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Abstract Protein-modifying enzymes regulate the dynamics of myriad post-translational modification (PTM) substrates. Precise characterization of enzyme-substrate associations is essential for the molecular basis of cellular function and phenotype. Methods for direct capturing global substrates of protein-modifying enzymes in living cells are with many challenges, and yet largely unexplored. Here, we report a strategy to directly capture substrates of lysine-modifying enzymes via PTM-acceptor residue crosslinking in living cells, enabling global profiling of substrates of PTM-enzymes and validation of PTM-sites in a straightforward manner. By integrating enzymatic PTM-mechanisms, and genetically encoding residue-selective photo-crosslinker into PTM-enzymes, our strategy expands the substrate profiles of both bacterial and mammalian lysine acylation enzymes, including bacterial lysine acylases PatZ, YiaC, LplA, TmcA, and YjaB, as well as mammalian acyltransferases GCN5 and Tip60, leading to discovery of distinct yet functionally important substrates and acylation sites. The concept of direct capturing substrates of PTM-enzymes via residue crosslinking may extend to the other types of amino acid residues beyond lysine, which has the potential to facilitate the investigation of diverse types of PTMs and substrate-enzyme interactive proteomics.

Abstract Background Increasing number of studies have demonstrated certain patterns of microbial changes in gynecological diseases; however, the interaction between them remains unclear. To evaluate the consistency or specificity across multiple studies on different gynecological diseases and microbial alterations at different sites of the body (gut and genital tract), we conducted a systematic review and meta-analysis. Methods We searched PubMed, Embase, Web of Science, and Cochrane Library up to December 5, 2022(PROSPERO: CRD42023400205). Eligible studies focused on gynecological diseases in adult women, applied next-generation sequencing on microbiome, and reported outcomes including alpha or beta diversity or relative abundance. The random-effects model on standardized mean difference (SMD) was conducted using the inverse-variance method for alpha diversity indices. Results Of 3327 unique articles, 87 eligible studies were included. Significant decreases were found in gut microbiome of patients versus controls (observed species SMD=-0.35; 95%CI, -0.62 to -0.09; Shannon index SMD=-0.23; 95%CI, -0.40 to -0.06), whereas significant increases were observed in vaginal microbiome (Chao1 SMD = 1.15; 95%CI, 0.74 to 1.56; Shannon index SMD = 0.51; 95%CI, 0.16 to 0.86). Most studies of different diagnostic categories showed no significant differences in beta diversity. Disease specificity was observed, but almost all the changes were only replicated in three studies, except for the increased Aerococcus in bacterial vaginosis (BV). Patients with major gynecological diseases shared the enrichment of Prevotella and depletion of Lactobacillus, and an overlap in microbes was implied between BV, cervical intraepithelial neoplasia, and cervical cancer. Conclusions These findings demonstrated an association between alterations in gut and genital microbiota and gynecological diseases. The most observed results were shared alterations across diseases rather than disease-specific alterations. Therefore, further investigation is required to identify specific biomarkers for diagnosis and treatment in the future.

—Extremely-Low Frequencies (ELF, 30∼300Hz) transmitting antennas in wireless communications are often limited by antenna size and complex impedance matching networks. In this paper, we propose an ultra-small Artificial Electret Type Mechanical Antenna (AETMA), which is composed of a single charge electret and a driving structure, with high radiation efficiency and small size. In order to improve the electric dipole moment of the mechanical antenna, we employ a pin-plate corona polarization technique and a unidirectional stretching treatment to obtain a porous thin-film electret that can stably store a large amount of charge. Its surface charge density can reach 5.355 mC/m2 and we analyze its surface potential stability. To assess the radiation capability of AETMA, the radiation field models of three kinds of mechanical antennas are established and verified by simulation. Additionally, we simulate and compare the planar electret and curved electret configurations to determine the optimal form of AETMA. The radiation intensity of the planar electret is found to be superior under the same moment of inertia. Finally, a 1m-scale artificial electret antenna is designed based on the optimal model. Comparative analysis with existing rotary mechanical antenna schemes confirms the great potential of the proposed AETMA for portable, miniaturized and high-performance wireless communication devices.

