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Accurate and fast recognition of vehicle license plates from natural scene images is a crucial and challenging task. Existing methods can recognize license plates in simple scenarios, but their performance degrades significantly in complex environments. A novel license plate detection and recognition model YOLOv5-PDLPR is proposed, which employs YOLOv5 target detection algorithm in the license plate detection part and uses the PDLPR algorithm proposed in this paper in the license plate recognition part. The PDLPR algorithm is mainly designed as follows: (1) A Multi-Head Attention mechanism is used to accurately recognize individual characters. (2) A global feature extractor network is designed to improve the completeness of the network for feature extraction. (3) The latest parallel decoder architecture is adopted to improve the inference efficiency. The experimental results show that the proposed algorithm has better accuracy and speed than the comparison algorithms, can achieve real-time recognition, and has high efficiency and robustness in complex scenes.

Background Genomic islands are associated with microbial adaptations, carrying genomic signatures different from the host. Some methods perform an overall test to identify genomic islands based on their local features. However, regions of different scales will display different genomic features. Results We proposed here a novel method “2SigFinder “, the first combined use of small-scale and large-scale statistical testing for genomic island detection. The proposed method was tested by genomic island boundary detection and identification of genomic islands or functional features of real biological data. We also compared the proposed method with the comparative genomics and composition-based approaches. The results indicate that the proposed 2SigFinder is more efficient in identifying genomic islands. Conclusions From real biological data, 2SigFinder identified genomic islands from a single genome and reported robust results across different experiments, without annotated information of genomes or prior knowledge from other datasets. 2SigHunter identified 25 Pathogenicity, 1 tRNA, 2 Virulence and 2 Repeats from 27 Pathogenicity, 1 tRNA, 2 Virulence and 2 Repeats, and detected 101 Phage and 28 HEG out of 130 Phage and 36 HEGs in S. enterica Typhi CT18, which shows that it is more efficient in detecting functional features associated with GIs.

Timely identification of emerging antigenic variants is critical to influenza vaccine design. The accuracy of a sequence-based antigenic prediction method relies on the choice of amino acids substitution matrices. In this study, we first compared a comprehensive 95 substitution matrices reflecting various amino acids properties in predicting the antigenicity of influenza viruses by a random forest model. We then proposed a novel algorithm called joint random forest regression (JRFR) to jointly consider top substitution matrices. We applied JRFR to human H3N2 seasonal influenza data from 1968 to 2003. A 10-fold cross-validation shows that JRFR outperforms other popular methods in predicting antigenic variants. In addition, our results suggest that structure features are most relevant to influenza antigenicity. By restricting the analysis to data involving two adjacent antigenic clusters, we inferred a few key amino acids mutation driving the 11 historical antigenic drift events, pointing to experimentally validated mutations. Finally, we constructed an antigenic cartography of all H3N2 viruses with hemagglutinin (the glycoprotein on the surface of the influenza virus responsible for its binding to host cells) sequence available from NCBI flu database, and showed an overall correspondence and local inconsistency between genetic and antigenic evolution of H3N2 influenza viruses.

MicroRNAs (miRNAs) regulate plant growth and development and plant responses to biotic and abiotic stresses. Although extensive studies show that miR159 family members regulate leaf and flower development in Arabidopsis thaliana, the roles of miRNAs in soybean (Glycine max) are poorly understood. Here, we identified six MIR159 genes in soybean, MIR159a–MIR159f, and investigate their expression patterns in plants under low-phosphorus (low-P), NaCl, or abscisic acid (ABA) treatments. In soybean leaves, MIR159e and MIR159f expression was induced by low-P treatment, while in roots, MIR159b, MIR159c, MIR159e, and MIR159f expression was upregulated. In flowers, low-P led to upregulation of MIR159a, MIR159b, MIR159c, and MIR159f but downregulation of MIR159d and MIR159e. In soybean nodules, MIR159b was upregulated but MIR159a, MIR159c, and MIR159d was downregulated under P deficiency. NaCl treatment induced MIR159a, MIR159b, MIR159c, and MIR159e expression in leaves and MIR159a–MIR159f expression in roots. ABA treatment upregulated MIR159a, MIR159b, and MIR159c but downregulated MIR159d, MIR159e, and MIR159f in leaves. These results suggest that miR159 family members function in plant abiotic stress responses. Moreover, total P content in leaves was significantly lower in plants overexpressing MIR159e than in the wild type, suggesting that miR159e may regulate P absorption and transport in soybean plants.

