一种基于Z-Curve映射下DNA序列信噪比的快速算法研究

DNA序列信号频谱3-周期特性被认为是用来区分编码区和非编码区的一个重要特征.针对信噪比计算量大的问题,给出基于Z-Curve映射下快速计算信噪比的方法,该方法有效避开计算离散傅立叶变换(DFT),从序列本身直接得到信噪比.实验结果表明,快速算法计算效率是DFT方法的百倍之上,极大减小基因的信噪比计算时间.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。     

差减杂交方法的原理和应用

本文结合胡场盐诱导基因和盐抑基因的筛选，介绍了差减杂交技术的试验原理，设计思想和应用方法。     

细菌RNA分离试剂盒

Bioscience Technology 2003年7月31页报道：美国Amersham Biosciences公司最近推出GenomiPhi^TM DNA扩增试剂盒。利用这种试剂盒可从有限的生物材料制备高质量的DNA。这是一种简单的等温扩增技术，它不需要使用热循环器。原材料可以是来自全血、颊面拭子、细胞印迹纸、     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.