We examine whether Latina elected officials, relative to their co-ethnic male counterparts, are more effective advocates for working class communities of color. Building upon the literature on political incorporation, gender politics, and ethnic politics, we hypothesize that Latina legislators are better positioned to be such advocates due to their unique capacity to leverage three primary resources: substantive policy focus, a multiple identity advantage, and a gender inclusive advantage. We refer to the combination of these three factors as strategic intersectionality. We test our model of strategic intersectionality using the National Latina/o State Legislator Survey (NLSLS), an original data set of thirty-minute telephone interviews with over half of all Latinos and Latinas who served in state legislatures during 2004. We find evidence for the presence of strategic intersectionality. However, its presence is not the same in all policy contexts. We conclude that strategic intersectionality is comprised of complex, multi-layered patterns of advocacy, representation, and policy influence.

This research would not have been possible without a magnificent team of undergraduate research assistants at Stanford University including Maria Lizet Ocampo, Teresa Mosqueda, Evan Otero, Kiyomi Burchill, Darya Landa, and Jessica Flores. We also thank the Office of the Vice Provost for Undergraduate Education (VPUE) and the Political Science Program for Undergraduate Research at Stanford. Luis R. Fraga would also like to thank the Immigrant Incorporation Cluster at the Radcliffe Institute for Advanced Study, Harvard University, for providing a wonderful environment in which to work during the 2003-04 academic year.

Prepared for delivery at the annual meeting of the American Political Science Association, Washington, DC, August 31-September 4, 2005.

The success of data standards and public databases in biology is the foundation for the current and continued success of machine learning in biology and medicine. This dissertation explores the interactions between biology, computers, and people in order to develop novel machine learning methods to model complex biological problems. Data is one of the main resources to do machine learning, and Chapters 1, 2, 3 are explicitly about data organization and quality assurance in the protein Nuclear Magnetic Resonance (NMR) spectroscopy discipline. Chapters 4 and 5 present new machine learning architectures to address learning tasks in genomic site recognition and NMR chemical shift prediction. Chapter 1 investigates the manner protein NMR chemical shift data is deposited at the Biological Magnetic Resonance Bank (BMRB) in order to build simple table look-up models to estimate protein chemical shifts. In Chapter 1, we find there is low sequence diversity and data redundancy in the BMRB that was a challenge to locate and filter out. Without filtering out BMRB entries with the same sequence, and possibly the same chemical shifts, look-up models will be more accurate due to data contamination in training and testing sets. Chapter 2 examines approaches to curate a large protein sample production and NMR database to create an NMR time-domain dataset. Quality assurance tests in this NMR sample/FID database uncovered data collisions and redundancies among the database records, which motivated the development of new NMR database management tools. Chapter 3 presents a relational database schema to archive protein NMR samples and associated time-domain data called SpecDB. SpecDB is open source and available at https://github.rpi.edu/RPIBioinformatics/SpecDB.git. Chapter 4 explores how deep neural networks can recognize genomic splice acceptor and donor sites from sequence alone, achieving 97% accuracy for highly used splice donor sites. Chapter 4 also investigates neural networks for intron/exon sequence classification, maximally reaching 77% accuracy. Chapter 5 presents the application of marginalized graph kernels to prediction of NMR chemical shifts for small organic molecules. Incorporating chemical descriptors to graph kernels reaches a 3.501 ppm mean absolute error for Carbon chemical shifts. In total, the following five dissertation chapters explore work in data integrity, organization, and learning techniques from data for applications to structural biology problems.

Sclerodermoid chronic graft-versus-host disease (scGVHD) is a rare form of cGVHD with an estimated prevalence of 3% to 11% in patients receiving allogeneic bone marrow transplants. scGVHD is believed to be an immune-mediated response characterized by aberrant T-cell function and dysregulation of tyrosine kinase cascades. Published literature on scGVHD is still limited and the mechanisms are yet to be fully understood. Thus, successful treatment of scGVHD remains largely unknown and many current options are hindered by potential side effects. This case provides an example of scGVHD localizing to areas of trauma and friction as a potential mechanism behind scGVHD and provides several case reports that document similar findings.