dataesr/scientific-paragraphs-categorization huggingface dataset:
However, their relative expression levels varied depending on the type of cell, for example ribosomal genes (RPL4, RPL14), VIM, SPARC, and low expressed genes such as BSCL2. In addition to this qualitative assessment, the transcriptome data set was analyzed using the R software package, DESeq  [13]. Principal component analysis of the DESeq normalized data confirmed the relevance of the data (Figure  2B ). The first axis explained 52% of expression variability and separated the two follicular compartments (O and GC). The second axis explained 15% of expression variability and separated the samples according to their follicular stages.
Validation of differential expression by quantitative real time RT-PCR To validate the RNA-seq analysis, the expression profiles of a subset of genes of interest were monitored using qRT-PCR. These genes were selected as follows: (i) genes showing significant up or down expression in oocytes (11 genes: Table  2 ), (ii) genes involved in particular canonical pathways (2 genes: Table  2 ) or (iii) compartment-specifically expressed genes (17 genes: 8 oocyte-specific genes and 9 GC-specific genes; Table  3 ). Statistical analysis confirmed the differential expression between compartments observed in RNA-seq data for all the genes tested (Table  2 ) and the compartment-specific expression of 13 out of 17 genes (Table  3 ). The accuracy of our gene expression measurement was also confirmed by the similarity between RNA-seq and qRT-PCR fold change (Additional file 1: Figure  S4 ).
Transcription regulators Oocyte and GC transcription factors (FIGLA, NOBOX, FOXL2, etc. ) play an important role in folliculogenesis  [14]. They control both oocyte development and GC function. To identify new transcription regulators, we performed IPA upstream regulator analysis on the DEG data set. For each compartment, we predicted the transcriptional regulators that were over expressed and involved in the regulation of this DEG: 47 GC regulators and 9 oocyte regulators (pval < 0. 05: Additional file 8). Among them, the oocyte transcription regulator NOBOX and 12 GC transcription regulators (GATA4, FOS, TP53, etc. ) were predicted to have their protein activated.
Annotation strategy In comparison to sheep, more gene annotations are available for cattle. In agreement with Jager  [70] , our preliminary analysis showed that the use of bovine transcripts for the annotation substantially increased the number of genes identified. Consequently, genomic fragments and de novo contigs were aligned with the bovine genome sequence  [71, 72]  (Ensembl Bos taurus UMD3. 1) using Blat. Blat results were filtered to keep only hits with genomic fragment or contig coverage > = 80%, identity > = 90% and best hit score 3 fold greater than other hits. Blat targets (i. e. cattle slices) were crossed with the Ensembl Bos taurus GTF file (Bos_taurus. UMD3. 1. 65. gtf). Iteratively, for Bos taurus slices not linked to a described gene, we searched for overlaps with downstream gene regions extended by 500 bp, 1 kb and 3 kb (Additional file 1: Figure  S6B ).
Dataset filtration As a gene was represented by a mean of eight genomic fragments (Additional file 1: Figure  S8 ) and because most of the RNA-seq reads are located at the end of the genes (amplification bias), the genomic dataset was filtered to conserve only the best genomic fragment per gene (the genomic fragment with the highest count and the most 3′ UTR localization) with more than 20-30 reads per condition (20 reads for PDO in the three replicates and 30 reads for the other conditions in the four replicates). This counting method was the most informative for this experiment and did not require gene length normalization. This genomic data set was completed with the annotated contigs from the de novo strategy missing in the genome strategy data set.
Statistical analysis The significance of differential gene expression was determined using DESeq package for multifactor design  [13]  in R software (R 2. 14. 0; DESeq release 1. 6. 1) (Additional file 1: Figure  S6C ). Our experimental design included 2 factors: stage (with 4 levels: PD, PM, SC and SA) and compartment (2 levels: O, GCs) with interactions. After DESeq normalization, the best dispersion estimation of our data set was obtained using the following arguments: method 'pooled' , sharingMode 'fit-only' , fitType 'local'. The GLM method was then used to estimate the coefficient and deviance for each gene. For inference, we specified three models: the full model regressing gene expression on both compartments and follicular stages and taking interaction effects into account (count ~stage*compartment), the additive model (count ~stage + compartment) and the reduced model (count ~1).
