Zero-Assignment Constraint for Graph Matching with Outliers

Graph matching (GM), as a longstanding problem in computer vision and pattern recognition, still suffers from numerous cluttered outliers in practical applications. To address this issue, we present the zero-assignment constraint (ZAC) for approaching the graph matching problem in the presence of outliers. The underlying idea is to suppress the matchings of outliers by assigning zero-valued vectors to the potential outliers in the obtained optimal correspondence matrix. We provide elaborate theoretical analysis to the problem, i.e., GM with ZAC, and figure out that the GM problem with and without outliers are intrinsically different, which enables us to put forward a sufficient condition to construct valid and reasonable objective function. Consequently, we design an efficient outlier-robust algorithm to significantly reduce the incorrect or redundant matchings caused by numerous outliers. Extensive experiments demonstrate that our method can achieve the state-of-the-art performance in terms of accuracy and efficiency, especially in the presence of numerous outliers

Absolute-energy-scale calibration of ARGO-YBJ for light primaries in multi-TeV region with the Moon shadow observation

In 2011 ARGO-YBJ experiment has reported a work to study the absolute rigidity scale of the primary cosmic ray particles based on the Moon's shadow observation. Given the progress in high energy hadronic interaction models with LHC data, in cosmic ray chemical composition measurement and in experimental data accumulation, more updates can be researched. This paper aims to further disentangle the composition dependence in absolute-energy-scale calibration by using specific moon-shadow data which mainly is comprised of light component cosmic rays. Results show that, 17% energy scale error is estimated from 3 TeV to 50 TeV. To validate the performance of this technique, the light component cosmic ray spectrum in the same energy region is shown. (C) 2017 Elsevier B.V. All rights reserved

Passive Smoking Exposure in Living Environments Reduces Cognitive Function: A Prospective Cohort Study in Older Adults

There is currently no consensus regarding the effects of passive smoking exposure on cognitive function in older adults. We evaluated 7000 permanent residents from six regions within Zhejiang Province, China, aged ≥60 years, without cognitive impairment at baseline and during follow-up examinations for two years. The Chinese version of the Mini-Mental State Examination was used to assess the participants’ cognitive function. Multivariate regression analyses were carried out to calculate the adjusted relative risks (RRs) as measures of the association between passive smoking exposure and cognitive impairment after adjusting for potential confounders. The results showed an association between passive smoking exposure in the living environment and increased risk of cognitive impairment (RR: 1.16; 95% confidence interval (CI): 1.01–1.35). No dose–response relationship between the cumulative dose of passive smoking exposure (days) and cognitive impairment was observed. The results of stratified analyses suggested a harmful effect of passive smoking exposure on cognitive function in non-smokers (RR: 1.24; 95% CI: 1.06–1.46), but not in smokers (RR: 1.11; 95% CI: 0.71–1.92). Therefore, passive smoking exposure increased the risk of cognitive impairment in older adults, especially non-smokers. More effective measures to restrict smoking in the living environment should be developed and implemented

Zinc-α-2-Glycoprotein: A Candidate Biomarker for Colon Cancer Diagnosis in Chinese Population

Zinc-α-2-glycoprotein (AZGP1) is a 41-kDa secreted glycoprotein, which has been detected in several malignancies. The diagnostic value of AZGP1 in serum of prostate and breast cancer patients has been reported. Analyzing “The Cancer Genome Atlas” data, we found that in colon cancer AZGP1 gene expression was upregulated at transcriptional level. We hypothesized that AZGP1 could be used as a diagnostic marker of colon cancer. First, we confirmed AZGP1 expression was higher in a set of 28 tumor tissues than in normal colonic mucosa tissues by real-time quantitative PCR and western blot in a Chinese population. We verified that serum concentration of AZGP1 was higher in 120 colon cancer patients compared with 40 healthy controls by ELISA (p < 0.001). Then receiver-operating characteristic (ROC) curve analysis was used to evaluate the predictive diagnostic value of AZGP1 in serum. The area under the curve (AUC) of AZGP1 was 0.742 (p < 0.001, 95% confidence interval (CI) = 0.656–0.827) in between the AUC of carcinoembryonic antigen (CEA) and the AUC of CA19-9, suggesting that predictive diagnostic value of AZGP1 is between CEA and Carbohydrate 19-9 (CA19-9). The combination of AZGP1 with traditional serum biomarkers, CEA and CA19-9, could result in better diagnostic results. To further validate the diagnostic value of AZGP1, a tissue microarray containing 190 samples of primary colon cancer tissue paired with normal colonic tissue was analysed and the result showed that AZGP1 was significantly upregulated in 68.4% (130 of 190) of the primary cancer lesions. In contrast, there was a weakly positive staining in 29.5% (56 of 190) of the normal colonic tissue samples (p < 0.001). Leave-one-out cross-validation was performed on the serum data, and showed that the diagnostic value of AZGP1 had 63.3% sensitivity and 65.0% specificity. Combination of AZGP1, CEA and CA19-9 had improved diagnosis value accuracy with 74.2% sensitivity and 72.5% specificity. These results suggest that AZGP1 is a useful diagnostic biomarker in tissues and serum from a Chinese population

New observations in neuroscience using superresolution microscopy

© 2018 the authors. Superresolution microscopy (SM) techniques are among the revolutionary methods for molecular and cellular observations in the 21st century. SM techniques overcome optical limitations, and several new observations using SM lead us to expect these techniques to have a large impact on neuroscience in the near future. Several types of SM have been developed, including structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), and photoactivated localization microscopy (PALM)/stochastic optical reconstruction microscopy (STORM), each with special features. In this Minisymposium, experts in these different types of SM discuss the new structural and functional information about specific important molecules in neuroscience that has been gained with SM. Using these techniques, we have revealed novel mechanisms of endocytosis in nerve growth, fusion pore dynamics, and described quantitative new properties of excitatory and inhibitory synapses. Additional powerful techniques, including single molecule-guided Bayesian localization SM (SIMBA) and expansion microscopy (ExM), alone or combined with super-resolution observation, are also introduced in this session