  ( INRA, Dijon ),  Marie Gosme  ( INRA, Versailles-Grignon ),  Robert Faivre  ( INRA, Toulouse ) and  Bruno Coulomb  ( INRA, Toulouse ) for their useful advices.
This study was carried out within a PhD project co-funded by  INRA  and  INPT EI Purpan , by the project  MICMAC design  ( ANR-09-STRA-06 ) supported by the  French National Agency for Research , and by the  Programme ''Assessing  and reducing environmental risks from plant protection products (pesticides)'', funded by the  French Ministry in charge of Ecology and Sustainable Development  (project ''ASPIB''). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
OBSERVATIONS AND DATA REDUCTION NuSTAR observed GRS 1915+105 on 03 July 2012, over a span of 59. 8 ks. The data were screened and processed using NuSTARDAS version 1. 1. 1. Spectra from the FPMA and FPMB detectors were extracted from 90" regions centered on the source position. Background spectra were extracted from regions of equivalent size on each detector; however, the background is negligible. Response files appropriate for the pointing (on-axis), source type (point, not extended) and region size were automatically created by the NuSTARDAS software. After all efficiencies and screening, the net exposure time for the resultant spectra was 14. 7 ks for the FPMA, and 15. 2 ksec for the FPMB. The net observing time is small compared to the total observing due to the source flux, and in part because the observation occurred very early in the mission, and in part owing to detector dead-time.
2001 , Trudolyubov 2001 , Fender & Belloni 2004). Observations with the RATAN-600 radio telescope found that GRS 1915+105 varied between 12 ± 3 and 6 ± 3 mJy at 4. 8 GHz (Trushkin, private communication) during the NuSTAR observation, consistent with relatively radio-faint "plateau" states. Version 1. 1. 1 of the NuSTARDAS software and calibration has verified the detector response over the 3-79 keV band, partly through careful comparisons to the Crab. In all cases, the FPMA and FPMB spectra of GRS 1915+105 were jointly fit over the 3-79 keV band. An overall constant was allowed to float between the detectors to account for any mismatch in their absolute flux calibration; in all cases, the value of this constant was found to be 1. 02 or less. In all fits, absorption in the ISM was fit using the "tbabs" model  (Wilms, Allen, & McCray 2000) , using corresponding abundances ("wilm") and cross sections ("vern";  Verner et al.
Acknowledgments This research was supported through an operational research course which was jointly developed and run by the  International Union Against Tuberculosis and Lung Disease, South-East Asia Regional Office, New Delhi, India ,  National Institute for Research in Tuberculosis (NIRT), Chennai, India and Desmond Tutu TB Centre, Stellenbosch University, Cape Town, South Africa. We are also thankful to programme managers of RNTCP who supported in the process of data collection for this project and study.
We confirmed the homozygous deletion in these patients using FISH (Figure  2C ) and array CGH (comparative genomic hybridization) analysis (data not shown). A breakpoint spanning PCR followed by sequencing defined the genomic deletion encompassing 106,960 bp (Figure  S1 ). Moreover, sequencing of all exons and exon/intron boundaries of the CERS3 gene showed the absence of PCR amplification of exon 13 and therefore confirmed the genomic deletion in the patients (D1, D2, C, and S). ADAMTS17 was not sequenced. One additional Tunisian individual (H) with isolated ARCI also showed a homozygous region on 15q26. 3, but did not carry the genomic deletion described above (Figure  2A ). However, sequencing of the CERS3 gene in this patient revealed a homozygous transversion of guanine to thymine affecting the exon 9 splice donor site (c.
Genotyping and sequencing Genome-wide SNP-genotyping was carried out in a total of 34 consanguineous families (120 individuals) using a human SNP array (Illumina 370k Quad, San Diego, CA). After quality control, the genotyping data were filtered for homozygous regions larger than 1 Mb. Both DNA strands from all subjects and controls were sequenced for the entire coding region and the exon/intron boundaries using standard protocols. Primers for CERS3 flanking the coding exons were designed with primer3. The splice site mutation in patient H is marked by an asterisk. The diagram shows the deletion and the limiting SNPs of the homozygous regions in patient H, D1, D2, C, and S (not to scale). (B) The scale illustration of the deleted region shows the missing SNPs and genes. The internal limit of the deletion is between SNP rs1080429 and rs7179355.
