薇甘菊(Mikania micrantha H B K.)是一种危害极大的外来入侵农林杂草。为了预测薇甘菊在中国的适生区,该文运用预设预测规则的遗传算法(genetic algorithm for rule-set production,GARP)和最大熵(Maximum Entropy,MaxEnt)模型对薇甘...薇甘菊(Mikania micrantha H B K.)是一种危害极大的外来入侵农林杂草。为了预测薇甘菊在中国的适生区,该文运用预设预测规则的遗传算法(genetic algorithm for rule-set production,GARP)和最大熵(Maximum Entropy,MaxEnt)模型对薇甘菊在中国的适生区进行预测,并运用受试者工作曲线(receiver operating characteristic,ROC)分析方法对2种模型的预测结果进行分析,选出最优模型进行预测,同时对环境变量进行刀切法分析,判断环境变量对薇甘菊分布的影响。结果表明,GARP和MaxEnt模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0.971,MaxEnt模型的AUC值更大,预测结果更准确,运行速度更快,更适合用于薇甘菊的适生区预测;对环境变量进行刀切法表明,海拔和季节性降水量方差对薇甘菊的分布影响最小,年温变化范围、年降水量、最湿月份降水量、最湿季度降水量、温度变化方差这5个环境变量对薇甘菊适生区预测影响最大;预测结果显示薇甘菊在中国大陆的适生区主要集中在海南、广东、广西、香港、澳门、云南、福建、西藏、贵州等省,其中西藏东南部和西南部、贵州西南部、福建中南部等地区应该加强监测及预警。展开更多
物种分布与环境因子之间存在着紧密的联系,因此利用环境因子作为预测物种分布模型的变量是当前最普遍的建模思路,但是绝大多数物种分布预测模型都遇到了难以解决的"高维小样本"问题。该研究通过理论和实践证明,基于结构风险...物种分布与环境因子之间存在着紧密的联系,因此利用环境因子作为预测物种分布模型的变量是当前最普遍的建模思路,但是绝大多数物种分布预测模型都遇到了难以解决的"高维小样本"问题。该研究通过理论和实践证明,基于结构风险最小化原理的支持向量机(Support vector machine,SVM)算法非常适合"高维小样本"的分类问题。以20种杜鹃花属(Rhododendron)中国特有种为检验对象,利用标本数据和11个1km×1km的栅格环境数据层作为模型变量,预测其在中国的潜在分布区,并通过全面的模型评估——专家评估,受试者工作特征(Receiver operator characteristic,ROC)曲线和曲线下方面积(Area under the curve,AUC)——来比较模型的性能。我们实现了以SVM为核心的物种分布预测系统,并且通过试验证明其无论在计算速度还是预测效果上都远远优于当前广泛使用的规则集合预测的遗传算法(Algorithm for rule-set prediction,GARP)预测系统。展开更多
Transforming growth factor （TGF）-β regulates a wide variety of cellular responses, including cell growth arrest, apoptosis, cell differentiation, motility, invasion, extracellular matrix production, tissue fibrosis...Transforming growth factor （TGF）-β regulates a wide variety of cellular responses, including cell growth arrest, apoptosis, cell differentiation, motility, invasion, extracellular matrix production, tissue fibrosis, angiogenesis, and immune function. Although tumor-suppressive roles of TGF-β have been extensively studied and well-characterized in many cancers, especially at early stages, accumulating evidence has revealed the critical roles of TGF-β as a pro-tumorigenic factor in various types of cancer. This review will focus on recent findings regarding epithelial-mesenchymal transition （EMT） induced by TGF-β, in relation to crosstalk with some other signaling pathways, and the roles of TGF-[~ in lung and pancreatic cancers, in which TGF-β has been shown to be involved in cancer progression. Recent findings also strongly suggested that targeting TGF-β signaling using specific inhibitors may be useful for the treatment of some cancers. TGF-β plays a pivotal role in the differentiation and function of regulatory T cells （Tregs）. TGF-β is produced as latent high molecular weight complexes, and the latent TGF-β complex expressed on the surface of Tregs contains glycoprotein A repetitions predominant （GARP, also known as leucine-rich repeat containing 32 or LRRC32）. Inhibition of the TGF-β activities through regulation of the latent TGF-β complex activation will be discussed.展开更多
