The Historic Role of Boards of Health in Local Innovation: New York City’s Soda Portion Case

Childhood and adult obesity pose major risks for cancer, diabetes, and cardiovascular disease, with the poor and racial minorities suffering from disproportionately high burdens of obesity and chronic disease. With current policies failing, cities and states have moved forward with creative prevention measures–-with boards of health driving policy innovation in many local jurisdictions. The New York City Board of Board of Health’s (NYCBH) soda portion limit pushed the boundaries of innovation, but was struck down on June 26, 2014 by New York State’s highest court, which held that the Board trespassed on the City Council’s authority. The Court’s decision ignored the critical role of local health agencies in responding to 21st century public health threats, including epidemics of obesity and chronic disease. The Court narrowly construed the NYCBH’s authority, characterizing its powers as administrative, and thus potentially stifling local innovation. The decision also obscured the fundamental truth that public health policymaking requires complex trade-offs and incremental action, as well as a multifaceted approach to reducing population weight gain. Policymaking often relies upon limited evidence, and agencies experiment with novel ideas while also transforming social norms and pushing the boundaries of public opinion. Although the portion rule would disproportionately affect disadvantaged individuals who drink the largest amount of soda, government’s failure to act represents a greater injustice. Enhancing opportunities to choose a healthy life path better serves the interests of justice, but the Court’s judgment takes us further away from realizing this social aspiration

Semantic annotation and summarization of biomedical text

Advancements in the biomedical community are largely documented and published in text format in scientific forums such as conference papers and journals. To address the scalability of utilizing the large volume of text-based information generated by continuing advances in the biomedical field, two complementary areas are studied. The first area is Semantic Annotation, which is a method for providing machineunderstandable information based on domain-specific resources. A novel semantic annotator, CONANN, is implemented for online matching of concepts defined by a biomedical metathesaurus. CONANN uses a multi-level filter based on both information retrieval and shallow natural language processing techniques. CONANN is evaluated against a state-of-the-art biomedical annotator using the performance measures of time (e.g. number of milliseconds per noun phrase) and precision/recall of the resulting concept matches. CONANN shows that annotation can be performed online, rather than offline, without a significant loss of precision and recall as compared to current offline systems. The second area of study is Text Summarization which is used as a way to perform data reduction of clinical trial texts while still describing the main themes of a biomedical document. The text summarization work is unique in that it focuses exclusively on summarizing biomedical full-text sources as opposed to abstracts, and also exclusively uses domain-specific concepts, rather than terms, to identify important information within a biomedical text. Two novel text summarization algorithms are implemented: one using a concept chaining method based on existing work in lexical chaining (BioChain), and the other using concept distribution to match important sentences between a source text and a generated summary (FreqDist). The BioChain and FreqDist summarizers are evaluated using the publicly-available ROUGE summary evaluation tool. ROUGE compares n-gram co-occurrences between a system summary and one or more model summaries. The text summarization evaluation shows that the two approaches outperform nearly all of the existing term-based approaches.Ph.D., Information Science and Technology -- Drexel University, 200

Semantic annotation and summarization of biomedical text

Advancements in the biomedical community are largely documented and published in text format in scientific forums such as conference papers and journals. To address the scalability of utilizing the large volume of text-based information generated by continuing advances in the biomedical field, two complementary areas are studied. The first area is Semantic Annotation, which is a method for providing machineunderstandable information based on domain-specific resources. A novel semantic annotator, CONANN, is implemented for online matching of concepts defined by a biomedical metathesaurus. CONANN uses a multi-level filter based on both information retrieval and shallow natural language processing techniques. CONANN is evaluated against a state-of-the-art biomedical annotator using the performance measures of time (e.g. number of milliseconds per noun phrase) and precision/recall of the resulting concept matches. CONANN shows that annotation can be performed online, rather than offline, without a significant loss of precision and recall as compared to current offline systems. The second area of study is Text Summarization which is used as a way to perform data reduction of clinical trial texts while still describing the main themes of a biomedical document. The text summarization work is unique in that it focuses exclusively on summarizing biomedical full-text sources as opposed to abstracts, and also exclusively uses domain-specific concepts, rather than terms, to identify important information within a biomedical text. Two novel text summarization algorithms are implemented: one using a concept chaining method based on existing work in lexical chaining (BioChain), and the other using concept distribution to match important sentences between a source text and a generated summary (FreqDist). The BioChain and FreqDist summarizers are evaluated using the publicly-available ROUGE summary evaluation tool. ROUGE compares n-gram co-occurrences between a system summary and one or more model summaries. The text summarization evaluation shows that the two approaches outperform nearly all of the existing term-based approaches.Ph.D., Information Science and Technology -- Drexel University, 200

