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The driver is one of the most important factors in road traffic. Monitoring the driver's driving status can greatly improve the safety and road operation efficiency of urban road traffic in the case of multiple traffic modes. Fatigue has a significant impact on drivers' safety on the road, particularly while driving in a monotonous environment for a long time. In this study, the eye movement parameters of 36 drivers were collected through the simulation experiment of a driving simulator. The pupil area and percentage of eye closure (PERCLOS) in driving scenes of the expressway and low-grade rural road were combined with the Stanford Sleepiness Scale (SSS) to determine the threshold of fatigue degree in different monotonous driving scenarios. A recognition model of different fatigue degrees of drivers is built based on the deep learning method of a long short-term memory network (LSTM) to detect the varied fatigue degrees of drivers. The result shows that the fatigue degree of drivers increases as driving time increases on both expressways and low-grade rural roads. In the same driving time, the driver felt tired faster on the expressway, and the fatigue degree was significantly higher than that on the country road. The recognition rate of the established fatigue degree recognition model for driver's awake state, mild fatigue, moderate fatigue, and severe fatigue is 100%, 93.1%, 98.4%, and 100% respectively, and the total recognition rate can reach 97.8%, which is higher than the recognition accuracy of the traditional machine learning approach., Author(s): Hao Han [1]; Kejie Li (corresponding author) [2]; Yi Li [2] 1. Introduction The trend of traffic intelligence is becoming more and more visible, thanks to the rapid development [...]

The microstructure of AZ61 alloy with Sm were investigated by XRD, OM, SEM and TEM. The results showed that the alloy mainly consists of α-Mg matrix, Mg17Al12 phase and Al2Sm phase. The Al2Sm phase can acting as nucleation site of a-Mg matrix and refine the microstructure of alloy.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method's robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data. Cell location information is important for understanding how tissue is spatially organized. Here, the authors develop CeLEry, a machine learning method that aims to recover cell locations for single-cell RNA-seq data by leveraging information learned from spatial transcriptomics. [ABSTRACT FROM AUTHOR]

The silkworm, which is considered a model invertebrate organism, was the first insect used for silk production in human history and has been utilized extensively throughout its domestication. However, sericulture has been plagued by various pathogens that have caused significant economic losses. To enhance the resistance of a host to its pathogens,numerous strategies have been developed. For instance, gene‐editing techniques have been applied to a wide range of organisms, effectively solving a variety of experimental problems. This review focuses on several common silkworm pests and their pathogenic mechanisms, with a particular emphasis on breeding for disease resistance to control multiple types of silkworm diseases. The review also compares the advantages and disadvantages of transgenic technology and gene‐editing systems. Finally, the paper provides a brief summary of current strategies used in breeding silkworm disease resistance, along with a discussion of the establishment of existing technologies and their future application prospects. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Muscle strength is highly heritable and predictive for multiple adverse health outcomes including mortality. Here, we present a rare protein-coding variant association study in 340,319 individuals for hand grip strength, a proxy measure of muscle strength. We show that the exome-wide burden of rare protein-truncating and damaging missense variants is associated with a reduction in hand grip strength. We identify six significant hand grip strength genes, KDM5B, OBSCN, GIGYF1, TTN, RB1CC1, and EIF3J. In the example of the titin (TTN) locus we demonstrate a convergence of rare with common variant association signals and uncover genetic relationships between reduced hand grip strength and disease. Finally, we identify shared mechanisms between brain and muscle function and uncover additive effects between rare and common genetic variation on muscle strength. Here, the authors provide an exome study of hand grip strength, a proxy of generalized muscle strength. They identify six exome-wide significant genes, with links to disease, and additivity of rare and common genetic variant effects on muscle strength. [ABSTRACT FROM AUTHOR]

Misuse and overuse of antibiotics have contributed to the rise of antimicrobial resistance as one of the top public health threats. Antibiotics and antibiotic resistance genes (ARGs) are prevalent in agricultural soils due to the widespread application of livestock and organic wastes. However, information about the occurrence, distribution, and risk of antibiotics and ARGs in agricultural soils is lacking for many scenarios. In this study, based on 2225 observations from 135 independent studies, we summarized the concentration or abundance of antibiotics or ARGs under different fertilizer sources and land-use patterns, analyzed the contributions of key environmental factors to the occurrence of antibiotics and ARGs in agricultural soils, and highlighted the potential ecological risk of typical antibiotics and the relationship between ARGs and mobile genetic elements (MGEs). Our results showed that cattle manure, chicken manure, swine manure, and sewage sludge were the primary pollution sources of antibiotics and ARGs in agricultural soils, and sulfonamides, tetracyclines, fluoroquinolones, and their corresponding ARGs were the main pollution types. Land-use type, soil, and climatic factors affected antibiotics concentration and ARG abundances. MGEs play a vital role in promoting the dissemination of ARGs, especially the sul1 gene is closely related to intI1. In conclusion, our findings and future research exploring these topics will contribute to better management of antibiotic and ARG contamination in agricultural soils and their risk to human health. [ABSTRACT FROM AUTHOR]

