Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks

Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model

Implementing Tablet PCs in a Distance Learning Environment

(First paragraph) The Commonwealth Graduate Engineering Program (CGEP) is a collaborative distance education program developed by leading Universities in the Commonwealth of Virginia. It is over 25 years old and its main goal is to deliver graduate engineering courses to qualified professionals located across the Commonwealth of Virginia. Traditionally, the courses delivered to students in this program are done through interactive video conferencing (IVC) technology and most students are required to drive to a physical location. However, an increasing number of working professionals are beginning to want more flexibility with the timing and locations of these classes. With these changes in market demand in mind, the CGEP directors are seeking new ways to deliver these courses combined with a systematic way to manage the change

Fracture Detection in Traumatic Pelvic CT Images

Fracture detection in pelvic bones is vital for patient diagnostic decisions and treatment planning in traumatic pelvic injuries. Manual detection of bone fracture from computed tomography (CT) images is very challenging due to low resolution of the images and the complex pelvic structures. Automated fracture detection from segmented bones can significantly help physicians analyze pelvic CT images and detect the severity of injuries in a very short period. This paper presents an automated hierarchical algorithm for bone fracture detection in pelvic CT scans using adaptive windowing, boundary tracing, and wavelet transform while incorporating anatomical information. Fracture detection is performed on the basis of the results of prior pelvic bone segmentation via our registered active shape model (RASM). The results are promising and show that the method is capable of detecting fractures accurately

Fishbowl Discussions: Promoting Collaboration between Mathematics and Partner Disciplines

A National Consortium for Synergistic Undergraduate Mathematics via Multi-institutional Interdisciplinary Teaching Partnerships project (SUMMIT-P) is a collaboration of institutions focused on revising first- and second-year mathematics courses with the help of partner disciplines with prerequisite mathematics courses. This paper describes the fishbowl discussion technique used by the consortium members to encourage interdisciplinary conversation. Vignettes describing the results of conversations that occurred at several consortium member institutions are provided by the co-authors

Fishbowl Discussions: Promoting Collaboration between Mathematics and Partner Disciplines

A National Consortium for Synergistic Undergraduate Mathematics via Multi-institutional Interdisciplinary Teaching Partnerships project (SUMMIT-P) is a collaboration of institutions focused on revising first- and second-year mathematics courses with the help of partner disciplines with prerequisite mathematics courses. This paper describes the fishbowl discussion technique used by the consortium members to encourage interdisciplinary conversation. Vignettes describing the results of conversations that occurred at several consortium member institutions are provided by the co-authors

Using Site Visits to Strengthen Collaboration

The SUMMIT-P project is a multi-institutional endeavor to leverage interdisciplinary collaboration in order to improve the teaching of undergraduate mathematics courses in the first two years of college. One goal of this work is to establish collaborative communities among the institutions involved. As part of the project, institutions visit one another on site visits that are structured according to a common protocol. The site visits have been valuable to the project. Participating institutions report the exchange of actionable ideas and feedback; members of the grant leadership team have used the site visits to direct the overall project, and evaluators have refined questions and identified trends that will help their assessment of the project. At a deeper level, the site visits have created a strong sense of community among those involved in every aspect of the SUMMIT-P project

The contribution of X-linked coding variation to severe developmental disorders

Abstract: Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders

An Automated Optimal Engagement and Attention Detection System Using Electrocardiogram

This research proposes to develop a monitoring system which uses Electrocardiograph (ECG) as a fundamental physiological signal, to analyze and predict the presence or lack of cognitive attention in individuals during a task execution. The primary focus of this study is to identify the correlation between fluctuating level of attention and its implications on the cardiac rhythm recorded in the ECG. Furthermore, Electroencephalograph (EEG) signals are also analyzed and classified for use as a benchmark for comparison with ECG analysis. Several advanced signal processing techniques have been implemented and investigated to derive multiple clandestine and informative features from both these physiological signals. Decomposition and feature extraction are done using Stockwell-transform for the ECG signal, while Discrete Wavelet Transform (DWT) is used for EEG. These features are then applied to various machine-learning algorithms to produce classification models that are capable of differentiating between the cases of a person being attentive and a person not being attentive. The presented results show that detection and classification of cognitive attention using ECG are fairly comparable to EEG