dataesr/scientific-paragraphs-categorization huggingface dataset:
[17]. Pooled estimates of continuous variables were made using the inverse variance method and reported as mean ± standard error (SE). The DerSimonian-Laird random effects model  [18]  was used to account for heterogeneity. For categorical variables, proportions with 95 % confidence intervals (CIs) were calculated using the Freeman-Tukey double-arcsine transformation  [19]. The heterogeneity of pooled data was estimated by calculating the Q and I 2 statistics and it was regarded as significant when p < 0. 05 or I 2 > 50 %. Statistical analyses were performed using MedCalc software  [20].
Study selection The initial search on PubMed, ISI Web of Science and the Cochrane Database of Systematic Reviews yielded 862 records, and 177 additional records were identified through forward search (Fig. 1 ). After duplicate removal and abstract screening, 274 articles were considered for full-text analysis. Among them, 248 were excluded because they did not fulfil inclusion criteria. These studies with reasons for their exclusion are listed in the Appendix. The remaining 26 studies (total 23,388 patients) were included in our review. The overall quality of evidence was very low (ESM Table  2 ).
Construction of Expression Plasmids β1and β6-conglutins were overexpressed using the pET28b construct (Novogen) 2  that contains an N-terminal polyhistidine (6xHis) tag. pUC57 vectors carrying synthesized β1, β2, β3, β4, or β6 conglutin sequences based on Genbank HQ670409 (β1), HQ670410 (β2), HQ670411 (β3), HQ670412 (β4) and HQ670414 (β6) sequences but altered for optimum bacterial codon usage with NcoI/XhoI restriction enzyme linkers were synthesized and constructed by GenScript 3  (Supplementary Figure  S1 ). The bacterial expression vectors for β-conglutins were obtained via NcoI/XhoI digestion of respective pUC57-β-conglutin constructs followed by ligation of the β-conglutin fragments into the pET28b vector.
Assays were performed in triplicate. Antifungal activity of β-conglutin proteins were expressed as the IC50 (µM) values for the fungi tested in Table  1. The mycelial growth inhibition assays were used to determine the concentration required for 50% growth inhibition (IC50), using a β-conglutin protein concentration range from 5 to 125 µM  (5, 10, 15, 20, 25, 35, 50, 75, and 125 µM)  for each β-conglutin. Results were expressed as mean ± standard deviation (SD). To determine the statistical significant differences of the β-conglutins antifungal activity on the growth of these fungal pathogens, the data was analyzed using statistical package SPSS 15. 0 (SPSS, Inc. , Chicago, IL, USA). Significant differences between the mean values of each cohort were determined using Tukey Kramer HSD test (p < 0.
05). To characterize the antifungal activity of β-conglutins further, a subsequent experiment with the necrotrophic fungal pathogen of NLL causing anthracnose disease (C. lupini isolates WAC8672 and WAC10444) was conducted, where the antifungal activity of β6-conglutin was determined by placing a mycelial plug in the center of a PDA plate and radial outgrowth determined over 14 days. For each time point significant differences were determined using a student's t-test using the JMP software v7. 0 (SAS Institute).
Subcellular Localization of β1and β6-Conglutin in Plant Cells Fusion proteins were expressed in 3-week-old N. benthamiana via Agrobacterium infiltration of leaves as previously described  (Sparkes et al. , 2006). Leaves were excised 3 days following infiltration and mounted with water under a 0. 17 mm coverslip and imaged using a Nikon A1Si confocal microscope (Nikon Plan Apo VC 60x NA1. 2 water-immersion objective). For GFP imaging, the 488 nm laser line and a 521/50 nm band pass filter was utilized, while a 561 nm laser line and 595/50 nm filter was used for RFP imaging. Images were analyzed using ImageJ software  (Schneider et al. , 2012). Images were converted to 8-bit grayscale and the intensity correlation analysis (ICA) method was used for determine the levels of colocalization and by using the JACoP plugin according to  (Bolte and Cordelières, 2006).
ACKNOWLEDGMENTS We thank  Roger Shivas  for the F. oxysporum f. sp. conglutinans strain Fo-5176,  John Irwin  for the F. oxysporum f. sp. medicaginis strain Fom-5190a ( BRIP 5190a ),  Kemal Kazan  ( CSIRO ) for the A. brassicicola ( UQ4273 ) and  S. sclerotiorum  ( UQ3833 ) isolates,  Mark Sweetingham  ( DAFWA ) for the R. solani isolates AG8-1 and AG2-1, and  Prof. Giles Hardy  ( Murdoch University ) for the P. nicotianae ( PAB12. 23 ). We thank the  Department of Agriculture and Food of Western Australia  for supplying the C. lupini isolates  WAC8672  and  WAC10444. We acknowledge the facilities, the scientific and technical assistance of the Australian Microscopy and  Microanalysis Research Facility at the Centre for Microscopy, Characterisation and Analysis , The  University of Western Australia , a facility funded by the  University, State and Commonwealth Governments.
