Stereo-Based Tracking-by-Multiple Hypotheses Framework for Multiple Vehicle Detection and Tracking

In this paper, we present a tracking-by-multiple hypotheses framework to detect and track multiple vehicles accurately and precisely. The tracking-bymultiple hypotheses framework consists of obstacle detection, vehicle recognition, visual tracking, global position tracking, data association and particle filtering. The multiple hypotheses are from obstacle detection, vehicle recognition and visual tracking. The obstacle detection detects all the obstacles on the road. The vehicle recognition classifies the detected obstacles as vehicles or non-vehicles. 3D feature-based visual tracking estimates the current target state using the previous target state. The multiple hypotheses should be linked to corresponding tracks to update the target state. The hierarchical data association method assigns multiple tracks to the correct hypotheses with multiple similarity functions. In the particle filter framework, the target state is updated using the Gaussian motion model and the observation model with associated multiple hypotheses. The experimental results demonstrate that the proposed method enhances the accuracy and precision of the region of interest. © 2013 Lim et al.1

Benserazide, the first allosteric inhibitor of Coxsackievirus B3 3C protease

AbstractCoxsackievirus B3 is the main cause of human viral myocarditis and cardiomyopathy. Virally encoded Coxsackievirus 3C protease (3Cpro) plays an essential role in viral proliferation. Here, benserazide was discovered as a novel inhibitor from a drug library screen targeting Coxsackievirus 3Cpro using a FRET-based enzyme assay. Benserazide, whose chemical structure has no electrophilic functional groups, was characterized as a non-competitive inhibitor by enzyme kinetic studies. A molecular docking study with benserazide and its analogs indicated that a novel putative allosteric binding site was involved. Specifically, a 2,3,4-trihydroxybenzyl moiety was determined to be a key pharmacophore for the enzyme’s inhibitory activity. We suggest that the putative allosteric binding site may be a novel target for future therapeutic strategies

Enhanced Interferon-β Response Contributes to Eosinophilic Chronic Rhinosinusitis

Type I interferon (IFN-I, including IFN-α and IFN-β) response has been implicated in eosinophilic inflammation, in addition to antiviral function. This study aimed to investigate the role of IFN-I in the pathogenesis of eosinophilic chronic rhinosinusitis (ECRS). IFN-α, IFN-β, cytokine expression, and IFN-β cellular localization in the sinonasal tissue from control subjects and ECRS patients with nasal polyps (NP) were determined using real time-PCR, ELISA, and immunohistochemistry. ECRS was induced in wild-type (WT) and IFNAR1 knockout (Ifnar1−/−) mice by intranasal challenge with Aspergillus protease and ovalbumin. Stromal cells cultured from NP tissue were stimulated by exogenous IFN-β, and their CCL11 production and IRF3, IRF7, STAT1, STAT2, and IRF9 gene and/or protein expression were measured. IFN-β, IL-5, IL-13, and CCL11 expression was higher in the NP tissue from ECRS patients, compared to the control group. IFN-β was highly colocalized with the CD11c+ cells in NP. IFN-β levels positively correlated with IL-5, IL-13, and CCL11 levels as well as the number of eosinophils in the NP tissue and CT score. The histological severity of ECRS, levels of IL-4, IL-5, IL-13, and CCL11 in the nasal lavage fluid, and total serum IgE levels were less in Ifnar1−/− mice than in WT mice. CCL11 production, and STAT1 and STAT2 mRNA and STAT1, phospho-STAT1, and phospho-STAT2 protein expression were significantly increased by exogenous IFN-β in NP stromal cells. Our data suggest that IFN-β response was upregulated in ECRS and may play role in ECRS development. IFN-β may contribute to ECRS by enhancing CCL11 production. Thus, increased IFN-β response in the sinonasal mucosa may underlie ECRS pathogenesis

Subtype heterogeneity and epigenetic convergence in neuroendocrine prostate cancer

Neuroendocrine carcinomas (NEC) are tumors expressing markers of neuronal differentiation that can arise at different anatomic sites but have strong histological and clinical similarities. Here we report the chromatin landscapes of a range of human NECs and show convergence to the activation of a common epigenetic program. With a particular focus on treatment emergent neuroendocrine prostate cancer (NEPC), we analyze cell lines, patient-derived xenograft (PDX) models and human clinical samples to show the existence of two distinct NEPC subtypes based on the expression of the neuronal transcription factors ASCL1 and NEUROD1. While in cell lines and PDX models these subtypes are mutually exclusive, single-cell analysis of human clinical samples exhibits a more complex tumor structure with subtypes coexisting as separate sub-populations within the same tumor. These tumor sub-populations differ genetically and epigenetically contributing to intra- and inter-tumoral heterogeneity in human metastases. Overall, our results provide a deeper understanding of the shared clinicopathological characteristics shown by NECs. Furthermore, the intratumoral heterogeneity of human NEPCs suggests the requirement of simultaneous targeting of coexisting tumor populations as a therapeutic strategy

Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques

Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient’s gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) using Precision, Recall and F-measure. Comparing the F-measure values of normal, mild cognitive impairment (MCI), and dementia, the MLP was the highest in the F-measure values of normal with 0.97, while the SVM appear to be the highest in MCI and dementia with 0.739. Using the proposed early diagnosis model for dementia reduces the time and economic burden and can help simplify the diagnosis method for dementia

A New Method of Parameter Estimation for Multinomial Naive Bayes Text Classifiers

Multinomial naive Bayes classifiers have been widely used for the probabilistic text classification. However, their parameter estimation method sometimes generates inappropriate probabilities. In this paper, we propose a topic document model approach for naive Bayes text classification, where their parameters are estimated with an expectation from the training documents. Experiments are conducted on Reuters 21578 and 20 Newsgroup collection, and our proposed approach obtained a significant improvement in performace over the conventional approach