The Alliance of Genome Resources: Building a Modern Data Ecosystem for Model Organism Databases.

Model organisms are essential experimental platforms for discovering gene functions, defining protein and genetic networks, uncovering functional consequences of human genome variation, and for modeling human disease. For decades, researchers who use model organisms have relied on Model Organism Databases (MODs) and the Gene Ontology Consortium (GOC) for expertly curated annotations, and for access to integrated genomic and biological information obtained from the scientific literature and public data archives. Through the development and enforcement of data and semantic standards, these genome resources provide rapid access to the collected knowledge of model organisms in human readable and computation-ready formats that would otherwise require countless hours for individual researchers to assemble on their own. Since their inception, the MODs for the predominant biomedical model organisms [Mus sp (laboratory mouse), Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Danio rerio, and Rattus norvegicus] along with the GOC have operated as a network of independent, highly collaborative genome resources. In 2016, these six MODs and the GOC joined forces as the Alliance of Genome Resources (the Alliance). By implementing shared programmatic access methods and data-specific web pages with a unified "look and feel," the Alliance is tackling barriers that have limited the ability of researchers to easily compare common data types and annotations across model organisms. To adapt to the rapidly changing landscape for evaluating and funding core data resources, the Alliance is building a modern, extensible, and operationally efficient "knowledge commons" for model organisms using shared, modular infrastructure

Combating Diabetes in Chittenden County: A Healthcare Provider Referral Campaign to Increase Patient Participation in the Vermont Diabetes Prevention Program

As of 2016, diabetes affects more than 29 million people in the United States and is the 7th leading cause of death nationwide. In Vermont, 1/10 people are diagnosed with either diabetes or prediabetes, with 6% of Vermonters affected by prediabetes and 5% of Chittenden Country affected by prediabetes. The public health burden of this chronic disease is immense: diabetes costs Vermont an estimated $543 million each year and is the leading cause of kidney failure, lower limb amputations, and adult-onset blindness. Prediabetes occurs when blood sugar is higher than normal but not at the diagnostic threshold of diabetes. Prediabetes does not definitely progress to type 2 diabetes if interventions are made, including healthier eating and physical activity to promote modest weight loss. The CDC has developed evidence-based curricula for lifestyle intervention in prediabetics with intensive individual counseling and motivational support on effective diet, exercise, and behavior modification. One of these curricula is currently run through the CDC-led National YMCA Diabetes Prevention Program (YDPP). Participation in this program reduces the risk of developing type 2 diabetes by 58% across all ethnic groups and sexes overall and by 71% in individuals over age 60. The Vermont YDPP had 325 participants in 2016, with only 21% (n=70) of those referrals to the program coming from healthcare providers. Healthcare providers have a unique role in the community of being the voices of health promotion. With the correct provider awareness of the YDPP and patient identification, awareness, and education, an increase in the percentage of YDPP-referring healthcare providers can increase the overall YDPP participation in Chittenden county. As of 2016, 60,038 people in Chittenden county have prediabetes, but only 8,026 are diagnosed, and an additional 52,012 people could be diagnosed with prediabetes by their healthcare professional and referred to the YDPP. This campaign intends to serve as a pilot project to create provider and patient awareness of the YDPP, identify patients with prediabetes, and ensure providers screen and refer prediabetic patients to the YDPP. Through exam room posters, panel query management, electronic medical record reprogramming, and targeted patient intervention, this study aims to increase YDPP patient participation via healthcare provider referral and refine the model for adaptation and implementation in other healthcare centers throughout Chittenden County.https://scholarworks.uvm.edu/fmclerk/1301/thumbnail.jp

Developing an Efficient DMCIS with Next-Generation Wireless Networks

The impact of extreme events across the globe is extraordinary which continues to handicap the advancement of the struggling developing societies and threatens most of the industrialized countries in the globe. Various fields of Information and Communication Technology have widely been used for efficient disaster management; but only to a limited extent though, there is a tremendous potential for increasing efficiency and effectiveness in coping with disasters with the utilization of emerging wireless network technologies. Early warning, response to the particular situation and proper recovery are among the main focuses of an efficient disaster management system today. Considering these aspects, in this paper we propose a framework for developing an efficient Disaster Management Communications and Information System (DMCIS) which is basically benefited by the exploitation of the emerging wireless network technologies combined with other networking and data processing technologies.Comment: 6 page

A Secure Lightweight Approach of Node Membership Verification in Dense HDSN

In this paper, we consider a particular type of deployment scenario of a distributed sensor network (DSN), where sensors of different types and categories are densely deployed in the same target area. In this network, the sensors are associated with different groups, based on their functional types and after deployment they collaborate with one another in the same group for doing any assigned task for that particular group. We term this sort of DSN as a heterogeneous distributed sensor network (HDSN). Considering this scenario, we propose a secure membership verification mechanism using one-way accumulator (OWA) which ensures that, before collaborating for a particular task, any pair of nodes in the same deployment group can verify each other-s legitimacy of membership. Our scheme also supports addition and deletion of members (nodes) in a particular group in the HDSN. Our analysis shows that, the proposed scheme could work well in conjunction with other security mechanisms for sensor networks and is very effective to resist any adversary-s attempt to be included in a legitimate group in the network.Comment: 6 page

Identifying how automation can lose its intended benefit along the development process : a research plan

Doctoral Consortium Presentation © The Authors 2009Automation is usually considered to improve performance in virtually any domain. However it can fail to deliver the target benefit as intended by those managers and designers advocating the introduction of the tool. In safety critical domains this problem is of significance not only because the unexpected effects of automation might prevent its widespread usage but also because they might turn out to be a contributor to incident and accidents. Research on failures of automation to deliver the intended benefit has focused mainly on human automation interaction. This paper presents a PhD research plan that aims at characterizing decisions for those involved in development process of automation for safety critical domains, taken under productive pressure, to identify where and when the initial intention the automation is supposed to deliver can be lost along the development process. We tentatively call such decisions as drift and the final objective is to develop principles that will allow to identify and compensate for possible sources of drift in the development of new automation. The research is based on case studies and is currently entering Year 2

Innovative in silico approaches to address avian flu using grid technology

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper presents how we are currently exploring how to integrate the existing data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations