Search for Baryon-Number-Violating Processes in B−B^- Decay to Final State Ξ‾c0Λ‾c−\overline{\Xi}_c^0 \overline{\Lambda}_c^- at Belle and Design of Belle II TOP Trigger System

This dissertation has two parts which describe my work on the Belle and Belle~II experiments. The first part provides a detailed account of my search for baryon-number violating processes using Belle data. The second part outlines my contributions to the Belle~II trigger based on information from Time-of-Propagation (TOP) counters. In the first part I report the results of the first search for $B^-$ decay to final state $\overline{\Xi}_{c}^{0} \overline{\Lambda}_{c}^{-}$ using 711~${\rm fb^{-1}}$ of data collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. The results are interpreted in terms of a direct baryon-number-violating $B^-$ decay and, alternatively, in terms of $\Xi_{c}^{0}-\overline{\Xi}_{c}^{0}$ oscillations which follow the Standard Model decay $B^- \to \Xi_{c}^{0} \overline{\Lambda}_{c}^{-}$. No evidence for baryon number violation is observed and 95\% confidence-level upper limits are set on the ratio of baryon-number-violating and Standard Model branching fractions ${\mathcal{B}(B^- \rightarrow \overline{\Xi}_{c}^{0} \overline{\Lambda}_{c}^{-})}/{\mathcal{B}(B^- \rightarrow \Xi_{c}^{0} \overline{\Lambda}_{c}^{-})} < 2.7\%$ and on $\Xi_{c}^{0} - \overline{\Xi}_{c}^{0}$ oscillation angular frequency $\omega < 0.76\ \mathrm{ps}^{-1}$. In the second part I report on my studies in the area of TOP-based trigger system (TOP~TRG) which provides precise collision timing information for the Belle~II Trigger system. The development of TOP~TRG system, including the hardware, firmware design, and performance analysis are presented. The standalone TOP~TRG achieves the efficiency of 92.5\% for barrel cosmic muons. By incorporating CDC-TOP matching, an efficiency of 75\% and a timing resolution of 12~ns are achieved for hadronic events. I discuss further possible TOP~TRG improvements

"Exploring Environmental Factors in Neurosurgical and Ophthalmology Operating Rooms to Mitigate Surgical Site Infections: An Observation Study."

Background: Surgical site infections (SSIs) are infections that patients can acquire after undergoing surgery in a hospital. These infections are quite common and can pose a significant risk to the patient's health. The operating room (OR) is a sterile space with regulated airflow, humidity, and pressure to ensure a clean environment. OR traffic should be minimized to maintain sterility and reduce SSI risks. Methods: This observational study aimed to understand how operating room staff behavior and environmental factors affect surgical outcomes. The study was conducted for 4 weeks at a teaching hospital and focused on 10 neurosurgical procedures. The research student leading the study observed OR traffic patterns and documented environmental parameters such as temperature, humidity, and pressure. Automated data acquisition was implemented for ophthalmology cases. Results: The study found correlations between particulate sizes and room conditions in an operating room. 0.3μm particulate size had a moderate to strong positive correlation of 0.737, while 1.0μm showed a very weak positive correlation of 0.087. The 5.0μm particulate size had a moderate positive correlation of 0.344, with 11.8% variability attributed to observed operating room traffic. The study also noted mild fluctuations in temperature, humidity, and pressure within the operating room. Conclusion: A prospective research study conducted at a university-affiliated teaching hospital suggests a potential link between the OR environment and the risk of developing surgical site infections (SSIs). However, the study's limitations and small sample size must be considered when interpreting the findings. Future research should address these constraints for accurate results. Understanding the OR environment is crucial in preventing SSIs, improving patient outcomes, and reducing the burden of postoperative complications. Public Health Significance: Surgical site infections (SSIs) are of a significant concern in the healthcare industry, particularly in neurosurgery and ophthalmology. SSIs result in physical discomfort, extended hospital stays, increased healthcare costs, and mortality. By prioritizing prevention of SSIs, healthcare resources can be utilized more efficiently, reducing the economic burden on the healthcare system. Controlling SSIs is crucial for maintaining high standards of patient care, reducing healthcare burden, preventing infections, and fostering a more equitable healthcare system

Exploring the impact of probiotics and tumor-resident commensal bacteria on cancer

