通过FGF401的构效关系研究发现一种新颖的FGFR4选择性抑制剂

设计并合成了一系列FGF401类似物以研究其FGFR4抑制、抗肿瘤活性及其构效关系.研究发现了N-(5-氰基-4-(2-甲氧基乙基氨基)吡啶-2-基)-7-甲酰基-6-(N-甲基四氢吡喃-4-甲酰胺)甲基-1,2,3,4-四氢-1,8-萘啶-1-甲酰胺(8ac)不仅在酶和细胞学水平上对FGFR4具有强效的的抑制活性,并表现出了出色的选择性.其活性及选择性优于阳性对照FGF401,并且在HCC (hepatocellular carcinoma)动物移植瘤模型中显著抑制肿瘤生长,还引起了肿瘤萎缩.     

Combined neural network and reduced FRF techniques for slight damage detection using measured response data

Summary  This paper deals with structural damage detection using measured frequency response functions (FRF) as input data to artificial neural networks (ANN). A major obstacle, the impracticality of using full-size FRF data with ANNs, was circumvented by applying a data-reduction technique based on principal component analysis (PCA). The compressed FRFs, represented by their projection onto the most significant principal components, were used as the ANN input variables instead of the raw FRF data. The output is a prediction of the actual state of the specimen, i.e. healthy or damaged. A further advantage of this particular approach is its ability to deal with relatively high measurement noise, which is a common occurrence when dealing with industrial structures. The methodology was applied to detect three different states of a space antenna: reference, slight mass damage and slight stiffness damage. About 600 FRF measurements, each with 1024 spectral points, were included in the analysis. Six 2-hidden layer networks, each with an individually-optimised architecture for a specific FRF reduction level, were used for damage detection. The results showed that it was possible to distinguish between the three states of the antenna with good accuracy, subject to using an adequate number of principal components together with a suitable neural network configuration. It was also found that the quality of the raw FRF data remained a major consideration, though the method was able to filter out some of the measurement noise. The convergence and detection properties of the networks were improved significantly by removing those FRFs associated with measurement errors. Received 9 March 2000; accepted for publication 12 December 2000     

Fixed Rank Filtering for Spatio-Temporal Data

Datasets from remote-sensing platforms and sensor networks are often spatial, temporal, and very large. Processing massive amounts of data to provide current estimates of the (hidden) state from current and past data is challenging, even for the Kalman filter. A large number of spatial locations observed through time can quickly lead to an overwhelmingly high-dimensional statistical model. Dimension reduction without sacrificing complexity is our goal in this article. We demonstrate how a Spatio-Temporal Random Effects (STRE) component of a statistical model reduces the problem to one of fixed dimension with a very fast statistical solution, a methodology we call Fixed Rank Filtering (FRF). This is compared in a simulation experiment to successive, spatial-only predictions based on an analogous Spatial Random Effects (SRE) model, and the value of incorporating temporal dependence is quantified. A remote-sensing dataset of aerosol optical depth (AOD), from the Multi-angle Imaging SpectroRadiometer (MISR) instrument on the Terra satellite, is used to compare spatio-temporal FRF with spatial-only prediction. FRF achieves rapid production of optimally filtered AOD predictions, along with their prediction standard errors. In our case, over 100,000 spatio-temporal data were processed: Parameter estimation took 64.4 seconds and optimal predictions and their standard errors took 77.3 seconds to compute. Supplemental materials giving complete details on the design and analysis of a simulation experiment, the simulation code, and the MISR data used are available on-line.     

Enhancing software reliability modeling and prediction through the introduction of time-variable fault reduction factor

Over the past three decades, many software reliability models with different parameters, reflecting various testing characteristics, have been proposed for estimating the reliability growth of software products. We have noticed that one of the most important parameters controlling software reliability growth is the fault reduction factor (FRF) proposed by Musa. FRF is generally defined as the ratio of net fault reduction to failures experienced. During the software testing process, FRF could be influenced by many environmental factors, such as imperfect debugging, debugging time lag, etc. Thus, in this paper, we first analyze some real data to observe the trends of FRF, and consider FRF to be a time-variable function. We further study how to integrate time-variable FRF into software reliability growth modeling. Some experimental results show that the proposed models can improve the accuracy of software reliability estimation. Finally, sensitivity analyses of various optimal release times based on cost and reliability requirements are discussed. The analytic results indicate that adjusting the value of FRF may affect the release time as well as the development cost.     

利用频响函数对阻尼结构进行模型修正的方法

基于频响函数和模型缩聚技术提出了一个动力结构模型修正方法。与传统频响函数法相比,由于引入了阻尼刚度比,减少了修正参数,提高了计算效率。并且针对传统频响函数法测试维数过高的缺点,引入模型缩聚技术,降低了模型测量维数的要求。数值算例证明了本方法的有效性与可行性。本方法可以对有阻尼的模型进行修正,克服了模态修正法的缺点;利用模型缩聚减小了测试点的数目,引进单元阻尼刚度比。与传统的频响函数修正法相比,减少了修正未知数,提高计算效率,并且修正结果也较准确。     

ODS & Modal Testing Using a Transmissi- bility Chain

In this paper, we show how Operating Deflection Shapes (ODS’s) and mode shapes can be obtained experimentally from measurements that are made using only two sensors and two short wires to connect them to a multi-channel acquisition system. This new test procedure is depicted in Figure 1. Not only is the equipment required to do a test much more cost effective, but this method can be used to test any sized test article, especially large ones.
The testing method introduced here involves moving a pair of sensors along together in a prescribed manor, and calculating the Transmissibility between them. The resulting chain of Transmissibility’s is then postprocessed to obtain a single reference set of cross-channel measurements, from which ODS’s and mode shapes can be extracted.
A “round trip” example is used to show how an original set of modal parameters can be recovered by curve fitting a single reference set of output-only Cross spectra, and a single reference set of FRFs.     

A new frequency domain based approach for a nonlinear aeroelasticity analysis

In this paper, a new frequency-domain based approach for the investigation of aeroelasticity problems is introduced that is capable of handling both linear and nonlinear problems. This approach is based on coupling a conceptual method used in a structural dynamic analysis and an optimum equivalent linear frequency response function (OELF). Additionally, a new criterion for determining the flutter speed and the instability of nonlinear systems is introduced that is based on the condition number of the aeroelastic matrices. Due to the global nature of the condition number, the new criterion proves to be efficient and simple to use. To examine the efficiency of the new technique, a two-dimensional nonlinear airfoil with an unsteady aerodynamic model is considered.     

基于界面主从位移控制的频响子结构方法

在复杂工程结构频响子结构分析中,各个子结构的频响函数往往是由不同的部门获得,各个子结构的频响函数可能是在不同的坐标系下得到,亦或有些子结构具有某种对称性,这需要对相应的子结构频响函数进行旋转和镜像变换后才能进行整体拼接。此外,子结构拼接时交界面的连接点有时可能存在不一致,为了满足位移协调和力平衡条件,本文基于界面主从位移控制原理,提出一种改进的频响子结构方法,该方法既能考虑子结构的旋转和镜像变换,也能考虑子结构界面间的柔性连接,并可大幅提高计算效率。当界面位移控制阵为单位阵时,改进的方法退化为传统的频响子结构方法。数值算例验证了所提方法的正确性和有效性。