Exosomes play an important role in numerous cellular processes. Fundamental study and practical use of exosomes are significantly constrained by the lack of analytical tools capable of physical and biochemical characterization. In this paper, we present an optical approach capable of imaging single exosomes in a label-free manner, using interferometric plasmonic microscopy. We demonstrate monitoring of the real-time adsorption of exosomes onto a chemically modified Au surface, calculating the image intensity, and determining the size distribution. The sizing capability enables us to quantitatively measure the membrane fusion activity between exosomes and liposomes. We also report the recording of the dynamic interaction between exosomes and antibodies at the single-exosome level, and the tracking of hit-stay-run behavior of exosomes on an antibody-coated surface. We anticipate that the proposed method will contribute to clinical exosome analysis and to the exploration of fundamental issues such as the exosome–antibody binding kinetics.
Extracellular vesicles (EVs), including exosomes, are promising circulating biomarkers for disease diagnosis. Conventional EVs analysis requires multiple instrumentations to obtain their phenotypic features, which limits its wide applications. Here, we present a plasmonic biosensor technology for multifunctional analysis of EVs. The system is based on a functionalized surface plasmon resonance (SPR) biosensor and an advanced plasmonic microscopy to capture and image EVs at single-particle level. SPR images are processed with a home-developed deep learning algorithm to identify EVs and quantify image intensity automatically. By combining immunosensing and single particle analysis, this approach enables both physical and chemical characterization of EVs. As a proof-of-concept, we applied it to analyze EVs secreted from human lung cancer A549 cell lines. Results show the capabilities in the detection of size, concentration and affinity constant. Due to the single particle imaging and multifunctional analysis capability, we anticipate that this technology will find use in clinical and scientific applications.
We introduce a general method to approximate the convolution of a program with a Gaussian kernel. This results in the program being smoothed. Our compiler framework models intermediate values in the program as random variables, by using mean and variance statistics. We decompose the input program into atomic parts and relate the statistics of the different parts of the smoothed program. We give several approximate smoothing rules that can be used for the parts of the program. These include an improved variant of Dorn et al. [DBLW15], a novel adaptive Gaussian approximation, Monte Carlo sampling, and compactly supported kernels. Our adaptive Gaussian approximation handles multivariate Gaussian distributed inputs, gives exact results for a larger class of programs than previous work, and is accurate to the second order in the standard deviation of the kernel for programs with certain analytic properties. Because each expression in the program can have multiple approximation choices, we use a genetic search to automatically select the best approximations. We apply this framework to the problem of automatically bandlimiting procedural shader programs. We evaluate our method on a variety of geometries and complex shaders, including shaders with parallax mapping, animation, and spatially varying statistics. The resulting smoothed shader programs outperform previous approaches both numerically and aesthetically.
Nearly every commodity imaging system we directly interact with, or indirectly rely on, leverages power efficient, application-adjustable black-box hardware image signal processing (ISPs) units, running either in dedicated hardware blocks, or as proprietary software modules on programmable hardware. The configuration parameters of these black-box ISPs often have complex interactions with the output image, and must be adjusted prior to deployment according to application-specific quality and performance metrics. Today, this search is commonly performed manually by "golden eye" experts or algorithm developers leveraging domain expertise. We present a fully automatic system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i.e., application-specific) metric. We leverage a differentiable mapping between the configuration space and evaluation metrics, parameterized by a convolutional neural network that we train in an end-to-end fashion with imaging hardware in-the-loop. Unlike prior art, our differentiable proxies allow for high-dimension parameter search with stochastic first-order optimizers, without explicitly modeling any lower-level image processing transformations. As such, we can efficiently optimize black-box image processing pipelines for a variety of imaging applications, reducing application-specific configuration times from months to hours. Our optimization method is fully automatic, even with black-box hardware in the loop. We validate our method on experimental data for real-time display applications, object detection, and extreme low-light imaging. The proposed approach outperforms manual search qualitatively and quantitatively for all domain-specific applications tested. When applied to traditional denoisers, we demonstrate that---just by changing hyperparameters---traditional algorithms can outperform recent deep learning methods by a substantial margin on recent benchmarks.
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