Cancer remains a main driver of morbidity and mortality worldwide, despite advancements in immunotherapies, which exploit a patient’s immune system to eradicate cancer cells. While immune checkpoint inhibitor (ICI) therapies have demonstrated unprecedented success in patients, only a small subset respond, resulting in a critical need to improve overall efficacy among non-responder patients. With the discovery of the gut microbiome composition influencing immunotherapy response, microbiome modulation has been proposed as a potential adjuvant, particularly via probiotic usage, however the potential anti-tumorigenic effects and the mechanisms of action remain enigmatic. To address this, we investigated the impact of several probiotics and revealed that Lactobacillus reuteri (Lr) is capable of potent tumor restraint in several preclinical cancer models. We elucidated that Lr restrained tumor outgrowth through a critical microbial-host crosstalk between Lr-derived aryl hydrocarbon receptor (AhR) agonist, indole-3-aldehyde (I3A), and CD8 T cells within the tumor microenvironment. This research revealed that Lr translocates to, colonizes, and persists within tumors, where via its released dietary tryptophan metabolite, I3A, it locally promotes interferon-gamma-producing CD8 T cells, thereby enhancing antitumor immunity and ICI efficacy. Moreover, we demonstrated that I3A is necessary and sufficient to drive antitumor immunity, and the loss of AhR signaling within CD8 T cells abrogates Lr’s antitumor effects. We found that a tryptophan-enriched diet is sufficient to potentiate both Lr- and ICI-induced antitumor immunity and required CD8 T cell-intrinsic AhR signaling. Demonstrating the translational relevancy, we provided evidence for a potential role of I3A in promoting ICI efficacy and survival in advanced melanoma patients. Discovering the presence of viable Lr within the tumor led us to further investigate translocation dynamics of commensal bacteria, as translocating microbes may be a potential mechanism by which gut microbiome modulation impacts immunotherapy response. We demonstrated that gut microbial modulation, through fecal microbiota transplants or dietary changes, induces alterations in the tumor microbiome. Both our identification of an impactful, tumor-intrinsic, anti-tumorigenic microbial-host crosstalk and the finding that gut modulation drives changes in the tumor microbiota composition will serve as a foundation for further investigation to explore the impact of various tumor-resident microbes on immunotherapy response in cancer

Photocatalytic synthesis of metallic nanoparticles and their applications

Photo-driven nanoparticle syntheses have remained relatively limited in scope compared to traditional synthetic methods, such as chemical reduction, thermal decomposition, laser-based syntheses, and nanolithography techniques. However, a photo-driven pathway offers numerous advantages, as light is highly tunable in its wavelength, intensity, timing, and duration of irradiation. The majority of light-driven syntheses which have been reported use either the localized surface plasmon resonance of existing or developing seed particles, or a photoexcited reagent which is depleted as the reaction proceeds, analogous to chemical reduction. Here, I introduce a photocatalytic reduction method for the synthesis of metallic nanoparticles, a novel synthetic technique which produces nanoparticles via a continuous nucleation pathway. In Chapter 2, I discuss this nucleation pathway and how light can be used to effectively “turn on” nanoparticle synthesis without impacting final nanoparticle morphology. Chapter 3 investigates the effects of reagent identity and the ratio of reagents on nanoparticle outcomes. Chapter 4 extends this photocatalytic reduction approach to the synthesis of bimetallic nanoparticles, and compares reaction outcomes to those of traditional chemical reduction based syntheses. In Chapter 5, the evolution of stoichiometry and chemical ordering in bimetallic nanoparticles synthesized via photocatalytic reduction is discussed, focusing on the gold/copper system. Finally, in Chapter 6 I apply this synthetic approach to the production of hydrogen through a photocatalytic water splitting reaction, and demonstrate the necessity of stable nanoparticles to catalyze the reaction. Taken together, this new approach to the synthesis of metallic nanoparticles via a photocatalytic reduction mechanism and the application of particles synthesized through this pathway offers insight into how light can be utilized as a reagent in nanochemistry, and lays the foundation for future studies to elucidate the underlying mechanism to further control nanoparticle outcomes

Biochemical Study of a New Family of Isomerocyclases in the Biogenesis of Hapalindole Type Alkaloids and Chemical Genetic Approach to Studying Bacterial Chromosome Segregation

