Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

In fluorescence microscopy,computational algorithms have been developed to suppress noise,enhance contrast,and even enable super-resolution(SR).However,the local quality of the images may vary on multiple scales,and these differences can lead to misconceptions.Current mapping methods fail to finely estimate the local quality,challenging to associate the SR scale content.Here,we develop a rolling Fourier ring correlation(rFRC)method to evaluate the reconstruction uncertainties down to SR scale.To visually pinpoint regions with low reliability,a filtered rFRC is combined with a modified resolution-scaled error map(RSM),offering a comprehensive and concise map for further examination.We demonstrate their performances on various SR imaging modalities,and the resulting quantitative maps enable better SR images integrated from different reconstructions.Overall,we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

High loading cotton cellulose-based aerogel self-standing electrode for Li-S batteries

Lithium-sulfur(Li-S) batteries have attracted considerable attention due to their high energy density(2600 Wh kg-1). However, its commercialization is hindered seriously by the low loading and utilization rate of sulfur cathodes. Herein, we designed the cellulose-based graphene carbon composite aerogel(CCA) self-standing electrode to enhance the performance of Li-S batteries. The CCA contributes to the mass loading and utilization efficiency of sulfur, because of its unique physical structure: low density(0.018 g cm-3), large specific surface area(657.85 m2 g-1), high porosity(96%), and remarkable electrolyte adsorption(42.25 times). Compared to Al(about 49%), the CCA displayed excellent sulfur use efficiency(86%) and could reach to high area capacity of 8.60 mAh cm-2 with 9.11 mgS loading. Meanwhile,the CCA exhibits the excellent potential for pulse sensing applications due to its flexibility and superior sensitivity to electrical response signals.

Super-resolution fluorescence-assisted diffraction computational tomography reveals the threedimensional landscape of the cellular organelle interactome

The emergence of super-resolution(SR)fluorescence microscopy has rejuvenated the search for new cellular substructures.However,SR fluorescence microscopy achieves high contrast at the expense of a holistic view of the interacting partners and surrounding environment.Thus,we developed SR fluorescence-assisted diffraction computational tomography(SR-FACT),which combines label-free three-dimensional optical diffraction tomography(ODT)with two-dimensional fluorescence Hessian structured illumination microscopy.The ODT module is capable of resolving the mitochondria,lipid droplets,the nuclear membrane,chromosomes,the tubular endoplasmic reticulum,and lysosomes.Using dual-mode correlated live-cell imaging for a prolonged period of time,we observed novel subcellular structures named dark-vacuole bodies,the majority of which originate from densely populated perinuclear regions,and intensively interact with organelles such as the mitochondria and the nuclear membrane before ultimately collapsing into the plasma membrane.This work demonstrates the unique capabilities of SR-FACT,which suggests its wide applicability in cell biology in general.

3D Hessian deconvolution of thick light-sheet z-stacks for high-contrast and high-SNR volumetric imaging

Due to its ability of optical sectioning and low phototoxicity,z-stacking light-sheet microscopy has been the tool of choice for in vivo imaging of the zebrafish brain.To image the zebrafish brain with a large field of view,the thickness of the Gaussian beam inevitably becomes several times greater than the system depth of field(DOF),where the fluorescence distributions outside the DOF will also be collected,blurring the image.In this paper,we propose a 3D deblurring method,aiming to redistribute the measured intensity of each pixel in a light-sheet image to in situ voxels by 3D deconvolution.By introducing a Hessian regularization term to maintain the continuity of the neuron dis-tribution and using a modified stripe removal algorithm,the reconstructed z stack images exhibit high contrast and a high signal-to-noise ratio.These performance characteristics can facilitate subsequent processing,such as 3D neuron registration,segmentation,and recognition.

ADAPTIVE CHOICE OF THE REGULARIZATION PARAMETER IN NUMERICAL DIFFERENTIATION

We investigate a novel adaptive choice rule of the Tikhonov regularization parameter in numerical differentiation which is a classic ill-posed problem. By assuming a general unknown Holder type error estimate derived for numerical differentiation, we choose a regularization parameter in a geometric set providing a nearly optimal convergence rate with very limited a-priori information. Numerical simulation in image edge detection verifies reliability and efficiency of the new adaptive approach.