The concrete-filled steel tubular (CFST) arch bridge has achieved significant development in recent years due to its unique mechanical performance and technical advantages. However, due to the lagging theoretical research compared to engineering practice, many problems have been exposed in the existing bridges, resulting in adverse social impacts and enormous economic losses. With the increasing prominence of safety issues in CFST arch bridges, it is necessary to assess their safety condition in service. This paper establishes a safety assessment index system for CFST arch bridges using the fuzzy analytic hierarchy process (AHP) based on an exponential scale. The assessment method proposed includes the following main points: (1) Bridge safety assessment is closely related to the load-bearing capacity of components. This study proposes an assessment index that comprehensively considers both the defect conditions and the design load-bearing capacity of components for the safety assessment. (2) The exponential scale method is introduced to safety assessment for the first time, and the AHP based on an exponential scale is applied to calculate the component weights. (3) Considering the specific structural characteristics of CFST arch bridges, this study provides a detailed division of component types and calculates the component weights. By combining the component assessment indexes, a comprehensive safety assessment index system is established, and a safety assessment method for CFST arch bridges is proposed. (4) Taking the Jiantiao Bridge in Zhejiang Province as an engineering case, the load-bearing capacity of components is calculated using finite element software ANSYS 19.1. Based on the established safety assessment index system, the safety of the bridge is assessed by integrating the inspection results. (5) Software for the safety assessment of a CFST arch bridge is developed using Visual Basic, and the assessment results align well with the actual condition of the bridge.

With the development of intelligent substations, inspection robots are widely used to ensure the safe and stable operation of substations. Due to the prevalence of grass around the substation in the external environment, the inspection robot will be affected by grass when performing the inspection task, which can easily lead to the interruption of the inspection task. At present, inspection robots based on LiDAR sensors regard grass as hard obstacles such as stones, resulting in interruption of inspection tasks and decreased inspection efficiency. Moreover, there are inaccurate multiple object-detection boxes in grass recognition. To address these issues, this paper proposes a new assistance navigation method for substation inspection robots to cross grass areas safely. First, an assistant navigation algorithm is designed to enable the substation inspection robot to recognize grass and to cross the grass obstacles on the route of movement to continue the inspection work. Second, a three-layer convolutional structure of the Faster-RCNN network in the assistant navigation algorithm is improved instead of the original full connection structure for optimizing the object-detection boxes. Finally, compared with several Faster-RCNN networks with different convolutional kernel dimensions, the experimental results show that at the convolutional kernel dimension of 1024, the proposed method in this paper improves the mAP by 4.13% and the mAP is 91.25% at IoU threshold 0.5 in the range of IoU thresholds from 0.5 to 0.9 with respect to the basic network. In addition, the assistant navigation algorithm designed in this paper fuses the ultrasonic radar signals with the object recognition results and then performs the safety judgment to make the inspection robot safely cross the grass area, which improves the inspection efficiency.

Mining-induced ground subsidence is a commonly observed geo-hazard that leads to loss of life, property damage, and economic disruption. Monitoring subsidence over time is essential for predicting related geo-risks and mitigating future disasters. Machine-learning algorithms have been applied to develop predictive models to quantify future ground subsidence. However, machine-learning approaches are often difficult to interpret and reproduce, as they are largely used as “black-box” functions. In contrast, stochastic differential equations offer a more reliable and interpretable solution to this problem. In this study, we propose a stochastic differential equation modeling approach to predict short-term subsidence in the temporal domain. Mining-induced time-series data collected from the Global Navigation Satellite System (GNSS) in our case study area were utilized to conduct the analysis. Here, the mining-induced time-series data collected from GNSS system regarding our case study area in Miyi County, Sichuan Province, China between June 2019 and February 2022 has been utilized to conduct the case study. The proposed approach is capable of extracting the time-dependent structure of monitored subsidence data and deriving short-term subsidence forecasts. The predictive outcome and time-path trajectories were obtained by characterizing the parameters within the stochastic differential equations. Comparative analysis against the persistent model, autoregressive model, and other improved autoregressive time-series models is conducted in this study. The computational results validate the effectiveness and accuracy of the proposed approach.

Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%.

The dynamic response of rock mass is largely restricted by its joint surface. Previous studies have shown that the degradation of joint surface can not be ignored when calculating the shear strength of structural plane under cyclic load. Although several studies have attempted to calculate the cyclic shear strength of a rock mass joint surface, an established and reliable method for calculating the cyclic shear strength of rock mass discontinuities is still lacking, thus necessitating further research. In this study, the deterioration effect of the shear strength of the joint surface under cyclic shearing was first analysed using cyclic shearing tests. The influence of vibration degradation in rock mass on the structural surface, undulant angle equation of the joint surface, and calculation method for the basic friction angle under a cyclic shearing load are proposed. Furthermore, the calculation method for the shear strength of the structural surface under the action of cyclic shearing is established. The proposed method is further validated through case analysis. The influence of the cutting and filling (produced during the shearing process) on the shear strength of the joint surface cannot be disregarded. The improved model proposed in this study is in good agreement with the experimental results; however, when the improved proposed method is used to estimate the cyclic shear strength of the joint surface where the normal stress is too large, calculation results may contain certain errors.

