金鱼DEAD-box家族基因p68和p110在配子发生中的表达特征

利用组织原位杂交技术,以地高辛标记的反义RNA为探针,检测了金鱼(Carassius auratus)DEAD-box家族基因p68和p110在卵子发生及精子发生中的表达分布特点。结果表明:在金鱼配子发生中,p68基因在各个时期的卵母细胞均有表达,Ⅰ、Ⅱ期卵母细胞中的表达水平较Ⅲ、Ⅳ期卵母细胞中更高。p68基因的表达只限于精原细胞和初级精母细胞中。p110基因在金鱼精子发生各阶段持续表达,其中精原细胞和初级精母细胞中表达水平较高;在卵子发生中,Ⅰ期卵母细胞中没有p110 RNA阳性信号,Ⅱ期卵母细胞中信号

黄鳝DEAD-box家族PL10基因的克隆与序列分析

通过PCR克隆的方法,从黄鳝(Monopterus albus)中得到两个PL10 基因的cDNA片段Mo PL10A和Mo PL10B,长度均为1.127 kb,推测其编码375个氨基酸的蛋白片段.结合其他PL10类同源物序列,对这两条cDNA进行了分析和初步的功能推测.根据此片段的氨基酸序列构建的系统发育树与形态分类结果一致.在不同组织中的RT PCR结果表明Mo PL10A和Mo PL10B的mRNA在各组织中的分布有差异.

Structural optimization of lattice materials

Lattice materials are characterized at the microscopic level by a regular pattern of voids confined by walls. Recent rapid prototyping techniques allow their manufacturing from a wide range of solid materials, ensuring high degrees of accuracy and limited costs. The microstructure of lattice material permits to obtain macroscopic properties and structural performance, such as very high stiffness to weight ratios, highly anisotropy, high specific energy dissipation capability and an extended elastic range, which cannot be attained by uniform materials. Among several applications, lattice materials are of special interest for the design of morphing structures, energy absorbing components and hard tissue scaffold for biomedical prostheses. Their macroscopic mechanical properties can be finely tuned by properly selecting the lattice topology and the material of the walls. Nevertheless, since the number of the design parameters involved is very high, and their correlation to the final macroscopic properties of the material is quite complex, reliable and robust multiscale mechanics analysis and design optimization tools are a necessary aid for their practical application. In this paper, the optimization of lattice materials parameters is illustrated with reference to the design of a bracket subjected to a point load. Given the geometric shape and the boundary conditions of the component, the parameters of four selected topologies have been optimized to concurrently maximize the component stiffness and minimize its mass. Copyright © 2011 by ASME.

A method for VVER-1000 fuel rearrangement optimization taking into account both fuel cladding durability and burnup

A method for VVER-1000 fuel rearrangement optimization that takes into account both cladding durability and fuel burnup and which is suitable for any regime of normal reactor operation has been established. The main stages involved in solving the problem of fuel rearrangement optimization are discussed in detail. Using the proposed fuel rearrangement efficiency criterion, a simple example VVER-1000 fuel rearrangement optimization problem is solved under deterministic and uncertain conditions. It is shown that the deterministic and robust (in the face of uncertainty) solutions of the rearrangement optimization problem are similar in principle, but the robust solution is, as might be anticipated, more conservative. © 2013 Elsevier B.V.

Multi-objective optimization for multiphase orbital rendezvous missions

A multi-objective optimization approach was proposed for multiphase orbital rendezvous missions and validated by application to a representative numerical problem. By comparing the Pareto fronts obtained using the proposed method, the relationships between the three objectives considered are revealed, and the influences of other mission parameters, such as the sensors' field of view, can also be analyzed effectively. For multiphase orbital rendezvous missions, the tradeoff relationships between the total velocity increment and the trajectory robustness index as well as between the total velocity increment and the time of flight are obvious and clear. However, the tradeoff relationship between the time of flight and the trajectory robustness index is weak, especially for the four- and five-phase missions examined. The proposed approach could be used to reorganize a stable rendezvous profile for an engineering rendezvous mission, when there is a failure that prevents the completion of the nominal mission.

Low-rank optimization for distance matrix completion

This paper addresses the problem of low-rank distance matrix completion. This problem amounts to recover the missing entries of a distance matrix when the dimension of the data embedding space is possibly unknown but small compared to the number of considered data points. The focus is on high-dimensional problems. We recast the considered problem into an optimization problem over the set of low-rank positive semidefinite matrices and propose two efficient algorithms for low-rank distance matrix completion. In addition, we propose a strategy to determine the dimension of the embedding space. The resulting algorithms scale to high-dimensional problems and monotonically converge to a global solution of the problem. Finally, numerical experiments illustrate the good performance of the proposed algorithms on benchmarks. © 2011 IEEE.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).