Detection of type III effector-induced transcription factors that regulate phytohormone content during symbiosis establishment in soybean.

Soybean is a pivotal protein and oil crop that utilizes atmospheric nitrogen via symbiosis with rhizobium soil bacteria. Rhizobial type III effectors (T3Es) are essential regulators during symbiosis establishment. However, how the transcription factors involved in the interaction between phytohormone synthesis and type III effectors are connected is unclear. To detect the responses of phytohormone and transcription factor genes to rhizobial type III effector NopAA and type III secretion system, the candidate genes underlying soybean symbiosis were identified using RNA sequencing (RNA-seq) and phytohormone content analysis of soybean roots infected with wild-type Rhizobium and its derived T3E mutant. Via RNA-seq analysis the WRKY and ERF transcription factor families were identified as the most differentially expressed factors in the T3E mutant compared with the wild-type. Next, qRT-PCR was used to confirm the candidate genes Glyma.09g282900, Glyma.08g018300, Glyma.18g238200, Glyma.03g116300, Glyma.07g246600, Glyma.16g172400 induced by S. fredii HH103, S. fredii HH103ΩNopAA, and S. fredii HH103ΩRhcN. Since the WRKY and ERF families may regulate abscisic acid (ABA) content and underlying nodule formation, we performed phytohormone content analysis at 0.5 and 24 h post-inoculation (hpi). A significant change in ABA content was found between wild Rhizobium and type III effector mutant. Our results support that NopAA can promote the establishment of symbiosis by affecting the ABA signaling pathways by regulating WRKY and ERF which regulate the phytohormone signaling pathway. Specifically, our work provides insights into a signaling interaction of prokaryotic effector-induced phytohormone response involved in host signaling that regulates the establishment of symbiosis and increases nitrogen utilization efficiency in soybean plants.

A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location.

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.

Three-Dimensional Localization Algorithm Based on Improved A and DV-Hop Algorithms in Wireless Sensor Network.

In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

An Effective Dense Co-Attention Networks for Visual Question Answering.

At present, the state-of-the-art approaches of Visual Question Answering (VQA) mainly use the co-attention model to relate each visual object with text objects, which can achieve the coarse interactions between multimodalities. However, they ignore the dense self-attention within question modality. In order to solve this problem and improve the accuracy of VQA tasks, in the present paper, an effective Dense Co-Attention Networks (DCAN) is proposed. First, to better capture the relationship between words that are relatively far apart and make the extracted semantics more robust, the Bidirectional Long Short-Term Memory (Bi-LSTM) neural network is introduced to encode questions and answers; second, to realize the fine-grained interactions between the question words and image regions, a dense multimodal co-attention model is proposed. The model's basic components include the self-attention unit and the guided-attention unit, which are cascaded in depth to form a hierarchical structure. The experimental results on the VQA-v2 dataset show that DCAN has obvious performance advantages, which makes VQA applicable to a wider range of AI scenarios.

Multi-Modal Explicit Sparse Attention Networks for Visual Question Answering.

Visual question answering (VQA) is a multi-modal task involving natural language processing (NLP) and computer vision (CV), which requires models to understand of both visual information and textual information simultaneously to predict the correct answer for the input visual image and textual question, and has been widely used in smart and intelligent transport systems, smart city, and other fields. Today, advanced VQA approaches model dense interactions between image regions and question words by designing co-attention mechanisms to achieve better accuracy. However, modeling interactions between each image region and each question word will force the model to calculate irrelevant information, thus causing the model's attention to be distracted. In this paper, to solve this problem, we propose a novel model called Multi-modal Explicit Sparse Attention Networks (MESAN), which concentrates the model's attention by explicitly selecting the parts of the input features that are the most relevant to answering the input question. We consider that this method based on top-k selection can reduce the interference caused by irrelevant information and ultimately help the model to achieve better performance. The experimental results on the benchmark dataset VQA v2 demonstrate the effectiveness of our model. Our best single model delivers 70.71% and 71.08% overall accuracy on the test-dev and test-std sets, respectively. In addition, we also demonstrate that our model can obtain better attended features than other advanced models through attention visualization. Our work proves that the models with sparse attention mechanisms can also achieve competitive results on VQA datasets. We hope that it can promote the development of VQA models and the application of artificial intelligence (AI) technology related to VQA in various aspects.