Antibiotic resistance presents a growing health challenge that will require a repertoire of new antibiotics that inhibit diverse targets. This includes the characterization of the biosynthesis of hapalindole type alkaloids (HTAs), which are produced by Stigonematalean cyanobacteria. I have also characterized the mechanism of action of a previously known antibiotic that disrupts phase separation of a bacterial biomolecular condensate involved in chromosome segregation. Then, I develop methods to study catalytically dead histidine kinases which are a part of a larger family of histidine kinases that regulate growth, virulence, and biofilm formation. Collectively, my work examines natural products and new antibiotic targets that could ultimately lead the way to next generation of antibiotics. In chapter 1, a combination of biochemical and biophysics approaches was extensively utilized to elucidate the U-type isomerocyclases heteromeric nature in the biogenesis of hapalindoles with the particular focus on hapalindole U and the confirmation of the calcium co-factor dependency for the U-isomerocyclases enzymatic activities. Recently, it has been recognized that bacterial cells leverage phase separation to form specialized compartments that regulate essential pathways. Past mechanistic studies suggest disruption of these assembly of these essential compartments could function as new antibiotic targets. In chapter 2, I will explore the discovery of a small molecule that specifically disrupts the phase separation properties of ParB, a CTPase involved in chromosome segregation in diverse bacterial species. Our studies pinpoint towards generalization of identifying lynchpin interactions that control the degree of multivalency as promising drug target strategies for biomolecular condensates associated with pathogens, cancer, and neurodegenerative diseases. In chapter 3, I will discuss models for how bacterial pseudokinases can utilize protein-protein interactions and allostery to serve as crucial signaling pathway regulators. Then we describe a protein engineering strategy to interrogate these models, emphasizing how signals flow within bacterial pseudokinases. In summary, the biochemical and biophysical characterizations of bioactive and structurally diverse HTAs biosynthesis will facilitate the structurally complicated small molecule in vitro biosynthesis and the ParB-parS-CTP biomolecular condensate will serve as an effective screening platform for antibiotics discovery

Exploring ML-Oriented Hardware for Accelerated and Scalable Feature Extraction

Machine-learning (ML) algorithms, tools, and devices continually grow intending to automate and accelerate many aspects of daily life. Hardware accelerators can enable these ML apps to achieve maximized performance. The first phase of this dissertation explores the maximum throughput performance of field-programmable gate arrays (FPGAs), CPUs, and GPUs on two architecturally different convolutional neural networks (CNNs) that are comprised of similar fundamental neural-network operations: GoogLeNet and AlexNet. Because of their highly parallel nature, GPUs achieved the highest inference throughput across models and devices, where additional tensor acceleration significantly boosts performance. To better understand the design and impacts of ML-oriented hardware and software, the second phase of this dissertation analyzes the subsequent generations of high-performance and embedded devices that feature ML optimizations in terms of latency and throughput. Tensor, vision, and other ML-focused architectures are also considered. Because many of these devices feature hardware for quantized and reduced-precision datatypes, GoogLeNet and AlexNet are quantized with more modern ML frameworks for optimized performance with state-of-the-art backend acceleration software. Though GPUs dominate in throughput and FPGAs achieve the lowest latencies, all of the devices use significant compute, memory, and power resources to achieve their respective performance. The final phase of this dissertation explores neuromorphic technology as an alternative solution to ML object classification to reduce the overall compute, memory, and power required. Neuromorphic sensors capture events at a microsecond resolution as opposed to generating entire frames to limit the amount of redundant data captured. These events can be related spatially, through algorithms such as k-means clustering, or spatio-temporally, through neuromorphic algorithms such as "A Hierarchy Of event-based Time Surfaces" (HOTS). FPGA accelerators for k-means clustering and HOTS are designed and optimized using state-of-the-art high-level synthesis tools and evaluated on multiple datasets. The highly scalable k-means clustering accelerator achieved an event-processing latency of 65 nanoseconds and throughput of 15.38 MEvt/s while using less than 2% of available FPGA resources and being competitive in accuracy. This dissertation benchmarked many state-of-the-art hardware accelerators, analyzed the impacts of ML hardware and software optimizations, and developed an ultra-low-latency, scalable alternative to ML object classification with neuromorphic technology

