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Utilizing data of Doppler wind lidar and L-band radiosonde system installed at Qingdao National Basic Meteorological Observing Station from April 2021 to December 2022, their detection capability and accuracy under low visibility weather conditions are compared and evaluated, specifically in terms of detection height, horizontal wind speed and wind direction, using data obtained from L-band radiosonde system as the reference standard. During non-precipitation weather, when visibility exceeds 10000 m, the wind lidar demonstrates a stable average maximum detection height of approximately 1200 m. The root mean square errors of the wind speed and direction fluctuate around 1.2 m·s-1 and 25°, respectively. However, when the visibility drops below 10000 m, the detection height and accuracy of the wind lidar are influenced by the level of interference, particularly in different visibility and relative humidity ranges. In situations where visibility is below 1000 m, the decrease in atmospheric visibility is attributed to increased water vapor content in the air, with relative humidity consistently exceeding 95%. This high humidity significantly interferes with laser transmission in the atmosphere, resulting in an average maximum detection height of less than 400 m. The correlation between horizontal wind speed and direction decreases to 0.91 and 0.94, and the root mean square error increases to 1.4 m·s-1and 42.7°, respectively. When the visibility ranges between 1000 m and 10000 m, the wind lidar's detection capability varies with the water vapor content in the atmosphere. In cases where relative humidity is below 90%, meeting the criteria for hazy days, the decrease in atmospheric visibility is primarily due to increased aerosol particle content. Under these conditions, the average maximum detection height remains stable above 1200 m. The correlation coefficients for horizontal wind speed and direction are as high as 0.97 and 0.98, with root mean square errors of about 1.1 m·s-1 and 22.3°, respectively. These results are comparable to the detection capability demonstrated under high visibility weather conditions. As relative humidity increases, the impact of water vapor attenuation on laser transmission starts to affect the detection height and accuracy of the wind lidar to varying degrees. When the relative humidity exceeds 95%, the average maximum detection height influenced by water vapor decreases to below 400 m. The correlation coefficient of wind speed decreases to 0.94, with a corresponding increase in the root mean square error to 1.5 m·s-1, and the accuracy of wind direction remains relatively stable under these conditions.

Improving the reproducibility of biomedical research results is a major challenge.Researchers reporting their research process transparently and accurately can help readers evaluate the reliability of the research results and further explore the experiment by repeating it or building upon its findings. The ARRIVE 2.0 guidelines, released in 2019 by the UK National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), provide a checklist applicable to any in vivo animal research report. These guidelines aim to improve the standardization of experimental design, implementation, and reporting, as well as the reliability, repeatability, and clinical translatability of animal experimental results. The use of ARRIVE 2.0 guidelines not only enriches the details of animal experimental research reports, ensuring that information on animal experimental results is fully evaluated and utilized, but also enables readers to understand the content expressed by the author accurately and clearly, promoting the transparency and integrity of the fundamental research review process. At present, the ARRIVE 2.0 guidelines have been widely adopted by international biomedical journals. This article is a Chinese translation based on the best practices of international journals following the ARRIVE 2.0 guidelines in international journals, specifically for the complete interpretation of the ARRIVE 2.0 guidelines published in the PLoS Biology journal in 2020 (original text can be found at https://arriveguidelines.org). The third part of the article includes the items 8-10 of ARRIVE 2.0 Essential 10, which covers "experimental animals" "experimental procedures" and "results". Its aim is to promote the full understanding and use of the ARRIVE 2.0 guidelines by domestic researchers, enhance the standardization of experimental animal research and reporting, and promote the high-quality development of experimental animal technology and comparative medicine research in China.

Objective: To compare the quality of low tube voltage coronary CT angiography (CCTA) images reconstructed with deep learning-based image reconstruction (DLIR) and with filter back projection (FBP) and with adaptive statistical iterative reconstruction-veo (ASiR-V). Methods: One hundred patients who underwent CCTA were included. The CCTA tube voltage were set as 70 kVp (n=50, BMI≤26 kg/m2) and 80 kVp (n=50, BMI>26 kg/m2) according to body mass index(BMI). The images were reconstructed with FBP (Group A), ASIR-V 50% (Group B), DLIR at medium (DLIR-M, Group C) and high DLIR(DLIR-H, Group D) levels, respectively. Objective evaluation indice including CT attenuation, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio(CNR) were measured or calculated between groups, and Likert 5-point scale was adopted for subjective image quality assessment. Results: There were significant differences in image noise, SNR, CNR among the 4 groups(P

Enhancing hazmat truck safety through advanced driving assistance systems (ADAS) relies on both system efficacy and driver reactions. This study investigates the driving behaviors of hazmat truck drivers in response to forward collision warnings (FCWs). Traditional warning triggering methods struggle to capture diverse and immediate driver responses; therefore, our research employs a vision‐based framework for driving data extraction and utilizes the K‐means++ clustering method for response‐based classification. Moreover, we propose an enhanced version of the intelligent driver model (IDM) based on the concept of a virtual vehicle to reproduce hazmat truck drivers' differential behaviors during risky car‐following periods, achieving results that depict improved driving simulations. This model is compared with classic benchmarks, including the IDM, optimal velocity model (OVM), and full velocity difference (FVD) model, demonstrating superior performance in terms of traffic stability and safety in extreme scenarios. Our findings highlight that preaction drivers tend to accelerate before receiving warnings, opting to overtake rather than maintain safe distances. In contrast, calm drivers decelerate in anticipation of the warning, showcasing their awareness of maintaining safety. The analysis reveals that aggressive drivers are predominantly in the 41–45 age group, indicating a higher skill level, while calm drivers are more commonly older, reflecting a trend in cautious driving behaviors. Overall, our research contributes to the development of effective ADAS by considering real‐time driver responses and emphasizes the potential of our model to revolutionize commercial ADAS adoption and enhance road safety for hazmat operations. [ABSTRACT FROM AUTHOR]

