dataesr/scientific-paragraphs-categorization huggingface dataset:
The communication benefits of providing sameday, on-site language-appropriate results to laboratorians and administrators encourage engagement from leadership in the improvement process. Furthermore, presenting reliable results in a graphical format leads to better understanding of problems and use of data for improvement. As with the TB-LAT e-Tool, this first version of the SLIPTA e-Tool has capacity for data exporting; however, some minor issues have been identified when transferring data between various versions of Microsoft ® Excel. Improvements to the tool should continue, based on feedback from end-users. In addition, studies are needed to provide a formal evaluation of the tool, including time and cost savings, improved accuracy and user preferences. Potential benefits of the e-Tool expand beyond improved auditing.
The tool incorporates auditing instructions for each SLIPTA question, which will enhance learning and consistency for laboratories performing internal audits. Furthermore, as additional auditors are trained, the e-Tool can be used for evaluation and validation of their competency and standardisation of audit criteria. Moreover, when unlocked, the electronic checklist can be customised and expanded to address additional laboratoryor country-specific data needs. The SLIPTA checklist e-Tool is now available for use by other countries and can easily be customised as needed; for example, optional languages can be added for local settings. Widespread use of the tool will allow development of a database for SLMTA programme managers at the global level in order to evaluate systematic gaps in laboratory quality and to enhance overall planning, implementation and sharing of results.
Acknowledgements The authors would like to extend their sincere thanks to  Dr Katy Yao ,  CDC , for her strong support and invaluable contribution to and review of the design of the SLIPTA e-Tool, as well as to  Dr Elizabeth Luman ,  CDC , for her patience and professional guidance in development of the manuscript. Finally, the authors would like to thank all endusers of the SLIPTA e-Tool for their invaluable feedback and contribution. This project has been supported by the  US President's Emergency Plan for AIDS Relief (PEPFAR)  through the  CDC  under the terms of Cooperative Agreement project number  PS001928  ( Vietnam Administration for Medical Services)  and  PS001285  ( American Society for Clinical Pathology ).
cDNA HYBRIDIZATION ON MICROARRAY RNA reverse transcription and fluorescent dye incorporation were carried out using the Promega ChipShot Direct Labeling and Clean-Up System (Promega, USA). For each sample, 5 µg of total RNA was reverse-transcribed and labeled with a single dye (Cy3) labeling procedure and used for microarray hybridization according to the manufacturer's indications (Promega). Each sample was run on custom-made 60-mers oligonucleotide microarrays specific for the S. glossinidius whole genome, and for the four plasmids (respectively, GenBank accession number, AP008232; NCBI RefSeq: NC_007183. 1; NCBI RefSeq: NC_007184. 1; NCBI RefSeq: NC_007186. 1; NCBI RefSeq: NC_007187. 1) with at least four oligonucleotide probes per gene (design is available at Gene Expression Omnibus under the accession number GPL17347).
5. 1. 1). Data were filtered for detectable expression level; only those showing a level of expression greater than the background noise in at least three of the four replicates were selected. Unsupervised hierarchical clustering was used to investigate relationships between samples and between genes. It was applied to median-centered data, using the Cluster and TreeView programs (average linkage clustering using Pearson correlation as the metric distance). Statistical analysis was performed using the TMeV5 Multi Experiment Viewer, v4. 5 software (http://www. tm4. org/mev. html) and two-class unpaired SAM (significant analysis of the microarray program) analysis method. One-way analysis of variance was applied to identify genes differentially expressed between infection self-cured and control flies.
A 5% predicted false discovery rate was used as the threshold for differential expression  (Reiner et al. , 2003). The identification of biological interpretation of differentially expressed genes was performed using DAVID software  (Dennis et al. , 2003). This program allows identification of the biological interpretation of genes in the basis of gene ontology (GO) terms. In addition, the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways were used to assess the specific biological pathways that were overrepresented.
QUANTITATIVE REAL-TIME PCR The microarray results were controlled, using quantitative PCR (qPCR), on a subset of four genes (SG0845, SG0858, SG0895, and SG1978) that were shown to be differentially expressed in microarray experiments between the two groups of flies; these genes were among the highest overexpressed in refractory flies. Primers, specific to these genes (Table  1 ) were designed using Primer-Blast software (http://www. ncbi. nlm. nih. gov/tools/ primer-blast/). cDNA was synthesized from 5 µg of original total RNA samples using random hexamers and Superscript II reverse-transcriptase (Invitrogen, France) according to the manufacturer's instructions. All qPCR reactions were performed in an Mx3005P QPCR System (Agilent Technologies) using the Brillant II Sybrgreen qPCR Kit (Agilent technologies) with 2 µl of cDNA of a known concentration in a 25-µl total volume.
