dataesr/scientific-paragraphs-categorization huggingface dataset:
5% of the identified isotigs varied in their expression level during B. henselae infection, reflecting probable molecular interactions between the pathogen and the vector. Among these isotigs, 62% were up-regulated, whereas 38% were down-regulated during bacterial infection. The balance observed between up-or down-regulated genes suggests a coevolutionary mechanism, which could guarantee both pathogen transmission and vector survival. Database searches for sequence identity led to the classification of translated sequences into nine families, however it is important to keep in mind that belonging to the same family does not necessarily imply having the same function. Conversely, changes in transcript expression profiles do not necessarily translate into variation in the corresponding proteins expression.
XYL was supported by funds from the  China Scholarship Council (CSC). This work was funded by  INRA  and  EU  grant  FP7-261504  EDENext and is catalogued by the EDENext Steering Committee as EDENext037 (http://www. edenext. eu). This work was also partially supported by the  Spanish Secretarı ´a de Estado de Investigacion, Desarrollo e Innovacion, Ministerio de Economı ´a y Competitividad  project  BFU2011-23896. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The microvine model enabled the application of this stress at several stages of berry development simultaneously. Changes in gene expression under heat stress were analyzed with whole genome 30 K microarrays (NimbleGen 090818 Vitis 12X) on green berries and two post-véraison stages. Two sets of gene annotations derived from  Grimplet et al. , 2012 [54]  and from the NCBI RefSeq [55] database were used for functional annotations. Depending on the developmental stage, considerable differences in the response of berries to heat stress between the day and night were revealed, emphasizing the necessity to include night-time in further studies on abiotic stress in plants.
The expression profiles of these genes were duly validated by real time PCR in order to confirm microarray data (Additional file 6). This immediate response by processes involved in secondary metabolism is remarkable given the brevity of the heat stress applied and has not been observed hitherto in temperature experiments on grapevine berries. It demonstrates that changes in gene expression involving secondary metabolism occur at the onset of sugar loading, before any changes in coloration can be detected. It has been shown here that coloration may be significantly delayed as compared to hexose accumulation, and that transcriptomic effects are usually masked by berry heterogeneity within bunches around véraison. Presumably the use of reconstructed groups of samples after single berry biochemical analyses and the inclusion of night sampling enabled the observation above to be made.
High temperatures impaired the expression of 1-deoxy-D-xylulose-5-phosphate synthase transcripts (VIT_11s0052g01730, VIT_11s0052g01780; cluster 7) required for isopentenylpyrophosphate (IPP) synthesis, the universal precursor for the biosynthesis of terpenes  [102]. Several transcripts coding for geraniol 10-hydroxylase, an enzyme thought to play an important role in indole alkaloid biosynthesis  [103] , were down-regulated at night by high temperatures at VS. However these transcripts are annotated as cytochrome P450 in the RefSeq  [54]  database. Further evidence for the impairment of terpene production arises from the repression at all growth stages of transcripts coding (-)-germacrene D synthase, a sesquiterpene synthase characterized recently in grapevine berries  [104]  (VIT_19s0014g02560, VIT_19s0014g02590; cluster 8, and VIT_19s0014g04840, VIT_19s0014g04880; cluster 3), in addition to linalool synthase (VIT_00s0271g00060; cluster 3; annotated nerolidol synthase in RefSeq [55]) which catalyses the formation of the acyclic monoterpene linalool from geranyl pyrophosphate  [105].
Microarray analysis cDNA synthesis, labelling, hybridization and washing reactions were performed according to the NimbleGen Arrays User's Guide (V 3. 2). Hybridization was performed on a NimbleGen® microarray 090818 Vitis exp HX12 (Roche, NimbleGen® Inc. , Madison, WI), containing 29,549 predicted genes representing 98. 6% of the 12X grapevine gene prediction version V1 http://srs. ebi. ac. uk/. The chip probe design is available at the following URL: http://ddlab. sci. univr. it/FunctionalGenomics/. The Robust Multi-array Analysis (RMA) algorithm was used for background correction, normalization and expression levels,  [149]. Differential expression analysis was performed with the Bayes t-statistics from the linear models for microarray data (limma)  [150]. P-values were corrected for multiple-testing using the Benjamini-Hochberg's method  [151].
