dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National Pour la Santé de la Recherche Médicale (INSERM)  and the  Agence Nationale de la Recherche  ( ANR-12-BSV2-0019 ENSINTED ). EG was supported by a PhD fellowship from the  Université Pierre et Marie Curie and Fondation ARC pour la Recherche sur le Cancer. We thank  C. Brakebusch  and  R. Fässler  for the β 1 neo/+ and β 1 fl/fl mice,  S. Srinivas  for the R26RYFP mice, and  A. Gautreau  and  J. L. Duband  for the kind gift of the anti-WAVE2 and  ES66  antibodies, respectively. We thank  M. Breau ,  S. Xing ,  L. Xu ,  R. Fodil  and  M. Depp  for their help in technical aspects, and  J. L. Duband  and  M. Breau  for critical reading of the manuscript. We thank the  NIMCE@Institut Curie-CNRS , the imaging staff of  PICT-IBiSA@Lhomond  and the  Institut Mondor de Recherche Biomédicale (IMRB) ,  U955 INSERM  and the  Mouse Facility staff of Institut Curie and IMRB.
The presentation timing was monitored using commercially available Presentation software (Neurobehavioral Systems, Inc. , Albany, CA, USA) and was synchronized with the scanner. Participants' responses were acquired with a five key-press button box that provided logic signals. The five buttons were placed in a configuration similar to the five fingers (thumb, forefinger, middle finger, ring finger, and pinkie) of the right hand, simulating the five levels of a Likert-type scale, respectively. Breathing was recorded using polyvinyl-chloride foot bellows (Herga Electric Limited, Suffolk, UK) secured to the subject's abdomen with a cotton belt. The participants' behavioral responses, breathing data, stimulation onset, and trigger signals from the MRI scanner were recorded online (100 Hz sampling rate) on a laptop equipped with a digital acquisition board I/O card (PCI-6527) (National Instruments R , Austin, TX, USA) using LabVIEW software package (National Instruments R ).
Functional and Structural Data Acquisition and Preprocessing Images were acquired using a 1. 5-Tesla MAGNETOM Sonata whole-body imager (Siemens Medical R , Erlangen, Germany) equipped with a 4-channel circularly polarized head coil. For functional imaging, we obtained 26 interleaved, 4-mm-thick axial slices using a T2 * -weighted echo-planar sequence with the following parameters: repetition time (TR) = 2500 ms, echo time (TE) = 50 ms, flip angle (FA) = 80 • , field-of-view (FOV) = 240 mm × 240 mm, and imaging matrix = 64 × 64 (voxel size: 3. 75 mm × 3. 75 mm × 4 mm). In total, 390 scans were collected for each functional run. A high-resolution structural T1-weighted anatomical image (inversion-recovery 3D Gradient-Echo sequence, 1 mm × 1 mm × 1 mm) parallel to the bicommissural plane and covering the entire brain was acquired over ∼10 min.
Foam wedges were used to restrict head motion. An oil-filled capsule was fixed on the right temple to subsequently locate the right side of the images. For each subject, the first five volumes of each functional run were discarded to avoid T2 * non-equilibration effects. We then processed all functional images using a pipeline in Nipype workspace  (Gorgolewski et al. , 2011 ) that provided a neuroimaging data processing framework in Python by implementation of Statistical Parametric Mapping software (SPM8, Wellcome Department of Cognitive Neurology, London, UK,  Friston et al. , 1995b). Slices of each remaining volume were slice-timing-corrected. All functional volumes were realigned to the median volume, co-registered to the anatomical image, spatially normalized to the Montréal Neurological Institute (MNI) standard brain T1.
, 2000). Because of uncertainty in the onset of sniffing the odor, we used the amplitude of the hrf in group randomeffect analysis, which removes potential bias in results caused by latency  (Calhoun et al. , 2004). Stimulus onset asynchronies were fixed at the time of odor delivery. Confounding factors (head motion) were included in the model. Random-effects analyses were performed to extrapolate statistical inferences at the population level, as described in the SPM8 software. Wholebrain analyses were performed on functional images for the different experimental conditions. We then contrasted activation functional images obtained for these two tasks between Pro and Anti [Pro vs. Anti] for either cheese or OFood. For these analyses, the level of significance was set at p < 0. 005, uncorrected at the cluster level for multiple comparisons across the much larger volume of the whole brain.