Polysaccharide decolorization has a major effect on polysaccharide function. In the present study, the decolorization of Rehmannia glutinosa polysaccharides (RGP) is optimized using two methods—the AB-8 macroporous resin (RGP-1) method and the H2O2 (RGP-2) method. The optimal decolorization parameters for the AB-8 macroporous resin method were as follows: temperature, 50 °C; macroporous resin addition, 8.4%; decolorization duration, 64 min; and pH, 5. Under these conditions, the overall score was 65.29 ± 3.4%. The optimal decolorization conditions for the H2O2 method were as follows: temperature, 51 °C; H2O2 addition, 9.5%; decolorization duration, 2 h; and pH, 8.6. Under these conditions, the overall score was 79.29 ± 4.8%. Two pure polysaccharides (RGP-1-A and RGP-2-A) were isolated from RGP-1 and RGP-2. Subsequently, their antioxidant and anti-inflammatory effects and mechanisms were evaluated. RGP treatment activated the Nrf2/Keap1 pathway and significantly increased the activity of antioxidant enzymes (p < 0.05). It also inhibited the expression of pro-inflammatory factors and suppressed the TLR4/NF-κB pathway (p < 0.05). RGP-1-A had a significantly better protective effect than RGP-2-A, likely owing to the sulfate and uronic groups it contains. Together, the findings indicate that RGP can act as a natural agent for the prevention of oxidation and inflammation-related diseases. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a systematic smooth control method for improving the stability of the two‐wheeled self‐balance robot under impact disturbances. For enhancing the robtot stability, a blend controller based on states feedback control embedded with the PID speed synchronization is estabilished, as well as a hybrid filter composes of RC network and Kalman algorithm. With the hybrid filter, disturbance signals are maximally attenuated or directly eliminated, and the system sensitivity to the impact disturbances significantly declines ; under the blend motion controller, the robot can not only keep balance under impacts but also achieve synchronization of the two driving wheels. The dynamic model, the blend controller, hybrid filter, and experimental results including application to transport are described, both of the simulation and experimental results are provided to verify the analysis.

Transmission line (TL) models are widely used within the simulation of the very fast transient over-voltages (VFTO) for gas-insulated substations (GISs); however, due to quasi-TEM approximation and model complexity, energy losses related to bushing radiation and frequency-dependent soil parameters are either not available or simply neglected, limiting the simulation accuracy of TL models. In this study, a new frequency-domain TL model is proposed, enabling the model to consider the influences of both radiation and soil losses in a VFTO simulation. Derived from the radiation power of a line antenna model, the radiation resistance of a “lossy” TL is put forward as an equivalent of the radiation loss for GIS bushing. For the soil loss, the Sunde approximation formula and the L-S soil dispersion expression are involved, and the parameters of the overhead busbar are also analyzed. The model gets validation by the measured VFTO generated at a full-scale 1100 kV GIS testing circuit. By considering radiation loss to suppress high-frequency response and soil loss to improve low-frequency response accuracy, the calculated and measured waveforms have the best consistency, which may greatly help assess the risk of insulation breakdown and the level of very fast transient disturbance in GISs. [ABSTRACT FROM AUTHOR]

To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals. [ABSTRACT FROM AUTHOR]

Recently, 2-D multiple signal classification (MUSIC) algorithm was applied to map lighting propagation with improved quality. However, the quality is improved at the cost of increasing the calculation load. To improve the efficiency, this article proposes an improved MUSIC-based lightning mapping algorithm, which transforms the 2-D direction of arrival (DOA) estimation problem for an L-shaped array into 1-D DOA estimation problem for the two arms of the array. Additionally, the lightning mapping array is arranged in a nonuniform way to reduce the risk of trivial ambiguity problem. The performance of the developed lightning mapping system is verified with numerical Monte Carlo simulation and further with real lightning observation results. The simulation results show that the nonuniform array outperforms the uniform array in terms of detection accuracy, spatial resolution, and antiambiguity. The observation results show that the MUSIC-based methods produce better lightning mapping quality compared with the interferometry method (15 777 sources mapped), due to better robustness for low signal-to-noise ratio (SNR) lightning very high-frequency (VHF) signals. Compared with the 2-D MUSIC method (34 620 sources mapped), the proposed low computational cost method (30 461 sources mapped) is proven to produce fairly similar path of lightning leader with significant lower computational cost. Specifically, the proposed method saves about 84.91% of calculation time compared with the 2-D MUSIC method in practice. [ABSTRACT FROM AUTHOR]

Author(s): Edward L. Huttlin (corresponding author) [1]; Raphael J. Bruckner [1]; Joao A. Paulo [1]; Joe R. Cannon [1]; Lily Ting [1]; Kurt Baltier [1]; Greg Colby [1]; Fana Gebreab [...]

Many fit-for-purpose bioinformatics tools generate plots to interpret complex biological data and illustrate findings. However, assembling individual plots in different formats from various sources into one high-resolution figure in the desired layout requires mastery of commercial tools or even programming skills. In addition, it is a time-consuming and sometimes frustrating process even for a computationally savvy scientist who frequently takes a trial-and-error iterative approach to get satisfactory results. To address the challenge, we developed bioInfograph, a web-based tool that allows users to interactively arrange high-resolution images in diversified formats, mainly Scalable Vector Graphics (SVG), to produce one multi-panel publication-quality composite figure in both PDF and HTML formats in a user-friendly manner, requiring no programming skills. It solves stylesheet conflicts of coexisting SVG plots, integrates a rich-text editor, and allows creative design by providing advanced functionalities like image transparency, controlled vertical stacking of plots, versatile image formats, and layout templates. To highlight, the sharable interactive HTML output with zoom-in function is a unique feature not seen in any other similar tools. In the end, we make the online tool publicly available at https://baohongz.github.io/bioInfograph while releasing the source code at https://github.com/baohongz/bioInfograph under MIT open-source license. [ABSTRACT FROM AUTHOR]