Temple University

About: Temple University is a education organization based out in Philadelphia, Pennsylvania, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32154 authors who have published 64375 publications receiving 2219828 citations.

The neural and genetic basis of executive function: Attention, cognitive flexibility, and response inhibition

Abstract: Executive function is a collection of cognitive processes essential for higher order mental function. Processes involved in executive function include, but are not limited to, working memory, attention, cognitive flexibility, and impulse control. These complex behaviors are largely mediated by prefrontal cortical function but are modulated by dopaminergic, noradrenergic, serotonergic, and cholinergic input. The ability of these neurotransmitter systems to modulate executive function allows for adaptation in cognitive behavior in response to changes in the environment. Because of the important role these neurotransmitter systems play in regulating executive function, changes in these systems can also have a grave impact on executive function. In addition, polymorphisms in genes associated with these neurotransmitters are associated with phenotypic differences in executive function. Understanding how these naturally occurring polymorphisms contribute to different executive function phenotypes will advance basic knowledge of cognition and potentially further understanding and treatment of mental illness that involve changes in executive function. In this review, we will examine the influence of dopamine, norepinephrine, serotonin, and acetylcholine on the following measures of executive function: attention, cognitive flexibility, and impulse control. We will also review the effects of polymorphisms in genes associated with these neurotransmitter systems on these measures of executive function.

Block-Based Neural Networks for Personalized ECG Signal Classification

Abstract: This paper presents evolvable block-based neural networks (BbNNs) for personalized ECG heartbeat pattern classification. A BbNN consists of a 2-D array of modular component NNs with flexible structures and internal configurations that can be implemented using reconfigurable digital hardware such as field-programmable gate arrays (FPGAs). Signal flow between the blocks determines the internal configuration of a block as well as the overall structure of the BbNN. Network structure and the weights are optimized using local gradient-based search and evolutionary operators with the rates changing adaptively according to their effectiveness in the previous evolution period. Such adaptive operator rate update scheme ensures higher fitness on average compared to predetermined fixed operator rates. The Hermite transform coefficients and the time interval between two neighboring R-peaks of ECG signals are used as inputs to the BbNN. A BbNN optimized with the proposed evolutionary algorithm (EA) makes a personalized heartbeat pattern classifier that copes with changing operating environments caused by individual difference and time-varying characteristics of ECG signals. Simulation results using the Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) arrhythmia database demonstrate high average detection accuracies of ventricular ectopic beats (98.1%) and supraventricular ectopic beats (96.6%) patterns for heartbeat monitoring, being a significant improvement over previously reported electrocardiogram (ECG) classification results.

Estimating the degree of emergency department overcrowding in academic medical centers: results of the National ED Overcrowding Study (NEDOCS).

Abstract: Objectives: No single universal definition of emergency department (ED) overcrowding exists. The authors hypothesize that a previously developed site-sampling form for academic ED overcrowding is a valid model to quantify overcrowding in academic institutions and can be used to develop a validated short form that correlates with overcrowding. Methods: A 23-question site-sampling form was designed based on input from academic physicians at eight medical schools representative of academic EDs nationwide. A total of 336 site-samplings at eight academic medical centers were conducted at 42 computer-generated random times over a three-week period by independent observers at each site. These sampling times ranged from very slow to severely overcrowded. The outcome variable was the degree of overcrowding as assessed by the charge nurse and ED physicians. The full model consisted of objective data that were obtained by counting the number of patients, determining patients' waiting times, and obtaining information from registration, triage, and ancillary services. Specific objective data were indexed to site-specific demographics. The outcome and objective data were compared using a multiple linear regression to determine predictive validity of the full model. A five-question reduced model was calculated using a backward stepdown procedure. Predictive validity and relationships between the outcome and objective data were assessed using a mixed-effects linear regression model, treating center as random effect. Results: Overcrowding occurred 12% to 73% of the time (mean, 35%), with two hospitals being overcrowded more than 50% of the time. Comparison of objective and outcome data resulted in an R2 of 0.49 (p < 0.001), indicating a good degree of predictive validity. A reduced five-question model predicted the full model with 88% accuracy. Conclusions: Overcrowding varied widely between academic centers during the study period. Results of a five-question reduced model are valid and accurate in predicting the degree of overcrowding in academic centers.

Noncontact Anterior Cruciate Ligament Injuries: Mechanisms and Risk Factors

Abstract: Significant advances have recently been made in understanding the mechanisms involved in noncontact anterior cruciate ligament (ACL) injury. Most ACL injuries involve minimal to no contact. Female athletes sustain a two- to eightfold greater rate of injury than do their male counterparts. Recent videotape analyses demonstrate significant differences in average leg and trunk positions during injury compared with control subjects. These findings as well as those of cadaveric and MRI studies indicate that axial compressive forces are a critical component in noncontact ACL injury. A complete understanding of the forces and risk factors associated with noncontact ACL injury should lead to the development of improved preventive strategies for this devastating injury.

Salient Object Detection via Structured Matrix Decomposition

Abstract: Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.