Biomedical text annotation and summarization

Advancements in the biomedical community are largely documented and published in text format in scientific forums such as conferences and journals. To address the scalability of utilizing the large volume of text-based information generated by continuing advances in the biomedical field, we propose a two-part text summarization system to reduce the volume of text which must be read by biomedical professionals. The contributions of the text summarization system are a) it utilizes biomedical concepts rather than terms to find the main points of a text, b) it uses two new concept-based algorithms to find important areas of a text for extracting sentences to form a summary, and c) it is supported by a new semantic annotation sub-system, which identifies biomedical concepts found in biomedical text documents. The semantic annotation subsystem uses a novel multiple-filter system architecture for online matching of concepts defined by a biomedical metathesaurus. The goal of semantic annotation is to show that online text-to-concept mapping can be performed without a significant loss of precision as compared to current offline systems. An evaluation shows the text summarization algorithms using concepts outperform existing summarization systems, and the semantic annotation system performs twenty times faster than a state-of-the-art system with no significant loss of precision

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Appraising spit dynamics and estary responses: a coastal management study from the Exe Estuary, UK

The paper describes geomorphological analysis and morphodynamic modelling of the double spit enclosed Exe Estuary, UK. The long-term morphodynamic behaviour of the Exe Estuary system and the surrounding shoreline is studied. The geomorphological analysis was based on available historic data on the estuary. The morphodynamic modelling methodology is two-fold: A systems model based on a Boolean approach is used to predict and investigate future morphodynamic response of the estuary to changes in external forcing. A 1-line shoreline model is used to investigate morphodynamics of Dawlish Warren spit (the only active spit at the mouth of the estuary) and its future morphologies. It was found that over long term time scales, the estuary will reach a stable morphological state or evolve cyclically between two morphological states, depending on future changes to environmental forcing such as waves and tides. It was also found that littoral transport control has a significant effect on the long term sustainability of the Dawlish Warren sand spit and the estuary as a whole

Acknowledgements

I would like to thank the members of my dissertation committee for their participation in my dissertation proposal and final defenses, for reviewing my papers, and for the many comments and suggestions which improved this work. I would particularly like to thank Dr. Han for the many hundreds of selfless hours spent molding me into a doctoral candidate. The members of my dissertation committee are: Dr. Hyoil Han, Advisor and Chairperso

Concept frequency distribution in biomedical text summarization

Text summarization is a data reduction process. The use of text summarization enables users to reduce the amount of text that must be read while still assimilating the core information. The data reduction offered by text summarization is particularly useful in the biomedical domain, where physicians must continuously find clinical trial study information to incorporate into their patient treatment efforts. Such efforts are often hampered by the highvolume of publications. Our contribution is two-fold: 1) to propose the frequency of domain concepts as a method to identify important sentences within a full-text; and 2) propose a novel frequency distribution model and algorithm for identifying important sentences based on term or concept frequency distribution. An evaluation of several existing summarization systems using biomedical texts is presented in order to determine a performance baseline. For domain concept comparison, a recent high-performing frequency-based algorithm using terms is adapted to use concepts and evaluated using both terms and concepts. It is shown that the use of concepts performs closely with the use of terms for sentence selection. Our proposed frequency distribution model and algorithm outperforms a state-of-the-art approach