dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the families who kindly agreed to be part of this study. This study makes use of data generated by the  DECIPHER Consortium. A full list of centres who contributed to the generation of the data is available from http:// decipher. sanger. ac. uk and via email from decipher@sanger. ac. uk. The research team acknowledges the support of the  National Institute for Health Research , through the  Comprehensive Clinical Research Network. We thank  Dr Samantha Knight ,  Oxford University , for performing the initial microarrays in case 9. Contributors AH: substantial contributions to the conception or design of the work; or the acquisition, analysis or interpretation of data for the work ( performed and analysed xenopus experiments). AP: substantial contributions to the conception or design of the work; or the acquisition, analysis or interpretation of data for the work (SOX11 sequencing and validation).
Using a new and unique dataset dealing with French small and medium-sized enterprise (SME) financing that provides detailed information about 1 116 firm-bank relationships, we test how the number of banks with which a firm works and the organizational structure of its main bank influence its risk-taking behavior. We find evidence that SMEs engaged with a decentralized main bank (a local or mutual one) invest in less risky projects, especially when they work with fewer than three banks (one or two). We also find evidence that single-bank SMEs engaged with a centralized bank (a large or foreign one) take significantly more risks than the others.
The data collected on banking relationships contains information about the names of firm main and secondary banks. We counted the number of lending banks for each firm. We defined a dummy variable called the "decentralized main bank", which is equal to 1 if the main bank is a local or mutual bank and 0 otherwise. For our control variables, we used corporate financial characteristics, geographical diversification indicators and corporate governance variables. We also considered year and industry fixed effects. The corporate financial characteristics were the same as those of  Morck et al. (1988) ,  Adams et al. (2005)  and  Cheng (2008). Firm size is a natural logarithm of total assets; age is a natural logarithm of firm age; leverage is total debt divided by total assets and growth opportunities are a natural logarithm of capital expenditures over total sales.
Data The sample used in this research is composed of SMEs for which it was possible to identify the main bank from the DIANE database 1 and secondary banks from the Kompass Europe 2 database in 2010. This year can be considered as a normal and representative period for bank relationships. It is two years after the bank crisis and there is no major institutional change for SME finance during this lapse of time 3. From DIANE, we identified 6 908 firm-main bank relationships. We obtained full information about banking relationships from Kompass Europe for 1 145 firms. We then excluded firms that do not meet the European Commission criteria for SMEs. Hence, the final sample consisted of data for 1 116 firms observed between 2004 and 2012. Half of these SMEs operate in the manufacturing sector, 23% in retailing, 9% in building, 5% in transport and the remaining ones in various other service sectors.
On average, the 1 DIANE is a financial database covering all firms registered in France, in particular SMEs. DIANE is managed by the Bureau van Dijk. This database is available on the following link: https://diane. bvdinfo. com/. 2 Kompass Europe is a commercial directory of firms which mainly includes product and limited banking information. This database is available on the following link: http://fr. kompass. com/. 3 Basel III capital requirements for banks have been voted in December 2010 but the implementation is scheduled for March 2019. 4(4), 166-174, 2015 sampled SMEs have 39 employees, generate a turnover of 10 million euros and display total assets of 15 million euros.
INTRODUCTION In many species of vertebrates, invertebrates, plants, and yeast  (Djebali et al. , 2012; Kapranov et al. , 2007; Katayama et al. , 2005; Li et al. , 2008; Pelechano et al. , 2013; Wang et al. , 2005; Zhang et al. , 2006) , genomic loci produce oppositely oriented transcripts that overlap. Analyses of sense and antisense transcripts produced by convergent transcription have focused on coding/non-coding RNA pairs that are prevalent in databases. This has highlighted the importance of non-coding RNAs (ncRNAs) in the regulation of many biological processes, including siRNA-induced gene silencing, cis-RNA-mediated chromatin modifications, transcriptional interference, or RNA editing  (Faghihi and Wahlestedt, 2009; Jacquier, 2009; Ponting et al. , 2009; Pelechano and Steinmetz, 2013).
