dataesr/scientific-paragraphs-categorization huggingface dataset:
Five micrograms of DNA were enriched by micro droplet PCR procedure (Raindance technology, Billerica, MA) to target 11,575 exons. Sorting and calling of SNP/InDel were performed using SAMTOOL and GATK softwares. Novelty was assessed by filtering the variants against a set of polymorphisms that are available in public databases such as dbSNP (http://www. ncbi. nlm. nih. gov/projects/SNP/), 1000 genomes (http://browser. 1000genomes. org/in-dex. html), Exome Variant Server (http://evs. gs. washington. edu/EVS/), and ExaC (http://exac. broadinstitute. org/). Only nonsynonymous variants or changes affecting splice sites were analyzed. All sequence variants were prioritized by scoring phylogenetic conservation and functional impact (SIFT and Polyphen-2). Candidate variants were selected and confirmed by Sanger sequencing, using the 3500XL Genetic analyzer and the Big Dye cycle sequencing Kit of Applied Biosystem technology.
Molecular genetics Sequencing of X-exome from patient II-4 and patient II-2 identified two missense variants that could be considered as potentially pathogenic according to our filters. The first variant was a c. 5922G>T substitution in exon 41 of the MED12 gene (GenBank accession number: NM_005120) leading to the substitution of a highly conserved Glutamine to a Histidine (p. Gln1974His). This mutation was absent from public databases of control individuals (Exome Variant Server, 1000 genomes, dbSNP135, and ExaC) and in > in-house 200 X-exomes of index patients from other XLID families. This mutation was confirmed by Sanger sequencing in the three affected boys as well as in the mother but was absent in the unaffected brother. This substitution was predicted to be deleterous by SIFT (score: 0) and possibly damaging by Polyphen-2 (score: 0.
642) softwares. Predictions with Mutation Taster were in favor of a diseasecausing variant (p value: 1). X-chromosome inactivation pattern was not skewed in the mother (60%;40%). The second variant was the c. 955G>A/p. Ala319Thr of the OGT gene (GeneBank accession number: NM_181672. 2). This variant has not been reported in public databases of control individuals (Exome Variant Server, 1000 genomes, dbSNP135, and ExaC data set) and in >200 X-exomes. In silico predictions are not in favor of a potentially pathogenic role. The OGT gene variant co-segregated with MED12 mutation in the three affected males. It was not found in the nonaffected brother.
Acknowledgments We are grateful to patients. We thank the team of the  Necher University Hospital High -throughput sequencing platform for the technical support in performing Xexome sequencing. This work was supported by the  Imagine Foundation  ( Necker Hospital ) and by INSERM  U781. We specially appreciated the clinical and genetic supervision of  Valerie Cormier-Daire  and  Jean Paul Bonnefont. We thank the  Physician Doctor Mohand Ousaid Sidali  from  Pi erre et Marie Curie University (Paris VI ) for his help in patients' photo development.
These response curves and their approximations are discussed in the results Section 3. 1. We are using standard numerical methods of bifurcation theory as implemented in the AUTO library with the python interface PyDSTools  (Clewley 2012). The local stability analysis for the fixed points of the dynamical system consists in the study of the set of parameters where some eigenvalues of the Jacobian are zero. Further study of the Jacobian are indicative of the type of instability at these points  (Kuznetsov 1998). A fold (or saddle-node) bifurcation curve separates regions with one stable fixed point from bistable regions with two stable fixed points separated by an unstable fixed point. Along those curves, the Jacobian has a zero eigenvalue and a stable fixed point and an unstable fixed point annihilate.
Methods All the following data were collected for each patient. All the exams were performed the same day for one patient. Clinical data. Interrogation and clinical data examination were performed to each patient to collect the following information: age, weight, height, body mass index (BMI), comorbidities, age at onset of menopause, hormonal replacement therapy, family history of fractures, personal history of fractures and their incidence condition, current treatment and calcium dietary intake. Spinal radiographs and vertebral fracture assessment. Each patient had antero-posterior and lateral thoracic and lumbar spine radiographs. Two trained investigators (at least a 10-year experience in rheumatology and bone diseases) who were unaware of the patient BMD status, analyzed radiographs independently in three steps: (a) detect vertebral fracture; (b) determine benign or malignant origin; and (c) classify the fracture as mild, moderate or severe.
