dataesr/scientific-paragraphs-categorization huggingface dataset:
FoxO1 Deacetylation Mediates Thyroid Hormone Action 04-1557; Millipore), ␤-actin (C4; SC-47778), and GAPDH (ab9485; Abcam) were used in this study. After overnight incubation in primary antibodies, membranes were washed three times in TBST and subsequently incubated with species-appropriate, peroxidase-conjugated secondary antibodies (Santa Cruz Biotechnology) for 1 h. Blots were washed three times with TBST and once with TBS without Tween and developed using an enhanced chemiluminescence system (GE Healthcare). Images were captured on the Gel-Doc system (Bio-Rad), and densitometry analysis was performed using ImageJ software (National Institutes of Health). The integrated density of target protein was normalized with indicated internal controls (␤-actin or GAPDH), and the mean was plotted in graphs.
Eluted DNA was purified with QIAamp DNA Mini Kit (Qiagen). 2 l of immune-precipitated DNA (1% input DNA) was used with a QuantiFast SYBR Green PCR kit (Qiagen) for 40 cycles of qPCR using Rotor-GeneQ qPCR machine (Qiagen). Primers and regions used to amplify PCK1 and G6PC promoter are given in Table  1. Statistical Analysis-Individual culture experiments were performed in triplicate and repeated at least three times independently using matched controls; the data were pooled. Results are expressed as mean Ϯ S. D. for all in vitro experiments and mean Ϯ S. E. for all in vivo experiments. The statistical significance of differences (p Ͻ 0. 05) was assessed by a two-tailed Student's t test. Ethics Statement and Study Approval-All mice were maintained according to the Guide for the Care and Use of Laboratory Animals (National Institutes of Health publication 1.
Acknowledgments-We thank  Prof. Mitchell A. Lazar  ( Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, The Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania ) and  Prof. Christopher B. Newgard  ( Sarah W. Stedman Nutrition  and  Metabolism Center, Departments of Medicine and Pharmacology and Cancer Biology ,  Duke University Medical Center, Durham, NC ) for critical reading and suggestions. We also thank  Dr. Chui Sun Yap ,  Dr. M. M. Siddique ,  Laura Martinez ,  Benjamin Livingston Farah  ( Cardiovascular and Metabolic Disorders Program ,  Duke-NUS Graduate Medical School ), and  Cyrielle Billon  ( Institut de Génomique Fonctionnelle de Lyon, Université de Lyon, Université Lyon 1, CNRS, Ecole Normale Supérieure de Lyon ), for helpful advice and constructive comments; and  Prof.
* This work was supported by  Duke-NUS Graduate Medical School Faculty Funds  (to P. M. Y. ) sponsored by the  Ministry of Health, Ministry of Education and Ministry of Trade, Singapore , and  A*StaR Singapore ;  Singapore National Medical Research Council (NMRC)   Early Development Grant  ( EDG)/1044/2011 ;  Singapore NMRC Clinical Investigator Research Grant   1340/2012 ; and  NMRC/CIRG/1340/2012  grants. This work also was supported by an  American Diabetes Association mentored research fellowship  (to S. H. Y. and  M. A. Lazar ).
pulex were sampled from the northern region of the Rhone River Valley (N1, N2, and N3), and three G. pulex gallicus populations were sampled from the southern region (S1, S2, and S3) (Fig. 1 ). For each region, the collecting sites were similar in terms of riparian vegetation cover (mainly alder and oak leaves in the north and oak leaves in the south), substratum type, width, and current velocity. At least 125 pre-copula pairs from each population were sampled in January 2012 (a total of 1,500 individuals) using a hand-net sampler. The female and male individuals of each pair were carefully separated directly in the field. To verify that the populations of the different sites belonged to the same species, DNA of 10 animals from each population was amplified with primers targeting the mitochondrial cytochrome oxidase subunit I (COI) gene (data not shown) and the threshold method defined by Lefebure et al.
