dataesr/scientific-paragraphs-categorization huggingface dataset:
Post-translational modifications such as phosphorylation and disulfide bridges were confirmed using the manual Single Protein mode. Proposed sequences with E-value o1 Â 10 À 2 were considered positively identified with a minimum of 10 matching fragment ions. The data were deposited with the ProteomeXchange Consortium (http://proteome central. proteomexchange. org) via the PRIDE partner repository  [3, 4]  with the dataset identifier PXD001303. A total of 62m/z were identified and attributed to 32 different molecular species corresponding to three intact and whole proteins and 58 peptides from 29 proteins (Supplementary Table  2 ). Forty-five of these peptides presented tryptic or semi-tryptic cleavages, suggesting protease activities by trypsin-like or kallicrein enzymes (Supplementary Table  2 ).
KiF (Knowledge in Frame) The whole system, training and prediction, has been implemented in KiF (Knowledge in Frame), a script language that has been implemented into the very fabric of the rule-based Xerox Incremental Parser (XIP). KiF offers a very simple way to hybridize a rule-based parser with machine learning technique. For instance, a KiF function, which evaluates a set a features to predict a class, can be called from a rule, which could then be fired along the output of that function. KiF is a multi-threaded programming language, which is available for all platforms (Windows, Mac OS, Linux). It provides all the necessary objects (strings, containers or classes) and many encapsulations of dynamic libraries from different C programs such as classifiers (liblinear and libsvm), database (SQLite), or XML (libxml2), which can be loaded on the fly.
General Methodology We focus on four main tasks: detecting the aspect terms and aspect categories and their corresponding polarities. While the detection of aspect terms and their corresponding polarities occurs at the grammar level, for the detection of aspect categories and their corresponding polarities we make use of the liblinear library  (Fan et al. , 2008)  to train our models. We train one classifier for detecting the categories and further, for each category we train a separate classifier for detecting the polarities corresponding to that particular category. For both settings, we use 10-fold cross-validation. The two modules for aspect category classification and aspect category polarity classification are described in details further.
Aspect Category Classification The sentence classification module is used to assign aspect categories to sentences. For each sentence, the module takes as input features the bag of words in the sentence as well as the information provided by the syntactic parser. The output consists of a list of categories corresponding to each sentence. In the pre-processing stage stop words are removed (determinants, conjunctions). Further, we use the L2-regularized logistic regression solver from the liblinear library to train a model. The features considered are the word lemmas from the sentence along with their frequencies (term frequency). Apart from this, the information provided by the rule based component is also taken into account to increase the term frequency for terms belonging to the detected categories.
Only two studies investigated the transcriptome of 5XFAD mice, one using RNA-seq in frontal cortex and cerebellum of 7 week-old transgenic mice  [30] , the other using whole-brain next-generation sequencing to compare young (M3-6) versus old (M12) mice from 5XFAD and Tg4-42 strains  [31]. Here, we carried out a longitudinal transcriptomic study on two major brain regions affected in AD, the hippocampus and the neocortex, obtained from 5XFAD female mice at presymptomatic (M1), prodromal-like (M4) and symptomatic stages (M6 and M9) of the pathology. We investigated how genes with a modulated expression are involved in functional networks through the use of two text-mining based softwares (Ingenuity and PredictSearch). Among the genes involved in these networks, a bibliographic search was performed to identify those reported in AD patients.
Although post-transcriptional regulation cannot be excluded, we can postulate that IFN-γ transcription occurs either transiently in brain cells or outside (See figure on previous page. ) Figure  3  Metabolic pathways associated to gene dysregulation in 5XFAD cortex and hippocampus at M4, M6 and M9: common versus age-specific processes. Biological functions and metabolic pathways associated to gene expression dysregulation were identified using Ingenuity Pathway Analysis (IPA). (A) Data from both the cortex and hippocampus were analyzed as one dataset and the main metabolic pathways affected were clustered according to age (M4, blue; M6, yellow; M9, red). The metabolic pathways affected across all three ages are represented in white and are related to inflammatory and immune responses. (B) The main metabolic pathway affected at M4, M6 and M9 relates to infectious disease, cellular function and antigen presentation.
