Inadequate levels of 5-methyltetrahydrofolate (5-MTHF) and the T variant of MTHFR C677T have been suggested to be associated with an increased risk of developing mental illness, whereas the PON1 SNP variant provides a protective role. However, reports validating the methodology for plasma 5-MTHF levels in schizophrenia patients are limited. A sensitive LC–MS/MS system using an amide column and calibration curve was determined by dialyzed human plasma, and applied to schizophrenia patients and healthy controls in Taiwan, and the differences between the subgroups were discussed. This analysis system meets regulation criteria, and the lower limit of quantification for 5-MTHF levels was 4 nM from 200 μL plasma, within 7 min. The mean plasma 5-MTHF levels in schizophrenia patients (n = 34; 11.70 ± 10.37 nM) were lower than those in the healthy controls (n = 42; 22.67 ± 11.12 nM) significantly (p < 0.01). 5-MTHF concentrations were significantly lower in male carriers than in female carriers (18.30 ± 10.37 nM vs. 24.83 ± 11.01 nM, p < 0.05), especially in subjects who were MTHFR CT/PON1 Q allele carriers. In conclusion, this quantitative system, which employed sensitive and simple processing methods, was successfully applied, and identified that schizophrenic patients had significantly lower levels of 5-MTHF. Lower plasma 5-MTHF concentrations were observed in male subjects.
In this paper, we propose an improved approach for image segmentation based on color and local homogeneity features. A given image is transformed into a quantized image by a self-constructing fuzzy clustering. Then, a color-based region image and an initial seeded region image are obtained from the quantized image by color-based and homogeneity-based region growing methods, respectively. After that, we combine these two images to generate a refined seeded region image and obtain an initial segmented image by a region-based region growing. Finally, merging based on color similarities and sizes of regions is performed for avoiding the problem of over-segmentation. Compared with the other method, experimental results show that the segmented regions obtained by our approach are more reasonable and precise.
In this paper, we proposed a method for selecting edgetype features for iris recognition. The AdaBoost algorithm is used to select a filter bank from a pile of filter candidates. The decisions of the weak classifiers associated with the filter bank are linearly combined to form a strong classifier. Real experiments have been conducted to assess the performance of the designed strong classifier. The results showed that the boosting algorithm can effectively improve the recognition accuracy at the cost of slightly increase the computation time.
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