Human Rights Writing: Activist Composition in a Global World

This dissertation explores the intersection of human rights and writing in global contexts, emphasizing the link between rhetoric, genre theory, and the shaping of human rights discourse. It argues that written documents play a pivotal role in human rights action. The failure to integrate human rights discourse into composition studies is a missed opportunity, limiting discussions of social justice to local and national issues and neglecting their global impact. Working within a theoretical framework that positions genres as dynamic and context-driven social actions, I demonstrate how genres play a significant role in shaping human rights realities. I begin by analyzing the United Nations Charter and the Universal Declaration of Human Rights, showing how they construct a collective humanity with moral authority over nation-states, while also reinforcing state sovereignty. The tension between the idealized moral concept of human rights and their manifestation as law is explored, exemplified by the UN Charter's dual characterization as a charter and a treaty. This tension persists in subsequent human rights writing as a gap between human rights ideals and practical effects. Texts such as human rights reports, and demonstrations of grassroots activism, like Amnesty International's letter campaigns, are caught between asserting humanity's authority over States while grappling with the State autonomy imposed by the charter and treaty genres. Having identified these dynamics of human rights writing through the constitutive and epistemic act of composing human rights genres, I turn to a case study of the Tumaini Festival and Homestay Program at the Dzaleka Refugee Camp in Malawi to explore the ways refugee activists deploy genres of the hospitality industry to reposition refugees as individuals with agency and economic value. This reframing offers refugees access to essential resources and opportunities, challenging the “rhetoric of illegibility” defining protracted encampment. This dissertation concludes by encouraging compositionists to engage critically but generatively with the intersections of human rights and writing studies to improve the work and impact of both in global issues of social justice

Molecular Modulator Approach for Controlling the Length of Chiral 1D Single-Helical Gold Nanoparticle Superstructures

Peptide-based methods have proven useful for constructing helical gold nanoparticle superstructures that exhibit strong plasmonic chiroptical activity. Superstructure syntheses using the amphiphilic peptide conjugate C16-(AYSSGAPPMoxPPF)2 typically yield 1D helices with a broad length distribution. In this study, we introduce a molecular modulator approach for controlling helix length. It represents a first step toward achieving narrowly disperse populations of single helices fabricated using peptide-based methods. Varying amounts of modulator, C16 (AYSSGA)2, were added to C16-(AYSSGAPPMoxPPF)2-based single-helix syntheses, resulting in decreased helix length and narrowing of the helix length distribution. Kinetic studies of fiber assembly were performed to investigate the mechanism by which the modulator affects helix length. It was found that the modulator leads to rapid peptide conjugate nucleation and fiber growth, which in turn results in large amounts of short fibers that serve as the underlying scaffold for the single-helix superstructures. These results constitute important advances toward generating monodisperse samples of plasmonic helical colloids

Polymer Infiltration and Pyrolysis of Silicon Carbide Ceramics Using Transient Liquid

The polymer infiltration and pyrolysis (PIP) method has a limitation in increasing the density of SiC ceramics after multiple PIP cycles. This is due to an increasing number of blocked pores that decreased the amount of polymer infiltrated deep into the interior of bulk materials. In this study, a new process that incorporates Ni and carbon nanoparticles into SiC polymer precursor is examined. Ni and carbon nanoparticles were uniformly distributed into porous SiC ceramics during the polymer infiltration. The reaction of nanomaterials with SiC polymer precursor results in a transient liquid phase during the pyrolysis, which mitigates the pore closure. Ni nanoparticles reacted with the SiC precursor to form a nickel silicide of low melting temperature such as Ni2Si and NiSi phases. During high temperature pyrolysis, these silicide phases turned to a liquid phase, facilitated the redistribution of the infiltrated material, and maintained the pore structure open. In later infiltration steps, the co-addition of carbon nanoparticles into the polymer precursor helps the conversion of nickel silicide to nickel carbide and decreases the amount of residual nickel silicide which may be harmful for mechanical strength at high temperature. Results of this study show significant improvement in the density of SiC ceramics in comparison to traditional PIP

Coherent Photonic Vector Processor for Scalable and Efficient Optical Computing.

Recent advancements in artificial intelligence (AI) have hinged on the capacity to train increasingly vast parameter sets within neural networks, a trend that has outpaced the computational capabilities of traditional digital hardware. This has led to a critical need for new computing paradigms that can simultaneously enhance computational efficiency and throughput. Photonic analog computing emerges as a promising solution to reduce latency and boost performance, particularly in addressing the memory-related challenges that underpin AI's success. This thesis presents an overview of the integration of optical and electrical memories with photonic hardware, underlining the pivotal role of memory technologies. It emphasizes the need for more efficient and compact memory for optical computing and presents an integrated coherent solution to process temporally multiplexed optical signals using a modular dot-product unit cell. We use these unit cells to demonstrate multiply accumulate operations on real- and complex-valued inputs using coherent detection and temporal integration. We then extend this to computing the covariance between stochastic bit streams which can be used to estimate correlation between data streams in the optical domain. Finally, we demonstrate a path to scaling up our platform to enable general matrix-matrix operations. This approach has the potential to enable highly efficient and scalable optical computing on-chip for a broad variety of AI applications