The accurate wind field observation of tropical cyclone (TC) boundary layer is of great significance to improve the TC track and intensity forecasting. To investigate the vertical structure of TC boundary layer during the landfall process of Super Typhoon Doksuri, three kinds of instruments including the coherent Doppler lidar (CDL), radar wind profiler (RWP) and automatic weather station (AWS) are deployed at two sites in Xiamen, Fujian province. A data fusion method is developed to obtain the complete wind speed profiles covering the whole Atmospheric Boundary Layer (ABL) based on the above instruments. The wind speeds in the near field blind zones of CDL observation are interpolated by combining the AWS measurements at 10 m. The CDL provides high temporal-spatial resolution wind speed profiles from tens of meters to its highest detection height. The wind speeds above the highest detection height of the CDL would be supplemented with the RWP measurements. The hourly mean wind speed profiles are compared with traditional models. Generally, the wind speed profiles fit well with the power law in the lower part of the ABL, before wind speed changes rapidly. However, it would cause a large error (up to 73 %) to describe the exact wind speed profiles with traditional models during and after the typhoon's passage, especially when the wind speed is almost constant with height or when wind shear exists. Then fine structures and evolutionary processes of the wind field in the ABL during the typhoon landfall are investigated. In addition, the wind field distribution and wind speed variation with distance from the typhoon center are statistical analyzed. The joint wind field measurements of CDL, RWP and AWS have the broad application prospects on the dynamics study of the TC boundary layer and the improvement of the boundary layer parameterization scheme in numerical forecast models. [ABSTRACT FROM AUTHOR]

In an era of consumer electronics, effective consumption recommendation contributes a lot to improving benefits for online shopping operators. However, consumers and products constitute a complex heterogeneous information network, in which extraction of structural features is essential. To construct more fine-grained feature space for modeling, we introduce the graph neural network (GNN) theory for this purpose. Accordingly, a multi-scale GNN-based personalized recommender system for online consumption decision is proposed in this paper. Firstly, we set up an encoder for consumption decision based on a multi-scale GNN structure, and define the loss function. Two realistic datasets are utilized as the research scenario, in order to complete the feature combination of online consumption. Then, personalized recommendation settings are completed based on a recurrent neural network structure. And a graph learning module is embedded in it to integrate cross-attention mechanism to establish the consumption decision algorithm. Finally, the proposed method is tested by comparing with relevant research methods under several metrics: adaptability, success rate, and stability. The results show that the proposed method has achieved some improvement in accuracy, coverage, and recommendation speed. [ABSTRACT FROM AUTHOR]

Drought is a significant global issue affecting agricultural production, and the utilization of beneficial rhizosphere microorganisms is one of the effective ways to increase the productivity of crops and forest under drought. In this study, we characterized a novel growth-promoting dark septate endophytes (DSE) fungus R16 (Dothideomycetes sp.) derived from blueberry roots. Hyphae or microsclerotia were visible within the epidermal or cortical cells of R16-colonized blueberry roots, which was consistent with the typical characteristics of DSE fungi. Inoculation with R16 promoted the growth of blueberry seedlings, and the advantage over the control group was more significant under PEG-induced drought. Comparison of physiological indicators related to drought resistance between the inoculated and control groups was performed on the potted blueberry plants, including the chlorophyll content, net photosynthetic rate, root activities, malondialdehyde and H2O2 content, which indicated that R16 colonization mitigated drought injury in blueberry plants. We further analyzed the effects of R16 on phytohormones and non-structural carbohydrates (NSCs) to explore the mechanism of increased drought tolerance by R16 in blueberry seedlings. The results showed that except for the gibberellin content, indole-3-acetic acid, zeatin and abscisic acid varied significantly between the inoculated and control groups. Sucrose phosphate synthase and sorbitol-6-phosphate dehydrogenase activities in mature leaves, the key enzymes responsible for sucrose and sorbitol synthesis, respectively, as well as sorbitol dehydrogenase, sucrose synthase, cell wall invertase, hexokinase and fructokinase in roots, the key enzymes involved in the NSCs metabolism, showed significant differences between the inoculated and control groups before and after drought treatment. These results suggested that the positive effects of R16 colonization on the drought tolerance of blueberry seedlings are partially attributable to the regulation of phytohormone and sugar metabolism. This study provided valuable information for the research on the interaction between DSE fungi and host plants as well as the application of DSE preparations in agriculture. [ABSTRACT FROM AUTHOR]

Type IV collagen is an integral component of basement membranes. Mutations in COL4A1, one of the key genes encoding Type IV collagen, can result in a variety of diseases. It is clear that a significant proportion of mutations that affect splicing can cause disease directly or contribute to the susceptibility or severity of disease. Here, we analyzed exonic mutations and intronic mutations described in the COL4A1 gene using bioinformatics programs and identified candidate mutations that may alter the normal splicing pattern through a minigene system. We identified seven variants that induce splicing alterations by disrupting normal splice sites, creating new ones, or altering splice regulatory elements. These mutations are predicted to impact protein function. Our results help in the correct molecular characterization of variants in COL4A1 and may help develop more personalized treatment options. [ABSTRACT FROM AUTHOR]

Background: The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. Purpose: To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. Material and Methods: The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. Results: The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). Conclusion: DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H. [ABSTRACT FROM AUTHOR]