1,775 research outputs found

    Services and support for IU School of Medicine and Clinical Affairs Schools by the UITS/PTI Advanced Biomedical Information Technology Core and Research Technologies Division in FY 2013 - Extended Version

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    The report presents information on services delivered in FY 2013 by ABITC and RT to the IU School of Medicine and the other Clinical Affairs schools that include the Schools of Nursing, Dentistry, Health and Rehabilitation Sciences, and Optometry; the Fairbanks School of Public Health at IUPUI; the School of Public Health at IU Bloomington; and the School of Social Work

    Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach

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    BACKGROUND: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. METHODS: Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. RESULTS: The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4. CONCLUSIONS: The combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum

    Usage of UITS advanced research cyberinfrastructure for 2011

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    IU has a proud tradition in open access to its research computing and cyberinfrastructure (CI) facilities, going back to the precedents set by Marshall Wrubel (appointed the first permanent director of the IU Research Computing Center in 1955). Starting in 1997 President Myles Brand and then-Vice President Michael McRobbie initiated a tremendous acceleration in growth of IU’s cyberinfrastructure facilities through developing and then executing the first Indiana University Information Technology Strategic Plan. Through a decade and a half of purposeful execution of excellent strategies in support for research and scholarly activities generally, University Information Technology Services (UITS) has provided exceptional support to a group of researchers. This includes usage from disciplines that are among the traditional users of high performance computing – physics, chemistry, and astronomy, as well as emerging areas of application of HPC including biology, business, and the arts

    Project development teams: a novel mechanism for accelerating translational research

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    The trend in conducting successful biomedical research is shifting from individual academic labs to coordinated collaborative research teams. Teams of experienced investigators with a wide variety of expertise are now critical for developing and maintaining a successful, productive research program. However, assembling a team whose members have the right expertise requires a great deal of time and many resources. To assist investigators seeking such resources, the Indiana Clinical and Translational Sciences Institute (Indiana CTSI) created the Project Development Teams (PDTs) program to support translational research on and across the Indiana University-Purdue University Indianapolis, Indiana University, Purdue University, and University of Notre Dame campuses. PDTs are multidisciplinary committees of seasoned researchers who assist investigators, at any stage of research, in transforming ideas/hypotheses into well-designed translational research projects. The teams help investigators capitalize on Indiana CTSI resources by providing investigators with, as needed, mentoring and career development; protocol development; pilot funding; institutional review board, regulatory, and/or nursing support; intellectual property support; access to institutional technology; and assistance with biostatistics, bioethics, recruiting participants, data mining, engaging community health, and collaborating with other investigators.Indiana CTSI leaders have analyzed metrics, collected since the inception of the PDT program in 2008 from both investigators and team members, and found evidence strongly suggesting that the highly responsive teams have become an important one-stop venue for facilitating productive interactions between basic and clinical scientists across four campuses, have aided in advancing the careers of junior faculty, and have helped investigators successfully obtain external funds

    2012 Annual Report - Advanced Biomedical Information Technology Core

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    This material is based upon work supported in part by the following funding agencies and grant awards: • Lilly Endowment, for its support of the Indiana Genomics Initiative (INGEN) – 2000; Indiana Metabolomics and Cytomics Initiative (METACyt); Indiana Pervasive Computing Research (IPCRES) initiative and Pervasive Technology Institute (1999 and 2008 respectively) • National Science Foundation under grants 01116050 MRI: Creation of the AVIDD Data Facility: A Distributed Facility for Managing, Analyzing and Visualizing Instrument-Driven Data (Michael A. McRobbie, PI); 0521433 MRI: Acquisition of a High-Speed, High Capacity Storage System to Support Scientific Computing: The Data Capacitor (Craig A. Stewart, PI); 0521433 ABI Development: National Center for Genome Analysis Support (Craig A. Stewart, PI) • National Institutes of Health NIAAA awards U24 AA014818-01 (Craig A. Stewart, PI) and U24 AA014818-04 (William K. Barnett, PI) Informatics Core for the Collaborative Initiative on Fetal Alcohol Spectrum Disorder • Subcontracts through the following NIH grant awards: 5P40RR024928 (Kenneth Cornetta, PI), 2U01AA014809 (Tatiana Foroud, PI), 1DP2OD007363-01 (Alexander Niculescu, PI), UL1RR025761-01 (Anantha Shekhar, PI), 3UL1RR025761-04S2 (Anantha Shekhar, PI), and 3UL1RR025761-04S3 (Anantha Shekhar, PI) • Funding from the general funds of Indiana University Any opinions expressed in this document are those of the authors and do not necessarily reflect the views of the funding agencies above

