dataesr/scientific-paragraphs-categorization huggingface dataset:
Explosive Precursors' Detection Using Pattern Recognition The above mentioned database was then used to compute PLS-DA classification models for the three target compounds. In this context, we performed a more comprehensive evaluation of the effect of several spectra pre-treatment methods in removing the uninformative variation while retaining the most informative one, so as to optimize model performance. To this aim, several PLS-DA models have been calculated for each target, considering the different spectra pre-processing methods listed in Table  2. The results obtained from this pre-treatment optimization step (data not shown for brevity) have indicated that the highest efficiency values in cross-validation and in prediction were achieved by applying pre-treatment 6: standard normal variate (SNV) followed by auto-scale.
Acknowledgments: This work has been supported by the  European Commission  Program  STREP-FP7-SEC-2010-1-Bomb  Factory Detection by  Networks of Advanced Sensors  grant agreement No. 261685. The authors would like to thank  Luigi Cassioli  and  Silvana Grossi  of the  Italian Air Force  for their support during the tests at the  Pratica di Mare (Italy)  base and  Pedro Santo Antonio  from  TEKEVER, Obidos (Portugal) , for developing the software connecting the sensors of the BONAS network. The authors would also like to thank  FOI (Swedish Defence Research Agency ) head of research and leading  Swedish explosive expert Henrik Östmark.
Data treatment and statistical analysis Data analysis was performed using the R statistical software  [35]. Leaf susceptibility, spore density on spore trap slides and disease severity at leaf scale were not normally distributed according to the Shapiro-Wilk normality test (P < 0. 05). We therefore used non-parametric tests to assess the impact of each factor (cultivar, leaf rank and rain type) on these three variables. Wilcoxon signed rank tests were used to compare samples two by two. Kruskal-Wallis tests were used to compare more than two samples and to detect shift between distributions. Multivariate analysis was performed using mean data measured on the first row (2 cultivars in pure stand x 2 rain types x 3 leaf ranks x 2 repetitions = 24 individuals), computed for 3 factors (cultivar, leaf rank, rain type) and for 7 variables (total lesion area at row scale, conditional severity, incidence, leaf susceptibility, leaf surface area and spore fluxes with and without canopy).
Acknowledgments We thank the  ABIES doctoral school for Tiphaine Vidal 's PhD grant and the  Wheatamix  project financed by the  National Agency of Research  ( ANR-13-AGRO-0008 , www6. inra. fr/wheatamix/) for financial support. We are grateful to  Michel Burban ,  Pascal Duprix ,  Alain Fortineau ,  Laurent Ge  ´rard,  Samuel Joseph  and  Francois Lecourt  for technical assistance, to Frederic Suffert and  Thierry Marcel  for helpful discussions and advice, and to  Bruno Andrieu ,  Christophe Gigot ,  Corinne Robert  and the anonymous reviewers for their comments on the manuscript. We have special thoughts for Witho Anatinae. We also thank  Jane Curtis  for English corrections.
In contrast, the GFP-negative fraction of AGM had a low frequency of CFU activity in both assay systems and generated predominantly unilineage colonies, consistent with reduction or loss of Hhex expression as progenitors progressively lose their multilineage potential and commit to a unique cell compartment. Finally, the primitive nature of Hhex-expressing AGM cells sorted from HhexB transgenic mice was assessed in adoptive transfer experiments. Critically, long-term reconstitution activity was restricted to the Hhex expressing cells, with close to 100% of all hematopoietic tissue being derived from the engrafted cells (Figure  4I ). No engraftment was detected in either peripheral blood or bone marrow in 5 out of 5 recipients injected with GFP-negative AGM cells; as GFPnegative cells cannot be detected by expression of the fluorescent reporter, the level of the reporter construct in the genomic DNA of recipient mice was assessed by PCR, relative to actin as a control gene (data not shown).
Study sample and data collection The recruitment was carried out at the University Hospitals of Geneva (Switzerland) and the University Hospital of Clermont-Ferrand (France) maternity wards by four research midwives. Women were recruited during their stay in the maternity ward and asked if they agreed to participate. The inclusion criteria were: speak and read French, primiparous, singleton foetus, gestational age greater than 37 weeks and the new born was not separated from his mother for medical reasons during the maternity stay. We excluded multiparous subjects because, in general, they were more satisfied in their childbirth experiences than primiparous subjects  [39]. We can explain this phenomenon by some obstetric factors, including: shorter labor, fewer interventions, and more realistic expectations according to their previous childbirth experiences.
