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Background: Interstitial Lung Disease [ILD] patients requiring Invasive Mechanical Ventilation [IMV] for Acute Respiratory Failure [ARF] are known to have a poor prognosis. Few studies have investigated determinants of outcomes and the utility of trialing Non-Invasive Positive Pressure Ventilation [NIPPV] prior to IMV to see if there are any effect[s] on mortality or morbidity. Methods: A retrospective study was designed using patients at four different intensive care units within one health care system. The primary objective was to determine if there are differences in outcomes for in-hospital and one-year mortality between patients who undergo NIPPV prior to IMV and those who receive only IMV. A secondary objective was to identify potential determinants of outcomes. Results: Out of 54 ILD patients with ARF treated with IMV, 20 (37.0%) survived until hospital discharge and 10 (18.5%) were alive at one-year. There was no significant mortality difference between patients trialed on NIPPV prior to IMV and those receiving only IMV. Several key determinants of outcomes were identified with higher mortality, including higher ventilatory support, idiopathic pulmonary fibrosis (IPF) subtype, high dose steroids, use of vasopressors, supraventricular tachycardias (SVTs), and higher body mass index. Conclusion: Considering that patients trialed on NIPPV prior to IMV were associated with no mortality disadvantage to patients treated with only IMV, trialing patients on NIPPV may identify responders and avoid complications associated with IMV. Increased ventilator support, need of vasopressors, SVTs, and high dose steroids reflect higher mortality and palliative care involvement should be considered as early as possible if a lung transplant is not an option.

EgeUn###, Rationale: Several common and rare genetic variants have been associated with idiopathic pulmonary fibrosis, a progressive fibrotic condition that is localized to the lung. Objectives: To develop an integrated understanding of the rare and common variants located in multiple loci that have been reported to contribute to the risk of disease. Methods: We performed deep targeted resequencing (3.69 Mb of DNA) in cases (n = 3,624) and control subjects (n = 4,442) across genes and regions previously associated with disease. We tested for associations between disease and 1) individual common variants via logistic regression and 2) groups of rare variants via sequence kernel association tests. Measurements and Main Results: Statistically significant common variant association signals occurred in all 10 of the regions chosen based on genome-wide association studies. The strongest risk variant is the MUC5B promoter variant rs35705950, with an odds ratio of 5.45 (95% confidence interval, 4.91-6.06) for one copy of the risk allele and 18.68 (95% confidence interval, 13.34-26.17) for two copies of the risk allele (P = 9.60 3 102295). In addition to identifying for the first time that rare variation in FAM13A is associated with disease, we confirmed the role of rare variation in the TERT and RTEL1 gene regions in the risk of IPF, and found that the FAM13A and TERT regions have independent common and rare variant signals. Conclusions: A limited number of common and rare variants contribute to the risk of idiopathic pulmonary fibrosis in each of the resequencing regions, and these genetic variants focus on biological mechanisms of host defense and cell senescence. Copyright © 2019 by the American Thoracic Society, Háskóli Íslands, HI University of Chicago University of Ulsan, UOU Monash University, MU University of Nottingham University of Edinburgh University of Colorado Denver Universitat de Barcelona University of Pittsburgh, Pitt Harvard School of Dental Medicine Massachusetts General Hospital, MGH University of Pennsylvania, Penn University College Cork, UCC City, University of London, City University of California, San Francisco, UCSF School of Medicine, Vanderbilt University Ege Üniversitesi University of Virginia, UV Aarhus Universitetshospital Central Manchester University Hospitals NHS Foundation Trust Columbia University Novartis Sunovion COPD Foundation Pfizer National Heart, Lung, and Blood Institute, NHLBI: R01-HL097163, UH3-HL123442, P01-HL092870 U.S. Department of Defense, DOD: W81XWH-17-1-0597, U01 HL089897, U01 HL089856 AstraZeneca Siemens Foundation North Carolina GlaxoSmithKline Foundation, 1National Jewish Health, Denver, Colorado; 2School of Public Health; 3Department of Medicine, and 54Department of Immunology, University of Colorado Denver, Denver, Colorado; 4Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee; 5Brigham and Women’s Hospital, Harvard School of Medicine, Boston, Massachusetts; 6Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma;7Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; 8MRC Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom; 9Respiratory Medicine Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom; 10Biomedical Research Centre, University of Nottingham, Nottingham, United Kingdom; 11Royal Brompton Hospital and Imperial College, London, United Kingdom; 12Simmons Center for Interstitial Lung Disease, University of Pittsburgh, Pittsburgh, Pennsylvania; 13Department of Medicine, University of California, San Francisco, San Francisco, California; 14Gilead Sciences, Foster City, California; 15Department of Medicine, University of Chicago, Chicago, Illinois; 16Department of Medicine, University of Virginia, Charlottesville, Virginia; 17Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania; 18National Hospital Organization Kinki-Chuo Chest Medical Center, Osaka, Japan; 19National Hospital Organization Tokyo National Hospital, Tokyo, Japan; 20Helmholtz Zentrum München, Neuherberg, Germany; 21University Hospital Munich, Munich, Germany; 22Department of Pulmonology, Ege University Hospital, Bornova, Izmir, Turkey; 23Royal Prince Alfred Hospital and University of Sydney, Sydney, Australia; 24Alfred Hospital and Monash University, Melbourne, Australia; 25Pulmonary Medicine, GB Morgagni Hospital, Forlì, Italy; 26Department of Diseases of the Thorax, Ospedale GB Morgagni, Forlì, Italy; 27Université Paris Diderot and Hôpital Bichat, Paris, France; 28Royal Papworth Hospital and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom; 29Respiratory Department, University Hospital of Bellvitge, University of Barcelona, Barcelona, Spain; 30National University Hospital of Iceland, University of Iceland, Reykjavik, Iceland; 31Department of Medicine, Columbia University Irving Medical Center, New York, New York; 32Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts; 33Department of Respiratory Diseases and Allergy, Aarhus University Hospital, Aarhus, Denmark; 34Instituto Nacional de Enfermedades Respiratorias “Ismael Cosio Villegas,” México City, México; 35Universidad Nacional Autónoma de México, México City, México; 36Cork University Hospital and University College Cork, Cork, Ireland; 37St. Vincent’s University Hospital, Dublin, Ireland; 38School of Medicine, University College Dublin, Dublin, Ireland; 39Department of Respiratory Medicine, First Faculty of Medicine Charles University and Thomayer Hospital, Prague, Czech Republic; 40University of British Columbia, Vancouver, Canada; 41Tokyo Medical and Dental University, Tokyo, Japan; 42Institute for Respiratory Health and; 43Centre for Cell Therapy and Regenerative Medicine, School of Biomedical Sciences, The University of Western Australia, Perth, Australia; 44Department of Medicine, Warren Alpert Medical School of Brown University, Providence, Rhode Island; 45Advanced Lung Disease and Transplant Program, Inova Fairfax Hospital, Falls Church, Virginia; 46Department of Pulmonary Medicine, Gazi University School of Medicine, Ankara, Turkey; 47Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama; 48Ruhrlandklinik, University Hospital, University of Duisburg-Essen, Essen, Germany; 49Department of Medicine, Tallaght University Hospital, Trinity College Dublin, Dublin, Ireland; 50CardioPulmonary Reserach Center, Alliance Pulmonary Group, Guaynabo, Puerto Rico; 51Department of Internal Medicine, University of Genoa, Genoa, Italy; 52Biomedical Genomics Center, University of Minnesota; Minneapolis, Minnesota; and 53Northwest Genomics Center, University of Washington, Seattle, Washington -- This research was supported by grants from the NHLBI (R01-HL097163, P01-HL092870, and UH3-HL123442) and the Department of Defense (W81XWH-17-1-0597). The COPDGene project was supported by Award Number U01 HL089897 and Award Number U01 HL089856 from the NHLBI. The COPDGene project was also supported by the COPD Foundation through contributions made to an industry advisory board comprised of AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion.