    The Structural and Functional Connectome and Prediction of Risk for Cognitive Impairment in Older Adults

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    The human connectome refers to a comprehensive description of the brain's structural and functional connections in terms of brain networks. As the field of brain connectomics has developed, data acquisition, subsequent processing and modeling, and ultimately the representation of the connectome have become better defined and integrated with network science approaches. In this way, the human connectome has provided a way to elucidate key features of not only the healthy brain but also diseased brains. The field has quickly evolved, offering insights into network disruptions that are characteristic for specific neurodegenerative disorders. In this paper, we provide a brief review of the field of brain connectomics, as well as a more in-depth survey of recent studies that have provided new insights into brain network pathologies, including those found in Alzheimer's disease (AD), patients with mild cognitive impairment (MCI), and finally in people classified as being "at risk". Until the emergence of brain connectomics, most previous studies had assessed neurodegenerative diseases mainly by focusing on specific and dispersed locales in the brain. Connectomics-based approaches allow us to model the brain as a network, which allows for inferences about how dynamic changes in brain function would be affected in relation to structural changes. In fact, looking at diseases using network theory gives rise to new hypotheses on mechanisms of pathophysiology and clinical symptoms. Finally, we discuss the future of this field and how understanding both the functional and structural connectome can aid in gaining sharper insight into changes in biological brain networks associated with cognitive impairment and dementia

    A Scientific Roadmap for Antibiotic Discovery: A Sustained and Robust Pipeline of New Antibacterial Drugs and Therapies is Critical to Preserve Public Health

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    In recent decades, the discovery and development of new antibiotics have slowed dramatically as scientific barriers to drug discovery, regulatory challenges, and diminishing returns on investment have led major drug companies to scale back or abandon their antibiotic research. Consequently, antibiotic discovery—which peaked in the 1950s—has dropped precipitously. Of greater concern is the fact that nearly all antibiotics brought to market over the past 30 years have been variations on existing drugs. Every currently available antibiotic is a derivative of a class discovered between the early 1900s and 1984.At the same time, the emergence of antibiotic-resistant pathogens has accelerated, giving rise to life-threatening infections that will not respond to available antibiotic treatment. Inevitably, the more that antibiotics are used, the more that bacteria develop resistance—rendering the drugs less effective and leading public health authorities worldwide to flag antibiotic resistance as an urgent and growing public health threat

    IU PTI/UITS Research Technologies Annual Report: FY 2014

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    This Fiscal Year 2014 (FY2014) report outlines IU community accomplishments using IU's cyberinfrastructure, as they relate to several IU Bicentennial Strategic Plan goals and ongoing principles of excellence. The report includes research and discovery highlights

    A Novel Joint Brain Network Analysis Using Longitudinal Alzheimer's Disease Data.

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    There is well-documented evidence of brain network differences between individuals with Alzheimer's disease (AD) and healthy controls (HC). To date, imaging studies investigating brain networks in these populations have typically been cross-sectional, and the reproducibility of such findings is somewhat unclear. In a novel study, we use the longitudinal ADNI data on the whole brain to jointly compute the brain network at baseline and one-year using a state of the art approach that pools information across both time points to yield distinct visit-specific networks for the AD and HC cohorts, resulting in more accurate inferences. We perform a multiscale comparison of the AD and HC networks in terms of global network metrics as well as at the more granular level of resting state networks defined under a whole brain parcellation. Our analysis illustrates a decrease in small-worldedness in the AD group at both the time points and also identifies more local network features and hub nodes that are disrupted due to the progression of AD. We also obtain high reproducibility of the HC network across visits. On the other hand, a separate estimation of the networks at each visit using standard graphical approaches reveals fewer meaningful differences and lower reproducibility
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