dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional genomic data sets To place our genomic data in a regional context, we reanalyzed raw paired-end Illumina reads from 107 additional P. vivax clinical isolates from the Americas. Fastq files were downloaded from the Sequence Read Archive (SRA) of the National Center for Biotechnology Information, United States, and processed in the same way as our newly obtained sequences. Three clinical isolates from Brazil had sequence data generated on an Illumina Genome Analyzer II platform at the Welcome Trust Sanger Institute (Hinxton, Cambridge, UK), as part of the P. vivax genome variation project coordinated by the MalariaGEN network  [14]. Other isolates-20 from Brazil, 34 from Peru, 31 from Colombia, and 19 from Mexico-had whole-genome sequence data generated on an Illumina HiSeq 2000 platform at the Broad Institute of MIT and Harvard (Cambridge, MA, USA), as part of the International Centers of Excellence for Malaria Research (ICEMR) program  [11].
We used GATK version 2. 0  [28]  for SNP calling following the GATK Best Practices (https://software. broadinstitute. org/gatk/best-practices/). GATK UnifiedGenotyper with the BaseAlignmentQuality option was used to obtain high-confidence SNPs by applying stringent VariantFiltration criteria: (a) coverage > 20×, (b) mapping quality > 30, (c) base quality > 30, read depth and allelic fraction by sample ! 1, and (d) haplotype score 3. We removed all SNPs with > 2 alleles and those with minor allele frequency < 0. 01 (reads counted across all sequenced samples). SnpEff  [29]  was used to identify SNPs mapping to coding sequences (further classified as synonymous or nonsynonymous), introns, and intergenic regions of the Sal-I reference genome. The resulting catalogue of 94,122 high-confidence SNPs was used to genotype each individual sample using GATK UnifiedGenotyper with default parameters except for the minimum phred-scaled confidence threshold, with a calling variant = 50 and emitting variant = 10.
Heterozygote calls were converted to the majority allele if !75% of the reads in that sample were the majority allele; otherwise, the allele was undetermined. Sites with < 5× coverage in a given sample were filtered out at this stage. The final data set of P. vivax nuclear genome sequences from the New World comprised our 9 newly sequenced samples from BRA and 75 high-quality SRA samples (2 from BRA, 23 from PER, 31 from COL, and 19 from MEX); 13 SRA samples were removed during the genotyping process because the number of SNPs identified (range: 13 to 2,889) was below the predefined minimum of 3,000 nucleotide differences compared to the Sal-I reference, consistent with poor sequencing coverage. Overall, we excluded 21 (out of 23) SRA samples from BRA and 11 (out of 34) SRA samples from PER at different stages of this analysis.
0 (http://www. megasoftware. net/). To estimate the ancestry shared between individual isolates, we used the ADMIXTURE software package  [34]  with either all 94,122 high-quality SNPs or a curtailed set of 12,762 SNPs that are not linked. To this end, we removed each SNP that had an r 2 value > 0. 1 with any other SNP within a 60-SNP sliding window advanced by 10 SNPs each time. The optimal number of clusters (K) was determined by performing multiple runs of the software under different K values (2-10) and selecting the K values (K = 2 and K = 3) associated with the lowest cross-validation error compared to other K values (S2 Fig).
These findings are particularly relevant to malaria-eliminating countries in the continent; if parasites can be assigned by molecular genotyping to their countries of origin, locally acquired and imported infections can theoretically be easily distinguished. Moreover, genetic analysis can also help to determine the likely origin of imported infections  [10, 48]. Accordingly, our PCA data revealed a clear clustering of BRA samples, which were separated from other populations by principal components C1 and C3 when the complete SNP data set was used. MEX was separated from other populations mostly by C1 and C2, while PER and COL were less clearly differentiated (Fig 4A ). We next tested whether a recently described P. vivax SNP barcode  [49]  would be able to correctly assign New World isolates to their countries of origin.