PCR efficiencies for each primer pair were calculated using tenfold dilutions of fly gut-extracted cDNA as previously described  (Hamidou Soumana et al. , 2013). PCR conditions were as follows:  calculation of relative expression quantification. Cycle thresholds (Ct) for each reaction were obtained using the MxPRO QPCR Software (Agilent Technologies). Relative quantification was calculated with the 2 -C(t) method as described by  Livak and Schmittgen (2001). Relative quantification for given genes with respect to the calibrator was determined and compared with the normalized expression values resulting from microarray experiments.
GENE FUNCTIONAL ANNOTATION Functional annotations of the differentially expressed S. glossinidius genes, with reference to the biological process GO terms and KEGG pathways, was investigated using DAVID software. The analysis showed an overrepresentation of GO terms related to metabolism and biosynthesis processes (Table  3 ). The modified Fisher exact test revealed a 17. 6-fold enrichment (P-value = 0. 055) for the GO term related to hexose metabolism (Table  3 ). Similarly, galactose metabolism KEGG pathways were found to be 28. 5-fold-enriched (P-value = 0. 088) (Table  4 ).
• C for 5 min (1×), followed by 94 • C for 45 s, 60 • C for 45 s, 72 • C for 1 min (39×), and then 72 • C for 10 min (1×). Melting curve analysis was performed to check the specificity of the PCR reaction and to verify the amplification efficiency. The housekeeping gene, Glossina tubulin (GenBank accession number HE861503), was used as the reference gene for the normalization.
ACKNOWLEDGMENTS The authors thank the " Région Languedoc-Roussillon -Appel d'Offre Chercheur d'Avenir 2011 ," the  "Service de Coopération et d'Action Culturelle de l'Ambassade de France au Niger " and the  "Institut de Recherche pour le Développement"  for their financial support. Illiassou Hamidou Soumana  is a PhD student funded by the  French Embassy in the Republic of Niger ,  Service de Coopération et d'Action Culturelle (SCAC). We extend our thanks to  Dr Aurélie Bergon  ( TAGC platform ) for her assistance in data registration in the Gene Expression Omnibus data bank.
Ewing sarcoma has low numbers of single-nucleotide and structural variants Our discovery set for whole-genome sequencing (WGS) comprised 112 Ewing sarcomas with matched germline DNA (Fig. 1 , Supplementary Table  S1 ). All Ewing sarcoma tumors, with the exception of one (SJ001301) that had insufficient tumor sample for analysis, expressed EWSR1-ETS fusions: EWSR1-FLI1 in 101 cases, EWSR1-ERG in 9 cases and EWSR1-ETV1 in one case. Tumor and germline DNA were sequenced at a median depth of 35X and 25X, respectively. Mapping, detection, and annotation of single-nucleotide variants (SNV), indels, and structural variants (SV), functional predictions, and copy-number alterations (CNA) were computed from the whole-genome sequencing data as previously described  (13) (14) (15). Eighty percent of the tumors had >70% tumor purity leading to a 98% power for detecting mutations present in the predominant tumor clones in this cohort (Supplementary Table  S2a ).
1 ). All mutations were missense, with the exception of one nonsense mutation (p. R317* according to NM_000546), and were described in the COSMIC database. After excluding the very large genes that are recurrently mutated in most cancer genome studies (TTN, CSMD1, MACF1, RYR2)  (19) , the third most frequently mutated genes were EZH2, BCOR, and ZMYM3, which each presented with 3 mutations (3/112, 2. 7%,) (Fig. 1  and Supplementary Table  S2 ). All three EZH2 mutations were missense mutations within the SET domain (Y646F, Y646H, and A682G according to NM_004456). BCOR exhibited one missense mutation (S1083I, according to NM_017745), one indel leading to a frameshift (M1259fs) and one 116-kb intragenic deletion (Fig. 1 , Supplementary Table  S2 ). ZMYM3 exhibited two indels (L82fs according to NM_201599) and one 17-kb intragenic deletion (Fig.