International audience; There has been increasing interest in the artificial intelligence community for influencer detection in recent years for its utility in singling out pertinent users within a large network of social media users. This could be useful, for example in commercial campaigns, to promote a product or a brand to a relevant target set of users. This task is performed either by analysing the graph-based representation of user interactions in a social network or by measuring the impact of the linguistic content of user messages in online discussions. We performed independent studies for each of these methods in the present paper with a hybridisation perspective. In the first study, we extract structural information to highlight influence among interaction networks. In the second, we identify linguistic features of influential behaviours. We then compute a score of user influence using centrality measures with the structural information for the former and a machine learning approach based on the relevant linguistic features for the latter.

Even though next-generation sequencing (NGS) has become an invaluable tool in molecular biology, several laboratories with NGS facilities lack trained Bioinformaticians for data analysis. Here, focusing on the variant detection application of NGS analysis, we have developed a fully automated pipeline, namely Variant Discovery and Annotation Tool-Graphical User Interface (VDAP-GUI), which detects and annotates single nucleotide polymorphisms and insertions/deletions from raw sequence reads. VDAP-GUI consolidates several proven methods in each step such as quality control, trimming, mapping, variant detection and annotation. It supports multiple NGS platforms and has four methodological choices for variant detection. Further, it can re-analyze existing data with alternate thresholds and generates easily interpretable reports in html and tab-delimited formats. Using VDAP-GUI, we have analyzed a publically available human whole-exome sequence dataset. VDAP-GUI is developed using Perl/Tk programming, and is available for free download and use at http://sourceforge.net/projects/vdapgui/. Electronic supplementary material The online version of this article (doi:10.1007/s13205-016-0382-1) contains supplementary material, which is available to authorized users.

