Biology subjects

Liphardt, J.

Publications and source records attributed to Liphardt, J..

3 recordsLinked to original sources

bioRxiv2021

Design considerations of a wearable electronic-skin for mental health and wellness: balancing biosignals and human factors

Chronic stress has been associated with a variety of pathophysiological risks including developing mental illness. Conversely, appropriate stress management, can be used to foster mental wellness proactively. Yet, there is no existing method that accurately and objectively monitors stress. With recent advances in electronic-skin (e-skin) and wearable technologies, it is possible to design devices that continuously measure physiological parameters linked to chronic stress and other mental health and wellness conditions. However, the design approach should be different from conventional wearables due to considerations like signal-to-noise ratio and the risk of stigmatization. Here, we present a multi-part study that combines user-centered design with engineering-centered data collection to inform future design efforts. To assess human factors, we conducted an n=24 participant design probe study that examined perceptions of an e-skin for mental health and wellness as well as preferred wear locations. We complement this with an n=10 and n=16 participant data collection study to measure physiological signals at several potential wear locations. By balancing human factors and biosignals, we conclude that the upper arm and forearm are optimal wear locations.

bioengineering↗

bioRxiv2019

NuSeT: A Deep Learning Tool for Reliably Separating and Analyzing Crowded Cells

Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that may be tightly packed is a major bottleneck in standard deep learning-based models. We report a Nuclear Segmentation Tool (NuSeT) based on deep learning that accurately segments nuclei across multiple types of fluorescence imaging data. Using a hybrid network consisting of U-Net and Region Proposal Networks (RPN), followed by a watershed step, we have achieved superior performance in detecting and delineating nuclear boundaries in 2D and 3D images of varying complexities. By using foreground normalization and additional training on synthetic images containing non-cellular artifacts, NuSeT improves nuclear detection and reduces false positives. NuSeT addresses common challenges in nuclear segmentation such as variability in nuclear signal and shape, limited training sample size, and sample preparation artifacts. Compared to other segmentation models, NuSeT consistently fares better in generating accurate segmentation masks and assigning boundaries for touching nuclei.

bioinformatics↗

bioRxiv2017

A fluorogenic nanobody array tag for prolonged single molecule imaging in live cells

Prolonged single molecule imaging in live cells requires labels that do not aggregate, have high contrast, and are photo-stable. To address these requirements, we have generated arrays of modular protein domains that function as fluorophore recruitment platforms. ArrayG, a linear repeat of GFP-nanobodies, recruits free monomeric wild-type GFP, which brightens ~15-fold upon binding the array. The fluorogenic ArrayG tag effectively eliminates background fluorescence from free binders, a major impediment to high-throughput acquisition of long trajectories in recruitment based imaging strategies. The photo-stability of ArrayG and consistently low background made it possible to continuously track single integrins for as long as 105 seconds (2100 frames). Prolonged tracking of both kinesin and integrin revealed repeated state-switching events, a measurement capability that is crucial to a mechanistic understanding of complex cellular processes. We also report an orthogonal array tag, based on a DHFR-nanobody, for prolonged dual color imaging of single molecules.

biophysics↗