Context Perception Parallel Decoder for Scene Text Recognition

Scene text recognition (STR) methods have struggled to attain high accuracy and fast inference speed. Autoregressive (AR)-based STR model uses the previously recognized characters to decode the next character iteratively. It shows superiority in terms of accuracy. However, the inference speed is slow also due to this iteration. Alternatively, parallel decoding (PD)-based STR model infers all the characters in a single decoding pass. It has advantages in terms of inference speed but worse accuracy, as it is difficult to build a robust recognition context in such a pass. In this paper, we first present an empirical study of AR decoding in STR. In addition to constructing a new AR model with the top accuracy, we find out that the success of AR decoder lies also in providing guidance on visual context perception rather than language modeling as claimed in existing studies. As a consequence, we propose Context Perception Parallel Decoder (CPPD) to decode the character sequence in a single PD pass. CPPD devises a character counting module and a character ordering module. Given a text instance, the former infers the occurrence count of each character, while the latter deduces the character reading order and placeholders. Together with the character prediction task, they construct a context that robustly tells what the character sequence is and where the characters appear, well mimicking the context conveyed by AR decoding. Experiments on both English and Chinese benchmarks demonstrate that CPPD models achieve highly competitive accuracy. Moreover, they run approximately 7x faster than their AR counterparts, and are also among the fastest recognizers. The code will be released soon

Finite Element Analysis for the Self-Loosening Behavior of the Bolted Joint with a Superelastic Shape Memory Alloy

A macroscopic constitutive model is proposed in this research to reproduce the uniaxial transition ratcheting behaviors of the superelastic shape memory alloy (SMA) undergoing cyclic loading, based on the cosine-type phase transition equation with the initial martensite evolution coefficient that provides the predictive residual martensite accumulation evolution and the nonlinear feature of hysteresis loop. The calculated results are compared with the experimental results to show the validity of the present computational procedure in transition ratcheting. Finite element implementation for the self-loosening behavior of the superelastic SMA bolt is then carried out based on the proposed constitutive model to analyze the curves of stress-strain responses on the bolt bar, clamping force reduction law, dissipation energy change law of the bolted joint for different external loading cases, and preload force of the bolt

SVTR: Scene Text Recognition with a Single Visual Model

Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient. In this study, we propose a Single Visual model for Scene Text recognition within the patch-wise image tokenization framework, which dispenses with the sequential modeling entirely. The method, termed SVTR, firstly decomposes an image text into small patches named character components. Afterward, hierarchical stages are recurrently carried out by component-level mixing, merging and/or combining. Global and local mixing blocks are devised to perceive the inter-character and intra-character patterns, leading to a multi-grained character component perception. Thus, characters are recognized by a simple linear prediction. Experimental results on both English and Chinese scene text recognition tasks demonstrate the effectiveness of SVTR. SVTR-L (Large) achieves highly competitive accuracy in English and outperforms existing methods by a large margin in Chinese, while running faster. In addition, SVTR-T (Tiny) is an effective and much smaller model, which shows appealing speed at inference. The code is publicly available at https://github.com/PaddlePaddle/PaddleOCR.Comment: Accepted by IJCAI 202

Identification and Pharmacological Characterization of Two Serotonin Type 7 Receptor Isoforms from <i>Mythimna separata</i>

Serotonin (5-hydroxytryptamine, 5-HT) is an important neuroactive molecule, as neurotransmitters regulate various biological functions in vertebrates and invertebrates by binding and activating specific 5-HT receptors. The pharmacology and tissue distribution of 5-HT receptors have been investigated in several model insects, and these receptors are recognized as potential insecticide targets. However, little is known about the pharmacological characterization of the 5-HT receptors in important agricultural pests. In this study, we investigated the sequence, pharmacology, and tissue distribution of 5-HT7 receptors from oriental armyworm Mythimna separata (Walker) (Lepidoptera: Noctuidae), an important migratory and polyphagous pest species. We found that the 5-HT7 receptor gene encodes two molecularly distinct transcripts, Msep5-HT7L and Msep5-HT7S, by the mechanism of alternative splicing in M. separata. Msep5-HT7S differs from Msep5-HT7L based on the deletion of 95 amino acids within the third intracellular loop. Two Msep5-HT7 receptor isoforms were activated by 5-HT and synthetic agonists α-methylserotonin, 8-hydroxy-DPAT, and 5-methoxytryptamine, resulting in increased intracellular cAMP levels in a dose-dependent manner, although these agonists showed much poorer potency and efficacy than 5-HT. The maximum efficacy of 5-HT compared to the two 5-HT isoforms was equivalent, but 5-HT exhibited 2.63-fold higher potency against the Msep5-HT7S than the Msep5-HT7L receptor. These two isoforms were also blocked by the non-selective antagonist methiothepin and the selective antagonists WAY-100635, ketanserin, SB-258719, and SB-269970. Moreover, two distinct mRNA transcripts were expressed preferentially in the brain and chemosensory organs of M. separata adults, as determined by qPCR assay. This study is the first comprehensive characterization of two splicing isoforms of 5-HT7 receptors in M. separata, and the first to demonstrate that alternative splicing is also the mechanism for producing multiple 5-HT7 isoforms in insects. Pharmacological and gene expression profiles offer important information that could facilitate further exploration of their function in the central nervous system and peripheral chemosensory organs, and may even contribute to the development of new selective pesticides

