dataesr/scientific-paragraphs-categorization huggingface dataset:
cshl. edu/fastx_toolkit/; version 0. 0. 13). Reads were mapped to the Arabidopsis reference genome (TAIR10) using GSNAP version 2012-07-20  (Wu and Nacu, 2010). The uniquely mapping reads were used for quantification on the gene level with htseq-count from the HTSeq python package  (Anders et al. , 2015). These analyses were performed using a local Galaxy instance (http://galaxyproject. org)  (Giardine et al. , 2005). Data were normalized with trimmed mean of M-values, implemented in edgeR  (Robinson et al. , 2010). Differentially expressed genes were defined by a twofold difference between samples with a P-value <0. 05 at false discovery rate (FDR) <0. 05. The significance of the overlap between the RNA-seq downregulated dataset and the TChAP-seq dataset was calculated using the hypergeometric distribution function.
The RNA-seq data discussed in this article have been deposited in the ArrayExpress database (http://www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-3755. For quantitative PCR (qPCR) confirmation of identified targets, RNA was isolated from the root tips prior to cDNA synthesis with the iScript cDNA Synthesis Kit (Bio-Rad). Relative expression levels were determined by qRT-PCR with the LightCycler 480 Real-Time SYBR Green PCR System (Roche). Two reference genes were used for normalization (ACTINE_2-2 AT3G18780 and AteEF1A-4 AT5G60390). For qPCR analysis of SRDX levels in the three Promoter_bZIP29:bZIP29-SRDX lines, RNA was isolated from seedlings at 21 DAS. Normalization was obtained with PP2A (AT1G13320) as reference gene.
2B ). Cis-regulatory elements targeted by bZIP29 were identified through a de novo motif discovery analysis. Both replicates revealed significant enrichment of similar motifs (Fig. 2C ), located preferentially near the peak summit (Fig. 2D ). Through comparison with a database of known motifs, high sequence similarity was observed with motifs linked to bZIP group I members bZIP30 and bZIP18  (Weirauch et al. , 2014)  (Fig. 2C ). The core of these motifs (mCAGCTGk) (Fig. 2E ) resembles the known VIP1 response element (VRE), which is responsible for activation of the stress genes encoding MYB44 and thioredoxin Trxh8  (Pitzschke et al. , 2009). In addition, it has been shown that VIP1 and bZIP29 have partially redundant functions in osmosensory response  (Tsugama et al. , 2012 (Tsugama et al.
S3 ). Group E members possess a proline residue in the third leucine heptad preventing homodimerization, and the TAP data confirmed that group E members form heterodimers with group I members, as previously demonstrated  (Shen et al. , 2007). Both group E members are highly co-expressed with each other and with two bZIP group I members (bZIP18 and bZIP52)  (Obayashi et al. , 2009) , further supporting the observed interactions. Moreover, interactions of bZIP29 with 9 out of 13 (DeLille et al. , 2001) different 14-3-3 proteins were identified (Table  1 ). These 14-3-3 general regulatory factors (GRFs) are scaffold proteins known to bind phosphorylated proteins and are mainly involved in signal transduction. Confirmation of interaction between bZIP29 and 14-3-3 proteins was obtained by Y2H (Arabidopsis Interactome Mapping Consortium, 2011).
, 2013) , bZIP29 has been identified to be phosphorylated during auxin-mediated lateral root initiation, supporting its expression in the lateral root primordia. Also for stomata, transcriptome and proteome profiling studies have validated bZIP29 expression in this cell type  (Zhao et al. , 2008; Obulareddy et al. , 2013)  and transcriptome data indicate that expression of bZIP29 in guard cells is down-regulated by ABA  (Pandey et al. , 2010). To get more insight into the downstream target genes that are regulated by bZIP29, we performed TChAP-seq analyses. In agreement with partially overlapping functions of bZIP29 and VIP1  (Tsugama et al. , 2014) , de novo motif analysis identified a motif resembling the VIP1-responsive element involved in stress signaling and the hypo-osmolarity-response element, and all known targets of VIP1 were present in the bZIP29 target list.
, 2012)  and orthologous protein sequences were obtained from UNIPROT  (Dimmer et al. , 2012). The PhyML tree was constructed using the 'one click' mode of the Phylogeny. fr software tool  (Dereeper et al. , 2008). The branch length is proportional to the number of substitutions per site and branches are annotated with their support values. (This figure is available in color at JXB online. ) This study broadens the knowledge of bZIP group I members, showing that this subfamily is essential for plant development, and revealing possible means of their post-translational regulation. This knowledge is projected in a model (Fig. 8 ), although further studies are needed to unravel the causality between the regulation of cell cycle or cell wall genes by bZIP29 and the control of leaf and root development.