Biological trends Ingenuity® Pathway Analysis software (IPA; http://www. ingenuity. com) was used to examine functional and molecular pathway enrichment for differentially expressed genes based on Fisher's exact test. This software combines functional annotations of differentially expressed genes (focus genes) and the corresponding bibliographic data to generate significant signaling pathways and regulation networks. The biological analysis focused on the list of differentially expressed genes. The Ingenuity Pathways Knowledge Base was used as the reference set. Functional category analysis identified the functions in the IPA library that were most significant to the input data set. The significance of the association between the data set and the functional category was determined by a P value calculated using Fischer's exact test corrected for multiple testing (p-value < 0.
05; FDR < 0. 05 (Benjamini-Hochberg test)). Canonical pathway analysis identified the pathways in the IPA library of canonical pathways that were most significant to the input data set. The significance of the association between the data set and the canonical pathway was determined based on two parameters: (1) the ratio of the number of genes from the data set that mapped to the pathway divided by the total number of genes that mapped to the canonical pathway and (2) a P value calculated using Fischer's exact test corrected for multiple testing to determine the probability that the association between the genes in the data set and the canonical pathway is due to chance alone (p-value < 0. 05; FDR < 0. 05-0. 25 (FDR threshold <0. 25 is proposed as appropriate for enrichment gene sets in BROAD GSEA analyses (http://www.
broadinstitute. org/ gsea/doc/GSEAUserGuideFrame. html). In these conditions, the result is likely to be valid 3 out of 4 times). Downstream effects analysis was used to identify the expected effect of the observed change in gene expression on the functions (expected to increase or decrease). This analysis examined genes in our data set that are known to affect functions, compared the gene direction of change to expectations derived from the literature, and provided a prediction for each function based on the direction of change (in the list of differential gene expression). IPA uses the regulation z-score algorithm to make predictions. In the same way, IPA upstream regulator analysis was used to understand the cause of the observed change in gene expression. This analysis identifies the cascade of upstream transcriptional regulators (any molecules that can affect the expression of other molecules) that can explain the observed change in gene expression in the dataset.
Quantitative real time RT-PCR analysis for gene expression Gene primers were designed from RNA-seq sequences (Additional file 10). The intron-exon organization of ovine genes was deduced by comparison with the Human genome using the NCBI database. Gene primers were designed preferentially in the 3′ UTR (last 1000 base pairs) or the last exon using LightCycler Probe De-sign2 software (Roche Diagnostics). The primer pairs were confirmed by Primer3 (http://frodo. wi. mit. edu/pri-mer3/). Sequences are available in Additional file 8. The LCM-derived aRNA samples were reverse transcribed as previously described  [12]. The resulting LCMderived cDNA samples were completed to 50 μl and diluted 1/20 before PCR. The assay for each gene consisted of four replicates per condition (except for PDO = 3) and negative controls.
Data was analyzed using Fluidigm Digital PCR Analysis software using the Linear (Derivative) Baseline Correction Method, the User (Global) Ct Threshold Method with the threshold set at 0. 01, and a Ct Range of 12 to 25 cycles. After determination of the threshold cycle (Ct) for each LCM-derived aRNA sample, the PFAFFL method was used to calculate the relative expression of each gene  [73]. The relative expression was normalized by the corresponding geometric average of three reference genes using geNorm v3. 4  [74] : β-actin, TMED4 and RPL19 genes, which were respectively slightly, moderately and highly expressed and not regulated during follicle development or between follicular compartments. The significance of compartment-specific and biomarkers differential gene expression was evaluated using Student's test after fourth square transformation.
The significance of the differential expression of genes involved in canonical pathways was tested using the oneway ANOVA model in the R statistical software system (the Comprehensive R Archive National, http://www. cran. r-project. org) after fourth square transformation of the data. For each gene, an ANOVA model was fitted, with the 2 factor stage (4 levels) and compartment (2 levels) with interactions. A backward variable selection procedure was applied as previously described  [12].
Acknowledgements We thank the  Langlade INRA  experimental farm that provided the lambs. LCM was processed at FR3450 set,  TRI platform Toulouse, Genopole  (http:// tri. genotoul. fr/index. php?id=36). RNA sequencing was processed at the genomic platform Toulouse, Genopole (http://genomique. genotoul. fr/). We thank  Katia Feve  for her help with Fluidigm experiment. We thank our colleagues at the  Biochips platform Toulouse Genopole  (http://biopuce. insatoulouse. fr/) for their help on this work. This work was supported by an  INRA   "Bioressources 2010" grant.
