Sandwich教学法在物理化学教学中的应用——以化学势表达式的推导为例

以化学势表达式的推导为例,探究了Sandwich教学法在物理化学教学中的应用。详细介绍了Sandwich教学法实施过程中教师如何提出问题以呈现教学目标,学生如何通过自主学习与全班交流来探究与解决问题,以及最后又如何通过总结与反馈回归教学目标。通过以学生为主体的Sandwich教学过程的实施,学生对化学势概念、热力学基本方程及状态函数法等重要的物理化学基本概念、公式及方法有了更深入的理解和掌握。Sandwich教学方法相较于传统教学模式更能激发学生学习的主动性和积极性,从而提高学生学习物理化学的兴趣与效果。     

Inference of gene regulatory networks with multi-objective cellular genetic algorithm

Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multi-objective formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty weights. A flexible and robust Multi-Objective Cellular Evolutionary Algorithm is adapted to perform the tasks of parameter learning and network topology inference for the proposed approach. The resulting software, called MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of trade-off solutions involving GRNs with different typologies and MSEs.     

Network analysis based on low-rank method for mining information on integrated data of multi-cancers

The noise problem of cancer sequencing data has been a problem that can’t be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.     

A class imbalance-aware Relief algorithm for the classification of tumors using microarray gene expression data

DNA microarray data has been widely used in cancer research due to the significant advantage helped to successfully distinguish between tumor classes. However, typical gene expression data usually presents a high-dimensional imbalanced characteristic, which poses severe challenge for traditional machine learning methods to construct a robust classifier performing well on both the minority and majority classes. As one of the most successful feature weighting techniques, Relief is considered to particularly suit to handle high-dimensional problems. Unfortunately, almost all relief-based methods have not taken the class imbalance distribution into account. This study identifies that existing Relief-based algorithms may underestimate the features with the discernibility ability of minority classes, and ignore the distribution characteristic of minority class samples. As a result, an additional bias towards being classified into the majority classes can be introduced. To this end, a new method, named imRelief, is proposed for efficiently handling high-dimensional imbalanced gene expression data. imRelief can correct the bias towards to the majority classes, and consider the scattered distributional characteristic of minority class samples in the process of estimating feature weights. This way, imRelief has the ability to reward the features which perform well at separating the minority classes from other classes. Experiments on four microarray gene expression data sets demonstrate the effectiveness of imRelief in both feature weighting and feature subset selection applications.     

Power-law distribution of gene expression fluctuations

Large-scale genomic technologies has opened new possibilities to infer gene regulatory networks from time series data. Here, we investigate the relationship between the dynamic information of gene expression in time series and the underlying network structure. First, our results show that the distribution of gene expression fluctuations (i.e., standard deviation) follows a power-law. This finding indicates that while most genes exhibit a relatively low variation in expression level, a few genes are revealed as highly variable genes. Second, we propose a stochastic model that explains the emergence of this power-law behavior. The model derives a relationship that connects the standard deviation (variance) of each node to its degree. In particular, it allows us to identify a global property of the underlying genetic regulatory network, such as the degree exponent, by only computing dynamic information. This result not only offers an interesting link to explore the topology of real systems without knowing the real structure but also supports earlier findings showing that gene networks may follow a scale-free distribution.     

基于分类回归树的个人信用客观评分指标体系(英文)

本文提出了一种客观的个人信用指标体系.首先利用分类回归树量化每个指标对信用状况的影响程度,并以此量化值为每个指标设置不同的评分权重;然后通过定义风险度量值来确定指标中各个取值的评分,进而建立了新的评估指标体系.通过选取现实样本数据对指标体系做了实证分析,分析结果表明,新建的指标体系能很好地对借款人进行风险评价.     

Study on the activity and mechanism of skimmianine against human non-small cell lung cancer

The present research was to investigate the effects of skimmianine (SK) in four non-small cell lung cancer (NSCLC) cells. We found that SK can significantly inhibit the growth of NSCLC cells and markedly induce apoptosis in NSCLC cells. The effects of growth inhibition and apoptosis induction were in a concentration–response relationship and caspase-dependent manner.     

Extracellular Matrix Proteins Involved in Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and characterized by cognitive and memory impairments. Emerging evidence suggests that the extracellular matrix (ECM) in the brain plays an important role in the etiology of AD. It has been detected that the levels of ECM proteins have changed in the brains of AD patients and animal models. Some ECM components, for example, elastin and heparan sulfate proteoglycans, are considered to promote the upregulation of extracellular amyloid-beta (Aβ) proteins. In addition, collagen VI and laminin are shown to have interactions with Aβ peptides, which might lead to the clearance of those peptides. Thus, ECM proteins are involved in both amyloidosis and neuroprotection in the AD process. However, the molecular mechanism of neuronal ECM proteins on the pathophysiology of AD remains elusive. More investigation of ECM proteins with AD pathogenesis is needed, and this may lead to novel therapeutic strategies and biomarkers for AD.