dataesr/scientific-paragraphs-categorization huggingface dataset:
The current study had two main aims. The first aim was to identify clinical phenotypes defined by a limited set of clinical variables that are easily acquired and can, therefore, be used in routine clinical practice or trials, and to assess their reproducibility and stability over time. The second aim was to describe these clinical phenotypes by their molecular characteristics, as assessed by whole genome expression of bronchial samples acquired by bronchoscopic biopsies and brushings. The study used clinical and biomarker data from two independent cohorts. Initial clustering that created the clinical phenotypes was performed on clinical data acquired in the ADEPT (Airway Disease Endotyping for Personalized Therapeutics) study  [10]  involving 158 asthmatics. The phenotypes thereby produced were assessed for stability within the longitudinal arm of the ADEPT study and validated for reproducibility using data from a subset of asthmatics from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcome) study  [11]  constituting an independent validation cohort.
Study design and study populations The ADEPT and U-BIOPRED study designs and study populations are published elsewhere  [10, 11]  and are described briefly here. The current study consisted of a combination of cross-sectional (ADEPT and U-BIOPRED) and longitudinal (ADEPT) studies. Participants in both cohorts were assessed clinically at baseline, using prespecified protocols. Clinical phenotypes were created by clustering 9 clinical variables (see Clustering methodology in the next paragraph) from the ADEPT study which served as the training set. Matching datasets from U-BIOPRED, excluding airway hyperresponsiveness (AHR), which was not assessed in U-BIOPRED, were used as the validation set for the ADEPT study-derived clinical phenotypes. After 3, 6 and 12 months of follow up, the ADEPT study participants were further reassessed to enable further validation, i.
Clinical phenotypes were created by clustering 9 clinical variables (see Clustering methodology in the next paragraph) from the ADEPT study which served as the training set. Matching datasets from U-BIOPRED, excluding airway hyperresponsiveness (AHR), which was not assessed in U-BIOPRED, were used as the validation set for the ADEPT study-derived clinical phenotypes. After 3, 6 and 12 months of follow up, the ADEPT study participants were further reassessed to enable further validation, i. e. assessment of the stability of the phenotypes through classification of the followed up participants into the ADEPT study-derived phenotypes created with baseline data. Relevant biological samples were collected to define the pathobiologic (transcriptomic) characteristics of the clinical phenotypes. Both the ADEPT  [10]  and U-BIOPRED  [11]  studies collected serum for protein array analysis.
GLMnet classification The GLMNet multinomial logistic regression classification algorithm (R package GLMNet v1. 9-8 7  [15] , http://www. jstatsoft. org/v33/i01/) was used with the R-package Caret (Classification and Regression Training, v. 6. 0-35; http://caret. r-forge. r-project. org/) for model cross-validation (leave-one-out cross-validation to identify optimal GLMNet alpha and lambda tuning parameters). Two models were used. Model A, the optimal classification model for ADEPT-asthma baseline clusters (Model A), based on the 8 clinical clustering variables (excluding PC20), was applied to classify U-BIOPRED subjects (n = 397, including patients on OCS and smokers), limited only to those without missing data for these 8 clustering variables. PC20 was not included in the classification because this variable was not longitudinally assessed in ADEPT and was unavailable for most U-BIOPRED participants.
The log 2 -normalized data matrix was imported into OmicSoft ArrayStudio software (Cary, NC; www. omicsoft. com) for subsequent analysis. For ADEPT biopsies and U-BIOPRED brushings, batch effects from RNA processing sets were observed (2 sets for each sample type), with the batch effect adjusted in the data matrices using linear modeling of batch (as random factor) and cohort. A log 2 -intensity threshold of 5. 5 for ADEPT biopsies and 4. 75 for U-BIOPRED brushings was established as the limit of reliable quantification (LOD) based on the 90 th percentile signal of merged nonspecific probesets distribution in the array and by the inflection point of maximum variance with decreasing signal in a standard deviation vs. mean intensity plot across all probesets. Probesets with mean log 2 intensity above this threshold in at least one of the 4 study cohorts were considered quantifiable and included in subsequent analyses (24033 and 21363 probesets for biopsies and brushings, respectively).