Acknowledgments This research was supported by  Agence Nationale de la Recherche  ( ANR 2010 BLAN 1222 01 ,  ANR-09-PIRI-0024 ,  2010-INTB-1401-02 ),  Sidaction ,  Région Ile-de-France  in the framework of  C'Nano IdF (The nanoscience competence center of Paris Region) ,  Fondation pour la Recherche Médicale en France (FRM)  and  Institut Pasteur. We thank  Jim McNally  for editing of the manuscript.
The intrusion signatures are represented as conditional rules (if condition then state). Furthermore, it triggers the communication and the alarm notification in order to launch advanced diagnostic. (ii) Communication manager that gathers information about, for example, the adjacent links as required by the diagnostic manager. Meanwhile, it answers the diagnostic requests. This component runs into a separated thread so that other IDAR operations are not blocked. (iii) Knowledge database includes the information that is extirpated from the logs and is provided by the communication manager. This database is realized in Mysql (http://www. mysql. com/). Stored information encompasses, for example, the 1-hop and 2-hops neighbors and the MPRs. (iv) Alarm notifier is responsible for alarming the network when an intrusion is detected.
Performance Evaluation. In order to evaluate the performance of IDAR, a mobile ad hoc network has been simulated using the network simulator NS3 (http://www. nsnam. org/) [36]. Each node in the simulated network is further coupled with a Linux Container (LXC) virtual machine (http://lxc. sourceforge. net/) [37]. The reason that motivates this coupling is twofold. First, NS3 offers the possibility to simulate a large-scale mobile ad hoc network. Second, LXC, an operating-system-level virtualization tool, permits to run multiple isolated machines (also called containers) on a single modified hosting kernel (up to 1024 containers over a single hosting kernel). Each container owns its proper resources (e. g. , process tree, network interface, and IP address). Thus, the resource consumption (e.
In practice, each node uses a Simple Network Management Protocol (SNMP) agent so as to collect audit data from the Management Information Base (MIB). After that, events are extracted from the collected audit data and are matched to the attack signature. The addressed attack aims at breaking the link between a victim node and its neighbors, and thus, a DoS takes place. To that end, the attacker impersonates the identity of the victim and sends a fake hello message advertising one of the victim symmetric neighbors with lost link status. Upon receiving the fabricated message, the neighbor changes the status of its link with the victim to "heard" and stops routing packets through the victim.
Several miR-210 predicted targets were found in the set of downregulated transcripts (Figure  1c ). We next analyzed this set using our web tool 'MiRonTop'  30  to better characterize this effect. The results indicated an overrepresentation of miR-210-predicted targets in the set of downregulated transcripts (enrichment factor ¼ 6. 55; Po0. 0001), according to the 'TargetScan' algorithm with 16 predicted targets (Figure  1d  and Table  1 ). Gene ontology analysis of these 16 genes showed an association with the 'ETC', confirming the downregulation of two ETC subunits, SDHD and NDUFA4, which we had previously identified. 28 Moreover, validated miR-210 targets ISCU1/2  25  and GPD1L,  31, 32  two genes associated with the ability of miR-210 to downregulate mitochondrial respiration or promote glycolysis, were confirmed.
The best enrichment score and P-value are obtained for miR-210. A significant score is also obtained for miR-147b, confirming the close functional proximity of miR-210 and miR-147b 11 Sixteen of the miR-210 predicted transcripts selected using the bioinformatics tool miRonTop (http://www. microarray. fr:8080/miRonTop/index) are listed. The Target Prediction Tool used was TargetScan. Cutoff values for selection are equal to 7 for logarithm (base 2) of the average intensity (AveExpr), to 0. 4 for the logarithm (base 2) of the ratio (pmiR-210/pmiR-Ctr) (logFC) and 0 for the B-value. Gene ID: corresponds to NCBI Gene ID (http://www. ncbi. nlm. nih. gov/gene) miR-210, hypoxia and radioresistance right panel). Finally, pmiR-210 cells showed greater lactate production compared with pmiR-Ctl cells (Figure  3e ), confirming the increase in glycolytic flux.
A pre-miR (ce-miR-67) from Caenorhabditis elegans was cloned into the lentiviral vector and used as a control. Virus production and cell transduction were performed following the manufacturer's protocol. The resulting pmiR-Ctl and pmiR-210 cells that stably overexpress miR-210 were selected by puromycin (1 mg/ml). Several pmiR-210/HIF-1 À A549 clones were obtained using two independent sequences of shRNA against HIF-1a. 35 We selected one clone for each sequence, based on the decreased mRNA level of HIF-1a (495% -data not shown) and the expression of HIF-1a and CAIX in hypoxia compared with the same conditions in pmiR-210 cells (Figure  6a ). As both clones were then tested under the same conditions and gave the same results, we only show the results from one clone (sequence pTerHIF-1bis).
