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Summary: Image quality can be measured visually. In the human visual system, a compressed image can be judged by the human eye. Image quality may not be perceived to decline in a region with low compression. However, image quality clearly declines in a region with high compression. As image compression increases, image quality gradually transitions from visually lossless to lossy. In this study, we aim to explain this phenomenon. A few images from different datasets were selected and compressed using JJ2000 and Apollo, which are well‐known image compression algorithms. Then, error‐based and correlation‐based metrics were applied to these images. The correlation‐based metrics agree with human‐vision evaluations in experiments, but the error‐based metrics do not. Inspired by the positive result of the correlation‐based metrics, a new metric named the simple correlation factor (SCF) was proposed to explain the aforementioned phenomenon. The results of the SCF show good consistency with human‐vision results for several datasets. In addition, the computation efficiency of the SCF is better than that of the existing correlation‐based metrics. [ABSTRACT FROM AUTHOR]

It is useful to increase the sharpness in medical images. This improvement can help medical diagnoses and treatment outcomes for patients. Noise overshoot and oversharpening effects are common artifacts of conventional sharpening algorithms. In this work, we propose an adaptive sharpness algorithm that achieves better sharpening effects than standard methods. As the pixel value changes abruptly at an edge, a method that adequately emphasized sudden variations in pixel values is effective for use in the sharpening process. Measurement of the gradient norm value of each pixel is calculated and compared to a threshold value to produce a curve. The curve quickly drops at the initial stage and then decreased more slowly for higher norm values. To distinguish the edge, an inflection point is determined by taking the second derivative of the curve and identifying the turnover point (where the curvature changes sign). Norm values higher than the inflection point are identified as belonging to the edge, and a simple sharpness filter is adaptively applied to these points. The proposed approach yielded better results than global filtering and a conventional unsharp masking approach when evaluated using Pratt's figure of merit. [ABSTRACT FROM AUTHOR]

Previous studies with biological and genetic evidence indicate that the myo-inositol monophosphatase 2 ( IMPA2) gene may influence schizophrenia. We performed a genetic association study in Han Chinese cohorts. Five single nucleotide polymorphisms within IMPA2 promoter region (rs971363, rs971362, rs2075824, rs111410794 and rs111610121), as well as one (rs45442994, in intron 1) that was positively associated in another study, were selected for genotyping in 1397 patients with schizophrenia and 1285 mentally healthy controls. Genotype and allele frequencies were assessed by gender stratification. Interestingly, rs2075824 showed a strong association with schizophrenia ( P = 4.1 × 10−4), and the T allele was more frequent in cases than controls [ P = 5.6 × 10−5, OR (95% CI) = 1.26 (1.13-1.41)]. In vitro promoter assay showed that the transcription activity of the T allele promoter was higher than that of the C allele promoter and the T allele of rs2075824 contributed to risk for schizophrenia. By stratifying males and females, we found a gender-specific association for IMPA2 and schizophrenia: the T allele of rs2075824 was more frequent in male cases compared with male controls [ P = 1.4 × 10−4, OR (95% CI) = 1.33 (1.15-1.55)]. Our data suggest that a promoter polymorphism of IMPA2 possibly contributed to risk for schizophrenia by elevating transcription activity in Han Chinese individuals. [ABSTRACT FROM AUTHOR]

The poisoning effects of minor compounds in soybean oil on the activity of nickel-based catalysts during hydrogenation was investigated. Several soybean oils prepared by different processes were used as the starting oils for hydrogenation. Soybean oil prepared by combining neutralization with degumming and then followed by bleaching leads to a slower hydrogenation rate than an oil prepared by sequential degumming, neutralization and bleaching with activated clay. The selection of bleaching earth used in the bleaching process affected the hydrogenation rate. Soybean oil bleached with neutral clay showed a slower hydrogenation rate. Higher amounts of phosphorus compounds, oxidation products, β-carotene and iron in these oils accounted for the slower hydrogenation rate. Storage of refined and bleached soybean oil greatly affected the hydrogenation rate. An increase in the oxidation products of RB soybean oil during storage was the major reason for the decrease in the hydrogenation rate. [ABSTRACT FROM AUTHOR]

The activities of several commercial nickel catalysts were determined by measuring their activation energies. Among these catalysts, G95E, Resan 22, Nysosel 222 and 325, all with low activation energy, were more active than DM3 and G95H, which had higher activation energy. However, the less active catalysts increased the linoleate selectivity of soybean oil during hydrogenation. The yields of both trans isomers and winterized oil were higher for the more selectively hydrogenated oil catalyzed by the less active catalysts. In the sensory evaluation, the fractionated solid fat that contained more trans isomers was lower in flavor scores than the fractionated liquid oil after hydrogenation and winterization of soybean oil. [ABSTRACT FROM AUTHOR]

Summary: This paper describes a new automated classification method for Wuyi rock tealeaves based on the best penalty parameter selection for the support vector machine with RBF (Radial Basis Function) kernel. A total of 3590 fresh tealeaf images of the representative Rou Gui and Shui Hsien varieties of Wuyi rock tea are collected in their natural habitat. Fourteen image features are extracted in terms of the leaf shape and texture. The automatic selection method is used to find the optimum RBF kernel parameter sigma, which is then applied to design an automatic parameter selection method to screen the best penalty parameter C for the classification of Wuyi rock tealeaves. In this study, the SVM classifier is used for the automated classification and recognition of the 14 image features. The contribution of the various features to the recognition rate of fresh tealeaves is evaluated to identify the key features for the classification and recognition of fresh Wuyi rock tealeaf images. The experimental results show that the proposed method improves the recognition rate of fresh tealeaves to 91.00%. [ABSTRACT FROM AUTHOR]