Recent advances in ocular graft-versus-host disease

Ocular graft-versus-host-disease (GVHD) remains a significant clinical complication after allogeneic hematopoietic stem cell transplantation. Impaired visual function, pain, and other symptoms severely affect affected individuals’ quality of life. However, the diagnosis of and therapy for ocular GVHD involve a multidisciplinary approach and remain challenging for both hematologists and ophthalmologists, as there are no unified international criteria. Through an exploration of the complex pathogenesis of ocular GVHD, this review comprehensively summarizes the pathogenic mechanism, related tear biomarkers, and clinical characteristics of this disease. Novel therapies based on the mechanisms are also discussed to provide insights into the ocular GVHD treatment

A rapid VEGF-gene-sequence photoluminescence detector for osteoarthritis

Osteoarthritis (OA) has become a serious problem to the human society for years due to its high economic burden, disability, pain, and severe impact on the patient’s lifestyle. The importance of current clinical imaging modalities in the assessment of the onset and progression of OA is well recognized by clinicians, but these modalities can only detect OA in the II stage with significant structural deterioration and clinical symptoms. Blood vessel formation induced by vascular endothelial growth factor (VEGF) occurs in the early stage and throughout the entire course of OA, enables VEGF relating gene sequence to act as a biomarker in the field of early diagnosis and monitoring of the disease. Here in, a facile rapid detection of VEGF relating ssDNA sequence was developed, in which manganese-based zeolitic imidazolate framework nanoparticles (Mn-ZIF-NPs) were synthesized by a simple coprecipitation strategy, followed by the introduction and surficial absorption of probe ssDNAs and the CRISPR/Cas12a system components. Furthermore, fluorescence experiments demonstrated that the biosensor displayed a low detection limit of 2.49 nM, a good linear response to the target ssDNA ranging from 10 nM to 500 nM, and the ability of distinguishing single nucleotide polymorphism. This finding opens a new window for the feasible and rapid detection of ssDNA molecules for the early diagnose of OA

Polar transformation system for offline handwritten character recognition

Offline handwritten recognition is an important automated process in pattern recognition and computer vision field. This paper presents an approach of polar coordinate-based handwritten recognition system involving Support Vector Machines (SVM) classification methodology to achieve high recognition performance. We provide comparison and evaluation for zoning feature extraction methods applied in Polar system. The recognition results we proposed were trained and tested by using SVM with a set of 650 handwritten character images. All the input images are segmented (isolated) handwritten characters. Compared with Cartesian based handwritten recognition system, the recognition rate is more stable and improved up to 86.63%

Using a circular grid for offline handwritten character recognition

Offline handwritten recognition is more challenging as indicated by the recognition technologies. This study demonstrates significantly higher rates recognition when compared with other comparable studies. In this paper, we present a circular grid zoning method applied on Polar transformation recognition system. It compares the circular grid zoning (CGZ) and standard zoning (SZ) feature extraction method on Polar and Cartesian coordinate system. We report recognition rates of 92.3%, which are considerably higher than previous studies of zoning based Polar transformation system (86.6%) and zoning based Cartesian recognition system (80.6%). Based on the finding, we propose that our circular grid zoning based Polar transformation system may provide improved classification rates for complex offline handwritten recognition.<br /

An Novel Image Recognition method based on Three-way Decision

The traditional recognition method takes the low-level information of the image as the foundation. The image recognition center of gravity is biased towards the typical features, and achieves the effect of recognition by region-dependent segmentation. Because the general image segmentation is a regular rectangle, easily lead to the same target is divided into different sub-blocks, ignoring the image of the fuzzy part, so the image recognition is not complete. An image recognition algorithm based on threeway decision is proposed. It takes full advantage of effective information in the image, improving the image recognition accuracy. First, this method divided the image into three regions: positive region, negative region and delay decision region. Second, an iterative process is performed on the region of the delay decision. Final, image recognition is performed on the positive sample region. Based on the basic theory of the three-way decision, the more obvious the decision result is, the more iterations are, and the information is added to the classifier until the blurred part of image cannot be divided. Finally, to achieve the realize effective image recognition. This method simulates the process of human cognition effectively, and makes the utilization of the effective information reach the maximum in the recognition process. The results of the experimental analysis showed that the method is more concise and efficient, and the recognition accuracy is more accurate