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks.

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

An Efficient Two-Factor Authentication Scheme Based on the Merkle Tree.

The Time-based One-Time Password (TOTP) algorithm is commonly used for two-factor authentication. In this algorithm, a shared secret is used to derive a One-Time Password (OTP). However, in TOTP, the client and the server need to agree on a shared secret (i.e., a key). As a consequence, an adversary can construct an OTP through the compromised key if the server is hacked. To solve this problem, Kogan et al. proposed T/Key, an OTP algorithm based on a hash chain. However, the efficiency of OTP generation and verification is low in T/Key. In this article, we propose a novel and efficient Merkle tree-based One-Time Password (MOTP) algorithm to overcome such limitations. Compared to T/Key, this proposal reduces the number of hash operations to generate and verify the OTP, at the cost of small server storage and tolerable client storage. Experimental analysis and security evaluation show that MOTP can resist leakage attacks against the server and bring a tiny delay to two-factor authentication and verification time.

QTL mapping pod dehiscence resistance in soybean (Glycine max L. Merr.) using specific-locus amplified fragment sequencing.

KEY MESSAGE: We constructed a high-density genetic linkage map comprising 4,593 SLAF markers using specific-locus amplified fragment sequencing and identified six quantitative trait loci for pod dehiscence resistance in soybean. Pod dehiscence is necessary for propagation in wild soybean (Glycine soja). It is a major component causing yield losses in cultivated soybean, however, and thus, cultivated soybean varieties have been artificially selected for resistance to pod dehiscence. Detecting quantitative trait loci (QTLs) related to pod dehiscence is required for molecular marker-assisted selection for breeding new varieties with pod dehiscence resistance. In this study, we constructed a high-density genetic linkage map using 260 recombinant inbred lines derived from the cultivars of Heihe 43 (pod-indehiscent) (ZDD24325) and Heihe 18 (pod-dehiscent) (ZDD23620). The map contained 4953 SLAF markers spanning 1478.86 cM on 20 linkage groups with an average distance between adjacent markers of 0.53 cM. In total, six novel QTLs related to pod dehiscence were mapped using inclusive composite interval mapping, explaining 7.22-24.44% of the phenotypic variance across 3 years, including three stable QTLs (qPD01, qPD05-1 and qPD08-1), that had been validated by developing CAPS/dCAPS markers. Based on the SNP/Indel and significant differential expression analyses of two parents, seven genes were selected as candidate genes for future study. The high-density map, three stable QTLs and their molecular markers will be helpful for map-based cloning of pod dehiscence resistance genes and marker-assisted selection of pod dehiscence resistance in soybean breeding.

Pattern recognition algorithm and software design of an optical fiber vibration signal based on Φ-optical time-domain reflectometry.

A phase-sensitive optical time-domain reflectometry with a detection distance of 10 km is used in this paper to recognize and to detect in real time events along the perimeter security monitoring system. To optimize the signal processing in the software system and achieve the design of distributed optical fiber vibration signal of the perimeter security monitoring system, a two-level vibration pattern recognition scheme based on the power spectral estimation is proposed. The monitoring system software of the proposed algorithm is designed and implemented, as well as the functional modules involved in the design of the software system. The experimental tests of the whole system show that the proposed algorithm can effectively identify in real time any intrusion event along the sensing cable, with an intrusion detection rate greater than 95%, including false alarms. Also, the corresponding monitoring system software can display the data collected in real time, with a processing time of around 1.6 s, which proves its reliability, thereby ensuring the system's validity.

