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Rice planthoppers, including Nilaparvata lugens, Sogatella furcifera, and Laodelphax striatellus, are major agricultural pests. Serpins, which function as serine protease inhibitors, play a pivotal role in the immune systems of these insects, especially within the Toll signaling pathway and the prophenoloxidase (PPO) cascade. This study presents a comparative analysis of serpin genes among these species, highlighting their roles in immunity and development. Utilizing genomic and bioinformatics approaches, we identified 11, 11, and 14 serpin genes in N. lugens, S. furcifera, and L. striatellus, respectively. Phylogenetic analysis revealed a close evolutionary relationship between these serpin genes and Bombyx mori BmSerpins, emphasizing the functional diversity of the serpin family. Structural analysis confirmed the presence of the reactive center loop (RCL) in all serpin proteins, with the Serpin7 subfamily showing a unique dual RCL configuration. Expression profiling showed species-specific serpin expression patterns across different life stages and adult tissues. Moreover, transcriptional analysis of serpin genes in the three planthoppers following Metarhizium infection uncovered distinct immune regulatory patterns two days post-infection. Notably, the expression of NlSerpin2-2/6, SfSerpin4/6/7-1, and LsSerpin4/5-2/6 was upregulated post-infection, potentially enhancing antifungal capabilities. In contrast, the expressions of NlSerpin1/7-1/9 and LsSerpin1/2/3/8/13 were downregulated, possibly suppressing immune responses. Moreover, Serpin6s, which share a conserved phylogenetic lineage, exhibited enhanced immune activity in response to fungal invasion. These insights into serpin-mediated immune regulation could contribute to the development of novel pest-control strategies.

In this paper, we propose a vehicle pedestrian detection and tracking method based on the improved YOLOv7 and DeepSort algorithms. We aim to improve the quality of vehicle pedestrian detection and tracking, addressing the challenges that current commercially available autonomous driving technologies face in complex and changing road traffic situations. First, the NMS (non-maximum suppression) algorithm in YOLOv7 is replaced with a modified Soft-NMS algorithm to ensure that targets can be accurately detected at high densities, and second, the CCBA (coordinate channel attention module) attention mechanism is incorporated to improve the feature extraction and perception capabilities of the network. Finally, a multi-scale feature network is introduced to extract features of small targets more accurately. Finally, the MobileNetV3 lightweight module is introduced into the feature extraction network of DeepSort, which not only reduces the number of model parameters and network complexity, but also improves the tracking performance of the target. The experimental results show that the improved YOLOv7 algorithm improves the average detection accuracy by 3.77% compared to that of the original algorithm; on the MOT20 dataset, the refined DeepSort model achieves a 1.6% increase in MOTA and a 1.9% improvement in MOTP; in addition, the model volume is one-eighth of the original algorithm. In summary, our model is able to achieve the desired real-time and accuracy, which is more suitable for autonomous driving.

Lung cancer is a leading cause of mortality worldwide, especially among Asian patients with non-small cell lung cancer (NSCLC) who have epidermal growth factor receptor (EGFR) mutations. Initially, first-generation EGFR tyrosine kinase inhibitors (TKIs) are commonly administered as the primary treatment option; however, encountering resistance to these medications poses a significant obstacle. Hence, it has become crucial to address initial resistance and ensure continued effectiveness. Recent research has focused on the role of long noncoding RNAs (lncRNAs) in tumor drug resistance, especially lncRNA H19. β-elemene, derived from Curcuma aromatic Salisb., has shown strong anti-tumor effects. However, the relationship between β-elemene, lncRNA H19, and gefitinib resistance in NSCLC is unclear. This study aims to investigate whether β-elemene can enhance the sensitivity of gefitinib-resistant NSCLC cells to gefitinib and to elucidate its mechanism of action. The impact of gefitinib and β-elemene on cell viability was evaluated using the cell counting kit-8 (CCK8) assay. Furthermore, western blotting and qRT-PCR analysis were employed to determine the expression levels of autophagy-related proteins and genes, respectively. The influence on cellular proliferation was gauged through a colony-formation assay, and apoptosis induction was quantified via flow cytometry. Additionally, the tumorigenic potential in vivo was assessed using a xenograft model in nude mice. The expression levels of LC3B, EGFR, and Rab7 proteins were examined through immunofluorescence. Our findings elucidate that the resistance to gefitinib is intricately linked with the dysregulation of autophagy and the overexpression of lncRNA H19. The synergistic administration of β-elemene and gefitinib markedly attenuated the proliferative capacity of resistant cells, expedited apoptotic processes, and inhibited the in vivo proliferation of lung cancer. Notably, β-elemene profoundly diminished the expression of lncRNA H19 and curtailed autophagic activity in resistant cells, thereby bolstering their responsiveness to gefitinib. Moreover, β-elemene disrupted the Rab7-facilitated degradation pathway of EGFR, facilitating its repositioning to the plasma membrane. β-elemene emerges as a promising auxiliary therapeutic for circumventing gefitinib resistance in NSCLC, potentially through the regulation of lncRNA H19-mediated autophagy. The participation of Rab7 in this dynamic unveils novel insights into the resistance mechanisms operative in lung cancer, paving the way for future therapeutic innovations.

