Calculating transmembrane voltage on the electric pulse-affected cancerous cell membrane: using molecular dynamics and finite element simulations.

Context: Electroporation is a technique that creates electrically generated pores in the cell membrane by modifying transmembrane potential. In this work, the finite element method (FEM) was used to examine the induced transmembrane voltage (ITV) of a spherical-shaped MCF-7 cell, allowing researchers to determine the stationary ITV. A greater ITV than the critical value causes permeabilization of the membrane.

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA.

With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper, we approach the forecasting task with an alternative technique-spatiotemporal machine learning.

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions.

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19.

Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms.

Objective: Although lower respiratory infections (LRI) are among the leading causes of mortality in the US, their association with underlying factors and geographic variation have not been adequately examined.

Methods: In this study, explanatory variables (n = 46) including climatic, topographic, socio-economic, and demographic factors were compiled at the county level across the continentalUS.Machine learning algorithms - logistic regression (LR), random forest (RF), gradient boosting decision trees (GBDT), k-nearest neighbors (KNN), and support vector machine (SVM) - were employed to predict the presence/absence of hotspots (P < 0.

Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States.

Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country.