AIMC Topic: Biophysics
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Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation.
Neuroscience
Aug 3, 2021
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channel...
Machine-Learning Provides Patient-Specific Prediction of Metastatic Risk Based on Innovative, Mechanobiology Assay.
Annals of biomedical engineering
Jan 22, 2021
Cancer mortality is mostly related to metastasis. Metastasis is currently prognosed via histopathology, disease-statistics, or genetics; those are potentially inaccurate, not rapidly available and require known markers. We had developed a rapid (~ 2 ...
Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.
Lab on a chip
Sep 16, 2020
The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols t...
Cell Line Classification Using Electric Cell-Substrate Impedance Sensing (ECIS).
The international journal of biostatistics
Dec 5, 2019
We present new methods for cell line classification using multivariate time series bioimpedance data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS technology, which monitors the attachment and spreading of mammal...
Estimating Multiscale Direct Causality Graphs in Neural Spike-Field Networks.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 28, 2019
Neural representations span various spatiotemporal scales of brain activity, from the spiking activity of single neurons to field activity measuring large-scale networks. The simultaneous analyses of spikes and fields to uncover causal interactions i...
Markerless 2D kinematic analysis of underwater running: A deep learning approach.
Journal of biomechanics
Mar 1, 2019
Kinematic analysis is often performed with a camera system combined with reflective markers placed over bony landmarks. This method is restrictive (and often expensive), and limits the ability to perform analyses outside of the lab. In the present st...
Multiparameter mechanical and morphometric screening of cells.
Scientific reports
Dec 2, 2016
We introduce a label-free method to rapidly phenotype and classify cells purely based on physical properties. We extract 15 biophysical parameters from cells as they deform in a microfluidic stretching flow field via high-speed microscopy and apply m...
Vector Symbolic Spiking Neural Network Model of Hippocampal Subarea CA1 Novelty Detection Functionality.
Neural computation
Feb 18, 2016
A neural network model is presented of novelty detection in the CA1 subdomain of the hippocampal formation from the perspective of information flow. This computational model is restricted on several levels by both anatomical information about hippoca...
Progress in deep Markov state modeling: Coarse graining and experimental data restraints.
The Journal of chemical physics
Dec 7, 2021
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems, such as proteins. In particular, the inclusion of physical constraints, e.g., time-reversibility, was a crucial ...
Parsing human and biophysical drivers of coral reef regimes.
Proceedings. Biological sciences
Feb 13, 2019
Coral reefs worldwide face unprecedented cumulative anthropogenic effects of interacting local human pressures, global climate change and distal social processes. Reefs are also bound by the natural biophysical environment within which they exist. In...
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