Transcriptomic analysis reveals bovine herpesvirus 1 infection regulates innate immune response resulted in restricted viral replication in neuronal cells.

Background: Bovine herpesvirus 1 (BoHV-1) is a major pathogen that affects the global bovine population, primarily inducing respiratory and reproductive disorders. Its ability to establish latent infections in neuronal cells and to reactivate under certain conditions poses a continual threat to uninfected hosts. In this study, we aimed to analyze the replication characteristics of BoHV-1 in neuronal cells, as well as the effects of viral replication on host cell immunity and physiology.

A hybrid learning-based stochastic noise eliminating method with attention-Conv-LSTM network for low-cost MEMS gyroscope.

Low-cost inertial measurement units (IMUs) based on microelectromechanical system (MEMS) have been widely used in self-localization for autonomous robots due to their small size and low power consumption. However, the low-cost MEMS IMUs often suffer from complex, non-linear, time-varying noise and errors. In order to improve the low-cost MEMS IMU gyroscope performance, a data-driven denoising method is proposed in this paper to reduce stochastic errors.

A Two-Stage Density-Aware Single Image Deraining Method.

Although advanced single image deraining methods have been proposed, one main challenge remains: the available methods usually perform well on specific rain patterns but can hardly deal with scenarios with dramatically different rain densities, especially when the impacts of rain streaks and the veiling effect caused by rain accumulation are heavily coupled. To tackle this challenge, we propose a two-stage density-aware single image deraining method with gated multi-scale feature fusion. In the first stage, a realistic physics model closer to real rain scenes is leveraged for initial deraining, and a network branch is also trained for rain density estimation to guide the subsequent refinement.

Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement.

This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task.

Semantic Segmentation Leveraging Simultaneous Depth Estimation.

Semantic segmentation is one of the most widely studied problems in computer vision communities, which makes a great contribution to a variety of applications. A lot of learning-based approaches, such as Convolutional Neural Network (CNN), have made a vast contribution to this problem. While rich context information of the input images can be learned from multi-scale receptive fields by convolutions with deep layers, traditional CNNs have great difficulty in learning the geometrical relationship and distribution of objects in the RGB image due to the lack of depth information, which may lead to an inferior segmentation quality.

Mesoporous carbon prepared by etching halloysite nanotubes (HNTs) with pyrrole as a precursor for a sulfur carrier of superior lithium-sulfur batteries.

Using pyrrole as a carbon precursor and halloysite nanotubes (HNT) as a templating agent, mesoporous carbon (MC) was prepared by template etching and combined with sulfur as a composite cathode for lithium-sulfur batteries. The mesoporous carbon/sulfur (MC/S) composite cathode exhibits a first cycle discharge specific capacity of 1355 mA h g at 0.2C, and the utilization rate of active sulfur can reach 80.