Computational models are a linchpin in our understanding of the neurocognitive basis of reading. These models can simulate idealized profiles of alexia syndromes, but in reality, individuals with alexia present with a wide range of mixed deficits rat... read more
Learning interpretable low-dimensional representations of whole-brain neuronal dynamics remains a major computational challenge in systems neuroscience. We present a wiring-agnostic deep-learning framework that couples a convolutional encoder with a ... read more
Metagenomic assembly can be a computationally intensive step in microbiome analysis, with memory requirements that vary widely depending on input data characteristics. In workflow systems like Galaxy and large-scale platforms like MGnify, which run t... read more
Inference of cancer cell states is essential for understanding oncogenic mechanisms and predicting clinical outcomes, yet current reliance on transcriptomic profiling limits scalability and real-time monitoring. Here, we show that cell morphology pro... read more
Molecular diagnostics to detect Vibrio cholerae (Vc) may be negatively impacted by pathogen-specific lytic bacteriophage (phage) predation. To address this problem, phage detection as a proxy for pathogen detection has been proposed. However, efforts... read more
Background - Previous studies evaluated large language model (LLM) performance on the American Society of Nuclear Cardiology (ASNC) Board Preparation Exam. Without domain-specific context, the best model (GPT-4o) achieved 63.1%, below the estimated 6... read more
Contactless assessment of cardiopulmonary function remains an unmet need, with current approaches relying either on subjective clinical examination or on resource intensive imaging. We evaluated a novel multipoint airborne ultrasound surface motion c... read more
Maximal oxygen consumption VO2max is the gold standard for cardiorespiratory fitness but requires resource-intensive physical testing. Recent reports show that machine learning models can extract additional information from ECGs, yet the potential of... read more
Machine learning models for causal inference aim to adjust for confounding factors that are associated with both an exposure and an outcome, creating a spurious biased association. But, these methods are rarely empirically evaluated to assess their s... read more
Background: The pivot shift (PS) test is the most specific clinical examination for anterolateral rotational instability in ACL deficient knees, yet grading remains subjective, as evidenced by poor interobserver reliability, particularly for Grade 2.... read more
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