Acknowledgments We thank all sample donors for their participation in this study;  Kezia K. G. Scopel  for help in field work;  Danielle S. Menchaca Vega  for laboratory support; and  Daniel E. Neafsey  for a critical reading of the manuscript. This publication uses sequence data generated by the  Wellcome Trust Sanger Institute (Hinxton, UK)  as part of the  MalariaGEN P. vivax Genome Variation project  (available at https://www. malariagen. net/data/p-vivax-genome-variation-may-2016data-release)and by the  Broad Institute of MIT  and  Harvard (Cambridge, MA) , as part of the  International Centers of Excellence for Malaria Research (ICEMR) program  of the  National Institute of Allergy and Infectious Diseases (NIAID)  of the  National Institutes of Health (NIH) ,  United States.
Funding: Research was supported by research grants from the  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico  (CNPq; http://cnpq. br/), Brazil  ( 590106/2011-2  to MUF), the  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP ; http://www. fapesp. br/),Brazil /51835-7 to MUF), and the  National Institute of Allergy and Infectious Diseases (NIAID) ,  National Institutes of Health  (NIH; https://www. niaid. nih. gov/),  USA  ( International Centers of Excellence in Malaria Research [ICEMR] program ,  U19 AI089681  to  Joseph M. Vinetz ,  University of California, San Diego ); PTR was supported by a scholarship from  CNPq , which also provided a senior researcher scholarship to MUF. RMG-L ( 2010/51938-0 ),  NFL  ( 2013/26928-0 ), and SB (2013/23770-6) were supported by scholarships from  FAPESP  and TCO was supported by a scholarship from the  Coordenacão de Aperfeicoamento de Pessoal de Nı ´vel Superior  (CAPES; http://www.
Small dust particle density measurements Turnkey environmental dust sensors need around thirty seconds to acquire enough data to compute an accurate measurement. The DCES microcode implements several tricks to lower that measurement period to only 1. 5 seconds, making it 20 times faster. But there is still one major problem remaining: the off the shelf SHARP sensor is not calibrated. Two approaches to the calibration issue are: (i) calibrate the sensor. (ii) completely skip it and work with uncalibrated data. At CERN a DCES and a calibrated dust sensor were installed together in a sealed box and volatile chemicals sprayed inside. The two sensors measured the same airflow containing the same density of volatile chemical droplets. Both readings were collected and linear calibration factors determined.
Large dust particle density measurements The simple and robust SHARP GP2Y1010AU0F dust sensor is a single wavelength sensor and therefore cannot directly measure the density of large dust particles. This limitation can be mitigated by optical physics. Mie scattering can be applied to this problem because the diameters of dust particles are similar to the wavelength of the infrared LED of the sensor. Mie theory describes the scattering of light by particles and it can be used to the light intensity received by the optical sensor photodiode after it has been scattered by an homogeneous cloud of spherical particles of a fixed diameter. The Mieplot [2] software was used to simulate the light intensities that the optical sensor would receive for various sizes of dust particles. The resulting graph in Figure  4  illustrates how the measured intensity fluctuates when the diameter of dust particles is over five times bigger than the wavelength of the scattering light (for the SHARP sensor it means particles larger than 4 microns).
Each DCES is Puppet managed and runs a standard SNMP agent that sends SNMP traps when abnormal levels of dust are detected. Dust information is sent through a lemon sensor and into CERN Elasticsearch instance, and simultaneously into an InfluxDB time series database that allows custom analysis in Jupyter notebooks. possibilities are endless thanks to DCES being a fully open source project. The DCES does not rely on any CERN specific IT infrastructure and has already been or is being integrated outside the CERN IT department.
Experimental data The data we have used come from experiments performed in different campaigns at the Neutron Time-Of-Flight (n TOF) facility at CERN  [6]. A very intense neutron flux is produced by spallation reactions on a lead target using a 20 GeV/c proton beam from the CERN Proton Synchrotron (PS). The water surrounding the spallation target acts as a moderator to produce a neutron flux covering a neutron energy range from thermal up to above 1 GeV. The long (185 m) flight path between the spallation target and the experimental area makes it possible to perform high-resolution time-of-flight measurements. More details on the facility and of the neutron beam spectrum can be found in  [7]. Fission events were detected using a reaction chamber containing Parallel Plate Avalanche Counters (PPACs) developed at IPN-Orsay.