Microarray data analysis Biological interpretation of the data was performed using two different programs. First, Ingenuity Pathway Analysis (IPA, Ingenuity Systems [165]) was used to identify biological functions from the lists of DEGs associated to transgenic animals. The main criteria to validate a differentially expressed gene was a fold change over 1. 5 or under -1. 5 when considering expression values in the transgenic group relative to the wild type control group. Upregulated and downregulated genes were analyzed in the same datasets to obtain the biologically relevant function categories. Right-tailed Fisher's exact test was used to calculate a p-value determining the top statistically significant biological functions assigned to the data set. Secondary analysis of the main metabolic pathways and their potential dysfunctions was performed using the Java/ Perl software Predictsearch® (Laboratoire Genex  [166] ), which has been previously described  [69, 74].
This software characterizes the pathways and functional networks in which the selected genes found to be up-or downregulated are involved. For this mechanistic analysis, only genes not differentially expressed across all time points in wild type animals (ratio between 0. 85 and 1. 2) but upregulated in transgenic animals (ratio over 1. 5) were considered. Predictsearch was then used to generate functional networks based on the total number of differentially expressed genes in both cortex and hippocampus.
Acknowledgements This work was supported by  CNRS ,  Aix Marseille University  and grants from the  French National Agency for Research (ANR)  funding to FF, SR and MK (ADHOC, TIMPAD, PREVENTAD) within the frame of the  French Alzheimer's Plan. The work was also supported by grants from  Fondation de l'Avenir  to FF, and by " Fonds Européen de Développement Régional" FEDER  in PACA. VL was recipient of a doctoral fellowship from the  French Ministry of Research. KB was granted a research associate fellowship by the  French Alzheimer's Plan.
Experimental Crystal data   Hydrogen-bond geometry (A ˚, ). D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA N1-H1AÁ Á ÁO4 i 0. 85 (3) 1. 94 (3) 2. 769 (2) 166 (2) N1-H1BÁ Á ÁO4 ii 0. 87 (3) 1. (i) x; Ày þ 1 2 ; z þ 1 2 ; (ii) Àx þ 1; y þ 1 2 ; Àz þ 3 2 ; (iii) Àx þ 2; y þ 1 2 ; Àz þ 3 2 ; (iv) x À 1; Ày þ 1 2 ; z À 1 2 ; (v) x À 1; y; z. Data collection: APEX2  (Bruker, 2006) ; cell refinement: SAINT  (Bruker, 2006) ; data reduction: SAINT; program(s) used to solve structure: SIR97  (Altomare et al. , 1999) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)   This work was supported by the Tunisian Ministry of H. E. Sc. R. Supporting information for this paper is available from the IUCr electronic archives (Reference: HB7193).
Computing details Data collection: APEX2  (Bruker, 2006) ; cell refinement: SAINT  (Bruker, 2006) ; data reduction: SAINT  (Bruker, 2006 ); program(s) used to solve structure: SIR97  (Altomare et al. , 1999) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Putz, 2005) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
RNA from all samples was pooled and submitted to high throughput RNA sequencing (RNAseq). Succeeding de novo assembly, bacterial contigs were assigned to the closest already-known taxa, revealing a list of zoonotic bacteria for the whole sample. Parallel, DNA samples were submitted to meta-barcoding approach: each sample was amplified by PCR using universal primers tagged at the V4 region of the 16S rRNA. The amplified templates were multiplexed and submitted to 454-pyrosequencing. The resulting dataset was demultiplexed using a home-made pipeline that assigns each read to a sample using the tagged primers, following these were processed using Mothur pipeline to construct OTUs and classify them using the RDP database. These methods allowed listing bacteria detected in each rodent and, so derive the prevalence, coinfections and bacteria interactions.
