dataesr/scientific-paragraphs-categorization huggingface dataset:
Unveil the Key Features The high accuracy of the reasoner robustness predictive models trained from featured datasets confirmed the validity of our assumption that particular ontology features would help tracking down the reasoner empirical behaviours. Henceforth, the robustness of reasoners would be explained in terms of the reduced set of ontology features, involved in their respective best predictive models. We have called these subsets, a reasoner local key features. We have also investigated possible presence of shared features across the different reasoners' best predictive models, i. e. global key features. We pinpointed their contribution to the reasoner robustness prediction, by computing their occurrences in the best models. A group of 8 features were recognized to be the most relevant indicators as they were involved in each of the 6 best predictive models.
TB Registration Data The TB occurrence data was acquired from TB clinics at district hospitals in Si Sa Ket. All the cases were collected from the Si Sa Ket TB registry, which included all confirmed TB cases notified in the residents from fiscal years 2004 to 2008. The data included every type of TB: pulmonary TB, meningitis TB, and TB in other organs. The latter two types of TB were commonly called extra-pulmonary TB (EPTB). The patients were further categorized based on their history of treatment according to the WHO and the Thailand National Tuberculosis Program (NTP) standards: new, relapsed, treatment after failure, treatment after defaulted, transferred in, and other previously treated [2,16]. To avoid duplication, only the new and relapsed cases were considered incidences in this study.
By using the standard definition of the NTP, each case record included the patient's gender, age at diagnosis, residential address at LAO level at the time of diagnosis, and the year of diagnosis. Age was included as a covariate and was categorized according to the NTP into seven groups: (i) 0-14; The residential address at the time of diagnosis for each case was reviewed and assigned an LAO code number. These cases were then geo-coded and matched to the LAO-level layers of polygon by LAO code using the software ArcGIS 9. 3 (ESRI Inc. , Redland, CA, USA). The latitude and longitude coordinates corresponding to the centroid of each LAO were also calculated. Personal identification of all TB cases was removed by the TB registrars, thus, no specific individual identifiers were used in the analysis.
Spatial Scan Statistic The spatial scan statistic was developed by Martin Kulldorff and implemented in a software program named SaTScan. SaTScan v9. 0. 1 was used to investigate the presence of statistically significant spatial clusters of TB and to identify their approximate locations  [27]. Spatial scan statistics analyses  [28]  with age adjustment were performed to eliminate the influence of difference in age structure of the population. Identification of spatial high or low clusters was done under Poisson probability model assumption where the number of events in an area was Poisson distributed according to a known underlying population at risk. Purely spatial analysis, which ignored the time dimension of the cases, was performed to detect the TB clusters in the area under study. The spatial scan statistic imposed a circular window on the map and lets the circle move over the area.
However, several publications have complimented usage of the indirect standardization approach  [33] [34] [35] [36] , especially in the situations where the population size is small or the incidence of cases is sparse (less than 20 cases). In this study, indirect age-standardization of the incidence was performed because the small number of incidences was witnessed in the study area. Instability might be found in the rates being generated by the direct method. Additionally, minimal fluctuation of the cases in a certain age group of the population in the small geographical unit like LAO might result in drastic changes in the directly standardized rates  [34]. This is in accordance with the studies conducted in England and Wales, Hong Kong, and Thailand  [37] [38] [39]. In terms of spatial clustering detection using SaTScan software with maximum spatial cluster size of ď5% of the total population, the spatial cluster analysis identified clusters of significantly high and low rates of TB in Si Sa Ket for 2004-2008.
DNA extraction and genome sequencing Bacterial cultures were grown over night in liquid Luria-Bertani (LB) media to an optical density (measured at 600 nm) of approximately three. Genomic DNA was isolated using the Wizard kit (Promega, Madison, WI, USA) according to manufacturer's instructions. Purified DNA was then sequenced at the Wellcome Trust Sanger Institute (WTSI). Paired end libraries 150 bp in length were generated and sequenced on the Illumina MiSeq instrument (San Diego, CA, USA) according to in house protocols  [10, 11] , with an approximately 500 bp insert size. Sequence data for each of the strains were deposited in the European Nucleotide Archive (accession numbers in Table  1 ).
