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Tillering and spike differentiation are key agronomic traits for wheat (Triticum aestivum L.) production. Numerous studies have shown that miR396 and growth-regulating factor genes (GRFs) are involved in growth and development of different plant organs. Previously, we have reported that wheat miR396b (tae-miR396b) and their targets TaGRFs (T. aestivum GRFs) play important roles in regulating wheat tillering. This study was to investigate the regulatory roles of tae-miR396b and TaGRFs played during wheat spike development. Wheat cultivar Guomai 301 (wild type, WT) and its three sipke mutants dwarf round spike mutant (drs), apical spikelet sterility mutant (ass) and prematurely terminated spike differentiation mutant (ptsd1) were studied. Three homeologous genes of tae-miR396b on the long arms of chromosomes 6A, 6B, and 6D were identified, and they encoded the same mature miRNA. Complementary sequences of mature tae-miR396b were identified in 23 TaGRFs, indicating they were the target genes of tae-miR396b. Tae-miR396b had different regulatory effects on TaGRFs between Guomai 301 and its mutants. TaGRF2-7A was confirmed to be the target gene of tae-miR396b by molecular interaction assay. The expression levels of tae-miR396b and TaGRFs were different between WT and mutants drs, ass and ptsd1 at the floret primordium visible (S1), the two awns/spikelet reaching apical meristem of the spikelet (S2), and the green anther stage (S3). The expression level of tae-miR396b in WT was significantly higher than that in mutants drs and ass. The most TaGRFs were negatively regulated by tae-miR396b. The abnormal expressions of TaGRF1 (6A, 6D), TaGRF2 (7A, 7B, 7D), TaGRF4 (6A, 6B), TaGRF5 (4A, 7A, 7D), and TaGRF10 (6A, 6B, 6D) were important causes for abnormal spike development in the three mutants. This study laid foundation for further elucidating functions of tae-miR396b and TaGRFs underlying wheat spike development. Regulating tae-miR396b and TaGRFs will be a new approach for wheat high yield breeding.

With the rapid advancement of quantitative trading technology, the demand for low-latency in Level 2 Deep Market Quote (L2DMQ) Decoding is ever-increasing. The L2DMQ decoder faces increasingly significant challenges in terms of bandwidth and performance. As a result, L2DMQ decoders based on FPGA and ASIC technologies are becoming mainstream solutions. Functional verification and testing of L2DMQ decoders implemented as digital circuits became a challenging task. Given the richness of message template types in L2DMQ and the expanded functionalities of decoders potentially leading to decreased stability in the verification platform, efficient and stable decoder functional verification becomes a complex issue. Furthermore, factors like long-term operation or environments with increased levels of electrostatic electricity or charged particle occurrences can lead to functional faults in the decoding circuit. Thus, error injection has become critically important for testing the reliability of the L2DMQ decoder. This paper investigates the encoding method of L2DMQ, providing a comprehensive complexity analysis. Based on the analysis, a message-template-based (MTB) random stimulus generation method is proposed. Compared to the traditional Message-Length-based (MLB) random stimulus generation method, our proposed MTB method simplifies the reference model of the verification platform and enhances its stability. The experimental results show that the verification platform reduces runtime by 26.69% to 69.29%. Building on this, we target a Universal Verification Methodology (UVM)-based fault-injection and detection platform with MTB random stimulus. Experimental results reveal that the obtained functional coverage reaches up to more than 99% when the number of fault injections ranges from 3750 to 22500. This demonstrates the efficiency of the proposed techniques when adopted using UVM and determines error severity levels through the functional coverage while varying different MTB random stimuli.

