dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS This work has been funded by  INRA-SPE  (D. T. ), and  PEPS  and  ANR-06-JCJC-0092-01  (J. -R. H. ) grants from  Le Centre national de la recherche scientifique. The authors thank  Joël Bonhomme  ( Institut National de la Recherche Agronomique [INRA] Rennes, Mixted Unit of Research (UMR ) Institute for Genetics, Environment and Plant Protection [IGEPP])  for technical advices on aphid rearing, and  Stéphanie Jaubert-Possamai  ( INRA Rennes, UMR IGEPP ) for reading the manuscript. Bernard Chaubet  ( INRA Rennes, UMR IGEPP ) is greatly acknowledged for the pictures of living aphids. Gérard Febvay ,  Yvan Rahbé , and  Marie-Gabrielle Duport  ( INRA Lyon ,  UMR BF2I ) are finally thanked for their advices on artificial diet use on the pea aphid.
A subsequent highsalt extraction was performed (2 M NaCl). After washes, the remaining insoluble proteins were fixed using 4% PFA. Confocal analyses were performed on a confocal microscope (LSM510 Meta; Carl Zeiss) with a Plan-Apochromat 63×/1. 4 NA oilimmersion objective at 20°C. Alexa Fluor 488, 594, 648 dye conjugates were used together with Vectashield mounting media containing DAPI (Vector Laboratories). Images were acquired with LSM510 software (Carl Zeiss). Increased resolution images were obtained with deconvolution software (AutoDeblur; ImageQuant) using standard blind iterative algorithms. 3D structured illumination microscope image acquisition Super-resolution structured illumination microscopy was performed on a structured illumination microscope (ELYRA S. 1; Carl Zeiss). A structured illumination pattern of the 561-nm diode laser was projected into the sample.
de Thé ),  Canceropôle Ile de France , the  European Research Council  ( STEMAPL advanced grant  to  H. de Thé ), PACRI and  Saint Louis Institute , and the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-11-PHUC-002 ). U. Sahin was supported by a fellowship of the  Fondation pour la Recherche Medicale. The authors declare no competing financial interests. Submitted: 29 May 2013 Accepted: 10 February 2014.
Sequences were then assembled using DNA Baser 3. 5. 3, and aligned using the MAFFT tool (http://mafft. cbrc. jp/alignment/server/) (December 2012) for species assignment. Phylogenetic analyses were conducted by the neighbour-joining method using MEGA 5. 05/5. 10 software  [40]. Bacterial sequences for the sequenced strains have been deposited in GenBank and accession numbers are available in Table  4. Biochemical profiles were analysed by using API 50 CHL test kit (Biomérieux, Marcy l'Étoile, France). Isolates belonging to the L. fermentum species were then typed with PFGE analyses as described by Delavenne et al. [41]. Effects of pH and organic acids on P. aeruginosa PAO1 growth An overnight culture of P. aeruginosa PAO1 was harvested by centrifugation, washed, and suspended in the same volume of saline solution (NaCl 0.
Statistics Statistical analyses were performed using the Microsoft Excel 2010 (Microsoft Corporation, Redmond, USA) and SAS 9. 3 (SAS Institute Inc. , Cary, USA) software products. In order to test the significance of the elastolytic activity and biofilm formation assays, the correlation between the three series of experiments was first assessed by the Student's inverse test (p < 0. 05). Once the correlation between the assays was established, significant differences within each assay were carried out with a one-way analysis of variance (ANOVA), and the least significant difference (LSD) test was used to detect antagonistic activities of lactobacilli strains against Pseudomonas aeruginosa. Statistical significance were set at a p = 0. 001 for elastolytic activity and p = 0. 05 for biofilm formation.
Acknowledgements The authors thank  Stéphanie Gouriou ,  Stella Debaets ,  Franck Déniel  and  Jérôme Mounier  for their implication in the sequencing of Lactobacillus spp. strains;  Emmanuel Nowak  for his work on statistics ( INSERM CIC 1412, Brest, France );  Danielle Arzur  for help in performing biofilm and elastolytic assays;  Michelle Virmaux  for support in PFGE analysis, and  Geneviève Héry-Arnaud  who provided an access to the mass spectrometry platform, and  Zarrin Alavi Menut  for her pertinent advice for submitting this manuscript ( INSERM CIC 1412, Brest, France ). This work was supported by a PhD fellowship from the  French Ministry of Higher Education and Research.