Interannual variations in prey relative abundance in the diet were analyzed using non-metric multidimensional scaling (NMDS) and the Bray-Curtis dissimilarity index  (Clarke and Gorley, 2006). Ordination was interpreted in ecological terms for stress values <0. 2. Prior to analysis, data were squareroot transformed to reduce the influence of abundant prey and to overcome the unity-sum constraint, and then submitted to Wisconsin double standardization to improve the gradient detection of the Bray-Curtis dissimilarity index  (Clarke and Gorley, 2006). Analysis of similarity (ANOSIM) was used to assess differences in diet between years with overall significance level corrected for multiple testing using the Bonferroni sequential correction. Similarity Percentage (SIMPER) analysis was used to identify prey categories with the highest contribution to diet dissimilarity.
All analyses were conducted using R (v3. 0. 1, R Development Core Team, 2014) and the Primer software v6. A total of 62 stomachs and 5839 prey items were analyzed. Percentage of occurrence data indicated that chironomids were the most frequent encountered prey, with trichoptera, hemipterans and ephemeropterans also present, though in differing frequencies between them (Table  1 ). Copepods and cladocerans were also taken frequently by L. gibbosus, being highly apparent in 2003 (Table  1 ). The relative abundance of chironomids was high in the diet of pumpkinseed in 1985 and 2013 (Table  1 ). However, in 2013, hemipterans were also abundant in the diet of both size classes and ephemeropterans in the diet of large individuals. Copepods dominated the diet of pumpkinseed in 2003 but were seldom consumed in the other years.
Acknowledgements. The authors gratefully acknowledge the financial support of " Fundação para a Ciência e Tecnologia" (FCT -Portugal ), through the postdoctoral fellowships that supported  C. Gkenas  ( SFRH/BPD/84859/2012 ) and  F. Ribeiro  ( SFRH/BPD/46761/2008 ), from the  Programa Operacional Potencial Humano/Fundo Social Europeu (POPH/FSE). This work was supported by the strategic plan of  MARE -Marine and Environmental Sciences Centre  ( UID/MAR/04292/2013 ). J. Cucherousset was supported by an  "ERG Marie Curie" grant  ( PERG08-GA-2010-276969 ) in the lab EDB, part of the  French Laboratory of Excellence project  " TULIP"  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ).
Data calculation Plaque assays were used to measure infectious phages in all samples. In previous experiments, it has been demonstrated that viruses genomes are stable under air sampling conditions used in this study  [15, 25, 33] , therefor, qPCR was used to measure total phage genomes in all samples. The ratio of infectious phages in air samples was calculated by dividing the number of infectious phages as determined by plaque assays by the number of phage genomes estimated by qPCR. Because infectious ratio is affected by RNA extraction, cDNA synthesis and qPCR efficiency  [27]  as well as by aggregates in plaque assays, the value may be below or above 1. These assays bias are phage-specific and therefore, infectious ratios should not be used to compare phages. However, infectious ratios can be used to compare the effect of environmental conditions (relative humidity and exposure time) on each phage since the limitations are likely the same for all samples of the same phage.
Aerosol characteristics Particle concentration in the aerosol chamber ranged between 4. 8 x 10 4 and 1. 2 x 10 5 particles per cubic centimeter. The median mass aerodynamic diameter (MMAD) was between 0. 9 and 1. 1 μm immediately after aerosolization. Particle concentration and MMAD inside the chamber were in accordance with previous data  [34]. Recorded temperature and relative humidity varied slightly within and between experiments. For all of the experiments, the temperature fluctuated between 18˚C and 21. 3˚C. The relative humidity levels ranged from 24. 6% to 26. 5% for experiments set at 25% RH, while RH ranged from 48. 2% to 52. 4% when set at 50%.
Effect of germicidal agents on relative infectivity ratios Once airborne, the effects of germicidal products, RH, and exposure time on phage infectivity are likely cumulative. The levels of infectious phage particles were determined after the exposure of four airborne phages to four germicidal products at 25% and 50% RH and compared to the reference values obtained without germicidal product at the same RH level and exposure times. As indicated in the Materials and Methods section, the data are presented as relative infectivity ratios, which allows for the analysis of the germicidal product effect alone without interference of the RH. The reference values obtained without germicidal product (relative infectivity ratio = 1) are represented by the dotted lines in Figs  2 3 4. All germicidal products had a significant effect on phage MS2 relative infectivity ratios at 25% RH (p<0.