Hence, initial fresh masses of the disks were converted into initial dry masses and these initial dry masses were compared to the final dry mass of the disks to compute the leaf consumption rate. The leaf consumption rates were calculated as: InitialDryLeafMass-FinalDryLeafMass-MeanMicrobialConsumption ð Þ AmphipodDryMass|10days ð Þ The leaf consumption rate was expressed in mg leaf dry mass day 21 mg 21 of gammarid dry mass  [26]. In addition, this study characterised the energetic state of the individuals (through the triglyceride stores) after nutritional experimentation. In crustaceans, triglycerides constitute an essential compound for long-term energy storage  [49] ,  [50] ,  [51]. Thus, individuals with a null leaf consumption rate (i. e. , starved animals) were removed from the dataset concerning changes in the triglyceride content (TG).
For all ANOVAs, Tukey's HSD tests were performed for pairwise comparisons. The linear correlations between the mean leaf consumption rate of northern or southern gammarid populations and the mean leaf species toughness were also tested. For each tree species, Spearman correlations were used to test the relationship between the mean percentage variation in the triglyceride content in gammarids and the leaf consumption rates. For this correlation, animals with null consumption rates were removed from the data set to the calculation of the leaf consumptions rates. All tests were performed using STATISTICA 7. 1 software (Statsoft TM , Tulsa, USA).
Acknowledgments Thanks to  Marion Javal  and  Florence Caty  (student trainees, Universite Ĺyon 1),  Felix Vallier ,  Marie-Rose Viricel  ( UMR CNRS 5023 ,  Universite Ĺyon 1 ) for the acquisition of data, and to  Katia Diadema  ( Conservatoire Botanique National Mediterraneen de Porquerolles, France ) for her help in collection of O. carpinifolia leaves.
The national research agency for funds (program Wetchange  ANR-09-CEP-006-01  of the  National Research Agency -Agence Nationale de la Recherche -ANR ) http://www. agence-nationale-recherche. fr/en/project-based-funding-to-advance-french-research/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
STATISTICAL ANALYSIS Data were expressed as a ratio of fold induction compared to nonactivated DCs. For each experiment, all experimental conditions were tested three times and each experiment was conducted at least three times for each strain. Statistical analyses were done using the non-parametric Mann-Whitney test. A p-value of less than 0. 05 was considered significant. All statistics were performed using SPSS 16. 0F for Windows software (SPSS Inc. , Chicago, IL, USA).
, 2004 Kranzer et al. , , 2005;; Kao et al. , 2006; Mitchell et al. , 2007; Rad et al. , 2009; Andres et al. , 2010; Kao et al. , 2010; Fehlings et al. , 2012). Our data were obtained using primary human DCs from several donors. All experiments were repeated with at least three different donors in order to ensure the reliability of the results. Significant productions of several cytokines (TNFα, IL-1β, IL-6, IL-8, IL-10, IL-12, and IL-23) were observed in H. pylori-activated DCs, TNFα and IL-6 being produced at the highest levels. In vivo, TNFα contributes to monocyte maturation after recruitment, whereas IL-6 supports the transition between the early stages of the infection and the sustained mononuclear influx into the infected gastric mucosa. Early production of TNFα and IL-6 upon LPS stimulation reflects the intrinsic property of E.
ACKNOWLEDGMENTS This work was supported by grants from  La Ligue contre le Cancer (Committee of Dordogne, France) , the  Structure Fédérative de Recherche TransBiomed (Bordeaux, France)  and the  Société Nationale Française de Gastroentérologie. Anaïs Hocès de la Guardia  received funding from the  French Ministry of Research. The authors thank  Jonathan Ferrand  ( Center for Innate Immunity and Infectious Disease, Monash Institute of Medical Research, Clayton, Australia ),  Patrick Blanco  ( CNRS UMR 5164, Bordeaux, France ) for fruitful discussions and  Lindsay Mégraud  for English revision of the manuscript.