Northern Blot Analysis RNA extracts and northern blots were performed as described previously  (Sinturel et al. , 2012). Blots were exposed to PhosphorImager screens, scanned using a Storm scanner (Molecular Dynamics), and quantified with ImageJ software. was performed on a Genome Analyzer IIx (Illumina). After removal of adaptor sequences using the Cutadapt tool  (Martin, 2011) , reads were uniquely mapped to the reference genome (S. cerevisiae S288C, 12/12/2011, retrieved from SGD; http://www. yeastgenome. org/) using version 0. 12. 7 of Bowtie  (Langmead et al. , 2009)  with a tolerance of three mismatches. All subsequent bioinformatic analyses were performed using uniquely mapped 19-to 23-nt small RNA reads. Small RNA Analysis 4,013 mRNAs (after having excluded mRNAs with annotated antisense ncRNAs and dubious ORFs) were defined (Table  S1 ).
1,456 convergent and 3 0 -overlapping mRNAs and 2,557 mRNAs having no antisense (called solo mRNAs) were analyzed. Among convergent mRNAs, we defined 1,142 mRNAs overlapping in their 3 0 UTR only (UTR only) and 315 additionally overlapping within their ORF (ORF and UTR). Only R23-nt overlaps between two convergent mRNAs were considered. For convergent mRNAs, 19-to 23-nt small RNA reads were counted within the region of overlap, on the considered strand. For solo mRNAs, reads were counted within the fulllength transcripts. For each gene class, total read counts were expressed in rpkm (reads per kilobase of transcript per million mapped reads). Annotations of UTRs were retrieved from the reference genome R63 and imported into our reference genome using Blastn alignments. For MA plot analysis, 1,923 solo mRNAs and 1,087 convergent mRNAs (red dots) are represented after filtering out the 1,003 mRNAs (25% of the 4,013 mRNA sets defined in Table  S1 ), showing the lowest density average in the four strains (WT +Dcr1+Ago1 + WT + xrn1D + Dcr1+Ago1 + xrn1D; Figure  S4 ; see Table  S2  for selected mRNAs).
ACKNOWLEDGMENTS This work has been supported by the  ANR   REGULncRNA and the ''nitiative d'Excellence program  from the  French government  (grant  DYNAMO ,  ANR-11-LABX-0011-01 ) to L. B. F. S. was a recipient of fellowship from the  Ministere pour la Recherche et la Technologie (MNRT) , from la  Fondation pour la Recherche Medicale (FRM) , and from la  Fondation Edmond de Rothschild. A. N. is supported by a doctoral grant from  ANR-11-LABX-0011-01. A. M. is supported by the  ANR REGULncRNA  and  ERC  EpincRNA starting grant. High-throughput sequencing was performed by the  Imagif NGS platform in Gif sur Yvette (France). We thank  Francois Lacroute  for providing Rpl1 antibody;  Roy Parker  and  David Bartel  for providing strains;  Mickae ¨l Cohen and Naı ¨ma Belgareh  for providing Pgk1 antibody, GFP and c-myc plasmids, and access to LI-COR instrument; and  Josette Banroques ,  Kyle Tanner , and  Agnes Le Seaux  for technical assistance and helpful discussions.
, 2007; Suttle, 2007). Extracellular DNA was digested with DNAse RQ1 (Promega) at 37 • C for 1 h. Viral DNA was extracted with the Nucleospin Extract II kit (Macherey-Nagel). Whole genome amplification (WGA) reactions were run in triplicate for each sample using Genomi-Phi following the instructions of the manufacturer (GE Healthcare) and subsequently pooled. Library construction and pyrosequencing was performed at the "Functional and Environmental Genomics" platform (OSUR, Rennes, France). After sequence quality and size trimming we obtained 481,402, and 618,487 reads from Fen and Bog, respectively, with an average length of 415 bp. Microviridae genomes reported in this publication have been deposited in the Sequence Read Archive under the study accession number KM589498-KM589516.
, 2009; Hyatt et al. , 2010; Roux et al. , 2012a,b). Semi-automated annotation was performed to identify genes by sequence similarity and coding probability using BLASTP  (Altschul et al. , 1997; Roux et al. , 2012b)  against the RefSeq NR protein database (GenBank), SWISSPROT (version 57. 11), and COG databases (COG + KOG, seven eukaryotic genomes). Manual annotation was completed within the above-mentioned platform. Gene order comparison was performed using the platform tool GENOMAPPER  (Desnues et al. , 2008; Zivanovic et al. , 2014)  combined with multiple alignment and phylogenetic analysis. To allow the interactive visualization of genomic fragment comparisons, we used Artemis Comparison Tool ACTv. 6  (Carver et al. , 2012; Rosario et al. , 2012). The presence of Microviridae in peat metagenomes that were constructed from corresponding peat samples without virus enrichment (Quaiser et al.
