Greenville, NC, United States
Greenville, NC, United States

East Carolina University is a public, coeducational, doctoral/research university in Greenville, North Carolina, United States. Named East Carolina University by statute and commonly known as ECU or East Carolina, the university is the third-largest university in North Carolina.Founded on March 8, 1907 as a teacher training school, today East Carolina is listed by Forbes Magazine as a "Best Buy" and 181st among "national universities" by U.S. News & World Report. It has historical academic strengths in education, nursing, business, music, theater, and medicine, and offers over 100 Bachelor degree programs, 85 master's degrees, 21 doctoral programs, Doctor of Medicine, Doctor of Dental Medicine, and 62 certificates.East Carolina has grown from 43 acres in 1907 to almost 1,600 acres today. The university's academic facilities are located on four properties: Main, Health science, West Research facility, and the Field Station for Coastal Studies in New Holland, North Carolina. The nine undergraduate colleges, graduate school, and four professional schools are located on these four properties. All of the non-health science majors are located on the main campus. The College of Nursing, College of Allied Health science, The Brody School of Medicine, and School of Dental Medicine are located on the health science campus. There are ten social sororities, 16 social fraternities, four historically black sororities, five historically black fraternities, one Native American fraternity, and one Native American sorority. There are over 300 registered clubs on campus including fraternities and sororities. Wikipedia.

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East Carolina University | Date: 2016-09-22

Embodiments of the present invention relate to methods of preparing a cell, tissue, organ or plant for cryopreservation, wherein the method includes contacting the cell, tissue, organ or plant with a composition including sucrose and/or sucralose.

Cornell University and East Carolina University | Date: 2016-12-14

The invention provides methods of preventing or treating insulin resistance in a mammalian subject. In particular, the present invention provides a peptide that can be used for normalizing blood glucose levels and/or normalizing insulin response in an insulin resistant mammalian subject.

Sun G.,East Carolina University
Plant Molecular Biology | Year: 2012

microRNAs (miRNAs) are an extensive class of newly identified small RNAs, which regulate gene expression at the post-transcriptional level by mRNA cleavage or translation inhibition. Currently, there are 3,070 miRNAs deposited in the public available miRNA database; these miRNAs were obtained from 43 plant species using both computational (comparative genomics) and experimental (direct cloning and deep sequencing) approaches. Like other signaling molecules, plant miRNAs can also be moved from one tissue to another through the vascular system. These mobile miRNAs may play an important role in plant nutrient homeostasis and response to environmental biotic and abiotic stresses. In addition, miRNAs also control a wide range of biological and metabolic processes, including developmental timing, tissue-specific development, and stem cell maintenance and differentiation. Currently, a majority of plant miRNA-related researches are purely descriptive, and provide no further detailed mechanistic insight into miRNA-mediated gene regulation and other functions. To better understand the function and regulatory mechanisms of plant miRNAs, more strategies need to be employed to investigate the functions of miRNAs and their associated signaling pathways and gene networks. Elucidating the evolutionary mechanism of miRNAs is also important. It is possible to develop a novel miRNA-based biotechnology for improving plant yield, quality and tolerance to environmental biotic and abiotic stresses besides focusing on basic genetic studies. © 2011 Springer Science+Business Media B.V.

Muoio D.M.,Duke University | Neufer P.D.,East Carolina University
Cell Metabolism | Year: 2012

The interplay between mitochondrial energetics, lipid balance, and muscle insulin sensitivity has remained a topic of intense interest and debate for decades. One popular view suggests that increased oxidative capacity benefits metabolic wellness, based on the premise that it is healthier to burn fat than glucose. Attempts to test this hypothesis using genetically modified mouse models have produced contradictory results and instead link muscle insulin resistance to excessive fat oxidation, acylcarnitine production, and increased mitochondrial H2O2-emitting potential. Here, we consider emerging evidence that insulin action in muscle is driven principally by mitochondrial load and redox signaling rather than oxidative capacity. © 2012 Elsevier Inc.

