Skin Manifestations of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a disease that affects the intestinal tract via an inflammatory process. Patients who suffer from IBD often have diseases that affect multiple other organ systems as well. These are called extraintestinal manifestations and can be just as, if not more debilitating than the intestinal inflammation itself. The skin is one of the most commonly affected organ systems in patients who suffer from IBD. The scientific literature suggests that a disturbance of the equilibrium between host defense and tolerance, and the subsequent over-activity of certain immune pathways are responsible for the cutaneous disorders seen so frequently in IBD patients. The purpose of this review article is to give an overview of the types of skin diseases that are typically seen with IBD and their respective pathogenesis, proposed mechanisms, and treatments. These cutaneous disorders can manifest as metastatic lesions, reactive processes to the intestinal inflammation, complications of IBD itself, or side effects from IBD treatments; these can be associated with IBD via genetic linkage, common autoimmune processes, or other mechanisms that will be discussed in this article. Ultimately, it is important for healthcare providers to understand that skin manifestations should always be checked and evaluated for in patients with IBD. Furthermore, skin disorders can predate gastrointestinal symptoms and thus may serve as important clinical indicators leading physicians to earlier diagnosis of IBD

Peer Evaluation of Video Lab Reports in a Blended Introductory Physics Course

The Georgia Tech blended introductory calculus-based mechanics course emphasizes scientific communication as one of its learning goals, and to that end, we gave our students a series of four peer-evaluation assignments intended to develop their abilities to present and evaluate scientific arguments. Within these assignments, we also assessed students' evaluation abilities by comparing their evaluations to a set of expert evaluations. We summarize our development efforts and describe the changes we observed in student evaluation behavior.Comment: 4 pages, 1 table, 2 figures, submitted to Summer 2014 PERC Proceeding

Macrophage-released ADAMTS1 promotes muscle stem cell activation.

Coordinated activation of muscle stem cells (known as satellite cells) is critical for postnatal muscle growth and regeneration. The muscle stem cell niche is central for regulating the activation state of satellite cells, but the specific extracellular signals that coordinate this regulation are poorly understood. Here we show that macrophages at sites of muscle injury induce activation of satellite cells via expression of Adamts1. Overexpression of Adamts1 in macrophages in vivo is sufficient to increase satellite cell activation and improve muscle regeneration in young mice. We demonstrate that NOTCH1 is a target of ADAMTS1 metalloproteinase activity, which reduces Notch signaling, leading to increased satellite cell activation. These results identify Adamts1 as a potent extracellular regulator of satellite cell activation and have significant implications for understanding the regulation of satellite cell activity and regeneration after muscle injury.Satellite cells are crucial for growth and regeneration of skeletal muscle. Here the authors show that in response to muscle injury, macrophages secrete Adamts1, which induces satellite cell activation by modulating Notch1 signaling

The Initial State of Students Taking an Introductory Physics MOOC

As part of a larger research project into massively open online courses (MOOCs), we have investigated student background, as well as student participation in a physics MOOC with a laboratory component. Students completed a demographic survey and the Force and Motion Conceptual Evaluation at the beginning of the course. While the course is still actively running, we have tracked student participation over the first five weeks of the eleven-week course.Comment: Accepted to PERC Proceedings 201

Reinforcement Learning Based Dual-Control Methodology for Complex Nonlinear Discrete-Time Systems with Application to Spark Engine EGR Operation

A novel reinforcement-learning-based dual-control methodology adaptive neural network (NN) controller is developed to deliver a desired tracking performance for a class of complex feedback nonlinear discrete-time systems, which consists of a second-order nonlinear discrete-time system in nonstrict feedback form and an affine nonlinear discrete-time system, in the presence of bounded and unknown disturbances. For example, the exhaust gas recirculation (EGR) operation of a spark ignition (SI) engine is modeled by using such a complex nonlinear discrete-time system. A dual-controller approach is undertaken where primary adaptive critic NN controller is designed for the nonstrict feedback nonlinear discrete-time system whereas the secondary one for the affine nonlinear discrete-time system but the controllers together offer the desired performance. The primary adaptive critic NN controller includes an NN observer for estimating the states and output, an NN critic, and two action NNs for generating virtual control and actual control inputs for the nonstrict feedback nonlinear discrete-time system, whereas an additional critic NN and an action NN are included for the affine nonlinear discrete-time system by assuming the state availability. All NN weights adapt online towards minimization of a certain performance index, utilizing gradient-descent-based rule. Using Lyapunov theory, the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates, and observer estimates are shown. The adaptive critic NN controller performance is evaluated on an SI engine operating with high EGR levels where the controller objective is to reduce cyclic dispersion in heat release while minimizing fuel intake. Simulation and experimental results indicate that engine out emissions drop significantly at 20% EGR due to reduction in dispersion in heat release thus verifying the dual-control approach

Reinforcement-Learning-Based Output-Feedback Control of Nonstrict Nonlinear Discrete-Time Systems with Application to Engine Emission Control

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NOx) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NOx\u27s are reduced by over 80% compared with stoichiometric levels

Near Optimal Output-Feedback Control of Nonlinear Discrete-Time Systems in Nonstrict Feedback Form with Application to Engines

A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. A Lyapunov function proves the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight, and observer estimation. Separation principle and certainty equivalence principles are relaxed; persistency of excitation condition and linear in the unknown parameter assumption is not needed. The performance of this controller is evaluated on a spark ignition (SI) engine operating with high exhaust gas recirculation (EGR) levels and experimental results are demonstrated