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Background The maize inbred line Chang7-2 and derived lines are important male donors for hybrid production, contributing significantly to the development of high-yield and stress-tolerant hybrids. Additionally, Chang7-2 serves as a valuable model inbred line for genetic and genomic studies, facilitating the discovery of genes underlying hybrid vigor and other agronomic traits. Results Here, a reference genome assembly and a chemical-induced mutant population (N = 1,716) through ethyl methyl sulfonate (EMS) treatments are generated using Chang7-2. Each EMS line is whole genome sequenced and compared to the Chang7-2 genome, identifying 2,586,769 mutations with 4,939 mutations causing premature stop codons or altered splicing sites. The effect estimation of mutations using two large language artificial intelligence (AI) models, namely the protein language model ESM1b and the DNA language model PlantCaduceus, reveals 15,264 and 18,326 deleterious mutations, respectively. Mutation effects estimated with AI models accelerate revelation of four causal mutations underlying phenotypes of albino leaf, reduced cuticular wax, altered seed color, and male sterility. In addition, allelic expression quantification of genic mutations in 13 EMS M1 lines and their M2 heterozygous progeny, which contain both wildtype and mutant alleles, shows that mutant alleles are overall accumulated at a lower level compared to wildtype. Such allelic disparity is observed for some synonymous mutations, indicating they may not be biologically inconsequential. Conclusions AI-based estimation of mutation effects offers cross-species evidence for functional impacts of mutations. Our study demonstrates its application in revealing deleterious EMS mutations and identifying causal mutations responsible for mutant phenotypes. 

Plant transformation efficiency is highly dependent on species, individual genotypes, and tissue types. In maize, immature embryos are regularly used for transformation. The process relies heavily on callus development, as it is intricately associated with somatic embryogenesis and subsequent plant regeneration, both of which directly affect transformation efficiency. Immature embryos of the segregation progeny derived from the two inbred parents, a transformation-amenable line A188 and a recalcitrant line B73, can be cultured to form two primary callus types: Type I and Type II. The Type II callus grows faster and is a favorable type for regeneration. Here, Type I and II calli from the B73xA188 F2 population were genotyped by Genotyping-By-Sequencing (GBS). Quantitative trait locus (QTL) analysis of the callus type identified QTLs at chromosomes 2, 5, 6, 8, and 9. The result was largely supported by the bulk segregant RNA-seq (BSR-seq) genetic analysis using RNA from separately pooled Type I and II calli. Both analyses revealed that an allele of A188 on chromosome 6 and B73 alleles on chromosomes 2, 5, 8, and 9 promoted the formation of the Type II callus. Differentially expressed genes (DEGs) between the Type II and I F2 calli were also identified. In addition, the A188 calli developed from the same immature embryos often exhibit heterogeneous morphology, including the fast- and slow-growing callus sectors. The transcriptional comparison between the two sectors was performed to identify DEGs. Both sets of DEGs were enriched in genes involved in cell-wall organization and wax biosynthesis pathways. 

PDF is a popular document file format with a flexible file structure that can embed diverse types of content, including images and JavaScript code. However, these features make it a favored vehicle for malware attackers. In this paper, we propose an image-based PDF malware detection method that utilizes pre-trained deep neural networks (DNNs). Specifically, we convert PDF files into fixed-size grayscale images using an image visualization technique. These images are then fed into pre-trained DNN models to classify them as benign or malicious. We investigated four classical pre-trained DNN models in our study. We evaluated the performance of the proposed method using the publicly available Contagio PDF malware dataset. Our results demonstrate that MobileNetv3 achieves the best detection performance with an accuracy of 0.9969 and exhibits low computational complexity, making it a promising solution for image-based PDF malware detection. 

Electricity theft is a type of cyberattack posing significant risks to the security of smart grids. Semi-supervised outlier detection (SSOD) algorithms utilize normal power usage data to build detection models, enabling them to detect unknown electricity theft attacks. In this paper, we applied feature engineering and ensemble learning to improve the detection performance of SSOD algorithms. Specifically, we extracted 22 time-series and wavelet features from load profiles, which served as inputs for the seven popular SSOD algorithms investigated in this study. Experimental results demonstrate that the proposed feature engineering greatly enhances the performance of SSOD algorithms to detect various false data injection (FDI) attacks. Furthermore, we constructed bagged ensemble models using the best-performing SSOD algorithm as the base model, with results indicating further improvements in detection performance compared to the base model alone. 

