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Biophysics

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Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.

Nature methods
Deep learning using neural networks relies on a class of machine-learnable models constructed using 'differentiable programs'. These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose, ...

Machine-Learning Provides Patient-Specific Prediction of Metastatic Risk Based on Innovative, Mechanobiology Assay.

Annals of biomedical engineering
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 ...

Across-Area Synchronization Supports Feature Integration in a Biophysical Network Model of Working Memory.

Frontiers in neural circuits
Working memory function is severely limited. One key limitation that constrains the ability to maintain multiple items in working memory simultaneously is so-called swap errors. These errors occur when an inaccurate response is in fact accurate relat...

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.

Lab on a chip
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
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
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
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...

Parsing human and biophysical drivers of coral reef regimes.

Proceedings. Biological sciences
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...

Multiparameter mechanical and morphometric screening of cells.

Scientific reports
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...

Multi-chip dataflow architecture for massive scale biophysically accurate neuron simulation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
State-of-the-art neuron simulators are capable of simulating at most few tens/hundreds of neurons in real-time due to the exponential growth in the communication costs with the number of simulated neurons. In this paper, we present a novel, reconfigu...