dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis No controlled data for clinical outcomes with MD1003 were available at the time of study design for sample size calculation. Data from seven MS patients treated with MD1003 for >3 months were used to estimate that >40% of MD1003-treated patients might show a reduction in disability. While progressive MS patients typically do not exhibit a confirmed decrease in EDSS scores,  13  and as a conservative approach, it was estimated that 10% of the placebo-treated patients might improve. It was estimated that at least 105 patients (70 in the MD1003 arm and 35 in the placebo arm) would be required to detect a difference in the proportion of patients demonstrating clinical improvement with 90% power at a 5% two-sided significance level. Differences in proportions were compared between arms with Fisher's exact test and differences in means with the non-parametric Mann-Whitney U test.
All statistical analyses were two-sided and conducted on an intention-to-treat (ITT) basis with a significance level of 5% using SAS software (version 9. 2). The primary endpoint was also analysed in patients who had assessments of EDSS or TW25 at screening, baseline, month 9 and month 12 without major protocol deviations (per-protocol population). The homogeneity of treatment effect for the primary endpoint was assessed in subgroup analyses stratified according to fampridine treatment, baseline EDSS value (4. 5-5. 5 or 6-7) and treatment centre. No imputation method was used to handle missing data for the primary endpoint as this could have overestimated the number of responding patients. The 'last observation carried forward' approach was used to handle missing data for all supportive analyses, except for the EDSS change, EDSS progression and TW25 change where no imputation was used.
Acknowledgements We thank  Yann Poulouin ,  MSc, the statistician at Biotrial, Rennes, France , who was responsible for the statistical analysis of study data and  Jacques Ropers, PharmD ,  URC Ambroise-Paré, Boulogne, France , for his help with the methodology and interpretation of data. Editorial support for the preparation of this manuscript was provided by  Jamie Ashman, PhD , of  Prism Ideas GmbH, Basel, Switzerland , and funded by  MedDay Pharmaceuticals. We also thank the patients and their families, the  MS-SPI  study coordinators, and all the staff at the study sites. Trial registration: EudraCT: 2013-002113-35.
X-ray diffraction analysis. Powder X-ray diffraction patterns were collected on a Philips X'Pert MPD X-ray diffractometer with a Bragg-Brentano geometry using Cu K α 1,2 radiation (10 < 2θ < 130°, with a step size of 0. 02°and a counting time of 30 s). The diffractograms were refined using the Rietveld method with the conventional reliability factors. The Fullprof program package was used. 16 The unit cell parameters, atomic positions and Debye-Waller factors were refined on the basis of the Fm3 ˉm space group corresponding to the elpasolite structure. ICP-OES measurements. The inductively Coupled Plasma -Optical Emission Spectroscopy (ICP-OES) was performed with a Varian 720ESD apparatus equipped with an argon plasma torch and a CDD detector display. The samples were prepared by their dissolution in chloric acid aqueous solutions.
Magnetic measurements. The magnetic measurements were performed in a static magnetic flux (DC) with a MPMS-5S SQUID (Superconducting Quantum Interference Device) apparatus. The magnetizations were performed between 2 and 100 K in an applied field ranging in the 0T-5T range within a zerofield-cooled process. Data collected at 100 K were fitted to extract the diamagnetic contribution from the sample holder and Rb 2 KInF 6 host.
Structural analyses The X-ray data obtained on a crushed LC (targeted concentration equal to 2%) and HC (targeted concentration equal to 10%) single crystal illustrate a pure elpasolite phase (Fig. 1 ). Fig. S1  † shows the platinum crucible after the two crystal growth attempts and the as-grown Ce-doped single crystals. Single crystals are located on the border of the crucible, whereas an opaque zone is visible in the centre (zone inside the dotted line). These solid residues were analysed by X-ray diffraction: polycrystalline mixtures were detected. These polycrystalline mixtures were composed of the Rb 2 KInF 6 elpasolite phase as the main phase and secondary KCe 2 F 9 (JCPDS no. 00-025-0632) and RbIn 3 F 10 (00-028-0908) phases for the batch with the lowest cerium content and KCeF 4 (no.
