饥饿胁迫对鲤形体、体成分及血液生理指标的影响

为了解饥饿胁迫对鲤形体、鱼体生化成分及血液生理的影响,在18～22℃条件下对鲤进行了0 d、10 d、20 d、30d的饥饿试验。试验结果表明,随着鲤饥饿时间的延长,肥满度、肠体比、肝体比呈现下降趋势,但差异均不显著（P〉0.05）;鲤肌肉中水分和粗灰分呈现升高的趋势,粗脂肪呈现下降的趋势,但差异均不显著（P〉0.05）,粗蛋白呈现下降的趋势,差异显著（P〈0.05）;血液中红细胞数量、血红蛋白值显著下降（P〈0.05）,白细胞数量呈现上升的趋势,血糖含量先下降后稳定下来。说明短期饥饿胁迫使鲤形体发生一定改变、随着饥饿时间的延长,鲤首先动用肌肉中的脂肪,再动用蛋白质来维持体内的正常代谢,同时血液生理指标也发生与饥饿相适应的变化。     

服装压力对人体的影响及其测试方法

阐述服装压力对人体生理、运动防护及病人伤口恢复的影响.总结国内外服装压力测试方法的原理,并分析服装压力的数学模型及软体假人在压力测试方面的应用,指出服装压力测试方法的发展趋势.     

老人住宅探讨

提出了老年人居住的建筑模式,就老年人群体所独具的特点进行了分析,详细地阐述了老年人室外居住环境和室内居住环境的设计应注意的问题,以供当前和未来的老年社区建设参考借鉴.     

Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering

In this article, a new data pre-processing method has been suggested to detect and classify vertebral column disorders and lumbar disc diseases with a high accuracy level. The suggested pre-processing method is called the Mean Shift Clustering-Based Attribute Weighting (MSCBAW) and is based primarily on mean shift clustering algorithm finding the number of the sets automatically. In this study, we have used two different datasets including lumbar disc diseases (with two classes-our database) and vertebral column disorders datasets (with two or three classes) taken from UCI (University of California at Irvine) machine learning database to test the proposed approach. The MSCBAW method is working as follows: first of all, the centres of the sets automatically for each characteristics in dataset by using the mean shift clustering algorithm are computed. And then, the mean values of each property in dataset are calculated. The weighted datasets by multiplying these mean values by each property value in the dataset that have been obtained by dividing the above mentioned mean values by the centres of the sets belonging to the relevant property are achieved. After the data weighting stage, three different classification algorithms that included the k-NN (k-Nearest Neighbour), RBF–NN (Radial Basis Function–Neural Network) and SVM (Support Vector Machine) classifying algorithms have been used to classify the datasets. In the classification of vertebral column disorders dataset with two classes (normal or abnormal), while the obtained classification accuracies and kappa values were 78.70% ± 0.455 (the classification accuracy ± standard deviation), 81.93% ± 0.899, and 80.32% ± 0.56 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 99.03% ± 0.977, 99.67% ± 0.992, and 99.35% ± 0.9852, respectively. In the classification of second dataset named vertebral column disorders dataset with three classes (Normal, Disk Hernia, and Spondylolisthesis), while the obtained classification accuracies and kappa values were 74.51% ± 0.581, 78.70% ± 0.659, and 83.22% ± 0.728 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 99.35% ± 0.989, 96.77% ± 0.948, and 99.67% ± 0.994, respectively. As for the lumbar disc dataset, while the obtained classification accuracies and kappa values were 94.54% ± 0.974, 94.54% ± 0.877, and 93.45% ± 0.856 using SVM, k-NN (for k = 1), and RBF–NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF–NN classifiers were obtained 100% ± 1.00, 99.63% ± 0.991, and 99.63% ± 0.991, respectively. The best hybrid models in the classification of vertebral column disorders dataset with two classes, vertebral column disorders dataset with three classes, and lumbar disc dataset were the combination of MSCBAW and k-NN classifier, the combination of MSCBAW and RBF–NN classifier, and the combination of MSCBAW and SVM classifier, respectively.     

Respiratory effects in people exposed to arsenic via the drinking water and tobacco smoking in southern part of Pakistan

In this study, a survey has been conducted during 2005-2007 on surface and groundwater arsenic (As) contamination and its impact on the health of local population, of villages located on the banks of Manchar lake, southern part of Sindh, Pakistan. We have also assessed the relationship between arsenic exposure through respiratory disorders in male subjects with drinking water and smoking cigarettes made from tobacco grown in agricultural land irrigated with As contaminated lake water. The biological samples (blood and scalp hair) were collected from As exposed subjects (100% smokers) and age matched healthy male subjects (40.2% smoker and 59.8% non smokers) belong to unexposed areas for comparison purposes. The As concentration in drinking water (surface and underground water), agricultural soil, cigarette tobacco and biological samples were determined by electrothermal atomic absorption spectrometry. The range of As concentrations in lake water was 35.2-158 µg/L (average 97.5 µg/L), which is 3-15 folds higher than permissible limit of World Health Organization (WHO, 2004). While the As level in local cigarette tobacco was found to be 3-6 folds higher than branded cigarettes (0.37-0.79 µg/g). Arsenic exposed subjects (with and without RD) had significantly elevated levels of As in their biological samples as compared to referent male subject of unexposed area. These respiratory effects were more pronounced in individuals who had also As induced skin lesions. The linear regressions showed good correlations between As concentrations in water versus hair and blood samples of exposed subjects with and without respiratory problems.