Abstract:This review presents an extensive overview of a large number of microvalve and micropump designs with great variability in performance and operation. The performance of a given design varies greatly depending on the particular assembly procedure and there is no standardized performance test against which all microvalves and micropumps can be compared. We present the designs with a historical perspective and provide insight into their advantages and limitations for biomedical uses.
We demonstrate residual channel attention networks (RCAN) for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data. First, we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy 4D super-resolution data, enabling image capture over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables class-leading resolution enhancement, superior to other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion (STED) microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy ground truth, achieving improvements of ~1.4-fold laterally and ~3.4-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.data, which we deconvolved to yield high SNR 'ground truth'. We then used 30 of these volumes for training and held out volumes for testing network performance. Using the same training and test data, we compared four networks: RCAN, CARE, SRResNET 20 , and ESRGAN 21 . SRResNet and ESRGAN are both class-leading deep residual networks used in image super-resolution, with ESRGAN winning the 2018 Perceptual Image Restoration and Manipulation challenge on perceptual image super-resolution 22 .For the mEmerald-Tomm20 label, RCAN, CARE, ESRGAN, and SRResNET predictions all provided 88 clear improvements in visual appearance, structural similarity index (SSIM) and peak signal-to-noise-89 ratio (PSNR) metrics relative to the raw input (Fig. 1b), also outperforming direct deconvolution on the noisy input data (Supplementary Fig. 1). The RCAN output provided PSNR and SSIM values competitive with the other networks (Fig. 1b), prompting us to investigate whether this performance held for other organelles. We thus conducted similar experiments for fixed U2OS cells with labeled actin, endoplasmic reticulum (ER), golgi, lysosomes, and microtubules (Supplementary Fig. 2), acquiring 15-23 volumes of training data and training independent networks for each organelle. In almost all cases, RCAN performance met or exceeded the other networks (Supplementary Fig. 3, Supplementary Table 3).An essential consideration when using any deep learning method is understanding when network performance deteriorates. Independently training an ensemble of networks and computing measures of network disagreement can provide insight into this issue 9,16 , yet such measures were not generally predictive of disagreement between ground truth and RCAN output (Supplementary Fig. 4). Instead, we found that estimating the per-pixel SNR in the raw input (Methods, Supplementary Fig. 4) seemed to better correlate with network ...
Light-sheet microscopy has emerged as the preferred means for high-throughput volumetric imaging of cleared tissues. However, there is a need for a user-friendly system that can address imaging applications with varied requirements in terms of resolution (mesoscopic to sub-micrometer), sample geometry (size, shape, and number), and compatibility with tissue-clearing protocols and sample holders of various refractive indices. We present a 'hybrid' system that combines a novel non-orthogonal dual-objective and conventional (orthogonal) open-top light-sheet architecture for versatile multi-scale volumetric imaging. Main TextRecent advances in tissue-clearing protocols greatly reduce optical scattering, aberrations, and background uorescence, enabling deep-tissue imaging with high resolution and contrast. These approaches have yielded new insights in many elds, including neuroscience, developmental biology, and anatomic pathology [1][2][3][4][5][6][7][8][9][10][11]. Light-sheet microscopy has emerged as a preferred means for highresolution volumetric imaging of cleared tissues due to its unrivaled speed and low photobleaching [12,13]. Many variants of light-sheet microscopes have been developed in recent years by academic researchers and commercial entities to tackle a diverse range of imaging applications (Error! Reference source not found. and Error! Reference source not found.) [14][15][16][17][18]. Whereas individual light-sheet systems are well-suited for a subset of cleared-tissue applications, trade-offs are inevitable. In particular, no current light-sheet microscope can satisfy all of the following requirements: (1) user-friendly mounting
In this paper, we present a monolithic PDMS micropump that generates peristaltic flow using a single control channel that actuates a group of different-sized microvalves. An elastomeric microvalve design with a raised seat, which improves bonding reliability, is incorporated into the micropump. Pump performance is evaluated based on several design parameters—size, number, and connection of successive microvalves along with control channel pressure at various operating frequencies. Flow rates ranging 0–5.87 μL min−1 were observed. The micropump design demonstrated here represents a substantial reduction in the number of/real estate taken up by the control lines that are required to run a peristaltic pump, hence it should become a widespread tool for parallel fluid processing in high-throughput microfluidics.
We demonstrate residual channel attention networks (RCAN) for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data. First, we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy 4D super-resolution data, enabling image capture over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables class-leading resolution enhancement, superior to other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ∼2.5-fold lateral resolution enhancement using stimulated emission depletion (STED) microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy ground truth, achieving improvements of ∼1.4-fold laterally and ∼3.4-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.
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