Genomic analysis Resulting sequencing reads were trimmed using Trimmomatic v0. 27  [12]  to remove adapters, bases with a PHRED score of <30, and remaining reads with lengths <50 bp. High quality reads were then mapped to relevant reference strains (Table  1 ), using SMALT (http://www. sanger. ac. uk/resources/software/smalt/) and Single Nucleotide Polymorphisms (SNPs) were called using Samtools  [13]. Nucleotides where mapping quality was below 30 and genotyping quality was below 50 were excluded from further analysis. Mapping coverage of all isolates was approximately 70-fold coverage. De novo assembly was performed using Velvet Optimiser  [14]  and contiguous sequences were annotated using Prokka  [15]. Clustering and BLAST comparisons were used to determine the presence/absence of genes in annotated assemblies as previously described  [16].
To prepare a multiple sequence alignment for phylogenetic analysis, sequencing data from strains in this study and from simulated fastq data created from published reference genomes were mapped to the chromosome of S. flexneri 2457T (GenBank accession: NC_004741. 1). The other reference isolates (and their accessions) used in this analysis were: S. sonnei Ss046 (NC_007384. 1), S. sonnei 53G (NC_016822. 1), S. flexneri 5 M90T (AGNM01000000), S. flexneri 5a 8401 (NC_008258. 1), S. flexneri 2a NCTC1 (LM651928), S. flexneri 2a 301 (NC_004337. 2), S. flexneri X 2002017 (NC_017328. 1) and S. boydii Sb 227 (NC_007613. 1). Core genes (n = 2,427) were identified that had 100% mapping coverage in all isolates and phylogenetic analysis was performed using RAxML software v7. 0. 3  [17]  on the 43,349 variable sites (subset from 2,306,256 bp) of these core genes.
Conclusions The work presented here increases the number of publically available Shigella genomes, including for the first time, sequencing data for S. sonnei 25931, two S. flexneri 1b, one S. flexneri 1a, one S. flexneri 3a and 8 S. flexneri six isolates. We provide details on the draft genomes generated from this sequencing data, and report SNP variation in strains maintained in different laboratories and after serial passage. We also described the relatedness of the strains and isolates used by the STOPENTERICS consortium, and have deposited this data as a public resource. Data presented in this work will guide the selection of strains for further development of vaccine and contribute to a growing awareness of diversity in Shigella.
Acknowledgements We thank  David Harris  ( Wellcome Trust Sanger Institute ) for sequencing the strains and  Mariaelena Caboni  ( Novartis Vaccines Institute for Global Health ) for providing S. sonnei 53G and S. flexneri 2a lines after several passages. The research received funding from the  European Union Seventh Framework Programme  [FP7/2007-2013] under Grant Agreement 261472 'STOPENTERICS' , and  Wellcome Trust  grant number  098051  also funded authors from the WTSI.
Bioinformatic Analysis of Phosphate Solubilization Systems in Selected Endophytic Strains The pqq operon and gcd genes in Pseudomonas fluorescens L321, L111, and L228 were identified in preliminary genome sequencing data (Dowling, unpublished data). The sequence data of these pqq and gcd genes were submitted to Genbank and can be found under accession numbers KP981419, KP981420, KP981421, KR002856, KR002857, and KR002858. Phylogenetic analysis were performed using MEGA6 software  (Tamura et al. , 2013). Amino acid sequences obtained from the NCBI database were concatenated in a FASTA formatted file and uploaded to MEGA6 software. Protein alignments were performed using the ClustalW  (Thompson et al. , 1994)  function, using an adjusted multiple alignment gap opening penalty of three and a gap extension penalty of 1.
8  (Hall, 2013). Evolutionary phylogenetic relationship for each strain was determined by aligning the predicted Pqq protein sequences concatenated in the order PqqF, PqqA, PqqB, PqqC, PqqD, and PqqE. Phylogeny was constructed using the Neighbor-joining method  (Saitou and Nei, 1987) , with 1000 bootstrap replications using partial deletion of gaps/missing data with a site coverage cut off of 95%. Details of Pseudomonas strains used in the alignment are described in Table  1. MEGA6 software  (McWilliam et al. , 2013)  was used to generate a graphic of the sequence alignment.
ChIP ChIP assays were carried out according to the protocol described in  Iacovoni et al. (2010)  and are detailed in the Supplemental Experimental Procedures. IP efficiencies were calculated as the percent of input DNA immunoprecipitated. For ChIP-chip, 8 ng of inputs and samples were amplified as in  Iacovoni et al. (2010) , labeled, and hybridized on Affymetrix tiling arrays covering human chromosomes 1 and 6. To plot data with respect to the 24 DSBs induced on chromosome 1 and 6  (Iacovoni et al. , 2010) , the ChIP-chip signal was averaged for 200-bp windows spanning 40 kb (P-ATM and P-DNA-PKcs) or 2 Mb (gH2AX) surrounding each annotated AsiSI site. For heatmap representations, the average ChIP-chip signal was determined in 500-bp bins for P-ATM and P-DNA-PKcs, and 50-kb bins for gH2AX, centered on each cleaved AsiSI site.