(2008) , and the boundary criteria proposed by  Mawlawi et al. (2001). The delineations were drawn from coronal slices of the caudate nucleus (from y = 20 mm anterior to y = -27 mm posterior to the anterior commissure), the anterior and posterior parts of the NAc (from y = 20 to 14 and from y = 13 to 6, respectively), and the dorsal and ventral parts of the putamen (from y = 17 to -20 and from y = 24 1 http://neuro. debian. net/pkgs/fsl-harvard-oxford-atlases. html 2 http://people. cas. sc. edu/rorden/mricron/index. html to 10, respectively). The mean activation signals were bilaterally extracted under two experimental conditions (food, task) for each subregion of the VOIs and for each of the 30 participants using the python package nibabel 3. Five-way ANOVA with repeated measures and multiple orthogonal comparisons were then performed to compare the levels of activation as a function of the different experimental conditions [group (Anti vs.
Pro), task (liking vs. wanting), food (cheese vs. OFood), area (ventral vs. dorsal), and side (right vs. left] for the Od-Pic stimuli only. Brain linear regression analyses were further performed to evaluate whether the activation data were correlated with the self-rated liking/wanting data. This assessment was performed for the structures of striatum and midbrain that, according to our hypotheses, were differentially activated between Anti and Pro: the internal and external segments of the globus pallidus (GPi/GPe), the SN and VTA.
fMRI Data As mentioned above, no significant differences were observed between the rating scores of the participants in the two groups stimulated with odors only. Therefore, we examined the brain imaging data obtained with the Od-Pic stimuli. We first investigated whether neural networks were differentially activated between Anti and Pro participants ([Anti -Pro] and [Pro -Anti] contrasts) exposed to cheese or OFood stimuli during the liking and wanting tasks (Figures  6A, B ). For cheese stimuli, we found a higher activation of the GPi/GPe during liking, and of the VTA, GPi/GPe and SN during wanting in Anti compared with Pro (Table  2 ; Figure  6A ). For OFood stimuli, we observed a higher activation of the VTA in Anti than Pro during wanting (Supplementary Table  S1 ). Second, we investigated whether the brain areas with increased activation by cheese in Anti compared with Pro (Table  2 ; Figure  6A ) exhibited similar activation in Anti when we compared the activation images for cheese and OFood.
ACKNOWLEDGMENTS We thank  P. Charrié ,  L. Morin-Audebrand , and the members of CERMEP (e. g. ,  D. Ibarrola ,  C. Vighi , and  F. Vey ) for their invaluable assistance. We are greatly indebted to  S. Garcia  for designing the software (with Python) to analyze the behavioral and breathing data and to  R. Soussignan  and  R. Margraff  for helpful comments on the manuscript. The authors gratefully thank the companies Mane,  René Laurent , and  Givaudan-Roure , who provided the odorants used as stimuli.
FUNDING This work was supported by the  Centre National de la Recherche Scientifique (Paris)  and the  French National Research Agency  ( ANR-06-PNRA , AROMALIM) to J-PR. NT was funded by the  AROMALIM  project. DM was funded by  LABEX Cortex  ( NR-11-LABX-0042 ) of  Université de Lyon  within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
The increased body weight gain is significantly correlated with the reduction of TRPC3 expression (Pearson r = 0. 71, p = 0. 01; Supp. Figure  3 ). Change in body weight gain in TRPC3 MBH KO mice is however not associated with changes in fasting blood glucose level or glucose tolerance (Figure  3E, F ). Together, these data show that MBH TRPC3 channels are critical for food intake and body weight control but do not seem to directly regulate glucose homeostasis. Central TRPC3 channels are required for central glucose detection. We then tested whether MBH TRPC3 channels are involved in brain glucose detection. To this end, we measured food intake and insulin secretion in response to increased brain glucose level in TRPC3 KO mice. As previously reported (35), ICV injection of glucose decreases food consumption in WT mice (Figure  4A ).
