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Due to the limited availability of power defect data， most current defect detection methods are unable to accurately detect power system anomalies. To overcome this challenge， a few-shot image generation method is employed. Building upon the improved local-fusion generative adversarial network （LoFGAN）， a context-aware few-shot image generator is designed to enhance the defect detection network’s capability to extract detailed features. A regularization loss based on LC-divergence is introduced to optimize the training effectiveness of the image generation model on limited datasets. Experimental results reveal that the few-shot image generation method can generate effective and diverse defect data for power scenarios. The proposed model can address the issue of data unavailability in power defect scenarios.

Due to the huge difference in the solubility‐product constants (Ksp) between the precipitates of Lu and Al, the synthesis of lutetium aluminum garnet (Lu3Al5O12, LuAG) nanopowders by coprecipitation method is prone to Lu loss, resulting in the creation of Al2O3 secondary phase. In this work, coprecipitated (Ce0.001Ca0.0015Lu(0.9975+x%))3Al5O12 (x = 0, 0.9, 1.2, 1.5) nanopowders with different starting stoichiometries are synthesized and combined with vacuum pre‐sintering and hot‐isostatic pressing to fabricate scintillating ceramics. The effect of compensated Lu content on the phase composition, microstructure, optical properties, and scintillation performance of ceramics is analyzed. The complete elimination of the secondary phase requires an additional compensation of 1.2% of Lu, which gives the best transmittance in ceramics. When x = 1.5, excessive Lu forms LuAl antisite defect, leading to the decrease of both light yield and intensity of the fast scintillation component. [ABSTRACT FROM AUTHOR]

Pleiotropy, the phenomenon in which a single gene influences multiple traits, is a fundamental concept in genetics. However, the evolutionary mechanisms underlying pleiotropy require further investigation. In this study, we conducted parallel gene knockouts targeting 100 transcription factors in 2 strains of Saccharomyces cerevisiae. We systematically examined and quantified the pleiotropic effects of these knockouts on gene expression levels for each transcription factor. Our results showed that the knockout of a single gene generally affected the expression levels of multiple genes in both strains, indicating various degrees of pleiotropic effects. Strikingly, the pleiotropic effects of the knockouts change rapidly between strains in different genetic backgrounds, and ∼85% of them were nonconserved. Further analysis revealed that the conserved effects tended to be functionally associated with the deleted transcription factors, while the nonconserved effects appeared to be more ad hoc responses. In addition, we measured 184 yeast cell morphological traits in these knockouts and found consistent patterns. In order to investigate the evolutionary processes underlying pleiotropy, we examined the pleiotropic effects of standing genetic variations in a population consisting of ∼1,000 hybrid progenies of the 2 strains. We observed that newly evolved expression quantitative trait loci impacted the expression of a greater number of genes than did old expression quantitative trait loci, suggesting that natural selection is gradually eliminating maladaptive or slightly deleterious pleiotropic responses. Overall, our results show that, although being prevalent for new mutations, the majority of pleiotropic effects observed are evolutionarily transient, which explains how evolution proceeds despite complicated pleiotropic effects. [ABSTRACT FROM AUTHOR]

Inspired by our previous work on the modification of diarylpyrimidine-typed non-nucleoside reverse transcriptase inhibitors (NNRTIs) and the reported crystallographic studies, a series of novel amino acids (analogues)-substituted thiophene[3,2-d]pyrimidine derivatives were designed and synthesized by targeting the solvent-exposed region of the NNRTI-binding pocket. The biological evaluation results showed that compound 5k was the most active inhibitor, exhibiting moderate-to-excellent potency against HIV-1 wild-type (WT) and a panel of NNRTI-resistant strains, with EC50 values ranging from 0.042 μM to 7.530 μM. Of special note, 5k exhibited the most potent activity against single-mutant strains (K103N and E138K), with EC50 values of 0.031 μM and 0.094 μM, being about 4.3-fold superior to EFV (EC50 = 0.132 μM) and 1.9-fold superior to NVP (EC50 = 0.181 μM), respectively. In addition, 5k demonstrated lower cytotoxicity (CC50 = 27.9 μM) and higher selectivity index values. The HIV-1 reverse transcriptase (RT) inhibition assay was further performed to confirm their binding target. Moreover, preliminary structure–activity relationships (SARs) and molecular docking studies were also discussed in order to provide valuable insights for further structural optimizations. In summary, 5k turned out to be a promising NNRTI lead compound for further investigations of treatments for HIV-1 infections. [ABSTRACT FROM AUTHOR]