文摘薇甘菊(Mikania micrantha H B K.)是一种危害极大的外来入侵农林杂草。为了预测薇甘菊在中国的适生区,该文运用预设预测规则的遗传算法(genetic algorithm for rule-set production,GARP)和最大熵(Maximum Entropy,MaxEnt)模型对薇甘菊在中国的适生区进行预测,并运用受试者工作曲线(receiver operating characteristic,ROC)分析方法对2种模型的预测结果进行分析,选出最优模型进行预测,同时对环境变量进行刀切法分析,判断环境变量对薇甘菊分布的影响。结果表明,GARP和MaxEnt模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0.971,MaxEnt模型的AUC值更大,预测结果更准确,运行速度更快,更适合用于薇甘菊的适生区预测;对环境变量进行刀切法表明,海拔和季节性降水量方差对薇甘菊的分布影响最小,年温变化范围、年降水量、最湿月份降水量、最湿季度降水量、温度变化方差这5个环境变量对薇甘菊适生区预测影响最大;预测结果显示薇甘菊在中国大陆的适生区主要集中在海南、广东、广西、香港、澳门、云南、福建、西藏、贵州等省,其中西藏东南部和西南部、贵州西南部、福建中南部等地区应该加强监测及预警。
文摘物种分布与环境因子之间存在着紧密的联系,因此利用环境因子作为预测物种分布模型的变量是当前最普遍的建模思路,但是绝大多数物种分布预测模型都遇到了难以解决的"高维小样本"问题。该研究通过理论和实践证明,基于结构风险最小化原理的支持向量机(Support vector machine,SVM)算法非常适合"高维小样本"的分类问题。以20种杜鹃花属(Rhododendron)中国特有种为检验对象,利用标本数据和11个1km×1km的栅格环境数据层作为模型变量,预测其在中国的潜在分布区,并通过全面的模型评估——专家评估,受试者工作特征(Receiver operator characteristic,ROC)曲线和曲线下方面积(Area under the curve,AUC)——来比较模型的性能。我们实现了以SVM为核心的物种分布预测系统,并且通过试验证明其无论在计算速度还是预测效果上都远远优于当前广泛使用的规则集合预测的遗传算法(Algorithm for rule-set prediction,GARP)预测系统。
文摘Transforming growth factor （TGF）-β regulates a wide variety of cellular responses, including cell growth arrest, apoptosis, cell differentiation, motility, invasion, extracellular matrix production, tissue fibrosis, angiogenesis, and immune function. Although tumor-suppressive roles of TGF-β have been extensively studied and well-characterized in many cancers, especially at early stages, accumulating evidence has revealed the critical roles of TGF-β as a pro-tumorigenic factor in various types of cancer. This review will focus on recent findings regarding epithelial-mesenchymal transition （EMT） induced by TGF-β, in relation to crosstalk with some other signaling pathways, and the roles of TGF-[~ in lung and pancreatic cancers, in which TGF-β has been shown to be involved in cancer progression. Recent findings also strongly suggested that targeting TGF-β signaling using specific inhibitors may be useful for the treatment of some cancers. TGF-β plays a pivotal role in the differentiation and function of regulatory T cells （Tregs）. TGF-β is produced as latent high molecular weight complexes, and the latent TGF-β complex expressed on the surface of Tregs contains glycoprotein A repetitions predominant （GARP, also known as leucine-rich repeat containing 32 or LRRC32）. Inhibition of the TGF-β activities through regulation of the latent TGF-β complex activation will be discussed.