n = 8 strains The sequencing of pheS gene (Genbank accession num- Figure 1 Flow-chart of the strains selection strategy. 38% and 47% the elastolytic activity of P. aeruginosa. Only one strain isolated from the oral cavity (L. zeae Od. 76), significantly reduced (minus 36%) the elastolytic activity. When grown in presence of lactobacilli during 22 h and despite a pH decrease ranging from 0. 9 to 1. 2 units of the BHI medium (pH 7. 4), no inhibition of P. aeruginosa growth was observed. Formal identification of lactobacilli active against P. aeruginosa Once several isolates were shown to be active against P. 3. 1D, K. V9. 3. 2B, aeruginosa PAO1, their identity was confirmed by 16S K. V9. 3. 2C and K. C6. 3. 1E) were L. fermentum isolated rRNA and rpoA genes sequencing, and API 50 CHL from raw milk.
fermentum 99. 7% K. C6. 3. 1E L. fermentum 2. 109 L. fermentum [KC762301] L. fermentum [KC861372] L. fermentum 99. 7% K. V9. 3. 2B L. fermentum 2. 163 L. fermentum [KC762302] L. fermentum [KC861373] L. fermentum 99. 7% K. V9. 3. 2C L. fermentum 2. 223 L. fermentum [KC762303] L. fermentum [KC861374] L. fermentum 99. 8% *An isolate was considered well identified at the species level with a MALDI-TOF mass spectrometry score ≥ 1. 9. **The API 50 CHL gallery is not designed to identify L. zeae. ***Od. 76 was formally identified as a L. zeae by sequencing the pheS gene (Genbank accession number: KJ402364).
Genetic markers The F1 sires were genotyped for a set of 9126 SNPs covering the available genome (assembly 2. 1 WASHUC2). A subset of 1536 SNPs was selected using MarkerSet  [11] , based on marker location and heterozigosity in the F 1 population to maximize both genome coverage and marker informativity. The average density was one SNP each 0. 66 cM, i. e. one SNP for 3 Mb. The 1362 offspring were then genotyped for those 1536 SNPs, at the National Genotyping Center (CNG, Evry, France) using Illumina Gold-enGate technology (Illumina, San Diego, CA, USA). MendelSoft  [12]  was used to correct data for Mendelian inconsistencies. Out of the 1536 markers, 191 were eliminated due to technical or inconsistence issues (call rate lower than 85% and/or Mendelian errors higher than 5%). The chicken linkage consensus map build by Groenen et al.
QTL mapping QTL interval mapping was performed using QTLMap software  [14]. A mixture of half and full-sib families was considered as pedigree structure, and only sire meioses were studied. For abdominal fat (AF) QTL interval mapping, sex (2 levels) and hatch group (5 levels) were used as fixed effects, while body weight at nine weeks (BW9) was used as co-variable to adjust data. A likelihood ratio test (LRT) was performed at each cM to compare the fit of two models (i. e. the model with a QTL at the location considered vs. the model without fitting any QTL effect). Chromosome-wide significance thresholds were evaluated through empirical calculations obtained by simulations under the null hypothesis. A total of 10,000 simulations was performed for each trait 6 chromosome combination and maximum LRT quantiles were calculated according to Harrel and Davis method  [15].
Polymorphism and gene annotations To identify positional candidate genes in highlighted QTL regions, AnnotQTL software  [22]  was used, providing the location of genes in a specific region using NCBI and Ensembl databases, and filtering them onto ontology annotation criterion. Thus, genes belonging to ''lipid metabolic process'' Gene Ontology (GO) class, and to ''diabetes mellitus'' and ''obesity'' Online Mendelian Inheritance in Men (OMIM) classes were considered as functional candidate genes. Variant annotation was performed using a two steps procedure. First, we used Ensembl Variant Effect Predictor (VEP)  [23]  for a global annotation on WASHUC2. 1, allowing us to focus on SNPs associated to ''missense'', ''stop gained'' or ''stop lost'' coding consequences, and on indels associated to ''inframe insertion'', ''inframe deletion'' or ''frameshift'' coding consequences.
NGS data allowed the identification of functional polymorphisms in coding regions within QTL regions As in all QTL fine-mapping studies, we first characterized genes located in each QTL region. Using AnnotQTL  [22] , based on both NCBI and Ensembl databases, all genes located in both QTL regions were listed. This led to the identification of 77 genes and 84 positional candidate genes located on GGA7 and GGA19 QTL regions, respectively. Using whole genome DNA sequencing data, we then characterized polymorphisms in both regions and revealed 39,781 and 19,755 SNPs and 4,613 and 1,829 indels (Table  2 ). As it is difficult to annotate and determine the impact of polymorphisms on regulatory regions -an issue that is still important in model species due to the lack of annotation in non-coding regions  [31] [32] [33]  -, only polymorphisms having an impact on coding sequences were further considered.