After submission of this manuscript, Brohl and colleagues published an article  (46)  describing the genomic landscape of Ewing sarcoma based mostly on exome sequencing and RNA-seq. The observed frequency of STAG2, TP53, and CDKN2A is similar to the findings reported in the present manuscript. They also observed the association of TP53 and STAG2 mutations. However, significant correlation with clinical outcome could not be demonstrated, possibly due to the smaller size of the patient cohort. Finally, the exclusive pattern of CDKN2A and STAG2 alterations is not reported in the Brohl et al dataset. The different techniques used in the two reports and the different sizes of the patient series may account for this discrepancy, which requires further investigation. In conclusion, our comprehensive genetic analysis of Ewing sarcoma identified recurrent mutations in STAG2, TP53, and epigenetic regulators.
Most patients were treated according to the EuroEwing protocol (  47 ). An anonymization procedure was performed before compilation of clinical, histologic, and biological information in a secure database with restricted access. All tumors included in this study were positive for the EWSR1-ETS fusion. Detailed clinicopathologic and sequencing information is provided in Fig. 1  and Supplementary Table  S1. The follow-up set comprising 199 tumor DNAs from EWSR1-ETS-positive Ewing sarcomas was distinct from the discovery set and consisted of patients treated according to the EuroEwing99 protocol.
Analysis of WGS data All samples were processed using the same analysis pipeline. Correspondence between sample and analysis numbers is indicated in Supplementary Table  S2b. WGS mapping, coverage, and quality assessment; single-nucleotide variants (SNVs); detection of small insertions or deletions (indels); tier annotation for sequence mutations; and prediction of adverse effects of missense mutations were previously described  (14, 15). SVs were analyzed by using the program CREST (13); CNAs were identified by comparing the read depth of matched tumor vs. normal tissue and were analyzed by using the CONSERTING algorithm (COpy Number SEgmentation by Regression Tree In Next-Gen sequencing). The reference human genome assembly GRCh37-lite was used to map all samples. We used the program cghMCR to identify recurrent copy number gain or loss.
Validation sequencing For 18 tumor samples (SJEWS001301-1320), the genomic coordinates of putative alterations identified by WGS, including SNVs, SVs, and indels, were used to generate a Nimbelgen Seqcap EZ bait set for enrichment of targeted regions (Roche). The baits were hybridized to Truseq sample libraries (Illumina) prepared from amplified genomic DNA (Roche). Pooled samples were sequenced on a HiSeq 2000 by using the paired-end multiplexed 100-cycle protocol. Resulting data were converted to FASTQ files by using CASAVA 1. 8. 2 (Illumina) and mapped with the Burrows-Wheeler Aligner (BWA) prior to pipeline analysis. Of the 6659 somatic SNVs identified in the 18 cases, we were able to design a validation assay by custom capture for 6,042. Of these, 5,779 were validated as somatic mutations (overall validation rate, 95.
Acknowledgments Financial support: This work was supported in part by a  Cancer Center   Support grant  ( CA21765 ) and grants to  M. A. D  ( EY014867 ,  EY018599 ,  CA168875 ) from the  U. S. National Institutes of Health , and by the  American Lebanese Syrian Associated Charities (ALSAC). M. A. D. is a  Howard Hughes Medical Institute Investigator. Whole-genome sequencing was supported as part of the  St. Jude Children's Research Hospital -Washington University Pediatric Cancer Genome Project. The whole genome sequencing of French cases was performed by the  CNAG in Barcelona  and supported by grants from the  French National Cancer Institute INCa  within the framework of the  ICGC program. Additional sequencing was performed by the next generation sequencing platform of the  Institut Curie , supported by grants  ANR-10-EQPX-03  and  ANR10-INBS-09-08  from the  Agence Nationale de la Recherche  (investissements d'avenir) and by the  Canceropôle Ile-de-France.
This work was also supported by grants from the  Ligue Nationale Contre Le Cancer  ( Equipe labellisée ), by European  PROVABES [ERA-NET TRANSCAN JTC-2011], ASSET [FP7-HEALTH-2010-259348 ] and  EEC  [ HEALTH-F2-2013-602856 ] grants. We also thank the following associations for their invaluable support: the  Société Française des Cancers de l'Enfant, Courir pour Mathieu , Dans les pas du Géant,  Olivier Chape ,  Les Bagouzamanon ,  Enfants et Santé  and les  Amis de Claire. Didier Surdez  is supported by the  Institut Curie -(SIRIC)  ( Site de Recherche Intégrée en Cancérologie) program. We are indebted to UNICANCER for providing access to the clinical databases. We thank  Fabien Calvo  for continuous support. We thank  Virginie Chêne ,  Stelly Ballet ,  Heather Mulder ,  Panduka Nagahawatte ,  Donald Yergeau ,  Yongjin Li ,  Michael Edmonson ,  Andrew Thrasher  and  Carlo Lucchesi  for their invaluable help,  Peter Brooks  for fruitful discussions,  Heinrich Kovar  for some of the cell lines and  Alban Lermine ,  Nicolas Servant ,  Philippe Hupé , and  Emmanuel Barillot  for their help in processing the NGS data.
