12 research outputs found
An accurate and real-time multi-view face detector using ORFs and doubly domain-partitioning classifier
We propose a novel multi-view face detector that operates accurately and fast in challenging environments. It consists of four consecutive functional components: background rejector, pose classifier, pose-specific face detectors, and face validator. The background rejector removes non-face patches quickly, the pose classifier estimates poses of the surviving patches, one or more selected pose-specific face detectors according to their estimated pose labels determine that a given patch is a face by using winner take all (WTA) strategy, and the face validator checks whether the face-like patch is really a face. For achieving strong discrimination power with low computing overhead, we devise several types of order relation features (ORF) that encode the order relation among feature elements as a unique code. The devised ORFs are placed in functional components appropriately to ensure fast operation of the multi-view face detector. For accurate classification, we propose a doubly domain-partitioning (DDP) classifier that consists of a coarse domain-partitioning weak classifier followed by a fine bin-partitioning weighted linear discriminant analysis (wLDA) classifier. For fast classification, we devise a feature sharing method that shares identical features between the background rejector and the pose classifier, and among all classes in the pose classifier. We evaluated the proposed multi-view face detector using the FDDB, AFW, and PASCAL face datasets. The experimental results show that the proposed multi-view face detector outperforms other state-of-the-art methods in terms of detection accuracy and execution time.11Nsciescopu
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Alternations of microRNAs, the microbiome, and gut-host interactions in gastrointestinal diseases
Over the past few decades, an ageing population combined with a shift towards a Western lifestyle has predisposed many individuals towards inter-connected gastrointestinal (GI) diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), gastric cancer (GC) and Clostridioides difficile (C. difficile) infection (CDI). anti-TNF-α treatment for IBD patients has a high unresponsive rate, by using bioinformatics approaches, I identified neutrophil chemotaxis may contribute to the treatment resistance and IL13RA2 is the best predictor to identify treatment unresponsive patients. On the other hand, in the intestinal tract, colonocytes consistently exfoliate and shed into the lumen, affecting gut microbiota composition. These molecular/microbial changes involved in disease pathogenesis can be detected in faeces. By using Taqman probe-based real-time polymerase chain reaction (RT qPCR) assay, several non-coding microRNAs (such as miR-18a, miR-20a, miR-221 and miR 135b) and gut microbes (including Fusobacterium nucleatum, Parvimonas micra, Gemella morbillorum, Peptostreptococcus anaerobius, Clostridium hathewayi and Lachnoclostrium sp.) are highly expressed/enriched in faeces in CRC individuals compared to control subjects. The use of a faecal immunological test (FIT) in combination with these biomarkers may improve the non-invasive CRC screening accuracy. Furthermore, Epstein-Barr virus (EBV) is an oncogenic virus and EBV-driven GC accounts for roughly 10% of total GC cases. GC cells infected with EBV alter the molecular aspect at whole-genome, transcriptome, and epigenome levels. For instance, AKT2 activated by mutation in EBV-positive GC cells affecting downstream MAPK and focal adhesion signalling pathways; AKT2 mutation associates with poor patient survival in EBV-positive GC. Furthermore, once patients have received GI treatments, it may suppress/interfere with the patients’ immune system, disrupt the gut flora homeostasis and trigger CDI. Faecal microbiota transplantation (FMT) has been demonstrated as an effective and alternative treatment strategy for CDI patients. However, it is still in clinical trials due to safety concerns. My study revealed that serum miRNAs such as miR-23a-3p, miR-150-5p, miR-26b-5p and miR-28-5p could be used to monitor FMT treatment in CDI patients, and these markers inversely correlate with IL-12B, IL-18, FGF21 and TNFSRF9 at serum protein and mRNA levels, respectively. Furthermore, miR-23a and miR-150 showed cytoprotective effects against C. difficile Toxin B (TcdB)