dataesr/scientific-paragraphs-categorization huggingface dataset:
-QOL scores at 6 months with validated French-language versions of the questionnaire (Cystic Fibrosis Questionnaire (CFQ) 14+)  [24]. Four domains were studied: physical functioning; energy/well-being; body image; and respiratory symptoms. -The need for analgesics. At the end of the study, all patients completed a questionnaire to collect data about their interest in the study and their satisfaction with the treatment received.
Data Collection At the base-line visit, we collected demographic data and information about the characteristics of the patient's CF, including respiratory function evaluated by measuring forced expiratory volume in one second (FEV1), and nutritional status evaluated with the body mass index (BMI). All patients were evaluated at the base-line visit (M0), after 3 months (M3) and after 6 months (M6) at the end of the study. Upon entry into the study, the participants were given a diary in which they recorded their pain symptoms(location and intensity) daily at baseline and during the trial; this diary was kept at home and brought for each study visit, and returned to the investigators at the end of the study. The CF physicians (DH or DG) checked the patient's diary and recorded the patient's pain after the patient had self-reported his pain on a VAS (mean value and number of painful days in the previous month), collected the CFQ 14+, listed concomitant treatment including analgesics, and adverse events.
Statistical Analyses All randomized patients were analyzed in their allocated group whatever the treatment received. Baseline data are described using means and standard deviations (SD) for quantitative variables, and medians and interquartile ranges (IQR) for variables with asymmetric distributions. For qualitative variables, frequencies and percentages were computed. Primary comparisons were between the OMT group (group A) and the sham OMT group+no additional treatment group pooled (group B+C). The difference for the primary outcome between the two groups was evaluated at 6 months using a linear mixed longitudinal model estimating the difference in change from baseline between the 2 groups (coefficient for time 6 group interaction) and taking into account the correlation for data of a same patient (random effect).
Illumina reads, trimmed using Trimmomatic (9) and quality filtered using Fastx-toolkit (http://hannonlab. cshl. edu/fastx _toolkit/), were assembled using the Spades software  (10, 11). These analyses yielded 19 scaffolds of 20 contigs containing 2,797,898 bp, for a 32. 74% GϩC content, representing 99. 14% of the completed genome using its parental strain S. aureus subsp. aureus NCTC8325 as a reference. We first verified that rsbU and tcaR genes were actually restored in the clone sequenced. However, the average nucleotide identity between the two strains is of 99. 97% suggesting that other mutations are present in strain HG003 compared to NCTC8325. The genome sequence was annotated by the NCBI Prokaryotic Genomes Annotation Pipeline (PGAP)  (12). The genome comprises 2,647 genes, 11 predicted RNAs (5S, 16S, and 23S), 50 tRNAs, one transfer-messenger RNA (tmRNA).
Among the 2,647 genes, 2,573 (97. 2%) were found to encode putative proteins and 488 (18. 96%) were assigned as hypothetical proteins. The genome encodes three intact Staphylococcus complete phages and four questionable clustered regularly interspaced short palindromic repeats (CRISPRs), according to the PHAge Search Tool (Phast)  (13)  and the CRISPERfinder online software  (14). Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. JPPU00000000. The version described in this paper is version JPPU01000000.
MALDI-MS analyses Protein digestion and peptide identification by MALDI-MS were performed as described  [26]. Briefly, MALDI TOF-TOF spectra were acquired in the positive and reflector mode, and an external calibration was performed using an equal mix of standard peptide solution Cal mix 1 and Cal mix 2 (Applied Biosystems). Monoisotopic peptide mass values extracted by Data Explorer (Applied Biosystems) were used for protein identification by searching against the UniProtKB/Swiss-Prot database using the on-line mascot search engine (http://www. matrixscience. com). The searches were conducted using the following settings: trypsin as digestion enzyme, one missed cleavage allowed, 30 ppm tolerance, carbamidomethyl as fixed modification and methionine oxidation as variable modification.
05%), and buffers A and B were 0. 1% formic acid in water and 0. 1% formic acid in CH 3 CN, respectively. Peptides were eluted at a flow rate of 300 nL/min from the reverse phase C18 column using a 5-35% CH 3 CN gradient for 40 min. MS/MS spectra were acquired with a Data Dependent acquisition method by selecting the 10 most intense precursors for CID fragmentation with Q1 quadrupole set at low resolution for better sensitivity. Raw data were processed with MS Data Converter software for generating MGF data files and analyzed with PeakView software (AB Sciex). Protein identification searches were performed using the MASCOT algorithm considering carbamidomethylation of cysteines and oxidation of methionines as variable modifications. Peptide and fragment tolerance were respectively set at 10 ppm and 0.
