A Real-Time License Plate Detection and Recognition Model in Unconstrained Scenarios.

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.

Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer.

Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.

Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection.

Genomic islands are fragments of foreign DNA that are found in bacterial and archaeal genomes, and are typically associated with symbiosis or pathogenesis. While numerous genomic island detection methods have been proposed, there has been limited evaluation of the efficiency of the genome information processing and boundary recognition tools. In this study, we conducted a review of the statistical methods involved in genomic signatures, host signature extraction, informative signature selection, divergence measures, and boundary detection steps in genomic island prediction. We compared the performances of these methods on simulated experiments using alien fragments obtained from both artificial and real genomes. Our results indicate that among the nine genomic signatures evaluated, genomic signature frequency and full probability performed the best. However, their performance declined when normalized to their expectations and variances, such as Z-score and composition vector. Based on our experiments of the E. coli genome, we found that the confidence intervals of the window variances achieved the best performance in the signature extraction of the host, with the best confidence interval being 1.5-2 times the standard error. Ordered kurtosis was most effective in selecting informative signatures from a single genome, without requiring prior knowledge from other datasets. Among the three divergence measures evaluated, the two-sample t-test was the most successful, and a non-overlapping window with a small eye window (size 2) was best suited for identifying compositionally distinct regions. Finally, the maximum of the Markovian Jensen-Shannon divergence score, in terms of GC-content bias, was found to make boundary detection faster while maintaining a similar error rate.

Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks.

BACKGROUND: More and more evidence showed that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human sophisticated diseases. Therefore, predicting human lncRNA-disease associations is a challenging and urgently task in bioinformatics to research of human sophisticated diseases. RESULTS: In the work, a global network-based computational framework called as LRWRHLDA were proposed which is a universal network-based method. Firstly, four isomorphic networks include lncRNA similarity network, disease similarity network, gene similarity network and miRNA similarity network were constructed. And then, six heterogeneous networks include known lncRNA-disease, lncRNA-gene, lncRNA-miRNA, disease-gene, disease-miRNA, and gene-miRNA associations network were applied to design a multi-layer network. Finally, the Laplace normalized random walk with restart algorithm in this global network is suggested to predict the relationship between lncRNAs and diseases. CONCLUSIONS: The ten-fold cross validation is used to evaluate the performance of LRWRHLDA. As a result, LRWRHLDA achieves an AUC of 0.98402, which is higher than other compared methods. Furthermore, LRWRHLDA can predict isolated disease-related lnRNA (isolated lnRNA related disease). The results for colorectal cancer, lung adenocarcinoma, stomach cancer and breast cancer have been verified by other researches. The case studies indicated that our method is effective.

A time series representation of protein sequences for similarity comparison.

Based on the physicochemical indexes of 20 amino acids and the Hungarian algorithm, each amino acid was mapped into a vector. And, the protein sequence can be represented as time series in eleven-dimensional space. In addition, the DTW algorithm was applied to calculate the distance between two time series to compare the similarities of protein sequences. The validity and accuracy of this method was illustrated by similarity comparison of ND5 proteins of nine species. Furthermore, homology analysis of eleven ACE2 proteins, which included human, Malayan pangolin and six species of bats, confirmed that the human had shorter evolutionary distance from the pangolin than those bats. The phylogenetic tree of spike protein sequences of 36 coronaviruses, which were divided into five groups, Class I, Class II, Class III, SARS-CoVs and COVID-19, was constructed.

A Generalized Iterative Map for Analysis of Protein Sequences.

AIM AND OBJECTIVE: The similarities comparison of biological sequences is an important task in bioinformatics. The methods of the similarities comparison for biological sequences are divided into two classes: sequence alignment method and alignment-free method. The graphical representation of biological sequences is a kind of alignment-free method, which constitutes a tool for analyzing and visualizing the biological sequences. In this article, a generalized iterative map of protein sequences was suggested to analyze the similarities of biological sequences. MATERIALS AND METHODS: Based on the normalized physicochemical indexes of 20 amino acids, each amino acid can be mapped into a point in 5D space. A generalized iterative function system was introduced to outline a generalized iterative map of protein sequences, which can not only reflect various physicochemical properties of amino acids but also incorporate with different compression ratios of the component of a generalized iterative map. Several properties were proved to illustrate the advantage of the generalized iterative map. The mathematical description of the generalized iterative map was suggested to compare the similarities and dissimilarities of protein sequences. Based on this method, similarities/dissimilarities were compared among ND5 protein sequences, as well as ND6 protein sequences of ten different species. RESULTS: By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to show the utility of our approach. The comparison results show that our approach has better correlations with ClustalW for all species than other approaches and illustrate the effectiveness of our approach. CONCLUSION: Two examples show that our method not only has good performances and effects in the similarity/dissimilarity analysis of protein sequences but also does not require complex computation.

Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur.

Data from the SEER reports reveal that the occurrence rate of a cancer type generally follows a unimodal distribution over age, peaking at an age that is cancer-type specific and ranges from 30+ through 70+. Previous studies attribute such bell-shaped distributions to the reduced proliferative potential in senior years but fail to explain why some cancers have their occurrence peak at 30+ or 40+. We present a computational model to offer a new explanation to such distributions. The model uses two factors to explain the observed age-dependent cancer occurrence rates: cancer risk of an organ and the availability level of the growth signals in circulation needed by a cancer type, with the former increasing and the latter decreasing with age. Regression analyses were conducted of known occurrence rates against such factors for triple negative breast cancer, testicular cancer and cervical cancer; and all achieved highly tight fitting results, which were also consistent with clinical, gene-expression and cancer-drug data. These reveal a fundamentally important relationship: while cancer is driven by endogenous stressors, it requires sufficient levels of exogenous growth signals to happen, hence suggesting the realistic possibility for treating cancer via cleaning out the growth signals in circulation needed by a cancer.

Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes.

A human papillomavirus type plays an important role in the early diagnosis of cervical cancer. Most of the prediction methods use protein sequence and structure information, but the reduced amino acid modes have not been used until now. In this paper, we introduced the modes of reduced amino acids to predict high-risk HPV. We first reduced 20 amino acids into several nonoverlapping groups and calculated their structure and physicochemical modes for high-risk HPV prediction, which was tested and compared with the existing methods on 68 samples of known HPV types. The experiment result indicates that the proposed method achieved better performance with an accuracy of 96.49%, indicating that the reduced amino acid modes might be used to improve the prediction of high-risk HPV types.

2SigFinder: the combined use of small-scale and large-scale statistical testing for genomic island detection from a single genome.

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.

RaaMLab: A MATLAB toolbox that generates amino acid groups and reduced amino acid modes.

Amino acid (AA) classification and its different biophysical and chemical characteristics have been widely applied to analyze and predict the structural, functional, expression and interaction profiles of proteins and peptides. We present RaaMLab, a free and open-source MATLAB toolbox, to facilitate studies on proteins and peptides, to generate AA groups and to extract the structural and physicochemical features of reduced AAs (RedAA). This toolbox offers 4 kinds of databases, including the physicochemical properties of AAs and their groupings, 49 AA classification methods and 5 types of biophysicochemical features of RedAAs. These factors can be easily computed based on user-defined alphabet size and AA properties of AA groupings. RaaMLab is an open source freely available at https://github.com/bioinfo0706/RaaMLab. This website also contains a tutorial, extensive documentation and examples.

An efficient genomic signature ranking method for genomic island prediction from a single genome.

Genomic islands that are associated with microbial adaptations and carry genomic signatures different from that of the host, and thus many methods have been proposed to select the informative genomic signatures from a range of organisms and discriminate genomic islands from the rest of the genome in terms of these signature biases. However, they are of limited use when closely related genomes are unavailable. In the present work, we proposed a kurtosis-based ranking method to select the informative genomic signatures from a single genome. In simulations with alien fragments from artificial and real genomes, the proposed kurtosis-based ranking method efficiently selected the informative genomic signatures from a single genome, without annotated information of genomes or prior knowledge from other datasets. This understanding can be useful to design more powerful method for genomic island detection.

Depth perception based 3D holograms enabled with polarization-independent metasurfaces.

Metasurfaces consist of dielectric nanobrick arrays with different dimensions in the long and short axes can be used to generate different phase delays, predicting a new way to manipulate an incident beam in the two orthogonal directions separately. Here we demonstrate the concept of depth perception based three-dimensional (3D) holograms with polarization-independent metasurfaces. 4-step dielectric metasurfaces-based fan-out optical elements and holograms operating at 658 nm were designed and simulated. Two different holographic images with high fidelity were generated at the same plane in the far field for different polarization states. One can observe the 3D effect of target objects with polarized glasses. With the advantages of ultracompactness, flexibility and replicability, the polarization-independent metasurfaces open up depth perception based stereoscopic imaging in a holographic way.

Functional Characterization of the Steroid Reductase Genes GmDET2a and GmDET2b form Glycine max.

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.

Theoretical study of polarization dependence of carrier-induced refractive index change of quantum dot.

The influences of dot material component, barrier material component, aspect ratio and carrier density on the refractive index changes of TE mode and TM mode of columnar quantum dot are analyzed, and a multiparameter adjustment method is proposed to realize low polarization dependence of refractive index change. Then the quantum dots with low polarization dependence of refractive index change (<1.5%) within C-band (1530 nm - 1565 nm) are designed, and it shows that quantum dots with different material parameters are anticipated to have similar characteristics of low polarization dependence.

Matrix completion with side information and its applications in predicting the antigenicity of influenza viruses.