036. Taking advantage of the available (p,f) experimental data we have adjusted these fission parameters for the three istopes considered in the present work and the results are listed in Table  1. It is to be pointed out that for the uranium isotopes close numerical results are obtained with both model chains, INCL++/ABLA07 and INCL++/GEMINI++, while for 209 Bi only the INCL++/ABLA07 chain yields a satisfactory fit to experimental data below 1 GeV. This apparent failure of the INCL++/GEMINI++ chain in the lead-bismuth region was already pointed out in Ref. [1]. Figure  2  compares the results obtained with both versions of the evaporation fission models for the ratio  between the (n,f) and (p,f) reactions for 238 U. It can be seen that this ratio is almost flat and near one from above 500 MeV up to more than 1 GeV.
Figures  4  and 5  show the (p,f) and (n,f) cross-sections for 235 U and 238 U, respectively. Besides the EXFOR available experimental datasets there are the Bologna's MC fits and the evaluation done in this work. It is worthwhile mentioning the discrepancy found with the IAEA values  [2]  (here in blue) between 200 MeV and 500 MeV. In Fig. 6  the results for bismuth are plotted, showing for the (p,f) cross sections only the numerical Monte Carlo fit obtained with the INCL++/ABLA07 chain, that gives a very nice agreement with the experimental data below   1 GeV, thus defining the fitting model parameters listed in Table  1. The evaluation done for 235 U and 238 U is compared with the above mentioned IAEA values as well as with the ones retrieved from JENDL/HE-2007 library, in Fig. 7. Finally, Table  2  lists the cross sections at different values of the incident neutron energy in the range from 190 MeV up to 2 GeV, as they are plotted in Figs.
Statistical analyses All statistical analyses were conducted using the software R v3. 1. 2 loaded with the packages car, lme4, MASS and lsmeans. The survival rate in between each developmental stage (defined here as "age") of offspring (entered using the cbind function) was tested using a generalized linear mixed-effects model (GLMM, with binomial error distribution). In this model, the age (second, third and fourth nymphal instars, and adults), early pathogen exposure (presence/absence) and early maternal presence (presence/absence) were entered as explanatory categorical factors, while the clutch identification (ID) was entered as a random factor to control for the fact that each clutch was used for each age. Because we interested in the survival rate of nymphs until their reach adulthood, adult males and females were pooled as "adults" in this model.
Immune parameters were then analyzed separately for nymphs (for which the sex was unknown) and adults (for which the sex was known). For each nymph and adult data set, hemocyte concentration and total-PO activity were analyzed using two LMMs, in which either the age of the nymphs (second, third and fourth nymphal instars) or the gender of the adults (male or female), early pathogen exposure, early maternal presence and the weight of the measured individual were entered as explanatory factors, whereas the ID was used as a random effect. In nymphs, the weight of each class of age was scaled and centered to correct for the inherent difference in weight between each instar. All models first included all interactions between the explanatory factors and were then simplified stepwise by removing the nonsignificant interaction terms (all P-values > 0.
Smaller effective population size of mtDNA due to linked selection is perhaps the primary reason of the increased Fst, but sex biased demographic history might also increase the Fst of mtDNA. The distribution of Fst along 1 kb windows of genomic sequence shows global differentiation at the whole genome scale (Fig. S8 ), with different extent among loci. We conclude from phylogenetic and Fst analyses that there is ) and rice strain (from MS_R1 to MS_R8) against the reference genomes of the C strain (left), the R strain (middle) and mitochondrial DNA (right). The average genetic distance between pairs of individuals was estimated by the comparison of the genotype (see supplementary information for mode detail) and the distance matrix was generated from these distances. The neighbour joining tree was reconstructed using neighbour program in the phylip package with 1,000 bootstrapping, and the consensus tree was generated using the consense program in the same package.
(b) Neighbour joining phylogenetic tree of mitochondrial genomes from natural populations of the corn (C1-C9) and the rice (R1-R9), reference sequences of the corn (Corn REF) and the rice strains (Rice REF) and outgroup species (Spodoptera litura and S. exigua). The DNA sequences of Spodoptera frugiperda were inferred from the VCF and those of outgroup species were downloaded from the NCBI homepage. Then, multiple sequence alignment was generated using the muscle software. The neighbour joining tree was reconstructed using MEGA software with 1,000 times of bootstrapping. significant genomic differentiation between the two strains genomes. We then investigated whether there was adaptive evolution according to the host-plant ranges. Almost no difference in number of chemosensory genes were found between the two strains (Table  1 ).