Deep learning has better detection efficiency than typical methods in photoelectric target detection. However, classical CNNs on GPU frameworks consume too much computing power and memory resources. We propose a multi-stream inference-optimized TensorRT (MSIOT) method to solve this problem effectively. MSIOT uses knowledge distillation to effectively reduce the number of model parameters by layer guidance between CNNs and lightweight networks. Moreover, we use the TensorRT and multi-stream mode to reduce the number of model computations. MSIOT again increases inference speed by 9.3% based on the 4.3–7.2× acceleration of TensorRT. The experimental results show that the model’s mean average accuracy, precision, recall, and F1 score after distillation can reach up to 94.20%, 93.16%, 95.4%, and 94.27%, respectively. It is of great significance for designing a real-time photoelectric target detection system.

Splines are irreplaceable in high-speed aviation fields due to their simplicity, reliability, and high specific power. Aviation splines are not only subjected to severe operating mechanical loads, but also sometimes operate under grease-lubricated and non-lubricated environments. All of this results in aviation splines suffering widespread failures. Since the 1960s, many researchers have carried out much research on aviation splines. The wide range of research topics demonstrates the technical challenges of understanding aviation spline. This paper reviews the research of aviation spline from the aspects of failure form, fatigue strength, surface contact stress, effects of lubrication, and misalignment on wear, as well as experiments. Relevant research shows crowned splines can mitigate the spline wear process induced by angular misalignment, and oil-lubricating splines experience almost no wear. This paper also looks forward to the future development directions of aviation splines.

Abstract Metal-semiconductor heterostructures integrate multiply functionalities beyond those of their individual counterparts. Great efforts have been devoted to synthesize heterostructures with controlled morphologies for the applications ranging from photocatalysis to photonic nanodevices. Beyond the morphologies, the interface between two counterparts also significantly influences the performance of the heterostructures. Here, we synthesize Au/CdSe Janus nanostructures consisting of two half spheres of Au and CdSe separated by a flat and high-quality interface. Au/CdSe with other morphologies could also be prepared by adjusting the overgrowth conditions. The photocatalytic hydrogen generation of the Au/CdSe Janus nanospheres is measured to be 3.9 times higher than that of the controlled samples with CdSe half-shells overgrown on the Au nanospheres. The highly efficient charge transfer across the interface between Au and CdSe contributes to the improved photocatalytic performance. Our studies may find the applications in the design of heterostructures with highly efficient photocatalytic activity.

The safe storage time for double base propellant (DBP or DB propellant) with stabilizers could usually be calculated to be greater than 40 years. However, the actual service life is far below that, which is largely caused by the decline of propellant mechanical performance. In this work polytetrafluoroethylene (PTFE) was introduced into the double base propellant formula as an additive. The tensile properties of this propellant before and after artificial aging were determined. The evaporation and diffusion characteristics of nitroglycerin (NG) in propellant were evaluated by thermogravimetry analysis (TGA). The results showed that mechanical properties of propellant were improved due to PTFE, especially for elongation at −40 °C, which was greatly increased by 115%. Moreover, the results of TGA showed that NG migration was reduced due to PTFE, which delayed the decline of propellant mechanical performance during aging. The reduction in elongation at −40 °C caused by aging was decreased by 68.5% for PTFE modified DBP. Enhanced mechanical properties and reduced NG migration could potentially prolong propellant service life.

Diabetic retinopathy(DR)is one of the major complications of diabetes mellitus, characterized by neurodegeneration and microangiopathy. Currently, the treatment of DR is mainly focused on the management of late complications and has not achieved the desired clinical outcome. Evidence suggests that the PI3K/AKT pathway, as one of the important intracellular signaling pathways during the cell cycle, is involved in the whole process of DR pathogenesis. This article focuses on the structural composition, activation and blocking pathways, conduction pathways, regulatory mechanisms and biological functions of the PI3K/AKT signaling pathway to review its role in DR and to explore the potential of targeting the PI3K/AKT pathway for the treatment of DR.