Varroa destructor is an obligate ectoparasite of honey bees and shifted from its original host Apis cerana to the new host Apis mellifera in the first half of the twentieth century. The host shift has resulted in a great threat to the health and survival of A. mellifera colonies worldwide. Chemical signals play a crucial role in all aspects of the Varroa life cycle, including host finding. However, the chemical cues that affect the host finding behavior of Varroa mites are still not fully understood. In this study, we systematically profiled the headspace volatiles of both worker and drone larvae of the two honey bee species by using solid phase micro-extraction coupled to gas chromatography-mass spectrometry (SPME-GC-MS), and then used electrophysiological recording and Y-tube olfactometer bioassay to study the potential roles of the selected compounds. The chemical profiling showed that there were four aliphatic esters, ethyl myristate (EM), methyl palmitate (MP), ethyl palmitate (EP), and ethyl oleate (EO) commonly detected from all four types of larval hosts. Among them, EM was a new substance identified from honey bee headspace volatiles. Results from electrophysiological recordings indicated that all the aliphatic esters could elicit significant responses of Varroa pit organs on its forelegs. Moreover, behavioral analyses revealed that EM could significantly attract V. destructor at a medium dosage (10 µg), while MP had no observable effect on the mites and both EP and EO were able to repel the parasites. Our findings suggest an important role of host-derived aliphatic esters in Varroa host finding, and provide new chemicals for Varroa monitoring and control.

In this paper, we propose a high-altitude platform (HAP) and unmanned aerial vehicles (UAVs) collaboration framework in non-orthogonal multiple access (NOMA)-enabled Internet of Things (IoT) networks with the presence of an eavesdropping UAV. For the considered framework, we investigate the uplink secure transmission by optimizing channel allocation from UAVs to HAP, users’ power, and UAVs’ three-dimensional (3D) position. To solve this non-convex problem, we adopt the K-means cluster pair algorithm to divide paired users into different groups and each cluster can be served by a corresponding UAV. Then, the formulated optimization problem is decoupled into three subproblems and tackled iteratively based on the block coordinate descent (BCD) algorithm. Finally, simulation results verify that the proposed network architecture can achieve a higher secure rate, faster convergence evolution, and lower complexity in comparison with the current works.

In this paper, we investigate tags in anti-collision applications of radio frequency identification (RFID) technology in unmanned aerial vehicle (UAV)-aided vehicular ad hoc networks (VANETs). The integration of RFID technology in UAV-aided VANETs can provide reliable traffic-related services for vehicles. However, multiple tags’ simultaneous responses to a reader mounted on a UAV, denoted as tag collision, gravely affect the correct tag detection on each vehicle. Therefore, in order to decrease the collision probability and improve the throughput, we propose a multi-frequency tag identification method. In the proposed scheme, we devise a tag grouping method based on adaptive power control to make the reader dynamically match the optimal frame length. Based on the above matching results, we introduce a tag estimation method using the optimal weight to improve the accuracy of tag estimation. We theoretically analyze the closed-form expression of the security outage probability expression. Finally, our simulation results demonstrate that the proposed tag anti-collision scheme achieved significant performance superiority in terms of the throughput and identification time slots.

Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) methods are promising techniques for improving the energy and spectrum efficiency of Fifth Generation/Beyond Fifth Generation (5G/B5G) networks. In order to take advantage of both techniques, we propose an RIS-assisted UAV secure communication scheme, where an UAV is equipped with RIS to facilitate secure transmission. To maximize the average secrecy rate, we jointly optimize the beamforming power, reflect phase shift, and UAV’s trajectory. For this non-convex problem, we decompose it into the power beamforming problem, the phase shift optimization problem, and the UAV’s trajectory design problem, and proposed an efficient iterative algorithm to solve the problem. The numerical results verify the superiority of the proposed scheme that can improve the average secure transmission rate by about 20% compared to that of Eavesdropping elimination methods.

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.

In this paper, we investigate the relay selection problem for the unmanned aerial vehicle (UAV)-assisted vehicular ad-hoc networks (VANETs). For the considered network, we first model and analyze the link quality of service (LQoS) from the source node (SN) to the neighbor node and the node forward capacity (NFC) from the neighbor node to the destination node (DN). Then, the relay selection problem is formulated as a multi-objective optimization problem by jointly considering the LQoS and the NFC. Afterward, we decompose the problem into two subproblems and propose a relay selection protocol with the storage-carry-forward (SCF) method. Moreover, we define a utility function with the node encounter frequency (NEF) and the message time-to-live (TTL) taken into account, based on which a redundant copy-deleting approach is devised. Furthermore, we analyze the security of the designed protocol. Finally, the simulation results demonstrate that the proposed relay selection protocol can improve the message delivery ratio, reduce the average end-to-end delay, and limit the overhead.

