Physical Sciences and Mathematics Commons^™

Conformational Change In A Four-Tetrad Dna G-Quadruplex Upon Intercalation Of A Small-Molecule Ligand Pydh2, Kailey N. Martin, Gwendolyn Lam , '24, O. Reznichenko, K. E. Brunner, M. J. Zdilla, Sawyer E. Mccarthy, Liliya A. Yatsunyk

Chemistry & Biochemistry Faculty Works

G-quadruplexes (G4s) are non-canonical DNA structures implicated in a number of biological processes. Small-molecule ligands can alter stability and folding of G4s, which can potentially be exploited for therapeutic purposes. In this work, we investigate the interaction of telomeric DNA fragment from Tetrahymena thermophila (TET25, 5′-G(TTGGGG)₄-3′) with a G4 ligand PyDH2 belonging to the bisquinolinium family. When alone, TET25 adopts a mixture of three conformations, with the most abundant being a four-tetrad hybrid G4. In the presence of PyDH2, surprisingly, TET25 folds into an antiparallel chair G4, with PyDH2 intercalated between G-tetrads 2 and 3, according to our crystal structure. …

2025 May 22 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Selected Artificial Intelligence Provisions In U.S. Fiscal Year 2025 National Defense Authorization Act, Bert Chapman

Libraries Faculty and Staff Presentations

The 2025 Fiscal Year National Defense Authorization Act contains multiple provisions relating to artificial intelligence (AI). These congressionally mandated provisions direct various sections of the Department of Defense (DOD) and individual U.S. armed service branches to execute congressional intent for AI policymaking. Examples of such intent include identifying and planning DOD's AI workforce, demonstrating AI biotechnology applications for national security, improving the human usability of AI systems, and establishing an AI security center. This presentation will note that reports on these initiatives must be prepared for relevant congressional oversight committees, and, in many cases, are in many cases, publicly released …

Re: Comment Letter For Draft Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed Sites Ur-05 Remedial Action Work Plan (Rawp) (Dated March 14, 2025), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Integrative Computational Modeling Of Distinct Binding Mechanisms For Broadly Neutralizing Antibodies Targeting Sars-Cov-2 Spike Omicron Variants: Balance Of Evolutionary And Dynamic Adaptability In Shaping Molecular Determinants Of Immune Escape, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this study, we conducted a comprehensive analysis of the interactions between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and four neutralizing antibodies—S309, S304, CYFN1006, and VIR-7229. Using integrative computational modeling that combined all-atom molecular dynamics (MD) simulations, mutational scanning, and MM-GBSA binding free energy calculations, we elucidated the structural, energetic, and dynamic determinants of antibody binding. Our findings reveal distinct dynamic binding mechanisms and evolutionary adaptation driving the broad neutralization effect of these antibodies. We show that S309 targets conserved residues near the ACE2 interface, leveraging synergistic van der Waals and electrostatic interactions, while S304 focuses on …

The Future Of The Us Marine Seafloor And Subseafloor Sampling Capabilities, Future 2024 Pi-Team, Olga Libman‐Roshal

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Recent changes in US oceanographic assets are impacting scientists' ability to access seafloor and sub-seafloor materials and thus constraining progress on science critical for societal needs. Here we identify national infrastructure needs to address critical science questions. This commentary reports on community-driven discussions that took place during the 3-day FUTURE of US Seafloor Sampling Capabilities 2024 Workshop, which used an “all-hands-on-deck” approach to assess seafloor and sub-seafloor sampling requirements of a broad range of scientific objectives, focusing on capabilities that could be supported through the US Academic Research Fleet (US-ARF) now or in the near future. Cross-cutting issues identified …

Protein Stability Retention Under Harsh Conditions, Kaylen Su

Student Theses and Dissertations

Plastic waste is a growing environmental concern due to its resistance to natural degradation, leading to long-term accumulation in landfills and oceans. One potential solution is enzymatic degradation, where specific enzymes break down plastic into smaller, biodegradable components. However, enzymes often lose their effectiveness under the extreme conditions plastics experience during manufacturing, such as high temperatures, mechanical stress, and dehydration. This study explores whether small molecules, known as ligands, can help stabilize enzymes and improve their ability to break down plastics.

