This study examined the effect of teacher well-being and the organizational climate in rural elementary schools on teachers' turnover intentions, as well as the effect of the interaction between teacher well-being and organizational climate on teachers' turnover intentions. Teachers from rural elementary schools in Nantou County, Taiwan, were the participants in this study. A questionnaire was distributed and surveys were collected from 254 teachers. SPSS statistical software was used to conduct confirmatory factor analysis, reliability analysis, descriptive statistics analysis, independent-samples t-tests, one-way ANOVA, Pearson's correlation analysis, and hierarchical regression analysis. The findings in this study were as follows: 1) Male and female teachers in rural elementary schools showed a significant difference in the professional sharing construct of school organizational climate. 2) Teachers of different age, marital status, part-time position, years of service, and native land showed significant differences in the well-being constructs of life satisfaction and negative emotions. 3) Teachers of different marital status showed a significant difference in the school organizational climate constructs of work supervision, teaching constraints, and turnover intentions. 4) Teachers from different places showed a significant difference in the estrangement construct. 5) The influence of teacher well-being on turnover intentions was negatively affected by the school organizational climate constructs of support, work supervision, and comradeship. The constructs of teaching constraints and estrangement had a significant and positive effect on turnover intentions. Thus, school organizational climate had a moderating effect on the relationship between teacher well-being and turnover intentions.
Host factors play a pivotal role in regulating virus infection. Uncovering the mechanism of how host factors are involved in virus infection could pave the way to defeat viral disease. In this study, we characterized a lipid transfer protein, designated NbLTP1 in Nicotiana benthamiana, which was downregulated after Bamboo mosaic virus (BaMV) inoculation. BaMV accumulation significantly decreased in NbLTP1-knockdown leaves and protoplasts compared with the controls. The subcellular localization of the NbLTP1-orange fluorescent protein (OFP) was mainly the extracellular matrix. However, when we removed the signal peptide (NbLTP1/ΔSP-OFP), most of the expressed protein targeted chloroplasts. Both NbLTP1-OFP and NbLTP1/ΔSP-OFP were localized in chloroplasts when we removed the cell wall. These results suggest that NbLTP1 may have a secondary targeting signal. Transient overexpression of NbLTP1 had no effect on BaMV accumulation, but that of NbLTP1/ΔSP significantly increased BaMV expression. NbLTP1 may be a positive regulator of BaMV accumulation especially when its expression is associated with chloroplasts, where BaMV replicates. The mutation was introduced to the predicted phosphorylation site to simulate the phosphorylated status, NbLTP/ΔSP/P(+), which could still assist BaMV accumulation. By contrast, a mutant lacking calmodulin-binding or simulates the phosphorylation-negative status could not support BaMV accumulation. The lipid-binding activity of LTP1 was reported to be associated with calmodulin-binding and phosphorylation, by which the C-terminus functional domain of NbLTP1 may play a critical role in BaMV accumulation.
To perform computer-aided diagnosis of thyroid nodules on ultrasound images, the nodule's location and its margin should be clearly defined. However, due to the nodule's biological characteristics, echo structure and quality, operator's subjective factors and operating conditions, identification of thyroid nodule boundary becomes quite difficult. In addition, manual identification of nodule boundary heavily relies on physician's subjective judgment. Even the same physician could give different results on the same image at different times. In this study, we proposed a novel and automatic method for thyroid nodule boundary detection based on Variance-Reduction statistics. Based the operator's initial inputs of the nodule's major and minor axes, the region of interest (ROI) is first generated. With grayscale values of pixels in the ROI, we then implement an algorithm to automatically detect the nodule boundary. The proposed method is validated with ultrasound images of 433 thyroid nodules, and the effectiveness of the method is shown by comparing the two boundary error metrics, the Hausdorff distance (HD) and the mean absolute distance (MD), to previously published results.
To perform computer-aided diagnosis of the thyroid nodules on ultrasound images, the location and boundary of nodules should be clearly defined. However, the identification of thyroid nodule boundary is a difficult issue due to the biological characteristics of the nodules, the physics and quality of ultrasound imaging, and the subjective factors and operating conditions of the operator. In this study, we propose a novel and semiautomatic method for detecting the boundary of thyroid nodule based on the Variance-Reduction (V-R) statistics without image preprocessing. The region of interest (ROI) is first automatically generated according to the initial inputs of the nodule's major and minor axes. The boundary candidate pixel points are then extracted by using the V-R statistics from the grayscale values of all pixel points in the ROI. Three filtering methods are further applied to eliminate the outlier pixel points to ensure that the remaining candidate pixel points are located on the nodule boundary. Finally, the remaining pixel points are smoothened and linked together to form the final boundary. The proposed method is validated with ultrasound images of 538 thyroid nodules, with manual delineation by experienced radiologist as gold standard. The effectiveness is evaluated and compared with previous publications using boundary error metrics and overlapping area metrics with the same data set. The results show that the normalized average mean boundary error is 1.02%, the true positive overlapping area ratio achieves 93.66% and false positive overlapping area ratio is limited to 7.68%. In conclusion, our proposed method is reliable and effective in detecting thyroid nodule boundary on ultrasound images.
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