PUBLIC RELEASE DATE:

14-Oct-2014

Contact: Kathryn Ryan kryan@liebertpub.com 914-740-2100 Mary Ann Liebert, Inc./Genetic Engineering News @LiebertOnline

New Rochelle, October 14, 2014 Big Data analytics are helping to provide answers to many complex problems in science and society, but they have not contributed to a better understanding climate science, despite an abundance of climate data. When it comes to analyzing the climate system, Big Data methods alone are not enough and sound scientific theory must guide data modeling techniques and results interpretation, according to an insightful article in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Big Data website.

In "A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science," James Faghmous, PhD and Vipin Kumar, PhD, The University of Minnesota--Twin Cities, explore the challenges and opportunities for mining large climate datasets and the subtle differences that are needed compared to traditional Big Data methods if accurate conclusions are to be drawn. The authors discuss the importance of combining scientific theory and First Principles with Big Data analytics and use examples from existing research to illustrate their novel approach.

"This paper is a great example of leveraging the abundance of climate data with powerful analytical methods, scientific theory, and solid data engineering to explain and predict important climate change phenomena," says Big Data Editor-in-Chief Vasant Dhar, Co-Director, Center for Business Analytics, Stern School of Business, New York University.

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About the Journal

Big Data , published quarterly in print and online, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data website.

About the Publisher

See the article here:
Can big data make sense of climate change?

PUBLIC RELEASE DATE:

14-Oct-2014

Contact: Kathryn Ryan kryan@liebertpub.com 914-740-2100 Mary Ann Liebert, Inc./Genetic Engineering News @LiebertOnline

New Rochelle, NY, October 14, 2014For the 5-15% of patients with mild traumatic brain injury (mTBI) who will have lingering physical, behavioral, or cognitive problems 3 to 6 months after their injury, identification of this at-risk population is essential for early intervention. Existing models used to predict poor outcomes after mTBI are unsatisfactory, according to a new study, and new, more relevant predictive factors are different than those used in cases of moderate or severe TBI, as described in the study published in Journal of Neurotrauma, a peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Journal of Neurotrauma website at http://online.liebertpub.com/doi/pdfplus/10.1089/neu.2014.3384 until November 14, 2014.

Hester F. Lingsma and a multidisciplinary, international team of authors evaluated two existing prognostic models for mTBI in patients selected from the TRACK-TBI Pilot observational study carried out at three medical centers in the U.S. Both models performed poorly. Based on further analysis, the authors identified older age, pre-existing psychiatric conditions, and less education as the three strongest predictors of poor outcomes, as they report in the article "Outcome Prediction after Mild and Complicated Mild Traumatic Brain Injury: External Validation of Existing Models and Identification of New Predictors Using the TRACK-TBI Pilot Study."

John T. Povlishock, PhD, Editor-in-Chief of Journal of Neurotrauma and Professor, Medical College of Virginia Campus of Virginia Commonwealth University, Richmond, notes that, "this is an extremely important study utilizing the TRACK-TBI database. This meticulously performed investigation highlights the dangers in assessing outcome following mTBI, emphasizing that other comorbid factors such as older age, preexisting psychiatric disorders, and less education, perhaps a function of socioeconomic status, can negatively impact outcome. This important communication should be considered routinely as we move forward in our assessments of outcomes following mTBI, whether or not these outcomes are framed in the context of advanced imaging, biomarker evaluation, and/or other metabolic/functional screens."

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About the Journal

Journal of Neurotrauma is an authoritative peer-reviewed journal published 24 times per year in print and online that focuses on the latest advances in the clinical and laboratory investigation of traumatic brain and spinal cord injury. Emphasis is on the basic pathobiology of injury to the nervous system, and the papers and reviews evaluate preclinical and clinical trials targeted at improving the early management and long-term care and recovery of patients with traumatic brain injury. Journal of Neurotrauma is the official journal of the National Neurotrauma Society and the International Neurotrauma Society. Complete tables of content and a sample issue may be viewed on the Journal of Neurotrauma website at http://www.liebertpub.com/neu.

About the Publisher

See original here:
Current models for predicting outcomes after mild traumatic brain injury perform poorly