Virtual-scanning light-field microscopy for robust snapshot high-resolution volumetric imaging.

High-speed three-dimensional (3D) intravital imaging in animals is useful for studying transient subcellular interactions and functions in health and disease. Light-field microscopy (LFM) provides a computational solution for snapshot 3D imaging with low phototoxicity but is restricted by low resolution and reconstruction artifacts induced by optical aberrations, motion and noise. Here, we propose virtual-scanning LFM (VsLFM), a physics-based deep learning framework to increase the resolution of LFM up to the diffraction limit within a snapshot. By constructing a 40 GB high-resolution scanning LFM dataset across different species, we exploit physical priors between phase-correlated angular views to address the frequency aliasing problem. This enables us to bypass hardware scanning and associated motion artifacts. Here, we show that VsLFM achieves ultrafast 3D imaging of diverse processes such as the beating heart in embryonic zebrafish, voltage activity in Drosophila brains and neutrophil migration in the mouse liver at up to 500 volumes per second.

Exploring a New Method to Study the Effects of Surface Functional Groups on Adsorption of CO2 and CH4 on Activated Carbons.

The commercial coconut shell-activated carbon was modified to change the number of oxygen-containing functional groups. N2 adsorption/desorption isotherms, Fourier transform infrared (FT-IR), and Boehm titration were adopted to describe the physical and chemical properties of the samples. The adsorption isotherms of CO2 and CH4 on both the unmodified and modified samples were measured. To better understand the effects of surface oxygen-containing functional groups on adsorption of CO2 and CH4, the overall adsorption could be considered as the result of adsorption within the pores and adsorption onto the oxygen-containing functional groups. Thus, a new way to understand different adsorption mechanisms by calculation was proposed. On the basis of the results, there is a significant correlation between the saturation adsorption capacity of CO2 and the number of oxygen-containing functional groups, especially carboxyl and hydroxyl. According to the values of enthalpy (-12.2 to -20 kJ/mol), it can be known that the adsorption caused by oxygen-containing functional groups is exothermic and belongs to physisorption. A semiempirical relationship between the variation of the surface oxygen-functional groups and the variation of the adsorbed amount was established. The method proposed in this paper provides a new way to study the effects of surface functional groups on the adsorption of CO2 and CH4 and can be even promoted in studying the adsorption mechanism of other adsorbates.

Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy.

Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.

The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.

Characterization of typical heavy metals in pyrolysis MSWI fly ash.

Thermal treatment methods are used extensively in the process of municipal solid waste incineration fly ash. However, the characterization of heavy metals during this process should be understood more clearly in order to control secondary pollution. In this paper, the content, speciation and leaching toxicity of mercury (Hg), plumbum (Pb), cadmium (Cd) and zinc (Zn) in fly ash treated under different temperatures and time were firstly analysed as pre-tests. Later, pilot-scale pyrolysis equipment was used to explore the concentration and speciation changes in the heavy metals of fly ash. Finally, the phase constitution and microstructure changes in fly ash were compared before and after pyrolysis using X-ray diffraction (XRD) and scanning electron microscope (SEM), respectively. The results showed that (a) The appropriate processing temperature was between 400°C and 450°C, and the processing time should be 1 h. (b) The stability of heavy metals in fly ash increased after pyrolysis. (c) XRD and SEM results indicated that phase constitution changed a little, but the microstructure varied to a porous structure similar to that of a coral reef after pyrolysis. These results suggest that pyrolysis could be an effective method in controlling heavy metal pollution in fly ash.