Acknowledgments We are grateful to  Serge Simard  for statistical analysis as well as to Gerald Kenanian for his assistance in the experiments. The authors acknowledge  Amanda Kate Toperoff  for the English revision of the manuscript. C. D. is a  FRQ-S senior scholar  and a member of the  FRQ-S Respiratory Health Network. S. M. holds a Tier 1  Canada Research Chair in Bacteriophages.
Methods Two methods were used to study the relationship between landscape diversity and adaptive strategies used by vulnerable households in food shortage periods. The first method is a characterization of the landscape at the village territory scale and the forest resources it contains. The second method is a household survey during a food shortage period including an assessment of the adaptive strategies households used to cope with the food shortages. To assess landscape and forest resources, we delineated the two villages' territories with village representatives in May 2012. We then used a grid of 100 m in the delineated territories. Following parallel transects with a GPS, we identified the limits between the different components of landscape. We then combined this information with remote sensing data and field observations to finalize the landcover and land-use maps of the two villages (Fig.
2 ), followed by an inventory of forest resources using a systematic sampling plan  (Gautier and Karr 2000)  with plots of 0. 5 ha in the parkland area (50 m radius) and plots of 0. 1 ha in the savannah and fallow areas (12 m radius). The area of the villages' territories and those of the different ecosystems were calculated using the extension compiled theme tools of ArcView software. The species richness is the number of tree species found in each village territory. Mean trees densities per hectare were calculated for each ecosystem type. We calculated also the standard deviation in order to highlight the variation of tree densities around the mean. A total of 60 households were randomly selected from each village on the basis of focus group workshops for the survey and the assessment of adaptive strategies.
Acknowledgments This research was supported by the project ' Forest and Adaptation to Climate Change and Forests in West Africa ' (ACFAO), co-funded by the  Fonds Francais pour l'Environnement Mondial (FFEM)  and the  CRP-FTA  ( Consortium Research Program on Forests, Trees , and Agroforestry). It was implemented by the  Center for International Forestry Research (CIFOR)  and its partners. It has benefited from the stimulating environment provided by the third  International Conference on Biodiversity  and the  UN Millennium Development Goals organized in Aix-en-Provence, France , in 2014. We would like to thank the villagers of Sorobouly and Kalembouly,  Burkina Faso , for their collaboration. We want to thank the two anonymous reviewers for their valuable comments and suggestions.
Whole-exome sequencing (WES) is increasingly applied to research and clinical diagnosis of human diseases. It typically results in large amounts of genetic variations. Depending on the mode of inheritance, only one or two correspond to pathogenic mutations responsible for the disease and present in affected individuals. Therefore, it is crucial to filter out nonpathogenic variants and limit downstream analysis to a handful of candidate mutations. We have developed a new computational combinatorial system UMD-Predictor (http://umd-predictor. eu) to efficiently annotate cDNA substitutions of all human transcripts for their potential pathogenicity. It combines biochemical properties, impact on splicing signals, localization in protein domains, variation frequency in the global population, and conservation through the BLOSUM62 global substitution matrix and a protein-specific conservation among 100 species.
We compared its accuracy with the seven most used and reliable prediction tools, using the largest reference variation datasets including more than 140,000 annotated variations. This system consistently demonstrated a better accuracy, specificity, Matthews correlation coefficient, diagnostic odds ratio, speed, and provided the shortest list of candidate mutations for WES. Webservices allow its implementation in any bioinformatics pipeline for next-generation sequencing analysis. It could benefit to a wide range of users and applications varying from gene discovery to clinical diagnosis.
Introduction The human genome is composed of approximately 3. 2 billion nucleotides and contains at least 25,000 genes. Despite very efficient systems to ensure the accurate replication of this genome, it has been estimated that the germ-line base substitution rate ranges from 1. 1 to 3 × 10 -8 per base per generation  [Roach et al. , 2010] ; therefore, each individual carries at least 10 de novo variations. In addition, early whole genome sequencing (WGS) experiments have revealed that every human carries about 3 million single-nucleotide polymorphisms (SNP)  [Levy et al. , 2007; Wang et al. , 2008; Wheeler et al. , 2008; Pushkarev et al. , 2009] , these data being consistently confirmed. The analysis of the dbSNP build 142 database reveals that 15% of identified SNPs are localized in genes, from which about 86% map to introns and 2.
