Fluorescent small molecule environment-sensitive probes change their emission properties (including emission wavelength, intensity or lifetime) in response to the changes of the environment around them, such as changes in temperature, viscosity, and polarity. Thus, these probes have found numerous applications in sensing and imaging, especially in biologically relevant systems. Ratiometic probes is a special group of molecules that has two or more emission wavelengths that exhibit a relative change in response to changes in the media, which provides an internal calibration, increases signal-to-noise ration, and improves the integrity of sensing. However, synthesis of such molecules is usually non-modular in nature, and it often requires multiple steps coupled with numerous purifications. In this presentation, we will highlight our synthetic efforts on the developments of several types of fluorescence ratiometric probes that are based on versatile fluorescence scaffolds, such as BODIPY and squaraine dyes.

Historically, drug design has focused on small molecule drugs, but macrocycles show potential to interact with targets that small molecules cannot, such as protein-protein interactions. These interactions do not have an active site that can be specifically targeted, so macrocycle drug design must explore as much structural diversity as possible. This work explores a new site for introducing structural diversity on the macrocycle backbone that does not compromise conformation or yields during cyclization. This glycine-containing macrocycle uses a two pot, four step synthesis where all but one intermediate can be isolated and characterized. This macrocycle is a dimer, in which the monomer is synthesized in three steps by three substitutions onto an aromatic triazine ring. These substitutions include BOC protected hydrazine, dimethyl amine, and glycine conjugated to an amino-ketone. The use of a ketone rather than an acetal during monomer synthesis introduces a new site for adding structural diversity to the macrocycle backbone. The molecule is purified via silica gel chromatography after every substitution to prevent side reactions and increase yield. Once the monomer is synthesized, dimerization occurs with acid-catalyzed imine formation. 1H and 13C NMR confirm the successful synthesis of each intermediate as well as the macrocycle. Additionally, COSY data confirms the structure of the macrocycle, while ROESY data confirms the shape and folding. The implication on future drug design is described.

FrogCrew is a comprehensive web-based system designed to simplify the management of TCU Athletics sports broadcasting crews. Traditional manual methods of scheduling, tracking availability, and assigning roles are inefficient and prone to errors. This often leads to miscommunication and scheduling conflicts. To solve these challenges, FrogCrew provides a unified platform for administrators. It enables them to manage game schedules, assign roles based on availability and qualifications, and automate notifications efficiently. Key features include customizable crew member profiles. These profiles allow users to update essential information such as availability, roles, and qualifications. The system also offers an automated scheduling tool that simplifies the process of creating game schedules and assigning roles. Additionally, FrogCrew includes a shift exchange feature. This feature allows crew members to request shift swaps, with automated notifications sent to administrators for approval. The system's reporting tools provide financial reports, position-specific insights, and individual performance analyses. These tools help administrators assess crew utilization and manage costs effectively. By automating core functions, FrogCrew reduces manual workload and minimizes errors. It also improves communication between administrators and crew members, ensuring optimal staffing - ultimately enhancing the execution of our TCU sporting events; Go Frogs!

Wearable smart devices, which continuously capture physiological signals such as heart rate, respiratory patterns, and blood oxygen levels, offer significant potential for the early detection of serious health conditions. Timely diagnosis of diseases such as arrhythmia and sleep apnea can greatly enhance patient outcomes by enabling early intervention. However, extensive collection of diverse, real-world wearable sensor data faces challenges due to privacy concerns, data scarcity, and logistical constraints. This research introduces a novel deep learning framework that integrates publicly available wearable sensor data with synthetic physiological signals generated by large language models (LLMs) to create comprehensive and privacy-compliant hybrid datasets.The proposed framework leverages convolutional neural networks (CNNs), optimized for time-series data analysis, alongside advanced machine learning techniques to identify early signs of arrhythmia, sleep apnea, and related health conditions from physiological data. The integration of synthetic data generated by LLMs addresses critical challenges of limited data availability and privacy concerns, enriching the training datasets with diverse scenarios and physiological variations. Preliminary experimental results demonstrate that the hybrid approach, combining publicly accessible wearable sensor data and LLM-generated synthetic signals, significantly enhances the model's accuracy, generalization capability, and resilience to data variability. Models trained on hybrid datasets consistently outperform those relying solely on real-world data, suggesting that synthetic data provides meaningful supplementation to traditional datasets. This study further highlights how synthetic physiological data can enhance the scalability and efficacy of AI-based health monitoring systems, reducing dependency on extensive clinical data collection. By exploring and validating this innovative data synthesis approach, the research contributes significantly to developing more effective, accessible, and proactive healthcare diagnostic tools, ultimately advancing AI-driven solutions in preventive healthcare.

The iPELiNT project develops an AI-powered patent analysis dashboard designed to streamline the patent prosecution process for attorneys and practitioners. This web application leverages modern technologies including Vue.js with Nuxt3 framework for frontend development, NodeJS with Express for backend services, MongoDB for database management, and integrates AI models from OpenAI to analyze patent documents.

The system features a user-friendly dashboard that allows practitioners to upload patent applications, analyze document health, view CPC prediction analytics, examine keyword relevance, and identify potential prior art conflicts. Key functionality includes document parsing, automated health checks, Art Unit prediction, and generation of actionable reports. The solution also incorporates user account management, notification systems, and specialized document generation tools.

Development followed an iterative process with clearly defined milestones and tasks distributed across team members. The project addresses a critical need in the patent industry by providing an all-in-one platform that simplifies complex patent analysis, replacing traditionally fragmented and cumbersome tools with a streamlined, intuitive interface.

The completed iPELiNT dashboard enhances efficiency for patent professionals, improving application quality through AI-powered insights, and ultimately streamlining the patent prosecution workflow with modern design principles and cutting-edge technology.