Serum sampling and analysis Serum was collected using standard Serum Separation Tubes (SST), frozen within 30 min and subsequently used for quantification of 1129 serum analytes applying the SomaScan v3 platform. Results for serum total immunoglobulin E (IgE) are presented from this panel, defining high IgE levels as those above the 95 th percentile of the HV distribution. In previous evaluations of the platform in asthmatics and HV, IgE measurements highly correlated (Pearson's correlation coefficient r > 0. 9) with those obtained from standard ELISA-based assays (data not shown).
Because CCL26 expression was below LOQ for most subjects across the study cohorts in U-BIOPRED biopsies and was also below LOQ in the brushings, an alternative Th2-activity indicator was selected, namely our own IL-13 ex vivo stimulation gene signature (IL-13 IVS) in endobronchial epithelial air-liquid interface cultures. The signature (genes in signature listed in Additional file 1: Table  S4 ) was defined by selecting genes commonly induced by IL-13 stimulation across 3 independent sets of experiments (data not shown). Enrichment was evaluated on a per-subject basis using the R-Bioconductor package Gene Set Variation Analysis (GSVA, v 1. 14. 1) [18]. Subjects with enrichment scores (ES) beyond the 95 th percentile of healthy control distribution were classified as airway T2-high.
The ADEPT clinical phenotypes The ADEPT asthmatics (n = 156) were partitioned into clusters, i. e. clinical phenotypes, based on baseline data of the 9 clustering variables. Partitioning the asthma population into four clusters was determined to be optimal based on maximizing between-cluster Silhouette metric Fc(U) and minimizing within-cluster distance metric Dc(U). The Fc(U)/Dc(U) values for options with 2, 3, 4, and 5 clusters were: 0. 742/0. 110, 0. 768/0. 080, 0. 786/0. 079 (optimal), and 0. 772/0. 082, respectively. >75% of participants in each cluster had >80% probability for their assigned phenotype, and only one subject per phenotype had <50% probability of 'belonging' to their phenotype (Fig. 2 ). The clinical and biomarker characteristics of the 4 ADEPT phenotypes (Clusters A1 to A4) are shown in Fig.
Classification of U-BIOPRED participants to the ADEPT baseline phenotypes As an alternate strategy to assess how well the ADEPT clinical cluster structure fits to an independent study population, GLMnet-classification model of ADEPTasthma baseline clinical clusters (8 clustering variables, excluding PC20; Model A) was applied to classify the subset of 397 participants from the U-BIOPRED asthma subjects (not restricted to ADEPT inclusion criteria and for whom all clustering clinical data were available). The classification probabilities for the classified ADEPT clusters are reported in Fig. 2  for both ADEPT (panel c, using baseline data to demonstrate model performance) and U-BIOPRED (panel D), with stratification by systemic steroid use for U-BIOPRED. For ADEPT-asthma, the classification concordance to actual clusters was excellent at 96% overall for all clusters.
Consistent with being the least eosinophilic, Phenotype A3 had the least (16%) CCL26high participants (Table  1 ). Phenotype A4 was intermediate, with 32% CCL26-high participants. A similar pattern among the phenotypes was observed for POSTN gene expression as an additional indicator of T2 phenotype (data not shown). Too few of the 82 U-BIOPRED asthma participants included in the clustering had endobronchial biopsy or brushings samples available for analysis (n = 16 and 17, respectively). For the 77 of 397 U-BIOPRED participants classified to ADEPT clusters who had available data on microarray gene expression in epithelial brushings, the T2 distributions (defined by IL-13 in vitro stimulation signature enrichment) were similar to those observed for ADEPT participants despite the differences in tissue and indicators for defining T2 status (Data not shown and Additional file 1: Figure  S4 ), with Phenotypes US2 and US4 having the highest proportions of T2-high participants (38%) and Phenotype US3 having the fewest (6%).
Discussion To our knowledge, this is the first study to identify reproducible phenotypes of asthma using data from two entirely independent cohorts of asthmatic patients. The four observed phenotypes have clinical relevance, representing patient subtypes that are commonly encountered in clinical practice, namely mild, well-controlled asthmatics, moderately-controlled asthmatics with severe AHR, asthmatic with less reversible airways disease, and severe asthmatics. The large size of the study, enrolling participants from across the EU, US and Canada, and the use of clinical variables that can be obtained easily in any pulmonary center provides a valuable novel methodological tool for asthma research. Initial clustering of the ADEPT study dataset resulted in four phenotypes that were robust to perturbation and generally stable over one year.