Relative Abundance of Microviridae We compiled available full-length major capsid protein sequences from the complete Microviridae genomes to a total of 88 sequences and attributed the taxonomic affiliation according to the phylogenetic analysis in Figure  1. BLASTX analysis against the major capsid protein sequences was performed for the 12 Sphagnum-peat viromes and 69 viromes from public databases. To assure the reliability of matches a strict cut-off value of 1e -10 was applied. The number of matches was normalized to the total number of reads in the different viromes.
Synteny analysis confirmed that the two prophages shared a common ancestor showing the same gene order (VP1-ORF1-ORF2-ORF3-VP2-VP4 for Parabacteroides distasonis and VP1-ORF1-VP2-VP4 for Parabacteroides merdae; Figure  3 ). The gene coding for an uncharacterized protein (ORF1) located downstream of the VP1 coding gene was specific to the two prophages and did not match with genes from other Microviridae genomes or from the NCBI non-redundant database. This strengthens the hypothesis that these prophages represent a distinct subfamily of Microviridae as suggested by the phylogenetic analysis of the major capsid protein sequences (Figure  1 ).
In one clade the gene order consists of VP1, VP2, VP3, VP4, and VP5 (i. e. , Fen672_31) while in the other clade the organization was VP1, VP2, VP4, VP5, and VP3 (Bog1183_53, Human_feces_E_009, and Human_feces_31_045). However, this distinction was not supported by phylogenetic analysis of the major capsid protein or the replication protein. The two genomes, representative for the Group D (Dragonfly associated and Fen7940_21), contain five genes. Compared to other groups, they possess two additional genes organized in the same order between VP1 and VP2. The gene situated downstream of VP1 was conserved with up to 50% similarity but only over 41% of their amino acid sequences (Figure  3 , Group D, pink). Since no other matches to this gene were found, neither in the NCBI non-redundant protein database nor in other peat Microviridae genomes, this gene was a unique feature for this clade confirming the distinction from other Microviridae clades.
The second protein encoded upstream of VP2 appeared to be unique to each genome as they exhibited no similarity between each other or with proteins found in the NCBI nr database. The three Stokavirinae genomes showed a similar organization. The protein encoded downstream of the major capsid protein gene was homologous within the Stokavirinae (Figure  3 , light green) but without similarity to Parabacteroidetes prophage proteins or to the NCBI NR database. Stokavirinae possess a characteristic additional ORF downstream of VP4 that is absent in other subfamilies confirming that they present a distinct clade. Interestingly, the two genomes Bog1249_12 and Fen51_42 showed 99% nucleic acid identity while the third Fen4704_41 was more distant. Both were assembled independently not only from different viromes but also from bog and fen samples recovered at different dates.
Quantitative estimations of viruses based on the detection of particular signature sequences is currently the best representative proxy, but should be considered with a grain of salt as WGA leads to overrepresentation of small ssDNA viruses  (Kim and Bae, 2011) , Nevertheless, to date WGA is the best way to recover sufficient viral genomic DNA for sequencing. Accordingly, all published viromes and the viromes included in this analysis were generated using this technique. Based on the phylogenetic analyses a database containing 88 major capsid protein sequences representing the different subfamilies of Microviridae was constructed. Each virome was searched with BLASTX against the major capsid protein sequence database and best matches were counted using strict count conditions (e-value 10 -10 ).
Acknowledgments We thank members of the 'Environmental and  Functional Genomics Platform' (Biogenouest Genomics)  for sequencing and the molecular ecology facility ( UMR 6553 ECOBIO ) for support. We thank  Mart Krupovic  for assistance and advice on capsid modeling. We thank  Daniel Revillini  for manuscript editing. This work was supported by funding from the  INSU-CNRS   EC2CO program  to  A-JF. (" VIRTOU ") and grants from the  University of Rennes/CNRS  and from the  "Observatoire des Sciences de l'Univers de Rennes" (OSUR)  to AQ.
