dataesr/scientific-paragraphs-categorization huggingface dataset:
Information about psychological events was gathered through patient self-evaluation of health outcome and through NC phone calls. In addition, during the telephone interview the NC questioned patients regarding their social and professional status or any other changes (e. g. , return to work, disability pensioning, personal resources). The resulting file, compiled by the NC, included physical, psychological, social and professional sections. The NC was in charge of forwarding this data to the oncologist, who summarized all the information and if necessary would call the patient or their GP for clarification, or as a last degree would call the patient in for a visit at the hospital. In each case, the oncologist then forwarded his conclusion to the GP by post. In some cases, symptom detection required referrals to additional clinical and psychosocial providers.
Data collection and analysis An anonymized database was used to collect all information related to the AMA-AC program. This database was secured and managed by an external service device in accordance with ad hoc regulatory committees. In order to determine the strength of the relationship between each of the variables (PTSD, HAD-Depression, HAD-Anxiety, SF36-MCS, and SF36-PCS scores) measured at M0, M3, M6, M9 and M12, we generated a Pearson correlation matrix. A correlation coefficient of 1. 0 indicated a positive correlation and a value of -1. 0 indicated a negative correlation. According to the guidelines by Cohen et al. [37] , a correlation coefficient between 0. 10 and 0. 29 corresponds to a small strength of correlation, 0. 30 to 0. 40 denotes a medium correlation and 0. 50 to 1. 0 signifies a high correlation between the variables.
We implemented a multivariate logistic regression model adjusted for variables statistically associated with the outcome in bivariate analyses with a risk alpha of 20 %, except for the first-line chemotherapy regimen which was forced in the model. Interactions between the covariates were verified for each model. Assumptions and model fit were measured using the Hosmer and Lemshow test. A two-sided p-value <0. 05 was considered as statistically significant for the multivariate model. Statistical analyses were performed using SAS®software version 9. 4 (SAS institute, Cary, NC).
Relapse Within the first year of survivorship nine patients relapsed: (n = 3 before M6, n = 6 between M6 and M12). In all cases the relapse was suspected by the patients themselves and was confirmed by clinical symptoms and examination by GPs. Consequently, patients were reexamined by an oncologist on demand to confirm the relapse by biopsy and histological analysis at the hospital. The CT scans performed at M6 (n = 108 examinations) played no part in detecting relapses (data not shown).
PCR amplification Heterologous oligonucleotide primers to amplify the Camelus dromedarius MSTN locus were designed as follows. First, BLAST analysis using the Vicugna pacos MSTN sequence, available at the Ensemble database (www. ensembl. org) was carried out, which returned, for the Camelus ferus species, a contig (GenBank Accession No AGVR01040332. 1) and a scaffold (GenBank Accession No NW_006211358. 1), both including the whole-gene MSTN sequence. Alignment of the above sequences was carried out using ClustalW (www. genome. jp/tools/clustalw/) in order to in silico detect possible variant sites. Primers were then designed on the Camelus ferus consensus sequence using Primer3 (www. primer3. ut. ee/, version 4. 0. 0). Primers and PCR conditions for each target region are provided below. Exon 1 -The sequence of the forward primer was 5'CCTTGGCATTACTCAAAAGCAA3'; the sequence of the reverse primer was 5'CTTACATACACACGAACAGCC3'.
DNA elution was carried out adding 30 μl of water to the membrane and centrifuging the column 1 min at 10000g. The concentration of the purified PCR products was evaluated with the Nanodrop 2000C spectrophotometer (Thermo Scientific). Sanger sequencing of the purified fragments was performed by the Eurofins MWG Operon service. Detection of sequence polymorphisms and differentially fixed nucleotide sites was carried out through alignment of Camelus dromedarius sequences against the Camelus ferus contig sequence (GenBank Accession No AGVR01040332. 1). Both sequence alignment and pair-wise sequence identity percentages among Camelus dromedarius and other species belonging to the Cetartiodactyla clade were obtained, for the MSTN locus, using ClustalW2  (Larkin et al. , 2007). The software Splitstree  (Huson and Bryant, 2006)  was used to construct a NJ tree from the pair-wise sequence identity matrix.