As one of the main applications of the Internet of things (IoT), the vehicular ad-hoc network (VANET) is the core of the intelligent transportation system (ITS). Air−ground integrated vehicular networks (AGIVNs) assisted by unmanned aerial vehicles (UAVs) have the advantages of wide coverage and flexible configuration, which outperform the ground-based VANET in terms of communication quality. However, the complex electromagnetic interference (EMI) severely degrades the communication performance of UAV sensors. Therefore, it is meaningful and challenging to design an efficient anti-interference scheme for UAV data links in AGIVNs. In this paper, we propose an anti-interference scheme, named as Mary-MCM, for UAV data links in AGIVNs based on multi-ary (M-ary) spread spectrum and multi-carrier modulation (MCM). Specifically, the Mary-MCM disperses the interference power by expanding the signal spectrum, such that the anti-interference ability of AGIVNs is enhanced. Besides, by using MCM and multiple-input multiple-output (MIMO) technologies, the Mary-MCM improves the spectrum utilization effectively while ensuring system performance. The simulation results verify that the Mary-MCM achieves excellent anti-interference performance under different EMI combinations.

High-precision and fast relative positioning of a large number of mobile sensor nodes (MSNs) is crucial for smart industrial wireless sensor networks (SIWSNs). However, positioning multiple targets simultaneously in three-dimensional (3D) space has been less explored. In this paper, we propose a new approach, called Angle-of-Arrival (AOA) based Three-dimensional Multi-target Localization (ATML). The approach utilizes two anchor nodes (ANs) with antenna arrays to receive the spread spectrum signals broadcast by MSNs. We design a multi-target single-input-multiple-output (MT-SIMO) signal transmission scheme and a simple iterative maximum likelihood estimator (MLE) to estimate the 2D AOAs of multiple MSNs simultaneously. We further adopt the skew line theorem of 3D geometry to mitigate the AOA estimation errors in determining locations. We have conducted extensive simulations and also developed a testbed of the proposed ATML. The numerical and field experiment results have verified that the proposed ATML can locate multiple MSNs simultaneously with high accuracy and efficiency by exploiting the spread spectrum gain and antenna array gain. The ATML scheme does not require extra hardware or synchronization among nodes, and has good capability in mitigating interference and multipath effect in complicated industrial environments.

Monitoring of low levels of chlorsulfuron in environmental water samples is important. Although several detection methods have been developed, they all have some drawbacks, such as being time-consuming, requiring expensive instruments and experienced operators, and consuming large volumes of organic solvents. There is an urgent need for a simple, rapid, and inexpensive detection method for chlorsulfuron. Herein, such a method was developed using anti-aggregation of gold nanoparticles (AuNPs) in the presence of acetamiprid in agricultural irrigation water samples. Aggregation of the AuNPs was induced by acetamiprid, and this produced a distinct color change from Bordeaux red to blue. However, the strong hydrogen bonding interaction between chlorsulfuron and acetamiprid could inhibit AuNP aggregation. The effect of chlorsulfuron on the anti-agglomeration behavior of AuNPs was monitored by ultraviolet–visiblespectroscopy (UV-Vis) and the naked eye over a concentration range 0.1–100 mg/L. The detection limit for chlorsulfuron was 0.025 mg/L (signal-to-noise ratio of three). This colorimetric method was successfully applied to the determination of chlorsulfuron in spiked tap water and agricultural irrigation water with satisfactory recoveries (76.3%–94.2%).

Various highly sensitive and selective analytical methods have been used to monitor metsulfuron-methyl residue in the environment. However, these methods involve costly instruments and complex, time-consuming operations performed in laboratories. Here, a rapid, convenient, and sensitive colorimetric sensor based on anti-aggregation of gold nanoparticles (AuNPs) is demonstrated for the rapid detection of metsulfuron-methyl in agricultural irrigation water. The AuNPs could be induced to aggregate in the presence of melamine and exhibited a distinct color change from wine-red to blue. The aggregation was suppressed by a strong hydrogen-bonding interaction between metsulfuron-methyl and melamine. The differences of the absorbance at 523 nm (ΔA523) and the color change was linearly related to metsulfuron-methyl concentration over the range 0.1–100 mg/L, as observed visually and by UV-vis (Ultraviolet-visible) spectrometry. The detection limit of the sensor was as low as 0.05 mg/L (signal/noise = 3), and was used to determine metsulfuron-methyl in spiked water and in agricultural irrigation water samples. Recoveries were in the range of 71.2–100.4%, suggesting that the colorimetric sensor was suitable for the determination of metsulfuron-methyl in agricultural water samples.