Agency: NSF | Branch: Fellowship | Program: | Phase: GRADUATE RESEARCH FELLOWSHIPS | Award Amount: 138.00K | Year: 2016

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based masters and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution.

Agency: NSF | Branch: Standard Grant | Program: | Phase: RSCH EXPER FOR UNDERGRAD SITES | Award Amount: 359.84K | Year: 2016

This project will establish a three-year REU site in software testing and analytics at East Carolina University (ECU). It will offer a ten-week research program for ten undergraduate students during summer semesters. The faculty-student interaction as well as interaction among students will take different forms such as meetings, seminars, tutorials, workshop, and field trips. The REU project will allow a diverse pool of undergraduate students to experience cutting-edge research experience that will help them to become self reliant in STEM research. Students will gain valuable research skills that will prepare them for their future fields of study, and their exposure to the research will help them to compete for high technology fields in an innovative job market. The research experience will also motivate them to continue onto graduate studies. The REU project also will provide students an opportunity to collaborate with their faculty mentors and student peers across the nation after the summer program.

The sample research projects cover open research topics in software testing and analytics. Software Testing and Analysis of Scientific Software is to investigate the technique for adequately testing complex scientific software systems. The experimental data generated from the testing will be analyzed with machine learning tools for improving the test efficiency and effectiveness. We expect students will master basic principles of software testing and become skillful in creating test strategies and using tools for testing scientific software. Fault Detection Effectiveness and MC/DC Coverage of Combinatorial Test Cases will investigate the integration of combinatorial testing and MC/DC (modified condition/decision coverage) testing. Studies such as how logical expressions can be effectively tested, sensitivity analysis of different partitions of the input domain and factors that may affect combinatorial-based test generation, and a cross comparison between tests generated using different combinatorial testing algorithms will be conducted. Students will receive rigorous training in software testing and software testing research in this project. Software Analytics for Mobile Domain Specific Language (DSL) Construction will analyze program analysis results for the improvement of the development of DSL, and Guided Test Generation for Web Applications will use program analysis results to derive tests for testing web applications. The two projects will offer students the opportunity to learn the principles, applications and experimental study of program analysis.

East Carolina University | Date: 2016-02-11

The present invention discloses methods of reducing injury resulting from cardiovascular disease, such as myocardial infarction, and/or promoting myocardial repair. The methods include administering an ephrin and pharmaceutical compositions including ephrins to a subject. Kits useful for accomplishing the same are also provided.

Some embodiments of the present inventive concept provide a system that uses two wavelengths of differential transmittance through a sample to apply laser speckle or laser Doppler imaging. A first of the two wavelengths is within the visible range that has zero or very shallow penetration. This wavelength captures the anatomical structure of tissue/organ surface and serves as a position marker of the sample but not the subsurface movement of blood flow and perfusion. A second wavelength is in the near Infra-Red (NIR) range, which has much deeper penetration. This wavelength reveals the underlying blood flow physiology and correlates both to the motion of the sample and also the movement of blood flow and perfusion. Thus, true motion of blood flow and perfusion can be derived from the NIR imaging measurement without being affected by the motion artifact of the target.

Agency: NSF | Branch: Standard Grant | Program: | Phase: ENVIRONMENTAL SUSTAINABILITY | Award Amount: 99.92K | Year: 2016

CBET 1644650 (Etheridge)

This award is under NSFs topic of Public Participation in Scientific Research. The investigators will assess the reliability, validity and trustworthiness of data collected by citizen scientists by investigating the drivers of flooding following storm events on Bogue Banks, a barrier island in North Carolina. Forty citizen scientists will be recruited for the the project to (a) document areas prone to storm water flooding, and (b) measure and record groundwater and surface water levels in coastal communities over a three month period.