Information on bank competition is extracted from the Global Financial Development Database. Competition in the banking industry is assessed by the degree of market concentration (share of assets held by the three largest banks) and by three non-structural measures  (Boone indicator, Lerner index and Panzar-Rosse H-statistic). The results show that financing constraints are alleviated in countries where banking markets are more competitive, irrespective of whether competition is measured by the Boone indicator or the Panzar-Rosse H-statistic, thus supporting the market power hypothesis. Employing the Lerner index to proxy market power suggests a similar conclusion, while the results are not statistically significant. The degree of concentration has no impact on credit availability. The empirical set-up allows us not only to test the information hypothesis against the market power hypothesis but also to investigate the channels by which competition affects credit availability.
Taking advantage of the broad scope of countries in our sample, we investigate a large set of country characteristics, including bank competition. The findings shed light on the important role played by country characteristics in explaining borrower discouragement and bank decisions. The rest of the paper is organized as follows. Section 2 presents the data and describes the construction of variables and the empirical methodology. The baseline results are discussed in Section 3. Section 4 presents some tests in order to understand how competition affects credit availability and the final section concludes.
Data The database used in this paper combines firm-and country-level data from various sources. The firm-level data come from the World Bank Enterprises Surveys (WBES). The dataset is supplemented with country-level data from diverse sources such as the World Development Indicators, the Global Financial Development Database, and the Doing Business database. Some filtering rules are applied. 2 The final sample includes 28,642 firms from 69 low-income and middle-income countries spanning four continents (Africa, Latin America, Europe and Asia). Two fifths of the firms were surveyed in 2006 and one third in 2009. Others were surveyed in  2007, 2008 and 2011. The sample considered has the advantage of grouping together economies with heterogenous characteristics.
Measuring bank competition The purpose of this essay is to investigate the implications of bank competition on credit availability. The choice of the appropriate proxy for bank competition is therefore crucial. All measures of bank competition are based on bank-level information. The Bankscope database, which covers a large range of banks in the world, is employed to extract balance sheet and income statement information on banks. Computations of indicators of competition are done by World Bank's staff and reported in the Global Financial Development Database. Despite some limitations with the structural approach, the share of assets held by the three largest banks is used as a proxy of the impact of market concentration. We complement the information on market structure that imperfectly captures bank conduct by using three indicators of competition based on the New Empirical Industrial Organization approach: the Boone indicator, the Lerner index and the Panzar-Rosse H-statistic.
Different degrees of competition may merely capture the differences in ownership structure of the banking industry. To test this hypothesis, we include as controls the share of banking assets owned by foreigners. We use the dataset built by  Claessens and Van Horen (2013a)  to proxy the share of banking assets held by foreigners. This variable is only available for 56 out of 69 countries of our sample. The results, reported in the Appendix, are robust insofar as coefficients associated to competition remain stable in terms of size and significance. The presence of foreign banks tends to play no role on credit availability. 6 he positive link between competition and access to credit can be explained by financial regulation. Less government interference in the banking system, especially less restrictions on bank activities, may alleviate credit constraints.
For instance, in less regulated markets, banks can offer new products more suitable for SME financing. Insofar as competition and financial regulations are correlated, the positive coefficient associated to competition can merely reflect the relationship between financial restrictions and credit availability. To control for this possibility, we include a measure of activity restrictions based on the World Bank Survey of Bank Regulation and Supervision database  (Barth et al. , 2005). Including the measure of financial freedom built by the Heritage Foundation instead of the variable of activity restrictions does not alter the conclusions. Results highlight that the coefficients associated to competition remain positive and significant. Another potential drawback concerns the measures of competition used.
How does competition affect credit availability? The baseline set-up shows that competition alleviates credit constraints in developing countries, which is in line with the market power hypothesis. In this section, we try to go one step further by investigating by which channels competition plays a role in facilitating access to finance. Recent works on developing countries have shown that the low use of formal credit is mainly explained by borrower discouragement rather than by banks' denial decisions  (Bigsten et al. , 2003; Brown et al. , 2011). Our data confirm this feature: 42% of firms with a need for external funds refused to apply, although only 15% of requests were turned down. An advantage of the database is that it provides the possibility of investigating to what extent the low incidence of bank credit in developing countries is the result of low credit demand and/or supply-side constraints and the factors influencing both.