These phenotypes were further validated in a subset of the U-BIOPRED cohort dataset, where four remarkably analogous phenotypes were observed. Importantly, differential biology across phenotypes was observed which will help develop tailored therapeutic options. Of relevance to current novel  therapies that target T2 immune mechanisms, the phenotypes could be differentiated in respect of expression of gene sets that are characteristic of the pathobiology driven by the T2 cytokine, IL-13. Phenotype A1, was remarkably similar to Phenotype US1 from the U-BIOPRED validation set. The low degree of airway inflammation in A1 was commensurate with absence of a decision to start controller medications, while in the US1 phenotype, where all the asthmatics were on ICS, this suggested adequate suppression of airway inflammation and, consequently, good disease control.
U-BIOPRED had a much broader representation of the overall asthmatic population, including severe asthmatics on maintenance OCS treatment and much lower FEV 1. Because of these differences, the U-BIOPRED primary validation set only included those participants most similar to those in ADEPT. Although PC 20 data were not available for most of the U-BIOPRED participants, the four ADEPT phenotypes largely mapped to the resultant 4 U-BIOPRED phenotypes for clinical variables. However, when applying the ADEPT classification model to a larger subset of the adult participants in U-BIOPRED cohort, the asthmatics on maintenance OCS and smoking-associated severe asthma also fitted well into the ADEPT phenotype classification structure, with similar relative distributions of the 8 clustering variables across the 4 classified ADEPT clinical phenotypes.
However, suppression of B cell and T cell lineage gene expression in biopsies below levels in healthy controls and mild asthmatics (not taking ICS), and suppression of mast cell lineage gene expression below that in mild asthma (data not shown), suggests that most subjects in the moderate and severe asthma groups were compliant in taking ICS, at least around the time of biopsy. The fitting of the U-BIOPRED severe asthmatics on oral CS to the clusters furthers confidence that the cluster formations were not overly influenced by lack of compliance to steroids. Nevertheless, even in the best of circumstances, compliance can only be evaluated over the short-term and imperfectly in absence of inpatient-monitoring. The strength of this study is represented by the independent, external validation and the longitudinal stability assessment.
Acknowledgements Keith Lasher  and the  Janssen Global Clinical Organization  for management of the ADEPT clinical trial. The investigators who participated in the ADEPT study:  I. Strambu ,  Arensia Exploratory Medicine ,  Sos. Viilor 90 ,  Bucharest 050159 ,  Romania; M. Laviolette ,  Institut Universitaire de Cardiologie et Pneumologie de Québec (IUCPQ) ,  2725 ,  Chemin Ste-Foy , Québec  G1V 4G5 ,  Canada ;  D. Singh ,  Medicines Evaluation Unit, University Hospital of South Manchester Foundation Trust, University of Manchester ,  Southmoor Road , Manchester  M23 9QZ , UK;  J. M. FitzGerald ,  Institute for Heart and Lung Health, The Lung Centre, 7th Floor, Gordon, Canada & Leslie Diamond Health Care Centre, 2775 Laurel Street, Vancouver ,  BC V5Z 1 M9 ,  Canada ;  S. Lam ,  Institute for Heart and Lung Health, The Lung Centre, 7th Floor, Gordon, Canada & Leslie Diamond Health Care Centre, 2775 Laurel Street, Vancouver ,  BC V5Z 1 M9 ,  Canada ;  S.
4. 3. 16) and Quinolinate synthetase (EC 2. 5. 1. 72), were only identified in C. renale CIP 52. 96. A distinctive characteristic of C. pilosum CIP103422 was the presence of potential activity for respiratory nitrate reductase (EC 1. 7. 99. 4), which was not detected in C. renale CIP 52. 96. Furthermore, analyses using PGAP software (8) with the MultiParanoid (MP) method with ≥50% of coverage and ≥50% of identity allowed the prediction of 1211 CDSs that were shared between both genomes. In these genes, CDS usually plays a crucial role in the maintenance of key aspects for the organism's biology and is a great target for drug development  (9). Additionally, we predicted 1169 CDSs exclusive for C. pilosum CIP103422 and 1007 CDSs for C. renale CIP 52. 96. These genes could confer an adaptive advantage and niche adaptation (10) (Fig.