As regards the various forms of 2-Cys Prxs detected in cells stimulated with IFN-γ and LPS (Fig. 5C ), only one spot very nearly vanished upon ascorbate/copper treatment (open arrowhead pointing up) presuming S-nitrosylation, whereas two other spots including spot X were resistant. To identify the modification of Prx1 spot X, we performed mass spectrometry analyses. The main Prx1 spot and the satellite spot nearby (X) were picked off the gel, trypsin-digested and subjected to LC-MS/MS analysis. The error tolerant search in the UniMod database allowed us to identify one triply charged peptide with a monoisotopic m/z 1057. 1783 (mass accuracy Δm ¼ 3. 61 ppm) corresponding to a homocysteine (Hcy) adduct (þ133. 01) on Cys-52 in spot X. Fig. 6  shows peptide fragmentation spectra. Mass difference between well visible y11 and y12 fragments indicates carbamidomethylation (Δm ¼ 57 Da) and S-homocysteinylation (Δm ¼ 133.
Feature extraction software (version 10. 7. 3. 1) with an hg19 annotated design file (021924_D_F_20111015) and a modified Agilent-provided extraction protocol (CGH_107_Sep09) (Agilent Technologies, Santa Clara, California, USA) were used to extract raw data from tiff files obtained after scanning. The arraybased CGH data were analyzed with the Agilent Genomic Workbench 7. 0 (Agilent Technologies, Santa Clara, California, USA). The Z-score algorithm was used with a threshold of 0. 2 and a 2-Mb sliding window to detect significantly aberrant genomic regions (QQ-plots on the P-value from the enrichment analysis confirmed that the chosen threshold did not over-call some aberrations). Our copy number variation data were compared to the Database of Genomic Variants  [19]  to exclude genomic variants.
Enrichment analysis Genomic intervals which have a significant overabundance of gains or losses in a given genomic region were identified with the hypergeometric tail algorithm available in Genomic Workbench 7. 0 (Agilent Technologies, Santa Clara, California, USA). In Genomic Workbench 7. 0, the genomic intervals are constructed from the data obtained with the aberration detection algorithm (Z-score algorithm) using a 2-Mb sliding window with a threshold at 0. 2. The genomics intervals calculated by the Z-score algorithm are then used in the enrichment analysis. P-values of significant chromosomal regions (P ≤0. 01) were -log-10 transformed and plotted according to the genomic position. Fluorescent in situ hybridization FISH was performed using the Histology FISH Accessory kit (DAKO, Glostrup, Denmark) on 5-μm sections from FFPE samples, as recommended by the manufacturer.
Taqman low-density arrays Customised Taqman low-density arrays from Applied Biosystems (Life Technologies, Carlsbad, California, USA) were used to study the expression of 65 genes belonging to the 17q25. 3 chromosomal region, as well as 18 putative housekeeping genes. For each tumor, one microgram of cDNA was loaded into two channels (500 ng cDNA/channel) of a microfluidic card, which was run on an Applied Biosystems 7900 HT Real-Time PCR System (Life Technologies, Carlsbad, California, USA) according to the manufacturer's recommendation (Protocol number 4400263 Rev. B). Best housekeeping genes (POLR2A, RPS11, GUSB, RPL37A, ACTB) for data normalization were chosen according to their stability score calculated by the Expression Suite 1. 0. 1 software (Life Technologies, Carlsbad, California, USA).
Statistical analysis FISH results were analyzed using the Mann-Whitney-Wilcoxon test. Statistical analysis of the Taqman lowdensity array data and the individual Taqman qPCR data was performed by Student's t-test using Graphpad Prism software (GraphPad Software, La Jolla, California, USA) (*P <0. 05; **P <0. 01; ***P <0. 001). The sensitivity was calculated as the proportion of BRCA1-mutated BC with a gain in 17q25. 3 (true positives/true positives + false negative). The specificity was calculated as the proportion of BC without BRCA1 mutation without a gain in 17q25. 3 (true negative/true negative + false positive).
