Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity

Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug–drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein–protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.     

个人住房抵押贷款模式及应用

提出了等比还贷,等差还贷,基于等额的灵活还贷和混合还贷4种模式,举例分析并指出了各模式适合的客户类型,最后对贷款利率调整时各模式的有关变化作了进一步的探讨,各种还贷模式可根据具体情况在实践中推广应用。     

Stock Index Prediction Based on Time Series Decomposition and Hybrid Model

The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.     

Sensitive electrochemical determination of α-fetoprotein using a glassy carbon electrode modified with in-situ grown gold nanoparticles,graphene oxide and MWCNTs acting as signal amplifiers

The authors describe an electrochemical immunoassay for α-fetoprotein (α-FP) using a glassy carbon electrode (GCE) modified with a nanocomposite made from gold nanoparticles, graphene oxide and multi-walled carbon nanotubes (AuNPs/GO-MWCNTs) and acting as a signal amplification matrix. The nanocomposite was synthesized in a one-pot redox reaction between GO and HAuCl₄ without using an additional reductant. The stepwise assembly of the immunoelectrode was characterized by means of cyclic voltammetry and electrochemical impedance spectroscopy. The interaction of antigen and antibody on the surface of the electrode creates a barrier for electrons and causes retarded electron transfer, this resulting in decreased signals in differential pulse voltammetry of hexacyanoferrate which is added as an electrochemical probe. Using this strategy and by working at a potential of 0.2 V (vs. SCE), a wide analytical range (0.01 - 100 ng∙mL‾¹) is covered. The correlation coefficient is 0.9929, and the limit of detection is as low as 3 pg∙mL‾¹ at a signal-to-noise ratio of 3. This electrochemical immunoassay combines the specificity of an immunological detection scheme with the sensitivity of an electrode modified with AuNPs and GO-MWCNTs.

Schematic illustration of the fabrication procedure of the immunosensor

     

Iron Catalyzed Isomerization of α-Alkyl Styrenes to Access Trisubstituted Alkenes

Stereoselective isomerization of α-alkyl styrenes is accomplished using a new iron catalyst supported by phosphine-pyridine-oxazoline (PPO) ligand.The protocol ...     

电动车辆电磁场辐射骚扰测试方法

与传统汽车相比,电动汽车采用的电气部件、电子控制单元等电气件更多。在低频段(9kHz～30MHz),其主要部件如逆变器和电动机等低频骚扰源与车辆的电子控制单元有严重的相互干扰。文章对电动车辆电磁场辐射骚扰的测量方法进行了探讨,通过对两台电动车辆测量数据的比较及低频段电动车辆可能发射源的分析,论述了GB/T18387-2001实施的必要性和可行性。     

The duality between Morrey spaces and its pre-dual spaces characterized by heat kernel

Let L be an elliptic operator, we give arguments about the duality estimate between Morrey spaces and its pre-dual spaces characterized by the heat kernel associated to L that is given in [6],[8].     

基于二维光栅分光的同步移相干涉测量技术

为了干涉测量的抗振目的,提出了一种新的同步移相干涉测量方案并搭建了实验装置。整个测量系统在迈克耳孙偏振移相干涉仪的基础上,利用一个正交的二维光栅产生对称分光,选取对于理想光栅衍射效率一致的(±1,±1)级衍射光作为测量分光路,使之分别通过偏振方向依次相差45°的一个偏振片组,从而分别形成0°、90°、180°和270°相移的四幅移相干涉图,按照传统的四步移相算法,对被测波面进行了复原。分析了光强畸变和移相误差对系统的测量误差的影响。利用该系统测量一球面系统,结果与在ZYGO干涉仪上相比较,球面系统的均方根误差相差0.012λ,峰谷值相差0.051λ。     

基于彩色扫描仪的图像光谱重构

针对彩色扫描仪的特点,采用主元分析法(PCA)和反向传播(BP)人工神经网络(ANN)相结合的方法对图像光谱重构进行研究。选择IT8.7/2标准色卡作为训练样本,将该色卡中的另一组色靶作为检验样本以讨论不同网络结构以及不同主元数和训练样本数对光谱重构的影响,再以自然色系统(NCS)色卡为检验样本来分析不同种类的训练和检验样本与光谱重构性能的关系。实验结果表明,采用3-14-6网络结构和6个主元数是最佳选择,训练样本和扫描目标之间的一致性是基于彩色扫描仪图像光谱重构的关键所在。     

低曝光量下投影算法的稳健性研究

研究了在低曝光量情况下灰度投影算法的稳健性,为提出的稳定成像系统的设计与实现提供可行性论证。通过合成视频实验探讨、实际视频实验论证相结合的研究方法,探讨曝光量不足对灰度投影算法性能的影响。实验结果表明,灰度投影算法在欠曝5档和存在一定随机噪声的情况下,仍能保持良好的运动矢量估计性能,从而说明了投影算法在一定的曝光量范围内仍能准确地得到运动矢量估计,是一种运算量小、实时性好且稳健性较强的电子稳像算法。投影算法在低曝光量情况下良好的稳像稳健特性,使其能在低亮度及高实时性要求等环境下应用于航拍、卫星遥感等稳定成像或高清晰成像场合。