Rice University Research Repository
The Rice Research Repository (R-3) provides access to research produced at Rice University, including theses and dissertations, journal articles, research center publications, datasets, and academic journals. Managed by Fondren Library, R-3 is indexed by Google and Google Scholar, follows best practices for preservation, and provides DOIs to facilitate citation. Woodson Research Center collections, including Rice Images and Documents and the Task Force on Slavery, Segregation, and Racial Injustice, have moved here.
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Delivery of Large Gene Circuits In vivo Using an Engineered Baculovirus Vector for Multifactorial Control of Gene Expression
(2024-12-06) Brown, Lucas Bernard Clatanoff; Bashor, Caleb; Bao, Gang
Many of the viral vectors used for gene therapy are limited by the cargo size they can deliver into cells in tissue. As a result, most therapies being actively considered today tend to consist of monomodal expression of one or two genes. While this modality is undoubtedly effective for many applications, there remains advantages to being able to deliver more genetic cargo. A viral vector with an increased cargo capacity could allow room not only for more and larger therapeutic genes, but also regulatory elements that permit complex, multifactorial regulation of therapeutic gene expression. Here we use the insect-derived baculovirus capable of packaging and delivering >100 kb of transgene DNA as a vector for complex gene circuits that regulate and enhance in vivo gene therapy. Baculovirus has many advantages over other vectors: the ability to transduce a broad spectrum of mammalian cells, a large packaging capacity, no replication in mammalian cells, and a low toxicity in vivo. However, while baculovirus has been used as a gene therapy vector previously, its potential has been limited by its transient expression, as well as its susceptibility to inactivation by the complement system.
We then implemented a hierarchical cloning scheme for the rapid generation and prototyping of baculovirus vectors containing up to 10 different expression units. We then address several shortcomings of the baculovirus by pseudo-typing the AcMNPV baculovirus with two proteins, the Vesticular stromatitis virus protein G and a fusion protein consisting of several complement regulatory domains. This engineered vector has increased transduction and persistence in mouse liver, muscle, and brain tissue. To our knowledge, this is the first time systemic delivery of baculovirus has been shown to be an effective delivery route. Using this engineered virus, we screened a library of 24 variations of a tamoxifen inducible circuit in order to select the architecture with the highest dynamic range, up to a 67-fold increase over uninduced. Finally, we demonstrate two orthogonal small molecule inducible systems (grazoprevir and tamoxifen) delivered by baculovirus in vivo, both as separate viruses and as one complete circuit. Our findings demonstrate the usefulness of complex regulation for the gene therapy field, as well as the utility of the baculovirus as a therapeutic vector.
Motional Dynamics in Trapped Ions and Rydberg Atoms, and Hybrid Quantum Algorithm for Classical Optimization
(2024-12-06) Zhang, Zewen; Hazzard, Kaden RA
Quantum information science has emerged as one of the most promising fields in contemporary research, relying on both software and hardware innovations. This thesis looks for both algorithms with quantum features that provide advantages over classical algorithms, and better hardware platforms for experiments and quantum computing. The work spans theoretical studies in both algorithm and hardware design, including hybrid quantum-classical algorithms and the development of quantum information processing platforms.
The algorithmic part has focused on the performance of a hybrid quantum algorithm - the Grover Quantum Approximate Optimization Algorithm (Grover QAOA) - designed for problems with multiple solutions. In practice, we find its potential for speedup in solution search and its ability to find all solutions. Furthermore, we propose a simplified protocol that reduces the classical complexity of optimizing the algorithm’s parameters, enhancing its practicality for future applications. Our implementation of Grover QAOA for multiple combinatorial optimization problems on trapped-ion quantum computers demonstrates that the algorithm can fulfill its fair-sampling advantage even on noisy devices.
In the hardware part, we mainly explore how the motion of cold atoms can either be used to engineer interactions or lead to previously overlooked decoherence. The first hardware platform we discuss is trapped ions, where we focus on implementing individual addressing to natively simulate new types of many-body systems. Our proposal leverages the exceptional controllability of trapped ions to explore dynamical models such as topological pumping. The second hardware platform we study is Rydberg atom lattices, where we investigate the
decoherence processes introduced by atomic motion during dynamics. Using the numerical tool of discrete truncated Wigner approximation, we simulate the coupled dynamics of electronic and motional degrees of freedom, demonstrating that atom motion induced by strong van der Waals interactions in Rydberg atoms can lead to significant decoherence in analog simulation experiments.
We have also explored specialized topics involving other quantum hardware platforms. One area of study is the reduction of frequency crowding in superconducting circuit quantum
chips. By properly designing the frequencies for each transmon qubit, we can improve the manufacturing yield of collision-free processors. Another area focuses on the SU(N) Fermi-Hubbard models on alkaline-earth-metal optical lattice platforms. We have obtained the
phase diagram of unit-filling models with imbalanced spin flavors. This work aids future experiments in searching for potential ground states of the unit-filling SU(N) model.
