Microfluidics deals with small amounts of fluids at the microscale. Miniaturization brings in many advantages such as reduced reagent use, shortened analysis time, increased throughput, and portability, making it an ideal enabling platform for a wide range of applications such as life science research, drug screening, and assistive technology. This talk summarizes Ren’s work on microfluidics which is dedicated to droplet microfluidics, protein fractionation, microwave sensing, and soft wearable robots for well-being. Droplet microfluidics will focus on unique active control of individual droplets with visual feedback, protein fractionation techniques will include isoelectric focusing, counterflow gradient focusing and pH elution, microwave sensing will discuss its application to metal, plastic and virus sensing, and soft robots will discuss low-cost systems for treating breast cancer-related lymphedema and providing a personalized fit of the prosthetic socket for amputees.
The study of yeast cell morphology requires consistent identification of cell cycle phases based on cell bud size. A computer-based image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The images were enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracy of various machine-learning classifiers is compared. The linear support vector machine, distance-based classification, and k-nearest-neighbor algorithm were the classifiers used in this experiment. The performance of the system under various illumination and focusing conditions were also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images.
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