75% to exons. Considering these exonic variations, more than 50% correspond to missense mutations and about 37% to synonymous changes. Nevertheless, these data do not provide information about the potential pathogenicity of these variations and may thus be biased for nonpathogenic events. However, the Human Gene Mutation Database  [Stenson et al. , 2003]  indicates that the vast majority of human pathogenic variations correspond to missense mutations (50%) corroborating that these mutations represent a key element in human genetics as their interpretation is challenging as next-generation sequencing (NGS) technologies are widely used both for research and clinical diagnosis. Thus, performing a whole-exome sequencing (WES) or a WGS will result in about 23,000 exonic SNPs with about 11,500 missense variations and 8,500 synonymous changes from which only one to two mutations, based on the mode of inheritance, are responsible for the Mendelian genetic disease.
The most widely used systems of this type are SIFT and Mutations assessor  [Reva et al. , 2011; Sim et al. , 2012]. The second approach takes into account the degree of similarity between amino acids as well as physical disruption of protein domains. Archetypes for this approach are the Polyphen-2, and SNPeffect systems  [Adzhubei et al. , 2010; De Baets et al. , 2012]. The third approach includes various semisupervised learning methods trained with positive (disease-causing mutations) and negative (nonpathogenic mutations) datasets from which rules have been established based on various features. The most widely used systems of this type are MutationTaster2, SNAP, and CADD  [Bromberg and Rost, 2007; Kircher et al. , 2014; Schwarz et al. , 2014]. Nevertheless, none of these approaches could be considered as a gold standard as they have limitations: a high sensitivity to multiple sequence alignments that could drastically alter predictions for the first type; availability of 3D structures that are not accessible for many proteins or only partially available for parts of a given-protein for the second type, and the requirement of very large experimentally validated datasets to train systems of the third type.
Thus, mutations affecting the ultimate base of exons are now well recognized to affect the donor splice site while often resulting in a synonymous change  [Desmet et al. , 2009]. The purpose of this work was to develop this combinatorial approach through the addition of new elements such as the conservation over 100 species and the variation frequency in the general population. In addition, as WES and WGS experiments result in annotated variations at the nucleotide level, this system was designed to handle large datasets, to predict the pathogenicity of missense and synonymous changes and make it available through Webservices allowing integration in any bioinformatics pipeline. To evaluate its efficiency, we compared our system with the seven most used and reliable prediction tools: SIFT 5.
1. 1  [Sim et al. , 2012] , Polyphen 2. 2. 2  [Adzhubei et al. , 2010] , Provean 1. 1. 3 [Choi et al. , 2012] , Mutation Assessor 2  [Reva et al. , 2011], CONDEL 1. 5 [González-Pérez and L ópez-Bigas, 2011] , Mu-tationTaster 2  [Schwarz et al. , 2014] , and CADD  [Kircher et al. , 2014]. To avoid any bias linked to a specific dataset or mutation type, we selected four widely used different datasets containing pathogenic and nonpathogenic variations: Varibench [Sasidharan  Nair and Vihinen, 2013]  combined with dbSNP  [Sherry et al. , 2001] (19,335 pathogenic, 7,897 nonpathogenic) ,  Uniprot [UniProt Consortium, 2014] (20,821 pathogenic, 36,825 nonpathogenic) , Clinvar  [Landrum et al. , 2014]  (10,669 pathogenic, 1,817 nonpathogenic), and PredictSNP  [Bendl et al. , 2014]  (24,082 pathogenic, 19,800 nonpathogenic).
UMD-Predictor Ecosystem We designed the UMD-Predictor ecosystem as a stand-alone platform that contains all predictions for all substitutions from any human transcript. It was conceptualized as a three-tier architecture. The Presentation Tier was developed in PHP, javascript, and html. This interface integrates the d3. js (http://d3js. org/) and jquery (http://jquery. com/) libraries to ensure easy access to the system (http://umd-predictor. eu). The Application Tier integrates the prediction algorithm itself and the data-tier integrates the Ensembl v71 human genome reference sequence and transcripts. All theoretical substitutions of protein-coding transcripts (280,315,899 mutations) have then been modeled, computed, and stored in a PostgreSQL (http://www. postgresql. org/) database allowing rapid analysis of large datasets.
