dataesr/scientific-paragraphs-categorization huggingface dataset:
This coincides also with the observation that the gene encoding enzyme of the first step of the alternative galactose pathway xyl1 is strongly induced. We also confirm the induction of genes encoding putative lactose transporters as recently reported  [24, 41]. Among them, the crt1 gene (Trire2:3405) has been recently described as having a pivotal role in the lactose induction of cellulase genes, either as a lactose transporter or a cellulose sensor  [24, 26, 42]. Three other transporters were highly up-regulated during growth on cellulose (Trire2:79202, Trire2:56684 and Trire2:54632). Several intracellular βglucosidases were also regulated  [23]. The relatively small number of detected genes and the fact that the majority of genes already being reported as having an impact on cellulase production are present in our dataset prompt us to believe that the data presented here is a very specific picture of cellulolytic enzymes induction by lactose.
Mutations in regulated genes described in other cellulase improved strains are shown. Mutations are described as single nucleotide variation (SNV) and deletions or insertions (InDel). Affected gene region is also provided. RUT C30,  [11] ) because it would regulate some genes in NG 14 that are no longer regulated in RUT C30. Based on our previous work on the cre1 regulon [9], we only found 66 genes in the present dataset to be potentially subject to carbon catabolite repression (data not shown). However, they were distributed in almost all the clusters, and have no common expression pattern with our previous study. While catabolite repression is probably operating in strain NG 14 (see below), the complex effect of CRE1 prevents us from formally identifying CRE1 ? fingerprints? in our dataset.
Downregulation of these genes in NG 14 could be an adaptation to lactose feeding and end of carbon starvation. Also, a large number of NG 14 up-regulated genes in cluster 1 (52) correspond to ribosome biogenesis. Note that ribosomal proteins were not identified in our previous study of cre1-deletants  [9] , and therefore this effect appears to be cre1-independent. However, without genetic engineering of NG 14 or RUT C30, we cannot formally rule out a direct or indirect CRE1 effect. Clusters 5 and 6 group most of the genes that are specific to RUT C30. Interestingly, a significant induction of these clusters occurs after the onset of protein production in RUT C30. Some of these genes might be responsible for the higher productivity of this strain. Indeed, these clusters feature the highest percentage of regulators and secretion-related genes of our dataset.
Only the ace3 gene (Trire2:77513) is a notable exception: it is at the same time induced in our datasets (and in other teams? [17] ), mutated in RUT C30, and has been identified as having an effect on cellulase production. The mutation in RUT C30 is a single nucleotide variation leading to a premature stop codon instead of a glutamine amino acid. This truncated C-terminus region is probably of regulatory importance, as in other Zn 2 Cys 6 transcription factors  [12]. This could lead to a relief from a potential repression, mimicking the effect of its overexpression  [17]. It is interesting to note that other mutations affect promoters, and that three of them concern genes in cluster 5 that are specific to RUT C30 and possibly linked to productivity. Intriguingly, a larger number of genes affected by mutations found in the independent lineages QM9414, KGD-12 and PC-3-7 were found in our dataset as well, and they are mostly located in clusters 1 and 5  [23] [24] [25].
Microarray hybridizations The microarray data and related protocols are available at the GEO website (http://www. ncbi. nlm. nih. gov/geo/), under the study number [GSE60908]. Briefly, the RNAs from each experiment were reverse-transcribed and labelled with Cy3 or Cy5 dyes using an indirect labelling procedure and dye-switch on the two biological replicates. Hybridization and scanner procedures for the T. reesei DNA chip manufactured by Agilent and designed using the Teolenn software have been described previously  [9, 50]. The data were normalized without background subtraction by the global lowess method performed with the Goulphar software  [51]. The background threshold was calculated by adding two standard deviations to the average intensity of all the ? not found? features. For each probe, the log2 hybridization ratio was linked to genome annotation from the Joint Genome Institute (JGI) website (http://genome.
jgi-psf. org/Trire2/Trire2. home. html). The final log2 ratio for each transcript was obtained by averaging the ? detectable? hybridization values from all probes located inside the coding sequence on the matching strand. Transcripts with no probe marked as ? detectable? were discarded from further analysis. For the two biological replicates on each of the four experiments, we applied to the pretreated results the linear modelling approach implemented by lmFit and the empirical Bayes statistics implemented by eBayes from the LIMMA R package  [52]. We selected the list of statistically regulated genes using a 5% significance threshold. Finally, we kept as the most highly regulated targets only transcripts with a final log2 hybridization ratio greater than 1 or less than -1.
