Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales would be an invaluable tool in biomedicine. However, conventional imaging modalities have stark tradeoffs precluding the fulfilment of all functional requirements.Here we propose the refractive index (RI), an intrinsic quantity governing light-matter interaction, as a means for such measurement. We show that major endogenous subcellular structures, which are conventionally accessed via exogenous fluorescence labeling, are encoded in 3D RI tomograms. We decode this information in a data-driven manner, thereby achieving multiplexed microtomography. This approach inherits the advantages of both highspecificity fluorescence imaging and label-free RI imaging. The performance, reliability, and scalability of this technology have been extensively characterized, and its application within single-cell profiling at unprecedented scales has been demonstrated..
The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.
We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising.
SummaryThe kinetics of the enzymatic hydrolysis of sucrose by invertase have been examined, with particular emphasis on high substrate concentration. Initial rates of reaction were determined by following the production of glucose directly as a function of time over a wide range of substrate concentrations (0.04M to 2.06M). The resulting data reveal a reaction rate that increases gradually until the sucrose concentration reaches about 0.2921.3, after which the reaction velocity decreases with increasing sucrose concentration. Previous workers (e.g., Xelson and Schubertl) have reported a peak reaction velocity as determined by indirect polarimetric measurements of glucose, a t a sucrose concentration of about 0.17M. These measurements, however, neglect the intermediate oligosaccharides formed by the transferase action of invertase,8-lo and assume equal amounts of glucose and fructose. According to Anderson et u1.,Io these oligosaccharides interfere by producing an erroneously low reaction rate. Experimental results of this work confirm Anderson's observations, and show a further reaction rate increase of nearly 20% between sucrose concentrations of 0.177M and 0.285M under the same conditions of temperature, pH, and enzyme concentration.Effects of substrate diffusion, solution viscosity, water concentration, and substrate inhibition were experimentally studied and the results incorporated into a kinetic model that has proven satisfactory in modeling the experimental results. This model takes into account inhibition by primary substrate, with concentration of the secondary substrate water, as a rate limiting factor at sucrose concentrations greater than 0.285M.The effects of mixing, in terms of volumetric power input, on the reaction rate have been tested. Approximately 40-fold increase in volumetric power input caused no increase in the reaction rate. These experiments have shown that bulk mass transfer is not a rate limiting factor under the experimental conditions.
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