Gene Regulatory Networks: Modeling, Intervention and Context

abstract: Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.Dissertation/ThesisPh.D. Computer Science 201

An Intelligence and Creative Computing Based Mental Health Care Framework

Artificial intelligence is being developed and applied in business, medical, tourism and other subjects. Health care can be related to artificial intelligence, serving more on protecting people’s health through supervising some indexes of people and providing advice for people’s normal life. As health care is required colleting the physical data, voice data and video data, wearable products are suitable. The user is connected with wearable products through minor cameras, sensors, and chips. In this research, an artificial intelligence-based health care framework is achieved for supervising and managing user’s health conditions and being applied with intelligent shoes. The aim of this research is to serve people on mental health care in normal life based on physical data analysis and mental data analysis, to protect people from being sub-health, to advise people on different aspects of being healthy. An applicable system can be generated with specific AI algorithms and machine learning algorithms, including body index data statistical model analysis, voice data analysis algorithm, and video data analysis. The framework can collect health related data and analyse it, generating health state reports and advice report for the user. The entire system is based on artificial intelligence theories and methods, and machine learning algorithms, completing health data processing. Abstraction method is combined with conventional nueral network and recurrent neural network are used for voice data analysis and video data analysis. Statistical model is created based on body index data categories. Three kinds of data can be processed for supervising the emotions of the user. This research will generate a mental health care framework and a mental health supervision and management system that can be set on the wearable products for the application

Seattle Pacific University Catalog 2010-2011

https://digitalcommons.spu.edu/archives_catalogs/1092/thumbnail.jp

Towards a Sustainable Life: Smart and Green Design in Buildings and Community

This Special Issue includes contributions about occupants’ sustainable living in buildings and communities, highlighting issues surrounding the sustainable development of our environments and lives by emphasizing smart and green design perspectives. This Special Issue specifically focuses on research and case studies that develop promising methods for the sustainable development of our environment and identify factors critical to the application of a sustainable paradigm for quality of life from a user-oriented perspective. After a rigorous review of the submissions by experts, fourteen articles concerning sustainable living and development are published in this Special Issue, written by authors sharing their expertise and approaches to the concept and application of sustainability in their fields. The fourteen contributions to this special issue can be categorized into four groups, depending on the issues that they address. All the proposed methods, models, and applications in these studies contribute to the current understanding of the adoption of the sustainability paradigm and are likely to inspire further research addressing the challenges of constructing sustainable buildings and communities resulting in a sustainable life for all of society

Seattle Pacific University Catalog 2011-2012

https://digitalcommons.spu.edu/archives_catalogs/1093/thumbnail.jp

Undergraduate course catalog (Florida International University). [2012-2013]

This catalog contains a description of the various policies, undergraduate programs, degree requirements, and course offerings at Florida International University during the 2012-2013 year.https://digitalcommons.fiu.edu/catalogs/1070/thumbnail.jp

Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data