RNA-seq library preparation and analysis Messenger (polyA+) RNAs were purified from 2 μg of total RNA using oligo(dT). Libraries were prepared using the strand non-specific RNA-seq library preparation Tru-Seq RNA Sample Prep Kits v1 (Illumina). Libraries were multiplexed by 2 on one single flowcell lane and subjected to 50 bp paired-end read sequencing on a HiSeq 2000 device. A mean of 53 ? 12 million passing Illumina quality filter reads was obtained for each of the two samples? RNA-seq data analysis. We performed RNA-seq analysis using the Eoulsan pipeline  [53]. Before mapping, poly N read tails were trimmed, reads ≤11 bases were removed, and reads with quality mean ≤12 were discarded. Reads were then aligned against the T. reesei genome (version 2 from the JGI website) using the Bowtie mapper (version 0.
12. 7) [54]. Alignments from reads matching more than once on the reference genome were removed using the Java version of SAMtools  [55]. To compute gene expression, T. reesei genome annotation from JGI (version 2) was used. All overlapping regions between alignments and referenced exons were counted. To analyse the gene expression level, the relative transcript abundance was measured in reads per kilobase of exon per million mapped sequence reads (RPKM)  [56]. The log2 ratios of the RPKM values were used to identify differentially expressed genes. To keep only the most differentially expressed genes, a threshold of 4 for the log ratio with a reads number greater than 100 was chosen. The RNA-seq gene expression data and raw fastq files are available in (Additional file 6: Table  S3 ).
Cluster analysis of microarray results An expression matrix was built from all the genes sorted as strongly regulated in at least one time point and in one strain. The GEPAS pipeline  [57]  was used to filter out genes with 30% missing values, leading to 568 selected genes. From these genes, a clustering analysis was performed using the MultiExperiment Viewer software  [58]. A combination of unsupervised hierarchical clustering and K-means algorithms was used to sort genes into clusters. First, an ascending hierarchical clustering was done using the Euclidean distance metric and the average linkage method. Results were used to determine the optimal number K of clusters (K = 10). The K-means algorithm was applied using Euclidean distance on the 568 gene expression profiles. In order to improve the robustness of results, five independent runs of K-means with a random initialization were performed.
Gene identification, functional prediction and classification Regulated genes were first identified according to their ID number by reference to the T. reesei genome website (http://genome. jgi-psf. org/Trire2/Trire2. home. html). In the case of genes that were either poorly or not yet annotated, orthologous genes in other fungal taxa (mainly Neurospora crassa, Saccharomyces cerevisiae and Aspergillus nidulans) were searched using the FUNGIpath database (http://embg. igmors. u-psud. fr/fungipath/) and the function predicted by similarity. Genes without orthologs were annotated with their domains either from the T. reesei genome website or from a search on Inter-Pro (http://www. ebi. ac. uk/interpro/). Identified proteins were categorized according to the Functional Catalogue (FunCat  [59] ) and they were manually curated to include only proteins with a clear function associated with a category.
Acknowledgements We thank  Sabine Prigent , H? l? ne Ipas and  Caroline Zemb  for technical assistance. This work was supported by the  Tuck Foundation  ( 2009-03  to AM, FBM and SLC and Ph. D. grant to TP). The IBENS high-throughput sequencing facility is a member of the ?France G? nomique ?consortium ( ANR-10-INBS-0009 ). BS was supported by grant  P24219  from the  Austrian Science Fund FWF. DPP Ph. D. was funded by  BPIFrance  (www. bpifrance. fr)through the  FUTUROL  project (http://www. projetfuturol. com/).
For a large number of individuals and all b i being equal, the IBM recovers the analytical model eq. 1. Simulation code for the IBM and for the analytical models is available at https://github. com/florianhartig/EvolutionOfRelativeNonlinearity/. Because strategies can go extinct in the IBM, especially during invasion, we use a different measure of invasion fitness than for the analytical models (eq. 7), namely the probability p that a strategy invading with one individual survives for at least 500 generations in a resident population at equilibrium. For computational efficiency, we approximate this value by simulating invasions with three individuals, resulting in estimates of the probability q (3) that a mutant strategy invading with three initial individuals goes extinct after 500 generations.