The aim of this work was to provide a theoretical and scientific basis for the treatment, prevention, and control of clinical drug-resistant bacterial infections by studying the molecular epidemiology and horizontal transfer mechanism of -carrying linezolid-resistant strains (LREfs) that were clinically isolated in a tertiary hospital in Kunming, China. Non-repetitive LREfs retained in a tertiary A hospital in Kunming, China. The strains were identified by Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The transferability and horizontal transfer mechanism of gene were analyzed using polymerase chain reaction (PCR), whole-genome sequencing (WGS), and conjugation experiments. A total of 39 LREfs strains were collected, and all of them were multi-drug resistant. There were 30 LREfs strains (76.9%) carrying the gene, The genes and mutations in the 23S rRNA gene were not detected. The conjugation experiments showed that only three of 10 randomly selected -carrying LREfs were successfully conjugated with JH2-2. Further analysis of one successfully conjugated strain revealed that the gene, located in the donor bacterium, formed the transferable fragment under the mediation of the mobile genetic element (MGE) , which was then transferred to the recipient bacterium via horizontal plasmid transfer. Carrying the gene is the primary resistance mechanism of LREfs strains. The gene could carry the and genes to co-transfer among . MGEs such as insertion sequence play an important role in the horizontal transfer of the gene.

Background Many content-based statistical features of secondary structural elements (CBF-PSSEs) have been proposed and achieved promising results in protein structural class prediction, but until now position distribution of the successive occurrences of an element in predicted secondary structure sequences hasn’t been used. It is necessary to extract some appropriate position-based features of the secondary structural elements for prediction task. Results We proposed some position-based features of predicted secondary structural elements (PBF-PSSEs) and assessed their intrinsic ability relative to the available CBF-PSSEs, which not only offers a systematic and quantitative experimental assessment of these statistical features, but also naturally complements the available comparison of the CBF-PSSEs. We also analyzed the performance of the CBF-PSSEs combined with the PBF-PSSE and further constructed a new combined feature set, PBF11CBF-PSSE. Based on these experiments, novel valuable guidelines for the use of PBF-PSSEs and CBF-PSSEs were obtained. Conclusions PBF-PSSEs and CBF-PSSEs have a compelling impact on protein structural class prediction. When combining with the PBF-PSSE, most of the CBF-PSSEs get a great improvement over the prediction accuracies, so the PBF-PSSEs and the CBF-PSSEs have to work closely so as to make significant and complementary contributions to protein structural class prediction. Besides, the proposed PBF-PSSE’s performance is extremely sensitive to the choice of parameter k . In summary, our quantitative analysis verifies that exploring the position information of predicted secondary structural elements is a promising way to improve the abilities of protein structural class prediction.

Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems, and road target detection is one of the most difficult tasks in the field of computer vision. The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments. However, because road targets are prone to complicated backgrounds and sparse features, it is challenging to detect and identify vehicle kinds fast and reliably. We suggest a new vehicle detection model called MEB-YOLO, which combines Mosaic and MixUp data augmentation, Efficient Channel Attention (ECA) attention mechanism, Bidirectional Feature Pyramid Network (BiFPN) with You Only Look Once (YOLO) model, to overcome this problem. Four sections make up this model: Input, Backbone, Neck, and Prediction. First, to improve the detection dataset and strengthen the network, MixUp and Mosaic data improvement are used during the picture processing step. Second, an attention mechanism is introduced to the backbone network, which is Cross Stage Partial Darknet (CSPDarknet), to reduce the influence of irrelevant features in images. Third, to achieve more sophisticated feature fusion without increasing computing cost, the BiFPN structure is utilized to build the Neck network of the model. The final prediction results are then obtained using Decoupled Head. Experiments demonstrate that the proposed model outperforms several already available detection methods and delivers good detection results on the University at Albany DEtection and TRACking (UA-DETRAC) public dataset. It also enables effective vehicle detection on real traffic monitoring data. As a result, this technique is efficient for detecting road targets.