Array CGH Genomic DNA from 4 individuals of family C (one patient, one non-affected brother, and parents) was analyzed on Microarray Sure Print G3 Human CGH+SNP chip 4x180K (Agilent, Santa Clara, CA). The probes were annotated against NCBI Build 37 (UCSC hg19, February 2009). The fragmentation, labelling, and purification of test DNA (reference DNA: HapMap sample of known genotype) as well as hybridization and washing was performed according to Agilent's protocol. The scanning step was performed with Agilent high resolution C scanner (3m) and Agilent Feature Extraction software. Agilent Cytogenomics software 2. 0. 6. 0 was used for imaging.
Immunofluorescent analysis and confocal microscopy Skin biopsies were fixed in 4% PFA, embedded in Tissue-Tek O. C. T. Compound (Sakura Finetek, Torrance, CA), shock frozen in liquid nitrogen, and sectioned to 8 mm. For double immunofluorescence staining, the skin biopsy was fixed in 4% PFA and embedded in Paraplast Plus (Leica Microsystems, Wetzlar, Germany) followed by sectioning to 8 mm. Antigen retrieval was performed at pH 6. 0 in a pressure cooker. The following commercial antibodies were used: rabbit polyclonal antibody to an N-terminal epitope of CERS3 (1:100, HPA006092, Sigma-Aldrich), mouse monoclonal antibody to ceramide (1:100, MAB_0011, Glycobiotech, Ku ¨kels, Germany), rabbit polyclonal antibody to loricrin (1:500, ab24722, Abcam, Cambridge, UK), mouse monoclonal antibody to involucrin (1:200, I9018, Sigma-Aldrich), and mouse monoclonal antibody to filaggrin (1:200, SPM181, Abcam) as primary antibodies and Alexafluor 488 donkey antibody to mouse IgG.
To separate polar lipids (glucosyl(acyl)ceramides) chromatograms were developed using the solvent system chloroform/methanol/water (70/30/5 v/v/v)  [42]. Lipid spots were visualized by carbonization after spraying the chromatograms with 10% CuSO 4 (w/v) and 10% H 3 PO 4 (v/v) and heating them to 150uC for 20 min. Developed chromatograms were photographed and signals were quantified using Quantity One 1-D Analysis software (Bio-Rad Laboratories, Hercules, CA). Lipid spots of acylceramides and glucosyl(acyl)ceramides were identified according to Breiden et al. [42]  using the epidermal lipid reference standard. After lipid extraction, cells were solubilized in 0. 1% (w/ v) SDS and 0. 3 N NaOH at 65uC over night, and the protein content was determined using BCA reagent (Pierce/Thermo Scientific) and BSA as standard.
Immunofluorescence Cells, grown on coverslips, were fixed with 4% paraformaldehyde in PBS for 30 minutes at 25°C and permeabilized as described  [5]. Cells were then incubated for 1h at 25°C with the following primary antibodies: mouse monoclonal anti-HA (1:50 in PBS; Covance, Berkeley, CA, USA) and rabbit polyclonal anti-K1 (1:50 in PBS; Covance). The primary antibodies were visualized using goat anti-mouse IgG-FITC (1:20 in PBS; Cappel) and goat anti-rabbit IgG-Texas Red (1:200 in PBS; Jackson Immunoresearch Laboratories, West Grove, PA, USA) for 30 minutes at 25°C. Nuclei were stained with DAPI (1:1000 in PBS; Sigma-Aldrich Inc. , Saint Louis, MO, USA). Fluorescence signals were analyzed by scanning cells in sequential sections with an ApoTome System (Zeiss); image analysis was performed by the Axiovision software (Zeiss) and 3D reconstruction of a selection of three central optical sections was shown in each figure.