Differential expression of genes was analysed between heat stress and control conditions at all developmental stages and time points. Transcripts were considered as significantly modulated when the absolute fold change was > 2 (log 2 fold change > 1) and the adjusted p value was < 0. 05 between heat stress and control at at least one stage and time point. Hierarchical clustering was carried out using the Multiple Experiment Viewer® version 4. 6. 2, using Pearson's correlation distance calculated on RMA log 2 transformed and mean centered gene expression profiles. The raw data is available at the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/info/linking. html) under the series GSE53409. Gene annotation was derived from Grimplet et al. , 2012  [54]. In order to compare functional annotation, protein sequences of significantly modulated genes were BLASTED against the NCBI RefSeq database  [54].
Alignment of sequences was considered as acceptable when the ratio between score and aligned sequence length was superior to 1. 6. Log 2 changes of day + night and night stage-specific differentially expressed transcripts were integrated using MapMan® software  [142, 75]. Functional categories were derived from  Grimplet et al. , 2012 [54]. In order to identify significant enrichment of functional categories Fisher's exact test was carried out to compare the genes list with non-redundant transcripts from the grapevine genome with the FatiGO analysis tool  [152]. Significant enrichment was considered in case of p value < 0. 01 and illustrated as fold change. To identify the MBF1c Arabidopsis thaliana regulon sequences, gene numbers were derived from  Suzuki et al. , 2011 [78]  and correspondence was found in the uniprot database (www.
Acknowledgments For technical support during climatic chamber experiments, support during sampling, with sample processing and advice we would like to thank,  Agnès Ageorges ,  Anne Pellegrino ,  Jérôme Grimplet ,  Remy de Marchi ,  Cléa Houel ,  Angélique Adivèze ,  Gilbert Lopez ,  Marc Farnos ,  Thérèse Marlin  and  Bertrand Muller.
Bed roughness index One way to quantitatively characterize the above-described bed conditions is to calculate bed roughness. The bed roughness index RI is obtained by applying a fast Fourier transform (FFT) to the bed elevation within a moving window  (Taylor et al. , 2004) : RI = f max f min |X[f ]| 2 N T x df , (1) where f min = 1/(N T x), f max = 1/(2 x), N T = 2 n is the number of data points in the window, x is the sampling interval (100 m in our case) and where X[f ] = N T d=1 x(d)e 2π i N T (d-1)(f -1). (2) Equation (  2 ) is the definition of the FFT for a dataset x(d) with index d in the range 1 ≤ d ≤ N T , and X[f ] is the same data set in the frequency domain with index f in the range f min ≤ f ≤ f max. In other words, the bed roughness index RI is the integral of the resultant power spectrum within each of the moving windows.
Acknowledgements. The radar survey was conducted by the  Alfred Wegener Institute  with further logistical support from the  Princess Elisabeth Station (Belgian Antarctic Research Expedition, BELARE) , and funded by the  European Facilities for Airborne Research (EUFAR). D. Callens  is funded through a  FNRS-FRIA fellowship  ( Fonds de la Recherche Scientifique ) and received an  Yggdrasil mobility grant  ( Research Council of Norway ). This work is in part supported by the  Centre for Ice, Climate and Ecosystems (ICE)  at the  Norwegian Polar Institute. Edited by:  C. R. Stokes.
High-throughput sequencing was performed by GATC Biotech (Konstanz, Germany) using a HiSeq 2000 system (Illumina). After removal of lowquality reads, 45. 7 million of 100-bp single-reads were filtered using Bowtie 2 to discard murine sequences  (8). Reads corresponding to RABV were selected by a similarity approach using BLASTn and BLASTx search tools with accession no. KC196743 as a reference (3). Final assembly was made using Ray software  (9). Each position was covered on average 7,800 times. Overall length was 11,923 nucleotides (nt). Coding sequence lengths were 1,353 nt for the nucleoprotein, 894 nt for the phosphoprotein, 609 nt for the matrix protein, 1,575 nt for the glycoprotein, and 6,384 nt for the polymerase genes. Only one similar complete genome (98% identity) was found in GenBank after a similarity search, corresponding to the DRV-NG11 virus isolated from a dog in Nigeria in 2011  (3).