Gene functional information allowed prioritization on genes related to adiposity and associated candidate polymorphisms Those polymorphisms, selected for being both coding and being functional (i. e. having a high propensity for impacting final protein product) were considered as strong candidates underlying QTLs. But, even if the causal polymorphisms may be located in genes that have not already been described as involved in the lipid metabolism, genes known as being related to the trait of interest stand for the first strong candidate genes to be considered. Using gene functional annotations could therefore allow prioritizing polymorphisms among pre-selected candidates. Considering the phenotype targeted in our study, such functional genes were selected using GO and OMIM databases (and genes related to lipid metabolic process, diabetes mellitus and obesity, see Methods).
Quality filtering and taxonomic affiliations of the sequences The sequences were processed using the MOTHUR 1. 28. 0 software  (Schloss, 2009)  following the standard operating procedure (http://www. mothur. org/wiki/Schloss_SOP) (Schloss et al. , 2011). First, flowgrams were extracted and demultiplexed according to their tag. The resulting 16 flowgrams were denoised using the MOTHUR 1. 28. 0 implementation of PyroNoise  (Quince, 2009). Primer sequences, TAG and key fragments were subsequently removed, and only sequences above 200 bp long, displaying less than eight homopolymers, were kept. The remaining sequences were dereplicated to unique sequences and aligned against the SILVA 108 database (http://www. arb-silva. de/) containing 62 587 eukaryotes SSU-18S rRNA sequences. Around 7 % of the sequences suspected of being chimeras were removed using the UCHIME software (http://drive5.
com/ usearch/manual/uchime_algo. html) (Edgar, 2011). The remaining sequences were clustered into operational taxonomical units (OTUs) at 97 % similarity threshold. Single singletons (unique amplicons after 97 % clustering that occurred exclusively in only one sample) were removed from downstream analyses, as these are most likely erroneous sequenc-ing products  (Reeder and Knight, 2009; Kunin et al. , 2010; Behnke et al. , 2010). This data set showed a representative overview of the diversity as indicated by the rarefaction curves reaching a plateau in most cases (Supplement Fig. S1 ). All OTUs were given a putative taxonomic affiliations based on BLAST  (Altschul et al. , 1990 ) identification of the closest cultured or uncultured relatives against the PR2  (Guillou et al. , 2013)  and the GenBank databases.
Data analysis Rarefaction curves and alpha diversity estimators within particular samples (richness estimator S Chao1 ; the heterogeneity of the diversity; Simpson and Berger-Parker indices) were calculated with the PAST 2. 17c software  (Hammer et al. , 2001). The S Chao1 approach uses the numbers of singletons and doubletons to estimate the number of expected species. According to S Chao1 , "missing" species information is mostly concentrated on those of low frequency counts. The Simpson index measures the "evenness" of the community and ranges from 0 (one taxon dominates the community) to 1 (all taxa are represented equally). Berger-Parker indicates the relative abundance of the dominant OTU in each sample (for more details, see  Magurran, 2004). Protistan assemblages, from the different samples, were compared using the Plymouth routines in the multivariate ecological research (PRIMER v.
6) software package  (Larke and Warwick, 2001). In order to identify inter-relationships between samples, Bray-Curtis similarities were analysed by cluster analysis and nonmetric MDS on square-root sequence abundance. The similarity profile (SIMPROF) permutation test was conducted in PRIMER v. 6 to establish the significance of dendrogram branches resulting from cluster analysis. Similarity percentage (SIMPER) analysis, also performed with PRIMER, was used to identify of the contribution of different OTUs to the observed similarity pattern.
, 2014; Lasbleiz et al. , 2014). In particular, Fragilariopsis kerguelensis, Pseudonitzschia spp. , Eucampia antarctica, and Chaetoceros spp. were found to be the four dominant diatom taxa, via microscopy  (Sackett et al. , this volume). However, while Pseudonitzschia-, Eucampia-and Chaetoceros-related OTUs represented 14 % of the Bacillariophyceae-related sequences, no Fragilariopsis-related OTUs were detected. Lie et al. , 2013). They can be related to extraction efficiency from thick walled diatoms  (Medinger et al. , 2010)  and/or amplification biases favouring species with high 18S rRNA gene copy number, such as ciliates and dinoflagellates  (Potvin and Lovejoy, 2009). It is also worth noting that 28 out of the 52 taxa identified by microscopy  (Armand et al. , 2008)  were not referenced in the GenBank.
