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Understanding Health-Related Discussions on Reddit: Development of a Topic Assignment Method and Exploratory Analysis.
JMIR Form Res
January 2025
Larner College of Medicine, University of Vermont, Burlington, VT, United States.
Background: Social media has become a widely used way for people to share opinions about health care and medical topics. Social media data can be leveraged to understand patient concerns and provide insight into why patients may turn to the internet instead of the health care system for health advice.
Objective: This study aimed to develop a method to investigate Reddit posts discussing health-related conditions.
Spiking Flip-Flop Memory in Resonant Tunneling Diode Neurons.
Phys Rev Lett
December 2024
University of Strathclyde, Institute of Photonics, SUPA Dept of Physics, Glasgow, United Kingdom.
We report a spiking flip-flop memory mechanism that allows controllably switching between neural-like excitable spike-firing and quiescent dynamics in a resonant tunneling diode (RTD) neuron under low-amplitude (<150 mV pulses) and high-speed (ns rate) inputs pulses. We also show that the timing of the set-reset input pulses is critical to elicit switching responses between spiking and quiescent regimes in the system. The demonstrated flip-flop spiking memory, in which spiking regimes can be controllably excited, stored, and inhibited in RTD neurons via specific low-amplitude, high-speed signals (delivered at proper time instants) offers high promise for RTD-based spiking neural networks, with the potential to be extended further to optoelectronic implementations where RTD neurons and RTD memory elements are deployed alongside for fast and efficient photonic-electronic neuromorphic computing and artificial intelligence hardware.
View Article and Find Full Text PDFLink to Densify: Topological Transitions and Origin of Hysteresis During the Compression and Decompression of Amorphous Ices.
Phys Rev Lett
December 2024
BM Research Europe, Hartree Centre, Daresbury WA4 4AD, United Kingdom.
In this Letter, we study the phase transition between amorphous ices and the nature of the hysteresis cycle separating them. We discover that a topological transition takes place as the system transforms from low-density amorphous ice (LDA) at low pressures to high-density amorphous ice (HDA) at high pressures. Specifically, we uncover that the hydrogen bond network (HBN) displays qualitatively different topologies in the LDA and HDA phases: the former characterized by disentangled loop motifs, with the latter displaying topologically complex long-lived Hopf-linked and knotted configurations.
View Article and Find Full Text PDFComparative Efficacy and Acceptability of Non-surgical Treatments with or without Exercise for Diastasis Recti Abdominis in Postpartum Women: A Network Meta-Analysis of Randomized Controlled Trials.
Sports Med
January 2025
Department of Nutrition, China Medical University, Taichung, Taiwan.
Background: Diastasis recti abdominis (DRA), commonly occurring in postpartum women, is not only an aesthetic issue but is also highly associated with functional impairments. Various conservative treatment modalities have been employed in clinical practice to alleviate DRA. However, the comparative efficacy of these non-surgical treatments for improving the inter-recti distance (IRD) remains to be determined.
View Article and Find Full Text PDFEnhanced O-glycosylation Site Prediction Using Explainable Machine Learning Technique with Spatial Local Environment.
Bioinformatics
January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.
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