Here, we report the draft genome sequence of Achromobacter sp. strain DMS1, which is 4.9 Mbp and has 3,727 coding sequences (CDSs), and is capable of degrading xenobiotic compounds and harboring genes for aromatic hydrocarbon metabolism. Its genome will unravel the basic mechanism involved in bioremediation of anthropogens.

International audience; Intelligent ‘services’ are increasingly used on e-commerce platforms to provide assistance to customers. Numerous preference elicitation methods developed in the literature are now employed for this purpose. However, it is commonly known that there is a real bottleneck in preference handling as concerns the elicitation of preferences because it does not cater to the wide range of preference representation languages available. Thus, as a first step in developing a decision-support tool using an AI based on such languages, this paper describes a preliminary study conducted to addressthis issue. We propose a method of eliciting real-time user preferences expressed in natural language (NL) which can be formally represented using comparative preference statements complying with different semantics, and provide a proof of concept to demonstrate its feasibility. Since we develop NL resources to detect preference semantics, we also make a comparative study with existing resources to underline the peculiarities of our model.

International audience; There has been a growing interest in the study of preferences for their utility in solving problems related to decision making. Most of the preference representation languages developed in the literature are based on comparative preference statements since they offer a simple and intuitive way for expressing preferences. They can be further interpreted following different semantics, imparting a greater flexibility on how outcomes can be compared. So far the main objective has been to rank-order the set of outcomes given a set of comparative preference statements and one or several semantics. Tackling this problem from a different angle, we look into the behavioural aspects of the preference semantics and statements by attempting to formalise the intuition behind them using postulates studied in preference logics and non-monotonic reasoning. We select the postulates w.r.t. three criteria: coherence, syntax independence and inference. Thus, our analysis provides a means to determine those properties that are satisfied for a given preference semantics.

International audience; This paper presents two anonymisation methods to process an SMS corpus. The first one is based on an unsupervised approach called Seek&Hide. The implemented system uses several dictionaries and rules in order to predict if a SMS needs anonymisation process. The second method is based on a supervised approach using machine learning techniques. We evaluate the two approaches and we propose a way to use them together. Only when the two methods do not agree on their prediction, will the SMS be checked by a human expert. This greatly reduces the cost of anonymising the corpus.

To determine the effect of medical treatment versus lung volume reduction surgery (LVRS) on pulmonary hemodynamics.Three clinical centers of the National Emphysema Treatment Trial (NETT) screened patients for additional inclusion into a cardiovascular (CV) substudy. Demographics were determined, and lung function testing, six-minute-walk distance, and maximum cardiopulmonary exercise testing were done at baseline and 6 months after medical therapy or LVRS. CV substudy patients underwent right heart catheterization at rest prerandomization (baseline) and 6 months after treatment.A total of 110 of the 163 patients evaluated for the CV substudy were randomized in NETT (53 were ineligible), 54 to medical treatment and 56 to LVRS. Fifty-five of these patients had both baseline and repeat right heart catheterization 6 months postrandomization. Baseline demographics and lung function data revealed CV substudy patients to be similar to the remaining 1,163 randomized NETT patients in terms of age, sex, FEV(1), residual volume, diffusion capacity of carbon monoxide, Pa(O(2)), Pa(CO(2)), and six-minute-walk distance. CV substudy patients had moderate pulmonary hypertension at rest (Ppa, 24.8 +/- 4.9 mm Hg); baseline hemodynamic measurements were similar across groups. Changes from baseline pressures to 6 months post-treatment were similar across treatment groups, except for a smaller change in pulmonary capillary wedge pressure at end-expiration post-LVRS compared with medical treatment (-1.8 vs. 3.5 mm Hg, p = 0.04).In comparison to medical therapy, LVRS was not associated with an increase in pulmonary artery pressures.

Background Although even randomization (that is, approximately 1:1 randomization ratio in study arms) provides the greatest statistical power, designed uneven randomization (DUR), (for example, 1:2 or 1:3) is used to increase participation rates. Until now, no convincing data exists addressing the impact of DUR on participation rates in trials. The objective of this study is to evaluate the epidemiology and to explore factors associated with DUR. Methods We will search for reports of RCTs published within two years in 25 general medical journals with the highest impact factor according to the Journal Citation Report (JCR)-2010. Teams of two reviewers will determine eligibility and extract relevant information from eligible RCTs in duplicate and using standardized forms. We will report the prevalence of DUR trials, the reported reasons for using DUR, and perform a linear regression analysis to estimate the association between the randomization ratio and the associated factors, including participation rate, type of informed consent, clinical area, and so on. Discussion A clearer understanding of RCTs with DUR and its association with factors in trials, for example, participation rate, can optimize trial design and may have important implications for both researchers and users of the medical literature.