Materials and Methods Detailed methods can be found in Supplementary Notes. Sequencing and assembly of the nuclear and mitochondrial genomes. Whole genome sequencing was performed with Illumina HiSeq. 2000 from DNA extracted from two male larvae for the C strain and one larva of the R strain. Sequences from paired-end and mate-pair reads of multiple libraries for the C strain were assembled using the ALLPathsLG software  42  and an in-house procedure was used to identify and correct mis-assemblies due to high levels of heterozygosity in the sequencing data. Sequences from paired-end reads of 150-170 bp DNA fragment libraries from the R strain were assembled using the Platanus software  43  that was specifically designed to assemble sequencing data with high level of heterozygosity. The SPAdes software  44  was used to assemble rDNA and mitochondrial DNA.
Genes prediction, TE annotation, validation of assemblies and gene predictions. Gene models of the C strain were automatically built using GAZE  45  , based on alignment of various proteins and RNA-Seq ressources and the SNAP ab initio gene prediction software  46. Gene models of the R strain were built using MAKER2  47  , and various ab initio gene predictors trained against a R strain reference transcriptome assembled using Trinity. A WebApollo server  48  was made available in the SfruDB Information system to members of the consortium for manual annotation of specific gene families Assemblies and gene predictions were validated by the mapping of the Benchmarking Sets of Universal Single-copy orthologs (BUSCO, 2,675 for arthropod species)  49  and/or BAC end sequences. Repetitive elements were annotated with the REPET package  50.
Orthology analysis. Orthology between insect species was inferred using OrthoMCL  51  and orthology between the two strains was assessed with OrthoMCL and the Inparanoid softwares  52. After aligning protein coding sequences from each 1:1 orthologous genes pair using the Prank software  53  , the signature of positive selection was tested based on the site model using the codeml software in the PAML 4. 8 package  54. Reference guided assembly procedure and analysis of synteny with Bombyx chromosome. The pairwise whole genome alignment of both S. frugiperda strains was conducted following the UCSC Lastz+chainnet pipeline  55  after masking repetitive elements. This approach led to two nets, one for each strain as reference, which allowed the detection of structural variation between the strain genomes.
Population genomics analysis. To investigate the genetic relationship among individuals from the lab strains and sympatric natural populations, we performed 125 bp paired-end whole genome re-sequencing (HiSeq. 2500) of nine individuals from C population and nine individuals from R populations using a HiSeq. 2500. Calling of SNPs was performed using the Samtools mpileup, followed by vigorous filtering. The average genetic distance between each pair of individuals was estimated and followed by reconstructing phylogenetic trees using the neighbor program in the phylip package  57. Weighted Fst using the vcftools 58 estimated the level of genetic differentiation between the C and the R populations. Evolution of host-range in the genus Spodoptera. The evolution of host-range in the genus Spodoptera was inferred under maximum likelihood using the phytools R package  59  , which allows to reconstruct ancestral states for a continuous trait (fastAnc function).
Acknowledgements This work was partially supported by  Genoscope  project  AP2010 , "The Spodoptera genome", by a grant from the  French National Research Agency  ( ANR-12-BSV7-0004-01 ; http://www. agence-nationale-recherche. fr/) for C. L. , G. J. K. , H. Q and E. A. including a post-doctoral fellowship for A. G. , Y. M. , F. M. , and by a grant from  Institut Universitaire de France  for N. N. Generation of transcriptome RNA-Seq data from R and C strains by H. W. F. was supported by  USDA AFRI  ( 2010-65106-20656 ) to D. S. L. , H. W. F. and J. H. M.
Another avenue is to develop agents that inhibit the endothelial p38 signal via disruption of p38 from interacting partners such as nucleophosmin. The combination of p38 inhibitors with conventional chemotherapeutic drugs is also a promising new avenue to treat some tumors. In this context, an oral p38 inhibitor, LY2228820/Ralimetinib, characterized as an antiangiogenic in vitro and in vivo  [283] , has been tested in a phase I study in combination with tamoxifen in advanced cancer patients  [284]. In this trial, Ralimetinib, showed acceptable safety, tolerability and pharmacokinetics for patients with advanced breast cancers. Additionally, 21. 3% of the patients achieved stable disease without progression. Interestingly, Ralimetinib in combination with gemcitabine and carboplatin is under investigation in a double blind, randomized phase II trial for women with platinum-sensitive ovarian cancer (https://clinicaltrials.