Acknowledgements We would like to thank  Thomas Banitz ,  Claudia Dislich ,  Simon Levin , and  Gita Benadi  for helpful comments. The models used in our analysis were implemented in Netlogo 4. 1 and 5. 0 [63]. Plots and statistical analysis were carried out with R version 3. 0. 2[64]. FH acknowledges support from the  ERC Advanced  Grant  233066. TM received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no  281422  ( TEEM-BIO ). UD gratefully acknowledges financial support by the  European Commission , the  European Science Foundation , the  Austrian Science Fund , the  Austrian Ministry for Science and Research, and the Vienna Science and Technology Fund.
Identification of novel A. gambiae miRNAs To detect putative A. gambiae miRNAs, we used miR-Deep2 and a pipeline developed in-house. Only mapped sequence reads were used for the further prediction and expression analyses. We considered a general guideline for microRNA annotation in deep-sequencing data  [32] [33] [34]. To refine results of the novel miRNA prediction, the following criteria were applied: i) detection of at least 10 sequence reads mapping to the miRNA hairpin per library; ii) valid miRNA precursors folding into stem-loop hairpins with folding energy less than -15 kcal/mol; iii) consistency of the 5'-end starting position, measured as an abundance of mapped sequence reads sharing the same 5'-terminus. Genuine miRNA loci produce so-called mature (miR) and star (miR*) sequences (originated from the preferred and non-preferred strands of miRNA duplex, respectively) that can be derived from both arms of the miRNA precursor.
Taken together our data suggest that the dramatic change in miR-92a levels resulted from the exogenous miRNA intake occurred during blood feeding. To assess the early responses of Anopheles miRNAs specific to P. berghei infection, we compared miRNA expression levels in females 3 h after regular and infectious blood feeding. We identified 6 miRNAs responding to the P. berghei parasite presence (Figure  6D ). Among those, two highly expressed miRNAs, miR-317 and miR-2940 were more than 5-and 3-fold upregulated by P. berghei infection, respectively. The remaining differentially expressed miRNAs showed a relatively low abundance. Overall, the Anopheles miRNAs identified here might represent early and dynamically regulated mosquito sensors that respond to normal and infectious blood meals.
miRNA identification and prediction Sequence reads were processed using the CASAVA1. 8 pipeline (Illumina). Non-coding RNA profiling was performed by the ncPRO-seq analysis pipeline  [52]. miR-Deep2 analysis  [53]  was employed to detect potential miRNAs from raw RNA sequencing data using default parameters. To quantify miR-71 associated sequence reads the following algorithm developed in-house was used: sequence reads mapped to Agamp3 genome assembly, maximum 2 nt mismatches were allowed. Multiple position mapping was enabled and the weight of multiple mapping reads was considered. We allowed 2 nt upstream and downstream shifts in the mapping window for sequenced miR-71-5p and miR-71-3p, if they fall within the same positions on the predicted miRNA-71 precursor. The thermodynamic stability of the secondary structures of the flanking genomic sequences was analyzed using RNAfold and Mfold  [54, 55].
IGV2. 0 viewer was used to visualize sequence reads mapped to the reference genome. To identify putative homologs, sequence alignment of A. gambiae miRNAs with mature miRNAs was performed using Ae. aegypti and D. melanogaster data sets (miRBase v19) and manually inspected. Mosquito miRNAs showing exact seed matches and sharing more than 70% of sequence similarity were considered as A. gambiae homologs of the corresponding known miRNAs. Anopheles miR-NAs with a low level of sequence similarity were further used for the seed sequence alignment. The alignment was performed using the first 10 nt at the 5'-end sequence of Anopheles miRNAs and sequences of known miRNA data sets, including Ae. aegypti (AaegL1), B. mori (SILKDB2. 0), D. melanogaster (BDGP5. 0), T. castaneum (Tcas3. 0) and H. sapiens (GRCh37.
p5). Multiple RNA sequence alignment was performed using the MARNA RNA tool. For quantification of miRNA expression levels, sequence reads with non-template additions at the 3'-end were included in the counts. The DESeq2. 6 package was used to quantify and to assess miRNA differential expression, which was considered as significant at p < 0. 05. The raw log value of all single miRNA reads and the 5p-and 3p-derived reads are listed in Additional file 2: Table  S1  and in Additional file 1: Figures  S1-2 , respectively. Detected M. musculus miRNAs in small RNA cDNA libraries are listed in Additional file 3: Table  S2. Small RNA expression in Ago-silenced mosquitoes was analyzed using miRBase v19, miRDeep2 and Rfam databases. The raw and normalized read frequencies of small RNAs are listed in Additional file 4: Table  S3.