It was subsequently normalized for the number of selected species in each group. For each mutation, we compared the Grantham score of the mutation itself and the highest Grantham score of natural variants of each group. If this variation is positive, the conservation score increases, otherwise it is decreased. The conservation score is thus translated into a new matrix ranging from -40 to +60. For example, if we consider a Glutamic acid residue (Glu) at position 1,003 of the FBN1 gene (ENSEMBL gene transcript ENST00000316623), the conservation displayed a natural variation to an Alanine residue (Ala -Grantham score of 107) in the Armadillo species (Supp. Fig. S1 ). A Glu>Lys mutation will result in a conservation score of 66. 8 as the Grantham score of the mutant residue is lower than the Grantham score of the natural variant.
Automatic Extraction of Key Residues Information from Swissprot In the original combinatorial algorithm, each database curator manually annotated a key residue parameter accounting for a 3D structure key element (Proline-induced hinges) or a functional key element (glycosylation site or disulfide bound). With the development of a global system, an automatic annotation system was required. We therefore used Uniprot/SwissProt data for the follow-ing terms: "CARBOHYD," "DISULFID," "ZN FNG," "MOD RES," "TRANSMEM," "CA BIND," "DNA BIND," "NP BIND," "MOTIF," "ACT SITE," "METAL," "BINDING," "SITE," "SE CYS," "LIPID," and "CROSSLNK. ".
Variation Frequency in the General Population As Mendelian-inherited human genetic diseases are considered rare, a high frequency of a specific variation in the human population is indicative of a nonpathogenic mutation also named polymorphism. Thus, we collected frequency information from the Ensembl database  [Cunningham et al. , 2015] , which include dbSNP data (build 139)  [NCBI Resource Coordinators, 2015]. If a variation frequency is available, a penalty score ranging from -50 to 0 was used for mutations with frequencies above 0. 001 with -50 for frequency above 0. 05.
Evaluation Datasets and Systems To allow unbiased comparison of tools, we selected large available datasets that contain annotated pathogenic and nonpathogenic mutations. These datasets are freely available and have already been used for tools evaluation. Note that only pathogenic mutations have been selected from the Varibench dataset and HUMAN  MUTATION, Vol. 37, No. 5, 439-446, 2016  combined to the dbSNP dataset in order to reach a significant number of both mutation types. We therefore used the four following datasets: Varibench [Sasidharan  Nair and Vihinen, 2013]  with db-SNP  [Sherry et al. , 2001]  (19,335 pathogenic, 7,897 nonpathogenic); Uniprot [UniProt Consortium, 2014] (20,821 pathogenic, 36,825 nonpathogenic); Clinvar  [Landrum et al. , 2014]  (10,669 pathogenic, 1,817 nonpathogenic); and PredictSNP  [Bendl et al.
, 2014]  (24,082 pathogenic, 19,800 nonpathogenic). Due to discrepancies in datasets genomic variations description (genomic or protein coordinates), not all predictors were able to perform analysis for all variants; therefore, accuracy was evaluated both on the overall dataset and on a common dataset corresponding to mutations for which all tools were able to perform predictions. The seven most used and reliable prediction tools were used for comparison: SIFT 5. 1. 1  [Sim et al. , 2012] , Polyphen 2. 2. 2  [Adzhubei et al. , 2010] , Provean 1. 1. 3 [Choi et al. , 2012] , Mutation Assessor 2  [Reva et al. , 2011] , CONDEL 1. 5 [González-Pérez and L ópez-Bigas, 2011], MutationTaster 2  [Schwarz et al. , 2014] , and CADD  [Kircher et al. , 2014 ]. For each system, the most recent version was used with default parameters.
Other Comparison Assessment Parameters The efficiency of prediction systems is mainly based on the prediction themselves but other parameters were also taken into account. They include: the time required for NGS datasets annotation; the ability to identify true pathogenic mutations; and the number of predicted pathogenic mutations. These parameters were evaluated using three clinical WES datasets from unrelated patients provided by the molecular genetics laboratory of the "La Timone Children Hospital" (Marseille, France). The molecular diagnostic of recessive diseases was confirmed by Sanger sequencing and family study for each proband (data not shown). The WES datasets contained 58,145, 54,006, and 57,936 variants. The assessment was performed using the on-line version of each system and the Webservices of the UMD-Predictor system.