DNA序列映射方法对蛋白质编码区预测准确率的影响

[目的]探讨当前主要的DNA序列映射方法对蛋白质编码区的预测准确率的影响,寻找有效的映射方法。[方法]以AC（Approximate Correlation）为碱基层的预测准确率的测度,采用FIR（Finite Impulse Response）窄通带滤波器预测算法研究各种映射方法对预测准确率的影响。[结果]在ALLSEQ和HMR195 2个DNA序列集上,Voss法和Z-curve方法是较PN（Paired Numeric）法、EIIP（Electron-Io Interaction Potential）法和复数法更为有效的映射方法。[结论]该研究结果为用预测结果的AC值来验证新映射方法的有效性奠定了基础,对利用生物信息学方法正确揭示DNA序列的结构具有重要意义。     

DNA序列映射方法对蛋白质编码区预测准确率的影响

[目的]探讨当前主要的DNA序列映射方法对蛋白质编码区的预测准确率的影响,寻找有效的映射方法。[方法]以AC(Approxi-mate Correlation)为碱基层的预测准确率的测度,采用FIR(Finite Impulse Response)窄通带滤波器预测算法研究各种映射方法对预测准确率的影响。[结果]在ALLSEQ和HMR195 2个DNA序列集上,Voss法和Z-curve方法是较PN(Paired Numeric)法、EIIP(Electron-IoInteraction Potential)法和复数法更为有效的映射方法。[结论]该研究结果为用预测结果的AC值来验证新映射方法的有效性奠定了基础,对利用生物信息学方法正确揭示DNA序列的结构具有重要意义。     

The response of elephants to the spatial heterogeneity of vegetation in a Southern African agricultural landscape

Based on the agricultural landscape of the Sebungwe in Zimbabwe, we investigated whether and how the spatial distribution of the African elephant (Loxodonta africana) responded to spatial heterogeneity of vegetation cover based on data of the early 1980s and early 1990s. We also investigated whether and how elephant distribution responded to changes in spatial heterogeneity between the early 1980s and early 1990s. Vegetation cover was estimated from a normalised difference vegetation index (NDVI). Spatial heterogeneity was estimated from a new approach based on the intensity (i.e., the maximum variance exhibited when a spatially distributed landscape property such as vegetation cover is measured with a successively increasing window size or scale) and dominant scale (i.e., the scale or window size at which the intensity is displayed). We used a variogram to quantify the dominant scale (i.e., range) and intensity (i.e., sill) of NDVI based congruent windows (i.e., 3.84 km × 3.84 km in a 61 km × 61 km landscape). The results indicated that elephants consistently responded to the dominant scale of spatial heterogeneity in a unimodal fashion with the peak elephant presence occurring in environments with dominant scales of spatial heterogeneity of around 457–734 m. Both the intensity and dominant scale of spatial heterogeneity predicted 65 and 68% of the variance in elephant presence in the early 1980s and in the early 1990s respectively. Also, changes in the intensity and dominant scale of spatial heterogeneity predicted 61% of the variance in the change in elephant distribution. The results imply that management decisions must take into consideration the influence of the levels of spatial heterogeneity on elephants in order to ensure elephant persistence in agricultural landscapes.