The reversible 2Ce 3+ + In 3+ ↔ 2Ce 4+ + In + process was studied as a function of the temperature and under several atmospheres. A 200 °C heat treatment seems to be sufficient to re-oxidize the In + UV-generated cations to the trivalent oxidation state. As expected, the redox process appears to be independent of the atmosphere. Finally, for the first time, cycling and printing tests were performed. A perfect "on-off switch" is visible for the LC Cedoped material, whereas a nice "bluish-green → orange switch" is observable for the HC Ce-doped material. Results are reproducible over at least four cycles, which confirms that these materials are in good agreement with UV dosimetry devices. Alain Garcia for fruitful discussion. The authors thank the CNRS and the Aquitaine region. This study was carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of a nonthematic program (ANR-2010-BLANC-0820) and in the frame of "the Investments for the future" Program IdEx Bordeaux -LAPHIA (ANR-10-IDEX-03-02).
Introduction Interpreting genetic variation from next-generation sequencing (NGS) datasets is essential for the advancement of personalized medicine. 1, 2 The vast majority of variants discovered by NGS are rare. 3, 4 Recent exome and genome sequencing studies have found that roughly 85% of nonsynonymous variants have alternate allele frequencies (AFs) less than 0. 5%, and roughly 100-400 rare nonsynonymous variants are discovered per sequenced individual. 3, 4 Rare coding variants play major roles in disease causation and might contribute to the missing heritability from genome-wide association studies. 5, 6 However, the majority of nonsynonymous variants discovered by NGS have unknown significance because experimental validation of large numbers of rare variants is infeasible, and association studies require prohibitively large sample sizes to detect rare variants with modest effect sizes with high statistical power.
Random Forest We trained a random forest on the set of variants described below by using the R ''randomForest'' package  32  with 1,000 binary classification trees. 33, 34 We selected the number of trees to be sufficiently large for the out-of-bag (OOB) error rate to plateau; sensitivity analyses showed that increasing the number of trees to 3,000 did not improve performance on the training dataset. The OOB prediction for a given training variant is the proportion of trees that classified the variant as pathogenic across only those trees in the forest that excluded the variant from their bootstrapped training sample. 33 Four features were selected at random as candidates for each split in the random forest trees, which was the default value for 18 features described below. To address the imbalance in the numbers of available disease and neutral training variants, we sampled the same number (n ¼ 6,182) of disease and neutral variants when generating the bootstrapped training set for each tree in the forest.
Training Variants REVEL was trained with putative rare neutral and disease missense variants. Disease variants were obtained from the Human Gene Mutation Database (HGMD)  35  version 2015. 2 and were restricted to the set of missense disease mutations (DMs) added to HGMD since August 1, 2012 to minimize overlap with variants previously used to train component features in the REVEL random forest. Missense exome sequencing variants (ESVs) were obtained from the Exome Sequencing Project (ESP)  4  European-American and African-American populations, the Atherosclerosis Risk in Communities (ARIC) study  36  European-American and African American populations, and the 1000 Genomes Project (KGP)  3  European, Yoruban, and Asian populations, as recorded in dbNSFP 31 version 2. 7. After excluding all disease variants in HGMD and the data sources for test sets 1 and 2 described below, the remaining missense ESVs were considered putatively neutral.
Features REVEL incorporates a total of 18 individual pathogenicity prediction scores from 13 tools as predictive features. MutPred scores were newly computed for this study with the UniProt 37 canonical protein sequence when available and the Ensembl 38 canonical transcript otherwise. PROVEAN  13  scores were obtained from dbNSFP v2. 9 (February 3, 2015). 16 additional scores were obtained from dbNSFP v2. 7 (September 12, 2014), including eight functional prediction scores (SIFT,  7  PolyPhen-2 HVAR and HDIV, LRT,  9  MutationTaster, MutationAssessor, 12 FATHMM v2. 3, and VEST 3. 0) and eight conservation scores (GERPþþ,  39  SiPhy,  40  three phyloP  41  scores for primates, placental mammals, and vertebrates, and three phastCons  42  scores for primates, placental mammals, and vertebrates).
