Measures for diagnosing and treating infections by a novel coronavirus responsible for a pneumonia outbreak originating in Wuhan, China.

On 10 January 2020, a new coronavirus causing a pneumonia outbreak in Wuhan City in central China was denoted as 2019-nCoV by the World Health Organization (WHO). As of 24 January 2020, there were 887 confirmed cases of 2019-nCoV infection, including 26 deaths, reported in China and other countries. Therefore, combating this new virus and stopping the epidemic is a matter of urgency. Here, we focus on advances in research and development of fast diagnosis methods, as well as potential prophylactics and therapeutics to prevent or treat 2019-nCoV infection

Neural Network Pruning as Spectrum Preserving Process

Neural networks have achieved remarkable performance in various application domains. Nevertheless, a large number of weights in pre-trained deep neural networks prohibit them from being deployed on smartphones and embedded systems. It is highly desirable to obtain lightweight versions of neural networks for inference in edge devices. Many cost-effective approaches were proposed to prune dense and convolutional layers that are common in deep neural networks and dominant in the parameter space. However, a unified theoretical foundation for the problem mostly is missing. In this paper, we identify the close connection between matrix spectrum learning and neural network training for dense and convolutional layers and argue that weight pruning is essentially a matrix sparsification process to preserve the spectrum. Based on the analysis, we also propose a matrix sparsification algorithm tailored for neural network pruning that yields better pruning result. We carefully design and conduct experiments to support our arguments. Hence we provide a consolidated viewpoint for neural network pruning and enhance the interpretability of deep neural networks by identifying and preserving the critical neural weights.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0345

A Tensor-Based Framework for Studying Eigenvector Multicentrality in Multilayer Networks

Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.Comment: 57 pages, 10 figure

Design of microstrip-line coupled kinetic inductance detectors for near infrared astronomy

Kinetic inductance detectors (KID) have great potential in astronomical observation, such as searching for exoplanets, because of their low noise, fast response and photon counting characteristics. In this paper, we present the design process and simulation results of a microstrip line coupled KIDs array for near-infrared astronomical observation. Compared with coplanar waveguide (CPW) feedlines, microstrip feedlines do not require air bridges, which simplify fabrication process. In the design part, we mainly focus on the impedance transforming networks, the KID structure, and the frequency crosstalk simulations. The test array has a total of 104 resonators with 8 rows and 13 columns, which ranges from 4.899~GHz to 6.194~GHz. The pitch size is about 200~$\mu$m and the frequency crosstalk is less than 50~kHz in simulation

Fruit Classification Based on Improved YOLOv7 Algorithm

With the rapid development of technology and advancements, unmanned vending machines have emerged as the primary contactless retail method. The efficient and accurate implementation of automated identification technology for agricultural products in their distribution and sales has become an urgent problem that needs to be addressed. This article presents an improved YOLOv7 (You Only Look Once) algorithm for fruit detection in complex environments. By replacing the 3×3 convolutions in the backbone of YOLOv7 with Deformable ConvNet v2(DCNv2), the recognition accuracy and efficiency of fruit classification in YOLOv7 are significantly enhanced. The results indicate that the overall recognition accuracy of this system for ten types of fruits is 98.3%, showcasing its high precision and stability

Pd-catalysed, Ag-assisted C2-H alkenylation of benzophospholes

A palladium-catalysed, silver-assisted regioselective C2-H alkenylation of benzophospholes with terminal alkenes has been developed. The palladium catalysis accommodates styrenes and electron-deficient alkenes including ester, ketone, nitrile, and phosphonate. Thus, this protocol enables the rapid construction of various benzophosphole-vinylene conjugations from the two simple C-H starting substrates. Optical properties of newly synthesized C2-alkenylated benzophospholes are also investigated.Tokura Y., Xu S., Kojima Y., et al. Pd-catalysed, Ag-assisted C2-H alkenylation of benzophospholes. Chemical Communications 58, 12208 (2022); https://doi.org/10.1039/D2CC04942B

The Role of Protein Arginine Methyltransferase 1 in Gastrointestinal Cancers

Mammals can produce nine kinds of arginine methylation enzymes that can be divided into three types (I, II, and III) according to their catalytic activity. Arginine methyltransferase 1 (PRMT1), as the first discovered arginine methyltransferase type I, has been reported to be involved in cell signal transduction, DNA damage repair, RNA transcription and other processes. Its imbalance or abnormal expression is also involved in cancer metastasis. PRMT1 is highly expressed in gastrointestinal tumors and promotes tumor biomarkers expression, chemotherapy resistance and tumorigenicity to promote cancer progression, while downregulation of PRMT1 expression can inhibit the migration and invasion of related tumor cells or promote tumor cells apoptosis and inhibit the progression of cancer. Therefore, PRMT1 may be a cancer therapeutic target. In this paper, arginine methylase 1 expression in various types of gastrointestinal tumors, the tumorigenic mechanism and the role of PRMT1 in tumorigenesis and development were reviewed

Picomolar Dichotomous Activity of Gnidimacrin Against HIV-1

Highly active antiretroviral therapy (HAART) has offered a promising approach for controlling HIV-1 replication in infected individuals. However, with HARRT, HIV-1 is suppressed rather than eradicated due to persistence of HIV-1 in latent viral reservoirs. Thus, purging the virus from latent reservoirs is an important strategy toward eradicating HIV-1 infection. In this study, we discovered that the daphnane diterpene gnidimacrin, which was previously reported to have potent anti-cancer cell activity, activated HIV-1 replication and killed persistently-infected cells at picomolar concentrations. In addition to its potential to purge HIV-1 from latently infected cells, gnidimacrin potently inhibited a panel of HIV-1 R5 virus infection of peripheral blood mononuclear cells (PBMCs) at an average concentration lower than 10 pM. In contrast, gnidimacrin only partially inhibited HIV-1 ×4 virus infection of PBMCs. The strong anti-HIV-1 R5 virus activity of gnidimacrin was correlated with its effect on down-regulation of the HIV-1 coreceptor CCR5. The anti-R5 virus activity of gnidimacrin was completely abrogated by a selective protein kinase C beta inhibitor enzastaurin, which suggests that protein kinase C beta plays a key role in the potent anti-HIV-1 activity of gnidimacrin in PBMCs. In summary, these results suggest that gnidimacrin could activate latent HIV-1, specifically kill HIV-1 persistently infected cells, and inhibit R5 viruses at picomolar concentrations