Brassinosteroids are important phytohormones for plant growth and development. In soybean (Glycine max), BR receptors have been identified, but the genes encoding BR biosynthesis-related enzymes remain poorly understood. Here, we found that the soybean genome encodes eight steroid reductases (GmDET2a to GmDET2h). Phylogenetic analysis grouped 105 steroid reductases from moss, fern and higher plants into five subgroups and indicated that the steroid reductase family has experienced purifying selection. GmDET2a and GmDET2b, homologs of the Arabidopsis thaliana steroid 5 α -reductase AtDET2, are proteins of 263 amino acids. Ectopic expression of GmDET2a and GmDET2b rescued the defects of the Atdet2-1 mutant in both darkness and light. Compared to the mutant, the hypocotyl length and plant height of the transgenic lines GmDET2a and GmDET2b increased significantly, in both darkness and light, and the transcript levels of the BR biosynthesis-related genes CPD, DWF4, BR6ox-1 and BR6ox-2 were downregulated in GmDET2aOX-23 and GmDET2bOX-16 lines compared to that in Atdet2-1. Quantitative real-time PCR revealed that GmDET2a and GmDET2b are ubiquitously expressed in all tested soybean organs, including roots, leaves and hypocotyls. Moreover, epibrassinosteroid negatively regulated GmDET2a and GmDET2b expression. Sulfate deficiency downregulated GmDET2a in leaves and GmDET2b in leaves and roots; by contrast, phosphate deficiency upregulated GmDET2b in roots and leaves. Taken together, our results revealed that GmDET2a and GmDET2b function as steroid reductases.

Brassinosteroids are important phytohormones for plant growth and development. In soybean ( ), BR receptors have been identified, but the genes encoding BR biosynthesis-related enzymes remain poorly understood. Here, we found that the soybean genome encodes eight steroid reductases (GmDET2a to GmDET2h). Phylogenetic analysis grouped 105 steroid reductases from moss, fern and higher plants into five subgroups and indicated that the steroid reductase family has experienced purifying selection. GmDET2a and GmDET2b, homologs of the thaliana steroid 5 α -reductase AtDET2, are proteins of 263 amino acids. Ectopic expression of and rescued the defects of the mutant in both darkness and light. Compared to the mutant, the hypocotyl length and plant height of the transgenic lines and increased significantly, in both darkness and light, and the transcript levels of the BR biosynthesis-related genes , , and were downregulated in and lines compared to that in . Quantitative real-time PCR revealed that and are ubiquitously expressed in all tested soybean organs, including roots, leaves and hypocotyls. Moreover, epibrassinosteroid negatively regulated and expression. Sulfate deficiency downregulated in leaves and in leaves and roots; by contrast, phosphate deficiency upregulated in roots and leaves. Taken together, our results revealed that GmDET2a and GmDET2b function as steroid reductases.

Traditional Monte Carlo integration using uniform random sampling exhibits degraded efficiency in low-regularity or high-dimensional problems. We propose a novel deep learning framework based on deterministic number-theoretic sampling points, which is a robust approach specifically designed to handle partial differential equations with rough solutions or in high dimensions. The sample points are generated by the generating vector to achieve the smallest discrepancy. The architecture integrates Physics-Informed Neural Networks (PINNs) with rigorous mathematical guarantees demonstrating lower error bounds compared to conventional uniform random sampling. Numerical validation includes low-regularity Poisson equations, two-dimensional inverse Helmholtz problems, and high-dimensional linear/nonlinear PDEs, systematically demonstrating the algorithm's superior performance and generalization capabilities.