© 2014 Razzauti et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/4. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Methods Materials and Methods: Fiftheen young adults (8 men, median 29 (23-41) yrs) with no risk factor for atherosclerosis were recruited. They all underwent 1. 5T MRI (Siemens, Avanto) at rest and during supine exercise using an amagnetic ergometer (Lode, the Netherlands). The following parameters were used for rest and exercise: cine = TR/TE = 30 ms/1. 8 ms, thickness = 6 mm, α = 65°, matrix = 148 × 256, resolution = 30 ms, retrospective gating; Phase contrast = TR/TE = 25 ms/2 ms, thickness = 6 mm, matrix = 256 × 256, flow encoding = 200 cm/s, α = 25°, resolution = 25 ms, retrospective gating). For exercise the volunteer was instructed to cycle for 2 min at 25W, work load was increased each 2 min for a minimum of 10 min, to obtained twice the resting heart rate. The volunteer was instructed to stop cycling and immediately following which a breath held stress dataset of 2 acquisitions were acquired.
© 2014 Bal-Theoleyre et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
The objective of our paper was therefore to study the influence of climatic and non-climatic factors on the spatio-temporal variations of annual incidence of MM serogroup A, the main etiologic agent over the study period, at the health centre catchment area (HCCA) scale in Niger, using a database of laboratory-confirmed cases and developing an explanatory Bayesian hierarchical model from 2004 to 2010 at the HCCA-year level.
Ethics statement This study was approved by the Clinical Research Committee of Institut Pasteur and authorized by the National Consultative Ethics Committee of Niger and the two French data protection competent authorities: CCTIRS (ComiteConsultatif sur le Traitement de l'Information en matiere de Recherche dans le domaine de la Sante) and CNIL (Commission Nationale de l'Informatique et des Libertes). The data collected involving patients were anonymized.
Cartographic data Spatial analyses were based on the National Health Map of Niger, created by the Centre de Recherche Medicale et Sanitaire (CERMES) in 2008, at the level of the HCCAs, areas which include all villages served by the same integrated health centre. We used the 2010 updated version of this National Health Map of 732 HCCAs, in the WGS84 geodetic system with UTM 32N projection. On average, a HCCA covers a 40640 km 2 area.
We found that mean relative humidity and occurrence of early rains were protective climatic factors and that a higher risk was associated with the presence of a road, the percentage of neighbouring areas having cases and the occurrence of early cases before January. These findings contribute to improve our understanding of MM epidemics in Africa and the associated factors, and might be used in the future for the subsequent development of an early warning system. Polymerase Chain Reaction (PCR) for all CSF. This enhanced surveillance system is active in the whole country since 2002, and has been described in detail elsewhere  [4]. We used the CERMES database for a retrospective study on confirmed MM A cases between 1 July 2003 and 30 June 2010. We aggregated MM A cases by HCCA and epidemiological year, defined as running from 1 July of the year n-1 to 30 June of the year n, in order to cover an entire meningitis season.
Vaccination data Health districts were contacted to obtain data on polysaccharide vaccines (number of delivered vaccine doses and/or vaccine coverage) at the HCCA level over the study period and the previous two years. Full vaccination records could be collected only for Tahoua region over 2002-2010. Missing data in records from other regions did not enable us to use them in our analyses. We thus studied the effect of previous vaccination campaigns conducted in Tahoua region during the years n-1 and n-2 on MM incidence of year n. We considered different forms for the vaccination covariate: either the coverage rate (as a continuous or categorized variable), the vaccination status (vaccinated: yes/no), or the exceedance of several coverage thresholds (above threshold: yes/no). The cumulative effect of successive vaccination campaigns could not be studied as only one HCCA was vaccinated two years in a row.
Demographic data The Institut National de la Statistique (INS) provided the number of inhabitants per village according to the 2001 national census. We aggregated the villages' populations at the HCCA level and applied a mean annual population growth rate of 3. 3% (provided by the INS). We computed the population density covariate as the number of inhabitants per HCCA divided by the HCCA surface area calculated in ArcGIS software (version 10. 0, ESRI Inc. Redlands, CA).
