Author: Scheetz, Marc H. ; Hauser, Alan R. ; Ward, Timothy ; Lee, Joanne ; Prashanth, Prarthana ; VanGosen, Ethan ; Valdes, Aliki ; Nozick, Sophie ; Niki, Issay ; Pincus, Nathan B. ; Nelson, Julia ; Kochan, Travis J. ; Cheng, William ; Mitra, Sumitra D. ; Bachta, Kelly E. R. ; Hynes, David ; Turner, Timothy L. ; Axline, Christopher M. R. ; Afzal, Tania

ABSTRACT Larvae of Galleria mellonella (the greater wax moth) are being increasingly used as a model to study microbial pathogenesis. In this model, bacterial virulence is typically measured by determining the 50% lethal dose (LD 50 ) of a bacterial strain or mutant. The use of G. mellonella to study Pseudomonas aeruginosa pathogenesis, however, is challenging because of the extreme sensitivity of larvae to this bacterium. For some P. aeruginosa strains, as few as 1–5 colony-forming units are sufficient to kill G. mellonella, which poses challenges for determining LD 50 values. For this reason, some groups have used time-to-death as a measure of P. aeruginosa virulence, but methodologies have not been standardized. We provide a detailed protocol for using the time at which 50% of larvae have died (LT 50 ) at a particular inoculum as a measure of P. aeruginosa virulence. We also describe a quality control metric for enhancing the reproducibility of LT 50 values. This approach provides an accurate and reproducible methodology for using G. mellonella larvae to measure and compare the virulence of P. aeruginosa strains. IMPORTANCEPseudomonas aeruginosa is a significant cause of morbidity and mortality. The invertebrate Galleria mellonella is used as a model to determine the virulence of P. aeruginosa strains. We provide a protocol and analytical approach for using a time-to-death metric to accurately quantify the virulence of P. aeruginosa strains in G. mellonella larvae. This methodology, which has several advantages over 50% lethal dose approaches, is a useful resource for the study of P. aeruginosa pathogenicity.

02 Jan 2025·International Journal of Occupational Safety and Ergonomics

Differentiating hand gestures from forearm muscle activity using machine learning

Article

Author: Hwang, Jaejin ; Puli, Sunil ; Cho, Ryan

This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the algorithm accuracies increased with RF from 85% to 97% due to the increased temporal resolution. It was also noticed that the RF performed better with an accuracy of 85% than the NN with accuracy 80% when the temporal resolution was smaller, indicating the RF will be efficient when quick-response time is important. As the window size increases, the NN showed higher performance, suggesting that NN will be useful when higher accuracy is required. Future studies should increase the sample size, include more hand gestures, use different feature extraction methods and test different algorithms to improve the accuracy and efficiency of the system.

03 Dec 2024·2024 IEEE Virtual Conference on Communications (VCC)

Evaluation of Spectrum Sharing Algorithms for Networks with Heterogeneous Wireless Devices

Author: Walishetti, Ankit ; Ward, Colin ; Berry, Randall ; Kim, Aidan ; Gutierrez, Eduardo ; Kadota, Igor

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