Acknowledgement We would like to acknowledge  Julien Soichot  and  Olivier Pichon  (IBMC);  Dana Tschierske  and  Hanne Krueger  (MPIIB) for maintaining mosquito colonies and technical assistance with mouse blood-feeding. We thank  Bernard Jost ,  Muriel Koch ,  Géraldine Bamba  and  Serge Vicaire (IGBMC)  for the assistance with small RNA libraries preparation and high-throughput sequencing. We thank  Alexandra Krupa  and  John R. Clayton  ( IBMC ) for helpful discussion, for reading of this manuscript and providing critical comments. This work was supported by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements no. 242095-EVIMalar  and no. 223601-MALVECBLOK.
Introduction: This report describes the first case, to the best of our knowledge, of pacemakerinduced endocarditis due to Gordonia bronchialis. Presentation: Pacemaker-induced endocarditis due to G. bronchialis infection was determined in a 92-year old man. This Gram-positive bacillus failed to be identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry technology, whereas the taxon was indexed in the database. 16S rRNA and rpoB gene sequencing were required to determine the correct strain identity.
This beaded Gram-positive bacillus was catalase positive and oxidase negative. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) identification was performed on a 96-target plate (Bruker Daltonics) with a Microflex mass spectrometer (Bruker Daltonics) using FlexControl software (version 3. 0). The spectrum was imported and compared with the database by the BioTyper software (version 2. 0; Bruker). The BioTyper database contains spectra of approximately 4613 species and is regularly updated by the manufacturer. Theoretically, an accurate identification score to species level is given by a score value >2. 0 according Bruker recommended cut-off values. The first result was not conclusive with a score of ,1. 5. The strain was retested after a protein extraction step, as described previously  (Bizzini et al.
, 2010; Khot et al. , 2012). Arthrobacter castelii (score51. 967) was considered as a reliable identification to species level Abbreviations: MALDI-TOF MS, matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry. The GenBank/EMBL/DDBJ accession numbers for the sequences of the Gordonia bronchialis strain isolated in this report are KF378766 (16S rRNA gene) and KF378767 (rpoB). (2012). However, the discrepancy between colonial aspects (i. e. smooth blade yellow colonies for Arthrobacter  (Heyrman et al. , 2005)  and wrinkled yellow orange colonies for our isolate) led us to perform 16S rRNA gene bacterial sequencing using the primers described by  Gauduchon et al. (2003). The 471 bp fragment obtained and compared with GenBank sequences using the BLAST algorithm (http://www.
ncbi. nlm. nih. gov/BLAST) showed 99 % identity with Gordonia bronchialis strain X0217 (GenBank accession no. HQ316182) and Gordonia terrae strain KNUC2111 (GenBank accession no. JN382216). These results were confirmed by bioinformatic bacterial identification analysis (http://pbil. univ-lyon1. fr/bibi/ query. php) with 19 choices for G. bronchialis and one for G. terrae. Sequencing of the rpoB gene was then performed with primers C2700F and C3130R described by  Khamis et al. (2004). The 413 bp fragment showed 99 % nucleotide similarity to G. bronchialis strain DSM 43247 (GenBank accession no. CP001802). Antimicrobial susceptibility testing performed by the disc diffusion method on Mueller-Hinton agar with the addition of 5 % blood and interpreted according to EUCAST (2014) criteria showed susceptibility to amoxicillin, piperacillin, cefotaxime, imipenem, aminoglycosides, trimethoprim-sulfamethoxazole, fluoroquinolones, vancomycin and linezolid and resistance to macrolides and tigecyclin.