The general distribution of the genomic aberrations was relatively similar between the two tumor groups and showed a high proportion of gains and losses in both BRCA1-mutated and non-mutated TNBC (mutated TNBC: mean 102 aberrations per genome, range 18 to 211; non-mutated TNBC: mean 134 aberrations per genome, range 14 to 246). In order to pinpoint the significant differences between the two profiles, data were analyzed with the software Genomic Workbench 7. 0 in combination with the hypergeometric tail algorithm (Figure  1C ). We considered aberrations with a difference in frequency of at least 20% between the two groups, and with a P-value below 0. 05. The most frequent discriminating genomic alterations in the BRCA1-mutated group as compared to the nonmutated group were gains in 7p22. Several other significant gains and losses were detected in the non-mutated group, but these corresponded to regions that were preserved in the BRCA1-mutated tumors, like the chromosomal 1q31.
3 region (Table  S2  in Additional file 2). Amongst all the relevant aberrations, the most consistent gain in BRCA1-mutated TNBC was a highly significant (P = 0. 0002) and recurrent gain of 2. 4 Mb located between the position 78644018 and 81029941 on the chromosome 17 (17q25. 3 (Human Genome built 37)). In the Database of Genomic Variant, most of the CNV in that region are losses, and we did not detect the 17q25. 3 gain in normal tissue adjacent to positive tumor tissue, suggesting that this gain is not a copy number polymorphism. The gain in the 17q25. 3 region was detected in 86. 96% of BRCA1-mutated TNBC (20/ 23; sensitivity of 87%), and was only detected in 28. 57% of non-mutated TNBC (6/21, specificity of 71%) (Figure  2A  and B ). Other gains were also highly significant, but were less frequent in the mutated samples.
Gene expression analysis of the 17q25. 3 chromosomal region in BRCA1-mutated and non-mutated TNBC Several studies have shown that genome amplification can lead to the over-expression of genes located in the amplified regions (amplicon). This over-expression may contribute to tumor development and progression. According to our CGH analyses, the 17q25. 3 subregion of interest spreads from the position 78644018 to 81029941 (NCBI built 37) and contains 65 protein coding genes. We investigated by low-density Taqman array the expression of the 65 genes of the 17q25. 3 region in BRCA1-mutated and non-mutated TNBC previously analyzed by array CGH. Relative expression levels were compared to identify significant differently expressed genes in mutated tumors. Expression data analysis identified a significant over expression of 17 genes (ARHG-DIA, AZI1, C17orf56, C17orf62, CSNK1D, DUS1L, FLJ9 0757, FN3KRP, GPS1, HGS, NPLOC4, RFNG, RPTOR, SIRT7, SCL25A10, SCL38A10, THOC4) in BRCA1-mutated TNBC (and CGH-positive for the gain in the 17q25.
As the RNA extracted from the tumor is probably contaminated by RNA extracted from normal cells, with TNBC often deeply infiltrated by numerous stromal and inflammatory diploid cells, the over expression in the tumor cells could be more important than what we have observed. To validate our experimental results, the expression of several genes of the 17q25. 3 region (ARHGDIA, AZI1, C17orf56, DUS1L, FN3KRP, HGS, SIRT7, SCL25A10) was compared in TNBC with or without a gain in the TCGA study (Z-scores values retrieved from  [23] ). In agreement with our own observations, tumors with copy number gains have an increased gene expression compared to cases without the 17q25. 3 gain (Figure  S2  in Additional file 4). To gain insight into the importance of the genes in the 17q25. 3 region, we analyzed publically available shRNA screen databases (COLT-cancer and genomeRNAi, Table  S8  and S9 in Additional file 4)  [24, 25].
Amongst these seven cases, four displayed a BRCA1like profile, with a gain in 17q25. 3. In the unscreened TNBC, BRCA1 promoter methylation was observed in three tumors (20%) and the gain in 17q25. 3 was detected in one TNBC (Figure  7 ). Altogether, the gain was detected in 50% (5/10) of the TNBC with BRCA1 promoter methylation. We did not detect BRCA1 promoter methylation in the LumA, Lum B and HER2+ BC. To confirm our results, BRCA1 promoter methylation data (HM27 beta value data, available for 311 samples) were retrieved for the TCGA study (samples with HM27 beta value >0. 5 and BRCA1 expression downregulation were considered as methylated). Methylation of the BRCA1 promoter was not detected in the 14 BRCA1 mutated samples from the TCGA study, but was detected in 16% of the TNBC without BRCA1 mutation (5/31 samples), in 0% of LumA (0/101) samples, in only 5% of LumB (3/64) samples and in 0% of HER2+ (0/31) samples (Figure  S3  in Additional file 4).
Acknowledgements This work was financed through the  Plan National Cancer -Action 29 (Belgium)  and supported by the  PACS08  study (UNICANCER, France). Jean-Baptiste Poullet  ( Stat'Rgy, Naninne, Belgium ) built the SVM model. We thank  Aline Van Maanen  and  Loic Campione  for help with statistical analysis. We thank  Dr François Duhoux  for critically reviewing the manuscript.