MOTIVATION: Low-rank matrix completion has been demonstrated to be powerful in predicting antigenic distances among influenza viruses and vaccines from partially revealed hemagglutination inhibition table. Meanwhile, influenza hemagglutinin (HA) protein sequences are also effective in inferring antigenic distances. Thus, it is natural to integrate HA protein sequence information into low-rank matrix completion model to help infer influenza antigenicity, which is critical to influenza vaccine development. RESULTS: We have proposed a novel algorithm called biological matrix completion with side information (BMCSI), which first measures HA protein sequence similarities among influenza viruses (especially on epitopes) and then integrates the similarity information into a low-rank matrix completion model to predict influenza antigenicity. This algorithm exploits both the correlations among viruses and vaccines in serological tests and the power of HA sequence in predicting influenza antigenicity. We applied this model into H3N2 seasonal influenza virus data. Comparing to previous methods, we significantly reduced the prediction root-mean-square error in a 10-fold cross validation analysis. Based on the cartographies constructed from imputed data, we showed that the antigenic evolution of H3N2 seasonal influenza is generally S-shaped while the genetic evolution is half-circle shaped. We also showed that the Spearman correlation between genetic and antigenic distances (among antigenic clusters) is 0.83, demonstrating a globally high correspondence and some local discrepancies between influenza genetic and antigenic evolution. Finally, we showed that 4.4%±1.2% genetic variance (corresponding to 3.11 ± 1.08 antigenic distances) caused an antigenic drift event for H3N2 influenza viruses historically. AVAILABILITY AND IMPLEMENTATION: The software and data for this study are available at http://bi.sky.zstu.edu.cn/BMCSI/. CONTACT: jialiang.yang@mssm.edu or pinganhe@zstu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting influenza antigenicity from Hemagglutintin sequence data based on a joint random forest method.

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.

Dual field-of-view step-zoom metalens.

A conventional optical zoom system is bulky, expensive, and complicated for real-time adjustment. Recent progress in metasurface research has provided a new solution to achieve innovative compact optical systems. In this Letter, we propose a highly integrated step-zoom lens with dual field of view (FOV) based on double-sided metasurfaces. With silicon nanobrick arrays of spatially varying orientations sitting on both sides of a transparent substrate, this ultrathin step-zoom metalens can be designed to focus an incident circular polarized beam with handedness-dependent FOVs without varying the focal plane, which is important for practical applications. The proposed dual FOV step-zoom metalens, with advantages such as ultracompactness, flexibility, and replicability, can find applications in fields that require ultracompact zoom imaging and beam focusing.

Comparative transcriptome analysis of the different tissues between the cultivated and wild tomato.

Although domesticated tomato is cultivated by wild tomato, there are a lot of differences between cultivated tomato and wild tomato, such as shape, physiological function and life history. Many studies show that wild tomato has better salt resistance and drought resistance. In addition to, domesticated tomato's fruit is bigger and has more nutritious than wild tomato. The different features are closely related to differentially expressed genes. We identified 126 up-regulated differentially expressed genes and 87 down-regulated differentially expressed genes in cultivated tomato and wild tomato by RNA-Seq. These differentially expressed genes may be associated with salt resistance, drought resistance and fruit nutrition. These differentially expressed genes also further highlight the large-scale reconstruction between wild and cultivated species. In this paper, we mainly study GO enrichment analysis and pathway analysis of the differentially expressed genes. After GO and pathway enrichment analysis, a set of significantly enriched GO annotations and pathways were identified for the differentially expressed genes. What's more, we also identified long non-coding RNAs and mRNAs in the two species and analyzed its essential features. In addition to, we construct a co-expression network of long non-coding RNAs and mRNAs, and annotate mRNAs associated with long non-coding RNAs as target genes, and speculate the regulation function of long non-coding RNAs. In total, our results reveal the effects of artificial and natural selection on tomato's transcript, providing scientific basis for tomato's research in the future.

A Novel Protein Characterization Based on Pseudo Amino Acids Composition and Star-Like Graph Topological Indices.

BACKGROUND AND OBJECTIVE: The rapidly growing number of protein data available creates necessity of computational methods with low complexity to infer accurate protein structure, function, and evolution. METHOD: A new description of proteins based on five topological indices of star-like graph representation and the occurrence frequency of 20 amino acids was proposed to compare the similarities of proteins. RESULTS: A phylogenetic tree of eight ND6 proteins was constructed to demonstrate the effectiveness and rationality of our approach. Analogously, we applied this method to RNA polymerase proteins of some subtypes of influenza virus to infer their phylogenetic relationship. The results showed that the phylogenetic relationship among RNA polymerase of influenza virus is closely related to distributions of species virus host and geographical distribution. CONCLUSION: This novel approach is based on a mapping which can be recaptured mathematically without loss of information.