Climate and aerosol data Gridded climate data from 2003 to 2010 were extracted from ERA-Interim reanalysis, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF)  [24]. We retrieved relative humidity, temperature, total precipitation, U (west-east) and V (south-north) wind components at a 0. 75u spatial resolution at a daily time-step. To characterize the wind-blown mineral dust emission from the Sahara, we used the Absorbing Aerosol Index (AAI), a dimensionless quantity which indicates the presence of ultraviolet-absorbing aerosols in the Earth's atmosphere  [25]. The AAI used in this study is derived from the reflectances measured by SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography) satellite instrument in the ultraviolet wavelength range  [26].
We retrieved monthly gridded data (1. 00u61. 25u latitude-longitude grid) from 2003 to 2010 (www. temis. nl/airpollution/absaai/). As we were interested in how the climate of a given year or season can influence the annual epidemic magnitude, we calculated multi-monthly means of each climatic variable, averaged over periods relevant to the meningitis season or the seasonal cycles of each climatic variable, both for each HCCA and for the whole study region (see Figure  S1  and Text S1 for further details).
Altitude data Shuttle Radar Topography Mission (SRTM) elevation data were obtained from the processed CGIAR-CSI (Consortium for Spatial Information) SRTM 90 m Digital Elevation Dataset version 4. 1  [27] , available as 5u65u tiles at a 3 arc second resolution (approximately 90 m). Six tiles were downloaded and combined in ArcGIS to cover the whole study region.
Database preparation Finally, we collated these multi-source and multi-format spatiotemporal datasets and reconciled data at the HCCA level (i. e. cartographic, epidemiological, vaccination and demographic data) and gridded data (i. e. landcover, climate, AAI and altitude data) by averaging the gridded values over each HCCA using the statistical computing software R (version 2. 15. 3, R Core Team, R Foundation for Statistical Computing, Vienna, Austria). Then, in addition to the covariates described above, we created supplementary variables to include in the statistical analyses. To take into account potential interactions with bordering countries, we calculated in ArcGIS the minimum distance of each HCCA to the closest border and expressed it as a binary variable (border versus no border) or a categorical variable (classes of distance and classes of bordering countries).
Bayesian multivariate model over the study region In the Bayesian multivariate model over the whole study region, the overdispersion parameter of the negative binomial (k 21 ) had a posterior mean value of 2. 586 (95% CI = 2. 223-2. 998) (Table  1 ). This was significantly different from zero, which confirmed that the negative binomial formulation was necessary to account for extra-Poisson variation in the dataset. Regarding fixed effects, five covariates were significantly associated with MM A incidence (the 95% CI of their associated IRR did not contain 1). A reduced risk was associated with higher average relative humidity during the meningitis season (November-June) over the study region (posterior mean IRR = 0. 656, 95% CI 0. 405-0. 949). Early rains in March in an HCCA represented a protective spatio-temporal factor (IRR = 0.
The Bayesian formulation allows to acknowledge the uncertainty associated with all model (hyper)parameters (fixed and random), to include a spatial correlation structure within a prior distribution  [32]  and leads to more robust estimates in particular when the geographical level is small and the disease rare  [33]. Such models are still rarely applied to MM in Africa. The modelling approach we adopted was a negative binomial GLMM using Bayesian estimation, to control for unobserved confounding factors and take into account the dependencies over space and time encountered in our dataset, incorporating year-specific and area-specific random effects  [32, 34]. Ignoring these multiple correlations could lead to overestimate the significance of the covariates  [31]. This model also accounted for extra-Poisson variation (overdispersion) in the count data via the negative binomial formulation, allowing the variance to be larger than the mean.
The difficulty to measure such factors made the inclusion of area-level random effects necessary. Finally, other unexplained factors, such as respiratory viral co-infections, might contribute to the residual spatio-temporal heterogeneity, via an effect on transmission, colonization and/or invasion  [3]. Although difficult to collect retrospectively, these factors should be further investigated at the health centre level and at least properly accounted for in any modelling attempt. Mathematical models, still little developed on this topic  [44] , could also help us to better understand the role of carriage and immunity in the epidemic dynamics. This study relied on a unique dataset which provided a very precise picture of MM A spatio-temporal dynamics in Niger over seven years, and has already been used in published spatiotemporal analyses  [4, 5].