GeneChip Operating Software (Affymetrix GCOS v. 1. 4) was used to obtain chip images, with quality control performed using the AffyQCReport software (Bioconductor, Berkeley, CA, USA). GSEA was performed on the Board Institute Platform  34, 35  and statistical significance (false discovery rate) was set at 0. 25. Microarray data are available from Gene Expression Omnibus (National Center for Biotechnology Information, Bethesda, MD, USA) under the accession number GSE45232. Cell culture. MiaPaCa2, Panc1, CaPan2 and BxPC-3 cells were obtained from ATCC (Manassas, VA, USA) and maintained in DMEM (Invitrogen, Carlsbad, CA, USA) supplemented with 10% FBS at 37 1C with 5% CO 2. INTERFERin reagent (Polyplus-transfection, New York, NY, USA) was used to perform siRNA transfections according to the manufacturer's protocol.
Primary antibodies used were as follows: anti-p27, anti-Cdk2, anti-Cdk4, anti-cyclin D1 and anti-Rb polyclonal antibodies were from Santa Cruz Biotechnologies; anti-Nupr1 monoclonal antibody was homemade and described previously; 17 FoxO3a monoclonal, phospho-FoxO3a (Ser253) polyclonal and phospho-Rb (Ser807/811) monoclonal antibodies were from Cell Signaling; anti-b-tubulin polyclonal and anti-vimentin monoclonal antibodies were from Sigma-Aldrich. Image acquisition was made in Fusion FX image acquisition system (Vilber Lourmat, VWR, Fontenay-sous-Bois, France) and bands were quantified using the ImageJ software (NIH, Bethesda, MD, USA). Reporter gene assay. The reporter assays were performed with Luciferase Assay System (Promega) according to the manufacturer's instructions. Cells were plated in 24-well plates and the following day co-transfected with 200 ng of pGl2-3 Â E2F and 20 ng of pRL-SV40 plasmids.
Data analysis was performed using the FlowJo (Treestar, Ashland, OR, USA) software. Statistics. Statistical analyses were performed using the unpaired two-tailed Student's t-test, and non-normal distribution, unpaired data were obtained using the Mann-Whitney test. All tests of significance were two-tailed and the level of significance was set at 0. 05. Values are expressed as mean±S. E. M. RT-qPCR data are representative of at least three independent experiments with technical duplicates completed.
Acknowledgements. We thank  Mariano Barbacid  and  Carmen Guerra  for their comments and  Marie-Noe  ´Lavaut for technical assistance. This work was supported by  La Ligue Contre le Cancer ,  INCa ,  Canceropole PACA ,  DGOS (labelization SIRIC)  and  INSERM  (to JLI),  National Institutes of Health  Grants  DK52913 , the  Mayo Clinic Center for Cell Signaling in Gastroenterology  ( P30DK084567 ) and the  Mayo Foundation  (to RU), and by  Fraternal Order of Eagles Cancer Award  (to GL). DG and MNG are supported by l' Association pour la Recherche sur le Cancer. DG and MNG are supported by  ARC  and TH is supported by  La Ligue Contre le Cancer.
MATERIALS AND METHODS Dermal bone samples of Compagopiscis (anterior ventrolateral plate), Polypterus (Cleithrum) and Rattus (Frontal) were imaged at beamline ID19, European Synchrotron Radiation Facility (ESRF), by propagation phase contrast synchrotron microtomography (PPC-SRmCT)  (Tafforeau et al. 2006). Samples were scanned with different set-ups according to size and density, with voxel sizes of 0. 678 mm (Rattus), 0. 744 mm (Polypterus), and 5. 05 mm (Compagopiscis). After ring-artefact correction, all data were converted from 32 to 8/16 bits for 3D processing. The scans were processed and rendered into threedimensional virtual models using VGStudioMax 2. 2 (Volume Graphics, Inc. , Heidelberg, Germany). The data will be made available through the ESRF palaeontology database (http:// paleo.
Acknowledgments We gratefully acknowledge the support of  ERC   Advanced Investigator Grant   233111  (P. E. A. , S. S. ),  Swedish Research Council  Grant  2012-4673 (T. H. )  and  ESRF  proposals  EC203  and  EC519  (S. S. , P. E. A. ), as well as institutional support from  Uppsala University  and  ESRF. Zerina Johanson  ( Natural History Museum, London ) and  Jan Ove Ebbestad  ( Museum of Evolution, Uppsala University ) kindly lent us specimens in their care.