gov/ct2/show/NCT01663857). Resistance to treatment has to be taken into account when considering cancer therapy and limiting resistance is a major challenge. In this context, inhibition of the p38 pathway limits resistance to irinotecan in colon adenocarcinoma  [285]. However, it is difficult to predict which type of tumors would benefit from these combined therapies. Predictive biomarkers would be very useful to select the patients who might benefit from the p38 inhibitors. In conclusion, important progresses have been done during the last 25 years in the understanding of the regulation and functions of p38. These progresses have been reached through multidisciplinary and translational approaches and we have no doubt that the next coming years will reveal to be fruitful in developing new drugs that will target p38 to improve therapeutic efficacy in diseases such cancer and cardiovascular pathologies.
work has been supported in part by the International Outgoing Fellowship, Marie Curie People Programme, the European Union's Seventh Framework Program FP7/2007-2013 (REA grant agreement No. 326512), and by a subvention from La Ligue contre le Cancer (Comités Loire-Atlantique, Morbihan, Côtes d'Armor) (to I. C. ), and the Natural Sciences and Engineering Research Council of Canada, the Canadian Institutes of Health Research and the Cancer Research Society (to JH).
Study design This study was the pilot phase of a multicentre clusterrandomized control trial conducted in several sites in the GMS and therefore limited to a sample size of four villages. It is registered at ClinicalTrials. gov: NCT01872702. The study was conducted in rural Eastern Kayin (Karen) state of Myanmar, a region endemic for P. falciparum and P. vivax malaria  17. In early 2013, 12 villages were screened using a high volume ultra-sensitive qPCR assay method (uPCR) with the original intention of identifying villages with >30% malaria prevalence, of which >30% was P. falciparum (Supplementary File 1: Table  S1 ). However, the P. falciparum prevalence was lower than expected, so after the survey the threshold proportion of P. falciparum for inclusion was lowered to 10%. Four villages fulfilling this revised entry criterion were selected after community engagement and agreement by the village leaders.
Detection of molecular markers of antimalarial resistance Assessment of mutations in PfKelch13. Polymorphisms in the PfKelch gene were assessed by nested PCR amplification covering the full length of the gene (total 2181 bp)  2  , and followed by sequencing of the gene by ABI Sequencer (Macrogen Inc, South Korea). Cross contamination was monitored by adding negative control samples in every run. Sequencing results were aligned against PfKelch13 of reference strain 3D7 (putative 9PF13_0238 NCBI Reference Sequence (3D7): XM_001350122. 1), using Bioedit software (Abbott, CA, USA). Polymorphic patterns were assessed by two individuals blinded to the origin of the sample.
Subjects and methods screening programs Data for this study were collected as part of the BALISTIC project (COPD in dairy farmers: screening, characterization and constitution of a cohort; ClinicalTrials. gov Identifier: NCT02540408) which was conducted from 2011 to 2015 at the University Hospital of Besançon in collaboration with the French national social security system for agricultural workers (Mutualité Sociale Agricole [MSA]) and the federation of community health practices of Franche-Comté (Fédération des Maisons de Santé Comtoises). This study was set up primarily to assess the prevalence and some specific characteristics of COPD in dairy farmers (COPD secondary to organic dust exposure) in comparison with COPD in patients without any occupational exposure and with matched controls without COPD.
Questionnaires During the characterization visit, several standardized questionnaires were administered to the four participating subgroups. A medical questionnaire (completed by the physician) collected data on anthropometric and demographic characteristics, symptoms and smoking history. Each patient was asked about their history of exacerbation, sputum and allergies. Dyspnea was assessed using two scales, namely, the Baseline Dyspnea Index (BDI) and the modified Medical Research Council scale (mMRC). A 6-minute walk test was performed to calculate the BMI, airflow Obstruction, Dyspnea and Exercise capacity (BODE) index. This score combines BMI, FEV 1 , mMRC and 6-minute walk test. 24 moking status was recorded and the number of pack-years was calculated. Subjects who smoked on average less than one cigarette (or cigar or pipe) a day for a year were categorized as never-smokers.