FUNDING This work was supported by the  European Research Program MARIE CURIE  ( FP7-PEOPLE-2011-IOF ) for the grant ref. number  PIOF-GA-2011-301550  to JJ-L and KS. JJ-L thanks the  Spanish Ministry of Economy and Competitiveness  for the grant ref. number  RYC-2014-16536  ( Ramon y Cajal Research Program ). SM was supported by a  CSIRO OCE   Postdoctoral Fellowship.
PUL function has been extensively dissected in Bacteroides thetaiotaomicron VPI-5482 (see reviews  [17, 18] ) but needs to be better decrypted in other species of the Bacteroides/Prevotella genus involved in PCW degradation like B. xylanisolvens. B. xylanisolvens XB1A T genome was predicted to contain 74 PULs by Terrapon et al. [19] , and the PUL numbering used hereafter is that given in the PULDB database: http:// www. cazy. org/PULDB/.
2 ). The results confirmed the over-expression of PUL 43 genes upon growth on OSX relative to glucose at mid-log phase. In PUL 70, we could verify the absence of induction of BXY_46540 and BXY_46550 encoding SusC-and SusDlike proteins as well as of BXY_46580 encoding a putative ABC transporter, and the over-expression of BXY_46600 encoding a GH10 endo-xylanase. Relative RT-qPCR was also carried out with B. xylanisolvens XB1A T grown on OSX at late-log phase. As expected, gene over-expression was higher at mid-log phase than at late-log phase, except for two genes (BXY_29180, BXY_29190) in PUL 43 and three genes in PUL 70 (BXY_46540, BXY_46550, BXY_46580) that were down-regulated at late-log phase (Fig. 2 ). Unfortunately, the RNA-seq data of the bacterium grown on OSX at late-log phase were not exploitable (see Additional file 1: Table  S1 ).
Functional analysis of PUL 43 Operon organization We further investigated the genetic organization of the PUL 43 cluster. This 33 kb region contains 18 ORFs (BXY_29170 to BXY_29340) that are present on the positive strand of the genome (Fig. 5 ). DNA sequence analysis tools (see Material & Methods) predicted the presence of three operons and of one gene being transcribed independently (Fig. 5c ). This was partly validated by predictions compiling genomic and RNA-seq data (Fig. 5d ). To underline the production of polycistronic transcripts, RT-PCR experiments were undertaken using RNAs extracted from strain XB1A T grown on OSX. Detection of a RT-PCR product corresponding to the intergenic region #1 and the absence of amplification for the intergenic regions #2 and #3, confirmed the first operon of two genes (BXY_29170 and BXY_29180) (Fig.
RNA-seq analyses cDNA libraries were prepared with 100 ng of mRNAenriched fractions following the protocols of the Illumina TruSeq Stranded Total RNA Library prep kit. The final libraries had an average fragment size of ∼ 250 bp and were quantified by qPCR before being sequenced with an Illumina HiSeq 2000 instrument on a single lane in paired end reads. The data are available in GEO datasets at NCBI (http://www. ncbi. nlm. nih. gov/) under the accession number GSE74379. Approximately 8 to 9 million paired reads per sample were obtained (Additional file 1: Table  S1 ). Quality filtering and adapter trimming were performed with Trimmomatic v0. 30  [33]  using Illumina TruSeq3 adapter sequences for adapter clipping. The B. xylanisolvens XB1A T genome (GenBank accession NC_021017. 1) was indexed using novoindex, and reads aligned with novoalign v.
3. 00. 05 (http://www. novocraft. com) against the indexed genome. For downstream gene expression analysis only the aligned R1 reads were used; these were extracted from the paired-end alignment file using samtools v0. 1. 19 [34]  using the bitwise flag for the first read for a read pair. Gene counts were determined for the aligned data using featureCounts v. 1. 4. 3-p1 [35]  and the NCBI GFF3 feature file (obtained from the NCBI FTP site Dec. 2013). Differential gene expression was performed using R 3. 0. 0 using edgeR/limma  [36, 37]. Samples were assessed for potential outliers based on counts using normalized counts (RPKM = Reads per Kilobase per Million) that would affect downstream analyses. From this analysis we determined that samples mRNA 1, 2, 3 and 10 were problematic and thus removed them from the analysis (Additional file 1: Table  S1 ).