Additionally, according to their predisposition to dissatisfaction, the primiparous subjects represented a population of choice to explore factors associated with the childbirth experience. We preferentially sent the questionnaire by e-mail but the option of sending by post was given to participants. The questionnaire was sent to participants at four weeks post-partum. Two recalls if necessary were done up to six weeks post-partum. The online questionnaire procedure was easier for gathering data, had minimal cost, decreased the possibility of data errors with responses automatically stored in a database, and could ensure women's privacy when answering potentially sensitive questions about perceived partner support during childbirth. The choice of the best time to collect the data regarding the childbirth experience was based on the results and recommendations from previous studies  [2, [39] [40] [41] [42] [43] [44].
The proportions were compared between groups, and the differences were tested using the Fisher exact test. To analyse the score data, we coded the answer format of the 4-point Likert scale, as follows: 1 (totally), 2 (in part), 3 (not so much), and 4 (not at all). The ratings of negatively worded statements were reverse-scored so that the higher scores more reflected a negative childbirth experiences. The means, with their standard deviations, were calculated for continuous variables, and the statistical significance of differences between groups was tested with Student's t-test. Statistical analyses were performed using SPSS 22. 0 for Windows (SPSS Inc. , Chicago, IL, USA), and Stata, version 13. 1 (StataCorp, College Station, TX, USA).
Acknowledgements We thank all of the participants who responded to the QACE. We thank  Delphine Courvoisier  for serving as the methodological expert for the questionnaires as well as  Ophélie Boucher  and  Antonina Chilin  (clinical midwives), and  Anne Gendre  and  Barbara Kaiser  (academic midwives). Their comments helped refine the questionnaire for the pre-test. We also thank  Corine Oberson  for the data management. Chloé Barasinski  and  Marine Julien  were the research midwives who collected the data at Clermont-Ferrand.
The light curve exhibits evident short-term variability. The bottom panel displays the light curve extracted from the background aperture region; the blue dashed lines in the top two panels show the mean background count rate scaled by the source region area. The light curves display the average count rate in each 1 ks time interval; note that during some of these time intervals, the effective exposure time is less than 1 ks due to Earth occultations or periods of poor data quality. sult, accurate background subtraction is more important when using the larger aperture regions and it is not surprising that our crude subtraction method, which assumes a spectrally flat background, for the model-independent fluxes leads to discrepancies with the spectral fluxes. 5. 4. X-ray variability NuSTAR's high time resolution allows us to characterize the timing properties of detected sources over a range of timescales.
Sources were flagged as longterm variable with an "l" in Table  6  if their 3 -10 keV photon flux differed by > 3σ based on their flux measured uncertainties; given the number of flux comparisons performed, this 3σ threshold should result in 1 spurious detection. NNR 1, 10, 11, 19, and 29 were found to be variable using this criterion. In addition, we compared Chandra and NuSTAR fluxes to check for variability on year timescales. For all sources with sufficient photon statistics, we compared the joint spectral fits to Chandra and NuSTAR data (see § 5. 7 for details), and identified sources with normalizations that differed at the > 90% confidence level. Since we performed these joint fits for 24 sources, we would expect as many as two spurious detections of variability, but we made the criterion more stringent by requiring that for a source to be considered variable between the Chandra and NuSTAR observations, its Chandra and NuSTAR normalizations must be inconsistent regardless of which of three different spectral models is adopted.
The ability to detect pulsations depends strongly on the source and background counts and number of search trials. For a sinusoidal signal, the aperture counts (source plus background) necessary to detect a signal of pulsed fraction f p is N = 2S/ f 2 p , where S is the power associated with the single trial false detection probability of a test signal ℘ = e -S/2 ; S is distributed as χ 2 with two degrees of freedom (van der Klis 1989). In practice, for a blind search, we need to take into account the number of frequencies tested N trials = T span / f Nyq , when T span is the data span and f Nyq = 250 Hz, the effective NuSTAR Nyquist frequency. In computing N we must allow for the reduced sensitivity of the search due to background contamination in the source aperture (N b ); the minimum detectable pulse fraction f p (min) is then increased by (N s + N b )/N s.
