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With the rapid development of urban rail transit, the existing track detection has some problems such as low efficiency and insufficient detection coverage, so an intelligent and automatic track detection method based on UAV is urgently needed to avoid major safety accidents. At the same time, the geographical distribution of IoT devices results in the inefficient use of the significant computing potential held by a large number of devices. As a result, the Dispersed Computing (DCOMP) architecture enables collaborative computing between devices in the Internet of Everything (IoE), promotes low-latency and efficient cross-wide applications, and meets users' growing needs for computing performance and service quality. This paper focuses on examining the resource allocation challenge within a dispersed computing environment that utilizes UAV inspection tracks. Furthermore, the system takes into account both resource constraints and computational constraints and transforms the optimization problem into an energy minimization problem with computational constraints. The Markov Decision Process (MDP) model is employed to capture the connection between the dispersed computing resource allocation strategy and the system environment. Subsequently, a method based on Double Deep Q-Network (DDQN) is introduced to derive the optimal policy. Simultaneously, an experience replay mechanism is implemented to tackle the issue of increasing dimensionality. The experimental simulations validate the efficacy of the method across various scenarios. [ABSTRACT FROM AUTHOR]

Vitamin D (VD) is one of the important nutrients required by livestock; however, VD deficiency is reported to be widespread. Earlier studies have suggested a potential role for VD in reproduction. Studies on the correlation between VD and sow reproduction are limited. The aim of the current study was aimed to determine the role of 1,25-dihydroxy vitamin D3 (1α,25(OH)2D3) on porcine ovarian granulosa cells (PGCs) in vitro to provide a theoretical basis for improving the reproductive efficiency of sows. We used chloroquine (autophagy inhibitor) and reactive oxygen species (ROS) scavenger N-acetylcysteine in conjunction with 1α,25(OH)2D3 to explore the effect on PGCs. The results showed that 10 nM of 1α,25(OH)2D3 increased PGC viability and ROS content. In addition, 1α,25(OH)2D3 induces PGC autophagy according to the gene transcription and protein expression levels of LC3, ATG7, BECN1, and SQSTM1 and promotes the generation of autophagosomes. 1α,25(OH)2D3-induced autophagy affects the synthesis of E2 and P4 in PGCs. We investigated the relationship between ROS and autophagy, and the results showed that 1α,25(OH)2D3-induced ROS promoted PGC autophagy. The ROS-BNIP3-PINK1 pathway was involved in PGC autophagy induced by 1α,25(OH)2D3. In conclusion, this study suggests that 1α,25(OH)2D3 promotes PGC autophagy as a protective mechanism against ROS via the BNIP3/PINK1 pathway. [ABSTRACT FROM AUTHOR]

Almost all cases of cervical cancer (CC) can be attributed to high‐risk human papillomavirus (HPVs) infections in keratinocytes. However, it is unknown whether HPV invades immune cells such as macrophages and T cells. We analyzed the single‐cell transcriptome of the CC and its adjacent tissues and found that HPV16 genes, including E1, E6, and E7, expressed in the macrophages and CD8+ T cells in addition to the malignant cells. HPV16+ macrophages highly expressed the genes that promote cell adhesion and the favorable genes such as WAS, IQCB1, MYO1F, and PDZD11 in CC prognosis. The transcription factor KLF5 potentially accounted for the induction of these protective genes and thus facilitated the infiltration of the immune cells in tumor tissues. Our single‐cell transcriptome analysis suggests the potential value of the HPV16+ macrophage in CC prognosis. However, extensive experimental studies investigating the characteristics and functions of the HPV+ immune cells are still required. [ABSTRACT FROM AUTHOR]

An innovative type of carbon fiber‐reinforced composite corrugated rib sandwich panel floor structure was designed and produced for a high‐speed train equipment compartment, complying with the applicable railway car body structural requirements and railway fire protection standards. Using the finite element analysis (FEA) method there was conducted a four‐step laminate design optimization for adjusting the mechanical properties of both the skin and corrugated ribs parts. A prototype was manufactured by an autoclave process and successfully submitted to static and fatigue mechanical testing for evaluation of the strength and stiffness of this structure as well as for correlation with the FEA model. It was finally concluded that requirements were met for this lightweight design and the weight reduction was approximately of 35.7% in comparison to the conventional metal structure. The research shown in this article does not only provide an effective carbon fiber composite equipment design scheme for the floor panel for high‐speed trains, but also entails a methodical design process for other applications of carbon fiber composite structural parts. [ABSTRACT FROM AUTHOR]

Background and Aims: Hyperactivated inflammatory responses induced by cytokine release syndrome are the primary causes of tissue damage and even death. The translation process is precisely regulated to control the production of proinflammatory cytokines. However, it is largely unknown whether targeting translation can effectively limit the hyperactivated inflammatory responses during acute hepatitis and graft-versus-host disease., Approach and Results: By using in vitro translation and cellular overexpression systems, we have found that the nonstructural protein gene NS2A of Zika virus functions as RNA molecules to suppress the translation of both ectopic genes and endogenous proinflammatory cytokines. Mechanistically, results from RNA pulldown and co-immunoprecipitation assays have demonstrated that NS2A RNA interacts with the translation initiation factor eIF2α to disrupt the dynamic balance of the eIF2/eIF2B complex and translation initiation, which is the rate-limiting step of translation. In the acetaminophen-induced, lipopolysaccharide/D-galactosamine-induced, viral infection-induced acute hepatitis, and graft-versus-host disease mouse models, mice with myeloid cell-specific knock-in of NS2A show decreased levels of serum proinflammatory cytokines and reduced tissue damage., Conclusions: Zika virus NS2A dampens the production of proinflammatory cytokines and alleviates inflammatory injuries by interfering translation process as RNA molecules, which suggests that NS2A RNA is potentially used to treat numerous acute inflammatory diseases characterized by cytokine release syndrome., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Cucumber is an important vegetable crop that has high nutritional and economic value and is thus favored by consumers worldwide. Exploring an accurate and fast technique for measuring the morphological traits of cucumber fruit could be helpful for improving its breeding efficiency and further refining the development models for pepo fruits. At present, several sets of measurement schemes and standards have been proposed and applied for the characterization of cucumber fruits; however, these manual methods are time-consuming and inefficient. Therefore, in this paper, we propose a cucumber fruit morphological trait identification framework and software called CucumberAI, which combines image processing techniques with deep learning models to efficiently identify up to 51 cucumber features, including 32 newly defined parameters. The proposed tool introduces an algorithm for performing cucumber contour extraction and fruit segmentation based on image processing techniques. The identification framework comprises 6 deep learning models that combine fruit feature recognition rules with MobileNetV2 to construct a decision tree for fruit shape recognition. Additionally, the framework employs U-Net segmentation models for fruit stripe and endocarp segmentation, a MobileNetV2 model for carpel classification, a ResNet50 model for stripe classification and a YOLOv5 model for tumor identification. The relationships between the image-based manual and algorithmic traits are highly correlated, and validation tests were conducted to perform correlation analyses of fruit surface smoothness and roughness, and a fruit appearance cluster analysis was also performed. In brief, CucumberAI offers an efficient approach for extracting and analyzing cucumber phenotypes and provides valuable information for future cucumber genetic improvements., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Wei Xue et al.)