Super-resolution structured illumination microscopy (SR-SIM) provides an up to two-fold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data, e.g. as a result of low excitation power or low exposure times, result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high quality reconstructed images. A residual encoding-decoding convolution neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the entirely deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well over various noise levels. The combination of computational reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change.
Planar photonic waveguides enable ultrathin sample illumination in fluorescence microscopy over exceedingly large fields of view. Their fabrication has been based on hard coatings requiring sputter deposition and ion-beam lithography, making volume production cumbersome and thereby limiting their availability. Additionally, they are typically fabricated on top of opaque silicon wafers, which restricts the use to upright microscopes. Here, we present a low-cost photonic waveguide chip based on standard 170 μm thick glass coverslips coated with a micrometer-thin layer of EpoCore, a photoresist polymer with a high refractive index. These chips enable wide-field excitation for fluorescence microscopy and nanoscopy on inverted microscopes using fluorescence detection through the transparent substrate. Channel waveguides with varying widths provide uniform near-field illumination by evanescent waves for fields of view up to the millimeter scale. We demonstrate multicolor fluorescence excitation resulting in high-contrast immunofluorescence and super-resolution imaging with a resolution of approximately 60 nm.
Seismic waves created during explosions are transmitted in an outward direction via the surrounding medium, creating a seismic effect that compromises the security of facilities. The energy released during explosions forms dynamic pressure, which creates gas pressure-induced blast waves that cause the ground to vibrate. The damage extent and influence of a blast are dependent on the energy released by the blast shock waves. Blast waves influence the stability of materials. Therefore, controlling vibration hazards is imperative in ensuring material security. This study investigated the effect of explosion-induced vibrations on the surface of a leveled landform. Changes in dynamic load over time were analyzed by conducting numerical simulations and actual onsite experiments. The Multi-Material Arbitrary Lagrangian-Eulerian algorithm were employed to develop a structural model for coupling fluid with solid grids, which was used to analyze the ground acceleration induced by the blasting effect. The results were used to determine the appropriate distance from which vibration reduction, disaster prevention, and safety protection can be achieved.
Super-resolution structured illumination microscopy (SR-SIM) pro- vides an up to two-fold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data, e.g. as a result of low excitation power or low exposure times, result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high quality reconstructed images. A residual encoding-decoding convolution neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the entirely deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well over various noise levels. The combination of computational reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change.
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