Using molecular dynamics simulations, we tested four ligands—poly(ethylene glycol) (PEG), 6-hydroxyhexanoic acid (HHA), benzalkonium chloride (BC), and 6-aminohexanoic acid …

Utilization Of Flow Cytometry, Metabolomic Analyses And A Feline Infectious Peritonitis Case Study To Evaluate The Physiological Impact Of Polyprenyl Immunostimulant, Irene Lee, Amar Desai, Akshay Patil, Yan Xu, Kelley Pozza-Adams, Anthony J. Berdis

Chemistry Faculty Publications

Measles, hepatitis C, and COVID-19 are significant human diseases caused by RNA viruses. While vaccines exist to prevent infections, there are a small number of currently available therapeutic agents that can effectively treat these diseases after infection occurs. This study explores a new therapeutic strategy using a small molecule designated polyprenyl immunostimulant (PI) to increase innate immune responses and combat viral infections. Using a multi-disciplinary approach, this study quantifies the effects of PI in mice and THP-1 cells using flow cytometry to identify immune phenotypic markers and mass spectroscopy to monitor the metabolomic profiles of immune cells perturbed by PI …

Enhanced Nuclear Binding Near The Proton Drip Line Opens Possible Bypass Of The 64Ge Rapid Proton Capture Process Waiting Point, Zachary Meisel, W.-J Ong, J. S. Ranhawa

Faculty Publications

We performed astrophysics model calculations with updated nuclear data to identify a possible bypass of the ⁶⁴Ge waiting point, a defining feature of the rapid proton capture (rp) process that powers type I X-ray bursts on accreting neutron stars. We find that the rp-process flow through the 64Ge bypass could be up to 36% for astrophysically relevant conditions. Our results call for new studies of ⁶⁵Se, including the nuclear mass, β-delayed proton emission branching, and nuclear structure as it pertains to the 64As(p, γ) reaction rate at X-ray burst temperatures.

A Complete Transfer Learning-Based Pipeline For Discriminating Between Select Pathogenic Yeasts From Microscopy Photographs, Ryan A. Parker, Danielle S. Hannagan, Jan H. Strydom, Christopher J. Boon, Jessica Fussell, Chelbie A. Mitchell, Katie L. Moerschel, Aura G. Valter-Franco, Christopher Cornelison

Faculty Articles

Pathogenic yeasts are an increasing concern in healthcare, with species like Candida auris often displaying drug resistance and causing high mortality in immunocompromised patients. The need for rapid and accessible diagnostic methods for accurate yeast identification is critical, especially in resource-limited settings. This study presents a convolutional neural network (CNN)-based approach for classifying pathogenic yeast species from microscopy images. Using transfer learning, we trained the model to identify six yeast species from simple micrographs, achieving high classification accuracy (93.91% at the patch level, 99.09% at the whole image level) and low misclassification rates across species, with the best performing model. …

Application Of Quechers Extraction Method For Optimization Of Organic Matters In Oil Sand By Ultrahigh Fourier Transform Infrared Orbitrap-Mass Spectrometry, Gbenga Aladekoyi, Emmanuel G. Olumayede, Samuel O. Olusanya, Simeon Schum

Michigan Tech Publications

This work focuses on the performance of composite solvents using the QuEchERs extraction (quick, easy, cheap, effective, rugged, and safe) method in bitumen recovery and determination of organic matters present in the oil sands of the Okitipupa area of southwestern Nigeria. Different correlated parameters, such as extraction time (h), stirring time (r/ min), change in temperature (°C), and type of solvent (heptane—toluene and dichloromethane—toluene), solvent ratios (2: 8, 3: 7, 5: 5, 7: 3, and 8: 2) were used to optimize dissolved organic matters from the oil sands using QuEchERs extraction method. Ratios 1 g/2.5 ml for each solvent mix …

Ai And Ethical Use Implication For Research, Kelley Plass, Sierra Campbell

May Institute

Artificial intelligence (AI) is becoming increasingly integrated into our daily lives, especially for students. While there are valid debates about the advantages and disadvantages of students using AI for their assignments, such as comparing tools like Grammarly and ChatGPT, one aspect that often goes unexamined is the information sources that AI relies on. AI is now integrated into search engines like Google and Bing, making it accessible to everyone. Additionally, with the emergence of AI research tools like ResearchRabbit, AI's role in everyday activities continues to expand beyond easily accessible resources such as subscription databases. AI is now an optional …

Ph Dependent Structural Characterization Of Ich Ensembles Needed For Catalysis, Kevin C. Prososki

Honors Program: Senior Projects (Public)

No abstract provided.