Sequence analysis of the N gene showed a close relationship with viruses found in Chad and Niger in  1987, 1990, 1996, and 2005-2006  (identity Ն99%). Phylogenetic analysis suggests that our isolate belongs to Africa II clade group e, which includes viruses from Chad, Niger, Cameroon, and Nigeria  (10). To our knowledge, this is the first reported complete genome of a RABV isolated from a human in Africa and one of a few of all the hosts combined. Nucleotide sequence accession number. The complete genome sequence is available in the DDBJ/EMBL/GenBank database under accession no. KF977826.
ACKNOWLEDGMENTS This study was financially supported by the  Programme Transversal de Recherche (PTR)  CEVACAR No. 385  funded by the  Institut Pasteur (Paris, France). All animal work has been approved by the local equivalent of the  Institutional Review Board. We gratefully thank  Josiane Leal  ( Anti-Rabies Dispensary ,  Institut Pasteur de Bangui) and Xavier Konamna (National Laboratory for Rabies, Institut Pasteur de Bangui)  for their technical help.
The in-gel profiling approach helped to determine the changes occurring in BEC differentiated in co-culture with astrocytes compared with endothelial cells cultured alone. The two-dimensional gel electrophoresis (2-DE) steps, including the isoelectric focusing and SDS-PAGE conditions, the gel staining, the image acquisition and the comparative study were detailed elsewhere  (23)  together with examples of protein identification by peptide mass fingerprint measured by MALDI-TOF-MS and complementary data issued from peptide fragmentation fingerprints that allow successful protein identifications. Peptide mass fingerprint and peptide fragmentation fingerprints analyses provide complementary datasets and thus, more comprehensive sequence coverage of the BEC proteome, especially when they are combined.
3 was retained for the regulation factor, 81 proteins were shown to be upregulated in BEC, with 34 and 47 in reinduced BBB and in limited BBB, respectively  (28). The combination of in-gel and off-gel approaches enabled the identification of about 430 Triton X-100soluble proteins from BEC with reinduced BBB functionalities  (27)  and thus constituted a first proteome reference for those cells. Information on the proteins identified after 1D-LC MS/MS was shared through the PRIDE database for convenient comparison with proteomic datasets from non-brain vascular endothelial cells (http://www. ebi. ac. uk/pride project PRD000250, accession numbers 12825e12830). Due to the extraction conditions, only a few proteins (!5%) were membrane-associated proteins. More than 75% of the identified proteins display binding, catalytic or structural functions.
Conclusions and Future Prospects The importance of quantitative proteomic analysis in BBB research is increasing and will lead to a better understanding of patients' BBB function as well as improving methodology for drug delivery to the brain. We expect quick developments after the publication of the draft of the human proteome  (43). A specialized database, ProteomicsDB (https://www. proteomicsdb. org/), which is a joint effort of the Technische Universit€ at M€ unchen and SAP AG, is dedicated to expedite the identification of the human proteome and its use across the scientific community. This will rapidly improve the available proteomic information about the healthy human BBB. A recent review  (19)  emphasized the important need to investigate, in the future, the BBB of patients with CNS diseases.
region surrounding Spok2 (Fig. S1 ). This was surprising since in this cross both Spok1 and Spok2 are in heterozygous configuration, which should enable killing by both Spok1 and Spok2 (Table  1 ). Possibly, Spok1 could act as a resistance factor to Spok2. To directly test this hypothesis, the 2-spored-asci progeny of Spok1 D x S crosses was analyzed. Data showed that all the recovered ascospores had two nuclei containing both the Spok2 gene (11 asci analyzed), suggesting that Spok2 exerted spore killing only when the cross was devoid of Spok1 (Table  1 ). Homokaryotic ''SKT20 Spok2 D '' strains, carrying a functional Spok1 recombined with a deleted Spok2, were successfully isolated in the progeny of a cross between SKT20 (which contain functional Spok1 and Spok2; Table  1 ) and Spok2 D. Upon crossing these SKT20 Spok2 D strains with SKT20, approximately 40% of homokaryotic descendants (9 of 23) carried the Spok2 D deletion.