8; 6 M urea; 30 % v/v glycerol, 2 % w/v SDS) containing 130 mM DTT for the first step and 135 mM iodoacetamide and trace of bromophenol blue for the second step. The second dimension (SDS-PAGE) was carried out with 12 % polyacrylamide gels in a PROTEAN® II XL Cell (Bio-Rad, France). Proteins were visualized by silver-staining according to Bradford et al. [40]. Silver stained 2-DE gels were scanned with a GS800 imaging densitometer (Bio-Rad, France) and spot detection and image analysis were performed with the PD-Quest software (version 7. 1, Bio-Rad). Three reproducible gels from each independent culture for each bacterial growth condition (OSX, xylose) were selected and included for image analysis. Statistical image analysis was performed using Progenesis Samespots software, version 4. 1 (Nonlinear Dynamics).
Using MALDI-TOF mass spectrometry (MS) (Voyager DE-Pro, Applied BioSystems) and Voyager software for data collection and analysis, positive-ion MALDI mass spectra were recorded in the reflectron mode. Identifications by nano-liquid chromatography (LC) coupled to electrospray ionization (ESI) and tandem mass spectrometry (MS/MS) (LTQVelos, Thermo Scientific) were performed when identification from MALDI-TOF MS failed. Monoisotopic peptide masses were assigned and used for database searches with Mascot v2. 2. 0. Interrogations were performed against a home database containing distinct entries corresponding to the predicted mature proteins in B. xylanisolvens XB1A T. The following parameters were considered for the searches: a maximum ion mass tolerance of 25 or 50 ppm, possible modification of cysteines by carbamidomethylation, as well as partial oxidation of methionine.
Acknowledgments We wish to acknowledge  Carine Le Bourvellec  and  Catherine M. G. C. Renard  for their support regarding the analysis of oat-spelt xylan. We also thank  Caroline Mirande  for the enzymatic (endo-xylanase) assays, Gérard Andant,  Maria-Gloria Lopes ,  Line Touloumet  and  Maria-Helene Giovanetti-Canteri  for technical assistance, as well as  Manon Depeyroux  and  Marion Chappuis  (Master students) for their experimental input in proteomic and transcriptomic analyses, respectively.
Generating transcriptome data sets suitable for investigating adaptive change can be complicated by the requirement for high quality RNA and sampling equivalent life stages and tissue types for all individuals 5. Short sequence lengths, high rates of allelic dropout 6 and missing data 7 , as well as the vagaries of the bioinformatics pipeline that is used 8 , can influence estimates of genetic diversity obtained with RAD-seq approaches. Target gene capture approaches offer a promising alternative to these methods. They permit sequencing of pre-selected regions of the genome (see the review from 9 ) that have been determined a priori to be informative for the research question and are generally effective across a range of sample types and qualities. This allows the generation of relatively unbiased, more complete datasets than can be obtained using RAD technologies, and at an affordable cost compared with whole-genome re-sequencing.
Such an approach is now tenable with the large datasets that are being generated using NGS and has important applications for the study of endangered animals, or of any organism for which specimens are not easily obtained. Here, we demonstrate the use of such an approach to study the demographic history of a non-model organism, the blacktip reef shark (Carcharhinus melanopterus). These animals are highly philopatric and are often found on remote coral islands and atolls  21, 22. Their proclivity for philopatry and strong habitat preference for coral reefs predisposes them to exhibit a disjunct meta-population structure over their widespread distribution throughout the Indo-Pacific. We devised a two-layered sampling scheme based on the above theoretical considerations associated with metapopulations 20 : we collected individuals from a single deme from Northern Australia and from a scattered sampling from various locations throughout the Indo-Pacific.
Genetic diversity and data summary The demographic history of the metapopulation of C. melanopterus was examined using two datasets: the first based on estimates from a "single deme" (SID) in Northern Australia, the second based on a collection of samples taken from various locations in the Indo-Pacific, which we refer to as the "scatter" sample (SCD) (see Fig. 1 , Table  1  and Supplementary Table  1 ). We sequenced 18 individuals in total with an average coverage of 75×. After applying strict filters and removing duplicate reads (see SI), we obtained sequence data for 995 loci for SCD and 998 loci for SID, spanning 606,647 bp and 632,160 bp respectively (Table  1  and Supplementary Table  S2 ). The average length of the regions is ~600bp and details of the length distribution for SCD and SID are shown in Supplementary Fig.