Discussion REVEL is an ensemble method for predicting the pathogenicity of rare missense variants. Rare variants are likely to comprise the vast majority of variants of unknown significance discovered in future sequencing studies. We have shown that REVEL consistently has the best overall performance as compared to existing methods, particularly for distinguishing disease mutations from uncommon neutral missense variants with an AF below 3%. To facilitate use by clinicians and researchers, we have pre-computed REVEL scores for all missense variants in dbNSFP v2. 7, a database of all potential nonsynonymous single-nucleotide variants in the human genome. REVEL thus addresses the need for a pathogenicity prediction tool with improved accuracy for interpreting rare genetic variants. The REVEL method has several strengths.
A key limitation of this study and others is the reliance on pathogenicity assertions from existing databases, which might be inaccurate and incomplete. Misclassification of training and test variants as disease or neutral would limit both the accuracy of the prediction method and the resulting performance estimates. Nonetheless, we expect that the putative disease variants used to train REVEL are enriched for true disease variants as compared to the putative neutral variants, allowing identification of key predictive features of pathogenic variants. An additional complication is that existing pathogenicity assertions for some variants might have been based in part on predictions from popular tools, such as SIFT and Poly-Phen-2, potentially resulting in inflated performance of these predictors and ensemble scores that use them.
Finally, the performance of REVEL and other ensemble methods is limited by the accuracy of the component predictors and could benefit from inclusion of additional predictors as they become available in the future. REVEL had the highest overall performance of any method in independent test sets, although its performance on common variants with AFs > 3%-5% was slightly worse than that of MetaLR or MetaSVM. The strong overall performance of REVEL reflects the fact that the majority of neutral variants in the training and test datasets were rare, as expected for novel variants discovered by NGS. Furthermore, although we carefully removed all variants used to train REVEL and its constituent features from the two test sets, we did not systematically exclude training variants for the comparator ensemble scores; thus, the performance estimates for the comparators could be overly optimistic.
MetaLR and MetaSVM had the next highest overall performance. These two ensemble methods included many of the same predictive features as REVEL, except for VEST, MutPred, and PROVEAN, and also included the AF, which could contribute to their greater sensitivity to the neutral variant AF. Condel is a weighted average of FATHMM and MutationAssessor, and its lower performance relative to some ensemble methods could be due to the inclusion of fewer predictive features. Eigen employs an unsupervised approach to separate variants into two classes and also uses fewer predictive features than REVEL, MetaLR, and MetaSVM. CADD and DANN differ from the other ensemble methods in their use of many basic genomic and protein annotations from ENCODE and Ensembl as features in addition to functional predictions from PolyPhen-2 and SIFT.
However, KGGSeq adaptively selects an optimal subset of features rather than using all features to predict the pathogenicity of each variant, in part to allow exclusion of features with missing data. Possible explanations for the improved performance of REVEL over KGGSeq include use of all features for all variants by first imputing missing scores, importance of MutPred as a predictive feature, and use of a random forest approach rather than logistic regression. 0] [21] [22] [23] [24] [25] [26] [27] [28]  The top-performing individual tools on our training dataset were VEST,  15  FATHMM,  14  and MutPred, 8 consistent with their high importance in the REVEL random forest. VEST predictions are based on a particularly large set of 86 basic genomic and protein annotations and had the best performance among the individual tools.