Modern advances in unmanned aerial vehicle (UAV) technology have widened the scope of commercial and military applications. However, the increased dependency on wireless communications exposes UAVs to potential attacks and introduces new threats, especially from UAVs designed with the malicious intent of targeting vital infrastructures. Significant efforts have been made from researchers and other United States (U.S.) Department of Defense (DoD) agencies for developing countermeasures for detection, interception, or destruction of the malicious UAVs. One promising countermeasure is the use of a counter UAV (CUAV) swarm to detect, track, and neutralize the malicious UAV. This paper aims to recognize the state-of-the-art swarm intelligence algorithms for achieving cooperative capture of a mobile target UAV. The major design and implementation challenges for swarm control, algorithm architecture, and safety protocols are considered. A prime challenge for UAV swarms is a robust communication infrastructure to enable accurate data transfer between UAVs for efficient path planning. A multi-agent deep reinforcement learning approach is applied to train a group of CUAVs to intercept a faster malicious UAV, while avoiding collisions among other CUAVs and non-cooperating obstacles (i.e. other aerial objects maneuvering in the area). The impact of the latency incurred through UAV-to-UAV communications is showcased and discussed with preliminary numerical results. 

The use of hand gestures for human-computer interaction (HCI) has gained popularity due to its ability to provide natural and intuitive communication in human dialogues. Hand gesture recognition (HGR) using surface electromyography (sEMG) signals is more reliable and user-friendly than traditional computer vision-based methods. This study proposes a deep network named DeepTPA-Net that utilizes multi-channel sEMG signals to recognize hand gestures. DeepTPA-Net employs a ResNet50 network as an automated feature extractor and a novel triple attention (3Attn) block that connects spatial, temporal, and channel attention modules in parallel to signify important features for HGR. We evaluated the performance of DeepTPA-Net using five publicly available benchmark sEMG hand gesture datasets, including CapgMyo DB-a, Csl-hdemg, NinaPro DB1, NinaPro DB2, and SeNic. The effectiveness of the proposed 3Attn block for HGR is demonstrated through a performance comparison with other attention mechanisms. We compared the performance of DeepTPA-Net with various baseline models, including its variations and other existing methods. The results show that DeepTPA-Net significantly outperforms the baseline models for all five benchmark datasets, indicating the superiority of DeepTPA-Net for sEMG-based HGR. 

In urban environments, tall buildings or structures can pose limits on the direct channel link between a base station (BS) and an Internet-of-Thing device (IoTD) for wireless communication. Unmanned aerial vehicles (UAVs) with a mounted reconfigurable intelligent surface (RIS), denoted as UAV-RIS, have been introduced in recent works to enhance the system throughput capacity by acting as a relay node between the BS and the IoTDs in wireless access networks. Uncoordinated UAVs or RIS phase shift elements will make unnecessary adjustments that can significantly impact the signal transmission to IoTDs in the area. The concept of age of information (AoI) is proposed in wireless network research to categorize the freshness of the received update message. To minimize the average sum of AoI (ASoA) in the network, two model-free deep reinforcement learning (DRL) approaches – Off-Policy Deep Q-Network (DQN) and On-Policy Proximal Policy Optimization (PPO) – are developed to solve the problem by jointly optimizing the RIS phase shift, the location of the UAV-RIS, and the IoTD transmission scheduling for large-scale IoT wireless networks. Analysis of loss functions and extensive simulations is performed to compare the stability and convergence performance of the two algorithms. The results reveal the superiority of the On-Policy approach, PPO, over the Off-Policy approach, DQN, in terms of stability, convergence speed, and under diverse environment settings 

Smart appliances’ run schedule and electric vehicles charging can be managed over a smart grid enabled home area network (HAN) to reduce electricity demand at critical times and add more plug-in electric vehicles to the grid, which eventually lower customers’ energy bills and reduce greenhouse gas emissions. Short range radio-based wireless communication technologies commonly adopted in a HAN are vulnerable to cyber attacks due to their wide interception range. In this work, a low-cost solution is proposed for securing the low-volume data exchange of sensitive tasks (e.g., key management and mutual authentication). Our approach utilizes the emerging concept of retro-reflector based visible light communication (Retro-VLC), where smart appliances, IoT sensors and other electric devices perform the sensitive data exchange with the HAN gateway via the secure Retro-VLC channel. To conduct the feasibility study, a multi-pixel Retro-VLC link is prototyped to enable quadrature amplitude modulation. The bit error rate of Retro-VLC is studied analytically, numerically and experimentally. A heterogeneous Retro-VLC + WLAN connection is implemented by socket programming. In addition, the working range, sniffing range, and key exchange latency are measured. The results validate the applicability of the Retro-VLC based solution.