ACKNOWLEDGMENTS We thank  V. Benes  at the  EMBL Genomic Core Facility  for microarrays hybridization. We thank  Dr. C. Normand  for time-lapse microscopy. We thank  Dr. D. Jullien  for the pEGFP-53BP1 and  Dr. O. Fernandez Capetillo  for the ATR inhibitor. T. C. and M. A. were supported by grants from the  Fondation pour la Recherche Medicale (FRM). P. C. was supported by a grant from the  Association Contre le Cancer (ARC). J. C. is supported by a grant from the  Ligue Nationale contre le Cancer. Work in the F. C. -L. laboratory is funded by grants and fellowships from the  Spanish Government  ( SAF2010-21017  and  BES-2011-047351 ) and the regional  Andalusian Government  ( CVI-7948 ). Funding in the G. L. laboratory was provided by grants from the  Agence Nationale pour la Recherche  ( ANR-14-CE10-0002-01  and  ANR-13-BSV8-0013 ), the  Institut National contre le Cancer (INCA) , the  Ligue Nationale contre le Cancer (LNCC) , and  Research Innovation Therapeutic Cancerologie (RITC).
Sedimentation Analysis of CshA The recombinant protein was subjected to zonal velocity sedimentation in a 15-30% glycerol gradient. Marker proteins catalase (native size 248 kDa), bovine serum albumin (66 kDa), and cytochrome c (12 kDa) were included as internal standards. CshA-His 6 (calculated to be a 57-kDa polypeptide) sedimented as a discrete peak (fraction 16) just slightly heavier than the BSA peak (66-kDa) (data not shown). The ATPase activity profile paralleled the abundance of the CshA-His 6 polypeptide and peaked at fraction 16 (Fig. 3 ). A plot of the S values of the 3 standards versus fraction number yielded a straight line (not shown). Interpolation to the internal standard curve determined an S value of 5. 6 for CshA-His by suggesting that CshA-His is a homodimer in solution.
We also checked the production of the proteins by western blot, using a polyclonal antibody raised against CshA. We noted a slight overproduction in comparison with the wild type level of CshA, probably due to the fact that the constructs are expressed from a plasmid on its native promoter (data not shown). Since some interactions could be mediated through RNA, we performed the lysis steps both in absence or presence of RNase A. The Strep/FLAG tagged versions of CshA and their potential interaction partners were purified using first a Strep-Tactin column. The corresponding elution fractions were pooled and subjected to a second round of purification on an a-FLAG resin. In order to be able to discriminate protein interactions from column background, we performed in parallel the same purifications with a lysate from a PR01 strain with the empty vector.
The eluted fractions were analyzed on a SDS-PAGE and the protein partners were identified by mass spectrometry. A total of 106 proteins were identified in the elution fractions coming from the lysates containing the tagged version of CshA, with or without RNase A treatment (Table  S2 , Table  S3  for the full data set). Among these proteins, we found proteins involved in each step of the RNA metabolism from transcription to decay, enzymes in DNA metabolism and others. Indeed, we found interactions with the b and b' subunits of the RNA polymerase, various ribosomal proteins, translation initiation and elongation factors. Most importantly, we also identified RNase J1, RNase J2, RNase Y and enolase, components of the putative degradosome. Interestingly, the PNPase was only detected without RNase A treatment, suggesting a lose association, if any, with the degradosome.
Acknowledgments We are grateful to  Vanessa Khemici  for continuous discussion and to  Eckhard Jankowsky  and  Patrick Viollier  for comments on the manuscript. RNA-sequencing experiments were performed at the iGE3 genomics platform of the  University of Geneva  (http:// www. ige3. unige. ch/genomics-platform. php). MS/MS protein identifications were performed at the proteomics core facility of the  Faculty of Medicine  (http://www. unige. ch/medecine/proteomique/protocols. html), and we are very grateful for their help and advices.