Statistical analyses Statistical analysis was performed using the software Statistical Package for the Social Sciences (SPSS V. 15. 0. 1, SPSS Inc. , IBM Corporation, Chicago, IL, USA). Baseline characteristics of patients were expressed in mean ± 1 s. d. for continuous variables and n (%) for categorical variables. The comparison of groups was performed for continuous variables by analysis of variance and for binary variables by the Pearson Χ 2. Differences were considered significant when P o0. 05. Logistic regression was performed to analyze factors associated with at least one vertebral fracture. As number of vertebral fractures is a criterion of fracture severity, logistic regression was also performed to analyze factors associated with at least two vertebral fractures. Multivariate analyses comprised age, sex, BMI, low BMD with a T-score o-2.
Main Outcome Measure: The ability of the algorithm screening tool is to identify patients at nutrition risk, to guide clinicians in logical renal-modified nutrition care process chains including follow-up on relevant parameters, and capture change in outcomes over 3 months. Statistics were performed using SPSS version 20. 0 and significance was set at P ,. 05. Results: One hundred patients on HD, enrolled by 29 dietitians, were included in this analysis. The average number of out-of-range screening parameters per patient was 3. 7 (standard deviation 1. 5, range 1-7), and the most prevalent risk factors were elevated parathyroid hormone (PTH; 62. 8%) and low serum cholesterol (56. 5%). At the initial screening step, 8 of the 14 factors led to chains with nonrandom selection patterns (by c 2 test with P ,.
Experimental procedures Ethics statement. All experimental protocols were approved by the French Institute of Health and Medical Research (INSERM). Epidural recordings. Recordings were performed 24/7 on adult male Wistar rats (n = 5). A recording skull screw was secured above hippocampal CA1 (-4. 0 mm posterior, +2. 0 mm lateral), and a reference skull screw was secured above cerebellum. The leads of the telemetry probe (Data Science International (DSI), St. Paul, MN, USA) were wrapped around the recording and reference screws and screws were all encased in dental cement. Rats were allowed 7 days post-surgical recovery before any further experimental procedures were conducted. Status epilepticus (SE) was induced with kainic acid injected once per hour at a dose of 5 mg/kg until SE was observed.
Expression of MDM2 was estimated from densitometry analysis performed with Bio-PROFIL Bio 1-D software (Vilbert Lourmat) on chemiluminescence images acquired using CHEMI-SMART 5000 documentation system and Chemi-Capt software (Vilbert Lourmat). Values of MDM2 were normalized against their correspondent actin value and then against DMSO-treated cells in the case of A549 and HCT116 and EV-or p53-transfected and DMSO-treated cells in the case of H1299 and Saos-2. 500 ng of DNA were used in transfection. (B) Samples from A were analyzed in parallel for the effect of p53 expression on endogenous mdm2 mRNA levels using relative RT-qPCR for p53-positive (top) and EV-of p53-transfected p53-negative (below) cell lines. Values were normalized against actin and are presented as fold change relative to DMSO-treated cells in the case of HCT116 and A549 and relative to EV-transfected and DMSO-treated cells for H1299 and Saos-2, set to 1 (mean § s.
For all experiments, 2 £ 10 5 cells were seeded 24 h before transfection in 6-well plates. Thapsigargin (THAP. , 100 mM) or DMSO treatments were done for 16 h. MDM2 was detected using 4B2 monoclonal antibody, HA-MDM2 was detected with an anti HA monoclonal antibody and both endogenous and exogenous p53 isoforms were detected using ACMDD serum (rabbit polyclonal antibody raised against peptide MDDLMLSPDDIEQC recognizing the N-terminus of p53/ 47). 30 BiP expression was used as a positive control for ER stress induction and b-Actin as loading control. Blots represent n 2. For all RT-qPCR, primers used to amplify MDM2 are: Forward 5 0 ATCTACAGGGACGCCATC 3 0 and Reverse 5 0 CTGATCCAACCAATCACCTGAA 3 0. In B top, Student's t-test compared data to the reference point as indicated. In B bottom and C down left, 2-way ANOVA compared data of the effect of treatment and transfection of p53 on mdm2 mRNA expression as indicated (for all, **P < 0.
The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses.