The citizen science team will deploy 20 automated water loggers in wells to collect independent groundwater data with which to compare data collected by citizen scientists. The citizen scientists will also be asked to identify areas prone to stormwater flooding where staff gages will be installed by the investigators to measure surface water ponding during storm events. The citizen scientists will be tasked with recording the water level and taking photos of the staff gages in flooded areas using smart phones or other devices. The investigators will then use image processing/machine vision software to extract quantitative data from the photos for comparison with the data recorded by the citizen scientists. A groundwater model will be used to simulate the impact of water table fluctuations on stormwater flooding events. One of the major outcomes of this project will be a determination of the reliability and validity of the groundwater and surface water level measurements made by citizen scientists along with the identification of other issues associated with their measurements in this context. The data collected by the citizen scientists will be used by the researchers to develop and evaluate models that relate groundwater level and precipitation data to stormwater flooding. Such models would be useful to water professionals/engineers in sustaining environmental systems impacted by stormwater flooding through the provision of information on anticipated flooding that is based on precipitation and depth to the water table.

Agency: NSF | Branch: Standard Grant | Program: | Phase: CRISP - Critical Resilient Int | Award Amount: 132.99K | Year: 2016

The US economy and social wellbeing depend on interdependent critical infrastructure systems (ICISs) such as transportation, energy, water, and food systems. These ICISs shape the countrys ability to meet community needs often successful, but not for all, and are susceptible to disruptions due to extreme natural events. This interplay between normal operation, chronic issues, and disaster-induced challenges is clearly evident when considering food security issues. Food access and affordability are persistent problems for more than 14 percent of Americans in normal times and are greatly exacerbated following disasters. Frameworks for understanding ICIS interdependencies, their interface with social and economic networks in response to natural hazards, and their roles in disaster recovery for vulnerable populations and food security are nascent. The food security of a community is a function of the pre-event vulnerabilities and the resilience of its food distribution network including the vulnerabilities of its infrastructural systems in isolation and their interdependencies. Furthermore, the demands posed by different hazards, the capacity of each physical network and system to respond to these demands, and the interactions between physical and social systems are highly uncertain. Accordingly, risk-informed approaches that can guide decision methods are crucial to characterize demand and impact on a community, to predict community response, and for designing community infrastructure systems that are resilient. Well-integrated decision methods that account for and integrate the performance of different ICISs in response to disasters have broad impacts. First, such methodologies will better frame questions on disaster mitigation and recovery, and will facilitate disaster planning activities and training for various disaster scenarios. Second, they will encourage policies that address chronic and acute food-security issues, balancing the mitigation of vulnerability with the promotion of resiliency. Finally, they will foster a shared language among social, behavioral, and economic (SBE) scientists, computational scientists, and engineers on the causes and characterization of hazards and risks and mitigation solutions. This project will engage a diverse set of students, including women and minorities, and in student-centered learning. It will integrate research and education throughout the project, and effectively disseminate the results. The methodologies developed will be integrated into courses such as Engineering Risk Analysis and Structural Reliability, Disaster Mitigation and Recovery and Planning Methods, and Risk and Regulation and into two NSF Research Experience for Undergraduate (REU) summer institutes which blend geography, computer science, health, planning and social science undergraduate students in food security, disparities, and health research projects.

This research will develop a decision platform that integrates computational models of ICISs at different spatial and temporal scales. These computational models will focus on the food distribution networks and include analytics of the socioeconomic causes of vulnerability. The decision platform may be used to examine issues related to reducing the risks associated with extreme hazards while enhancing community resilience with respect to food security. The project brings together three distinct disciplines: Engineering, SBE sciences, and Computer/Computational Sciences. Achieving project goals requires a deep collaboration between these three broad disciplines. Engineering is needed to understand and model the physical components of each sector and their interdependencies. SBE sciences are essential to understand and model food distribution from wholesale to households with a focus on vulnerable populations. Computer and Computational Science are needed to develop comprehensive models representing communities and their infrastructure and are the basis for assessing policy and organizational interventions that lead to greater robustness and resilience. The interdisciplinary nature of this research will also forge new channels of communication through models that integrate social and physical aspects of risk and vulnerability.

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