Western blot analysis HaCaT cells were lysed as described  [10] ; 50 µg of total protein were resolved under reducing conditions by 8% SDS-PAGE and transferred to reinforced nitrocellulose (BA-S 83, Schleider and Schuell, Keene, NH, USA). The membranes were blocked with 5% non fat dry milk in PBS 0. 1% Tween 20, and incubated with anti-Bek (C-17, Santa Cruz) polyclonal antibodies, anti-K1 (Covance) polyclonal antibodies and p63 monoclonal antibody (4A4, Santa Cruz). The membranes were rehydrated and probed again with anti-actin (Sigma) monoclonal antibody, to estimate the protein equal loading. Densitometric analysis was performed using Quantity One Program (Bio-Rad Laboratoires, Hercules, CA, USA). The resulting values from three different experiments were then normalized and expressed as fold increase respect to the control value.
Data analysis WHO criteria  [39]  were adopted to define resistance status of the mosquito populations. When less than 80% mortality was observed the population was considered 'resistant'; between 80 and 97% mortality the population was considered 'tolerant' (or 'suspected of resistance' in the literature) and when the mortality was above 97% the population was considered 'susceptible'. Knockdown data were analyzed using the PoloPlus 1. 0 software (LeOra Software). KDT 50 and KDT 95 were generated by means of a logtime probit model. The KDT 50 and KDT 95 generated were compared with that of the An. gambiae Kisumu reference susceptible strain by estimates of KDT 50 and KDT 95 ratios (RR). Biochemical assay data (enzymatic activity per mg protein, levels of MFO, NSE and GST) of Kisumu and M'Bé An.
Acknowledgements The study has been financially supported by the  Institut de Recherche pour le Développement through the research network "Anopheles Biology and Control" (ABC Network). The  ABC Network  is an integral part of the  WHO Collaborating Centre  for pesticide evaluation in public health. We are very grateful to all the staff at the  Institut Pierre Richet, Bouaké, Côte d'Ivoire and Centre de Recherche Entomologique de Cotonou, Benin , for their hard work during the field and laboratory experiments. Special thanks also go to  Aboubacar Koné ,  Jean-Paul Kabran Kouamé  and  Aboubacar Sidick  for technical assistance.
The effect of anisotropic diffusion on hydrodynamic instabilities in the context of Inertial Confinement Fusion (ICF) flows is numerically assessed. This anisotropy occurs in indirect-drive when laminated ablators are used to modify the lateral transport  [1, 2]. In direct-drive, non-local transport mechanisms and magnetic fields may modify the lateral conduction  [3]. In this work, numerical simulations obtained with the code PERLE [4], dedicated to linear stability analysis, are compared with previous theoretical results  [1]. In these approaches, the diffusion anisotropy can be controlled by a characteristic coefficient which enables a comprehensive study. This work provides new results on the ablative Rayleigh-Taylor (RT), ablative Richtmyer-Meshkov (RM) and Darrieus-Landau (DL) instabilities.
Numerical simulations We tested the theoretical predictions accuracy comparing with numerical simulations from the code PERLE. This code solves a 1D Lagrangian system including thermal conduction, laser energy deposition and a perfect gas equation of state. The bidimensional linear perturbation is developed in Fourier series. The code solves for each wavelength the corresponding linearized equations by means of specific numerical schemes. It is used to study the linear perturbation behavior of unsteady flows to understand the main physical mechanisms involved in specific situations  [4, 12]. The anisotropy diffusion coefficient D was introduced in PERLE for this study.
Identifying the independent associations between PA and CRF with different measures of adiposity may be important for preventive purposes because increasing population levels of PA may be more achievable than increasing CRF. Recent data in elderly showed that higher CRF and MVPA are independently associated with lower waist circumference  (18). However, previous studies have mainly relied on CRF measures combined with self-reported data on PA and sedentary time when examining their independent relationships with BMI and waist circumference as measures of obesity  (18) (19) (20). We hypothesized that the associations between CRF, PA, MVPA, and sedentary time with anthropometric markers are independent of each other and differ in their magnitudes of association. Therefore, we quantified the independent associations between objectively measured total PA, MVPA sedentary time and CRF, and anthropometric markers in apparently healthy European men and women.
