North Carolina School of Science and Mathematics

Walking impairments affect millions worldwide, reducing independence and increasing the risk of falls, yet current assistive devices primarily provide passive support without actively retraining gait mechanics. This paper presents STABLE (Smart Tracking Assistance for Balance and Locomotion Enhancement), a wearable rehabilitation shoe that integrates real-time pressure sensing, gyroscopic orientation tracking, corrective piston-based actuation, and haptic feedback to stabilize and retrain walking patterns. Unlike conventional orthotics, STABLE establishes a personalized baseline during calibration, allowing its embedded control system to detect deviations in plantar pressure or foot rotation and respond with mechanical corrections alongside vibrational cues that reinforce proper gait. The system's closed-loop architecture ensures rapid detection and correction at high frequency, while onboard logging and wireless communication allow long-term progress tracking by patients and therapists. By combining immediate stabilization with neuromuscular training, STABLE represents a proactive, adaptive, and user-centered advance in mobility rehabilitation technology.

As augmented reality (AR) applications increasingly require 3D content, generative pipelines driven by natural input such as speech offer an alternative to manual asset creation. In this work, we design a modular, edge-assisted architecture that supports both direct text-to-3D and text-image-to-3D pathways, enabling interchangeable integration of state-of-the-art components and systematic comparison of their performance in AR settings. Using this architecture, we implement and evaluate four representative pipelines through an IRB-approved user study with 11 participants, assessing six perceptual and usability metrics across three object prompts. Overall, text-image-to-3D pipelines deliver higher generation quality: the best-performing pipeline, which used FLUX for image generation and Trellis for 3D generation, achieved an average satisfaction score of 4.55 out of 5 and an intent alignment score of 4.82 out of 5. In contrast, direct text-to-3D pipelines excel in speed, with the fastest, Shap-E, completing generation in about 20 seconds. Our results suggest that perceptual quality has a greater impact on user satisfaction than latency, with users tolerating longer generation times when output quality aligns with expectations. We complement subjective ratings with system-level metrics and visual analysis, providing practical insights into the trade-offs of current 3D generation methods for real-world AR deployment.

Leg cramps are a common condition that causes severe, abrupt pain, and disrupts daily life, often at night or during exercise. Current devices mainly address prevention or assisting a muscle in general but cannot provide immediate relief once the cramp has started. This paper presents SENSE, Sensor-based Electrostimulation for Nerve Support & Ease, a wearable device with the ability to detect and treat leg cramps in real time. SENSE combines EMG sensors with TENS technology in a comfortable leg brace worn below the knee. The device works by constantly monitoring muscle activity and uses a unique algorithm to identify when a cramp is about to occur. Upon detecting abnormal muscle signals, SENSE automatically delivers electrical stimulation to the site of the cramps, disrupting pain signals, and thereby providing the muscle with rapid, hands-free relief. The device does this through an automatic feedback loop that continues to sense and stimulate until the cramp ends, making it a responsive and convenient device. Through the integration of real time sensing and instant relief, SENSE offers a new method for enhancing the quality of life of patients who suffer from leg cramps.

Information generated from longitudinally sampled microbial data has the potential to illuminate important aspects of development and progression for many human conditions and diseases. Identifying microbial biomarkers and their time-varying effects can not only advance our understanding of pathogenetic mechanisms, but also facilitate early diagnosis and guide optimal timing of interventions. However, longitudinal predictive modeling of highly noisy and dynamic microbial data (e.g. metagenomics) poses analytical challenges.To overcome these challenges, we introduce a robust and interpretable machine-learning-based longitudinal microbiome analysis framework, LP-Micro, that encompasses (i) longitudinal microbial feature screening via a polynomial group lasso, (ii) disease outcome prediction implemented via machine learning methods (e.g. XGBoost, deep neural networks), and (iii) interpretable association testing between time points, microbial features, and disease outcomes via permutation feature importance. We demonstrate in simulations that LP-Micro can not only identify incident disease-related microbiome taxa, but also offers improved prediction accuracy compared with existing approaches. Applications of LP-Micro in two longitudinal microbiome studies with clinical outcomes of childhood dental disease and weight loss following bariatric surgery yield consistently high prediction accuracy. Moreover, LP-Micro highlights critical time points and associated microbial changes: oral microbial changes, including Streptococcus mutans, are most informative for predicting childhood dental disease at around 39 months of age, while gut microbial changes shortly after bariatric surgery strongly predict future weight loss. These findings are both informative and aligned with clinical expectations. The tool LP-Micro can be seen at https://github.com/IV012/LPMicro.