Conclusion Whether competition helps or hinders small firms' access to finance is in itself a much debated question in the economic literature and in policy circles. Despite the policy relevance of this issue, empirical evidence on the effect of interbank competition on access to finance in developing countries remains inconclusive. Using surveys on firms in developing economies, this article sheds new light on this debate. The database covers a large number of developing countries. Furthermore, this paper employs several non-structural measures of competition  (Boone indicator, Lerner index, and H-statistic)  in addition to market structure. By confronting the results using several measures, this paper gives a more complete picture of the role of competition on credit availability. In addition we also examine the potential channels through which bank competition may affect the use of credit by firms.
Acknowledgements We thank  Pierrick Baraton ,  Vianney Dequiedt ,  Hans Degryse ,  Samuel Guérineau ,  Olena Havrylchyk ,  Lars Norden ,  Alexandre Sauquet ,  Laurent Weill  and two anonymous referees as well as seminar participants at the 3rd CInSt Banking Workshop (Moscow), the  ADRES Doctoral Conference (Paris)  and the  LARGE/BOFIT Workshop  on banking systems in emerging countries ( Strasbourg, France ) for their useful comments. This paper benefited from the financial support of the  FERDI (Fondation pour les Études et Recherches sur le Développement International ) and from the  Agence Nationale de la Recherche  of the  French government through the program ''Investissements d'avenir' '  ANR-10-LABX-14-01  The usual disclaimers apply.
The overall weight loss was r2 wt%. X-ray powder diffraction patterns (XRD) were recorded to check the phase purity and determine the lattice parameters for all samples. Data were collected on a Bruker D8 Advance instrument with monochromated CuKα 1 radiation. The X-ray diffraction patterns were analysed using the JANA2006 software package  [30]. The microstructure and chemical composition of a subset of the samples was analysed using a Quanta 650 FEG ESEM equipped with an Oxford Instruments X-max 150 N detector for elemental mapping. The working distance, beam spot size and collecting time were 10 mm, 4. 5 and 4-10 frames, respectively. Prior to analysis the samples were polished using fine Al 2 O 3 sand paper down to 0. 5 μm roughness. Rectangular bars were cut using a diamond saw and used for transport measurements.
3  and Table  1. The MnSi 1. 75 sample has the most surface structure while the other samples appear better sintered, and had a better finish after polishing. Elemental mapping for MnSi 1. 75 (not shown) showed a homogenous distribution of Mn and Si, and also revealed small amounts of elemental Si impurities not evident from the X-ray powder diffraction. An average of three large area elemental maps (212 Â 146 μm 2 ) taken on different phase pure regions of the ingot yielded a chemical composition MnSi 1. 67  (3)  , where the standard deviation is the variation between the three datasets. Similar analysis on homogenous areas of the Fe and Al substituted samples yielded the following chemical compositions: Mn 0. 90 Fe 0. 098(7) Si 1. 60(1) Al 0. 063  (7)  , Mn 0. 80 Fe 0. 18(1) Si 1. 48(2) Al 0.
Acknowledgments We acknowledge the  EPSRC  ( EP/J000884/1  and  EP/K036408/1 ),  Leverhulme Trust  ( RPG-2012-576 ) and  Royal Society  for support, and  Jim Buckman ,  Institute of Petroleum Engineering, Heriot-Watt University , for his help during collection of the SEM-EDX data. Appendix  A. Supporting  information.
The analysis showed that the load application in the mechanical cases caused pronounced strain localization within the deforming sheet, whereas significantly more uniform strain distributions are observed for electromagnetic loading. The delayed failure in electromagnetic forming is thus mainly attributed to the increased strain field uniformity, whereas the increase in ductility due to strain rate and temperature effects is seen as secondary. The first author is grateful for the partial financial support through the French National Research Agency (Grant ANR-11-BS09-0008, LOTERIE).