Physical activity measurement We used a validated combined heart rate and movement sensor (Actiheart, CamNtech, Cambridge, UK), which was attached to the chest via two standard ECG electrodes  (26). This device measures vertical acceleration by a piezoelectric element included in the Actiheart with a frequency of 32 Hz. Following the step test, the Actiheart sensor was initialized for longterm recording of PA summarized into 1-minute epochs. Participants were instructed to constantly wear the monitor for a minimum of 4 days. Data collected by the Actiheart sensor were centrally cleaned and processed at the MRC Epidemiology Unit, Cambridge, UK. This included estimation of activity intensity (J/min/kg) for each time point by acceleration  (27). Next, the identification of nonwear periods from the combination of nonphysiological heart rate and prolonged periods of inactivity was performed.
We converted activity counts obtained by the movement sensor into units of acceleration (m/s 2 /d) as recommended in the literature  (28). We used vertical acceleration as the measure of PA in the analysis as it was shown to be a reliable indicator for body movement, especially walking, and increases steadily with increasing PA intensity (1 m/s Finally, we excluded measurement periods with less than 48 hours of data and averaged daily estimates of time spent in different intensity categories from the two 4 day measurements. We also weighted PA, MVPA, and sedentary behavior outcomes to account for divergence from the optimum monitoring duration of two 4 day measurement periods (i. e. , individuals with at least 4 1 4 days were weighted 1. 0, whereas those with fewer days, e. g. , 4 1 3 days, were weighted less, e.
In the second and third model, we further adjusted for PA and CRF, respectively, in order to examine if the associations are independent of each other. We built Z-scores of all analyzed variables in order to facilitate the comparison of the strength of associations. To better account for measurement precision, we performed a sensitivity analysis with beta-coefficient adjustment for measurement error (where we used the interclass correlation coefficients (ICC) of our two measurements: PA ICC 5 0. 55 and CRF ICC 5 0. 84) as the correction factor  (30). For sedentary time and MVPA models PA ICC was used. All statistical analyses were performed with SAS Enterprise software release 9. 2.
The results from both occasions were averaged which provides data with a lower measurement error. This approach reflects more accurately the habitual PA and CRF status of an individual than a single measurement  (31). Further, different anthropometry parameters were measured by trained personnel and included in the analyses allowing studying the impact of PA and CRF in detail. A potential limitation represents the way CRF was assessed. The validity of this specific step test is not yet determined against a gold standard. However, a similar step test, shorter in duration and administrated in less standardized conditions in the participants home (The Canadian Home fitness Test) correlates strongly (r 5 0. 88) with directly measured oxygen uptake  (32). Also, the objective PA measurement method might have introduced some inaccuracy because of the Hawthorne effect  (33)  which implies that participants are more likely to change their activity during the measurement.
Other studies have examined the independent associations between PA and CRF with metabolic outcomes and suggested that PA is associated with clustered metabolic risk and insulin resistance independent of CRF  (37, 38). Additionally Franks et al. found that CRF modifies the association between physical activity energy expenditure (PAEE) and metabolic syndrome in which the association between PAEE and metabolic syndrome was much stronger in unfit individuals  (38). We observed a positive association between sedentary time with obesity outcomes. Our measure of sedentary time included sleep which has previously been shown to be inversely related to anthropometry (39). However, excluding sleeping time between 12 pm and 6 am did not materially change the magnitude of associations between sedentary time and the outcomes (data not shown).
Acknowledgments We are grateful to all participants who gave their time and effort to the study. We would also like to thank all persons who contributed to the data collection across the study sites. Special thanks to the MRC Epidemiology Unit physical activity technical team:  Mark Betts ,  Laura Lamming , and  Stefanie Mayle  who assisted with data reduction, cleaning, and processing. Many thanks for statistical advice to  Stephen Sharp  and  Sven Kn€  uppel. This study was supported by funding from the  European Union  ( Integrated  Project  LSHM-CT-2006-037197  in the Framework Programme 6 of the  European Community ) and the  Medical Research Council, UK.