7 2. 6 5. 5 5. 4 SA1281 SA1279-SA1281 Uncharacterized protein SA1281 2. 7 2. 3 2. 0 2. 8 SA1282 SA1282-SA1283 Holliday junction resolvase RecU recU 3. 6 3. 0 2. 5 2. 4 SA1283 SA1282-SA1283 penicillin binding protein 2 pbp2 2. 6 2. 5 2. 2 1. 5 SA1337 Uncharacterized protein, weakly similar to SA1337 2. 3 2. 2 1. 9 2. 0 transcriptional regulator SA1375 SA1375-SA1380 Uncharacterized protein, similar to Probable metallo- SA1375 2. 0 2. 0 2. 1 2. 1 hydrolase YqgX SA1376 SA1375-SA1380 Uncharacterized protein, Similar to YqgV from B. SA1376 2. 3 2. 2 2. 2 2. 4 subtilis SA1377 SA1375-SA1380 Glucokinase glcK 2. 5 2. 3 2. 8 3. 1 SA1474 SA1474-SA1475 Uncharacterized protein SA1474 2. 3 2. 0 1. 7 2. 0 SA1490 Uncharacterized protein SA1490 2. 8 2. 7 2. 1 6. 3 SA1536 SA1535-SA1538 Uncharacterized protein, weakly similar to UPF0721 SA1536 2.
(ref. YO4C; CellASIC, Hayward, CA) , using the Microfluidic Perfusion Platform (ONIX) driven with the interface software ONIX-FG-SW (CellASIC). Cells were resuspended in SD medium containing 0. 1M HEPES, pH 4, and trapped and maintained in a uniform plane. Cells were then perfused with 0. 1 M SD medium containing 0. 1 M HEPES, pH 7, and imaged. Normal growth conditions were reproduced by adjusting the ambient temperature at 30°C with a thermostated chamber and flowing cells with the indicated culture medium at 3 psi. The microfluidics device was coupled to a DMI6000 (Leica, Wetzlar, Germany) microscope equipped with an oil immersion Plan Apochromat 100×/numerical aperture 1. 4 objective, a QuantEM cooled electron-multiplying charge-coupled device camera (Photometrics, Tucson, AZ), and a spinning-disk confocal system CSU22 (Yokogawa, Tokyo, Japan).
ACKNOWLEDGMENTS We thank  L. Robinson  for strains  LRB341  and  LRB362. This work was supported by Grant  BFU2008-02005  from the  Spanish Ministerio de Ciencia e Innovación  and by the  Fondation ARC pour la Recherche sur le Cancer  ( SFI20121205762  to S. L. and  DOC20130606445  to M. B. ). A. H. is the recipient of a  Spanish National Research Council   JAE Predoctoral Fellowship.
1B ), consistent with previous findings  (Marin et al. , 1996; Michaud et al. , 1997). Apart from CG14207 and CG13133, all the other small HSPs were also found to be heat-inducible (Fig. 1C ). Expression of CG13133 was below the detection limit at both control and heat-shock conditions. The most strongly induced nonclassical members were HSP67BC and CG4461, while HSP67BA, L(2)EFL, and CG7409 were moderately induced (Fig. 1C ). qPCR data were subsequently used to estimate the relative contribution of each member to the total pool of small HSP mRNA before and after heat shock. In S2 cells, the most abundantly constitutively expressed small HSPs are HSP23 (17. 6% of the total pool), HSP26 (16. 9%), HSP27 (14. 7%), and CG14207 (17%) (Fig. 1D ). After a heat shock and a recovery period of 1 h, the pool of small HSP mRNA has shifted toward a more homogenous distribution with comparable levels of HSP23, HSP26, HSP27, HSP67BC, HSP67BA, and CG4461 (Fig.
melanogaster HSP70Aa or HSC70-4. PolyQ aggregates were detected using the filter trap assay using two dilutions. Samples coexpressing HSP67BC were loaded at higher concentrations to better visualize trapped aggregation. Vent DNA polymerase (New England Biolabs) was used for preparative polymerase chain reactions. DNA sequencing reactions were carried out by ServiceXS (Leiden, the Netherlands). Blast algorithms  (McGinnis & Madden, 2004)  were used to screen databases at the National Center for Biotechnology Information (NCBI) (Bethesda, MD, USA). Drosophila small heat-shock protein sequences were retrieved from the NCBI database using the D. melanogaster HSP27 sequence as input for a protein BLAST search. Results were analyzed by CLUSTALX  (Larkin et al. , 2007)  and visualized by TREEVIEW  (Page, 1996).