The cases were all biologically confirmed by CERMES laboratory, which allowed us to exclude misclassified infectious agents that give similar clinical signs of meningitis. Databases commonly used by most statistical studies on MM in the Belt (e. g. [17] [18] [19] [20] [21] ) gather clinically-suspected cases of meningitis, and thus include a mixture of different Nm serogroups and other bacteria such as S. pneumoniae and H. influenzae. In Niger, over our study period, 40% of positive CSFs were infected by another agent that Nm A. Relying only on suspected cases would therefore introduce a large number of misclassified cases. If etiological confirmation by conventional PCR may have led to underestimation of positive cases  [45] , this should have affected the data in a spatially and temporally consistent way, as biological testing was performed with the same PCR assay by the same laboratory all along the study period.
Acknowledgments We thank all the doctors and health staff who took part in the surveillance system, the  Nigerien Ministry of Health , the  Direction of Surveillance and Response to Epidemics (DSRE) , the  General Directors of CERMES , and all the CERMES staff from the  Biology and Epidemiology Units , with many thanks to  Noemie Phulpin ,  Oumarou Alto  and  Florian Girond  for health map and database development. We also thank the  MERIT community  and in particular the MAMEMA group for helpful discussions.
For each important reaction we performed an extensive bibliographic review that included chemical databases such as the KIDA database  (Wakelam et al. 2012). When no information existed, we calculated the presence and the value of the entrance barrier using density functional theory (DFT) calculations (with the hybrid M06-2X functional developed by  Zhao & Truhlar 2008) , which is well suited to calculations involving molecules and radicals with C, N, H, O, and S atoms, associated with aug-cc-pVTZ basis sets  (Dunning 1989 ) using Gaussian09 (11 reactions are concerned). For barrierless reactions, the rate constants k(T ) were calculated using simplified capture rate theory, which led to an upper limit value of the rate constant  (Georgievskii & Klippenstein 2005). For reactions with a barrier, the rate constants were calculated using conventional transition state theory or are estimated by comparison with similar reactions involving oxygen analogs.
Particular attention was paid to photodissociation processes. When no data existed, the absorption cross section were calculated using the EOM-aug-cc-pVTZ method using Gaussian09. Branching ratios for the photolysis of sulfur containing species are poorly known. When no data exist they are deduced from the oxygen analogs or deduced from thermodynamic data leading to large uncertainties. The present chemical scheme contains 153 species (26 sulfur species), 1060 reactions (91 including sulfur species), 199 photolysis processes (28 including sulfur species), and the dissociation of N 2 by galactic cosmic rays. The coupling between sulfur chemistry and nitrogen or oxygen chemistry is limited to the main compounds. The complete list of reactions, with references or information regarding the estimation of rate constants and their uncertainty factors, is available upon request and can be downloaded from the KIDA database  (Wakelam et al.
5% expressed tet(M)  21. Furthermore, the tet(M) gene was detected in only 25% of the 51 bovine GBS genome sequences and 10% of the genome of 22 GBS strains isolated from fish that were available at the NCBI database as of 15 May, 2014. These values highlight a very high incidence of tet(M)-associated TcR within human isolates compared with animal GBS. It is known that the use of antibiotics results in a strong positive selection for antibiotic-resistant clones and an overall reduction in bacterial population diversity  22. To identify a possible link between the population structure of human GBS and antibiotic resistance, we analysed the resistome of the 229 sequenced genomes (Table  3 ). In agreement with the epidemiological surveys, the most frequent determinants in the 216 human isolates were tet(M) (n = 183, 84%) and tet(O) (n = 10, 5%) both encoding ribosomal protection proteins, and erm(B) (n = 18, 9%) and erm(TR) (n = 12, 5.