New technologies around biomarkers and their interplay with drug development

What conductors are to their orchestras, biomarkers are to their associated technologies. Building fundamental science, supporting early diagnosis of diseases and following their progression, improving efficacy and safety of treatments, optimizing patient selection and adapting dosing of drugs, helping decide which therapy is most appropriate; these are examples of a few contexts in which biomarkers are key players. Technology development can definitely not escape being associated with these steps. In other words, today’s biomarkers are the thermometers of tomorrow’s therapies. This review provides an overview of recently established platforms as well as new and upcoming technologies for biomarker development in the context of drug development. The roles as well as the pros and cons of different disciplines such as genetics, genomics, proteomics, metabonomics, and assay development will be discussed.     

非线性流固耦合问题的ALE分步有限元数值方法

推导了一种基于ALE（Arbitrary Lagrangian-Eulerian,即任意的拉格朗日-欧拉）描述下的带有自由液面不可压液体与运动结构非线性耦合问题的一种有限元数值计算方法,在时间域上采用分步格式计算,这种方法与混合插值方法比较其优点是速度和压力可使用同阶线性插值,给数值计算带来很大方便,将所得到的方法应用到结构与TLD（调谐式（液体阻尼器）装置之间的耦合问题,数值计算结果验证了本文所推导的方法。     

二层供应链网络均衡模型的研究

利用均衡理论和二层规划理论来研究供应链网络均衡问题。针对供应链网络中上下层成员之间具有的Stackelberg博弈特征以及同层成员之间具有的非合作博弈特征,构建了二层供应链网络的均衡模型,该模型实际上一个均衡约束的二层规划问题。此外,为了使得供应链网络在整体上实现最优,本文还在模型中引入回收契约以协调供应链网络。最后,利用罚函数法对模型进行了求解,算例分析说明了模型的合理性和有效性。     

A New Method of Constructing the Functional Integral Directly from Hamiltonian in N (> 2)-Dimensional State space

Using the new method of constructing the functional path integral in state space, we accomplished the construction of the functional path integral in N(> 2)-dimensional state space, and illustrated it with the example of n = 3 dimensional case that leads to the same results with others.     

Particle image velocimetry in aerodynamics: Technology and applications in wind tunnels

Particle image velocimetry (PIV) is increasingly used for aerodynamic research and development. The PIV technique allows the recording of a complete flow velocity field in a plane of the flow within a few microseconds. Thus, it provides information about unsteady flow fields, which is difficult to obtain with other experimental techniques. The short acquisition time and fast availability of data reduce the operational time, and hence cost, in large scale test facilities. Technical progress made in the last years allowed DLR to develop a reliable, modular PIV system for use in industrial wind tunnels. The features of this system are summarized and results of recent PIV applications are presented.     

Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements

In this study, an algorithm for growing neural networks is proposed. Starting with an empty network the algorithm reduces the error of prediction by subsequently inserting connections and neurons. The type of network element and the location where to insert the element is determined by the maximum reduction of the error of prediction. The algorithm builds non-uniform neural networks without any constraints of size and complexity. The algorithm is additionally implemented into two frameworks, which use a data set limited in size very efficiently, resulting in a more reproducible variable selection and network topology.

The algorithm is applied to a data set of binary mixtures of the refrigerants R22 and R134a, which were measured by a surface plasmon resonance (SPR) device in a time-resolved mode. Compared with common static neural networks all implementations of the growing neural networks show better generalization abilities resulting in low relative errors of prediction of 0.75% for R22 and 1.18% for R134a using unknown data.     

The Structure of Diastereomeric Oxaprotiline Metabolites. An NMR Study

The structure of phenolic metabolites of oxaprotiline, obtained from a study with rats and dogs, are assigned with NMR-spectroscopic methods in combination with MS data and an X-ray structure analysis of synthetic (9R*,2′S*)-2-chlorooxaprotiline, the metabolites are shown to be (9R*,2′R*)-2-hydroxy, 3-hydroxy-, and 2-hydroxy-3-methoxyoxaprotiline, and (9R*,2′S*)-2-hydroxy-, 3-hydroxy-, and 3-hydroxy-2-methoxyoxaprotiline, as well as 2,6-dihydroxyoxaprotiline.