MEGAPOLI campaigns and car MAX-DOAS measurements The car MAX-DOAS observations in and around Paris were described in detail in  Shaiganfar et al. (2015). Here, we give only a brief overview. Two extensive measurement campaigns were organised within the framework of the MEGAPOLI project  (Baklanov et al. , 2011 ; see also http: //megapoli. dmi. dk/) in June and July 2009 and in January and February 2010. Car MAX-DOAS measurements were performed on 25 days in summer and 29 days in winter. One major aim of the car MAX-DOAS measurements was to quantify the total NO x emissions from Paris. For that purpose we applied the CIM by carrying out car MAX-DOAS measurements of the tropospheric vertical column density (VCD) along closed circles around Paris. Details on the data analysis of the car MAX-DOAS measurements are given in  Shaiganfar et al.
Since individual MAX-DOAS measurements were performed for limited periods of time (about 1 min), the integral is substituted by a sum over the fluxes calculated for the segments corresponding to individual measurements: F NO 2 ≈ i VCD i • ω i • sin (β i ) • s i. (2) Here, VCD i represents the NO 2 VCD obtained from measurement i; ω i represents the average wind speed during the period of the car MAX-DOAS measurements. β i represents the angle between the (average) wind direction and the driving direction at the location of measurement i; s i indicates the distance between the location of measurement i and i +1. As wind data we used the wind fields from the model simulations at different heights (see Sect. 3 and Fig. 3 ). From this data set we calculated the average wind speed and direction for all individual locations and times of the car MAX-DOAS measurements along the circle (for the vertical averaging, see below).
For both days, large differences in the NO x emissions derived using either averaged or variable wind data were obtained (36 % for 24 January; more than 100 % for 29 January). We also determined the NO x emissions using the spatiotemporally varying wind fields (interpolated to the exact locations and times of the individual car MAX-DOAS measurements) and compared them to the results derived from using the averaged wind data. Figure  12  displays the relative differences in the derived NO x emissions with either averaged or spatio-temporally varying wind fields for all days versus the wind-related errors, calculated as described at the beginning of this subsection. For most days, the relative differences are small (< 10 %), but larger differences are found for days with large errors caused by the variability in the wind fields (see also Fig.
Acknowledgements. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  FP/2007-2011 within the project  MEGAPOLI , grant agreement no. 212520. We are very thankful to several drivers, who supported the car MAX-DOAS measurements:  Thierry Marbach ,  Tobias Tröndle ,  Steffen Dörner ,  Christoph Hörmann ,  Bastian Jäcker  and  Sven Krautwurst. The authors would like to acknowledge:  Jean-Charles Dupont  and the  SIRTA team  for providing the wind cube lidar data used in this study. We acknowledge Airparif (see http://www. airparif. asso. fr/en/index/index)for providing the emission data for the greater Paris region. The article processing charges for this open-access publication were covered by the  Max Planck Society. Edited by:  Robert McLaren Reviewed  by: two anonymous referees.
Sequence analysis Amplified sequences were used as query in a BLAST search in the NCBI database to confirm that the amplified sequences were the target genes. The DNA sequences were edited in Geneious R6 (Biomatters, http://www. geneious. com/). Sequences of each gene were independently aligned using MACSE (Multiple Alignment of Coding SEquences accounting for frame shifts and stop codons)  (Ranwez et al. , 2011)  for COI and CAD genes. For 16S and 28S rDNA sequences, alignments were generated using Muscle  (Edgar, 2004). For each alignment, segments that had too many variable positions or gaps were removed using Gblocks 0. 91b  (Castresana, 2000)  to make alignments more appropriate for phylogenetic reconstruction. The reading frames and sequence statistics were checked under MEGA v6. 0  (Tamura et al.
Selected sites with P < 0. 05 were reported. The following sequences were used in this study: COI sequences from a C. bolitinos Meiswinkel population from Reunion Island (A. Desvars, J. C. Delecolle, F. Biteau, G. Gerbier, F. Roger and T. Baldet, unpublished data) [accession numbers: HQ447061. 1 and HQ447062. 1], a C. fulvus Sen and Das Gupta population from Australasia  (Gopurenko et al. , 2015)  [accession numbers: KT352267. 1, KT352340. 1, KT352547.
2. 3 (Ronquist et al. , 2012). Two simultaneous and independent runs consisting of 16 Metropolis-coupled Markov chain Monte Carlo (MCMC) running 50 million generations were used, with a tree sampling every 1000 generations to calculate posterior probabilities (PP). In order to investigate the convergence of the runs, we investigated the split frequencies and Effective Sample Size (ESS) of all the parameters, and plotted the log-likelihood of the samples against the number of generations in Tracer 1. 5 (http://BEAST. bio. ed. ac. uk/ Tracer). A value of ESS > 2710 was found as a good indicator of convergence. The ML analyses were conducted with the best model selected using PhyML 3. 0  (Guindon et al. , 2010)  for each dataset and RAxML for the concatenated dataset  (Stamatakis, 2014). We performed 1000 bootstrap replicates to investigate the level of support at each node.