ACKNOWLEDGEMENTS Authors are thankful to  Dr. Thierry Alquier  ( University of Montreal, Montreal Diabetes Research Center, Montreal, Canada ) for insightful comments and advices on this manuscript. We thank  Dr. Richard Palmiter  ( University of Washington, Seattle, WA, USA ) and  Dr. Serge Luquet  ( University Paris Diderot, Paris, France ) for providing the plasmid sequence of the AAVcre/GFP. We are also thankful to  A. Lefranc ,  L. Decocq ,  E. Lalarme  and  A. Mathou  ( CSGA, Dijon, France ) for animal care taking. This work was supported by grants from  Marie Curie Foundation (CIG NeuROSenS   PCIG09GA2011293738 , XF),  Société Francophone du Diabète (XF). This work was also supported by the following grants:  ANR13JVS1000301  to AB;  ANR11BSV10007  to CL; SFB 894 by  DFG  to VF;  Z01ES101684  to LB.
A MDE 150 Stand Polarographic (Radiometer, Copenhagen) Tracelab 50, controlled by the computer software Tracemaster 5 through a Polarographicanalyser POL 150 was used. MTs results were reported in g MTs g -1 ww. 2. 4. 2. 6. Alpha-tocopherol levels. Tocopherol extraction was performed in cephalothorax and abdomen of shrimp as described by  Griboff et al. (2014). The ␣-tocopherol (␣-Toco) content was determined by high performance liquid chromatography with fluorescence detection (excitation wavelength 276 nm and emission wavelength 316 nm). ␣-tocopherol concentrations were reported in g gr -1 ww.
Boosted regression trees and integrated biomarker response The aim of this analysis is to determine those biomarkers with higher capacity than other to explain the variation in the exposure concentrations of CPF. Boosted regression trees (BRT) were fitted according to procedure described by  Bertrand et al. (2016). Briefly, the model was constructed with the GBM packages  (Ridgeway, 2013)  in R (version 0. 98. 953), using the code described by  Elith et al. (2008)  including those biomarkers with significant differences with respect to control condition. This method builds a sequence of models of increasing complexity, in order to describe relationships between the response variable (exposure CPF concentration) and the predictor variables (effect biomarkers) getting the variance percentage of the response variable explained by each biomarker (BRT%).
Acknowledgments This work was supported by grants from the  Agencia Nacional de Promoción Científica y Técnica  ( FONCyT-PICT/2011-1597 ; 2013-1348 ),  Secretaría de Ciencia y Técnica (SECyT)  and  CONICET (National Research Council, Argentina). The present work is part of the PhD thesis of  L. Bertrand , who gratefully acknowledges fellowship from  CONICET. The authors thank  Mélanie Bruneau  for her technical assistance for MT measurements.
Eligibility Criteria, Data Sources and Search Strategy Cross-sectional, retrospective and case-control studies on Ghanaian participants published in peer-reviewed journals were considered for inclusion. For the purposes of this systematic review, a "child" was defined as a person below the age of five years. ScienceDirect, PubMed, MEDLINE via EBSCO and Google Scholar were searched for published articles reporting on the influence of social factors on child health in Ghana. The search terms used included: "social determinants", "child health", "social factors influencing health" and "social determinants of child health" in combination with "Ghana", "West Africa" or "sub-Saharan Africa". As an example, the PubMed search strategy was: (("social determinants of child health Ghana" [MeSH Terms] OR "child health" [All Fields] AND "social determinants Ghana" [All Fields]) OR ("child health" [All Fields] AND "social determinants" [All Fields] AND "Ghana" [All Fields]) OR ("social factors influencing health Ghana" [MeSH Terms]) OR ("social factors" [All Fields] AND "health" [All Fields] AND "Ghana" [All Fields]) OR ("infant" [MeSH Terms] OR "adolescent" OR "child" AND "West Africa" [All Fields])).
The identified articles were evaluated using their titles, abstracts and full texts to select those that would fit our inclusion criteria (Fig  1 ). The references cited in the identified articles were also scanned for potentially useful articles that might have been missed during our search. The inclusion criteria were that the study must (i) have reported on social determinants of health on child health, (ii) have been conducted in Ghana or focused on Ghana, and (iii) have been indexed in the databases up until 30 th May, 2015. Apart from not meeting the inclusion criteria given above, studies were also excluded if they focussed entirely on discussing the health of individuals older than five years.
