U(1)U(1) gauge vector field on a codimension-2 brane

In this paper, we obtain a gauge invariant effective action for a bulk massless $U(1)$ gauge vector field on a brane with codimension two by using a general Kaluza-Klein (KK) decomposition for the field. It suggests that there exist two types of scalar KK modes to keep the gauge invariance of the action for the massive vector KK modes. Both the vector and scalar KK modes can be massive. The masses of the vector KK modes $m^{(n)}$ contain two parts, $m_{1}^{(n)}$ and $m_{2}^{(n)}$, due to the existence of the two extra dimensions. The masses of the two types of scalar KK modes $m_{\phi}^{(n)}$ and $m_{\varphi}^{(n)}$ are related to the vector ones, i.e., $m_{\phi}^{(n)}=m_{1}^{(n)}$ and $m_{\varphi}^{(n)}=m_{2}^{(n)}$. Moreover, we derive two Schr\"{o}dinger-like equations for the vector KK modes, for which the effective potentials are just the functions of the warp factor.Comment: 15 pages,no figures, accepted by JHE

Ferritin level prospectively predicts hepatocarcinogenesis in patients with chronic hepatitis B virus infection

Previous studies have detected a higher level of ferritin in patients with hepatocellular carcinoma (HCC), but a potential causal association between serum ferritin level and hepatocarcinogenesis remains to be clarified. Using a well-established prospective cohort and longitudinally collected serial blood samples, the association between baseline ferritin levels and HCC risk were evaluated in 1,152 patients infected with hepatitis B virus (HBV), a major risk factor for HCC. The association was assessed by Cox proportional hazards regression model using univariate and multivariate analyses and longitudinal analysis. It was demonstrated that HBV patients who developed HCC had a significantly higher baseline ferritin level than those who remained cancer-free (188.00 vs. 108.00 ng/ml, P\u3c0.0001). The patients with a high ferritin level (≥200 ng/ml) had 2.43-fold increased risk of HCC compared to those with lower ferritin levels [hazard ratio (HR), 2.43; 95% confidence interval, 1.63-3.63]. A significant trend of increasing HRs along with elevated ferritin levels was observed (P for trend \u3c0.0001). The association was still significant after multivariate adjustment. Incorporating ferritin into the α-fetoprotein (AFP) model significantly improved the performance of HCC prediction (the area under the curve from 0.74 to 0.77, P=0.003). Longitudinal analysis showed that the average ferritin level in HBV patients who developed HCC was persistently higher than in those who were cancer-free during follow-up. HCC risk reached a peak at approximately the fifth year after baseline ferritin detection. Moreover, stratified analyses showed that the association was noted in both males and females, and was prominent in patients with a low AFP value. In short, serum ferritin level could independently predict the risk of HBV-related HCC and may have a complementary role in AFP-based HCC diagnosis. Future studies are warranted to validate these findings and test its clinical applicability in HCC prevention and management. © 2018, Spandidos Publication

Scalable Image Retrieval by Sparse Product Quantization

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method.Comment: 12 page