, 2006). We conducted the bGMYC model using ultrametric gene trees inferred in the BEAST 1. 8. 0  (Drummond et al. , 2012)  without outgroups under a strict clock model and a Speciation: Yule Process Tree Model. The runs consisted of 10 million generations sampled every 1000 cycles. Convergence was assessed by ESS values. A conservative burn-in of 10% was performed after checking the log-likelihood curves in Tracer 1. 5. As recommended by  Reid & Carstens (2012) , 100 trees sampled at intervals from the posterior distribution of trees using LogCombiner 1. 8. 0  (Drummond et al. , 2012)  were used to perform the bGMYC analyses. Species delimitation analyses were conducted in R using the package 'bGMYC'. For each of the 100 trees selected, the analyses consisted of 250 000 generations with a burnin of 25 000 and a thinning parameter of 100, as performed in  Toussaint et al.
DNA sequences Of 153 samples considered in this study, we obtained 139 sequences for the COI gene, 147 for 16S rDNA and 146 for 28S rDNA. Many samples failed sequencing to the CAD gene (66 of 153 samples amplified). DNA sequences of COI, 16S rDNA and 28S rDNA genes generated in this study are deposited in GenBank (see Table  S1  for additional details): COI (MF399674-MF399811); 16S rDNA (MF422796-MF422942); 28S rDNA (MF422943- MF423087). The final concatenated alignment of COI, 16S rDNA and 28S rDNA yielded 132 sequences of 1493 bp for 31 morphological units corresponding to all studied Culicoides species excluding C. trifasciellus and C. sp. #54 pale form. Information relative to sequence statistics and best-fit partitioning scheme and partition-specific substitution model are provided in Tables  2  and 3.
Phylogenetic relationships Phylogenetic analyses conducted with COI, 16S rDNA and 28S rDNA genes concatenated, and the CAD gene are represented in Figs  3  and 4 , respectively. A concatenated COI, 16S rDNA and 28S rDNA alignment of 132 specimens which included all studied Culicoides species excluding C. trifasciellus and C. sp. #54 pale form, contained six clades: C1-C6 (Fig. 3 ). CAD alignment of 72 sequences, including six sequences from GenBank: KJ163009. 1,.
C. kibatiensis ( ): Tip of 2 nd radial cell pale and intrudes into the 2 nd costal spot. 3 rd post stigmatic pale spot in r3 cell does not touch the wing margin. and C. tuttifrutti) were well-supported and resolved at the concatenated phylogenies constructed, but less so at the CAD gene. Culicoides gulbenkiani sequence from GenBank and C. kibatiensis were nested among the Imicola group in the CAD phylogeny (Fig. 4 ). The clade C3 cluster includes two groups.
1, KT352629. 1 and KT352696. 1], a C. miombo Meiswinkel population from Benin (B. Mathieu, C. Garros, T. Balenghien, E. Candolfi, J. C. Delecolle, and C. Cetre-Sossah, unpublished data) [accession numbers: KF417704. 1 and KF417705. 1] and a C. similis Carter population from India (Harrup et al. , 2016) [accession numbers: KT307841. 1-KT307842. 1]; and CAD sequences from C. kwagga, C. loxodontis Meiswinkel, C. gulbenkiani Caeiro, C. tuttifrutti Meiswinkel and C. bolitinos populations from South Africa [accession numbers: KJ163032. 1, KJ163034. 1, KJ163025. 1, KJ163044. 1, KJ163009. 1], and C. imicola Kieffer from France [accession number: KJ163028. 1] (Bellis et al. , 2013).
Acknowledgements This study was funded by  UMR 117 ASTRE, Cirad. M. T. B. was granted by the  Islamic Development Bank. M. T. B. would like to thank  Christine Chevillon , IRD, UR 224 MIVEGEC, Montpellier, France; and  Nabil Haddad, Laboratory of Immunology and Vector Borne Diseases, Lebanese University. The authors are grateful to all the people who gave assistance in Culicoides collection. This work was supported by the high-performance cluster of Cirad. M. T. B. and C. G. designed the study; K. L. and M. F. contributed the collection and identification of Culicoides; M. T. B. , K. H. , B. M. and C. G. analysed the data; K. L. , B. M. , K. H. , G. V. , A. G. F. , T. B. , L. G. G. G. and J. B. contributed to the manuscript after its first draft by M. T. B. and C. G. ; all authors read and commented on the final manuscript version.
