Endocrinology

Osteoporosis

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CaML: Chemistry-informed machine learning explains mutual changes between protein conformations and calcium ions in calcium-binding proteins using structural and topological features.

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and th...

Feb 1 2025 39865355

Sensitivity of Quantitative Susceptibility Mapping in Clinical Brain Research

Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitorin...

Single-neuron deep generative model uncovers underlying physics of neuronal activity in Ca imaging data

Calcium imaging has become a powerful alternative to electrophysiology for studying neuronal activity, offering spatial resolution and the ability t...

Enhancing Coronary Artery Calcium Scoring via Multi-Organ Segmentation on Non-Contrast Cardiac Computed Tomography

Despite coronary artery calcium scoring being considered a largely solved problem within the realm of medical artificial intelligence, this paper ar...

Deep Learning-based Feature Discovery for Decoding Phenotypic Plasticity in Pediatric High-Grade Gliomas Single-Cell Transcriptomics

By use of complex network dynamics and graph-based machine learning, we identified critical determinants of lineage-specific plasticity across the s...

Jaxley: Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

Biophysiscal neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the questio...

A Biologically Inspired Attention Model for Neural Signal Analysis

Understanding how the brain represents sensory information and triggers behavioural responses is a fundamental goal in neuroscience. Recent advances i...

Oxytocin neurons signal state-dependent transitions from rest to thermogenesis and behavioral arousal in social and non-social settings

Core body temperature (Tb) is defended within narrow limits through thermoregulatory behaviors like huddling, nesting, and physical activity as well a...

Selective changes in cortical cholinergic signaling during learning

Cognition relies on the function of local and long-range neural circuits in the neocortex, which are dynamically regulated by neuromodulatory signals ...

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatia...

Circuit inhibition promotes the dynamic reorganization of prefrontal task encoding to support cognitive flexibility

The mammalian prefrontal cortex encodes variables related to goal-directed behavior, and enables flexibility during environmental changes, making it c...

Calcium Binding Affinity in the Mutational Landscape of Troponin-C: Free Energy Calculation, Coevolution Modeling and Machine Learning

Mutation in calcium-binding proteins (CBPs) can significantly influence Ca2+ binding affinity (BA), resulting in substantial impairment in the signali...

Parvalbumin interneurons mediate spontaneous hemodynamic fluctuations

Resting-state hemodynamic fluctuations are closely linked to gamma-band neural activity, yet the cellular drivers of this neurovascular coupling remai...

Independence and Coherence in Temporal Sequence Computation across the Fronto-Parietal Network

Time processing requires distributed and coordinated cortical dynamics. Flexible yet robust temporal representations can arise from two distinct compu...

Predicting individual learning trajectories in zebrafish via the free-energy principle

The free-energy principle has been proposed as a unified theory of brain function, and recent evidence from in vitro experiments supports its validity...

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments (e.g., all-o...

Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity

Quantitative phenotyping of Caenorhabditis elegans is essential across numerous fields, yet data extraction remains a significant analytical bottlenec...

Deep learning-based classification of complex intracellular calcium concentration patterns

Intracellular calcium ion (Ca2+) exhibits diverse dynamical behaviors, including complex oscillations such as bursting and chaos. The distinction of d...

Automated generation of personalized trajectories of aging phenotypes with DyViA-GAN

With a general increase in human lifespan, the need for technological advances to develop strategies for healthy aging has assumed great importance. I...

Axon termination of the SAB motor neurons in C. elegans depends on pre- and postsynaptic activity

Axon termination is a critical step in neural circuit formation, but the contribution of activity from postsynaptic targets to this process remains un...

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