Based on availability of case management services, drug-involved women offenders entered either a probation case management (PCM) intervention (n = 65) or standard probation (n = 44). Participants were placed in the case management condition until all slots were filled, then placed in standard probation until case management slots opened. Participants were interviewed at program entry and at 6- and 12-month follow-up using measures of substance abuse, psychiatric symptoms, and social support. Results showed modest change over time in both conditions, but PCM did not result in more services or treatment, or better outcomes than standard probation. These findings are discussed in the context of study limitations and in the context of state initiatives like those in Arizona and California designed to apply treatment as an alternative to incarceration.

Using the Internet to improve access to water quality and agricultural information.

Mammalian brain cells show a remarkable diversity, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Cytosine DNA methylation, a covalent modification in the genome, is a critical player in brain cell gene regulation. Recent advances in single-cell technologies allow studying this epigenomic modification at an unprecedented resolution. During my Ph.D., I have developed and used single-cell methylome and multi-omic technologies to profile mouse and human brains to establish the brain cell atlas. In chapter 1, I access the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing to profile 103,982 nuclei from the mouse cerebrum. I constructed cell taxonomies containing 161 epigenetic clusters and annotated each cluster with signature genes, regulatory elements, and transcription factors. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. By combining multi-omic datasets from single nuclei and annotating the regulatory genome of these cell types, the DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum. In chapter 2, colleagues and I devised single-nucleus methylcytosine, chromatin accessibility, and transcriptome sequencing (snmCAT-seq) and applied it to postmortem human frontal cortex tissue. We developed a cross-validation approach using multi-modal information to validate fine-grained cell types. We reconstructed regulatory lineages for cortical cell populations and found specific enrichment of genetic risk for neuropsychiatric traits, enabling the prediction of cell types associated with diseases. In chapter 3, I developed a comprehensive computational package called ALLCools to solve the computational challenges brought up by the single-cell methylome and multi-omic dataset. This package includes data formats specialized for the single-cell DNA methylation data and various functions covering cellular and genomic analysis. As a result, my Ph.D. study has produced high-resolution single-cell DNA methylome datasets in the mouse brain, developed new multi-omic technologies applicable to the human brain, and established comprehensive analysis frameworks to analyze the extensive data. Together, these works create concrete deliverables that extend the understanding of brain epigenome diversity and enable future studies to understand the molecular source of brain cellular diversity and link the psychological diseases to their molecular basis.

Measurement of the decay of the Higgs boson to charm quarks provides a direct probe of the Higgs coupling to second-generation quarks. Therefore, it is crucial for understanding the structure of Yukawa couplings. In this thesis, a search for the Higgs boson decaying to charm quarks with the CMS experiment is presented. The search is designed for Lorentz-boosted Higgs bosons produced in association with vector (V) bosons (W or Z bosons). A novel approach that reconstructs both quarks from the Higgs boson decay with a single large-radius jet is adopted. The charm quark pair is identified with an advanced deep learning--based algorithm. This approach leads to a highly competitive result: Using proton-proton collision data corresponding to an integrated luminosity of 35.9 fb-1, an observed (expected) upper limit on the VH cross section times the H->cc branching fraction of 71 (49) times the standard model expectation at 95% confidence level is obtained.

A detailed description of the deep learning--based boosted object identification algorithm is also presented in this thesis. It is a versatile algorithm designed to identify and classify hadronic decays of highly Lorentz-boosted top quarks and W, Z, Higgs bosons. Using deep neural networks to directly access and process the raw information of all constituent particle-flow candidates of a jet, this advanced algorithm has achieved significant performance improvements compared to traditional approaches.