Expanding cities and expanding waistlines: Urban sprawl and its impact on obesity, how the adoption of smart growth statutes can build healthier and more active communities

For decades, American towns and cities have expanded from their established cores into the surrounding rural areas. U.S. population has grown but the land that we use has grown at an even faster pace, and our country has now become a largely suburban nation. Americans moved and continue to move out to the suburbs in search of better lives – for clean and healthy living, for larger homes, and for better resources. In many ways and for many Americans, the suburban lifestyle has been a great success. However, there are some unintended public health consequences of urban sprawl that must be recognized. As most Americans no longer walk or bicycle, increasingly sedentary lifestyles now contribute to greater levels of obesity, diabetes and other associated chronic diseases. This thesis reviewed the impacts of urban sprawl on the public's health specifically, as sprawl relates to decreased physical activity rates and increased obesity rates. The health effects and their connection with sprawl were identified, and available evidence was reviewed. Finally, this thesis described legal and policy solutions for addressing the health effect through improving the design of our built environment and by recommending that governments adopt and implement Smart Growth statutes that incorporate a public health component and require public health involvement. ^

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.