Finally, regarding the 27 diatom taxa that were "identified" only by pyrosequencing -based on sequence similarity with the closest existing cultured relatives in GenBank -they mainly belonged to the genera previously observed in this area  (Armand et al. , 2008). The accuracy of BLAST-derived taxonomy, especially at low-level taxa, depends on sequence length, variability of the 18S region, database coverage for the specific taxonomic group, and correct identification of the reference sequence  (Bik et al. , 2012). Sequences belonging to the nano-and picophytoplanktonic groups of Bolidophyceae, Pelagophyceae, Chrysophyceae and Cryptophyta were found at relatively low abundances in all samples. Moreover, Haptophyta were dominated by an OTU affiliated as Phaeocystis antarctica (100 % sequence identity).
HNLC station Based on trophic organization, HNLC areas seem conceptually similar to oligotrophic regions dominated by small producers and an active microbial food web (e. g. Hall and Safi, 2001; Oliver et al. , 2004; Christaki et al. , 2014 Christaki et al. , , 2008;; Obernosterer et al. , 2008). The characteristic contributors of the HNLC cluster (i) were Haptophyta, Chlorophyta and MAST, which included mainly nanoplanktonic organisms. During KEOPS2, the relative importance of small-sized cells at the HNLC station is in accordance with the flow cytometry data (4. 8 ± 1. 9 10 3 mL -1 nano-picophytoplankton cells in comparison to 1. 8 ± 1. 3 10 3 mL -1 at the bloom stations; KEOPS2 data). The factors influencing phytoplankton community composition (e. g. diatoms vs. Phaeocystis sp. ) in the Southern Ocean are a complex interplay between bottomup (iron-silicate-light availability; controlling growth) and top-down effects (grazing; mortality)  (Cullen, 1991; Arrigo et al.
Acknowledgements. KEOPS was financed by  INSU-CNRS ,  IPEV  and  ANR  and the  French Ministry of Higher Education  through a  PhD grant  to  C. Georges. We thank our many colleagues who participated in the collection of various data sets, the KEOPS co-ordinator  S. Blain , the chief scientist on board  B. Quéguiner  and the crew aboard the  R/V Marion Dufresne  for their help in the successful completion of the cruise. We also thank www. englisheditor. webs. comfor the paper's English proofing. Edited by:  G. Herndl.
The new species can be found in northwestern Argentina (Salta and Jujuy provinces) and southern Bolivia (Santa Cruz, Chuquisaca and Tarija departments) in Yungas montane forest. Mazaeras yungasensis n. sp. can be found between 467m to 2540m (data from Instituto Fundación Miguel Lillo (IFML), Museo de La Plata (MLP), Museo de Zoología de la Universidad Nacional de Córdoba (MZUC) and personal collection: Benoit Vincent (BVC) and Michel Laguerre (MLC) (Fig. 11 ). Genitalia were prepared using a hot KOH solution (10%). Illustrations images of abdomen and genital male were taken with a camera attached to a stereoscopic microscope Zeiss Stemi 2000-C stereomicroscope. The morphology analyses were made with specimens of IFML, MLP, MZUC, BVC and MLC. Genitalic terminology follows Vincent et  al. (2014).
Acknowledgements We want to thank  Dr. Fernando Navarro  ( IFML ) for the reviews of the manuscript and the two anonymous reviewers for their suggestions and comments  Dr. Adriana Zapata  ( MZUC ) for her help in the museums visited and for providing specimens. Dr. Analia Lanteri  ( MLP ) for access to specimens deposited in Museo La Plata. Fernando Navarro ,  María José Barrionuevo ,  Adriana Chalup  and  Gabriela Acosta  for their help during the expedition. This study was funded by  CONICET ,  Argentina. Michel Laguerre  which provided us with the distribution map.
Integrated view of the differences in accumulation of metabolites, proteins and transcripts As in a previous study aimed at integrating the three different "omics" investigations when maize plants were grown under N-limiting conditions  [18] , we have made a similar attempt to integrate the transcriptome, proteome and metabolome datasets and the impact of the two gln1 mutations at two key stages of plant development. Such an integrated "omics" picture can provide important information on the impact of the two gln1 mutations on the physiological adaptation of the plant. Irrespective of their biological functions, the quantitative differences observed in mRNA transcript, protein and metabolite accumulation in relation to the plant developmental stage are presented in Table  4. In addition, the relative importance of these processes in terms of number of metabolites, proteins and transcripts exhibiting differences within common functional categories across the three "omic" studies is presented in Figure  1.