1. Overall, 2605 and 1946 high quality single nucleotide polymorphisms (SNPs) were called for the scatter and single deme dataset, respectively. As expected, we detected a higher number of SNPs from SCD (considering samples from different locations) than SID, and SNP density is higher in introns than in exons for both datasets (no significant difference was found between SNP density in intron 5 ' and intron 3 ' ; Supplementary Table  S2 ). We also performed a Principal Component Analysis (PCA) including all 18 samples to assess the level of population structure within our dataset (Supplementary Fig. S2 ). At least three distinctive clusters are suggested by the first two principal components, explaining ~40% of the variance and separating Australia from the Indo-Pacific and Oman samples. Additional population sub-structure within the Australian and the Indonesian clusters is highlighted by the third principal component (Supplementary Fig.
g. post-glacial expansion), as described for other marine species  30, 31. Posterior probabilities of model CHG1 and model CHG2 are similar for both datasets (Supplementary Table  S4 ). When the effective population size was estimated at different time points in the past, CHG1 and CHG2 showed the same pattern (Fig. 3  and Supplementary Fig. S3 ). The estimation of Tc1 in CHG2 for both datasets reflects the estimate of Tc in CHG1 suggesting that CHG2 identifies only the change in effective population size corresponding to the demographic expansion that was also identified by CHG1.
Demographic inferences: direct approach Considering the results from the indirect approach, we therefore explored the demographic signals in the data by fitting a non-equilibrium finite island model 20 (model FIM; Fig. 2, panel d ) with 100 demes to both datasets. The choice of an island model is justified by the absence of panmixia in the PCA and by the near-uniform Gst matrix (Supplementary Table  S5 ), suggesting similar genetic distances across all samples except Oman. Model FIM is defined by three parameters, namely Nm, the time of the onset of the island Ti and the effective size of the ancestral deme, Nanc. Demographic parameters of the metapopulation were estimated directly for the two different sampling schemes, SCD and SID. The FIM model showed the highest posterior probability in the model choice for both SID and SCD (Table  2 ).
Cross-validation of the model choice confirmed that FIM is distinguished from COS and CHG1 with high probability for both datasets (see SI and Supplementary Table  S6 ). Importantly, the two datasets produced similar estimates of both Nm and Ti (Table  3 ). We further confirmed this result by applying FIM to the complete dataset of 18 samples (SCD+SID without shared samples) (Table  3 ). With this value of Nm ≈ 40 in the metapopulation, the signature of the ancestral expansion at Ti is lost in SID but not in SCD, consistent with expectation based on results in simulation studies of range expansion (see 19,32,33 ). Indeed, for similar Nm values the gene genealogy of lineages sampled from a single deme is a mixture of recent coalescent events occurring during the scattering phase and more ancient coalescent events occurring during the collecting phase.
Detection of a recent bottleneck We used ABC to test whether the absence of a bottleneck in our dataset reflects the real demographic history of C. melanopterus, or a lack of power to detect it. To this end, we simulated pseudo observed data sets (pods) under a modified FIM model (FIM-BOTT) characterised by two additional parameters (Supplementary Fig. S4 , panel b): Tbott (the time when an instantaneous decrease in Nm occurred) and Ibott (the ratio of the ancestral Nm to the new one). When Ibott=1 FIM-BOTT reduces to FIM (Fig. 2 panel d ). FIM-BOTT is defined by two major events: the onset of the island at Ti and the reduction in connectivity (i. e. , a reduction in the effective population size of a deme, a reduction in the migration rate, or both) at Tbott. We simulated 1,000 pods under model FIM-BOTT for each parameter combination and we reanalysed them using the CHG2 model.
When we fit the FIM model independently to both SCD and SID, we found a metapopulation characterised by a value of Nm ~40 (95% CI: 20-110), with an onset of formation around ~59,000 (95% CI: 6,000-165,000) generations ago. This level of connectivity is consistent with philopatry in this species, as previously described in Moorea and Tetiaroa (French Polynesia)  35. Unfortunately our sampling scheme does not allow us to test males and females separately, but it would be interesting to investigate possible differences in the level of connectivity related to sexspecific migration. We further apply the same FIM model to the whole dataset and we obtained similar values of both Nm and Ti (Table  3 ). In summary, the indirect approach of comparing gene genealogies under different sampling schemes and directly applying the FIM model (applied to three largely independent datasets) provided consistent results, suggesting that this approach is effective for reconstructing the demography of this species.
