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2,236 result(s) for "Richard Woodward"
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Teprotumumab for Thyroid-Associated Ophthalmopathy
Teprotumumab for Thyroid-Associated Ophthalmopathy
In patients with thyroid-associated ophthalmopathy, responses to treatment are rare and usually minor. Teprotumumab, an antibody to the insulin-like growth factor I receptor, led to significant responses in 69% of patients and to decreased proptosis. Medical therapies for moderate-to-severe thyroid-associated ophthalmopathy (Graves’ orbitopathy) that have proved to be effective and safe in adequately powered, prospective, placebo-controlled trials are lacking. This unmet need is due to the incompletely understood pathogenesis of the disease. 1 Current treatments are inconsistently beneficial and often associated with side effects, and their modification of the ultimate disease outcome is uncertain. 1 – 3 Previous clinical trials, which were rarely placebo-controlled, suggest that high-dose glucocorticoids, alone 3 – 5 or with radiotherapy, 6 , 7 can reduce inflammation-related signs and symptoms in patients with active ophthalmopathy. However, glucocorticoids and orbital radiotherapy minimally affect proptosis and can cause dose-limiting adverse . . .
Human body : a visual encyclopedia
Presents comprehensive information on the human body, covering such topics as the musculoskeletal system, immunology, digestion and nutrition, the human life cycle, the nervous system, and the respiratory system-- Source other than Library of Congress.
Adapting myoelectric control in real-time using a virtual environment
Adapting myoelectric control in real-time using a virtual environment
Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device. Here we present an engaging and interactive virtual reality environment for optimal training of a myoelectric controller. Using this tool, we evaluated the importance of training a classifier actively (i.e., moving the residual limb during data collection) compared to passively (i.e., maintaining the limb in a single, neutral orientation), and whether computational adaptation through serious gaming can improve performance. We found that actively trained classifiers performed significantly better than passively trained classifiers for non-amputees (P < 0.05). Furthermore, collecting data passively with minimal instruction, paired with computational adaptation in a virtual environment, significantly improved real-time performance of myoelectric controllers. These results further support previous work which suggested active movements during data collection can improve pattern recognition systems. Furthermore, adaptation within a virtual guided serious game environment can improve real-time performance of myoelectric controllers.
A coupled recreational anglers' decision and fish population dynamics model
A coupled recreational anglers' decision and fish population dynamics model
The effective management of fish populations requires understanding of both the biology of the species being managed and the behavior of the humans who harvest those species. For many marine fisheries, recreational harvests represent a significant portion of the total fishing mortality. For such fisheries, therefore, a model that captures the dynamics of angler choices and the fish population would be a valuable tool for fisheries management. In this study, we provide such a model, focusing on red drum and spotted seatrout, which are the two of the main recreational fishing targets in the Gulf of Mexico. The biological models are in the form of vector autoregressive models. The anglers' decision model takes the discrete choice approach, in which anglers first decide whether to go fishing and then determine the location to fish based on the distance and expected catch of two species of fish if they decide to go fishing. The coupled model predicts that, under the level of fluctuation in the abundance of the two species experienced in the past 35 years, the number of trips that might be taken by anglers fluctuates moderately. This fluctuation is magnified as the cost of travel decreases because the anglers can travel long distance to seek better fishing conditions. On the other hand, as the cost of travel increases, their preference to fish in nearby areas increases regardless of the expected catch in other locations and variation in the trips taken declines. The model demonstrates the importance of incorporating anglers' decision processes in understanding the changes in a fishing effort level. Although the model in this study still has a room for further improvement, it can be used for more effective management of fish and potentially other populations.
Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data
Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data
Electromyography (EMG) is the standard technology for monitoring muscle activity in laboratory environments, either using surface electrodes or fine wire electrodes inserted into the muscle. Due to limitations such as cost, complexity, and technical factors, including skin impedance with surface EMG and the invasive nature of fine wire electrodes, EMG is impractical for use outside of a laboratory environment. Mechanomyography (MMG) is an alternative to EMG, which shows promise in pervasive applications. The present study used an exerting squat-based task to induce muscle fatigue. MMG and EMG amplitude and frequency were compared before, during, and after the squatting task. Combining MMG with inertial measurement unit (IMU) data enabled segmentation of muscle activity at specific points: entering, holding, and exiting the squat. Results show MMG measures of muscle activity were similar to EMG in timing, duration, and magnitude during the fatigue task. The size, cost, unobtrusive nature, and usability of the MMG/IMU technology used, paired with the similar results compared to EMG, suggest that such a system could be suitable in uncontrolled natural environments such as within the home.
The Impact of Exogenous Pollution on Green Innovation
The Impact of Exogenous Pollution on Green Innovation
Does environmental quality affect firms’ activities that might improve that quality? In this paper, we use China's public heating policy as a quasi-experiment to investigate the impact of exogenous pollution differences on green innovation behavior. We use a regression discontinuity model, and carry out a suite of robustness tests. We consistently find that firms located in cities with an exogenous source of heavy pollution tend to adopt green innovation at a lower rate while we find no difference in the rate at which they adopt non-green innovation. We find a strong causal effect: being north of the boundary, where pollution levels are higher, leads firms to adopt less green innovation. Firms located in the heating areas report roughly 1 less green innovation per billion RMB of assets, a substantial difference given the average number of green innovations per billion RMB of assets of northern firms is 0.641.
Performance of a wearable acoustic system for fetal movement discrimination
Performance of a wearable acoustic system for fetal movement discrimination
Fetal movements (FM) are a key factor in clinical management of high-risk pregnancies such as fetal growth restriction. While maternal perception of reduced FM can trigger self-referral to obstetric services, maternal sensation is highly subjective. Objective, reliable monitoring of fetal movement patterns outside clinical environs is not currently possible. A wearable and non-transmitting system capable of sensing fetal movements over extended periods of time would be extremely valuable, not only for monitoring individual fetal health, but also for establishing normal levels of movement in the population at large. Wearable monitors based on accelerometers have previously been proposed as a means of tracking FM, but such systems have difficulty separating maternal and fetal activity and have not matured to the level of clinical use. We introduce a new wearable system based on a novel combination of accelerometers and bespoke acoustic sensors as well as an advanced signal processing architecture to identify and discriminate between types of fetal movements. We validate the system with concurrent ultrasound tests on a cohort of 44 pregnant women and demonstrate that the garment is capable of both detecting and discriminating the vigorous, whole-body 'startle' movements of a fetus. These results demonstrate the promise of multimodal sensing for the development of a low-cost, non-transmitting wearable monitor for fetal movements.
Practical Precautionary Resource Management Using Robust Optimization
Practical Precautionary Resource Management Using Robust Optimization
Uncertainties inherent in fisheries motivate a precautionary approach to management, meaning an approach specifically intended to avoid bad outcomes. Stochastic dynamic optimization models, which have been in the fisheries literature for decades, provide a framework for decision making when uncertain outcomes have known probabilities. However, most such models incorporate population dynamics models for which the parameters are assumed known. In this paper, we apply a robust optimization approach to capture a form of uncertainty nearly universal in fisheries, uncertainty regarding the values of model parameters. Our approach, developed by Nilim and El Ghaoui (Oper Res 53(5):780–798, 2005), establishes bounds on parameter values based on the available data and the degree of precaution that the decision maker chooses. To demonstrate the applicability of the method to fisheries management problems, we use a simple example, the Skeena River sockeye salmon fishery. We show that robust optimization offers a structured and computationally tractable approach to formulating precautionary harvest policies. Moreover, as better information about the resource becomes available, less conservative management is possible without reducing the level of precaution.