One is the break-apart pattern ('classic' pattern) with one fusion signal (native ROS1) and two separated 3′ and 5′ signals. The other positive pattern is an isolated 3′ signal pattern, usually an isolated green signal, ('atypical' pattern) with one fusion signal (native ROS1) and one 3′ signal without the corresponding 5′ signal (Fig. 2 ). Table  3  summarises the criteria for ROS1 FISH interpretation in NSCLC  [23, 32, 35, 36]. For optimal FISH results, there are a number of relevant factors. Use of sections older than 6 months may result in poor hybridisation. In the post-analytic phase, it is important that only intact tumour cells with non-overlapping nuclei are scored. Furthermore, the use of an automated software system (e. g. BioView Duet system, Rehovot, Israel) can facilitate FISH scoring.
statistical analysis All results were presented as mean ± standard deviation (SD) for the quantitative variables. The qualitative variables were expressed in percentages. Statistical analyses were performed using SPSS 17. 0 statistical software (SPSS Inc. , Chicago, IL, USA). The determination of the dependent variable, the 1-minute STS repetitions at baseline, and other independent variables were made using the univariate linear regression analysis. These included anthropometric parameters, the data of the combined COPD assessment A, B, C, and D categories, the Charlson and body mass index, airflow obstruction, dyspnea and exercise capacity index pulmonary function parameters (from the forced expiratory maneuver, plethysmography, and respiratory muscle strength), cardiopulmonary exercise tests parameters, 6MWT, and QMVC parameters.
Notes: The tests were performed at the inclusion (Baseline) and at the end of the Pr. Data presented as mean ± standard deviation. Abbreviations: 6MWD, 6-minute walk distance; QMVC, quadriceps maximal voluntary contraction; sTs, sit-to-stand. In the whole group in average, there was an improvement in the number of repetitions and a reduction in the perception of dyspnea at the end of PR. The differences were not significant for the heart rate and pulse oxygen saturation, which is probably related to the short duration of the test even if it is perceived as a maximal task by the patient. It provides additional information that cannot be compared to more standardized incremental exercise tests, such as the incremental shuttle walk test or cardiopulmonary exercise test. While STS tests explored the balance quality and the efficiency of the biomechanical properties of lower limbs, the incremental exercise tests explored the maximal cardiorespiratory and metabolic capacities of the patient.
METHODS This study was a retrospective analysis of prospectively collected data from a regional colorectal cancer (CRC) screening program; 2979 complete colonoscopies from 18 endoscopists were included. Variability in performance between endoscopists for detection of at least one adenoma (A), one proximal adenoma (PA), one distal adenoma (DA), and one proximal serrated polyp (PSP) was assessed by using multilevel logistic regression models.
58% and 2. 26%, respectively. Positive testing was followed by a colonoscopy in 92. 6% and 91. 4% of the subjects, respectively. Finally, 2295 and 1848 colonoscopies were performed from 2003 to 2007 in the first and second rounds, respectively. The 18 endoscopists who had performed at least 30 colonoscopies following a positive test in each of the first two rounds of the screening program were included. Fourteen of the 18 endoscopists were in private practice, and 4 worked in public hospitals. Overall, the 18 endoscopists performed 3487 (84. 2%) of the 4143 white-light colonoscopies of the 2 screening rounds. Although high cecal intubation rates were recorded for rounds 1 and 2 (96. 3% and 95. 9%, respectively), we included only complete examinations of the colon in this study. The data from both rounds were pooled because no difference in the colonoscopy findings was noted between the two rounds.
We previously reported that individual endoscopists who had participated in the CRC screening program as a factor was not a significant predictor of CRC detection but was a significant predictor of adenoma detection  [15]. In the present study, a secondary analysis of the colonoscopy data was done to explore variations in the detection rate of at least one adenoma according to its location in the colon and to compare interendoscopist variability in polyp detection rates in the proximal colon according to histological subtype (serrated polyps and adenomas). The CRC screening program was declared and approved by the CNIL "Commission Nationale de l'Informatique et des Libertés" on August 30 th 2002 (n° 812571). Research was approved by the CCTIRS "Comité Consultatif pour le Traitement de l'Information en matière de Recherche dans le domaine de la santé".