Comparing the Curative Efficacy of Different Skin Grafting Methods for Third-Degree Burn Wounds.

BACKGROUND Our research purpose was to compare the curative efficacy of different skin grafting methods for treating third-degree burn wounds. MATERIAL AND METHODS A total of 105 patients with third-degree burns were involved in this study. The burn wounds of these patients were treated using three different methods: Meek skin grafting, Stamp skin grafting, and Microskin grafting. Patients treated with different methods were placed in different groups. The skin graft survival rate, skin graft fusion time, wound healing time, total time of surgery, and 1% total body surface area (TBSA) treatment costs in each group were evaluated during and after the grafting procedures. After the operations, patients were followed up for 3 to 18 months in order to evaluate the postoperative outcomes. RESULTS The skin graft survival rate was significantly higher in the Meek group compared to the rates in the Stamp and Microskin groups (both P<0.01). In addition, the skin graft fusion time, wound healing time, and 1% TBSA treatment costs were significantly lower in the Meek group compared to those in the Stamp and Microskin groups (both P<0.01). Furthermore, the Meek group exhibited better results with respect to curative efficacy, scarring status, and joint activity in comparison to the other two groups (both P<0.05). CONCLUSIONS The Meek skin grafting method showed better clinical efficacy for treating large wound areas in third-degree burn patients compared to the Stamp and Microskin skin grafting methods.

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

[Occurrence characteristics and molecular genetic basis of pod shattering in soybean].

Pod shattering is a natural property of wild soybean (Glycine soja) for propagation and also a major cause of yield loss in cultivated soybean (Glycine max L. Merr). Thus, studies on occurrence characteristics and molecular genetic basis of pod shattering in soybean can provide insights into both molecular mechanisms and potential application in legume crop improvement. In this review, we summarize the occurrence features and phenotypic identification methods of pod shattering based on analysis of the cellular microstructure of shattering-resistant soybean pod. We also introduced the identification and breeding of shattering-resistant germplasms, the progress of molecular genetic studies on shattering-resistant phenotype in soybean as well as perspectives on future studies of pod-shattering trait and application in crop improvement.

The application of similar image retrieval in electronic commerce.

Traditional online shopping platform (OSP), which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers' experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system.

Bio-based melamine formaldehyde resins for flame-retardant polyurethane foams.

The polyurethane (PU) foams can be functionally tailored by modifying the formulation with different additives. One such additive is melamine (MA) formaldehyde resin for improving their flame-retardant properties. In this work, the glycerol-modified (GMF), sodium alginate (SGMF)- and lignosulfonate-modified melamine formaldehyde (LGMF) were prepared and used as flame retardants reacting with isocyanate to prepare the corresponding rigid polyurethane foams (GMF-PU, SGMF-PU and LGMF-PU). The thermomechanical properties and flame-retardant properties of the foams were characterized. The results showed that the specific compression strength of GMF-PU, SGMF-PU and LGMF-PU increased substantially compared to the foams from physical addition of MA, sodium alginate and lignosulfonate, all of which were greater than that of the foam without any flame retardant (PPU). Meanwhile, the cell wall of the foam pores became thicker and the closed pore ratio increased. The sodium alginate and lignosulfonate played a key role in enhancing foam thermal stability. The limiting oxygen index values and cone calorimetry results indicated the flame-retardant efficiency of GMF-PU, SGMF-PU and LGMF-PU was significantly enhanced relative to PPU. Meanwhile, the heat and smoke release results indicated sodium alginate and lignosulfonate could reduce the amount of smoke generation to different degrees during the combustion of the foam.

Oceanobacillus picturae alleviates cadmium stress and promotes growth in soybean seedlings.