Research On Grey-Box Modeling Method Of Digital Twins For Cantilever Structure, Wenjia Zhang, Heming Zhang

Journal of System Simulation

Abstract: The construction of accurate and highly real-time digital twin models in complex industrial setting presents several challenges. Traditional model construction approaches based only on mechanism or data show certain limitations. Therefore, this study is based on the idea of grey-box modeling, taking the cantilever structure within a boom-type roadheader as the object, and proposes a novel modeling approach that combines the characteristics of the mechanism model and introduces a self-attention mechanism. This method performs grayscale transformation on the original input and splices it with physical features to achieve organic fusion of mechanism information, which not only enhances the expressiveness …

An Extended Image Features Based Uncalibrated Visual Servoing Method, Shuzhen Zhang, Yukun Cheng, Yangbo Liu, Fusheng Zha

Journal of System Simulation

Abstract: Aiming at the traditional uncalibrated visual servo relying on the estimation of image Jacobi matrix and the coupling of the motion of each degree of freedom of the camera, on the basis of imagebased uncalibrated visual servo, an extended image features based uncalibrated visual servo method is proposed. By analyzing the relationship between image features and camera frames change in the visual servoing process, the visual servoing process in the image space is decomposed into four basic processes: translation, stretching, rotation and scaling; by analyzing the changing of image features in the visual servoing process, extended image features are …

A Modeling And Simulation Method For Firepower Intelligent Decision-Making Of Directed Energy System Basedon Joint Dqn, Changhong Qu, Junjie Wang, Kun Wang, Qingyong Cui, Jiangyang Chen, Xinpeng Wang

Journal of System Simulation

Abstract: In order to solve the problem of dynamically addressing firepower intelligent decision-making in anti-UAV cluster combat using a directed energy system, a deep reinforcement learning model is established. Based on the high multi-agent state and action space dimensions of this model, a modeling and simulation method of firepower intelligent decision-making of directed energy system based on joint deep Q network (DQN) is proposed. The state space is constructed from the state of directed energy system, UAV cluster and the directed energy system deployment area. The joint mechanism is used to share the state information of each equipment and the …

Histone Methyltransferase Ash1l Primes Metastases And Metabolic Reprogramming Of Macrophages In The Bone Niche, Chenling Meng, Kevin Lin, Wei Shi, Hongqi Teng, Xinhai Wan, Anna Debruine, Yin Wang, Xin Liang, Javier Leo, Feiyu Chen, Qianlin Gu, Jie Zhang, Vivien Van, Kiersten L Maldonado, Boyi Gan, Li Ma, Yue Lu, Di Zhao

Faculty, Staff and Student Publications

Bone metastasis is a major cause of cancer death; however, the epigenetic determinants driving this process remain elusive. Here, we report that histone methyltransferase ASH1L is genetically amplified and is required for bone metastasis in men with prostate cancer. ASH1L rewires histone methylations and cooperates with HIF-1α to induce pro-metastatic transcriptome in invading cancer cells, resulting in monocyte differentiation into lipid-associated macrophage (LA-TAM) and enhancing their pro-tumoral phenotype in the metastatic bone niche. We identified IGF-2 as a direct target of ASH1L/HIF-1α and mediates LA-TAMs' differentiation and phenotypic changes by reprogramming oxidative phosphorylation. Pharmacologic inhibition of the ASH1L-HIF-1α-macrophages axis elicits …

Adaptive Multi-Scale Feature Pyramid Network For Occlusion Pedestrian Detection, Huaping Zhou, Tao Wu, Kelei Sun

Journal of System Simulation

Abstract: To address the issue of current pedestrian detectors, which struggle to extract complete features in occlusion-heavy environments and consequently have low detection accuracy. A novel adaptive multiscale feature pyramid network is proposed. A multi-scale feature enhancement module (MFEM) is developed. It captures the visible area of pedestrians at different scales through a multi-branch network with different receptive fields. An AFM (adaptive fusion module) is proposed. It calculates the importance of different pixels by optimizing the mean variance at the spatial and feature levels. It enhances the texture and semantic features of pedestrians and fuses the features of different scales …

