Application of ACO algorithm in protein structure prediction

The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved ACO algorithm for the protein structure prediction. In the algorithm, the "lone"ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.     

无定位图的预测误差差值扩展可逆数据隐藏*

将Tian差值扩展技术应用于彩色图像中,提出一种利用预测误差差值进行扩展嵌入的彩色图像可逆数据隐藏算法。针对传统差值扩展技术存在过分修改像素灰度值、须嵌入定位图等缺点,首先利用色彩分量间的相关性减小差值,并将差值扩展量分散到两个色彩分量中;其次,对直方图平移技术进行改进,使得同等嵌入率下图像质量达到最佳;最后由两个色彩分量中像素的预测值之和决定可用于扩展嵌入的像素,无须保存溢出定位图,提取端在提取信息时可无损地恢复原始图像。实验结果表明,与其他算法相比,该算法在同等嵌入率下可取得更好的图像质量,算法复杂度     

A study of structural determinants in the interleukin-1 fold

The structures of interleukin-1ß, basic fibroblastgrowth factor and Erythrina trypsin inhibitor have been analysedin order to determine whether the hydrophobic core remains conserved,even when the structures have extremely low sequence similarities.We find that there are significant differences in the way eachprotein achieves a satisfactory arrangement of core residuesand that positions which contribute to the core of one structureare not guaranteed to contribute to the integrity of another.Furthermore, the side-chain packing arrangements of these coreresidues vary significantly between the three structures. Duringthis analysis the side-chain rotamers for three independentlydetermined interleukin-1ß structures were also compared.It was found that although buried residues are generally inagreement the remaining residues frequently occupy differentrotamers in the three structures. This suggests that althoughmeaningful studies are possible for buried side-chains the resultsobtained from equivalent analyses of accessible residues shouldbe treated with caution. These results are discussed with specificreference to the optimization of side-chain packing in proteinsof known structure.     

基于循环维纳滤波的振动信号去噪算法研究

振动信号的去噪需要在滤除噪声的同时尽可能避免相位失真。传统的锐截止低通滤波器或FIR滤波器无法在去噪效果和线性相位之间取得平衡,难以满足振动信号的去噪需求。针对这一问题,将线性相位的维纳滤波算法引入到振动信号的去噪中,并结合振动信号的循环平稳特性,形成了振动信号的循环维纳滤波算法。文中以直接维纳滤波为基础对循环维纳滤波算法的原理进行了深入分析和仿真实现,并基于实测汽轮机振动信号对算法的去噪性能进行了讨论。理论分析及算法仿真表明,循环维纳滤波算法在保证线性相位的前提下有较好的去噪效果,能够解决传统去噪算法所存在的去噪效果与线性相位难以均衡的问题。     

应用于临床生存时间预测的二次学习算法研究

生存时间预测在医学、经济和工程等领域有着广泛的应用。随着机器学习技术和数据挖掘技术的发展和广泛应用,研究人员提出了很多基于机器学习技术的生存时间预测算法。这些算法虽然都取得了良好的效果,但预测精度均有提升的空间。因此,提出了一种基于二次学习风范的生存时间预测算法,并结合最近邻算法在截尾样本估计上的应用以及支持向量机在泛化性能上的优势,实现了对临床生存时间的建模。实验结果表明,该算法能够获取精确的生存时间,且具有预测精度上的性能优势。     

A predicted three-dimensional structure of human cytochrome P450: implications for substrate specificity

A three-dimensional structure for human cytochrome P450IA1 waspredicted based on the crystal coordinates of cytochrome P450camfrom Pseudomonas putida. As there was only 15% residue identitybetween the two enzymes, additional information was used toestablish an accurate sequence alignment that is a prerequisitefor model building. Twelve representative eukaryotic sequenceswere aligned and a net prediction of secondary structure wasmatched against the known -helices and ß-sheets ofP450cam. The cam secondary structure provided a fixed main-chainframework onto which loops of appropriate length from the humanP450IA1 structure were added. The model-built structure of thehuman cytochrome conformed to the requirements for the segregationof polar and nonpolar residues between the core and the surface.The first 44 residues of human cytochrome P450 could not bebuilt into the model and sequence analysis suggested that residues1–26 formed a single membrane-spanning segment. Examinationof the sequences of cytochrome P450s from distinct gene familiessuggested specific residues that could account for the differencesin substrate specificity. A major substrate for P450IA1, 3-methyl-cholanthrene,was fitted into the proposed active site and this planar aromaticmolecule could be accommodated into the available cavity. Residuesthat are likely to interact with the haem were identified. Thesequence similarity between 59 eukaryotic enzymes was representedas a dendrogram that in general clustered according to genefamily. Until a crystallographic structure is available, thismodel-building study identifies potential residues in cytochromeP450s important in the function of these enzymes and these residuesare candidates for site-directed mutagenesis.     

CJ91胶粘剂在螺旋振动光整机中的应用

螺旋振动光整机槽内壁的橡皮保护层由于老化及磨损等原因出现裂口甚至脱落现象，严重影响设备的使用寿命和闸把壳体的外观质量。通过分析和试验筛选出螺旋振动光整机的理想胶粘荆——CJ91胶粘剂并介绍了粘接修补工艺。     

基于深度网络的出租车Pick-up需求预测

在智慧城市建设中,区域的出租车需求预测是一个十分重要的模块。为了预测指定区域未来时刻的出租车需求,本文通过拓展已有序列模型,提出一个多时间分辨率的基于层次注意力机制的循环高速网络（Multi-Time Resolution Hierarchical Attention-Based Recurrent Highway Networks, MTR-HRHN）。MTR-HRHN将对外生数据时空特征的提取和目标数据的时空建模整合到单个框架中,并且通过多分辨率（例如每个小时或者每天）对序列数据不同的时间属性建模,从而捕获更全面的时间模式。最后,在纽约市出租车数据集上评估MTR-HRHN的预测性能。实验结果表明,与其他经典时间序列预测方法相比,MTR-HRHN在多个高需求区域的短期需求预测上表现出更好的预测性能。     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.