Pexophagy is a selective autophagy process that degrades damaged and/or superfluous peroxisomes in the yeast vacuole or in mammalian lysosomes. The molecular mechanisms of pexophagy are well studied in yeast. Peroxisomes can be rapidly induced by oleate in the budding yeast, Saccharomyces cerevisiae, and by oleate or methanol in the methylotrophic yeast, Pichia pastoris. A number of peroxisomal matrix enzymes, such as 3-ketoacyl CoA thiolase (thiolase) and alcohol oxidase (AOX), are upregulated correspondingly to meet metabolic demands of the cells. Removal of these peroxisome-inducing carbon sources creates conditions wherein peroxisomes are superfluous and results in pexophagy and the degradation of these peroxisomal matrix enzymes. In this chapter, we discuss different assays to monitor pexophagy in yeast. These assays rely on tracking the localization of the BFP-SKL protein (a peroxisomally targeted version of the blue fluorescent protein) by microscopy, biochemical analysis of the degradation of peroxisomal matrix proteins, thiolase and AOX, and/or measuring the reduction of AOX activity during pexophagy.

An optical laser trap with fluorescence excitation/emission capability has been integrated in a flow cytometric geometry for the study of microparticle confinement and off-axis fluorescence detection in laminar flow streams. Measurements of particle escape velocity, trapping efficiency, and fluorescence intensity are presented for 2 μm diameter dye-tagged latex microspheres in laminar flow streams having velocities of up to 12 mm/s. Experimental results are compared with theoretical values for flow velocity and fluorescence intensity and found to be in excellent agreement. © 1995 American Institute of Physics.

We report what is to our knowledge the first experimental demonstration and theoretical analysis of an optical laser trap that uses a pair of mutually phase-conjugate beams. A primary trapping beam derived from an argon laser (514.5 nm) together with its counterpropagating phase-conjugate beam creates a self-aligned dual-beam laser trap that provides stable three-dimensional confinement for micrometer-sized dielectric particles. The transverse trapping efficiency, experimentally measured for low-numerical-aperture (N.A. 0.40-0.85) objective lenses, is found to be comparable with that produced by a single-beam gradient force trap. A theoretical analysis, which compares the performance of the self-aligned dual-beam trap against that of single-beam gradient force and conventional counterpropagating dual-beam laser traps, shows that phase-conjugate trapping provides a slight improvement in axial trapping efficiency over the other trapping geometries. The advantages of combining laser trapping with photorefractive optical phase conjugation for simultaneous sample micromanipulation and optical image processing are discussed. © 1997 Optical Society of America.

A novel data-model-fusion prognostic framework is developed in this paper to improve the accuracy of system state long-horizon forecasting. This framework strategically integrates the strengths of the data-driven prognostic method and the model-based particle filtering approach in system state prediction while alleviating their limitations. In the proposed methodology, particle filtering is applied for system state estimation in parallel with parameter identification of the prediction model (with unknown parameters) based on Bayesian learning. Simultaneously, a data-driven predictor is employed to learn the system degradation pattern from history data so as to predict system evolution (or future measurements). An innovative feature of the proposed fusion prognostic framework is that the predicted measurements (with uncertainties) from the data-driven predictor will be properly managed and utilized by the particle filtering to further update the prediction model parameters, thereby enabling markedly better prognosis as well as improved forecasting transparency. As an application example, the developed fusion prognostic framework is employed to predict the remaining useful life of lithium ion batteries through electrochemical impedance spectroscopy tests. The investigation results demonstrate that the proposed fusion prognostic framework is an effective forecasting tool that can integrate the strengths of both the data-driven method and the particle filtering approach to achieve more accurate state forecasting. © 2012 Elsevier Ltd. All rights reserved.