Cadmium (Cd) is a heavy metal that significantly impacts human health and the environment. Microorganisms play a crucial role in reducing heavy metal stress in plants; however, the mechanisms by which microorganisms enhance plant tolerance to Cd stress and the interplay between plants and microorganisms under such stress remain unclear. In this study, Oceanobacillus picturae (O. picturae) was isolated for interaction with soybean seedlings under Cd stress. Results indicated that Cd treatment alone markedly inhibited soybean seedling growth. Conversely, inoculation with O. picturae significantly improved growth indices such as plant height, root length, and fresh weight, while also promoting recovery in soil physiological indicators and pH. Metabolomic and transcriptomic analyses identified 157 genes related to aspartic acid, cysteine, and flavonoid biosynthesis pathways. Sixty-three microbial species were significantly associated with metabolites in these pathways, including pathogenic, adversity-resistant, and bioconductive bacteria. This research experimentally demonstrates, for the first time, the growth-promoting effect of the O. picturae strain on soybean seedlings under non-stress conditions. It also highlights its role in enhancing root growth and reducing Cd accumulation in the roots under Cd stress. Additionally, through the utilization of untargeted metabolomics, metagenomics, and transcriptomics for a multi-omics analysis, we investigated the impact of O. picturae on the soil microbiome and its correlation with differential gene expression in plants. This innovative approach unveils the molecular mechanisms underlying O. picturae's promotion of root growth and adaptation to Cd stress.

Comparing the Salt Tolerance of Different Spring Soybean Varieties at the Germination Stage.

Salinization is a global agricultural problem with many negative effects on crops, including delaying germination, inhibiting growth, and reducing crop yield and quality. This study compared the salt tolerance of 20 soybean varieties at the germination stage to identify soybean germplasm with a high salt tolerance. Germination tests were conducted in Petri dishes containing 0, 50, 100, 150, and 200 mmol L-1 NaCl. Each Petri dish contained 20 soybean seeds, and each treatment was repeated five times. The indicators of germination potential, germination rate, hypocotyl length, and radicle length were measured. The salt tolerance of 20 soybean varieties was graded, and the theoretical identification concentration was determined by cluster analysis, the membership function method, one-way analysis of variance, and quadratic equation analysis. The relative germination rate, relative germination potential, relative root length, and relative bud length of the 20 soybean germplasms decreased when the salt concentration was >50 mmol L-1, compared with that of the Ctrl. The half-lethal salt concentration of soybean was 164.50 mmol L-1, and the coefficient of variation was 18.90%. Twenty soybean varieties were divided into three salt tolerance levels following cluster analysis: Dongnong 254, Heike 123, Heike 58, Heihe 49, and Heike 68 were salt-tolerant varieties, and Xihai 2, Suinong 94, Kenfeng 16, and Heinong 84 were salt-sensitive varieties, respectively. This study identified suitable soybean varieties for planting in areas severely affected by salt and provided materials for screening and extracting parents or genes to breed salt-tolerant varieties in areas where direct planting is impossible. It assists crop breeding at the molecular level to cope with increasingly serious salt stress.

Salt altered rhizosphere fungal community and induced soybean recruit specific species to ameliorate salt stress.

Different crop genotypes showed different adaptability to salt stress, which is partly attributable to the microorganisms in the rhizosphere. Yet, knowledge about how fungal communities of different genotypes in soybean respond to salt stress is limited. Here, qPCR and ITS sequencing were used to assess the response of rhizobial fungal communities of resistant and susceptible soybean to salt stress. Moreover, we isolated two fungal species recruited by resistant soybeans for validation. The assembly of fungal community structure might be strongly linked to alterations in fungal abundance and soil physicochemical properties. Salt stress derived structural differences in fungal communities of resistant and susceptible genotypes. The salt-resistant genotype appeared to recruit some fungal taxa to the rhizosphere to help mitigating salt stress. An increase of fungal taxa with predicted saprotrophic lifestyles might help promoting plant growth by increasing nutrient availability to the plants. Compared with the susceptible genotypes, the resistant genotypes had more stronger network structure of fungi. Lastly, we verified that recruited fungi, such as Penicillium and Aspergillus, can soybean adapt to salt stress. This study provided a promising approach for rhizospheric fungal community to enhance salt tolerance of soybean from the perspective of microbiology and ecology.