Motivating example: The DALIA-1 trial The DALIA-1 trial is a phase 1 therapeutic HIV vaccine trial whose details are described on http://clinicaltrials. gov (ClinicalTrial. gov identifier: NCT00796770) and in  [5]. The vaccine candidate was based on ex-vivo generated interferon-α dendritic cells loaded with HIV-1 lipopeptides and activated with lipopolysaccharide. The objectives of the trial were to evaluate the safety of the strategy and to evaluate the immune response to the vaccine. For the purpose of the present paper, we focus on the gene expression component of this study. Gene abundance in whole blood was measured through Illumina HumanHT-12 v4 Expression BeadChips. The DALIA-1 trial design. All of the nineteen HIV infected patients received the therapeutic vaccine while under antiretroviral treatment.
The patients received four injections at week 0, 4, 8 and 12. This vaccination period was followed by an antiretroviral treatment interruption (ATI) at week 24. The patients were followed up to week 48. Antiretroviral treatment was resumed from week 24 to week 48 at any time under the following criteria: i) if the patients or their doctors wished so; ii) if CD4+ T-cell count was < 350 cells/μL and < 25% of total lymphocytes. Fourteen time points (five in pre-ATI from week 0 to week 22, and nine in post-ATI from week 24 to week 44) were used for this analysis (see  Fig 6). One patient was removed from the analysis as his/her antiretroviral treatment compliance was irregular during the vaccination phase. In the following analysis, two distinct datasets were considered: pre-ATI and post-ATI. The two datasets were normalized separately-via a normal-exponential convolution model  [55, 56] , followed by the application of the ComBat method  [57]  to correct for batch effects.
Splitting the data allows us to study separately the vaccine effect and the treatment interruption, otherwise the ATI effect would mask any noticeable vaccine effect, because of the huge modification of gene expression related to viral replication  [1, 2]. We investigated the gene sets defined by Chaussabel et al. [13] , which are oriented towards the immune system. The definition and annotations of those 260 gene sets (called 'Modules') are available online (http://www. biir. net/public_wikis/module_annotation/V2_Trial_8_Modules). Pre-ATI: The vaccination phase. During the vaccination period, a standard gene-by-gene mixed model analysis, with a cubic polynomial function of time, did not found any significant change of gene abundance at a 5% False Discovery Rate (see Table  1 ). However, during this vaccination phase, cytokines production analysis of the same blood samples (as measured by Luminex or intracellular staining) have showed that a response was induced by the vaccine at week 16  [5].
Post-ATI: After antiretroviral treatment interruption. Eq (1) was then fitted to the data after antiretroviral treatment interruption that occurred at week 24: 216 gene sets out of 260 were found to be significant with a cubic polynomial function of time. Fig 4 displays the raw and estimated expressions of nine of those significant gene sets. It features heterogeneous gene sets, such as M4. 16 and M7. 1, which are both also good examples of the shrinkage that occurs with the estimations (see Methods). Meanwhile, GSEA for time series identifies 67 significant, whereas CAMERA identified only 2. These results are consistent with the lower statistical power found in our simulation study. The large number of significant gene sets post-ATI illustrates the tremendous impact of the treatment interruption on the organism.
[13]. They investigated the changes of gene expression in those 62 gene sets for each of the seven time points from day 1 to day 28 in regards of the baseline, that was considered as the average of the two measurements at days -7 and 0. So hierarchical structure of the data was not taken into account. The three arms (saline, flu and pneumococcal) were analyzed separately, and only significant gene sets at day 1 and day 7 (not further on) are presented in their paper. Changes in eight gene sets were common to both vaccines: M4. 6 (inflammation), M6. 6 and M6. 13 (apoptosis/cell death) and modules M4. 1 and M4. 15 (T cells), M4. 3 (protein synthesis), M5. 11, and M6. 9 (no functional annotation). Nine gene sets were uniquely changing after the influenza vaccine, three were associated with antiviral responses (M1.
