基于薯蓣皂苷元催化合成N-甲基吲哚孕烯醇酮化合物及其抗肿瘤活性

利用N-甲基吲哚对类固醇药物的前驱体16-脱氢孕烯醇酮乙酸酯(16-DPA)的D环C16位进行修饰,采用ZrCl₄-乙酸乙酯廉价催化体系,合成了16个3β-乙酰氧基-16α-3’-吲哚孕烯醇酮化合物和6个3β-羟基-16α-3’-吲哚孕烯醇酮衍生物.该方法具有收率高、立体选择性好和底物适应性强等优点.通过噻唑蓝(MTT)比色法测试了22个化合物对三阴性乳腺癌细胞(MDA-MB-231)的抗肿瘤活性.初步测试结果表明, 3β-乙酰氧基-16α-3’-吲哚孕烯醇酮化合物6h和6i对MDA-MB-231癌细胞有较好的抑制活性,其半数抑制浓度(IC₅₀)分别为18.07和23.22μmol/L;而化合物7a～7f均对MDA-MB-231癌细胞有较好的抑制活性,其中化合物7e的抗肿瘤活性最好,其IC₅₀为12.50μmol/L.目标化合物为药物筛选提供了一定的参考.     

基于多特征和多分类融合的视频概念检测

The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systematically investigated, and kernel-based learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systems on both classifier-level and kernel-level fusion that contribute to a more robust system. Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision (MAP) score is much better than the benchmark performance, which proves that our concepts detection engine develops a generic model and performs well on both object and scene type concepts.     

语音识别的非线性方法

语音信号是一个复杂的非线性过程，这使得基于线性系统理论发展起来的传统语音识别技术性能难以进一步提高。近年来人们开始逐渐重视非线性在语音识别技术中的应用，本文概括地介绍了非线性理论在语音识别技术中的所取得的成果和发展方向，除了涉及较为流行的隐马尔柯夫过程和人工神经网络在语音识别中的应用外，文中着重论述了近年来发展迅猛的混沌，分形理论在语音识别中的应用，本文最后还提到不可忽视的分形理论在语音编码中的应