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Biology subjects

Mohammadi, R.

Publications and source records attributed to Mohammadi, R..

2 recordsLinked to original sources

Experiments in micro-patterned model membranes support the narrow escape theory

The narrow escape theory (NET) predicts the escape time distribution of Brownian particles confined to a domain with reflecting borders except for one small window. Applications include molecular activation events in cell biology and biophysics. Specifically, the mean first passage time [Formula] can be analytically calculated from the size of the domain, the escape window, and the diffusion coefficient of the particles. In this study, we systematically tested the NET in a disc by variation of the escape opening. Our model system consisted of micro-patterned lipid bilayers. For the measurement of [Formula], we imaged diffusing fluorescently-labeled lipids using single-molecule fluorescence microscopy. We overcame the lifetime limitation of fluorescent probes by re-scaling the measured time with the fraction of escaped particles. Experiments were complemented by matching stochastic numerical simulations. To conclude, we confirmed the NET prediction in vitro and in silico for the disc geometry in the limit of small escape openings. Significance StatementIn the biological context of a cell, a multitude of reactions are facilitated by diffusion. It is astonishing how Brownian motion as a cost-efficient but random process is mediating especially fast reactions. The formalism of the narrow escape theory is a tool to determine the average timescale of such processes to be completed (mean first passage time, MFPT) from the reaction space and diffusion coefficient. We present the systematic proof of this formalism experimentally in a bio-mimetic model system and by random walk simulations. Further, we demonstrate a straightforward solution to determine the MFPT from incomplete experimental traces. This will be beneficial for measurements of the MFPT, reliant on fluorescent probes, that have prior been inaccessible.

biophysics↗

A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers

It is well established that lack of physical activity is detrimental to overall health of an individual. Modern day activity trackers enable individuals to monitor their daily activity to meet and maintain targets and to promote activity encouraging behavior. However, the benefits of activity trackers are attenuated over time due to waning adherence. One of the key methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics. In this work we developed a machine learning model to dynamically adjust the activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. This model prescribes activity target for the forthcoming week. We considered individual user-specific personal, social, and environmental factors, daily step count through the current week (7 days). In addition, we computed an entropy measure that characterizes the pattern of daily step count for the current week. Data for training the machine learning model was collected from 30 participants over a duration of 9 weeks. The model predicted target daily count with mean absolute error of 1545 steps. The proposed work can be used to set personalized goals in accordance with the individuals level of activity and thereby improving adherence to fitness tracker.

bioinformatics↗