Historically, pharmaceutical companies have created small molecule drugs designed to interfere with chemical reactions. An alternative strategy for therapy relies on inhibiting protein-protein interactions, but larger molecules are required. Nature uses large ring-shaped molecules (macrocycles) to accomplish this task. These molecules present challenges to synthesis: forming rings typically is difficult, expensive, time-consuming and inefficient. In addition, the rules required to make macrocyclic drugs are poorly understood when compared to those for small molecules. Here, a strategy for creating macrocycles is described that addresses the challenges of synthesis: they can be prepared quickly and inexpensively. The basis for this chemistry is stepwise substitution of cyanuric chloride, allowing the target to be prepared in three steps. The advantage of using highly electrophilic molecules like cyanuric chloride is that virtually any primary or secondary amine or amino acid could be used to make a macrocycle. Using a variety of amines has shown to affect properties like hydrophobicity and size, which allows for the creation of a large library of molecules to be tested for biological activity, which mirrors how current drug development programs work. The macrocycle is characterized by NMR spectroscopy and screened for other physical (drug-relevant) properties, such as logP and pKa.

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.

Designed to empower students with transparent, real-time insights, this innovative digital platform provides comprehensive reviews of classes and instructors, enabling informed academic decision-making. It aggregates detailed evaluations of course content, teaching effectiveness, workload, and overall classroom experience, offering a dynamic alternative to traditional end-of-semester surveys that frequently deliver delayed or insufficient feedback. Backed by survey research underscoring the vital role of timely, honest assessments in shaping successful academic journeys, the platform bridges the gap between institutional data and the practical needs of students. Its intuitive, user-friendly interface allows seamless navigation through a wealth of peer-generated feedback, making it easier for students to select courses that align with their educational goals and personal learning styles. Moreover, by establishing a constructive feedback loop, it provides educators with actionable insights to refine their teaching methods and foster continuous improvement. This collaborative environment not only enhances individual learning experiences but also contributes to building a more effective, accountable educational community. Through open dialogue and shared knowledge, the platform drives positive change, promoting excellence and ensuring that every academic decision is supported by reliable, student-centered information. By continuously evolving based on extensive user feedback, the platform remains dedicated to advancing educational quality and student success.