6014 hsa-miR-30c 0. 3051 12. 6626 hsa-miR-223 0. 3115 143. 2608 hsa-miR-126 0. 3248 1. 6386 hsa-miR-574-3p 0. 3270 1. 3218 hsa-miR-21 0. 3293 1307. 4137 hsa-miR-203 0. 3362 0. 0168 hsa-mir-125b 0. 3529 1. 9219 One μg of total RNA from DCs cultured for 48 h in the absence or presence of H. pylori B38 at MOI 1 was retro-transcribed using the RT2 miRNA First Strand Kit (Qiagen, Courtaboeuf, France). The first strand reaction was mixed with RT2 SYBR Green qPCR mix and distributed onto PCR array plates contain- ing primers for 88 miRNA involved in human immunopathology (SABioscience, Qiagen) according to manufacturer's protocol. The amplification profiles were measured on a Stratagene Mx3005P instrument (Life Technologies, Saint Aubin, France). Data were analyzed by the C t method (Livak and Schmittgen, 2001), using the average C t of the housekeeping RNA for normalization.
For 1013 sequences out of 2091, a relevant blast result was obtained. For the 1078 remaining sequences we did not obtain a significant blast result due to either poor sequence quality or the absence of positive match in Genbank library. The e-values distribution of relevant blast results shown that 52% of sequences were above 10 -140 , supporting an accurate identification of the genes (Figure  1 ). 114 sequences had good homology with avian mitochondrial genes and 115 sequences had homology with avian BAC clones or complete avian cDNA of putative protein of unknown function. 784 sequences have been clearly identified, to date, as avian genes and corresponding to 210 different genes. These genes belong to several different functional families: cell cycle, metabolism, immune response, cytoskeleton network.
Functional characterisation of differentially expressed genes In order to further characterize differentially expressed sequences, sequencing data and microarray results were cross-analysed using Gene Ontology annotations with the Ingenuity Pathway Analysis software. Due to the poverty of bibliographic information for the chicken annotated genome and the duck genome, comparison was made by analogy to the orthologous genes annotation from the well-documented and annotated human, mouse and rat genomes. Among the 210 genes identified from our subtracted libraries, only 158 were referenced in Ingenuity database for analysis, the remaining 52 genes are predicted genes with no relevant annotation in the database. Trachea responses, i. e. differentially expressed genes in HPAIand LPAI-infected tracheas, were compared for each time post-infection.
Using the Ingenuity Pathway Analysis software, interaction networks between selected genes were inferred based on the known direct or indirect relation between these genes stored in Ingenuity's database (related to literature). In a first time the analysis was conducted on the genes obtained after comparison to mock-infected sample. For LPAI-infected tracheas, 5 gene interaction networks can be identified and three of these networks were connected together by one or two genes. For HPAIinfected tracheas, 5 gene interaction networks were also identified and only two of these networks interacted together by one gene, and the three others were not connected to another one. Induced and repressed genes by HPAI-or LPAI-infection included in interaction networks are presented in Additional file 1: Table  S1.
Validation of differential expression of genes by quantitative PCR Differential duck tracheal gene expression after LPAI-or HPAI-infection was assessed for a selected set of genes by real-time PCRs. This set was constituted by various modulated genes i. e. induced or repressed genes either in HP or in LP infection. As shown in Table  1 , quantitative PCR confirmed only for a limited number of genes the result obtained into microarray data analysis (SAM analysis) but not for the other. In particular, at 4 h p. i. , we observed high variations in gene expression between the different samples from the same experimental condition (LPAI-or HPAI-infected tracheas) which resulted in not-statistical significant gene expression variations. Indeed, Only H-Ras, DDX3X and DCN were found statistically significantly down-regulated in HPAI-    ).
The Ingenuity Pathway Analysis software was used to organise significant differentially expressed genes into different signalling pathways in which they are involved and according to the calculated -log (p-values). Data significance is represented by ratio and p-value. Ratios (yellow lines) represent the part of differentially expressed genes from a pathway related to the total number of genes for this same pathway. The p-value is calculated using a right-tailed Fisher's exact test and corresponds to the probability that the association between genes and a given pathway is not due to random chance. The threshold corresponds to a limit of significance set by the software (p < 0. 05).