Sampling, library preparation and sequencing A total of 18 samples of C. melanopterus were included from 8 different locations (Fig. 1 ): Northern Territory and Queensland, Australia (N=11), Oman (N=1), Philippines (N=1), Thailand (N=1), Malaysia (N=1), South Kalimantan, Indonesia (N=1), West Kalimantan, Indonesian Borneo (N=1) and East Kalimantan, Indonesia (N=1). Samples were divided in two datasets called "scatter" (SCD) and "single deme" (SID) with 9 and 11 samples respectively (Table  1 , Supplementary Table  S1 ). The SCD dataset aims to mimic a "scatter sample", sensu Wakeley, where all samples come from independent geographical locations distributed in close proximity to each other. We also included one sample from Oman in order to infer the demographic history of the metapopulation at a larger scale and to cover a more extensive area of the Indian Ocean.
Analyses were performed in the R environment 54 with the library abc 59. We performed cross-validation for both model selection and parameter estimation by randomly generating pods from the prior distributions under each model. For each crossvalidation experiment we generated 1000 pods and we applied the same inferential procedure as for the observed data (see SI, Supplementary Fig. S10  and S11 ). In addition, a posterior predictive test 60 was carried out to test whether the data can be reproduced under a specific demographic model. Bayesian p-values computed from the posterior distribution of the number of polymorphic sites showed that none of the four models in both datasets could be rejected (SI, Supplementary Fig. S12 ).
Acknowledgments The authors thank  Giorgio Bertorelle  and  Oscar Lao  for helpful comments and suggestions and  Laurent Excoffier  for sharing fastsimcoal v2. 5. 2 before publication. Data analysis was performed on the computer cluster Genotoul bioinformatics platform (www. bioinfo. genotoul. fr). This work was supported by the  Agence Nationale de la Recherche  ( ANR-12-BSV7-0012  to P. M. D. ) and by the  US National Science Foundation  ( DEB-1132229 ).
pods (see SI). All bottleneck scenarios were compared to the ABC-skyline reconstructed from 59 Csillery, K. , Francois, O. & Blum, M. G. B. abc: an R package for approximate Bayesian computation (ABC). Methods Ecol Evol 3, 475-479, (2012). 60 Gelman, A. , Carlin, J. , Stern, H. & Rubin, D. Bayesian Data Analysis. (CRC Press, 2004). A vcf file containing all variants used in this study is being submitted to the Dryad database (http://datadryad. org/).
ATPase assay The ATPase assays were carried out in the remodeling reaction buffer with ATP concentration at 120 μM. To the reaction mixture 0. 1 μl (3. 3 μM) of the source 32 P ATP was added. The remodelling reaction was carried out at 29°C for 50 minutes. Reaction aliquots were analyzed by both, native 5% PAGE (sliding assay) and 15% acrylamide-50% urea denaturing gel (ATPase assay). The native PAGE was dried, exposed overnight on a phosphor imager screen and quantified by using the Multi Gauge V3. 0 software. The wet denaturing gels (ATPase assay) were covered by saran, exposed for 30' and the digital images quantified. Briefly, % ATP hyd = 100[P Ã ]/([P Ã ]+[ATP Ã ]) (where the values in brackets represents the background corrected integrated volumes of the corresponding radioactive bands on the digital gel image.
fondationarc. org/Grant  n°SFI20101201424  to DA, the  Institut National du Cancer-INCa  http://www. e-cancer. fr/grant agreement numbers  INCa_4496 ,  INCa_4454  and  INCa_PLBIO15-245  to AH and SD, the  Agence Nationale de la Recherche-ANR  http://www. agencenationale-recherche. fr/grant agreement numbers:  ANR-12-BSV8-0018  to AH and  SD ,  ANR-14-CE09-0019  to AH and  SD  and  ANR-12-BSV5-0017  to DA and SD, the  Fondation pour la Recherche Médicale -FRM https://www. frm. org/grant  n°DEP20131128521  to SD, the  Université de Strasbourg Institut d'Etudes Avancées  http://www. usias. fr/grant  USIAS-2015-42  to AH, the  Ligue Nationale  contre le Cancer https:// www. ligue-cancer. net/Équipe labellisée to AH and SD, and post-doc support to HM. Funding for open access charge: the  Agence Nationale de la Recherche-ANR  http://www.