Nutritional Intake Analysis Estimated nutritional intake data from the Food4Me FFQ were generated automatically by the online Food4Me programmed system, as described by Forster et al  [18]. Composition of the food items listed in the FFQ were derived from WISP (Tinuviel Software, Anglesey, UK)  [28]  and modified to include recipes of composite dishes, generic commercial foods, new foods on the market, and current manufacturers information. The 4-day WFR intakes were analyzed using WISP (Tinuviel Software, Anglesey, UK)  [28]. For the purpose of the current study, consumption of dietary supplements was not included in the analyses.
Statistical Analysis Statistical analyses were performed using SPSS version 20. 0 (IBM Corp, Armonk, NY, USA). Mean nutrient intakes and standard deviations were calculated for baseline characteristics, repeated Food4Me FFQ, and 4-day WFR. Differences in participant characteristics and energy intakes (kcal) were assessed using a paired 2-sample t test. Nutrient intakes were compared using general linear model (GLM) analysis controlling for energy and gender where there was significant interaction between gender and nutrient intake. Data were checked for normality using the Shapiro-Wilk test and, depending on the outcome, the association between dietary intake methods and repeated Food4Me FFQ were assessed using either Pearson product-moment correlation or Spearman correlation coefficient (SCC, rho).
As per the Bland-Altman methodology, dietary records were considered comparable/ repeatable if greater than 95% of data plots lay within 2 standard deviations of the mean. GraphPad PRISM version 6 was used to produce the Bland-Altman plots (GraphPad Software, Inc, La Jolla, CA, USA). Differences in food group intakes between the FFQ and WFR and repeated Food4Me FFQ were also examined. For this purpose, food items in the Food4Me FFQ and 4-day WFR were arranged into 35 food groups as per previous validation by Forster et al  [18]. SCC were calculated to assess the strength of association between methods for estimated intakes of the 35 food groups. To assess the relative agreement between the dietary methods and repeated FFQ for daily food group intake, food groups were also cross-classified to estimate the percentage of participants classified by the 2 methods into quartiles of exact agreement, exact agreement plus adjacent, disagreement, and extreme disagreement.
Summary A total of 121 participants were screened for inclusion in the study, of which 113 were deemed eligible. Reasons for exclusion included self-diagnosed food intolerance (n=7) and medication use (n=1). Before completion, 10 participants dropped out of the study and a further 3 were excluded from analysis due to a reported energy intake >4500 kcal  [26]. The final dataset for analysis included 100 participants, of whom 49 had also completed the 4-day WFR, as illustrated in Figure  2. A total of 48 participants completed the Diet Record Usability-Rating Questionnaire.
After controlling for energy and, where appropriate, gender, vitamin C intakes were no longer significantly different. Removing underreporters from the dataset reduced the agreement between the 2 methods for folate, vitamin B6, and vitamin E, but improved agreement for energy-controlled carbohydrate and vitamin D, with no significant differences observed between estimates of these nutrients (data not shown). Bland-Altman plots for mean energy (kcal), total fat (%TE), protein (%TE) and carbohydrate (%TE) for the 4-day WFR and FFQ1 are shown in Figure  4. Overall, less than 5% of cases fell outside of the limits of agreement for all plots indicating good agreement between the methods. The mean difference (bias) between energy intakes was relatively small (178 kcal/day) with greater values being estimated in the Food4Me FFQ, as was the case for energy derived from total fat and protein.
Acknowledgments This work was funded by  Food4Me  ( KBBE. 2010. 2. 3-02 , Project no  265494 ). The authors would like to thank  Kate Currie  and  Marzena Kucharska-Kelly  for their assistance with the running of the study,  Crème Global (Dublin, Ireland)  for their support with the online Food4Me FFQ, and  Breige McNulty  for her assistance with analysis of the WFR.