Study design and outcomes This was a cross-sectional study that used data retrieved from a prospectively collected database. Three adenoma detection rates, which were expressed as the proportion of complete colonoscopies with at least one adenoma, were calculated for each endoscopist as follows: The distal adenoma detection rate (DA. DR) for at least one adenoma detected in the distal colon (i. e. , below the splenic flexure including the flexure), the proximal adenoma detection rate (PA. DR) for at least one adenoma in the proximal colon (i. e. , proximal to the splenic flexure) and the A. DR for at least one colorectal adenoma regardless of its location in the colon. Colonoscopies with CRC, including those with malignant polyps harboring intramucosal carcinoma, were not included in the analysis because additional polyps in these patients were not recorded in the database.
To account for existing uncertainties on the configuration of the Drake Passage and of the Caribbean Seaway, sensitivity experiments assessing the effect of the depth specified for these two marine gateways are also presented in the discussion. Data and model-based atmospheric CO 2 level estimates for the Late Cretaceous are between two and eight times the pre-industrial atmospheric CO 2 concentration (280 p. p. m. )  [64] [65] [66] [67] [68] [69]. Accordingly, three atmospheric CO 2 concentrations were tested for the two palaeogeographies: 560, 1,120 and 2,240 p. p. m. The solar luminosity was specified as 99% of modern. Orbital parameters and other greenhouse gases were set to present-day values. River routing is specified using the Sewall et al. reconstruction. The experiments were integrated for 2,000 years without flux corrections or deep ocean acceleration.
Study Design The LixiLan-O study was an open-label, randomized, parallel-group multinational, multicenter phase 3 clinical trial (NCT02058147). It was initiated (first patient enrolled) on February 12, 2014 and ended (last patient completed) on June 17, 2015. Supplementary Fig. 1  summarizes the study design. Patients (aged ≥18 years) with type 2 diabetes diagnosed at least 1 year prior to screening were eligible if they showed inadequate glycemic control despite being treated for at least 3 months with metformin ± a second oral glucose-lowering therapy. Inadequate glycemic control was defined as HbA1c ≥7. 5% and ≤10. 0% (58-86 mmol/mol) for those on metformin alone and ≥7. 0% and ≤9. 0% (53-75 mmol/mol) for patients previously treated with metformin and a second oral glucose-lowering therapy, namely a sulfonylurea, glinide, sodium-glucose cotransporter 2 (SGLT2), or a dipeptidyl peptidase 4 (DPP-4) inhibitor.
Acknowledgments The authors thank the study participants, trial staff, and investigators for their participation. Principal investigators at the clinical sites are listed in Supplemental File 2. M. D. wishes to thank the  NIHR Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit. Editorial assistance for this publication was provided by  Lisa Tatler  and  Steve Smith  ( Caudex, Oxford, UK ) and  Mark Greener  (for Caudex, Oxford, UK) and was funded by  Sanofi.
Duality of Interest This study (NCT02058147) was sponsored by  Sanofi. J. R. has served on scientific advisory boards and received honoraria or consulting fees from  AstraZeneca ,  Boehringer Ingelheim ,  Daiichi Sankyo ,  Eli Lilly ,  Intarcia ,  Janssen ,  Lexicon ,  Merck ,  Novo Nordisk , and  Sanofi ; and has received grants/research support from  Asahi ,  AstraZeneca ,  Boehringer Ingelheim, Bristol-Myers Squibb ,  Daiichi Sankyo ,  Eli Lilly ,  GlaxoSmithKline ,  Hanmi ,  Intarcia ,  Janssen ,  Lexicon ,  MannKind ,  Merck ,  Novartis ,  Novo Nordisk ,  Pfizer ,  Sanofi , and  Takeda. R. A.
, 2007). In selected experiments, to determine spatial visual acuity, mice were exposed to pattern stimuli with varying spatial frequencies (0. 05-0. 7 c/deg) at constant contrast, luminance, and temporal frequency. Spatial acuity was calculated as the spatial frequency of the intercept between the linear regression among data points in a graph, where spatial frequencies were represented on a logarithmic scale, and the noise level measured upon presentation of a blank screen. Flash ERG. Mice were dark adapted for Ն4 h before being anesthetized under dim red light by intraperitoneal injections of urethane (2 g/kg), and gently restrained as described above. The pupils were dilated with the mydriatic agent atropin at 1% in physiological solution. The gold recording electrode (0. 25 mm diameter) was gently placed on the corneal sur-face encircling the dilated pupil without interfering with vision.