TAT Nanobody Exerts Antiviral Effect against PRRSV In Vitro by Targeting Viral Nucleocapsid Protein

Porcine reproductive and respiratory syndrome (PRRS) is caused by the PRRS virus (PRRSV), which has brought huge economic losses to the pork industry worldwide since its first discovery in the late 1980s in North America. To date, there are no effective commercial vaccines or therapeutic drugs available for controlling the spread of PRRSV. Due to their unique advantages of high affinity and high specificity, nanobodies (Nbs) have received increasing attention in the process of disease diagnosis and treatment. Trans-activator transcription (TAT) can serve as a vector to carry specific proteins into cells by passing through cell membranes. In our previous study, a specific Nb against the PRRSV nucleocapsid (N) protein was screened using phage display technology. For this study, we developed a novel recombinant protein constituting a TAT-conjugated Nb, which we call TAT-Nb1. The target cell entry efficiency of TAT-Nb1 and its effect on PRRSV infection and replication were then investigated. Our results indicate that TAT delivered Nb1 into Marc-145 cells and porcine alveolar macrophages (PAMs) in a dose- and time-dependent manner. Furthermore, TAT-Nb1 dose-dependently suppressed PRRSV infection and replication, where this antiviral effect was independent of PRRSV strain. Co-immunoprecipitation results revealed that Nb1 efficiently interacted with the N protein of PRRSV. Taken together, the presented results suggest that TAT-Nb1 can effectively suppress PRRSV replication, and it may be considered as a new anti-PRRSV candidate drug

Improving Unsupervised Object-Based Change Detection via Hierarchical Multi-Scale Binary Partition Tree Segmentation: A Case Study in the Yellow River Source Region

Change detection in remote sensing enables identifying alterations in surface characteristics over time, underpinning diverse applications. However, conventional pixel-based algorithms encounter constraints in terms of accuracy when applied to medium- and high-resolution remote sensing images. Although object-oriented methods offer a step forward, they frequently grapple with missing small objects or handling complex features effectively. To bridge these gaps, this paper proposes an unsupervised object-oriented change detection approach empowered by hierarchical multi-scale segmentation for generating binary ecosystem change maps. This approach meticulously segments images into optimal sizes and leverages multidimensional features to adapt the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) algorithm for GaoFen WFV data. We rigorously evaluated its performance in the Yellow River Source Region, a critical ecosystem conservation zone. The results unveil three key strengths: (1) the approach achieved excellent object-level change detection results, making it particularly suited for identifying changes in subtle features; (2) while simply increasing object features did not lead to a linear accuracy gain, optimized feature space construction effectively mitigated dimensionality issues; and (3) the scalability of our approach is underscored by its success in mapping the entire Yellow River Source Region, achieving an overall accuracy of 90.09% and F-score of 0.8844. Furthermore, our analysis reveals that from 2015 to 2022, changed ecosystems comprised approximately 1.42% of the total area, providing valuable insights into regional ecosystem dynamics

Development of an Effective Double Antigen Sandwich ELISA Based on p30 Protein to Detect Antibodies against African Swine Fever Virus

African swine fever (ASF), the highly lethal swine infectious disease caused by the African swine fever virus (ASFV), is a great threat to the swine industry. There is no effective vaccine or diagnostic method to prevent and control this disease currently. The p30 protein of ASFV is an important target for serological diagnosis, expressed in the early stage of viral replication and has high immunogenicity and sequence conservatism. Here, the CP204L gene was cloned into the expression vector pET-30a (+), and the soluble p30 protein was successfully expressed in the E. coli prokaryotic expression system and then labeled with horseradish peroxidase (HRP) to be the enzyme-labeled antigen. Using the purified recombinant p30 protein, a double-antigen sandwich ELISA for ASFV antibody detection was developed. This method exhibits excellent specificity, sensitivity and reproducibility in clinical sample detection with lower cost and shorter production cycles. Taken together, this study provides technical support for antibody detection for ASFV