D-HÁ (i) x þ 1; y; z; (ii) Àx þ 1; Ày þ 2; Àz þ 1; (iii) Àx þ 1; Ày þ 1; Àz þ 1; (iv) Àx þ 1; Ày þ 1; Àz þ 2; (v) x þ 1; y þ 1; z; (vi) Àx; Ày þ 1; Àz þ 1; (vii) x; y; z À 1; (viii) x À 1; y; z. Data collection: APEX2  (Bruker, 2013) ; cell refinement: SAINT  (Bruker, 2013) ; data reduction: SAINT; program(s) used to solve structure: olex2. solve (Bourhis et al. , 2015) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: OLEX2  (Dolomanov et al. , 2009) ; software used to prepare material for publication: OLEX2.
2013b). Here, we use data collected on over 6000 nests of Montagu's harrier monitored throughout France over six years, with the aim to identify the most effective protection measures and highlight areas where an increase in volunteers' effort could result in highest benefits to the species across the country. More specifically, we compare breeding performance at nests under different protection measures in relation to unprotected nests, accounting for nesting habitat types representing different pressures (e. g. harvest damage). Among the different protection measures, we pay particular attention to those protecting from harvesting alone, and those that also allow protecting from predation. Finally, we derive a nationwide map showing areas of mismatch between expected gain from nest protection and availability of volunteers' workforce.
In these three models, we used data from unprotected nests with known clutch size, and land use was included as a categorical predictor (with three classes). Clutch size or brood reduction may vary between land uses because of either differences in food abundance, quality variation among harriers breeding in different land uses, or both. A difference in productivity between land uses may be caused by varying predation levels, food availability or harvest destruction (in nests in annual and pluriannual land uses). We hypothesized that if clutch size and brood reduction, but not fledgling production, were similar between the three land uses, this would indicate that differences between habitats were mainly due to harvesting or predation rather than to food availability or individual quality.
The expected gain from protection will ultimately depend on harrier density and the difference in productivity between protected and unprotected nests: if the difference in productivity and/or density is low, expected gain of increasing protection there will also be low. Thus, we first calculated the difference between average productivity of protected nests (considering only nests protected with a fence, e. g. fenced buffer, fenced relocation or fence alone on perennial land) minus that of all unprotected nests at a resolution of 100-km 2 squares (see Fig. S1a ,b, Supporting information). Because not all 100-km 2 squares across France had available nest data, we interpolated the values from the squares with available data across the whole country using ordinary kriging with an exponential shape derived from an empirical variogram investigation (see Fig.
S2 , Supporting information) from observed data. Next, we multiplied the interpolated expected gain (average extra chicks produced per nest following protection) by the density of breeding pairs per square to derive an overall expected gain of chicks per square assuming protection of all nests. Data on breeding harrier density were derived from predictive density models that accounted for land-use and climatic conditions using survey data from the years 2000-2002 (see  Thiollay & Bretagnolle 2004  for methodology of the French raptor breeding survey, and Le Rest, Pinaud & Bretagnolle 2013 for details on the density modelling methodology). We derived a measure of volunteers' workforce (i. e. available effort), expressed as average number of man-days of fieldwork per year for each region of France.
Acknowledgements We thank the thousands of volunteers and farmers who have been helping and collaborating on this study, but more particularly  J. -L. Bourrioux ,  T. Printemps  and  B. van Hecke  for data and network management of the  French Harriers programme  and  K. LeRest  for  GIS assistance and geostatistics. LPO Mission Rapaces (previously FIR)  initiated and promoted for the last 40 years the conservation of harriers in France through volunteerbased programmes, which are now integrated and coordinated in the  French Harrier Network. We also thank  R. Fuller ,  A. Tulloch  and  M. Gr€  uebler for insightful comments that greatly helped to improve this paper. A. S. was supported by the  Finnish School in Wildlife Biology, Conservation and Management (LUOVA)  and by the  Kone Foundation.
(c) Percentage of risky responses as a function of trial number (both placebo and ketamine sessions were pooled). The green (respectively, blue) curve represents the choices following the cue that was positive (respectively, negative) in the first block. Bold lines represent means; color-delimited areas represent inter-subject s. e. m (corrected for the variance across subject: the grand mean of each subject was removed from its data before computing sem). Vertical dashed lines indicate reversals. anxiety-depression, elevated mood, grandiosity, hallucination and unusual thought content) were also assessed. Dissociative symptoms were assessed by the Clinician Administered Dissociative States Scale. 33 Subjects then performed the probabilistic learning task in the fMRI scanner. Subjects also performed two other cognitive tasks while in the fMRI scanner.