We further checked that the infection occurred homogeneously in the entire tracheas prepared whatever the virus used (LPAI or HPAI). M gene was detected by real-time RT-PCR and ciliostasis was observed all along the trachea for both the LPAI-and HPAI-H5N1 infected samples indicating that the virus infected and replicated all along the tracheas. The efficacy of the SSH strategy for host response analysis and discovery of modulated genes in poorly-studied species, in association or not with microarray, has been well established  [38] [39] [40] [41] [42] [43] [44]. The suppressive subtractive hybridisation (SSH) procedure is designed to subtract cell responses that occur in both control and infected tracheas allowing focusing only on the differences between the two sets of RNA used. Even if this strategy allowed us to generate and analyse gene datasets from duck, it however suffers some drawbacks: -First, and foremost, there is an important loss of information occurring after sequencing of our subtracted libraries with only half of the sequences generating a pertinent blast result.
At the end of the process only 210 different avian genes were identified. -Secondly, several sequences corresponded to genes encoding proteins with unknown function or predicted proteins that were not annotated in databases. In these conditions the annotation based analysis, with tools like the Ingenuity Pathway Analysis software, are performed with a reduced number of sequences and in fine poorly informative. Another limit of this approach resides in the fact that, due to the relative paucity of the data concerning avian species in the literature, as compared to the mammalian species, our annotation based analysis assume a functional equivalence of the avian and duck genes with their mammalian (human, rat and mouse) orthologs which is not demonstrated and most probably exaggerated. However, many signaling pathways, including pathways concerning immunity, have been described and involve the same genes in various species.
Despite these limitations, our subtracted libraries corresponding to induced or repressed sequences at 4 h and 8 h p. i. contained 1141 and 950 clones, respectively. Using statistical analysis, only 19% of these clones were differentially expressed. We observed that between LPAI-and HPAI-infection, there were significant differentially expressed sequences which possibly might be related to the difference of pathogenicity between LPAI and HPAI viruses. In order to identify if these sequences correspond to genes implicated in some specific mechanisms involved in influenza infection (HPAI and/or LPAI) in duck trachea, gene ontology annotations using Ingenuity Pathway Analysis software was used. Among identified pathways, our findings (i. e. a variation of cellular gene expression between HP-and LP-infected tracheas, are consistent with previous studies which demonstrate differential modulation of the immune response according to the AI strains  [45] [46] [47] [48].
Sequences analysis and clone identification The four duck tracheal subtracted libraries were sequenced in order to identify corresponding genes. To this purpose, isolated bacterial colonies were grown for plasmid isolation with the Wizard SV 96 Plasmid DNA purification System (Promega). The DNA sequences of purified products were determined using ABI Prism DyeTerminator Cycle Sequencing Ready Reaction Kit (Applied Biosystems) and a primer flanking the cloning site of pGEM (M13rev or T7 primer) on an automatic DNA sequencer ABI 373XL (Applied Biosystems). Sequences were cleaned from vector sequences and blasted using the non redundant genbank library and also against the Gallus gallus genome (www. ncbi. nlm. nih. gov/genbank/ and www. ncbi. nlm. nih. gov/genome/ or www. ensembl. org/Gallus_gallus/).
The best 5 results were considered for final identification with a maximum e-value cut-off arbitrarily set up to 2. 10 -5. Corresponding gene identifiers (approved symbols) were used for the annotation of the duck sequences. This heterologous annotation allowed Gene Ontology and pathway analysis using the Ingenuity Pathway Analysis data mining suite (Ingenuity Systems Inc. ). Sequences were deposited into the Genbank dbEST database.
Microarray analysis Microarray data were processed as previously described  [17, 56]. Briefly, the raw data were normalised (Lowess) then subjected to a statistical analysis using the significance analysis of microarray (SAM) software to identify differentially expressed genes  [57]. SAM software also calculated a false discovery rate (FDR) for each analysis performed. In a first analysis, results obtained with HPAI-or LPAI-infected tracheas were compared to those obtained with mock-infected tracheas at 4 h or 8 h p. i. (SAM one-class analyses). In a second analysis, results obtained with HPAI-infected tracheas were compared to those obtained with LPAI-infected tracheas (SAM two-class analyses). These analyses resulted in a total of 6 datasets (HPAI versus test at 4 and 8 h pi, LPAI versus test at 4 and 8 h pi, HPAI versus LPAI at 4 and 8 h pi) of genes differentially expressed from our different experimental conditions.
