111 Research Drive

Bethlehem, PA, United States

111 Research Drive

Bethlehem, PA, United States
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Govindan J.,Lehigh University | Iovine M.K.,Lehigh University | Iovine M.K.,111 Research Drive
Gene Expression Patterns | Year: 2015

Extracellular matrix plays a dynamic role during the process of wound healing, embryogenesis and tissue regeneration. Caudal fin regeneration in zebrafish is an excellent model to study tissue and skeletal regeneration. We have analyzed the expression pattern of some of the well characterized ECM proteins during the process of caudal fin regeneration in zebrafish. Our results show that a transitional matrix analogous to the one formed during newt skeletal and heart muscle regeneration is synthesized during fin regeneration. Here we demonstrate that a provisional matrix rich in hyaluronic acid, tenascin C, and fibronectin is synthesized following amputation. Additionally, we observed that the link protein Hapln1a dependent ECM, consisting of Hapln1a, hyaluronan and proteoglycan aggrecan, is upregulated during fin regeneration. Laminin, the protein characteristic of differentiated tissues, showed only modest change in the expression pattern. Our findings on zebrafish fin regeneration implicates that changes in the extracellular milieu represent an evolutionarily conserved mechanism that proceeds during tissue regeneration, yet with distinct players depending on the type of tissue that is involved. © 2015 Elsevier B.V. All rights reserved.

PubMed | 111 Research Drive and University of Manitoba
Type: Journal Article | Journal: BMC health services research | Year: 2016

A patients trajectory through the healthcare system affects resource use and outcomes. Data fields in population-based administrative health databases are potentially valuable resources for constructing care trajectories for entire populations, provided they can capture patient transitions between healthcare services. This study describes patient transitions from the emergency department (ED) to other healthcare settings, and ascertains whether the discharge disposition field recorded in the ED data was a reliable source of patient transition information from the emergency to the acute care settings.Administrative health databases from the province of Saskatchewan, Canada (population 1.1 million) were used to identify patients with at least one ED visit to provincial teaching hospitals (n=5) between April 1, 2006 and March 31, 2012. Discharge disposition from ED was described using frequencies and percentages; and it includes categories such as home, transfer to other facilities, and died. The kappa statistic with 95% confidence intervals (95% CIs) was used to measure agreement between the discharge disposition field in the ED data and hospital admission records.We identified N=1,062,861 visits for 371,480 patients to EDs over the six-year study period. Three-quarters of the discharges were to home, 16.1% were to acute care in the same facility in which the ED was located, and 1.6% resulted in a patient transfer to a different acute care facility. Agreement between the discharge disposition field in the ED data and hospital admission records was good when the emergency and acute care departments were in the same facility (=0.77, 95% CI 0.77, 0.77). For transfers to a different acute care facility, agreement was only fair (=0.36, 95% CI 0.35, 0.36).The majority of patients who attended EDs did not transition to another healthcare setting. For those who transitioned to acute care, accuracy of the discharge disposition field depended on whether the two services were provided in the same facility. Using the hospital data as reference, we conclude that the discharge disposition field in the ED data is not reliable for measuring transitions from ED to acute care.

PubMed | 111 Research Drive, U.S. National Institute of Diabetes and Digestive and Kidney Diseases and University of Cambridge
Type: Journal Article | Journal: Journal of chemical theory and computation | Year: 2015

The C-terminal -hairpin of protein G is a 16-residue peptide that folds in a two-state fashion akin to many larger proteins. However, with an experimental folding time of 6 s, it remains a challenging system for all-atom, explicitly solvated, molecular dynamics simulations. Here, we use a large simulation data set (0.7 ms total) of the hairpin at 300 and 350 K to interpret its folding via a master equation approach. We find a separation of over an order of magnitude between the longest and second longest relaxation times, with the slowest relaxation corresponding to folding. However, in spite of this apparent two-state dynamics, the folding rate determined based on a first-passage time analysis depends on the initial conditions chosen, with a nonexponential distribution of first passage times being obtained in some cases. Using the master equation model, we are now able to account quantitatively for the observed distribution of first passage times. The deviation from the expected exponential distribution for a two-state system arises from slow dynamics in the unfolded state, associated with formation and melting of helical structures. Our results help to reconcile recent findings of slow dynamics in unfolded proteins with observed two-state folding kinetics. At the same time, they indicate that care is required in estimating folding kinetics from many short folding simulations. Last, we are able to use the master equation model to obtain details of the folding mechanism and folding transition state, which appear consistent with the zipper mechanism inferred from the experiment.

News Article | October 26, 2016
Site: www.eurekalert.org

On Monday, October 31, Lehigh University is hosting a symposium to advance the understanding and usage of probabilistic modeling across science and engineering academia, especially in the U.S. Midlantic region. Probabilistic modeling provides essential tools for analyzing vast amounts of data that have become available in science, scholarship, and everyday life; increasingly, it is becoming an important skillset for all scientists and engineers. Organized by Lehigh's Probabilistic Modeling Group, the symposium will gather researchers with expertise in theoretical and applied probability to share experiences from different fields of science and engineering. According to organizer Dr. Paolo Bocchini, assistant professor of civil and environmental engineering at Lehigh, the symposium will also underscore the probabilistic approach as a pillar of scientific curricula at all academic levels. "Three keynote speakers from different scientific fields will be on hand to make the case for probabilistic modeling," says Bocchini, who leads an interdisciplinary, NSF-funded Lehigh project at Lehigh entitled Probabilistic Resilience Assessment of Interdependent Systems (PRAISys). "All of them are renowned leaders in the theory and application of these techniques." The symposium will also include a panel discussion and question/answer session on the future of research and education in probability. The symposium is FREE for all the participants, yet registration is required by October 25, 2016. Lunch will be provided. Although not required, all researchers and students who register are encouraged to bring a poster describing their research work in probabilistic modeling and to share their approach and results with other attendees. The symposium will be held in the Wood Dining Room of Iacocca Hall, on Lehigh's scenic Mountaintop campus, 111 Research Drive, Bethlehem, PA 18015. For more details and to register, please visit http://www. .

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