Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly

Spinal cord injury without radiographic abnormalities caused by rotation-stretching injury manifesting as Brown-Sequard syndrome: a case report

Spinal cord injury without radiographic abnormality (SCIWORA) is a term that denotes clinical symptoms of traumatic myelopathy without radiographic or computed tomographic features of vertebral fracture or instability. However, SCIWORA in adults is very rare, especially that involving the thoracic spine. We describe the case of a 38-year-old man who complained of weakness in the right lower extremity for two hours. The injury occurred due to rapid spinal cord rotation-stretching. The patient was diagnosed with SCIWORA at the T4 level, manifesting as Brown-Sequard syndrome (BBS). Finally, he was able to walk independently without assistance after two-month treatment. SCIWORA due to spinal cord rotation-stretching injury, manifesting as BSS, is a very rare mechanism of injury. When X-ray and CT scans rule out the diagnosis of spinal fractures, SCIWORA should be suspected. ---Continu

Detecting dense text in natural images

Most existing text detection methods are mainly motivated by deep learning‐based object detection approaches, which may result in serious overlapping between detected text lines, especially in dense text scenarios. It is because text boxes are not commonly overlapped, as different from general objects in natural scenes. Moreover, text detection requires higher localisation accuracy than object detection. To tackle these problems, the authors propose a novel dense text detection network (DTDN) to localise tighter text lines without overlapping. Their main novelties are: (i) propose an intersection‐over‐union overlap loss, which considers correlations between one anchor and GT boxes and measures how many text areas one anchor contains, (ii) propose a novel anchor sample selection strategy, named CMax‐OMin, to select tighter positive samples for training. CMax‐OMin strategy not only considers whether an anchor has the largest overlap with its corresponding GT box (CMax), but also ensures the overlapping between one anchor and other GT boxes as little as possible (OMin). Besides, they train a bounding‐box regressor as post‐processing to further improve text localisation performance. Experiments on scene text benchmark datasets and their proposed dense text dataset demonstrate that the proposed DTDN achieves competitive performance, especially for dense text scenarios

Integrated analysis of m6A regulator-mediated RNA methylation modification patterns and immune characteristics in Sjögren's syndrome

The epigenetic modifier N6-methyladenosine (m6A), recognized as the most prevalent internal modification in messenger RNA (mRNA), has recently emerged as a pivotal player in immune regulation. Its dysregulation has been implicated in the pathogenesis of various autoimmune conditions. However, the implications of m6A modification within the immune microenvironment of Sjögren's syndrome (SS), a chronic autoimmune disorder characterized by exocrine gland dysfunction, remain unexplored. Herein, we leverage an integrative analysis combining public database resources and novel sequencing data to investigate the expression profiles of m6A regulatory genes in SS. Our cohort comprised 220 patients diagnosed with SS and 62 healthy individuals, enabling a comprehensive evaluation of peripheral blood at the transcriptomic level. We report a significant association between SS and altered expression of key m6A regulators, with these changes closely tied to the activation of CD4+ T cells. Employing a random forest (RF) algorithm, we identified crucial genes contributing to the disease phenotype, which facilitated the development of a robust diagnostic model via multivariate logistic regression analysis. Further, unsupervised clustering revealed two distinct m6A modification patterns, which were significantly associated with variations in immunocyte infiltration, immune response activity, and biological function enrichment in SS. Subsequently, we proceeded with a screening process aimed at identifying genes that were differentially expressed (DEGs) between the two groups distinguished by m6A modification. Leveraging these DEGs, we employed weight gene co-expression network analysis (WGCNA) to uncover sets of genes that exhibited strong co-variance and hub genes that were closely linked to m6A modification. Through rigorous analysis, we identified three critical m6A regulators - METTL3, ALKBH5, and YTHDF1 - alongside two m6A-related hub genes, COMMD8 and SRP9. These elements collectively underscore a complex but discernible pattern of m6A modification that appears to be integrally linked with SS's pathogenesis. Our findings not only illuminate the significant correlation between m6A modification and the immune microenvironment in SS but also lay the groundwork for a deeper understanding of m6A regulatory mechanisms. More importantly, the identification of these key regulators and hub genes opens new avenues for the diagnosis and treatment of SS, presenting potential targets for therapeutic intervention