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- Creators: College of Liberal Arts and Sciences
Society is heavily dependent on a reliable electric supply; all infrastructure systems depend on electricity to operate. When the electric system fails, the impacts can be catastrophic (food spoilage, inoperable medical devices, lack of access to water, etc.). The social impacts, defined as the direct and indirect impacts on people, of power outages must be explored as the likelihood of power outages and blackouts are increasing. However, compared to other hazards, such as heat and flooding, the knowledge base on the impacts of power outages is relatively small. The purpose of this thesis is to identify what is currently known about the social impacts of power outages, identify where gaps in the literature exist, and deploy a survey to explore power outage experiences at the household level. This thesis is comprised of two chapters, a systematic literature review on the current knowledge of the social impacts of power outages and a multi-city survey focused on power outage experiences.
The first chapter comprised of a systematic literature review using a combined search of in Scopus which returned 762 candidate articles were identified that potentially explored the social impacts of power outages. However, after multiple filtering criteria were applied, only 45 articles met all criteria. Four themes were used to classify the literature, not exclusively, including modeling, social, technical, and other. Only papers that were classified as “social” - meaning they observed how people were affected by a power outage - or in combination with other categories were used within the review.
From the literature, populations of concern were identified, including minority demographics - specifically Blacks or African Americans, children, elderly, and rural populations. The most commonly reported health concerns were from those that rely on medical devices for chronic conditions and unsafe generator practices. Criminal activity was also reported to increase during prolonged power outages and can be mitigated by consistent messaging on where to receive assistance and when power will be restored. Providing financial assistance and resources such as food and water can reduce the crime rate temporarily, but the crime rate can be expected to increase once the relief expires. Authorities should expect looting to occur, especially in poorer areas, during prolonged power outages. Gaps in the literature were identified and future directions for research were provided.
The second chapter consists of a multi-city survey that targeted three major cities across the United States (Detroit, MI; Miami, FL; and Phoenix, AZ). The survey was disseminated through Amazon’s Mechanical Turk and hosted by Qualtrics. 896 participants from the three cities qualified to complete the full version of the survey. Three criteria had to be met for participants to complete the full survey including residing in one of the three target cities, living at their primary address for a majority of the year, and indicate they had experienced a power outage within the last five years.
Participants were asked questions regarding the number of outages experienced in the last five years, the length of their most recent and longest outage experienced, if they owned a generator, how they managed their longest power outage, if participants or anyone in their household relies on a medical device, the financial burden their power outage caused, and standard demographic- and income-related questions. Race was a significant variable that influenced the outage duration length but not frequency in Phoenix and Detroit. Income was not a significant variable associated with experiencing greater economic impacts, such as having thrown food away because of an outage and not receiving help during the longest outage. Additional assessments similar to this survey are needed to better understand household power outage experiences.
Findings from this thesis demonstrate traditional metrics used in vulnerability indices were not indicative of who experienced the greatest effects of power outages. Additionally, other factors that are not included in these indices, such as owning adaptive resources including medical devices and generators in Phoenix and Detroit, are factors in reducing negative outcomes. More research is needed on this topic to indicate which populations are more likely to experience factors that can influence positive or negative outage outcomes.
RESEARCH QUESTION: Does Online "Working Out Work" as a Treatment and Prevention for Depression in Older Adults? An Analysis of a Prescribed and Monitored Exercise Program Administered via the Internet for Senior Adults with Depression.
OBJECTIVE: The purpose of this study is to investigate and access the effectiveness of an online prescribed and monitored exercise program for the treatment of depression in Older Adults. The Dependent Variable for the study is Depression. The Independent Variable for the study is the Effects of Exercise administered via the Internet and the population is geriatric adults defined as senior adults aged 50 and older. Depression is defined by Princeton University Scholars (Wordnet, 2006) as a mental state characterized by a pessimistic sense of inadequacy and a despondent lack of activity.
METHODS: The presence and severity of depression will be assessed by using The Merck Manual of Geriatrics (GDS-15) Geriatric Depression Scale. Assessments will be performed at baseline, before and after the treatment is concluded. The subjects will complete the Physical Activity Readiness Questionnaire (PAR-Q) prior to participating in an exercise program three times per week.
LIMITATIONS OF RESEARCH: The limitations of this study are: 1) There is a small sample size limited to Senior Adults aged 50 - 80, and 2) there is no control group with structured activity or placebo, therefore researcher is unable to evaluate if the marked improvement was due to a non-specific therapeutic effect associated with taking part in a social activity (group online exercise program). Further research could compare and analyze the positive effects of a muscular strength training exercise program verses a cardiovascular training exercise program.
The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates.
Methods
This study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t.
Results
The study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas.
Conclusions
The study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.
The maintenance of chromosomal integrity is an essential task of every living organism and cellular repair mechanisms exist to guard against insults to DNA. Given the importance of this process, it is expected that DNA repair proteins would be evolutionarily conserved, exhibiting very minimal sequence change over time. However, BRCA1, an essential gene involved in DNA repair, has been reported to be evolving rapidly despite the fact that many protein-altering mutations within this gene convey a significantly elevated risk for breast and ovarian cancers.
Results
To obtain a deeper understanding of the evolutionary trajectory of BRCA1, we analyzed complete BRCA1 gene sequences from 23 primate species. We show that specific amino acid sites have experienced repeated selection for amino acid replacement over primate evolution. This selection has been focused specifically on humans and our closest living relatives, chimpanzees (Pan troglodytes) and bonobos (Pan paniscus). After examining BRCA1 polymorphisms in 7 bonobo, 44 chimpanzee, and 44 rhesus macaque (Macaca mulatta) individuals, we find considerable variation within each of these species and evidence for recent selection in chimpanzee populations. Finally, we also sequenced and analyzed BRCA2 from 24 primate species and find that this gene has also evolved under positive selection.
Conclusions
While mutations leading to truncated forms of BRCA1 are clearly linked to cancer phenotypes in humans, there is also an underlying selective pressure in favor of amino acid-altering substitutions in this gene. A hypothesis where viruses are the drivers of this natural selection is discussed.
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