This document is an editorial from the International Journal of Molecular Sciences titled "Antiviral Drug Discovery." It highlights the urgent need for antiviral agents, especially in light of the COVID-19 pandemic. The editorial discusses the success of HIV-1 and influenza antivirals in managing these diseases, but also emphasizes the challenges of emerging viruses and drug resistance. The document covers various topics related to antiviral drug discovery, including in silico studies for SARS-CoV-2, new targets for HIV-1, IAV antiviral drugs, and the mechanism of baicalin inhibiting IBV. It also addresses the evaluation of interactions between antivirals and the immune system. The editorial concludes by thanking the authors, reviewers, and journal staff for their contributions. [Extracted from the article]

In the water transportation, ship speed estimation has become a key subject of intelligent shipping research. Traditionally, Automatic Identification System (AIS) is used to extract the ship speed information. However, transportation environment is gradually becoming complex, especially in the busy water, leading to the loss of some AIS data and resulting in a variety of maritime accidents. To make up for this deficiency, this paper proposes a vessel speed extraction framework, based on Unmanned Aerial Vehicle (UAV) airborne video. Firstly, YOLO v4 is employed to detect the ship targets in UAV image precisely. Secondly, a simple online and real time tracking method with a Deep association metric (Deep SORT) is applied to track ship targets with high quality. Finally, the ship motion pixel is computed based on the bounding box information of the ship trajectories, at the same time, the ship speed is estimated according to the mapping relationship between image space and the real space. Exhaustive experiments are conducted on the various scenarios. Results verify that the proposed framework has an excellent performance with average speed measurement accuracy is above 93% in complex waters. This paper also paves a way to further predict ship traffic flow in water transportation., Author(s): Jiansen Zhao [1]; Yanjun Chen [1]; Zhenzhen Zhou [1]; Jingying Zhao [1]; Shengzheng Wang [1]; Xinqiang Chen (corresponding author) [2] 1. Introduction In recent years, the development of economy [...]

This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that the Yonsei University (YSU) scheme and the Quasi-Normal Scale Elimination (QNSE) scheme performed optimally for wind speed and wind direction. We also conducted a sensitivity test of the simulation results for atmospheric pressure, air temperature, and relative humidity. The statistical analysis showed that the YSU scheme performed optimally, while the MRF and BL schemes performed poorly. Following this, the wind energy distribution in the coastal hilly areas of the Jiaodong Peninsula was simulated using the YSU boundary-layer parameterization scheme. The modeled wind energy density in the mountainous and hilly areas of the Jiaodong Peninsula were higher than that in other regions. The wind energy density exhibits a seasonal variation, with the highest values in spring and early summer and the lowest in summer. In spring, the wind energy density over the Bohai Sea is higher than over the Yellow Sea, while the opposite trend is modeled in summer. [ABSTRACT FROM AUTHOR]

Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not sufficiently comprehensive. Taking into account the application of ship detection and tracking technology, this study proposes a deep learning-based ship speed extraction framework under the haze environment. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm is used to detect ships in dehazed marine images, and a simple online and real-time tracking method with a Deep association metric (Deep SORT) is used to track ships. Then, the ship's displacement in the images is calculated based on the ship's trajectory. Finally, the speed of the ships is estimated by calculating the mapping relationship between the image space and real space. Experiments demonstrate that the method proposed in this study effectively reduces haze interference in maritime videos, thereby enhancing the image quality while extracting the ship's speed. The mean squared error (MSE) for multiple scenes is 0.3 Kn on average. The stable extraction of ship speed from the video achieved in this study holds significant value in further ensuring the safety of ship navigation. [ABSTRACT FROM AUTHOR]