University of North Carolina at Charlotte

About: University of North Carolina at Charlotte is a education organization based out in Charlotte, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8772 authors who have published 22239 publications receiving 562529 citations. The organization is also known as: UNC Charlotte & UNCC.

Terrorism, Acute Stress, and Cardiovascular Health: A 3-Year National Study Following the September 11th Attacks

Abstract: Context The terrorist attacks of 9/11 (September 11, 2001) present an unusual opportunity to examine prospectively the physical health impact of extreme stress in a national sample. Objective To examine the degree to which acute stress reactions to the 9/11 terrorist attacks predict cardiovascular outcomes in a national probability sample over the subsequent 3 years. Design, Setting, and Participants A national probability sample of 2729 adults (78.1% participation rate), 95.0% of whom had completed a health survey before 9/11 (final health sample, 2592), completed a Web-based assessment of acute stress responses approximately 9 to 14 days after the terrorist attacks. Follow-up health surveys reassessed physician-diagnosed cardiovascular ailments 1 (n = 1923, 84.3% participation rate), 2 (n = 1576, 74.2% participation rate), and 3 (n = 1950, 78.9% participation rate) years following the attacks. Main Outcome Measures Reports of physician-diagnosed cardiovascular ailments over the 3 years following the attacks. Results Acute stress responses to the 9/11 attacks were associated with a 53% increased incidence of cardiovascular ailments over the 3 subsequent years, even after adjusting for pre-9/11 cardiovascular and mental health status, degree of exposure to the attacks, cardiovascular risk factors (ie, smoking, body mass index, and number of endocrine ailments), total number of physical health ailments, somatization, and demographics. Individuals reporting high levels of acute stress immediately following the attacks reported an increased incidence of physician-diagnosed hypertension (rate ratios, 2.15 at 1 year and 1.75 at 2 years) and heart problems (rate ratios, 2.98 at 1 year and 3.12 at 2 years) over 2 years. Among individuals reporting ongoing worry about terrorism post-9/11, high 9/11-related acute stress symptoms predicted increased risk of physician-diagnosed heart problems 2 to 3 years following the attacks (rate ratios, 4.67 at 2 years and 3.22 at 3 years). Conclusion Using health data collected before 9/11 as a baseline, acute stress response to the terrorist attacks predicted increased reports of physician-diagnosed cardiovascular ailments over 3 years following the attacks.

Epidemiology of Posttraumatic Osteoarthritis.

Abstract: Osteoarthritis is a leading cause of disability whose prevalence and incidence continue to increase. History of joint injury represents an important risk factor for posttraumatic osteoarthritis and is a significant contributor to the rapidly growing percentage of the population with osteoarthritis. This review will present the epidemiology associated with posttraumatic osteoarthritis, with particular emphasis on the knee and ankle joints. It is important to understand the effect of posttraumatic osteoarthritis on the population so that sufficient resources can be devoted to countering the disease and promoting optimal long-term health for patients after joint injury.

Multispectral Deep Neural Networks for Pedestrian Detection

Abstract: Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.