Microarrays Expression microarrays: SurePrint G3 Human Gene Expression 8 Â 60 K Microarray from Agilent Technology (Loveland, CO, USA) were used. RNA were labeled and hybridized following the manufacturer's instructions. Two biological replicates were performed for each comparison. The experimental data and microarray design have been deposited in the NCBI Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) under series GSE43622. Statistical analysis: Normalization was performed using the Limma package available from Bioconductor (http://www. bioconductor. org). Intra-and interslide normalization was performed using the Print Tip Loess and the quantile methods, respectively. Means of ratios from all comparisons were calculated and B-test analysis was performed. Differentially expressed genes were selected using a B-value40.
Biological theme analysis: Data from expression microarrays were analyzed for enrichment in biological themes (Gene Ontology molecular function and biological process) using Ingenuity Pathway Analysis software (http://www. ingenuity. com/) and Mediante (http://www. microarray. fr:8080/merge/index). miR-210 targets analysis was performed with MiRonTop (available at http:// www. microarray. fr:8080/miRonTop/index) as previously described. 30 ansmission electronic microscopy. Cells were fixed in situ with 1. 6% glutaraldehyde in 0. 1 M phosphate buffer at room temperature for at least 1 h and then conserved at 4 1C. Samples were rinsed in the same buffer and then postfixed with 1% osmium tetroxide and 1% potassium ferrocyanide in 0. 1 M cacodylate buffer for 1 h at room temperature to enhance the staining of cytoplasmic membranes.
Cells were allowed to form colonies during 7 days. At this time, they were stained with GIEMSA and counted using ImageJ software. For chemotherapy sensitivity, 400 000 cells were plated in 25 cm 2 flask and cultivated for 2 days. At this point, cells were treated for 24 h with cisplatin or 4 h with docetaxel or for 2 h with bleomycin, then immediately trypsinized and plated in triplicate (1000 cells per well in 6-well plates) for clonogenic survival. Cells were allowed to form colonies during 10 days. At this time, they were fixed and stained with crystal violet and counted using ImageJ software. Confocal microscopy. Cells were grown on glass coverslips and then fixed in 3. 3% paraformaldehyde for 30 min at RT and permeabilized with 0. 2% Triton-X-100 for 5 min. Cells were blocked with PBS containing 0.
Acknowledgements. We thank  Pascale Bertrand  for helpful discussion and suggestions. We acknowledge the excellent support of the  Nice-Sophia Antipolis Functional Genomics Platform and of the 'Centre Commun de Microscopie Appliquee'  from the  University of Nice-Sophia-Antipolis. We thank  Nicolas Pons  and  Sandra Scha  ¨fer (IPMC) for excellent technical assistance. We thank  Thomas Maurin  and  Eric Lai  for providing us with miR-210 sensor construct. This work was supported by  CNRS ,  INSERM ,  Association pour la Recherche contre le Cancer (ARC, Projet Libre   SFI20101201798  to BM),  Canceropole PACA ,  French National Research Agency (ANR) ,  Ligue Nationale Contre le Cancer (JP, NM) , the  Centre Antoine Lacassagne, European Community  (MICROENVIMET,  FP7-HEALTH-F2-2008-201279  to PB, BM and METOXIA to JP, NM).
, 2008; Ndione et al. , 2009). In order to release risk maps (consisting of the combination of hazards and vulnerability) in quasi-real time, automatic hazard maps were since produced through a model using a process chain of causality in the interactive data language (IDL) code (https://www. cfa. harvard. edu/~scranmer/Ay201a/Data/i dl_basics. pdf) based on ITTVIS software (http:// www. itres. com/products/supporting/hyperspectral_ima ge_analysis_software/), which is supported by Windows XP and Windows 7 by ENVI 4. 7 and IDL 7. 1 (http://www. ittvis. com/) providing at least one gigabyte random access memory and graphic facilities with at least 800 x 600 pixels. Three main functions were used to establish the final product: (i) analysis of rainfall products from the tropical rainfall measuring mission (TRMM); (ii) analysis of pond dynamics (filling-up and flushing-out) based on data obtained from the normalised difference pond index (NDPI) according to  Lacaux et al.