The highly dynamic properties of maneuvering targets make it intractable for radars to predict the target motion states accurately and quickly, and low-grade predicted states depreciate the efficiency of resource allocation. To overcome this problem, we introduce the modified current statistical (MCS) model, which incorporates the input-acceleration transition matrix into the augmented state transition matrix, to predict the motion state of a maneuvering target. Based on this, a robust resource allocation strategy is developed for maneuvering target tracking (MTT) in a netted opportunistic array radar (OAR) system under uncertain conditions. The mechanism of the strategy is to minimize the total transmitting power conditioned on the desired tracking performance. The predicted conditional Cramér–Rao lower bound (PC-CRLB) is deemed as the optimization criterion, which is derived based on the recently received measurement so as to provide a tighter lower bound than the posterior CRLB (PCRLB). For the uncertainty of the target reflectivity, we encapsulate the determined resource allocation model with chance-constraint programming (CCP) to balance resource consumption and tracking performance. A hybrid intelligent optimization algorithm (HIOA), which integrates a stochastic simulation and a genetic algorithm (GA), is employed to solve the CCP problem. Finally, simulations demonstrate the efficiency and robustness of the presented algorithm.

Abstract Background We aim to clarify the controversial associations between EBV-related antibodies and gastric cancer risk. Methods We analysed the associations between serological Epstein-Barr nuclear antigen 1 immunoglobulin A (EBNA1-IgA) and viral capsid antigen immunoglobulin A (VCA-IgA) by enzyme-linked immunosorbent assay and the risk of gastric cancer in a nested case-control study originated from a population-based nasopharyngeal carcinoma (NPC) screening cohort in Zhongshan, a city of southern China, including 18 gastric cancer cases and 444 controls. Conditional logistic regression was used to calculate the odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Results All the sera of cases were sampled before diagnosis and the median time interval was 3.04 (range: 0.04, 7.59) years. Both increased relative optical density (rOD) values of EBNA1-IgA and VCA-IgA were associated with higher risks of gastric cancer with age adjusted ORs of 1.99 (95%CI: 1.07, 3.70) and 2.64 (95%CI: 1.33, 5.23), respectively. Each participant was further classified as high or medium/low risk based on a combination of two anti-EBV antibody levels. Participants in the high-risk group had substantially higher odds of developing gastric cancer than that in the medium/low risk group with an age adjusted OR of 6.53 (95%CI: 1.69, 25.26). Conclusions Our research reveals positive associations between EBNA1-IgA and VCA-IgA and gastric cancer risk in southern China. We thus postulate that EBNA1-IgA and VCA-IgA might appear to be potential biomarkers for gastric cancer. More research to further validate the results among diverse populations and investigate its underlying biological mechanism is needed.

In this paper, an antenna array aperture resource management scheme of opportunistic array radar (OAR) based on chance-constraint programming (CCP) is proposed for multiple target tracking (MTT). In the multi-target tracking scenario, multiple beams need to be synthesized to illuminate the targets. According to the predicted distance of targets, the OAR can intelligently segment the antenna array aperture, and select the optimal combination of array elements to synthesize the satisfactory beams with the element-level antenna steering. Owing to the unique array arrangement and work mode of opportunistic array, the element number in working modes is uncertain. To represent the uncertainty, a fuzzy CCP model is introduced. The element number in working modes is viewed as a fuzzy variable. The maximum tracking error, which is denoted by Bayesian Cramér-Rao lower bound(BCRLB), of the targets is regarded as the objective function. The fuzzy simulation algorithm is embedded into genetic algorithm (GA) to compose a hybrid intelligent optimization algorithm to solve the CCP problem. The simulation results verify the validity and practicability of the antenna array aperture resource allocation scheme.

In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize an existing Riccati-like recursion to accomplish effective resource allocation. The prior Cramér-Rao lower boundary (prior CRLB-like) is compared with the upper boundary of the desired tracking error range to determine the adaptive sampling interval, and the Bayesian CRLB-like (BCRLB-like) gives a criterion used for measuring power allocation. In addition, considering the randomness of target radar cross section (RCS), we adopt the CCP to package the deterministic resource management model, which minimizes the total transmitted power by effective resource allocation. Lastly, the stochastic simulation is embedded into a genetic algorithm (GA) to produce a hybrid intelligent optimization algorithm (HIOA) to solve the CCP optimization problem. Simulation results show that the global performance of the radar system can be improved effectively by the resource allocation scheme.