The genomes of P. anserina and other fungi contain genes related to Spok1 and Spok2 Mining available databases of complete genome sequences showed that homologues of Spok genes are present and prevalent in the genomes of many filamentous ascomycetes (spore sac fungi; Fig. 6 ). They are present in all major classes of Pezizomycotina except in the basal classes Orbiliomycetes and Pezizomycetes, but in a patchy distribution with closely related species having or lacking presence/absence of a PCR-amplification product when probed with Spok genes-specific primers. 2 number of nucleotide differences with the Spok genes of the S and T strains. 3 fertility of this cross is very low and few asci were obtained, all had only two ascospores. doi:10. 1371/journal. pgen. 1004387. t002 Spok-related genes.
Materials and Methods All protocols for cultivation, genetic and molecular analysis with P. anserina are available at http://podospora. igmors. upsud. fr. Similar culture techniques were used for S. macrospora. Crosses were performed on M2 minimal medium using the Sderived strains as females and T-derived strains as males (supplementary Table  S1 ). Sk was detected by the presence of 2-spored asci in the F1 progeny and assignments to Spok1 or Spok2 were made by measuring the ratio of 2-spored versus 4spored asci and by backcrossing the F1 progeny to the S, T, Spok1 D and Spok2 D strains and observing the F2 progeny. The sequence of Spok1 has been deposited in GenBank with accession nu JX560967.
021 using a Bruker D8 diffractometer operating with CuK α radiation. Lattice parameters were calculated by Full-profile matching using FULL-PROF program and the Rietveld method  [25, 26]. Chemical composition was controlled with an energy-dispersive X-ray (EDS) detector, coupled at a scanning electron microscope. The cationic ratio, Sm/Cr ¼0. 98, determined by the EDS analysis is close to the nominal one. Magnetic properties of the polycrystalline samples were measured over the temperature range 5-300 K in an applied field of 1 kOe under both zero field cooling (ZFC) and field cooling (FC) conditions, using the VSM option in the QD-PPMS device. Raman spectra were collected, between 80 and 300 K, using a Renishaw Invia Reflex and a commercial temperature control stage (Linkam THMS600). The excitation wavelength of 514.
BU case data This study relied on the analysis of the registry  [29]  of BU patients included in the BU management intervention at Akonolinga District Hospital, for which the Centre Pasteur du Cameroun (CPC) performed biological confirmation of MU infection diagnosis as the National Reference Laboratory. All new patients treated for BU in Akonolinga after clinical diagnosis, and with a documented place of residence in Akonolinga district, were included in the analysis. Patients without a documented village of residence or with an unidentified village of residence were excluded. These data were collected routinely at Akonolinga District Hospital and at Centre Pasteur du Cameroun as part of the BU Disease Surveillance system of the National Control Program. A clinical case was defined as a patient with a clinical diagnosis of BU, made at the Akonolinga District Hospital, by trained specialized health practitioners in charge of the BU treatment.
Population data Population data for Akonolinga health district villages were obtained from the national population census bureau (BUCREP). These population data were a 2010 projection based on the detailed results of the 2005 census. Population settlements in the Centre region are typically hamlets relatively close to each other which form a village (''chefferie'') under the administrative authority of a traditional chief  [34, 35]. Since cases were reported at the village level, we aggregated the population of hamlets at village level. For the towns of Akonolinga and Endom, the urban neighbourhoods were aggregated.
Administrative data In accordance with Cameroonian laws (decree 77/245), a village was defined as the collection of all hamlets under the jurisdiction of the same traditional chief and was represented on the maps as the surface encompassing all hamlets. Hamlets had been either geolocated using a GPS during previous fieldwork  [30, 36]  or identified on a 1/2,00 000 scale map (Institut National de Cartographie, Yaounde, sheets of Yaounde, Nanga Eboko and Akonolinga). Topographical and environmental data were extracted using a circular 5 km-radius buffer around the village centroid. This value of 5 km was chosen based on a socio-anthropologic evaluation done in the region (described in  [37] ) and it approximated the distance that could easily be walked by inhabitants for their daily activities: fishing, farming, going to school.