Fig. 3  shows that the ranks corresponding to these values vary approximately from 0. 85 to 0. 95. These model calculations agree with calculations using various experimental data (not shown); in particular, this includes experimental data from PDB entries with low, medium and extremely high solvent content. This also agrees with the previous observation by  Ioerger & Sacchettini (2002). Other scaling methods, e. g. choosing another value [for example such that max n |(n)| = 100 or using a so-called 'absolute scale'] or another nonlinear scheme (for example,  Bhat, 1988; Lunin et al. , 2000)  are known, but we will not review this issue here in detail.
Incomplete low-resolution data sets 4. 2. 1. Explaining an apparent contradiction between low correlation coefficients and similar contour maps. A model F calc exp(i' calc ) Fourier synthesis (referred to as incomplete ) computed for PDB entry 1nh2 by (2) using reflection indices from the deposited data set  (Bleichenbacher et al. , 2003;  highest resolution 1. 9 A ˚; data completeness 95%) shows part of the structure very poorly (Fig. 2a ). Fig. 2.
(b) shows a model Fourier synthesis complete calculated with the same coefficients Table  1  Numerical comparison of the syntheses for the peptide model. For definition of the syntheses (Fig. 1 ) and the correlation coefficients between a and b , see the text. using all theoretically possible reflections up to 1. 9 A ˚resolution. The correlation coefficient CC calculated using (4) between the two syntheses is 0. 70. Since both syntheses were calculated with the model data, the only source of difference is the missing reflections, essentially the lowest resolution reflections (there are 300 reflections missing from 408 with resolution below 10 A ˚; all 59 reflections with resolution below 20 A ˚are missing). A similar comparison of incomplete with complete for another test case  (PDB entry 3cr1; MacElrevey et al.
Effect of low-resolution incompleteness on crystals with various solvent contents. The examples in x4. 2. 1 illustrate the effect of low-resolution data incompleteness. It seemed possible that the strength of this effect might depend on the fraction of bulk solvent in the crystal. We made a comparative analysis considering three cases of bulksolvent content: near the very common value of 50% (PDB entry 1zud;  Lehmann et al. , 2006 ; solvent content 0. 47), very high (PDB entry 1q09;  Changela et al. , 2003 ; solvent content 0. 84) and very low (PDB entry 1ous;  Loris et al. , 2003; solvent content 0. 24). For each of these structures, we calculated a complete set of structure factors F calc exp(i' calc ) from the atomic model at a resolution of 2 A ˚. We call the Fourier synthesis calculated with these structure factors 2-1 (n) = complete (n).
formally speaking, the calculation of contour maps (although the latter is not always the case in practice). These data are used for validation  (Bru ¨nger, 1992)  and to estimate statistical parameters  (Lunin & Skovoroda, 1995; Pannu & Read, 1996; Murshudov et al. , 1997). In general, the reflections for the test set are chosen randomly and uniformly across reciprocal space. There is an old and frequently asked question whether excluding such reflections noticeably distorts the Fourier contour maps. We do not analyze this question in detail here, but simply illustrate the effects for a typical protein structure under typical conditions. To do so, we used the IF2 structure that we recently solved  (Simonetti et al. , 2013; PDB entry 4b3x). The corresponding crystals belonged to space group P2 1 2 1 2 1 , with unit-cell parameters a = 45.
PyMOL (DeLano, 2002). PVA, TCT and PDA thank the  NIH  (grant  GM063210 ) and the  PHENIX Industrial Consortium  for support of the  PHENIX  project. This work was supported in part by the  US Department of Energy  under Contract No. DE-AC02-05CH11231  (PVA, TCT and PDA) and by  Russian Foundation for Basic Research  grant  13-04-00118 -a (VYL). AU thanks the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01  and Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI). We thank the referees for their very fruitful and constructive comments.
Life-history data are essential for providing answers to a wide range of questions in evolution, ecology, and conservation biology. While life history data for many species, especially plants, are available online, life history traits of European reptiles are available only widely scattered in different languages and primarily in printed media. For this reason, we generated a comprehensive trait database covering all European reptile species. Data were compiled by searching the peer-reviewed and non-peer-reviewed literature. The database covers the whole of Europe and neighbouring Asian and African countries. Traits were categorised under five main headings: Activity / Energy / Habitat; Phenology; Movement; Sexual Maturity; and Morphometry. To ensure that the data were standardised, we defined trait data categories before we started compiling data.