RNA preparation Total RNA was extracted as described in  [72]  from leaves that had been stored at -80°C. Total RNAs (50 μg) for transcriptome and quantitative Real Time-Polymerase Chain Reaction (qRT-PCR) studies were treated and prepared as previously described by  [18]. Reverse transcription reactions and quantitative first strands were synthesized according to  [18]. Primers for qRT-PCR and RT-PCR cloning were designed from Bacterial Artificial Chromosomes (BAC) sequences found in the public maize genome databases (Maizesequence. org, PlantGDB, Genebank). The sequences of the primers used in RT-PCR and qRT-PCR are presented in Additional file 1: Table  S14.
Gene expression profiles using maize cDNA microarrays and statistical analysis of data Whole genome leaf transcript profiling was performed using the maize 46 K arrays obtained from the maize oligonucleotide array project (http://www. maizegdb. org/ microarray. php#mes) essentially as described previously in  [18]. The maize 46 K spotted oligonucleotide array contains 46,000 unique probes from maize. Its detailed description, composition and gene putative annotation can be found at the Gene Expression Omnibus (GEO); (http:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GPL6438). Starting with 3 μg of total leaf RNA, non-modified amplified antisense RNA (aRNA) products were prepared using the Amino Allyl MessageAmp™ aRNA Kit (Ambion, Foster City, CA, USA). Following this, 2 μg-aliquots of aRNA were labelled using the SuperSript™ Indirect cDNA Labeling System Kit (Invitrogen, Carlsbad, CA, USA) and the purification steps were carried out using QIAquick® PCR columns (QIAGEN, Hilden, Germany).
After IEF, strips were equilibrated to improve protein transfer to the two-dimensional gel (2-D gel). The second separation was performed in an 11% SDS-PAGE gel. Separation was carried out at 20 V for 1 h and subsequently at a maximum of 30 mA/gel, 120 V overnight, until the bromophenol blue front had reached the end of the gel. After SDS-PAGE, the gels were subsequently stained with colloidal Coomassie blue. Scanning was carried out at 300 dpi with a 16-bit greyscale pixel depth using an image scanner (Amersham Biosciences), and then gel images were analyzed using the Progenesis and SameSpot softwares (Nonlinear Dynamics Ltd). The SAS package (procedure GLM for one way ANOVA analysis) was used to examine modifications of individual protein spot volumes. A protein spot was selected if its variation had a p value <0.
A multiple-threshold filter was applied at the peptide level: Xcorr magnitude were up to 1. 7, 2. 2, 3. 3 and 4. 3 for peptides with one, two, three and four isotopic charges respectively; peptide probability lower than 0. 05, ΔCn >0. 1 with a minimum of two different peptides for an identified protein. A database search was performed with Bioworks 3. 3. 1 (Thermo Electron). The TIGR maize gene indice database v 16, 72047*6 EST (http:// maize. jcvi. org/) sequences was used.
Ryk-ICD Is Increased in the Nucleus of Mutant htt Striatal Cells The model supported by our data for Ryk to be toxic in HD suggests that Ryk-ICD levels might be increased in mutant polyQ cells. To explore this possibility, we used a newly obtained rabbit polyclonal Ryk-ICD antibody (anti-Ryk ICD )  [33]  to perform immunocytostaining of mouse striatal cells followed by confocal analysis. This antibody was raised against the Ryk-ICD fragment, and it recognizes Ryk species that contain the ICD including fulllength Ryk and the Ryk-ICD fragment  [33]. Depending on the type of cells and level of expression in these cells, this antibody may detect one or more of all possible Ryk species. The detection of ICD cDNA (4 ng/ml) in touch receptor neurons using the mec-3 promoter is sufficient to abolish the neuroprotective activity of lin-18 LOF in 128Q nematodes with no effect detected in 19Q nematodes as tested in two independent extrachromosomal arrays (128Q: A1 is ID1333, A2 is ID1334; 19Q: A1 is ID1331, A2 is ID1332; see also Table  S8 ) per polyQ genotype.
The expression of LIN-18 ICD cDNA was confirmed by RT-PCR for all of the arrays generated. Data are mean 6 SEM (more than 200 animals tested). **p,0. 001 compared to 128Q animals, **p,0. 001 compared to 128Q;lin-18 animals. ns, not significant. Significance was tested using one-way ANOVA, with correction for multiple testing by Tukey's Multiple Comparison Test. (B) Expression of LIN-18 ICD cDNA (4 ng/ml) in touch receptor neurons using the mec-3 promoter is also sufficient to abolish the protective activity of lin-18 LOF on axonal swelling in the PLM neurons of 128Q nematodes as tested in two independent extrachromosomal arrays (Lin-18 ICD: A1 is ID1333, A2 is ID1334; Lin-18: A1 is ID1325, A2 is ID1326). The expression of LIN-18 ICD cDNA was confirmed by RT-PCR for all of the arrays generated.