2, M3. 4, M5. 12) and included genes coding for interferon (IFN)inducible molecules. Six gene sets were uniquely responsive to the pneumococcal vaccine. Of these, five were modules including genes associated with inflammation: M3. 2, M4. 2, M4. 13, M5. 1 and M5. 7. TcGSA results. To compare the gene expression at the gene set level between the vaccine arm (flu or pneumococcal) and the placebo (saline) arm, we applied TcGSA on these data using Eq (2) (for each vaccine separately). In both vaccines, a large response is observed at Day 1. To avoid smoothing down the expression at t i = 1, we used the following function of time to model the dynamic evolution of gene expression: f m ðt i Þ ¼ ðZ m þ h g Þ1 ft i ¼1g þ ðZ 0 m þ h 0 g Þ1 ft i 6 ¼1g with ðh g ; h 0 g Þ $ N ð0; S h Þ, and m the group (either vaccine or placebo).
Assessment of statistical performances on simulated data In order to assess the behavior of the proposed method, a simulation study of TcGSA has been performed. The simulation scheme was chosen to be very close to the motivating exemple: the DALIA-1 trial. In each simulation run, gene expression data was simulated for 20 patients at 8 time points. 5,000 genes were simulated, divided into 100 non overlapping gene sets of 50 genes each. Each of the 100 gene sets was either simulated under (H 0 ) or (H 1 ). The proportion of genes under (H 1 ) varied between 0%, 27% (which corresponds to results found in pre-ATI) and 85% (which corresponds to results found in post-ATI). When there are gene sets simulated under (H 1 ), 75% of those were homogeneous (simulated with parameters close to those estimated for gene set M1.
e. for declaring a gene set significant when it actually is. Four methods are evaluated: i) TcGSA, the proposed approach, fitted either with a linear or with a cubic function of time; ii) the GlobalANCOVA procedure  [29]  in which p-values are either computed by permutation (10,000) or approximated; iii) the GSEA for time series  [14] , iv) CAMERA  [31]. Default values are used for the various methods (see S1 Text and S1 Software). doi:10. 1371/journal. pcbi. 1004310. t002 (MAR) processes (i. e. when the probability of missing data is associated to the previously measured information) may lead to biased estimates when using least squares or generalized estimating equations  [37]. TcGSA can cope with such issues because of the use of Maximum Likelihood to estimate the parameters of the mixed models.
This is an advantage of the TcGSA approach over those of Hummel et al. [29]  or Nueda et al. [32]. An increasing number of gene sets databases are available, such as KEGG  [11] , Gene Ontology  [12] , Modules  [13]. An immune related subset of Gene Ontology as well as an immune related subset of KEGG pathway have been used in additional analyses (see S3 Fig). The choice of the database used for the analysis impacts the interpretability but also the limitations of TcGSA. The efficiency of TcGSA will vary according to the number of genes represented in each gene set. The size of a given gene set has an impact on its significance, as the more genes it includes, the more likely a significant variation will be detected. The average size of the Chaussabel's V2 modules is 55 genes. 17% of the 260 modules include more than 100 genes, and 31% less than 20 genes.
For small gene sets, the normality assumptions of random effects of the Eqs (1), (1bis) and (  2 ) are questionable (even though inference should still be robust  [59] ). Nevertheless, even though we expect that the models could be misspecified in many cases (if not all), the objective of such an analysis is to detect any significant variation over time (in the spirit of Shahbaba et al. [19] , a significant variability of the trajectories between the genes inside a gene set indicates a change over time regardless of the fixed effects specification). The use of flexible time functions may help to get a better fit of dynamics although beyond cubic polynomials it did not have a substantial impact in our motivating example-see Table  1. These results vary according to the dataset and the number of time points available, and we recommend to try several models to check the robustness of the results.
Acknowledgments The authors wish to thank all participants of the DALIA-1 trial and the members of the trial committees and of the  ANRS/VRI  study team. Trial Committee:  Jacques Banchereau ,  Geneviève Chêne ,  Carson Harrod ,  Christine Lacabaratz ,  Yves Levy ,  Monica Montes ,  Karolina Palucka ,  Laura Richert , Louis Sloan. DSMB members:  William Duncan ,  Roy M. Gulick ,  Daniel R. Kuritzkes ,  James Neaton ,  Richard Pollard  (Chair). Event Validation Committee members:  Joseph Fay ,  Ronald Mitsuyasu ,  Jean-Paul Viard. Trial management:  Derek Blankenship ,  Céline Boucherie ,  David Jutras ,  Bryan King ,  Sophie Pérusat ,  Elisa Priest ,  Charlie Quinn ,  Anna Laura Ross ,  Mathieu Surenaud. Vaccine/GMP team:  Susan Burkeholder ,  Amanda Cobb ,  Charles McWilliams ,  Jennifer Finholt-Perry ,  Lee Roberts.