Leveraging Artificial Intelligence In Education To Drive Cross-Sector Innovation, Brent Terwilliger, John Faraca

Publications

As artificial intelligence (AI) reshapes educational practices, particularly in technical fields such as uncrewed systems, robotics, and aviation/ aerospace, its integration raises promise and complexity. This exploratory study features an investigation of the impact AI tools adoption has on instruction, curriculum support, and workforce preparation, with a focus on online learning environments. Drawing from pilot survey data across aviation and aerospace education stakeholders and hands-on evaluation of AI video production platforms, findings reveal diverse applications, perceived benefits, and critical concerns, including ethical, pedagogical, and institutional challenges. Additionally, the analysis explored how AI-enabled education intersects with broader industry and government innovation …

Investigating Potential Sugar-Related Uncertainties In A Proxy For Ancient Co2, Liam Cooper

Student Theses and Dissertations

The ε_p proxy for ancient pCO₂ can potentially estimate ancient pCO₂ as from the evolution of rubisco-based photosynthesis, using the difference in stable carbon isotope ratios (δ¹³C) between fixed and inorganic carbon, assuming this difference is proportional to pCO₂. This study aims to address uncertainty with the use of photosynthetic microbes in proxies for ancient pCO₂. While previous studies address uncertainties related to the δ¹³C of initial fixed photosynthate (e.g., growth rate), we aim to address the effects of downstream carbon allocation. Specifically, if a significant fraction of fixed …

Historical Perspectives In Volatility Forecasting Methods With Machine Learning, Zhiang Qiu, Clemens Kownatzki, Fabien Scalzo, Eun Sang Cha

All Faculty Open Access Publications

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to …

Book Review Of, Participatory Engineering Of Algorithmic Socialtechnologies: An Extended Book Review Of David G. Robinson's Voices Inthe Code, Rajesh Venkatachalapathy

Complex Systems Faculty Publications and Presentations

David G. Robinson’s Voices in the Code, reviewed here, approaches the issue of governance and regulation of algorithmic social technologies with a case study orientation that offers a grounding in the details, illuminating the fundamentally sociological and processual nature of modern governance issues that are mostly missed or left implicit in academic, regulatory, and legislative discussions. This important book urges us to design adaptive, malleable, and evolvable participatory organizational structures instead. Accepting the author’s invitation to carry the torch forward, this review builds on and extends the book’s arguments by presenting broad and transferable insights on regulation and governance …

Modeling And Simulation Of Intelligent Underwater Acoustic Countermeasure Based On The Matrix Game, Huijin Zhao, Yu Chen

Journal of System Simulation

Abstract: Due to the great threat of torpedo against surface warships, an efficient hydroacoustic countermeasure system must output real-time strategies to accommodate varied antagonizing scenarios. Aiming at the decision-making problem in the anti-torpedo hydroacoustic countermeasure cases, an intelligent adversarial strategy is proposed based on the game theory. By discretizing the strategy space of both sides, a matrix game model is established where the payoff is characterised by the capture probability of attacking torpedo. An improved simplex algorithm is then developed to get the mixedstrategy Nash equilibrium of the game model, which can be used to obtain preferred avoidance strategies to …

Optimal Scheduling Of Virtual Power Plant With Coupled Operation Of Ccs-P2g Considering Wind And Photovoltaic Uncertainty, Xurong Jin, Jiang Yin, Guohua Yang, Wei Li, Guobin Wang, Lele Wang, Na Yang, Xuenian Zhou

Journal of System Simulation

Abstract: In order to solve the problem that the uncertainty of wind power and photovoltaic power generation output easily affects the scheduling of virtual power plant, a new optimal scheduling model of virtual power plant is proposed based on information gap decision theory (IGDT) . In order to reduce the carbon emission of the system, carbon capture and storage (CCS) is installed on the combined heat and power units; in order to improve the utilization rate of renewable energy, the power to gas (P2G) device is introduced into the system, and the operation mode of CCS-P2G coupling is proposed; based …

A Quadrotor Trajectory Tracking Control Method Based On Deep Reinforcement Learning, Guohua Wu, Jiaheng Zeng, Dezhi Wang, Long Zheng, Wei Zou