Simulated discharge times series are obtained using a hydrological model forced with past variations of meteorological variables available for the region. When meteorological observations required for such analysis do not cover the whole target period, they can also be reconstructed. A classical reconstruction is obtained using external data (proxy data) from long-term series of observations available from one or several neighbouring stations. The most popular reconstruction approach is based on linear (multiple-)regression models but a variety of other approaches have been proposed, including non-linear multiple regression (e. g. neural networks), kriging methods and copula-based methods  (Coulibaly and Evora, 2007; Teegavarapu, 2012; Bárdossy and Pegram, 2014). Local meteorological data can alternatively be reconstructed from past climate variations.
The recent release of two major atmospheric reanalyses covering the entire 20th century (from 1871 for the NOAA 20CR,  Compo et al. , 2011 and from 1900  for the ECMWF ERA-20C,  Poli et al. , 2013)  provides a great opportunity for such a reconstruction. Unfortunately, their spatial and temporal resolutions rarely fit the resolution needs (typically sub-daily time step, up to 1000 km 2 ) of hydrological applications. In such a case, the required local meteorological data can be obtained through downscaling. This study compares three different statistical approaches for the reconstruction of high-resolution precipitation and temperature data. Reconstructions are respectively obtained from observations available at a neighbouring station, from large-scale (mesoscale) atmospheric variables extracted from the 20CR reanalysis and from a combination of both.
For a qualitative assessment of the reconstructed series, five monthly time series from the HISTALP project database (monthly series,  Auer et al. , 2007)  were also used. They also go back to the 1870s. For air temperature, the selected stations are located around the southeastern part of the Alps, at University of Genoa, Milan-Brera, Montpellier, Nice airport and Nîmes airport. For precipitation, they are closer to the Durance watershed, located in the cities of Aix-en-Provence, Nice (Cap Ferrat), Orange, Saint-Paul-les-Durance and Toulon. A. Kuentz et al. :  The ANATEM model 2721.
Mixing formulation: the ANATEM model Both local and large-scale predictors are available for the 1870-2010 period. The local model (LM) and the analogue model (ANA) can therefore be used to produce two different reconstructions of precipitation or air temperature for this period, one based on local observed data (another station with available data), the other from large-scale atmospheric information (mesoscale variables). The originality and strength of the ANATEM model introduced here lies in an approach that combines the two previous models. In this way, it can take advantage of both local and large-scale information and produce an original representation of uncertainties, conditioned by atmospheric circulation patterns. The principle of ANATEM is the following: for any target day, the analogue model allows the identification of n analogue days in terms of atmospheric circulation (see Sect.
Evaluation process The data presented in Sect. 2 can be used to reconstruct the daily air temperature and precipitation series for the 22 selected watersheds over the period 1883-2010. The reconstruction is deterministic for the LM model. For ANATEM and ANA, 50 reconstructed time series are generated. In the present section, the three reconstruction models are evaluated based on their reconstruction skill for the 1948-2010 observation period. The evaluation is based on three criteria. The ratio β between the mean estimated value and mean observed value of the variable evaluates the bias of the reconstruction. The ratio between the standard deviations of the reconstructed and observed time series (α) evaluates the ability of the reconstruction to reproduce the observed variability of the variable.
Conclusions Reconstructing local-scale meteorological variables over long periods is a challenging but necessary task in order to obtain a better understanding of the low-frequency variability of regional climate and climate-driven variables. Three models are compared in the present work, using different types of data for the reconstruction: the regression-based local model (LM) uses local observations of the variable from neighbouring stations as a predictor; the analogue model (ANA), a so-called downscaling model, uses large-scale information concerning atmospheric circulation and the ANATEM model  The multi-criteria and multi-scale performance assessment highlights that the best reconstructions are obtained when local information is used. The ANA model is clearly less efficient than the two other models, particularly concerning low-frequency (annual) air temperature variability or highfrequency (daily) precipitation variability.
Acknowledgements. The authors are grateful to  Meteo-France  and the  HISTALP  project  (Historical Instrumental Climatological Surface Time Series Of The Greater Alpine Region ) for providing long-term climatic data, as well as to NOAA for the Twentieth Century Reanalysis Project data set. The authors also wish to thank warmly  Vazken Andréassian ,  Anne-Catherine Favre  and  Cristian Perret  for their support and their helpful comments during different phases of this work. Last but not least, the authors are very grateful to  Bettina Schaefli  and the three anonymous reviewers for their positive, encouraging and demanding comments that significantly contributed to the improvement of this paper. Edited by:  B. Schaefli.