Results mDA Neuron Gene Expression in the SNpc of En1+/-Mice RNA sequencing (RNA-seq) analysis on microdissected SNpc was performed in WT and En1+/mice. Comparable reads were obtained in WT and En1+/mice with 989 differentially expressed genes (p < 0. 05) (Figure  S1A ; Table  S1 ). Analysis was performed on 6-week-old animals when all neurons are still present in the in En1+/mice  (Sonnier et al. , 2007). Pathway Studio Ontology (gene set enrichment analysis, Pathway Studio software) indicates that the three most represented and significant groups are DNA repair (p = 0. 002), chromatin remodeling (p = 0. 004), and transcription factors (p = 0. 007); Cell Process Pathways analysis also revealed differential apoptosis regulation genes (p = 0. 01) (Figure  S1B ). Genes within these ontologies and pathways were ranked by increasing p values; Figure  1A  highlights those with significant differences in read numbers.
Acknowledgments We thank  Professor Edith Heard  and  Dr. Ariel Di Nardo  for reading of the manuscript and helpful discussions. We also acknowledge  Dr. Alain Joliot  for his generous gift of EnHD-VP64 and the CIRB imaging and animal facilities for their help. RNA-seq was done with the participation of  Fanny Coulpier  and  Laurent Jourdren  at the genomic platform of the  Ecole normale supérieure Institute of Biology , which is part of the  France Genomique network. The study was supported by  Région Ile de France ,  Fondation Bettencourt Schueller , GRL program  2009-00424 , and  European Research Council Advanced  Grant  HOMEOSIGN 339379.
Data and specimen collection A standardized questionnaire was used to collect socio-demographic characteristics (sex, age), clinical presentation (dehydration, fever, vomiting), medical care (rehydration treatment, hospitalization, inclusion in a nutritional program) and outcomes. At the health centers, stool specimens were collected using sterile plastic containers and systematically tested for rotavirus using Vikia Rota-Adeno rapid test (bioMérieux, Marcy l'Etoile, France)  [32]. Stool samples were then transferred onto Cary-Blair medium (Oxoid, Basingstoke, UK) using sterile swab and stored at +2-8°C until transported in cold containers to the Epicentre laboratory in Maradi city every other day. At the Maradi regional hospital, we maintained the fresh stools at +2-8°C before transfer to the laboratory within 1 hour after sampling.
Data analysis Double data entry was done using EpiData 3. 1 (EpiData, Odense, Denmark), and data analysis using Stata 12. 1 (College Station, Texas, USA). Severe diarrhea was defined using the 20-point Vesikari scale with a score 11  [49]. We performed a weighted analysis by month and district of inclusion to take into account the selection process when estimating the proportion and seasonality of the bacterial pathogens. For Salmonella and Shigella spp. , only the isolates that were confirmed at FNRC-ESS are considered in this analysis.
This suggests that malnutrition could be a risk factor for Salmonella infection in general, and not only for invasive non-typhoidal Salmonella infection  [17]. The Salmonella peak around November 2010 corresponds to end of the annual malnutrition peak in Niger, and the number of children included in a nutritional program during this period in our study was decreasing (data not shown), suggesting that the 2 phenomena are temporally disconnected. This period also generally corresponds to the malaria peak in Niger, but we could not study possible associations between malaria and Salmonella isolation in diarrhea patients here since diagnosis of malaria was not recorded. In addition, although there was a slight increase in the number of Salmonella detected around November 2011, there was no clear peak and the seasonality of infection remains to be confirmed.
Acknowledgments We would like to thank the children and their families for their participation in the study. We also thank the staff from the health centers and hospitals where the study was implemented in health districts of Madarounfa, Aguié,  Maradi  and  Guidan Roumdji ,  Niger. We are also thankful to  Abdoulaye Inoussa  and  Chou-ayb Yahaya  from the  Epicentre laboratory in Niger. We thank  Isabelle Carle ,  Monique Lejay-Collin ,  Corinne Ruckly ,  Véronique Guibert ,  Marie Accou-Demartin ,  Laëtitia Fabre ,  Martine Garnier ,  Brigitte Chavinier-Jové ,  Chrystelle Roux ,  Erwan Trochu  and  Lucile Sontag  for their excellent technical assistance.