Amplification products were analyzed using the ABI 3730 and ABI 3130 Genetic Analyzers (Applied Biosystems). Electrophoresis results were analyzed using Genscan v. 3. 7 and Genotyper v. 3. 7 (Applied Biosystems). Y-STR data of haplogroup O3a2b Ã -P164 (xM134) and O3a-M324 Ã (xM7,M134) in Austronesian populations were also collected from the literature  [28, 30, 31]. According to Mirabal et al. [27] , O3a-M324 Ã (xM7,M134) samples from Austronesian populations in Oceania should actually be O3a2b Ã -P164(xM134). Therefore, Y-STR from Delfin et al. [30]  were also included in this study. The haplotypes for which data was available for all 15 Y-STR polymorphisms (excluding DYS385a/b from 17 Y-STRs) were used to construct the median-joining network using the program NETWORK 5. 0. 0. 0 (Fluxus Engineering)  [37].
Others biomarkers, like MYC or MYCN amplifications, occurring at lower frequency in pHGGs, were reported associated with histone mutations, especially in diffuse intrapontine gliomas (DIPGs) and in histone G34V mutated pHGGs  [28] [29] [30] [31] [32] [33] [34]. In terms of hypoxia, pHGGs display the classical characteristics of microvascular proliferation and/or necrosis, associated with intra-tumor hypoxia, although HIF-1 or HIF-2 over-expressions have not been specifically studied, but involvement only suggested. In those highly resistant tumors to chemotherapy and radiotherapy, a strategy of bi-therapy consisting of inhibiting both mTORC1 by rapamycin and HIF-1α by irinotecan might be efficient by completely abolishing HIF-1α accumulation  [35, 36]  and was evaluated in a pediatric phase I on malignant refractory tumors (ClinicalTrials.
gov Identifier: NCT01282697, results are currently finalized). To establish, the crucial role of mTOR/HIF-1α axis in pHGGs, we, first, assessed its protein and transcriptomic deregulations in a pediatric cohort of 26 pHGGs and in pHGG cell lines, cultured in both normoxic and hypoxic conditions. Secondly, based on the concept of a bi-therapy targeting mTORC1 and HIF-1α in pHGGs, we hypothesized that the distinctive genomic alterations of RAS/ERK and PI3K/AKT upstream signals and HIF-1α/HIF-2α/MYC relative expressions would differentially modulate the response to this double inhibition and impact on specific metabolic changes. The final objective of this preclinical study was to determine the predictive metabolomic, transcriptomic and protein profiles of response to this therapeutic combination in pHGG in vitro models.
mRNA expression profiling analyses Expression profiling of 5 pHGGs, compared to 10 pilocytic astrocytomas and 4 grade 1 gliomas, was performed. Those 2 DIPGs, 2 thalamic and 1 hemispheric pHGG are including in our whole cohort of 26 patients. The cell lines (KNS42, SF188 and UW479) were analyzed in normoxia and hypoxia conditions at different treatment checkpoints: before any treatment, after 24h treatment exposure with rapamycin (10 nM), irinotecan (5 μM) and both drugs. All those experiments were performed in duplicate. Gene expression analysis was performed using Affymetrix U133 Plus 2. 0 chips. Data were read with the affy package and normalized with rma. Gene expression was annotated with ensembl genes from build 37 assembly 71.
HRMAS NMR spectroscopy HRMAS NMR spectroscopy was performed for KNS42, SF188 and UW479 in normoxic and hypoxic conditions of proliferation and after 24h exposure with rapamycin (10 nM), irinotecan (5 μM) and both drugs. Six replicates for each condition were analyzed. For cell lines, a pellet of 10 7 cells was collected and directly snap-frozen in liquid nitrogen. HRMAS NMR spectra were analyzed on a Bruker Avance III 500 spectrometer operating at a proton frequency of 500. 13MHz. HRMAS NMR data acquisition and spectra processing were performed as previously described  [60, 61]. A one-dimensional (1D) proton spectrum was acquired for each sample. The chemical shift was referenced to the peak of the methyl proton of L-lactate at 1. 33 ppm. Two-dimensional (2D) homonuclear and heteronuclear experiments were also recorded in order to confirm resonance assignments.