Space experimental studies of microgravity fluid science in China

Microgravity fluid physics is an important part of microgravity sciences, which consists of simple fluids of many new systems, gas-liquid two-phase flow and heat transfer, and complex fluid mechanics. In addition to the importance of itself in sciences and applications, microgravity fluid physics closely relates to microgravity combustion, space biotechnology and space materials science, and promotes the developments of interdisciplinary fields. Many space microgravity experiments have been per- formed on board the recoverable satellites and space ships of China and pushed the rapid development of microgravity sciences in China. In the present paper, space experimental studies and the main re- sults of the microgravity fluid science in China in the last 10 years or so are introduced briefly.

H.R. 4502 and S. 1966, the Biotechnology Competitiveness Act [microform] : hearing before the Subcommittee on Natural Resources, Agriculture Research, and Environment and the Subcommittee on Science, Research, and Technology of the Committee on Science, Space, and Technology, House of Representatives, One Hundredth Congress, second session, July 14, 1988.

"No. 138."

Main Progress of Microgravity Sciences and Space Life Sciences Research in China

The progress of the research activities on space material sciences, microgravity ‰uid physics and combustion, space life sciences and biotechnology research, fundamental Physics in China are brie‰y summarized in the present paper. The major space missions and experimental results obtained on board the Chinese recoverable/non-recoverable satellites and the Chinese manned spaceship named ``Shen-Zhou'' are presented summarily. The recent main activities of the ground-based studies in China are introduced in brief.

Main Progress of Microgravity Sciences and Space Life Sciences Research in China

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Numerical approximation of a fractional-in-space diffusion equation (II) - with nonhomogeneous boundary conditions

In this paper, a space fractional di®usion equation (SFDE) with non- homogeneous boundary conditions on a bounded domain is considered. A new matrix transfer technique (MTT) for solving the SFDE is proposed. The method is based on a matrix representation of the fractional-in-space operator and the novelty of this approach is that a standard discretisation of the operator leads to a system of linear ODEs with the matrix raised to the same fractional power. Analytic solutions of the SFDE are derived. Finally, some numerical results are given to demonstrate that the MTT is a computationally e±cient and accurate method for solving SFDE.