Due to what is known as the “biologically desert fallacy” and the pervasive westernized ideal of wilderness that has influenced widespread American Conservation culture for millennia, urban areas have long been deemed as areas devoid of biodiversity. However, cities can contribute significantly to regional biodiversity and provide vital niches for wildlife, illuminating the growing awareness that cities are crucial to the future of conservation and combating the global biodiversity crisis. In terms of the biodiversity crisis, bats are a relevant species of concern. In many studies, different bat species have been broadly classified according to their ability to adapt to urban environments. There is evidence that urban areas can filter bat species based on traits and behavior, with many bats possessing traits that do not allow them to live in cities. The three broad categories are urban avoiders, urban adapters, or urban exploiters based upon where their abundance is highest along a gradient of urban intensity. A common example of an urban exploiter bat is a Mexican Free-tailed bat, which can thrive and rely on urban environments and it is found in the Phoenix Metropolitan area. Bats are important as even in urban environments they play vital ecological roles such as cactus pollination, insect management, and seed dispersal. Bat Crazy is a thesis project focused on urban enhancement and the field of urban biodiversity. The goals of this thesis are to observe how bio-conscious urban cities that work to promote species conservation can serve as a positive tool to promote biodiversity and foster community education and engagement for their urban environment.

Early detection of disease is essential for alleviating disease burden, increasing success rate and decreasing mortality rate especially for cancer. To improve disease diagnostics, many candidate biomarkers have been suggested using molecular biology or image analysis techniques over the past decade. The receiver operating characteristics (ROC) curve is a standard technique to evaluate a diagnostic accuracy of biomarkers, but it has some limitations especially for heterogeneous diseases. As an alternative of the ROC curve analysis, we suggest a jittered dot plot (JDP) and JDP-based evaluation measures, above mean difference (AMD) and averaged above mean difference (AAMD). We demonstrate how JDP and AMD or AAMD together better evaluate biomarkers than the standard ROC curve. We analyze real and heterogeneous basal-like breast cancer data.