Water holding capacity, texture and colour Water holding capacity was measured by the compression test on filter paper according to  Grau and Hamm (1953) , measuring the area of released fluid. Texture analyses were carried out using a Zwick Roell ® 109 texturometer (Zwick Roell, Ulm, Germany) with Text Expert II software, equipped with a 200 N load cell. The Warner-Bratzler (WB) shear test was performed on a sample of 5×5 cm surface area, taken from the central part of the fillet (one measurement for each fillet), including both the hypaxial and epaxial regions. A straight blade (width of 7 cm), perpendicular to muscle fibre direction, was used at a crosshead speed of 30 mm/min to 50% of total deformation. Maximum shear force, defined as maximum resistance of the sample to shearing  (Veland and Torrissen, 1999)  was determined.
All measurements were made at room temperature. All parameters were determined from the plot of force (N) compared with deformation (%). A Spectro-color ® 116 colorimeter (Bell Technology Ltd. , Auckland, New Zealand), using Spectral qc 3. 6 software, was utilised for colorimetric measurement in the CIELab system (Commission Internationale de l'Éclairage, 1976). In this system, lightness (L*) is expressed on a 0 to 100 scale from black to white; redness index (a*) ranges from red (+60) to green (-60) and yellowness index (b*) ranges from yellow (+60) to blue (-60). Colour was measured in duplicate on epaxial, ventral and caudal positions on fillets and expressed as mean.
Statistical analysis Data was analysed using SAS Proc GLM (SAS, 2007) with the following model: Yijk=µ+Si+Fj+(S*F)ij+b*Xijk+Eijk where: Y=k th observation of the i th strain and the j th farm; S=strain effect (i=1…5); F=farm effect (j=1…3); X=independent variable (body weight); E=random error effect. For the texture parameters, fillet thickness was used as a further covariate. Differences between least square means were tested with Student's t-test. All the statistical analyses on meat traits were performed separately for raw and cooked fillets. The coefficients of the residual (after the above model) correlation between physical and chemical traits were also calculated. Physical and chemical parameters also underwent Principal Component Analysis (PCA)  (Naes et al. , 1996) , using SAS Proc FACTOR  (SAS, 2007)  with Varimax Rotation and the first three factors underwent ANCOVA analysis with the above model.
Principal Component Analysis In order to analyse the joint behaviour of physico-chemical traits, PCA was applied to the dataset using the Varimax Rotation that optimises and balances variance partition between defined factors  (SAS, 2007). Because of the reduced number of samples analysed for collagen content, collagen was excluded from PCA. Table  7  reveals that the first three Factors explained about 57% of the total variance of the parameters in raw fillets and almost 49% in cooked fillets. In raw fillets, Factor1 associated the variables colour, moisture and lipids. In particular, lipids, a* and b* were positively associated and showed similar loading values, in the opposite direction to moisture and L*. No significant association was found between the abovementioned parameters and the texture indicators that influenced Factor2, where shear force, hardness and chewiness were linked and showed similar high loading values.
Ultraviolet-visible absorption spectra of the crystals were measured offline before X-ray irradiation at 100 K using a HR2000 (OceanOptics) on a microspectrophotometer setup at beamline ID29S (ESRF) 36 (Supplementary Fig. 2 ). Data collection and structure analysis. Diffraction data collections of more than 500 crystals were performed at 100 K using synchrotron X-ray sources from ESRF and BESSY II. The best diffraction data for the highest resolution of Ops*-ArrFL-1 was collected at synchrotron beamline ID29 (ref. 37) at ESRF with a Pilatus 6 M detector at 0. 9791 nm wavelength. The crystal-to-detector distance was fixed at 521 mm for Ops*-ArrFL-1. All images were indexed, integrated and scaled using the XDS programme package  38  and the CCP4 programme SCALA  39, 40. Ops*-ArrFL-1 crystals belonged to the rhombohedral space group H32 (for Ops*-ArrFL-1:   9a, b, c ) yield an apparent K D , from which the true K D value of peptide binding to Meta II can be derived.
Further experimental details are given in the Methods. a ¼ 242. 62 Å, b ¼ 242. 62 Å, c ¼ 110. 22 Å, a ¼ 90°, b ¼ 90°, g ¼ 120°). Table 1 summarizes the statistics for crystallographic data collection and structural refinement. Initial phases for Ops*-ArrFL-1 were obtained by the conventional molecular replacement protocol (rotation, translation, rigid-body fitting) using the Ops*-G t aCT structure (PDB accession 3DQB 3 ) without G t aCT peptide as the initial search model, which was used in the programme Phaser  41. A simulated annealing procedure with the resulting model was performed using a slow-cooling protocol and a maximum likelihood target function, energy minimization and B-factor refinement by the programme PHENIX  42  , which was carried out in the resolution range of 40. 04-2. 75 Å for the Ops*-ArrFL-1 structure.