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A multilevel mHealth drug abuse and STI/HIV preventive intervention for clinic settings in the United States: A feasibility and acceptability study
Get PDFBackground: Drug abuse and sexually transmitted infections (STIs), including the human immunodeficiency virus (HIV), remain significant public health concerns in the United States. Youth are at disproportionate risk of drug use and STIs/HIV, yet interventions aimed at improving STI and HIV testing and reducing STI/HIV risk behaviors through technology-based engagement in clinic settings are limited. The purpose of this study was to examine the feasibility and acceptability of Storytelling 4 Empowerment (S4E), a multilevel mobile-health drug abuse and STI/HIV preventive application (app) for clinic settings. We also explored uptake of STI/HIV testing among youth immediately post-intervention. Method: Employing community-based participatory research principles and a multi-method research design, we developed a clinician-facing app, and examined the feasibility and acceptability of S4E among clinicians (n = 6) and youth (n = 20) in an urban youth-centered community health clinic. S4E aimed to improve clinician–youth risk communication and youths’ drug use and STI/HIV knowledge, self-efficacy, and refusal skills. We also explored youths’ uptake of STI and HIV testing. Quantitative data were analyzed by computing mean scores and proportions, and qualitative analyses followed the tenets of content analysis. Results: Among eligible participants, 86.9% of youth and 85.7% of clinicians enrolled in the study, suggesting the feasibility of recruiting participants from the targeted clinic. Most clinicians identified as non-Hispanic white (83%) and female (66.7%). Among the youth, 70% identified as non-Hispanic white, followed by 30% African American, and 50% identified as female with a mean age of 19.6 (SD = 1.5, Range = 16–21). The quantitative findings suggest that the acceptability of S4E is high, as indicated by the Client Satisfaction Questionnaire (mean score = 25.2, SD: 4.8). Immediately post-intervention, all youth who reported past 90-day condomless sex or having never been tested for STIs or HIV in their lifetime, were tested for both STIs and HIV. Qualitative themes revealed four overarching themes, including S4E: (1) faciliated timely, targeted, and tailored prevention and risk reduction strategies; (2) shaped clinician and youth communication and interaction during the clinic visit; (3) may have improved uptake of STI/HIV testing and increased STI/HIV knowledge and self-efficacy; and (4) had high feasibiliy and acceptability among youth and clninicans. Conclusions: Findings suggest the feasibility and acceptability of S4E in an urban community-based health clinic setting. A next important step is to examine the efficacy of S4E in a randomized controlled trial design.</p
Putin Ballots in the Box: A Geospatial Analysis of Manipulation in Russian Elections
Get PDFVladimir Putin’s twenty year long regime has time and time again faced scrutiny and criticism from international election observers and citizens alike. Countless videos of ballot stuffing have emerged on the internet and many suspect that the election results reported by the Russian government are fraudulent. Building on previous election forensics projects studying Russian elections, this thesis aims to expand understanding of the geographic spread of electoral fraud in Russia. Using a stochastic kernel density resampling method, I estimate regional levels of contamination in Russian presidential elections from 2000-2018. The indicator of interest is round integer (multiple of 5) reports of voter turnout and incumbent vote share. Mapping the relative frequency of round integer reports of vote share across the election years, I determine that Russia’s ethnic republics most consistently report abnormally high turnout and vote share. I find that the amount of election manipulation has increased throughout the course of Putin’s regime. Moreover, statistical analysis points to the bifurcation of the electorate, signifying that regions which had previously reported abnormal results are doing so at much higher rates in subsequent election years. This is reflected in the Kernel Density Estimates for each year, which demonstrate abnormal spikes at round integers and show a remarkable shift towards a non-normal distribution over the years of Putin’s regime
Multiscale Molecular Modeling of Biomolecular Self-Assembly: From Molecular Dynamics to Machine Learning
No full textThe self-assembly of large biomolecular aggregates underlies a wide spectrum of biological processes, from cytoskeletal filament dynamics to the formation of viral capsids and engineered protein-based biomaterials. These processes are inherently multiscale, involving thousands of interacting subunits, complex conformational rearrangements, and cooperative transitions that span nanometers (nm) to microns (μm), and microseconds (μs) to minutes (min). Capturing such long time- and length-scale events poses significant challenges for both experiment and computation. Experimental approaches often provide only static or ensemble-averaged snapshots, while conventional all-atom molecular dynamics (MD) simulations are computationally prohibitive for assemblies of this large size and complexity. Consequently, it is challenging to elucidate the molecular-level mechanisms that governs the pathways of large-scale