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Malvern, PA, United States

Luo R.,University of Delaware | Cannon L.,University of Delaware | Hernandez J.,University of Delaware | Piovoso M.J.,Penn State Great Valley | And 2 more authors.
Journal of Process Control | Year: 2011

Evolution has long been understood as the driving force for many problems of medical interest. The evolution of drug resistance in HIV and bacterial infections is recognized as one of the most significant emerging problems in medicine. In cancer therapy, the evolution of resistance to chemotherapeutic agents is often the differentiating factor between effective therapy and disease progression or death. Interventions to manage the evolution of resistance have, up to this point, been based on steady-state analysis of mutation and selection models. In this paper, we review the mathematical methods applied to studying evolution of resistance in disease. We present a broad review of several classical applications of mathematical modeling of evolution, and review in depth two recent problems which demonstrate the potential for interventions which exploit the dynamic behavior of resistance evolution models. The first problem addresses the problem of sequential treatment failures in HIV; we present a review of our recent publications addressing this problem. The second problem addresses a novel approach to gene therapy for pancreatic cancer treatment, where selection is used to encourage optimal spread of susceptibility genes through a target tumor, which is then eradicated during a second treatment phase. We review the recent in vitro laboratory work on this topic, present a new mathematical model to describe the treatment process, and show why model-based approaches will be necessary to successfully implement this novel and promising approach. © 2010 Elsevier Ltd. All rights reserved. Source

Barb A.S.,Penn State Great Valley | Shyu C.-R.,University of Missouri
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

This article proposes a methodology to reduce overfitting when ranking high-resolution satellite images by domain semantics. Our approach uses PathFinder Network Scaling ensemble methods. We generate cross-fold co-occurrence matrices for relevance of feature subspaces to each semantic. Each matrix is then reduced using the PathFinder network scaling algorithm. Irrelevant nodes are removed using node strength metrics resulting in an optimized model for ranking by semantic that generalizes better to new images. The experiments show that, when using this approach, the quality of ranking by semantic can be significantly improved. Results show that Mean Average Precision (MAP) of ranking over cross-fold experiments increased by a 13.2% while standard deviation of MAP was reduced by 16.8% relatively to experiments without PathFinder network scaling. © 2012 IEEE. Source

Jablokow K.,Penn State Great Valley | Myers M.,SAP America
Proceedings - 5th International Conference on Global Software Engineering, ICGSE 2010 | Year: 2010

The challenges faced in global collaboration are often described in terms of logistical issues (e.g., language, time, distance) and issues related to observed workplace differences, which are frequently attributed to the influence of national culture. In this paper, we suggest that another less visible but equally important factor is at work - namely, cognitive diversity, or differences in the preferred ways in which individuals solve problems. In bringing to light key findings related to this additional factor, we review and integrate core concepts from Hofstede's cultural diversity research and Kirton's cognitive diversity research, respectively. This enables us to explore the potential causes of team conflict from a broader perspective and to create a more comprehensive view of diversity management in general. In addition, we will discuss practical strategic approaches for mitigating both cultural and cognitive differences, making recommendations for their use in the context of global IT teams. © 2010 IEEE. Source

Reychav I.,Ariel University | Stein E.W.,Penn State Great Valley | Weisberg J.,Bar - Ilan University | Glezer C.,Ariel University
International Journal of Knowledge Management | Year: 2012

This study examines the relationship between creativity and innovation at the individual level and how knowledge sharing mediates the relationship between these two constructs. A survey was conducted that measured individual creativity, innovativeness, and four types of knowledge sharing: explicit knowledge and tacit knowledge (e.g., experience, know-how, and expertise) sharing. It was postulated that the type of knowledge mediates the relationship between creativity and the innovativeness of task performance among systems analysts. The results show that creativity was positively related to task innovativeness. This relationship was mediated negatively by explicit knowledge sharing but positively mediated by tacit knowledge sharing based on know-how among project team members. These results have implications for system development and implementation projects. Copyright © 2012, IGI Global. Source

Barb A.S.,Penn State Great Valley | Shyu C.-R.,University of Missouri
2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, HEALTHCOM 2011 | Year: 2011

With recent advances in diagnostic medical imaging, huge quantities of medical images are produced and stored in digital image repositories. While these repositories are difficult to be analyzed manually by medical experts, they can be evaluated using computer-based methods to enrich the process of decision making. For example, query by image methods can be used by medical experts for differential diagnosis by displaying previously evaluated cases that contain similar visual patterns. Also, less experienced practitioners can benefit from query-by-semantic methods in training processes especially for difficult-to-interpret cases with multiple pathologies. In this article we develop a methodology for ranking medical images based on Dirichlet process nonparametric distributions. Our approach uses natural groupings of images in a generated feature space to evaluate associative semantic mappings. Relevant semantic mappings are then used to generate additive computer models of semantic understanding of visual patterns found in images. We evaluate the performance of our method using mean average precision and precision-recall charts. © 2011 IEEE. Source

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