Cosmological and astrophysical probes of axionlike particles
(2024-12-06) Hagimoto, Ray Mitchell; Long, Andrew J; Amin, Mustafa A
Axionlike particles (ALPs), pseudo Nambu-Goldstone bosons arising from the spontaneous breaking of global U(1) symmetries, appear in solutions to open issues in fundamental physics and are ubiquitous in string theory compactifications. Furthermore, ALPs have a rich phenomenology that provides numerous ways to search for evidence of their existence. This work explores two potential discovery channels for ALPs. The first considers the possibility that hyperlight ALPs, with masses less than 10^(-28) eV and a Chern-Simons coupling to electromagnetism, formed a cosmic string network in the early Universe that survives beyond recombination. In this scenario, cosmic microwave background (CMB) photons passing through string loops in the network experience a rotation in their plane of polarization, an effect known as CMB birefringence that may be within reach of future CMB probes. I use existing CMB birefringence power spectrum data to constrain axion string network parameters, then discuss non-Gaussian features of axion string-induced CMB birefringence maps, and finally explore how a neural network could estimate axion string network parameters from these maps. The second potential discovery channel examines how ALPs with lepton flavor-violating couplings and masses less than 1 MeV affect the cooling rates of neutron stars. Through these studies, I develop tools that would assist in identifying signatures of ALPs in cosmological and astrophysical observations.
Reliable Medical LLM and Vision-Language RAG through Multi-Agent Orchestration and Single-Step Preference Alignment
(2024-12-06) Pahwa, Khushbu; Hu, Xia (Ben)
Medical RAG systems and long-context models like Med-PaLM face distinct yet
interconnected challenges in processing complex medical information. While RAG
systems struggle with hallucinations due to noisy retrievals and incomplete fact verification, long-context models, despite their ability to process extended inputs, suffer from attention dilution and context retention issues. Current BioNLP RAG systems have particularly overlooked the critical balance between retrieved context and parametric knowledge, often leading to hallucinations from over-reliance on retrieved information. Our HALO-MMedRAG framework addresses these challenges through a comprehensive multi-agent architecture. The system’s effectiveness stems from four innovative components: query-intent parser agent, multi-query generation agent, coarse retrieval agent, fine-grained hallucination aware retrieval agent with a perplexity and NLL based hybrid scoring for the chunks, generation agent, a light-weight fact-verification agent and an orchestrator agent that manages a CoT reasoning debate among 3 agents to provide the final hallucination free response, grounded in factuality.
The notion of Retrieval Augmented Generation in the context of Multimodal Medical LLMs has not been given due consideration from the lens of hallucination mitigation. Further, the existing approaches have been limited in their coverage of the medical domains, often limited to X-Ray. Medical Multimodal LLMs, when utilized for Multi-Modal Retrieval Augmented Generation, face critical challenges in maintaining factual accuracy while integrating complex visual and textual information. Our innovative approach addresses these challenges through a unified Triple Preference Optimization framework with three-stage preference dataset curation, focusing on cross-modal alignment, retrieval balance, and a dual staged visual feedback agent. Unlike existing solutions, our method employs a single-step optimization process that simultaneously handles multiple aspects of alignment while maintaining computational efficiency. Through careful curation of preference datasets that capture different levels of alignment quality, combined with a visual feedback agent for precise visual grounding to provide visual prompting for the Vision Language Model to improve its response, our approach significantly reduces hallucinations and improves medical response accuracy. Extensive evaluation across diverse medical domains, including radiology, ophthalmology, pathology, magnetic resonance imaging and CT scan demonstrates superior performance compared to the existing multimodal medical RAG methods, making our solution titled Align-MedRAG-VL, both practical and reliable for real-world medical applications where hallucination mitigation is paramount.
Leveraging Graph Networks for Health and Wellbeing Prediction
(2024-12-05) Khalid, Maryam; Sano, Akane
Health and well-being prediction plays an essential role in mental healthcare and well-being-aware computing. The complex nature of well-being, resulting from its dependency on a person’s physiological health, mental state, and surroundings, makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported well-being metrics. In addition to a person’s physiology, we incorporate the environment’s impact through weather and social network data. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users within the graph network and integrates it with the temporal dynamics of data to predict well-being outcomes for all users.
To address the dynamic nature of social networks, we introduce GEDD (Graph Extraction for Dynamic Distribution), an approach that automatically adapts to fluctuating network sizes. GEDD utilizes graph properties, including connectivity and components, to transform variable-sized graphs into a standardized format, ensuring no user data is discarded. The proposed architecture supports online learning, making it feasible to scale to large networks without adding ecological momentary assessments (EMAs) or additional data collection burdens, thus preserving user privacy.
Through extensive evaluations, we show that social network incorporation improves prediction accuracy, although node influence, especially in users with high eigenvector centrality, can amplify noise. To address this, we propose a robust system that leverages attention and social contagion in well-being behaviors through graph networks and integrates it with physiological and phone data from ubiquitous mobile and wearable devices. This system is designed to predict well-being outcomes, such as sleep duration and other health metrics while mitigating the challenges posed by noisy and incomplete data.
Finally, we further leverage the graph structure to reduce the user burden associated with collecting health and well-being metrics, which are often captured at a much lower resolution than sensing data through surveys and EMAs. To this end, we introduce a benchmark framework to evaluate existing state-of-the-art graph-based active learning (AL) strategies in dynamic sensing environments. Our framework assesses AL strategies in terms of adaptability to real-time, user-centric data by evaluating performance over time in a stream-based setting. We also introduce new metrics, including sampling entropy, coverage ratio, and time-gap analysis, to quantify user burden, sampling diversity, and generalization performance. These metrics provide a holistic view of the AL strategies’ effectiveness, helping to identify those that best balance predictive accuracy and user engagement. This comprehensive evaluation framework supports scalable and efficient health prediction systems, facilitating practical, large-scale deployment.