Cholera is typically considered endemic in West Africa, especially in the Republic of Guinea. However, a three-year lull period was observed from 2009 to 2011, before a new epidemic struck the country in 2012, which was officially responsible for 7,350 suspected cases and 133 deaths. To determine whether cholera re-emerged from the aquatic environment or was rather imported due to human migration, a comprehensive epidemiological and molecular survey was conducted. A spatiotemporal analysis of the national case databases established Kaback Island, located off the southern coast of Guinea, as the initial focus of the epidemic in early February. According to the field investigations, the index case was found to be a fisherman who had recently arrived from a coastal district of neighboring Sierra Leone, where a cholera outbreak had recently occurred.
Cholera cases and deaths, rainfall, population and geographical data The Republic of Guinea spans 245,857 km 2 and is administratively divided into 33 prefectures plus the capital Conakry. In 2012, the country had an estimated population of 12 million inhabitants. At that time, the Guinean national health surveillance system prospectively reported all suspected cholera cases based on the WHO definition of the disease  [15]. Each Prefectural Health Directorate (DPS -Direction Prefectorale de la Sante) tallied new cases recorded at the various health structures of the prefecture on a weekly basis. Aggregated morbidity and mortality cholera data were then transmitted to the Directorate of Prevention and Disease Control (DPLM -Direction de la Prevention et de la Lutte contre la Maladie), which compiled the information in a national database of 7,350 cases.
DPLM also retrospectively compiled a line list of 6,568 patients, which included the date of consultation and geographical origin down to the village level and was anonymized prior to analysis. To limit notification bias, both databases were subsequently compared and merged, which enabled the retrieval of 393 additional cases. The use of these data for epidemiological, research and publication purposes was approved by the Guinean Ministry of Health (Ministere de la SantePublique et de l'Hygiene Publique). Dailyaccumulated rainfall data were obtained from satellite estimates (TMPA-RT 3B42RT derived) provided by the National Aeronautics and Space Administration (available at: http://disc2. nascom. nasa. gov/Giovanni/tovas/realtime. 3B42RT_daily. 2. shtml). As most cases were recorded in Maritime Guinea and, to a lesser extent, Middle Guinea, daily rainfall data were averaged on the position 9.
Author Summary Cholera is a potentially deadly diarrheic disease caused by the toxin-secreting bacterium Vibrio cholerae. In many poor countries, this prototypical waterborne disease is considered endemic and linked to the climate-driven proliferation of environmental reservoirs of the pathogen. Although such a statement implies radical public health consequences, it has never been proven in Africa. The present study aimed to elucidate the origin of the cholera epidemic that struck the Republic of Guinea in 2012 following a three-year lull period. This investigation integrated a spatiotemporal analysis of the national case databases, field investigations and thorough genetic analyses of 38 clinical bacterial isolates sampled throughout the Guinean epidemic. The Guinean V. cholerae DNA sequence results were aligned and compared with the sequences of nearly 200 strains isolated throughout the world over the past 60 years.
MLVA-based genotyping of sampled strains Genotyping of the V. cholerae strains was performed via MLVA (Multiple Loci VNTR (Variable Number Tandem Repeat) Analysis) of 6 VNTRs (Table  1 ), including 4 previously described assays  [17, 18]  and 2 assays specifically designed for this study to improve the discriminating power of the analysis. The novel VNTR assays were designed based on the reference strain El Tor N16961 (GenBank accession numbers AE003852. 1 and AE003853. 1) using Perfect Microsatellite Repeat Finder webserver (currently unavailable). Specific primer pairs were subsequently designed using the Primer3 program (http://simgene. com/ Primer3) (Table  1 ). Fluorescent-labeled primers were purchased from Applied Biosystems. For each PCR assay, DNA amplification was carried out by mixing 0.
Whole-genome sequencing To better characterize the V. cholerae strains responsible for the epidemic, whole-genome sequencing was performed on a strain isolated at the onset of the epidemic (strain G298_Guinea) using a GS FLX+ System (454 Life Science, a Roche company). The DNA sequence was assembled using Newbler, from GS De novo Assembler (http://454. com/products/analysis-software/index. asp). To perform a phylogenetic assessment of the core V. cholerae genome based on genome-wide SNPs (single nucleotide polymorphisms), strain G298_Guinea DNA was re-sequenced using a HiSeq Illumina System (Illumina).