Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conform...Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conformational variability of complexes and affects most 3D structure determination methods that rely on signal averaging. Here, an approach is described that allows sorting structural states based on a 3D statistical approach, the 3D sampling and classification (3D-SC) of 3D structures derived from single particles imaged by cryo electron microscopy (cryo-EM). The method is based on jackknifing & bootstrapping of 3D sub-ensembles and 3D multivariate statistical analysis followed by 3D classification. The robustness of the statistical sorting procedure is corroborated using model data from an RNA polymerase structure and experimental data from a ribosome complex. It allows resolving multiple states within heterogeneous complexes that thus become amendable for a structural analysis despite of their highly flexible nature. The method has important implications for high-resolution structural studies and allows describing structure ensembles to provide insights into the dynamics of multi-component macromolecular assemblies.展开更多
针对无线传感器网络中近似四面体内点三维(APIT—3D)定位算法存在的问题,提出一种基于球切割的APIT(APIT—SC)定位算法。该算法改善在节点分布不均匀时定位精度和定位覆盖率差的问题,用体积规则减少PIT—3D测试中出现Out To In和In To ...针对无线传感器网络中近似四面体内点三维(APIT—3D)定位算法存在的问题,提出一种基于球切割的APIT(APIT—SC)定位算法。该算法改善在节点分布不均匀时定位精度和定位覆盖率差的问题,用体积规则减少PIT—3D测试中出现Out To In和In To Out错误。以球切割法和轮回选择法改善算法性能,降低计算复杂度。仿真实验表明:500个节点随机部署在100 m×100 m×100 m的理想网络环境下,APIT—SC算法定位覆盖率可达91%,定位误差在23%左右。与APIT—3D算法相比,降低了计算复杂度,提高了定位精度。展开更多
文摘Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conformational variability of complexes and affects most 3D structure determination methods that rely on signal averaging. Here, an approach is described that allows sorting structural states based on a 3D statistical approach, the 3D sampling and classification (3D-SC) of 3D structures derived from single particles imaged by cryo electron microscopy (cryo-EM). The method is based on jackknifing & bootstrapping of 3D sub-ensembles and 3D multivariate statistical analysis followed by 3D classification. The robustness of the statistical sorting procedure is corroborated using model data from an RNA polymerase structure and experimental data from a ribosome complex. It allows resolving multiple states within heterogeneous complexes that thus become amendable for a structural analysis despite of their highly flexible nature. The method has important implications for high-resolution structural studies and allows describing structure ensembles to provide insights into the dynamics of multi-component macromolecular assemblies.
文摘针对无线传感器网络中近似四面体内点三维(APIT—3D)定位算法存在的问题,提出一种基于球切割的APIT(APIT—SC)定位算法。该算法改善在节点分布不均匀时定位精度和定位覆盖率差的问题,用体积规则减少PIT—3D测试中出现Out To In和In To Out错误。以球切割法和轮回选择法改善算法性能,降低计算复杂度。仿真实验表明:500个节点随机部署在100 m×100 m×100 m的理想网络环境下,APIT—SC算法定位覆盖率可达91%,定位误差在23%左右。与APIT—3D算法相比,降低了计算复杂度,提高了定位精度。
