In the last decade, California’s imprisoned population of women has increased by nearly 400% (Chesney-Lind, 2012). The focus of this thesis is to discuss the treatment—or lack thereof—of women within California’s criminal justice system and sentencing laws. By exploring its historical approach to two criminal actions related to women, the Three Strikes law (including non-violent drug crimes) and the absence of laws accounting for experiences of female victims of domestic violence who killed their abusers, I explore how California’s criminal code has marginalized women, and present a summary of the adverse effects brought about by the gender invisibility that is endemic within sentencing policies and practice. I also discuss recent attempted and successful reforms related to these issues, which evidence a shift toward social dialogue on sentencing aiming to address gender inequity in the sentencing code. These reforms were the result of activism; organizations, academics and individuals successfully raised awareness regarding excessive and undue sentencing of women and compelled action by the legislature.
By method of a feminist analysis of these histories, I explore these two pertinent issues in California; both are related to women who, under harsh sentencing laws, were incarcerated under the state’s male-focused legislation. Responses to the inequalities found in these laws included attempts toward both visibility for women and reform related to sentencing. I analyze the ontology of sentencing reform as it relates to activism in order to discuss the implications of further criminal code legislation, as well as the implications of the 2012 reforms in practice. Through the paper, I focus upon how women have become a target of arrest and long sentences not because they are strategically arrested to equalize their representation behind bars, but because the “tough on crime” framework in the criminal code cast a wide and fixed net that incarcerated increasingly more women following the codification of both mandatory minimums and a male-oriented approach to sentencing (Chesney-Lind et. al, 2012).
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.
Digital learning tools have become ubiquitous in virtual and in person classrooms as teachers found creative ways to engage students during the COVID 19 pandemic. Even before the pandemic and widespread remote learning, however, digital learning tools were increasingly common and a typical part of many classrooms. While all digital learning tools are worthy of study, math digital learning tools (MDLTs) designed for K - 8th grade in particular raise questions of efficacy and usefulness for classrooms. This paper shows that MDLTs are an effective tool to raise students’ math achievement across K - 8th grade, and that time spent on MDLTs can lead to better understanding of a topic than traditional, teacher led instruction. However, if the MDLT is being delivered in a language the student is not familiar with, that student will not be able to benefit from MDLTs in the way other students do. This is also true of students who receive Special Education services. Additionally, higher quality MDLTs that provide feedback that attaches meaning to students’ work creates a better learning environment for students than one with simpler feedback. Based on my experiences with student teaching this year and using the popular MDLT IXL frequently, I recommend that MDLTs not just be used for independent practice time, but for whole class, problem solving sessions where students have to use mathematical thinking in new content areas. This will build deeper conceptual learning and a greater sense of achievement in students.
Media witnessing and storytelling for environmental justice (EJ) provide an avenue to understand the relationships between “multiple realities of environmental injury” and to analyze “fleeting phenomena with lasting form; thereby transforming phenomena that are experienced in a plurality of lives into publicly recognized history” (Houston, 2012, 419, 422). This creates opportunities to challenge and eradicate the oppressive structures that deem certain individuals and groups disposable and ultimately protect the possessive investment in whiteness. Therefore, for the purposes of EJ, media witnessing creates space for dynamic, citizen-based storytelling which can undermine narratives that promote the life versus economy framework that has perpetuated oppression, injustice, and state sanctioned violence. Media witnessing in an EJ context demonstrates the potential for collective understanding and action, political opportunities, and healing.<br/>This paper is an analysis of the process of media witnessing in regards to the Flint Water Crisis and the construction of the Dakota Access Pipeline (DAPL) and will apply an EJ lens to this phenomenon. It will discuss how media witnessing in response to these two crises can be used as a precedent for understanding and utilizing this framework and digital storytelling to address the crises of 2020, primarily the COVID-19 pandemic and racial injustice. It will then examine how the intersectionality of race, gender, and age has implications for future media witnessing and storytelling in the context of EJ movements. Finally, it will explain how media witnessing can motivate holistic policymaking in the favor of EJ initiatives and the health and wellbeing of all Americans, as well as how such policymaking and initiatives must acknowledge the double-edged sword that is social media.
Neoliberal feminism has gained significant popularity in fourth-wave feminist media. In this paper, I analyze the 2017 limited television series "Big Little Lies" to uncover the intricacies of neoliberal feminist theory in practice, particularly how it speaks to gender, race, and class relations.
Sex, Love, & Dating During the COVID-19 Pandemic is a creative thesis project that addresses two main issues: 1) the overall lack of resources and information available to the public about how to proceed with respect to sex, love, and dating during a global pandemic; and 2) my inability as director of Devils in the Bedroom (an on-campus sexual health club at ASU) to get condoms and other sexual health materials into the hands of students while in quarantine. A resource was developed, an informational pamphlet on the three main topics (sex, love, and dating), as well as a program to distribute the materials by mail, the sexual health care packages.
Food insecure populations suffer from the ability to access affordable and nutritious foods as a result of financial and transportation needs. Often these populations are concentrated in areas referenced as food deserts. A food desert is an area that does not have a supermarket or large grocery store within a mile and often is saturated with small non-traditional food stores and fast- food establishments. In this study, 21 food deserts along Grand Avenue in Downtown Phoenix were analyzed to better understand their access to food, population statistics and barriers to being food secure. The research question analyzed is the impact food insecurity has on communities in Phoenix, Arizona. The findings are presented in the form of a research paper, as well as 15 black and white film photographs accompanied by descriptions. There is primary qualitative data presented through photographs and observations, as well as secondary quantitative data analyzed from Census data. The food deserts studied consist of communities that are low-income and majority minority with little to no access to nutritious food in their area. The economics of food insecurity and grocery stores, racial discrimination, access to transportation, impacts on health and education and the sustainability of food deserts are all aspects of food insecurity discussed in the research. Possible solutions such as community gardens and subsidized grocery stores are also presented. The study revealed that food insecurity has several negative impacts on the affected populations and communities and disproportionately impacts low-income and minority communities.