To avoid the irregularities during the level set evolution, a fractional distance regularized variational model is proposed for image segmentation. We first define a fractional distance regularization term which punishes the deviation of the level set function (LSF) and the signed distance function. Since the fractional derivative of the constant value function outside the starting point is nonzero, the fractional gradient modular of the LSF does not approach infinity where the integer order gradient modular is close to 0. This prevents the sharp reverse diffusion of LSF in flat areas and ensures the smooth evolution of LSF. Then, we use the Grünwald-Letnikov (G-L) fractional derivative to derive the discrete forms of the conjugate of fractional derivatives and fractional divergence. To facilitate the calculation of fractional derivatives and their conjugates, we designed their covering templates. Finally, a numerical solution to the minimization of the energy functional is obtained from these discrete forms and covering templates. Numerical experiments of medical images with different modalities show that the model in this paper can well segment weak images and intensity inhomogeneity images.
Gestational diabetes is one of the most common diseases in pregnancy. In the present work, the possible relationship between serum selenium concentration and gestational diabetes was investigated. Blood samples of 234 pregnant women were collected, including 98 subjects with impaired glucose tolerance (IGT), 46 subjects with gestational diabetes mellitus (GDM), and 90 normal pregnant women (NPW). An additional 17 samples of normal women of fertile age (NW) were collected for comparison. The hydride generation atomic fluorescence spectrometry was used for selenium determination. The mean serum selenium levels obtained for each group were 0.0741 +/- 0.0167 mg/L for NPW, 0.0631 +/- 0.0132 mg/L for IGT, 0.0635 +/- 0.0120 mg/L for GDM, and 0.108 +/- 0.0170 mg/L for NW. Serum selenium levels were significantly lower in pregnant woman with IGT (p < 0.001) and GDM (p < 0.001) than in NPW. Furthermore, an inverse correlation between the serum selenium concentration and the gestational period was also observed. Selenium supplementation during gestation for pregnant women, especially with IGT and GDM, should be considered.
Diabetes mellitus is characterized by hyperglycemia and is closely related to trace elements. Quite a few pregnant women suffer from impaired glucose tolerance (IGT) or gestational diabetes mellitus (GDM). Investigation of the changes of elemental contents in serum of the pregnant women with IGT and GDM is significant in the etiological research and cure of the diseases. In the present work, the elements Cu, Zn, Ca, Sr, Mg, P, Fe, and Al in the serum of pregnant women were determined. The elemental contents in different experimental groups were compared. Also, the correlation between elemental contents and gestational period was observed. The results showed that compared with normal pregnant women, the Cu contents in serum of pregnant women with GDM increased, but Zn contents had a decreasing trend. In addition, for all pregnant women, the Ca contents in serum had an obvious inverse correlation with gestational period.
The inert C(sp 3)–H bond and easy overoxidation of toluene make the selective oxidation of toluene to benzaldehyde a great challenge. Herein, we present that a photocatalyst, constructed with a small amount (1 mol %) of amorphous BiOCl nanosheets assembled on TiO2 (denoted as 0.01BOC/TiO2), shows excellent performance in toluene oxidation to benzaldehyde, with 85% selectivity at 10% conversion, and the benzaldehyde formation rate is up to 1.7 mmol g–1 h–1, which is 5.6 and 3.7 times that of bare TiO2 and BOC, respectively. In addition to the charge separation function of the BOC/TiO2 heterojunction, we found that the amorphous structure of BOC endows its abundant surface oxygen vacancies (Ov), which can further promote the charge separation. Most importantly, the surface Ov of amorphous BOC can efficiently adsorb and activate O2, and amorphous BOC makes the product, benzaldehyde, easily desorb from the catalyst surface, which alleviates the further oxidation of benzaldehyde, and results in the high selectivity. This work highlights the importance of the microstructure based on heterojunctions, which is conducive to the rational design of photocatalysts with high performance in organic synthesis.
We investigate in this paper the problem of face verification in the presence of face makeups. To our knowledge, this problem has less formally addressed in the literature. A key challenge is how to increase the measured similarity between face images of the same person without and with makeups. In this paper, we propose a novel approach for makeup-robust face verification, by measuring correlations between face images in a meta subspace. The meta subspace is learned using canonical correlation analysis (CCA), with the objective that intra-personal sample correlations are maximized. Subsequently, discriminative learning with the support vector machine (SVM) classifier is applied to verify faces based on the low-dimensional features in the learned meta subspace. Experimental results on our dataset are presented to demonstrate the efficacy of our approach.
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