REVEL can be used to prioritize the most likely clinically or functionally relevant variants among the sea of rare variants that are increasingly discovered as sequencing studies expand in scale. For example, REVEL scores have been used by the International Consortium of Prostate Cancer Genetics as weights for combining variants discovered by exome sequencing in gene-level case-control studies. Pre-computed REVEL pathogenicity scores for all possible human missense variants, based on GENCODE v9 gene annotations  54  for hg19, are available for download (see Web Resources). To aid interpretation, we also provide estimates of REVEL sensitivity and specificity for different score thresholds and the quantiles of the REVEL score in over one million ESVs observed in KGP, ESP, and ARIC. Future studies might explore the application of REVEL to specific genes to evaluate its clinical utility for interpreting variants of unknown significance for a broad spectrum of clinical conditions.
In the last section, several numerical results are presented, discussed and compared with those provided by a commercial Finite Element software. 1 Formalism  In this paper, the cylindrical surface presents a rotational symmetry, the variable θ is thus omitted in what follows. The coordinate matching consists in connecting two surfaces (interfaces) of coordinates by an analytical transition function denoted by h(u). Fig. 2  presents a slice view of the geometry: the tube's wall is delimited by two surfaces represented by two profiles a 1 (z) and a 2 (z). In order to be able to write easily boundary conditions on the two walls on the tube, from the usual cylindrical coordinates system (r, θ, z), let us introduce a change of coordinates so that the the interfaces are aligned along the coordinates lines.
METHODS This was a population-based study of a cohort of patients identified from the EGB database, a 1/97th representative random sample of the Système national d'information inter-régimes de l'Assurance maladie (SNIIR-AM),  [19] [20] [21]  a French nationwide population-based record of individual and anonymized data on all reimbursements for health care utilization, including therapy and outpatient medical and nursing care. No direct information on the medical indication is linked with each reimbursement, but the SNIIR-AM includes information on long-term disease (LTD) status coded in International Classification of Diseases, 10th Revision (ICD-10) codes. LTD status allows patients to receive treatment for severe and costly conditions without out-of-pocket payment. SNIIR-AM also contains information on free-access-to-care status, which enables patients of lower socioeconomic status to receive free medical care.
Information from the SNIIR-AM database and medical information from the French hospital discharge database (Programme de Médicalisation des Systèmes d'Information) about all patients admitted to hospital in France, including discharge diagnoses coded in ICD-10 codes, medical procedures, and French diagnosis-related groups, are cross-referenced. This observational study was conducted on anonymized data, and the National Informatics and Liberty Committee has delivered an overall authorization to use EGB data for research purposes. This study was performed after approval by the French Institute for Health Data (Institut des Données de Santé, approval no. 94, September 9, 2014).
Likewise, patients with cystic fibrosis were excluded (E84 ICD-10 code). So were patients receiving omalizumab (R03DX05 Anatomical Therapeutic Chemical code) because their asthma is mostly managed in secondary care by respiratory physicians. For each patient, we defined the entry date as the date of the third dispensation of respiratory medications that resulted in case identification. Within the cohort, we selected patients who experienced asthma-related hospitalization, defined as a hospital discharge with asthma (J45 and J46 ICD-10 codes) as primary or related reason for hospitalization. The date of the first asthma-related hospitalization was defined as the index date. In line with similar studies,  16, 22  we requested at least a 12-month baseline period between the entry date and the index date, during which patients with asthma-related hospitalization were excluded to ensure that there were sufficient longitudinal data to assess drugs received before the index date.
ANALYSIS All analyses were performed in SAS V. 9. 3 (SAS Institute, Carry, NC). Subjects were categorized by an unsupervised classification algorithm that classified patients on the basis of controller therapy recorded, that is, ICS in a single canister, longacting beta agonists (LABAs) in a single canister, or fixed-dose combinations (FDCs) of LABAs and ICS. Drug use was assessed in the 12 months before the index date, even if patients had more than 12 months between entry and index dates. Ward's minimum-variance hierarchical clustering was performed using an agglomerative (bottom-up) approach, and Ward's linkage using 3 numerical variables (number of units of LABAs alone, ICS alone, and FDCs of LABAs þ ICS) to categorize patients on drug received in the 12 months before the index date. With this algorithm, every possible cluster combination is considered at each step of agglomerative hierarchical clustering, and samples are merged into larger clusters to minimize the within-cluster sum of squares and to maximize the between-cluster sum of squares.