Effects of salt stress on soil enzyme activities and rhizosphere microbial structure in salt-tolerant and -sensitive soybean.

Salt is recognized as one of the most major factors that limits soybean yield in acidic soils. Soil enzyme activity and bacterial community have a critical function in improving the tolerance to soybean. Our aim was to assess the activities of soil enzyme, the structure of bacteria and their potential functions for salt resistance between Salt-tolerant (Salt-T) and -sensitive (Salt-S) soybean genotypes when subject to salt stress. Plant biomass, soil physicochemical properties, soil catalase, urease, sucrase, amylase, and acid phosphatase activities, and rhizosphere microbial characteristics were investigated in Salt-T and Salt-S soybean genotypes under salt stress with a pot experiment. Salt stress significantly decreased the soil enzyme activities and changed the rhizosphere microbial structure in a genotype-dependent manner. In addition, 46 ASVs which were enriched in the Salt-T geotype under the salt stress, such as ASV19 (Alicyclobacillus), ASV132 (Tumebacillus), ASV1760 (Mycobacterium) and ASV1357 (Bacillus), which may enhance the tolerance to soybean under salt stress. Moreover, the network structure of Salt-T soybean was simplified by salt stress, which may result in soil bacterial communities being susceptible to external factors. Salt stress altered the strength of soil enzyme activities and the assembly of microbial structure in Salt-T and Salt-S soybean genotypes. Na+, NO3--N, NH4+-N and Olsen-P were the most important driving factors in the structure of bacterial community in both genotypes. Salt-T genotypes enriched several microorganisms that contributed to enhance salt tolerance in soybeans, such as Alicyclobacillus, Tumebacillus, and Bacillus. Nevertheless, the simplified network structure of salt-T genotype due to salt stress may render its bacterial community structure unstable and susceptible.

Genome-wide signatures of the geographic expansion and breeding of soybean.

Soybean is a leguminous crop that provides oil and protein. Exploring the genomic signatures of soybean evolution is crucial for breeding varieties with improved adaptability to environmental extremes. We analyzed the genome sequences of 2,214 soybeans and proposed a soybean evolutionary route, i.e., the expansion of annual wild soybean (Glycine soja Sieb. & Zucc.) from southern China and its domestication in central China, followed by the expansion and local breeding selection of its landraces (G. max (L.) Merr.). We observed that the genetic introgression in soybean landraces was mostly derived from sympatric rather than allopatric wild populations during the geographic expansion. Soybean expansion and breeding were accompanied by the positive selection of flowering time genes, including GmSPA3c. Our study sheds light on the evolutionary history of soybean and provides valuable genetic resources for its future breeding.

Pre-training Model Based on Parallel Cross-Modality Fusion Layer.

Visual Question Answering (VQA) is a learning task that combines computer vision with natural language processing. In VQA, it is important to understand the alignment between visual concepts and linguistic semantics. In this paper, we proposed a Pre-training Model Based on Parallel Cross-Modality Fusion Layer (P-PCFL) to learn the fine-grained relationship between vision and language. The P-PCFL model is composed of three Encoders: Object Encoder, Language Encoder, and Parallel Cross-Modality Fusion Encoder, with Transformer as the core. We use four different Pre-training missions, namely, Cross-Modality Mask Language Modeling, Cross-Modality Mask Region Modeling, Image-Text Matching, and Image-Text Q&A, to pre-train the P-PCFL model and improve its reasoning and universality, which help to learn the relationship between Intra-modality and Inter-modality. Experimental results on the platform of Visual Question Answering dataset VQA v2.0 show that the Pre-trained P-PCFL model has a good effect after fine-tuning the parameters. In addition, we also conduct ablation experiments and provide some results of Attention visualization to verify the effectiveness of P-PCFL model.