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Treadmill running is a common workout for individuals across fitness levels. In this paper, we propose mm-RunAssist, a first-of-its-kind mmWave-based system that enhances treadmill workouts by monitoring respiration waveforms, running rhythm (i.e., coordination between breathing and strides), and detecting fall-off events. Extracting respiration from moving subjects using RF signals is challenging due to dominant motion artifacts. While prior deep learning efforts use adversarial or contrastive learning to mitigate such artifacts, they have been evaluated primarily under low-intensity activities like walking. To address this gap, mm-RunAssist introduces a Dual-task Variational U-Net that shares latent representations between respiration and upper-body movement tracking. This dual-task setup, guided by belt and depth sensors during training, improves reconstruction under intense body motion. Our system not only recovers fine-grained respiratory patterns during running but also supports cadence analysis through arm swing tracking. Extensive experiments with three state-of-the-art baselines under various conditions demonstrate mm-RunAssist's robustness and accuracy in treadmill running scenarios. Results show that mm-RunAssist advances RF sensing by effectively extracting vital signs even during vigorous body movements, offering new capabilities for fitness monitoring and non-intrusive health assessment. 

Accurate and real-time gait analysis is essential for enhancing performance and reducing injury risks in treadmill running. In this paper, we introduce VibRun, an unobtrusive gait analysis system that estimates key physiological metrics, such as cadence, ground contact time, stride time, center of pressure, and plantar pressure distribution, through footstep vibrations captured by low-cost treadmill-mounted sensors. Leveraging advanced multi-task transformer models, our system offers a robust, real-time solution to monitor and analyze running biomechanics without requiring intrusive wearable devices. This approach enables seamless integration into virtual sports, gaming platforms, and immersive exercise environments, enhancing the running experience by providing personalized feedback. By offering precise biomechanical insights in real-time, VibRun paves the way for future applications in virtual sports, gamified fitness, and interactive training programs, empowering users to engage more effectively in their training sessions while improving overall performance and reducing injury risks. Extensive evaluations with 17 participants across varied treadmill running scenarios demonstrate VibRun's accuracy in real-time gait analysis. For instance, VibRun achieves a mean error of 28.8 ms in ground contact time and a distance of 13.66 mm in the center of pressure, among other measured metrics, highlighting its precise performance across multiple gait parameters. 

ABSTRACT Predicting the structure of ligands bound to proteins is a foundational problem in modern biotechnology and drug discovery, yet little is known about how to combine the predictions of protein‐ligand structure (poses) produced by the latest deep learning methods to identify the best poses and how to accurately estimate the binding affinity between a protein target and a list of ligand candidates. Further, a blind benchmarking and assessment of protein‐ligand structure and binding affinity prediction is necessary to ensure it generalizes well to new settings. Towards this end, we introduceMULTICOM_ligand, a deep learning‐based protein‐ligand structure and binding affinity prediction ensemble featuring structural consensus ranking for unsupervised pose ranking and a new deep generative flow matching model for joint structure and binding affinity prediction. Notably,MULTICOM_ligand ranked among the top‐5 ligand prediction methods in both protein‐ligand structure prediction and binding affinity prediction in the 16th Critical Assessment of Techniques for Structure Prediction (CASP16), demonstrating its efficacy and utility for real‐world drug discovery efforts. The source code for MULTICOM_ligand is freely available on GitHub. 

ABSTRACT Themaintenance ofcarboxysomedistribution (Mcd) system comprises the proteins McdA and McdB, which spatially organize carboxysomes to promote efficient carbon fixation and ensure their equal inheritance during cell division. McdA, a member of the ParA/MinD family of ATPases, forms dynamic gradients on the nucleoid that position McdB-bound carboxysomes. McdB belongs to a widespread but poorly characterized class of ParA/MinD partner proteins, and the molecular basis of its interaction with McdA remains unclear. Here, we demonstrate that the N-terminal 20 residues ofH. neapolitanusMcdB are both necessary and sufficient for interaction with McdA. Within this region, we identify three lysine residues whose individual substitution modulates McdA binding and leads to distinct carboxysome organization phenotypes. Notably, lysine 7 (K7) is critical for McdA interaction: substitutions at this site result in the formation of a single carboxysome aggregate positioned at mid-nucleoid. This phenotype contrasts with that of an McdB deletion, in which carboxysome aggregates lose their nucleoid association and become sequestered at the cell poles. These findings suggest that weakened McdA–McdB interactions are sufficient to maintain carboxysome aggregates on the nucleoid but inadequate for partitioning individual carboxysomes across it. We propose that, within the ParA/MinD family of ATPases, cargo positioning and partitioning are mechanistically separable: weak interactions with the cognate partner can mediate positioning, whereas effective partitioning requires stronger interactions capable of overcoming cargo self-association forces. 

Artificial intelligence-assisted drug design is revolutionizing the pharmaceutical industry. Effective molecular features are crucial for accurate machine learning predictions, and advanced mathematics plays a key role in designing these features. Persistent homology theory, which equips topological invariants with persistence, provides valuable insights into molecular structures. The standard homology theory is based on a differential rule for the boundary operator that satisfies [Formula: see text] = 0. Our recent work has extended this rule by employing Mayer homology with generalized differentials that satisfy [Formula: see text] = 0 for [Formula: see text] 2, leading to the development of persistent Mayer homology (PMH) theory and richer topological information across various scales. In this study, we utilize PMH to create a novel multiscale topological vectorization for molecular representation, offering valuable tools for descriptive and predictive analyses in molecular data and machine learning prediction. Specifically, benchmark tests on established protein-ligand datasets, including PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016, demonstrate the superior performance of our Mayer homology models in predicting protein-ligand binding affinities.