To compare the clusters, ANOVA, Kruskal-Wallis, and chi-square tests were used. Ward's method is a widely used algorithm that minimizes the variance within clusters. 23, 24 In Ward's minimum-variance method, the distance between 2 clusters is the ANOVA sum of squares between the 2 clusters added up over all the variables. At each generation, the within-cluster sum of squares is minimized over all partitions obtainable by merging 2 clusters from the previous generation. The sums of squares are easier to interpret when they are divided by the total sum of squares to give proportions of variance (squared semipartial correlations). SAS procedures CLUSTER and TREE were used for the cluster analysis and illustration. The following variables were explored as illustrative variables: age at entry date, number of visits to general practitioners (GPs), lung specialists, pediatricians, and other specialists in the 12 months before the index date, and the number of dispensations of short-acting beta agonists (SABAs), leukotriene receptor antagonists (LTRAs), and oral corticosteroids (OCs) during the 3-month period before the index date.
RESULTS Between 2006 and 2014, we identified 73,692 patients with 3 or more dispensations for asthma-related medications during any 12-month period, corresponding to 11. 3% of the French population recorded in the EGB between 2006 and 2014. Among selected patients, 22,586 (30. 6%) were aged 6 to 40 years at the third dispensation. We excluded 259 patients (1. 1%) with chronic obstructive pulmonary disease, 27 patients (0. 1%) with cystic fibrosis, and 5 patients (0. 02%) with dispensation of omalizumab. Among 17,846 remaining patients with at least 12 months of follow-up data available before and after the entry date, we finally selected 275 patients (1. 5%) with asthma-related hospitalization of 12 months or more after the entry date (Figure  1 ).
However, hospitalization is less preventable in this group, as the absence of controller therapy suggests intermittent asthma, at lower risk for adverse outcomes than persistent asthma. Information on symptoms and airflow limitation would have allowed characterization of persistent asthma and asthma severity, but these variables are not recorded in the database. Altogether, these data suggest a population of more deprived patients, not appropriately managed for their asthma, possibly poor perceivers of bronchoconstriction, and possibly with rapidly deteriorating asthma. Patients from cluster 2 had regular controller therapy (FDCs), besides OCs and SABAs, and relatively more frequent medical contacts. Furthermore, the high frequency of free-access-to-care status (18%) and the high percentage of LTD status (27%) suggest more severe and not well-controlled asthma, possibly with poor quality of use of inhalers.
Actually, the prevalence of asthma in our study was comparable to the prevalence observed in other studies,  36  supporting the validity of our identification algorithm. Additional limitations were the absence of information in the SNIR-AM on lung function or the therapy that was actually prescribed by health care providers. In this study, treatment clusters were identified only in hospitalized patients, but different phenotypes based on medication utilization could possibly be identified in all patients (hospitalized or not). Indeed, it would have been desirable to compare findings in the hospitalized cohort to findings in a control group of patients who were not hospitalized. It was, however, not possible to identify a valid control group in this study because asthma discharge diagnoses were specifically used to identify cases with maximal validity, which would not be true for nonhospitalized controls, for whom equivalent diagnostic data would be mostly missing.
Another potential marker of asthma, acute dispensings of OCs, would not be as reliable as a hospital discharge diagnosis because OCs may be used for several indications beyond asthma. This study should also be replicated using larger sample sizes, targeting populations at high risk, such as patients who experienced near-fatal asthma exacerbation or death. Furthermore, in our study, dispensation of controller therapy was evaluated in the 12 months before the hospitalization. However, in severe acute asthma, exacerbation could happen quickly, and precise information on drug use during the shorter time span before exacerbation could be more informative. Furthermore, clinical variables might be added to the hierarchical clustering method to better characterize profiles of patients. Also, data are not capturing "use," but dispensing as a surrogate for use.