PCR amplification of bacterial 16S rRNA gene was done using the universal primers 28F (5′ GAG TTT GAT CNT GGC TCA G 3′ ) and 519R (5′ GTN TTA CNG CGG CKG CTG 3′ ) covering the V1-V3 variable regions. The amplicons were pyrosequenced using Roche 454 Titanium chemistry. Elongation was performed from the forward primer. Raw data are available on NCBI under BioProject number PRJNA275539. Sequence processing pipeline. The post-sequencing processing were completed using the open-source program mothur v. 1. 33. 0 software (http://www. mothur. org)  29 following the pipeline described by Comeau et al. 30. Raw 454 reads were first processed to remove low-quality reads, such as (i) the presence of one or more uncertain bases (N), (ii) sequences shorter than 150 nt (nucleotides), (iii) unusually long reads that extended more than 100 nt over the amplicon size, (iv) reads that had long homopolymers sequences (more than 8), and (v) reads that had incorrect forward primer sequences.
The final aligned reads were clustered into OTUs at ≥ 95% similarity level using the furthest neighboring cluster in mothur, thus allowing for the identification of the bacterial genus  33. The measure of diversity and community similarity analysis were also obtained based on mothur analysis. Sequences were taxonomically identified using the Ribosomal Database Project (RDP) classifier version 2. 6  34  trained on 16S rRNA training set 9.
Diversity analysis. Rarefaction curves were generated within the software mothur to evaluate the sufficiency of the sampling effort using equalized data (i. e. random selection of a determined number of sequences from each library according to the size of the smallest library to avoid bias due to variable sampling efforts). The abundance-based coverage estimator (Ace) and the Shannon index, also generated with mothur, were used to estimate the diversity among the bacterial populations using the same data. To proceed with the diversity analyzes among the bacterial communities, a maximum likelihood phylogenetic tree was constructed with MEGA 5. 2  35  using a representative sequence of each previously generated OTU and an Archaea sequence as the outgroup. A sample ID mapping file was also built to indicate to which sample each OTU belongs as well as the number of times each sequence was observed (sequence abundance).
9  38. The representative sequence of each of the 90 OTUs found in abundance (≥ 1% of sample abundance), as well as their closely related sequences identified by BLASTN against NCBI database were aligned together with the MUSCLE  39  algorithm implemented in MEGA. A maximum likelihood phylogenetic tree was built with FastTree 2. 1. 7  40 using the GTR model with 1,000 resampling to estimate node support values. Additionally, a Venn diagram was also generated with mothur to observe the partition of the OTUs across the three environments sampled. Finally, a principal coordinate analysis (PCoA) was also achieved with Fast UniFrac to assess whether different bacterial communities were distributed along the axes of variation using the same previous reference files.
Each node of the dendrogram was recovered with more than 99. 9% accuracy, showing the validity of the similarity clustering. To further investigate the taxonomic identification of the most abundant OTUs across the three ecological niches, a maximum likelihood phylogenetic tree was constructed using FastTree based on the representative 16S sequences of each OTU and their closely related sequences identified by BLASTN (Fig. 3 ). Phylogenetic analysis revealed taxa related to genera Pseudomonas, Pantoea, Erwinia, Rahnella, Serratia and Ewingella on the surface of the insect's cuticle. Whereas, in the interior of the insect body, most OTUs (56) formed a well-delineated and monophyletic cluster representing potentially novel bacterial genera belonging to the family Enterobacteriaceae. Among the closest relatives of this cluster Closely related sequences were identified using BLASTN against NCBI database.
simplex adult ectomicrobiome. Abundant bacteria found associated to the endomicrobiome were almost all Gammaproteobacteria, with also Firmicutes. In the phylogenetic tree (Fig. 3 ), the majority of endomicrobial OTUs are divergent from known bacterial species present in Genbank. These well-delineated OTUs might belong to novel bacterial genera or/and species. Enterobacteriaceae bacteria have been already found in beetle's gut, where they were implicated in nitrogen-fixing processes and cellulolytic activities  [7] [8] [9] 43. Pse doxanthomonas, Gilvibacter, Terriglobus and Raoultella are also associated with the gut of several Dendroctonus species  8, 10. It is thus possible that some of these apparently new identified bacteria are involved in important nutritional roles. No shared bacterial genus was observed between the ectomicrobiome and endomicrobiome of D.