Advancements in the Development of non-BET Bromodomain Chemical Probes

The bromodomain and extra terminal (BET) family of bromodomain-containing proteins (BCPs) have been the subject of extensive research over the past decade, resulting in a plethora of high-quality chemical probes for their tandem bromodomains. In turn, these chemical probes have helped reveal the profound biological role of the BET bromodomains and their role in disease, ultimately leading to a number of molecules in active clinical development. However, the BET subfamily represents just 8/61 of the known human bromodomains, and attention has now expanded to the biological role of the remaining 53 non-BET bromodomains. Rapid growth of this research area has been accompanied by a greater understanding of the requirements for an effective bromodomain chemical probe and has led to a number of new non-BET bromodomain chemical probes being developed. Advances since December 2015 are discussed, highlighting the strengths/caveats of each molecule, and the value they add toward validating the non-BET bromodomains as tractable therapeutic targets.     

Progressive damage modelling of SMC composite materials

The present work deals with the modelling of damage behaviour for sheet moulding compound (SMC) composite materials using a finite element analysis package. Specifically, a comparison is made between the results obtained experimentally for a three-point bending test, and those obtained from numerical simulation using a material model already implemented. The simulation has been performed for the material models available within the PAM-CRASH software. The simulation results are compared and validated with respect to experimentation.     

Model validation and model structure determination

This paper gives a tutorial overview of basic approaches for model validation and model structure determination.Work partially supported by the Swedish Research Council for Engineering Sciences under contract 98-654.     

Sandia National Laboratories Validation Workshop: Structural dynamics application

This article specifies a virtual structural dynamic subsystem and some systems, as well as mathematical models that approximately simulate them. The purpose is to define a setting for model validations conceived by participants in the Sandia National Laboratories Validation Workshop. Some broad guidelines for the model validations are set, as well as a regulatory requirement to be satisfied by the target system. Participants are directed to present techniques for subsystem model validation, to analytically predict whether or not the regulatory requirement will be met, and to estimate the likelihood of accuracy of the prediction.     

Constructing a short form of the Smoking Consequences Questionnaire with adolescents and young adults.

The goal of the present studies was to construct and validate a short form of the 50-item Smoking Consequences Questionnaire (SCQ; T. H. Brandon & T. B. Baker, 1991), a measure of smoking outcome expectancies. In Study 1, a 21-item short form (S-SCQ) was derived from a sample of 107 young adults previously treated for substance abuse. In Study 2, the measure was cross-validated on 125 adolescents in treatment for substance abuse. Confirmatory factor analyses revealed good model fit and factorial invariance for the 4 S-SCQ subscales across both samples. Validation analyses on each sample found that subscale scores generally correlated significantly with smoking-related variables. The present studies provide initial evidence for the utility of the S-SCQ when used with young adults and adolescents. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

Progressive events in supervisory control and compositional verification

This paper investigates some limitations of the nonblocking property when used for supervisor synthesis in discrete event systems. It is shown that there are cases where synthesis with the nonblocking property gives undesired results. To address such cases, the paper introduces progressive events as a means to specify more precisely how a synthesised supervisor should complete its tasks. The nonblocking property is modified to take progressive events into account, and appropriate methods for verification and synthesis are proposed. Experiments show that progressive events can be used in the analysis of industrial-scale systems, and can expose issues that remain undetected by standard nonblocking verification.     

基于支持向量回归的动力电池参数辨识

根据铅酸蓄电池在放电过程中内部电化学反应导致外部电特性变化的特点,提出一种基于支持向量机原理的电解液密度辨识模型。利用支持向量机理论非线性回归的特性,简化测量电解液密度的过程,在恶劣环境下检测动力电池的电解液密度更显其优越性。预测实验表明,采用改进的交叉验证预测模型具有泛化能力强、稳定性好的特点,并且在小样本的条件下能达到预期的辨识精度。