Model-free behavioral analysis The overall percentage of risky response and button presses were compared between sessions in order to assess drug effects on choice and motor impulsivity, respectively. To assess drug effects on learning, the percentage of optimal responses (risky choice for the positive cue, less risky choice for the negative cue) were collapsed across the two cues and averaged within six bins of 10 consecutive trials. These data were then submitted to repeated-measure analysis of variance with three experimental factors (bin*block*session) and subjects as random factor. Post-hoc comparisons were performed to characterize the learning deficit observed under ketamine.
ACKNOWLEDGMENTS The authors are grateful to  Mael Lebreton  and  Jean Daunizeau  for helpful conversations and advices. FV was supported by the  Groupe Pasteur Mutualité. RG was supported by the  Fondation pour la Recherche Médicale  and the  Fondation Bettencourt Schueller. SP is supported by a  Marie Curie Intra-European fellowship  ( FP7-PEOPLE-2012-IEF ). AF was supported by  National Health and Medical Research Council  grants (IDs:  1050504  and  1066779 ) and an  Australian Research Council   Future Fellowship  (ID:  FT130100589 ). This work was supported by the  Wellcome Trust  and the  Bernard Wolfe Health Neuroscience Fund.
Experimental Procedures Site description, sampling strategy, and data collection The study was carried out on a monitored landscape covering 13 km 2 in Burgundy (France, Lat: 47°14 0 N, Long: 5°03 0 E), characterized by oak hornbeam deciduous forests (3. 86 km 2 ) and intensive agricultural croplands (9. 22 km 2 ). The site is under continental climate, with a mean annual air temperature of 10. 4°C and a mean annual rainfall of 762 mm  (period 1968 -2011). The whole area is situated on deep calcisol (IUSS Working Group, WRB 2006) of mainly silty or silty clay texture and is slightly sloping. Croplands were planted with winter crops (winter wheat, oilseed rape) in rotation with late sown crops (spring barley). Crop species and management practices were recorded from 2004 to 2011 over the whole study area.
PCR amplification and pyrosequencing of 16S rRNA gene sequences Amplification targeted the 16S rRNA V3-V4 gene region using primers F479 and R888 and a nested PCR strategy to add an 10-bp multiplex identifier (MID) barcode, as initially described by  Plassart et al. (2012). Equal amounts of each sample were pooled, and all further steps (adapter ligation, emPCR and 454-pyrosequencing) were carried out by Beckman Coulter Genomics (Danvers, MA, http:// www. beckmangenomics. com/) on a 454 GS-FLX-Titanium sequencer (Roche, Basel, Switzerland). The raw data sets are publicly available in the EBI database system (in the Short Read Archive) under project accession no. PRJEB5219.
Bioinformatics sequence analysis The bioinformatics analyses were performed using the GnS-PIPE at the GenoSol platform  Terrat et al. (2012). Sequences obtained after an initial quality filtering step (>350 bp, no base ambiguity), were aligned with Infernal alignments using a secondary structure of the 16S rRNA gene  (Cole et al. 2009) , and clustered at 95% sequence similarity into operational taxonomic units (OTU). Clustering was done with a custom PERL program that does not take into account differences in homopolymer length, which can constitute one of the major 454 sequencing errors  (Balzer et al. 2011). Procedure details are provided in Table  S1. A subsample of 10,800 quality sequences for each sample was randomly selected to allow rigorous comparison of the data. Bacterial diversity was characterized by OTU richness, evenness, and Shannon index  (Haegeman et al.
Acknowledgments This work, through the involvement of technical facilities of the  GenoSol platform of the infrastructure ANAEE France , received a grant from the  French  state through the  National Agency for Research under the program  " Investments for the Future"  (reference  ANR-11-INBS-0001 ), as well as a grant from the  Regional Council of Burgundy. We thank the farmers of F enay for providing information about agricultural practices and for allowing soil sampling in their fields. Thanks are also extended to  Diana Warwick  for her comments to the manuscript.
Acknowledgment Both IMA and ELS are part of the ASPERA-3 experiment on the  ESA Mars Express spacecraft. We are indebted to all of the involved national agencies and to the  European Space Agency  for conducting the  Mars Express program , especially the  Swedish National Space Board , CNRS in France, and  NASA  (contract  NASW-00003 ) in the USA. The lead author (MY) wishes to thank the social welfare (personal assistant) program in Sweden for providing assistance to physically disabled individuals which made it possible for him to conduct this research.