Spatial Disparity of Meteorological Impacts on Carbon Monoxide Pollution in China during the COVID-19 Lockdown Period.

Lockdown due to the novel coronavirus disease 2019 (COVID-19) pandemic offers a unique opportunity to study the factors governing the variation in air pollution. A number of studies have investigated the cause underlying the occurrence of heavy haze pollution around the world during the lockdown period. However, information about spatiotemporal variations in gaseous pollutants and detailed quantifications of potential meteorological (METRO) impacts are limited.

Application of a modified biological flocculant in total nitrogen treatment of leather wastewater.

Leather wastewater harms the ecological environment and human health. In this study, a modified bio-flocculant was prepared to facilitate treatment of leather wastewater. A bio-flocculant produced by Bacillus cereus was combined with amphoteric starch and modified using a cerium ammonium nitrate initiator.

Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.

We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy.

Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens.

Isotropic 3D histological imaging of large biological specimens is highly desired but remains highly challenging to current fluorescence microscopy technique. Here we present a new method, termed deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM), to enable fast, isotropic light-sheet fluorescence imaging on a conventional wide-field microscope. After integrating a minimized add-on device that transforms an inverted microscope into a 3D light-sheet microscope, we further integrate a deep neural network (DNN) procedure to quickly restore the ambiguous z-reconstructed planes that suffer from still insufficient axial resolution of light-sheet illumination, thereby achieving isotropic 3D imaging of thick biological specimens at single-cell resolution.

Minutes-timescale 3D isotropic imaging of entire organs at subcellular resolution by content-aware compressed-sensing light-sheet microscopy.

Rapid 3D imaging of entire organs and organisms at cellular resolution is a recurring challenge in life science. Here we report on a computational light-sheet microscopy able to achieve minute-timescale high-resolution mapping of entire macro-scale organs. Through combining a dual-side confocally-scanned Bessel light-sheet illumination which provides thinner-and-wider optical sectioning of deep tissues, with a content-aware compressed sensing (CACS) computation pipeline which further improves the contrast and resolution based on a single acquisition, our approach yields 3D images with high, isotropic spatial resolution and rapid acquisition over two-order-of-magnitude faster than conventional 3D microscopy implementations.

Deep learning-enabled efficient image restoration for 3D microscopy of turbid biological specimens.

Though three-dimensional (3D) fluorescence microscopy has been an essential tool for modern life science research, the light scattering by biological specimens fundamentally prevents its more widespread applications in live imaging. We hereby report a deep-learning approach, termed ScatNet, that enables reversion of 3D fluorescence microscopy from high-resolution targets to low-quality, light-scattered measurements, thereby allowing restoration for a blurred and light-scattered 3D image of deep tissue. Our approach can computationally extend the imaging depth for current 3D fluorescence microscopes, without the addition of complicated optics.

High-throughput, high-resolution deep learning microscopy based on registration-free generative adversarial network.

We combine a generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, the neural network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. Its capacity has been broadly demonstrated via imaging various types of samples, such as USAF resolution target, human pathological slides, fluorescence-labelled fibroblast cells, and deep tissues in transgenic mouse brain, by both wide-field and light-sheet microscopes.