UPOCR: Towards Unified Pixel-Level OCR Interface

In recent years, the optical character recognition (OCR) field has been proliferating with plentiful cutting-edge approaches for a wide spectrum of tasks. However, these approaches are task-specifically designed with divergent paradigms, architectures, and training strategies, which significantly increases the complexity of research and maintenance and hinders the fast deployment in applications. To this end, we propose UPOCR, a simple-yet-effective generalist model for Unified Pixel-level OCR interface. Specifically, the UPOCR unifies the paradigm of diverse OCR tasks as image-to-image transformation and the architecture as a vision Transformer (ViT)-based encoder-decoder. Learnable task prompts are introduced to push the general feature representations extracted by the encoder toward task-specific spaces, endowing the decoder with task awareness. Moreover, the model training is uniformly aimed at minimizing the discrepancy between the generated and ground-truth images regardless of the inhomogeneity among tasks. Experiments are conducted on three pixel-level OCR tasks including text removal, text segmentation, and tampered text detection. Without bells and whistles, the experimental results showcase that the proposed method can simultaneously achieve state-of-the-art performance on three tasks with a unified single model, which provides valuable strategies and insights for future research on generalist OCR models. Code will be publicly available

Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluation

This paper presents a comprehensive evaluation of the Optical Character Recognition (OCR) capabilities of the recently released GPT-4V(ision), a Large Multimodal Model (LMM). We assess the model's performance across a range of OCR tasks, including scene text recognition, handwritten text recognition, handwritten mathematical expression recognition, table structure recognition, and information extraction from visually-rich document. The evaluation reveals that GPT-4V performs well in recognizing and understanding Latin contents, but struggles with multilingual scenarios and complex tasks. Specifically, it showed limitations when dealing with non-Latin languages and complex tasks such as handwriting mathematical expression recognition, table structure recognition, and end-to-end semantic entity recognition and pair extraction from document image. Based on these observations, we affirm the necessity and continued research value of specialized OCR models. In general, despite its versatility in handling diverse OCR tasks, GPT-4V does not outperform existing state-of-the-art OCR models. How to fully utilize pre-trained general-purpose LMMs such as GPT-4V for OCR downstream tasks remains an open problem. The study offers a critical reference for future research in OCR with LMMs. Evaluation pipeline and results are available at https://github.com/SCUT-DLVCLab/GPT-4V_OCR

Construction and Evaluation of the Brucella Double Gene Knock-out Vaccine Strain MB6 Δbp26ΔwboA (RM6)

Brucellosis is a serious zoonotic infection worldwide. To date, vaccination is the most effective measure against brucellosis. This study was aimed at obtaining a vaccine strain that has high protective efficacy and low toxicity, and allows vaccination to be differentiated from infection. Using homologous recombination, we constructed a double gene-deletion Brucella strain MB6 Δbp26ΔwboA (RM6) and evaluated its characteristics, safety and efficacy. The RM6 strain had good proliferative ability and stable biological characteristics in vivo and in vitro. Moreover, it had a favorable safety profile and elicited specific immune responses in mice and sheep. The RM6 strain may have substantial practical application value

Development and Efficacy Evaluation of an SP01-adjuvanted Inactivated Escherichia Coli Mutant Vaccine Against Bovine Coliform Mastitis

Escherichia coli ( E. coli ) is one of the most common pathogens causing clinical mastitis in cattle, but no vaccine is available to prevent this disease in China. Therefore, development of an E. coli vaccine against bovine clinical mastitis is urgently needed. The candidate vaccine (Ch-O111-1) and challenge (LZ06) strains were screened from milk samples of cows with clinical mastitis. To extend the cross-protection of the Ch-O111-1 strain, we deleted the galE gene fragment of the Ch-O111-1 strain through homologous recombination between the Ch-O111-1 strain and pCVD442/ΔgalE plasmid, which was identified through conventional methods, including PCR, SDS-PAGE and sequencing. The Ch-O111-1/ΔgalE (Z9) strain was characterized by extensive cross-reactivity and attenuated virulence. We prepared inactivated Z9 vaccines with different adjuvants. Immunization of inactivated Z9 antigen induced adjuvant-, dosage- and inoculation time-dependent antibody titers in cows and mice. Furthermore, immunization with SP01-adjuvanted inactivated Z9 vaccine protected cows against severe clinical mastitis caused by LZ06 and protected mice against death due to LZ06. An SP01-adjuvanted inactivated Z9 vaccine was successfully developed and found to protect cows against severe mastitis caused by Escherichia coli

Purified Immunoglobulin F(ab′) 2 Protects Mice and Rhesus Monkeys against Lethal Ricin Intoxication

Ricin is a highly toxic ribosome-inactivating lectin derived from castor beans. To date, no antidote is available to treat ricin-poisoned patients, and the development of a safe and effective antidote is urgently needed. First, ricin was prepared and used to construct a mouse model and a rhesus monkey model of ricin intoxication. Second, pepsin-digested F(ab′) 2 fragments of serum IgG from horses injected with Freund’s-adjuvanted purified ricin were prepared. Third, the protective efficacy was evaluated in mouse and rhesus monkey models of lethal ricin intoxication. The purity quotient of the prepared ricin and F(ab′) 2 fragments exceeded 90% and 85% in the mouse and monkey models, respectively. The LD 50 of ricin in mice and rhesus monkeys was 2.7 and 9 μg/kg, respectively. A quantity of 6.25 and 1.85 mg/kg F(ab′) 2 was sufficient to treat lethal ricin intoxication in the mice and rhesus monkeys, respectively. Finally, the effect of this therapeutic antibody on peripheral blood immune cells was examined by analysis of peripheral blood immune cells through single cell sequencing. The underlying mechanism was found to involve restraining neutrophil activation, proliferation, and differentiation. Purified F(ab′) 2 fragments administered with needle-free devices fully protect mice and rhesus monkeys against lethal doses of ricin intoxication

Preparation of Equine Immunoglobulin F(ab′) 2 against Smallpox and Evaluation of its Immunoprotective Effect

Smallpox, a severe infectious disease caused by the smallpox virus, causes a death rate as high as 30% within 15-20 days after infection. Therefore, development of anti-Smallpox product as a strategic reserve is urgently needed. We prepared and tested pepsin-digested F(ab′) 2 fragments of serum IgG from horses. Transmission electron microscopy indicated that the purified virus showed morphology consistent with VVTT. The titer was above 1.0 × 10 7 PFU/mL. The purity of the antigen exceeded 90%, according to HPLC. After purification and cleavage, the yield of the purified product F(ab′) 2 was approximately 1.3%, its purity exceeded 90%, and the neutralizing antibody titer exceeded 1:3200. F(ab′) 2 fragments had good preventive and therapeutic effects in mice at antibody doses of 5.2 mg/mL and 2.6 mg/mL. The viral loads of the drug-treated mice were suppressed to varying degrees, and the higher dose groups (5.2 and 2.6 mg/mL) showed a 2-3 fold lower viral load than that in the control group. A process for producing equine immunoglobulin F(ab′) 2 against VVTT was established. The prepared horse anti-smallpox immunoglobulin product had good neutralizing antibody effects on VVTT. The highly purified preparation may serve as a potential candidate for smallpox treatment

AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms -- the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/datasetComment: Published in Medical Image Analysi