The advent of generative AI has opened up more avenues for mental health challenges among youth worldwide. This work represents a comprehensive survey of the recent progress of generative AI in youth mental healthcare concerning their impact, enabling technologies, challenges, and future directions. Some of the key technologies that form the bedrock for many applications involve NLP, transformer models, GAN, VAE, reinforcement learning, and Affective AI in the development of AI-powered chatbots, virtual therapists, personalized interventions, and early detection. We assess the role of open-source technologies such as orchestration frameworks, vector databases, large and small language models, front-end tools, and other enablers that empower RAG systems to elevate the capability for contextual relevance and effectiveness in Generation AI solutions. We present the positive impacts of generative AI: enhanced accessibility, increased engagement, and decreased cost, while also discussing critical concerns on ethical issues, data privacy, bias, and informed consent. We also probe barriers to mainstreaming, including technical limitations, policy gaps, and social resistance, and presented a critical review of pathways toward surmounting the difficulties. In the final portion of the study, we make conclusions and recommendations with regard to strategic future research and policy development recommendations that will ensure that generative AI is safe, ethical, and effective to deploy within youth mental healthcare for improved mental health outcomes among young populations globally.

Oral fluids provide ready detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and host responses. This study sought to evaluate relationships between oral virus, oral and systemic anti-SARS-CoV-2-specific antibodies, and symptoms. Oral fluids (saliva/throat wash (saliva/TW)) and serum were collected from asymptomatic and symptomatic, nasopharyngeal (NP) SARS-CoV-2 RT-qPCR+ human participants (n = 45). SARS-CoV-2 RT-qPCR and N-antigen detection by immunoblot and lateral flow assay (LFA) were performed. RT-qPCR for subgenomic RNA (sgRNA) was sequence confirmed. SARS-CoV-2-anti-S protein RBD LFA and ELISA assessed IgM and IgG responses. Structural analysis identified host salivary molecules analogous to SARS-CoV-2-N-antigen. At time of enrollment (baseline, BL), LFA-detected N-antigen in 86% of TW and was immunoblot-confirmed. However, only 3/17 were saliva/TW qPCR+ . Sixty percent of saliva and 83% of TW demonstrated persistent N-antigen at 4 weeks. N-antigen LFA signal in three anti-spike sero-negative participants suggested potential cross-detection of 4 structurally analogous salivary RNA binding proteins (alignment 19–29aa, RMSD 1–1.5 Angstroms). At enrollment, symptomatic participants demonstrated replication-associated sgRNA junctions, were IgG+ (94%/100% in saliva/TW), and IgM+ (63%/54%). At 4 weeks, SARS-CoV-2 IgG (100%/83%) and IgM (80%/67%) persisted. Oral and serum IgG correlated 100% with NP+ PCR status. Cough and fatigue severity (p = 0.010 and 0.018 respectively), and presence of weakness, nausea, and composite upper respiratory symptoms (p = 0.037, 0.005, and 0.017, respectively) were negatively associated with saliva IgM but not TW or serum IgM. Throat wash IgM levels were higher in women compared to men, although the association did not reach statistical significance (median: 290 (female) versus 0.697, p = 0.056). Important to transmission and disease course, oral viral replication and persistence showed clear relationships with select symptoms and early oral IgM responses during early infection. N-antigen cross-reactivity may reflect mimicry of structurally analogous host proteins.

Nuclear Magnetic Resonance (NMR) is a fundamental tool in chemistry for elucidating the molecular structure of unidentified substances. The evaluation of 13C NMR spectra can be challenging due to the numerous factors that affect the peaks and their locations (chemical shifts). Chemists can use NMR spectroscopy in synthesizing new molecules to confirm the identity of the molecule produced. Since the NMR spectrum for the molecule does not exist, the chemists cannot compare their spectra with preexisting spectra to verify their results. To address this, an algorithm that predicts the chemical shifts of the 13C NMR spectra for compounds based on their molecular structure emerges as a solution, generating artificial spectra for comparison with real ones. This paper delineates a method to formulate such an algorithm using machine learning techniques. Multiple graph neural networks and a classical neural network underwent training on an NMR database to predict the chemical shifts of the 13C NMR spectra for several molecules. The accuracy of each neural network was tested by analyzing the difference between the predictions and the actual chemical shift values in the database using Mean Absolute Error (MAE). Notably, the graph neural networks had a higher accuracy and precision than the classical neural network. Among them, the Graph Transformer Network emerges as the most proficient performer. Chemists can utilize the Graph Transformer Network model to validate the synthesis of new compounds within a margin of error of approximately 2.599 ppm.

The use of air ambulance services, a critical component of emergency medical response, has become increasingly prevalent in transporting critically ill or injured patients to medical emergency sites. This essay aims to provide a comprehensive understanding of air ambulance services by exploring their definition, importance in trauma care, and the economic and personal finance aspects associated with these life-saving interventions. The significance of air ambulance services in trauma care is underscored by empirical research, highlighting the advantages of high speed, rapid transportation, and access to specialized medical teams for trauma patients. Furthermore, the laws and current economic conditions of air ambulance services is examined through cost comparisons and analyses of factors influencing cost effectiveness. The findings shed light on the balance between the economic viability of air ambulance services and their life-saving potential. Any approach aimed at addressing these economic issues must consider the many perspectives: economic, medical, political, and more.