Acknowledgments We are grateful to  Martin Sander  ( Bonn Universita ¨t ),  Yoshitsugu Kobayashi  ( Hokkaido University Museum ), and  Hideki Endo  ( Tokyo University Museum ) for support throughout this study and to  Vivian de Buffrenil (Museum National d'Histoire Naturelle, Paris) ,  Koen Stein ,  Nicole Klein  ( Bonn Universita ¨t ),  Naoki Kohno  ( National Science Museum of Tokyo ),  Hikaru Uno  ( National Institute for Agro-Environmental Sciences Carbon and Nutrient Cycles Division) ,  Daisuke Koyabu  ( University of Zu ¨rich ) and  Daisuke Suzuki  ( Sapporo Medical University ) for helpful advices throughout this study. The authors thank  Isao Segawa ,  Katsuie  and  Katsumi Yabuki  ( Ashoro Museum of Paleontology ),  Seiichi Toshimitsu  ( Geological Museum, Geological Survey of Japan, AIST ),  Tadasu K.
Background The identification of mold in the clinical laboratory is classically based on macroscopic and microscopic examination of the colonies grown on mycological culture media. It is a slow and complex process requiring highly skilled mycologists, and misidentifications may occur, even in experienced reference laboratories  [1]. Additionally, some distinct species, which are identified via DNA sequence analysis, are morphologically indistinguishable  [2] [3] [4]. Therefore, multilocus DNA sequence analysis represents the recommended approach to accurately identify these microorganisms. Nevertheless, the DNA sequence-based identification of filamentous fungi is primarily limited by the following: i) low DNA extraction yields because mold cells are difficult to lyse, ii) the presence of PCR inhibitors, iii) the presence of misidentified sequences in non-curated public DNA sequence databases, and iv) the cost and time required for sequencing.
These results were dramatically improved compared with those obtained using library B1, which included only one isolate per species and one subculture per isolate. Indeed, using the B1 library, we only obtained 449 (64%) concordant identifications, 40. 09% of which displayed LS values higher than 1. 7 (180/449) and only 15. 59% were higher than 2. 000 (70/449). Modulation of the MSP creation parameters, while considering the B1 library, tended to show that the performance of the database could be improved by an increased peak frequency minimum, regarding the number of concordant identifications and the Log Score.
Discussion In contrast with recurrent efforts to improve the reproducibility of the MS-based identification of filamentous fungi by standardizing the pre-treatment procedures, we report the first study aiming to improve identification by comparing the effectiveness of distinct RMS library architectures. However, in a recently published study aiming to identify filamentous fungi using MS, de Carolis et al. [22]  have shown that some of the mass spectra data obtained during routine diagnosis matched preferentially with the RMS obtained from either young or mature cultures of the same species. Regarding Scedosporium identification, Coulibaly et al. [16]  have shown that both the culture media and the duration of culture had a significant impact on MALDI-TOF assay results. However, the standard recommendation to address problems associated with the heterogeneity of microorganism species is merely to increase the number of strains per species in the library.
Our findings confirm this hypothesis; however, it is particularly challenging to increase the number of well-characterized strains included in the RMS library for each fungal species. Numerous species have been described to play a role in human infections and, in many cases, only a single strain or a few strains of the same species are preserved in international collections. In the current study, we demonstrated that increasing the number of mass spectra generated from distinct subcultures of a given strain yields a significant improvement in the process of filamentous fungi identification and can partially offset the relatively low number of specific strains available to construct RMS libraries. Modulating MSP creation parameters yielded discrepant results depending on the database that was taken into account.
As the B7 database appears ideal for filamentous fungi identification, Bruker's default parameters for the MSP creation method seem to be more suitable for library construction. Conversely, the number of spectra derived from a strain (4, 10, 20, or 40) that were used to construct RMS did not result in a marked improvement of the identification performance. This straightforward optimization of RMS library architecture significantly enhanced the identification effectiveness. In this study, we used quadruplicates of the clinical samples to test the various RMS libraries. By taking only the spectrum with the highest LS value into account, we observed an increased percentage of concordant identifications (e. g. , ranging from 87% to 90% with library B7). In parallel, using the four clinical replicates to construct an MSP and then compare it to the various libraries did not alter the results but instead tended to complicate the procedure, as this cannot be performed with RTC software during routine analyses.