This work was supported by the  Investissements d'Avenir program  managed by the  ANR  under reference  ANR-10-LABX-77  and by the  Vaccine Research Institute (VRI) , F-94010 Creteil, France. recipient of a Ph. D. fellowship from The  DALIA-1 study  was supported by a grant from the  French National Agency for Research on AIDS and Viral Hepatitis (ANRS: L'Agence Nationale pour la Recherche contre le SIDA  et les hépatites virales). The VRI participated in study design, data collection and analysis for the DALIA trial. The funders had no role in decision to publish, or preparation of the manuscript.
The data sets supporting the results of this article are available in the GEO (Gene Expression Omnibus) repository. The gene expression data of the DALIA-1 trial are available under the following identifier: GSE46734 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE46734). The data from the vaccine study of Obermoser et al. are available under the following identifier: GSE30101 (http://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE30101).
CMR analysis CMR images were analyzed by two blinded experienced observers and in case of discordance by a third observer for consensus. Myocardial segmentation was assessed using the 17-segment model according to the consensus of the North American Society of Myocardial Imaging. Endocardial and epicardial borders were manually traced on end-diastolic and end-systolic short-axis cine images from base to apex. LV volumes and ejection fraction were derived by summation of epicardial and endocardial contours (Argus® Software, Siemens, Erlangen, Germany). Myocardial edema was assessed as a hypersignal that was present before contrast injection on black-blood T2-weighted images. The extent of myocardial edema on T2-weighted images, of early gadolinium enhancement (EGE) on cine images and of late gadolinium enhancement (LGE) was assessed through the use of a semi-quantitative analysis.
Each of the 17 LV myocardial segments was divided into 3 layers (outer, mid and inner). The presence of myocardial lesions was determined in each of the 3 layers for each myocardial segment. Finally, myocardial lesions on CMR data were delineated by planimetry using an automated threshold greater than 4SD above mean myocardial signal intensity and expressed as percentage of LV myocardial surface area. Myocardial damage on LGE-CMR was localized in details in the sub-epicardium, midwall or transmural and its shape was characterized as nodular or linear.
Statistical analysis Statistical analysis was performed using STATA 10. 1 (Statacorp LP, Texas USA). Baseline descriptive statistics are presented as frequency and percentage for categorical variables and mean ± SD or median (interquartile range) for continuous variables. The normality of data was assessed using the Skewness and Kurtosis normality test. The differences between the groups were assessed with the chi-square test or Fisher exact test for categorical data and Student t-test for continuous data. Differences between groups were considered statistically significant if the 95 % CI around the difference of the means did not contain zero. In a prespecified analysis, baseline predictors for MACE were identified using Cox multivariate analysis including variables showing a p value <0. 20 in association with MACE by univariate analysis.
Acknowledgements The authors acknowledge the  Jacques Cartier Hospital Review Board. They acknowledge  Mr Gilles AUVRAY ,  Florent BERGHMAN ,  Jean-Luc BOUCHE , and  Cyrille SCNHOELLER  for their technical skills and CMR acquisitions throughout the study. They acknowledge  Mrs Sandy SOURDEVAL  for administrative assistance. Received: 21 April 2015 Accepted: 17 August 2015.
Background: Monocentric cohorts suggested that radiation-induced CD8 T-lymphocyte apoptosis (RILA) can predict late toxicity after curative intent radiotherapy (RT). We assessed the role of RILA as a predictor of breast fibrosis (bf+) after adjuvant breast RT in a prospective multicenter trial. Methods: A total of 502 breast-cancer patients (pts) treated by conservative surgery and adjuvant RT were recruited at ten centers. RILA was assessed before RT by flow cytometry. Impact of RILA on bf+ (primary endpoint) or relapse was assessed using a competing risk method. Receiver-operator characteristic (ROC) curve analyses were also performed in intention to treat. This study is registered with ClinicalTrials. gov, number NCT00893035 and final analyses are presented here. Findings: Four hundred and fifty-six pts (90.