A Note on the Single Genotype Resolution Problem

This note settles the complexity of the single genotype resolution problemshowing it is NP-complete. This solves an open problem raised by P. Bonizzoni, G.D. Vedova, R.Dondi, and J. Li. The same proof also gives an alternative and simpler reduction of the NP-hardnessof Maximum Resolution problem.

Visualization of atomic scale reaction dynamics of supported nanocatalysts during oxidation and ammonia synthesis using in-situ environmental(scanning) transmission electron microscopy

Reaction dynamics in gases at operating temperatures at the atomic level are the basis of heterogeneous gas-solid catalyst reactions and are crucial to the catalyst function.Supported noble metal nanocatalysts such as platinum are of interest in fuel cells and as diesel oxidation catalysts for pollution control,and practical ruthenium nanocatalysts are explored for ammonia synthesis.Graphite and graphitic carbons are of interest as supports for the nanocatalysts.Despite considerable literature on the catalytic processes on graphite and graphitic supports,reaction dynamics of the nanocatalysts on the supports in different reactive gas environments and operating temperatures at the single atom level are not well understood.Here we present real time in-situ observations and analyses of reaction dynamics of Pt in oxidation,and practical Ru nanocatalysts in ammonia synthesis,on graphite and related supports under controlled reaction environments using a novel in-situ environmental(scanning) transmission electron microscope with single atom resolution.By recording snapshots of the reaction dynamics,the behaviour of the catalysts is imaged.The images reveal single metal atoms,clusters of a few atoms on the graphitic supports and the support function.These all play key roles in the mobility,sintering and growth of the catalysts.The experimental findings provide new structural insights into atomic scale reaction dynamics,morphology and stability of the nanocatalysts.

Continuous subcellular resolution three-dimensional imaging on intact macaque brain

To decipher the organizational logic of complex brain circuits,it is important to chart long-distance pathways while preserving micron-level accuracy of local network.However,mapping the neuronal projections with individual-axon resolution in the large and complex primate brain is still challenging.Herein,we describe a highly efficient pipeline for three-dimensional mapping of the entire macaque brain with subcellular resolution.The pipeline includes a novel poly-N-acryloyl glycinamide(PNAGA)-based embedding method for long-term structure and fluorescence preservation,high-resolution and rapid whole-brain optical imaging,and image post-processing.The cytoarchitectonic information of the entire macaque brain was acquired with a voxel size of 0.32μm×0.32μm×10μm,showing its anatomical structure with cell distribution,density,and shape.Furthermore,thanks to viral labeling,individual long-distance projection axons from the frontal cortex were for the first time reconstructed across the entire brain hemisphere with a voxel size of 0.65μm×0.65μm×3μm.Our results show that individual cortical axons originating from the prefrontal cortex simultaneously target multiple brain regions,including the visual cortex,striatum,thalamus,and midbrain.This pipeline provides an efficient method for cellular and circuitry investigation of the whole macaque brain with individual-axon resolution,and can shed light on brain function and disorders.

Adaptive Densely Residual Network for Image Super-Resolution

Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without consider-ing different contributions of different depth features to the network,these de-signs have the problem of evaluating the importance of different depth features.To solve this problem,this paper proposes an adaptive densely residual net-work(ADRNet)for the single image super resolution.ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features.An adaptive densely residual block(ADRB)was designed,combining 3 residual blocks(RB)and dense connection was added.It learned the attention score of each dense connection through adaptive dense connections,and the at-tention score reflected the importance of the features of each RB.To further en-hance the performance of ADRB,a multi-direction attention block(MDAB)was introduced to obtain multidirectional context information.Through comparative experiments,it is proved that theproposed ADRNet is superior to the existing methods.Through ablation experiments,it is proved that evaluating features of different depths helps to improve network performance.