Amplifications were performed using the following conditions: 2 min at 50°C, 10 min at 95°C, followed by 45 amplification cycles (15 sec at 95°C and 1 min at 60°C). The uniqueness of the amplification product was assessed by a dissociation stage after the last amplification cycle. Amplifications were carried out in 96-well plates with 3 replicates per sample, using Applied Biosystem 7500 Real-Time PCR Systems and analysed with the Sequence Detection Software. Results were normalized by the control house-keeping gene expression in each sample. Relative amounts of each gene expression were calculated using the R = 2 -ΔΔCT equation  [59]  with gene expression in mock-infected tracheas as relative reference. Two-sample Student t-test was performed to compare gene expression variation between HPAI-and LPAI-infected tracheas, using Systat 9 software (Systat Software Inc.
In many fields, due to the increasing number of automatic sensors and devices, there is an emerging need to integrate georeferenced and temporal data into decision support tools. Geographic Information Systems (GIS) and Geostatistics lack some functionalities for modelling and reasoning using georeferenced data. Soft computing techniques and software suited to these needs may be useful to implement new functionalities and use them for modelling and decision making. This work presents an open source framework designed for that purpose. It is based upon open source toolboxes, and its design is inspired by the fuzzy software capabilities developed in FisPro for ordinary non-georeferenced data. Two real world applications in Agronomy are included, and some perspectives are given to meet the challenge of using soft computing for georeferenced data.
Spatial data specificities are likely to open novel research topics in soft computing. For instance, the notion of zone is not clearly defined in GIS, it is often mistaken for a projection of a classification achieved in the attribute space without considering geographic continuity. This concept is central in spatial reasoning and essential in decision making, particularly in these fields, As in practice, decisions need to be applied to management zones, satisfying geographical contiguity and shape criteria. For realistic decision support, zones must be defined with respect to the imprecision and uncertainty of available data and knowledge. This work presents the outline of a decision support system framework for spatial data. It is freely available and based upon open source toolboxes as well as on the authors' experience in soft computing software, through the former development of FisPro a , that offers a high level of semantics and human-machine interaction.
It could be part, as a spatial package, of a wider project like the GNU Fuzzy one proposed in the 2007 Fuzz'Ieee Conference  8. The paper organization is as follows. The next section presents a state of the art of the available software environments for spatial data. The proposed architecture, including FisPro and the Ge-oFIS b framework, is introduced in Section 3. Section 4 presents a soft computing-based distance, available in FisPro and GeoFIS. The framework functionalities are illustrated with two real world applications in Section 5. Finally, Section 6 summarizes the main conclusions and the open challenges. GIS use digital data and a spatio-temporal (space-time) location as the key index variable for all information, allowing information from different sources to be related by accurate spatial information.
An add-on to GRASS provides fuzzy membership functions, fuzzy operators and fuzzy rules to implement fuzzy inference systems for classification tasks. Fuzzy k-means clustering may be used for mining GIS data. In recent work  9  the authors propose an extended fuzzy k-means method for GIS, that allows cluster centers to be hyperspheres, and apply it to find fire-point event hotspots from georeferenced data. Recent publications, for instance  10  that uses a fuzzy GIS-based spatial multi-criteria framework for irrigated agriculture, take place in the application fields of Agronomic and Environmental Sciences. On a different note, several advanced packages (spatial, geoR, gstat. ), are available for the open source R 11 software. They provide multivariate geostatistical functions for kriging, analysis and simulation, and often include GIS support (GRASS for gstat) for querying data and executing scripts.
They are intended for researchers or engineers having a good background in Statistics. SAGA (System for Automated Geoscientific Analyses) d offers an open source comprehensive set of geoscientific methods. The need for modelling using georeferenced data is increasing, in many application fields, but particularly so in Life Sciences. The large amount of available spatial data has begun to open new avenues of scientific inquiry into behaviors and patterns of pre-viously considered unrelated information. However, the software tools presented above, including GIS and R, are complex and require lengthy training and specialised skills to be taken over. This is a limiting factor for the practical use of spatial modelling in some domains, such as Agronomic and Environmental Sciences where the stakeholders are not specialists of spatial data.