Study Selection and Data Assessment All articles meeting the criteria outlined above were selected for further analyses. Studies reporting on social determinants of health in Ghana but focused on maternal, paternal or general health rather than child health were excluded. No restrictions were made in terms of study design, although duplicates and studies conducted outside Ghana were excluded. Fig  1 illus trates the article-selection process which followed the PRISMA guidelines (S1 PRISMA Checklist). Information extracted included publication data, study settings, study design, sample characteristics, key findings and identified social determinants.
Studies reporting on these aspects were published between 1988 and 2015. Twenty of these studies analysed data from nationally-representative samples, with the remaining examining data from various regions of the country, namely Eastern (2 studies), Northern (2 studies), Ashanti (3 studies), Greater Accra (2 studies), Volta (1 study), Central (1 study) and Upper East (1 study) regions. Additionally, one study was conducted among participants in the Volta and Eastern regions while another was done in the Ashanti and Eastern regions. The rest examined government policies. An intriguing aspect of the social determinants reported is that they appeared to be important for almost every disease studied. The health conditions associated with these factors included infant or child mortality, nutritional status of children, seeking treatment after burns, completion of immunisation programmes, health seeking and hygiene practices.
Acknowledgments TKK was funded by the  Biotechnology and Biological Sciences Research Council  (BBSRC; http://www. bbsrc. ac. uk) grant number  BB/J014532/1  through the  Midlands Integrative Biosciences Training Partnership. EQ was funded by a PhD studentship from  De Montfort University , Leicester, UK. TKK acknowledges support from the  Research Councils UK (RCUK)  block grant at the  University of Warwick  for the payment of open access charges. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Parameters such as the time needed for the enzymatic hydrolysis, temperature, nature and concentration of enzymes, pH, and raw material pretreatment have shown their impact on the extraction  [8]. Response surface methodology is based on the fit of statistical models to measured data obtained in relation to an experimental design. This methodology has been shown to be useful for improving, developing, and optimizing biochemical processes to extract the maximum number of compounds of interest  [8]. The Box-Behnken design was chosen to investigate the influence of three parameters on the extraction yield of water-soluble compounds, notably the time of extraction, the temperature, and the enzyme quantity used on dried seaweeds. In this study, the isolation of antiviral extracts from the red macroalga Solieria chordalis was performed by using enzymes.
Acknowledgments This work has received financial support from the single  Inter-Ministry Fund (FUI)  within the  VB2  project  (Valorization of Breton Biomass ). The authors thank the members of LBCM and of the project  VB2  for their help in collecting seaweeds, especially  Christel Marty  for the maintenance of the cell line and of the virus,  Declan Bennett , an undergraduate student from the  National University of Ireland, Galway , for helping in the analyses of the biochemical composition of seaweed extracts and  Nicole Geslin  for proof-reading. The authors have no conflict of interest to declare.
albus strain 7 and SY3 and none in strain 8  (Ding et al. , 2001; Rinc on et al. , 2004; Rincon et al. , 2005; Jindou et al. , 2006; Miller et al. , 2009; Dassa et al. , 2014). These organisms occupy only a small fraction of the rumen ecosystem  (Jami and Mizrahi, 2012b)  and are currently considered to be the only ones that carry cellulosomal components within the rumen environment. In the present study, advances in sequencing technologies served to examine complex microbial environments, notably the rumen environment, and to gain insight into the taxonomical composition and protein functionality using metagenomics approaches. Hence, we have used an ultra-deep two bovine rumen metagenome database, previously described by  Hess et al. (2011)  as a basis to understand the complex microbial ecosystem of the rumen.
Retrieval of dockerin and cohesin-containing sequences from the rumen metagenome Local BLAST  (Altschul et al. , 1997)  searches were carried out among the contigs which were sequenced from a bovine rumen metagenome  (Hess et al. , 2011) , and sequences of known cohesin and dockerin modules were analysed against an in-house cohesin-dockerin database, which was constructed in the Bayer laboratory. All hits below E-value of 10 24 were retrieved and inspected individually by examining their characteristic sequence features.
Species and functional characterisation of cohesin/ dockerin-bearing proteins In order to obtain information about the species of the cohesin/dockerin protein sequences we used Blast2Go  (Conesa et al. , 2005). In the process of characterizing the cohesin-and dockerin-containing sequences, we performed BLAST searches (blastp) of these protein sequences against the NCBI-nr database (non-redundant protein sequences) with Evalue of 10 25. Prediction of each protein function was carried out by MG-RAST  (Meyer et al. , 2008) , a pipeline which produces functional prediction of gene sequences by comparing it to proteins and nucleotide databases. The default maximum E-value cut-off used by MG-RAST was 10 25.