Topographical data Topography has been shown to be a major driver in most studies  [21, 23, 27]. A digital elevation model was used to obtain elevation data (Shuttle Radar Topography Mission, available from the U. S. Geological Survey). A map of 90 m-topographic wetness index (TWI), an indicator of zones where water tends to accumulate due to abundant runoff from the surrounding area and a low slope, was obtained from Africa Soil Information Service (http://www. africasoils. net). TWI was categorised around the value 18, since following fieldwork, TWI. 18 corresponded best to the bottoms of valleys which were most likely to represent wetlands. The percentage of each buffer area within this class was used as an indicator of the abundance of wetlands. Data on the distribution of rivers and roads were obtained from IFORA project and Institut National de Cartographie du Cameroun.
Environmental data First, we used aggregated measures to quantify vegetation cover in each buffer. We used a vegetation index calculated from remote sensing multispectral data measured by the MODIS satellite. The Enhanced Vegetation Index (EVI) is available from U. S. Geological Survey as a monthly image with 1 km 2 resolution averaging measurements performed with a 16-day period (30-Day L3 Global 1 km product -MOD13A3). EVI was used as a measurement of overall forest cover: it is directly related to photosynthetic activity and biomass and was developed specifically for high biomass areas such as tropical forests  [38]. Using images from December 2001 to December 2011, we calculated the mean EVI during the dry season for each village buffer to approximate vegetation cover. During the dry season, contrast was expected to be maximal between herbaceous or cultivated zones, where annual plants require rain for their growth, and forest where perennial vegetation relying on deep soil water, would still present a high photosynthetic activity  [39].
Deforestation was one of the major human-driven changes that we expected in this area. We calculated the mean EVI over months December to February at the beginning and end of the study period (dry seasons  2001-2002 and 2011-12). The difference between the two values was included as a crude proxy for quantitative vegetation change over the study period. Second, we characterised the environment in more detail (forest type, cultivated areas…) using two distinct Land-use/Land-cover (LULC) datasets. The first dataset was a classification constructed using two Landsat images from February and March 2001 which were selected for low cloud content. Initial exploratory maps were classified using multi-spectral decision trees in the software ENVI, version 4. 8 (Exelis Visual Information Solutions, Boulder, Colorado).
Following ground truthing of these initial maps in November 2012, they were refined using object orientated image analysis in the software eCognition (eCognition Developer version 8. 9. 1, Trimble Geospatial Imaging, Munich, Germany). This resulted in regions classified as Urban, Road, Forest, Crop, Flood plain or Swamp categories. The second dataset was a map of forest types established in 2002 and obtained from the Forest Atlas of Cameroon  [40]. The study area presented 9 classes of vegetation: primary forest (dense humid evergreen or with raffia trees), secondary forest (young or adult, cultivated or not), forested wetlands/swamps, wetlands, and savannah. Secondary forest represents forest growing after being cleared (completely or partially). Two categories are distinguished according to the time elapsed since clearing.
Young secondary forest corresponds to the first 5 to 20 years after clearing. It hosts mainly plant species that grow rapidly and in the light. With time, the number and variety of plant species increase, the canopy closes and the forest becomes adult secondary forest, characterised by a high biodiversity. In Akonolinga region, clearing resulted mainly from familial agriculture. The forest category indicated the intensity of human pressure on the environment. The first dataset was used mainly for urban, agricultural, and wetland land-cover characterisation, which were combined with detailed forest data from the second dataset. New classes or new attributions were derived, such as ''cultivated wetlands'' corresponding to areas listed as cultivated in one dataset and swamp or swamp forest in the other.
Statistical analyses All analyses were performed using R software version 3. 0. 2 (R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria), including packages DCluster, SPODT, FactoMineR, bcp; and the software ArcGIS version 10. 0 (ESRI Inc. Redlands, CA), including the extension Spatial analyst. Graphics were drawn using the ggplot2 R-package and maps were drawn using ArcGIS. Incidence rate calculation and mapping. In order to analyse the distribution of cases in Akonolinga district, a map of the mean monthly incidence rate of BU per village was drawn for each phase and for the cumulative series. Mean incidence rate was expressed in cases per 1,000 person. years (cases/1,000py) and allowed comparisons between villages with different population sizes and different exposure times.