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Co-methylation networks associated with cognition and structural brain development during adolescence.
Front Genet
January 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, United States.
Introduction: Typical adolescent neurodevelopment is marked by decreases in grey matter (GM) volume, increases in myelination, measured by fractional anisotropy (FA), and improvement in cognitive performance.
Methods: To understand how epigenetic changes, methylation (DNAm) in particular, may be involved during this phase of development, we studied cognitive assessments, DNAm from saliva, and neuroimaging data from a longitudinal cohort of normally developing adolescents, aged nine to fourteen. We extracted networks of methylation with patterns of correlated change using a weighted gene correlation network analysis (WCGNA).
KalmanFormer: using transformer to model the Kalman Gain in Kalman Filters.
Front Neurorobot
January 2025
Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China.
Introduction: Tracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity. However, real-world applications often involve non-linear dynamics, making it challenging for traditional Kalman Filters to achieve accurate state estimation.
View Article and Find Full Text PDFPreliminary evaluation of the FastCAP for users of the Nurotron cochlear implant.
Front Neurosci
January 2025
The First Affiliated Hospital of Soochow University, Suzhou, China.
Background: Electrically evoked compound action potential (ECAP) can be used to measure the auditory nerve's response to electrical stimulation in cochlear implant (CI) users. In the Nurotron CI system, extracting the ECAP waveform from the stimulus artifact is time-consuming.
Method: We developed a new paradigm ("FastCAP") for use with Nurotron CI devices.
Exploring the performance of large language models on hepatitis B infection-related questions: A comparative study.
World J Gastroenterol
January 2025
Department of Gastroenterology, Jiangxi Provincial Key Laboratory of Digestive Diseases, Jiangxi Clinical Research Center for Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi Province, China.
Background: Patients with hepatitis B virus (HBV) infection require chronic and personalized care to improve outcomes. Large language models (LLMs) can potentially provide medical information for patients.
Aim: To examine the performance of three LLMs, ChatGPT-3.
Noise-agnostic quantum error mitigation with data augmented neural models.
npj Quantum Inf
January 2025
QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong.
Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise.
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