Identification of carbohydrate-degrading enzymes Dockerin-containing proteins were annotated by the Carbohydrate-Active enZymes (CAZy) database (http://www. cazy. org) (Lombard et al. , 2014)  in order to bioinformatically analyze their catalytic modules. The CAZy database contains information about enzymes that assemble, modify and breakdown polysaccharides. This includes identification of the catalytic modules and their classification for glycoside hydrolases (GHs), carbohydrate esterases (CEs), polysaccharide lyases (PLs) and carbohydrate-binding modules (CBM). Additional conserved domains of the proteins were analysed using the NCBI Conserved Domain Database  (Marchler-Bauer et al. , 2014)  (http://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi) which serve as a resource for the annotation of proteins with the location of conserved domain footprints.
Results The role of cellulosomal complexes is not exclusive to plant fibre degradation In order to identify cohesin-and dockerin-containing proteins in the rumen metagenome, we applied BLAST searches against a database of known dockerins and cohesins, 649 dockerins and 61 cohesins were thus revealed. Dockerin modules are usually fused to an enzyme that serves a polysaccharide-degrading function within the cellulosomal complex. Therefore, we sought to understand the functional attributes of the dockerin-containing proteins found in the rumen metagenome. The putative dockerincontaining proteins were subjected to annotation using the Carbohydrate-Active enZymes (CAZy) database, in an attempt to identify catalytic modules related to carbohydrate degradation. This database is specific and curated for catalytic modules, such as glycoside hydrolases (GHs), carbohydrate esterases (CEs) or polysaccharide lyases (PLs) and the non-catalytic modules, carbohydrate-binding modules (CBMs).
Among the 649 dockerin-containing proteins, 158 (24. 3%) contained a variety of predicted carbohydrate-active catalytic units and CBMs. These include 135 GHs from 21 different families, 21 CEs, 5 PLs and 65 CBMs. The list of dockerin-bearing proteins and associated CAZy modules is presented in the Supporting Information (Table  S4 ). In addition, we submitted the 491 proteins which did not contain carbohydrate-active catalytic units or CBMs according to CAZy, to MG-RAST analysis  (Meyer et al. , 2008). This server produces automated functional assignments of sequences in the metagenome by comparing them to a large number of protein databases (GenBank, IMG, KEGG, PATRIC, RefSeq, SEED, SwissProt, TrEMBL, eggNOG, M5NR). Our analysis revealed that out of 491 dockerin-containing proteins 16. 1% were predicted as proteins of known function with a total of 79 different functions (Supporting Information Table  S5 ).
Blast analysis of these sequences confirmed that six of these proteins contain two putative functions, whereas in the seventh protein the Blast analysis confirmed only the CAZy annotation with about 300 amino acids at the C-terminus of the protein remaining unannotated. None of the 61 cohesin-containing proteins were annotated with predicted functions using the same databases, which is in accordance with the fact that cohesins, and therefore scaffoldins, are non-enzymatic subunits of the cellulosomes. Since the dockerin-containing proteins are supposed to be secreted extracellularly, we further analysed our dataset for the existence of a secretion signal with the SignalP server. Out of the overall dataset, only 55% of the proteins contained a signal peptide. The absence of signal peptide in 45% of the proteins could be the result of performance problems of the detection method or truncated 5 0 ends of the ORFs in the metagenome.
No significant species hits were found for 62 dockerins and 2 cohesins. These findings indicate that the cellulosomal strategy is adopted by a surprisingly wide range of phylogenetic groups and by a number of uncharacterized bacteria. Usually more than one cohesin was discovered on a scaffoldin module. Indeed, when we investigated the 61 presumed cohesin-containing proteins, they were distributed among 40 different putative scaffoldins. Some of the cohesin-containing scaffoldins were found to carry also dockerin modules. Phylogenetic analysis of the cohesincontaining proteins with a database of known cohesins indicated that some of these scaffoldins were closely related to known scaffoldins from R. flavefaciens and shared similar molecular arrangement (Fig. 3 ). We asked further whether the taxonomical composition of the rumen cellulosomal components is similar in terms of abundance to that of the fibre-adherent rumen microbiome.