biomolecular systems, motivating the development of new computational methodologies and solutions to bridge the gap between atomistic detail and emergent mesoscale phenomena. Multiscale coarse-graining (CG) theory offers a powerful framework to address these challenges by systematically connecting all-atom resolution with coarse-grained models that capture collective behavior over extended spatiotemporal scales. Coarse-graining is indispensable for the study of ultra-large biomolecular systems, as it can reduce the number of degrees of freedom while preserving essential structural and dynamics features. However, the fidelity of CG simulations depends critically on how atomic sites are mapped to CG sites. Existing mapping strategies, many of which rely on sequence contiguity, often fail to represent functionally important domains when residues that move collectively are distant in sequence but adjacent in three-dimensional space. This limitation restricts the ability of CG models to capture cooperative conformational changes and emergent pathways in massive protein complexes. A central challenge, therefore, is to design mapping methods that are both systematic and physically grounded, and that can also leverage emerging machine learning (ML) techniques to extend coarse-graining frameworks to even larger assemblies and more complex dynamics. In this dissertation, I present a body of work that bridges molecular dynamics, multiscale coarse-graining theory, and machine learning for applications to extremely large biomolecular assembly systems. Specifically, we introduce a generalized K-means clustering coarse-graining (KMC-CG) methodology that systematically identifies optimal mappings by incorporating both spatial proximity and correlated motions of residues. This approach removes the constraints of sequence-based algorithms and yields physically intuitive, transferable CG models of unprecedented robustness for very large biomolecules. In addition, we explore how ML concepts, such as clustering, dimensionality reduction, and data-driven acceleration, can be integrated into coarse-graining workflows to enhance efficiency and accuracy. The utility of these methods is demonstrated in two distinct but complementary contexts. First, in collaboration with experimental partners, we employ multiscale simulations with artificial intelligence (AI)–driven approaches to guide the design of megamolecule self-assembly systems, where computational predictions of building block architecture and metal–ligand coordination inform synthesis and characterization. Second, we investigate the dynamic instability of microtubules (MTs), developing multiscale models that accelerate relaxation dynamics and reveal nucleotide-dependent differences in structure, mechanics, and effective interactions at the growing tip. Building on this foundation, we are further constructing a low-resolution CG model for the microtubule system and, subsequently, a polymerizable multiscale model of MT tips that couples CG molecular dynamics with the kinetic Monte Carlo (KMC) framework for subunit addition, GTP hydrolysis, and dissociation. Collectively, these studies highlight how methodological innovations—combining molecular dynamics, coarse-graining, and machine learning—enable efficient and accurate simulations of ultra-large biomolecular assemblies, advancing both the rational design of synthetic self-assembling systems and the mechanistic understanding of dynamic cytoskeletal processes that are fundamental to cellular organization
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Hierarchical, Intermingled and Segmented: The Organization of Neural Activity in Mouse Sensorimotor Cortex
Get PDFCortical control of movement is a distributed computation spanning multiple, densely interconnected regions. Although we have rich anatomical atlases and a coarse understanding of how function maps to these areas and subareas, we lack a detailed account of how behaviorally relevant activity is organized across the cortical sheet. Progress requires two key factors: (i) a challenging sensorimotor task with many conditions to engage sensorimotor circuits, and (ii) large-scale, single-neuron-resolution sampling across the full set of involved areas. In this dissertation, I address these gaps with two studies that pair a motorically demanding behavior with dense sampling of mouse sensorimotor cortex. First, we trained mice to perform a challenging reach-to-grasp task while recording neural activity in primary motor cortex (M1) and forelimb somatosensory cortex (S1-fl). Detailed kinematic information was similarly present in both regions; however, movement-related signals emerged earlier and were more persistent in M1, whereas S1-fl responses were more time-varying. These findings support a distributed control framework and are consistent with somatosensory areas receiving and transforming efference-copy-like information. Second, we expanded the task to fifteen target locations and performed cortex-wide, single-neuron sampling across much of sensorimotor cortex. Using statistical models on tuning features, we identified interleaved subpopulations with distinct response profiles. This large and rich dataset reveals a parallel-processing architecture where local specializations sit inside a distributed, flexibly routed network
Multiscale Molecular Modeling and AI-Accelerated Discovery of Polymeric Materials for Sustainability
