Bradley Department of Electrical and Computer Engineering

Permanent URI for this community

https://hdl.handle.net/10919/18738
From pervasive computing, to smart power systems, Virginia Tech ECE faculty and students delve into all major areas of electrical and computer engineering. The main campus is in Blacksburg, and the department has additional research and teaching facilities in Arlington, Falls Church, and Hampton, Virginia.

Browse

Browsing Bradley Department of Electrical and Computer Engineering by Subject "06 Biological Sciences"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements
    Chen, Xi; Neuwald, Andrew F.; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua (PLoS, 2021-07-01)
    Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIPseq profiling of multiple TFs makes it feasible to infer functional TF modules. However, when inferring TF modules based on co-localization of ChIP-seq peaks, often many weak binding events are missed, especially for mediators, resulting in incomplete identification of modules. To address this problem, we develop a ChIP-seq data-driven Gibbs Sampler to infer Modules (ChIP-GSM) using a Bayesian framework that integrates ChIP-seq profiles of multiple TFs. ChIP-GSM samples read counts of module TFs iteratively to estimate the binding potential of a module to each region and, across all regions, estimates the module abundance. Using inferred module-region probabilistic bindings as feature units, ChIP-GSM then employs logistic regression to predict active regulatory elements. Validation of ChIPGSM predicted regulatory regions on multiple independent datasets sharing the same context confirms the advantage of using TF modules for predicting regulatory activity. In a case study of K562 cells, we demonstrate that the ChIP-GSM inferred modules form as groups, activate gene expression at different time points, and mediate diverse functional cellular processes. Hence, ChIP-GSM infers biologically meaningful TF modules and improves the prediction accuracy of regulatory region activities.
  • Thumbnail Image
    Modeling the Metabolic Reductions of a Passive Back-Support Exoskeleton
    Alemi, Mohammad Mehdi; Simon, Athulya A.; Geissinger, Jack H.; Asbeck, Alan T. (2022-01-13)
    Despite several attempts to quantify the metabolic savings resulting from the use of passive back-support exoskeletons (BSEs), no study has modeled the metabolic change while wearing an exoskeleton during lifting. The objectives of this study were to: 1) quantify the metabolic reductions due to the VT-Lowe's exoskeleton during lifting; and 2) provide a comprehensive model to estimate the metabolic reductions from using a passive BSE. In this study, 15 healthy adults (13M, 2F) of ages 20 to 34 years (mean=25.33, SD=4.43) performed repeated freestyle lifting and lowering of an empty box and a box with 20% of their bodyweight. Oxygen consumption and metabolic expenditure data were collected. A model for metabolic expenditure was developed and fitted with the experimental data of two prior studies and the without-exoskeleton experimental results. The metabolic cost model was then modified to reflect the effect of the exoskeleton. The experimental results revealed that VT-Lowe's exoskeleton significantly lowered the oxygen consumption by ~9% for an empty box and 8% for a 20% bodyweight box, which corresponds to a net metabolic cost reduction of ~12% and ~9%, respectively. The mean metabolic difference (i.e., without-exo minus with-exo) and the 95% confidence interval were 0.36 and (0.2-0.52) [Watts/kg] for 0% bodyweight, and 0.43 and (0.18-0.69) [Watts/kg] for 20% bodyweight. Our modeling predictions for with-exoskeleton conditions were precise, with absolute freestyle prediction errors of <2.1%. The model developed in this study can be modified based on different study designs, and can assist researchers in enhancing designs of future lifting exoskeletons.