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Vicinal oxygen chelate (VOC) proteins are members of an enzyme superfamily with dioxygenase or non‐dioxygenase activities. However, the biological functions of VOC proteins in plants are poorly understood. Here, we show that a VOC in Nicotiana benthamiana (NbVOC1) facilitates viral infection. NbVOC1 was significantly induced by infection by beet necrotic yellow vein virus (BNYVV). Transient overexpression of NbVOC1 or its homolog from Beta vulgaris (BvVOC1) enhanced BNYVV infection in N. benthamiana, which required the nuclear localization of VOC1. Consistent with this result, overexpressing NbVOC1 facilitated BNYVV infection, whereas, knockdown and knockout of NbVOC1 inhibited BNYVV infection in transgenic N. benthamiana plants. NbVOC1 interacts with the basic leucine zipper transcription factors bZIP17/28, which enhances their self‐interaction and DNA binding to the promoters of unfolded protein response (UPR)‐related genes. We propose that bZIP17/28 directly binds to the NbVOC1 promoter and induces its transcription, forming a positive feedback loop to induce the UPR and facilitating BNYVV infection. Collectively, our results demonstrate that NbVOC1 positively regulates the UPR that enhances viral infection in plants. [ABSTRACT FROM AUTHOR]

Electrochemical water splitting offers an advancing approach to producing highly pure hydrogen and oxygen, motivated by the prevalence of a low‐carbon economy and the goal of a sustainable future. The customized modulation of electronic structures enables the electrocatalyst to directionally promote hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), which is a promising shortcut to overall water splitting (OWS). Herein, 3D homologous WSeS/CoSeS heterojunction nanoarrays (WSeS/CoSeS NAs) and W‐doped CoSeS nanoarrays (W‐CoSeS NAs) are investigated. Abundant heterointerfaces within WSeS/CoSeS NAs facilitate HER kinetics, boosting mass diffusivity, and increasing carrier separation and transfer process. High‐valence W6+ doping into CoSeS prevents phase separation and stabilizes Co sites by charge offset effect, leading to enhanced OER. Consequently, the WSeS/CoSeS NAs and W‐CoSeS NAs reach 10 mA cm−2 at an overpotential of 43.8 and 233.3 mV in 1.0 m KOH electrolyte for HER and OER, respectively. Moreover, when asymmetrically engaged as an electrolyzer, this configuration exhibits extraordinary electrocatalytic performances (cell voltage of 1.51 V at 10 mA cm−2) with satisfying stability and mechanical robustness (over 1000 h at 1000 mA cm−2). The modulation and manufacture of reaction‐property‐oriented materials are experimentally and theoretically validated potential, illuminating the light of inspiration for multiple applications. [ABSTRACT FROM AUTHOR]

As the most widely used supervised classification algorithm, K nearest neighbor (KNN) algorithm is often inefficient in the processing of large-scale and multidimensional data. Therefore, an improved KNN algorithm for high dimensional large data processing is proposed. Firstly, deep neural networks (DNN) is used as feature extractor and dimension reduction is carried out to learn the most appropriate representation form of depth feature. Then, the appropriate number of clusters and the initial clustering center, obtained through the density Canopy algorithm become the input parameters of the subsequent K-means clustering. Finally, the learned data are clustered, and Hashing strategy in approximate similarity search (ASS) is used to cluster partitioning according to its approximate similarity, and the result is token as a new training sample of KNN classifier. In addition, considering that the nearest neighbor samples to be searched may fall in different clusters and the performance of KNN search is reduced, an additional clustering enhancement strategy is adopted in clustering, which effectively alleviates this situation. Five different data sets are used for comparison test. The results show that, compared with the experimental algorithms, this algorithm can not only greatly improve the accuracy of KNN classification, but also effectively improves the classification efficiency of the algorithm, reduces the distance required for searching, and has good robustness for noise data.

A non‐precious metal‐based catalyst for water electrolysis provides great promise for cost‐effective and highly efficient sustainable hydrogen production. It herein rationally synthesizes uniform superminiature CoNi nanoparticles (2.6 nm) embedded in 3D N‐doped randomly oriented and erected porous carbon nanosheets (CoNi@N‐PCNS). Taking advantage of the large specific surface area, expedited intermediate transport, and effectively exposed active sites of the hierarchical architecture, located CoNi nanoparticles yield a high atom utilization efficiency. Density functional theory calculations indicate that synergetic and cooperative interactions inside CoNi alloy modulate the d‐band center, leading to a moderate adsorption and desorption energy of reaction intermediates, further accelerating both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) kinetics. Accordingly, the as‐synthesized CoNi@N‐PCNS catalyst establishes superb catalytic activities for HER and OER, revealing overpotentials of 71.2 and 263.8 mV at 10 mA cm−2, respectively. Remarkably, when assembled as a two‐electrode electrolyzer, a satisfying cell voltage of 1.59 V at 10 mA cm−2, and superior stability are demonstrated, highlighting great promise toward water electrolysis. [ABSTRACT FROM AUTHOR]

Unlike dense and regularly distributed 2D raster images, 3D point clouds are irregular and disordered, thus applying convolution on them can be difficult. A method for direct convolution of original 3D point clouds is proposed. This method uses Gaussian kernel density estimation and multi-layer perceptron (MLP) network to learn the density function. Besides, the density scale of the learning point and the relative position of the point are fitted by the weight function of the approximated MLP network to obtain the weight value of each point in the local area. The whole convolution kernel can be seen as a nonlinear function of 3D local coordinates composed of the weight function and the density function, which can be used for translation invariant and permutation invariant convolution of any set of points in 3D space. In addition, multi- scale sampling grouping and normal features are employed to achieve the best effect of this network. In the classification experiment of point cloud datasets of ModelNet40 and ModelNet10, the accuracy of the network is 92.8% and 94.7% respectively, which acquires better performance than baseline. The image datasets of CIFAR-10 and MNIST are converted into point clouds to test, experiments on aforementioned datasets show that the performance of the network in 2D images is basically equivalent to that of the traditional 2D convolutional network. [ABSTRACT FROM AUTHOR]

The effect of immunotherapy strategy has been affirmed in the treatment of various tumors. Nevertheless, the latent role of RNA 5-methylcytosine (m 5 C) modification in gastric cancer (GC) tumor microenvironment (TME) cell infiltration is still unclear. We systematically explore the m 5 C modification patterns of 2,122 GC patients from GEO and TCGA databases by 16 m 5 C regulators and related these patterns to TME characteristics. LASSO Cox regression was employed to construct the m 5 Cscore based on the expression of regulators and DEGs, which was used to evaluate the prognosis. All the GC patients were divided into three m 5 C modification clusters with distinct gene expression characteristics and TME patterns. GSVA, ssGSEA, and TME cell infiltration analysis showed that m 5 C clusters A, B, and C were classified as immune-desert, immune-inflamed, and immune-excluded phenotype, respectively. The m 5 Cscore system based on the expression of eight genes could effectively predict the prognosis of individual GC patients, with AUC 0.766. Patients with a lower m 5 Cscore were characterized by the activation of immunity and experienced significantly longer PFS and OS. Our study demonstrated the non-negligible role of m 5 C modification in the development of TME complexity and inhomogeneity. Assessing the m 5 C modification pattern for individual GC patients will help recognize the infiltration characterization and guide more effective immunotherapy treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Sun, Sun, Lu, Gao, Shen and Qin.)