Get PDFAdvancing a sustainable, low-carbon future depends critically on the discovery of materials with exceptional performance, durability, and manufacturability. Polymeric materials—particularly polymer electrolytes and polyelectrolytes—are central to such technologies, enabling functions in fuel cells, electrolyzers, batteries, and selective membranes. Their chemical diversity and architectural tunability allow for precise control over ionic conductivity, selectivity, and stability. Yet these properties emerge from hierarchical structures spanning molecular to macroscopic scales, and small changes in chemistry or morphology can propagate unpredictably across these scales. The vast combinatorial design space, coupled with the complexity of structure–property relationships, renders traditional trial-and-error development inefficient and incomplete. This thesis advances a unified framework for polymer materials discovery that integrates multiscale molecular modeling with artificial intelligence (AI). First, molecular simulations at atomistic and coarse-grained resolutions, anchored to experimental observables such as scattering and transport measurements, are used to reveal the microscopic interactions and mesoscale morphologies that govern ion transport. These studies identify physically meaningful descriptors—such as hydration structure, medium-range correlations, and percolation connectivity—that directly link chemical design to macroscopic performance. Second, physics-guided AI models, built on hierarchical, functional-group-based representations, predict key properties and propose chemically plausible candidates with optimized, multi-objective performance. The resulting design strategy couples mechanistic insight with data-driven exploration, enabling targeted navigation of vast chemical spaces. By uniting fundamental insight with predictive and generative tools, this work transforms qualitative heuristics into quantitative rules for designing polymeric materials. The methodology is broadly applicable and provide a pathway to accelerate the discovery of materials for sustainable technologies of the future
Seeing the Beauty of the Lord: Mystics on Nature as Theophany
Get PDFMystics are often thought to have little interest in the natural world, given their concern with the inner self. Many mystics, however, have had a profound sense of the beauty of creation. Their interest is not in nature as such, but in the world as a manifestation (theophania), a veil in which and through which God reveals and conceals Godself. This essay will sketch the line of “theophanic mysticism” in three figures. In several texts (e.g., Confessions 9.10; City of God 22.24), Augustine meditates on natural beauty as revealing God. In his “Canticum Solis” (Hymn of Brother Sun), Francis of Assisi presents a distinctive view of the natural and human worlds as praising God in a “familial chorus.” John of the Cross, who at times seems to reject the world, insists that when the soul is emptied of all false attachments, it will finally be able to see and love the beauty of creation. The essay concludes with a look at Pope Francis’s “Laudato Si’” as a contemporary revival of theophanic mysticism and an important ethical option in the midst of the current ecological crisis
The Monolith Cracking: American Response to the Sino-Soviet Split, 1961-1963
Get PDFScholars interested in Sino-Soviet-US triangular relations have generally been contending that during the early 1960s, the John F. Kennedy administration had committed itself to the wedge strategy – namely, to exacerbate the increasingly furious Sino-Soviet dispute – of which the American pursuit of the Limited Test Ban Treaty was a crucial part. However, I, as this thesis shows, am convinced that the Kennedy administration had been more concerned about exploiting than aggravating the Sino-Soviet split, although it did take pains to avoid the reconciliation between the Chinese and the Soviets. This not only had to do with the constant uncertainties about the future of Sino-Soviet relations but also was out of the American hope to use the Soviet restraint of China, which demanded the residual Soviet leverage on the Chinese. Therefore, I view the American quest for the test ban not as the wedge strategy’s embodiment but as an excellent case of the US utilizing the Sino-Soviet discord to stifle China’s nuclear program. Actually, with the American struggle for the underdeveloped world, China’s global revolutionary initiatives and nuclear ambitions made it the archenemy of the United States, much more dangerous than the Soviets advocating peaceful coexistence. Lastly and fundamentally, the Americans, embracing Wilsonian liberalism, had not expected much from the communist infighting
Minimax Hypothesis Testing in Large-Scale Inference
Get PDFThis thesis studies some hypothesis testing, and related functional estimation, problems in models motivated by large-scale inference applications. Specifically, problems in sparse mixture detection, null distribution estimation, global null testing under correlation, variance estimation in compound decision theory, and testing with heteroskedastic counts are addressed. The perspective is minimax, and the goal is to characterize either the optimal functional estimation rate or the optimal testing rate in the style of Ingster. A recurring motif in the results is the emergence of rate phenomena and phase transitions distinct from the corresponding theory of estimating the underlying, high-dimensional parameter