Acknowledgements The partners would like to thank managers of the programme " Gestion et Impacts du Changement Climatique" (GICC, French Ministère de l'Écologie, du Développement Durable et de l'Énergie , or MEDDE), for their unconditional support and funding the "AdaptFVR" project. Many thanks to the  CNES ISIS  programme (http://www. isis-cnes. fr/) by providing SPOT data to facilitate European space scientific research. Dr. Samuel Somot  from the  "Centre National de Recherches Météorologiques" (CNRM) of Météo-France  allowed us to access the CORDEX-Africa data in record time. His collaboration is highly appreciated. Thanks also go to  Thomas Manga  (Barkedji veterinarian and head of livestock breeding) for his expertise and discriminating knowledge of the area. This work is designated as LDEO contribution #  7705.
glabrata plasma and S. mansoni primary sporocyst extracts, we recently identified a new, putative cytolytic protein from B. glabrata that displays similarities to members of the b-PFT superfamily known to form channels in targeted membranes  [30]. The most studied members of this superfamily are the aerolysin toxins secreted by several Aeromonas spp. [31, 32]. Other members of this b-PFT superfamily include the a-toxin produced by Clostridium septicum  [33] , the etoxin from Clostridium perfringens  [34] , the MTX (mosquito toxin)type proteins secreted by Bacillus sphaericus  [35] , parasporin 2 and 4 from Bacillus thuringiensis  [36, 37] , monalysin from Pseudomonas entomophila  [38] , and the vibrioaerolysin of Vibrio splendidus  [39]. Most of these proteins are produced by bacteria, as confirmed by a recent bioinformatic analysis of protein database entries displaying an aerolysin signature, which revealed that 70% of the putative b-PFTs identified came from bacteria  [40].
Semi-quantitative RT-PCR The tissue distribution of Biomphalysin mRNA was analyzed by preparing samples of albumen gland, head-foot, hepato-pancreas, and ovotestis, collected from ten snails under a binocular dissection microscope. Hemocytes from fifty snails were also collected from hemolymph after centrifugation at 100006g for 10 min at 4uC. Total RNA from these different tissues, hemocytes, and Bge cells was extracted using TRIzol Reagent solution (Invitrogen) according to the manufacturer's protocol. Then, total RNA (10 mg) was treated with rDnase I (1 U/mg DNA; Ambion) to remove contaminating genomic DNA. cDNA was synthesized from DNA-free RNA (1 mg) by reverse transcription (RT) using random hexamer primers and Revertaid H minus M-MuLV reverse transcriptase (Fermentas). The absence of contaminating genomic DNA was confirmed using PCR primers (forward: 59-CCC ATC TAT TGT TGG CAG ACC-39, reverse: 59-GTT TAG AGG TGC CTC TGT GAG-39) for the actin gene (accession number U53348.
Real-time quantitative PCR Total RNA was extracted either from a pool of seven snails or from Bge cells using TRIzol reagent. Reverse transcription was performed as described previously  [21]. Real-time quantitative PCR (Q-PCR) was performed on cDNAs (diluted 50-fold with nuclease-free water) using the Light Cycler System 480 (Roche, Idaho Technologies). Sequences of primers used for amplification of Biomphalysin (forward: 59-CTG ATT ACA CCT GGG C-39, reverse: 59-ACC CTT TCG TCC CAT AC-39) and ribosomal protein S19 (forward: 59TTC TGT TGC TCG CCA C, reverse: 59-CCT GTA TTT GCA TCC TGT T-39) were designed using LightCycler Probe Design software version 1. 0 (Roche Diagnostics). Q-PCR reactions were performed according to the Light Cycler procedure. Amplification conditions were as follows: 20 s denaturation at 95uC followed by 40 cycles of 5 s at 95uC, 7 s at 60uC, and 12 s at 72uC.
After amplification, the specificity of PCR products was determined by analyzing melting curves, acquired by heating the product at 95uC, cooling at 70uC for 20 s, and then slowly remelting (0. 31uC/s) up to 95uC. Each PCR product was verified by sequencing. The results of melting curve analyses were quantified by determining the crossing-point value (Cp) using the second derivative maximum method of the Light Cycler Software 3. 3 (Roche Diagnostics). Expression data were normalized to ribosomal protein S19 (accession number CK988928) levels  [29]. The Biomphalysin-to-S19 transcription ratio was calculated using the relative quantification analysis module of the LightCycler 480 software. All Q-PCR experiments reported in the present study were repeated at least three times.