Journal of System Simulation

Abstract: Traditional quadrotor controllers, constrained by fixed model equation structures, encounter challenges in addressing control errors stemming from variations in parameters and environmental disturbances. This paper proposes a deep reinforcement learning solution for the quadrotor trajectory following control problem. We present the PPO-SAG algorithm incorporated into the PPO framework, utilizing adaptive mechanisms and PID expert knowledge to enhance training convergence and stability. Target functions incorporating distance constraint penalties and entropy policies are designed in alignment with the characteristics of the given problem. We also devise innovative disturbance-adaptive structures and trajectory feature selection mechanisms to augment control error information and extract …

Optimization Of Cargo Location Allocation In Four-Way Shuttle Warehousing System Based On Two-Stage Hybrid Algorithm, Zisong Wu, Daofang Chang, Yuchun Gai

Journal of System Simulation

Abstract: To address issues such as the dense distribution of storage locations and the potential congestion of shuttle vehicles in the four-way shuttle dense storage system, a grid-based approach to the storage location distribution is developed. A location allocation model is then constructed with the goals of ensuring shelf stability, improving warehousing efficiency, and balancing equipment utilization. A twostage hybrid algorithm is designed for the model. In the first stage, the local search strategy of nondominant sequencing genetic algorithm(NSGA-II) is enhanced by incorporating the hill climbing algorithm to address a set of Pareto front sets. In the second stage, the …

Research On A Credibility Information Management Method Of Simulation Modeling, Qiming Gu, Jiazhi Chen, Guoyan Cao, Chenchu Zhou, Dengxiu Yu, Haifeng Hu

Journal of System Simulation

Abstract: A proposed method for managing credibility information in simulation modeling draws insights from the NASA-STD-7009 standard for modeling and simulation. From the perspective of credibility information recording, definition, and assessment, this method aims to address the core issue of whether the credibility requirements are met in simulation modeling research. A simulation system credibility information extraction tool is designed to extract three types of information including modeling credibility information, data credibility information, and development process credibility information, enabling recording, definition, and traceability of credibility information in simulation models. Taking a servo system credibility evaluation as an example, a simulation system …

Hierarchical Optimal Scheduling Of Integrated Energy System With Electric Vehicles Based On Empc, Miaomiao Ma, Zijuan Long, Zhiwei Ren, Yongqiang Cheng

Journal of System Simulation

Abstract: A hierarchical real-time optimization (HRTO) based on economic model predictive control is designed to address the issues of randomness and uncertainty of renewable energy and demand-side in integrated energy systems (IES) with electric vehicles. The optimization problem of the entire system is divided into three sub-problems: day-ahead rolling optimization, real-time rolling optimization, and tracking control. The day-ahead optimization strategy based on economic model predictive control is constructed to ensure that the operational units can meet users' demands. The optimal steady-state operating points of the entire IES are obtained through the real-time optimization layer. The tracking model predictive controller is …

Method For Dynamic Coalition Formation Of Wargame Agent For Force Cooperation, Changhua Yao, Shanning Bi, Rufei Ma, Xiaohan Yu, Jiaqiang Li, Jinli Chen

Journal of System Simulation

Abstract: Regarding the issue of cooperative task alliance formation and adjustment in multi-agent dynamic confrontation scenarios at the tactical level, this method comprehensively considers factors such as target value, task allocation, and operator characteristics, as well as the benefits and costs of executing different types of tasks. we propose a targeted force coordination adjustment for dynamic task alliance formation based on behavioral constraints. The “MiaoSuan-Wise Winning Instant Strategy Human-Computer Confrontation Platform” of Chinese Academy of Sciences (CAS) is used as an experimental platform to conduct confrontation experiments. The experiment demonstrates that the proposed method improves the dynamic coordination ability of …

Research On Decision-Making Of Autonomous Driving In Highway Environment Based On Knowledge And Large Language Model, Xiang Wang, Guozhen Tan

Journal of System Simulation

Abstract: Aiming at the lack of continuous learning and interpretability of current autonomous driving system, a decision model with cognition, generalization and learning ability is proposed. The model utilizes large language model (LLM) and attention mechanisms to understand and explain driving scenes. the system can accumulate and learn from driving experiences, continuously improving its decisionmaking ability. In a simulation environment, the closed-loop test decision model is applied in high-speed scenarios.The simulation results show that the success rate of the knowledge-driven model is 7% and 4% higher than those of the rule-based and data-driven methods. Additionally, the model exhibits generalization and …