VCSRA:A fast and accurate multiple sequence alignment algorithm with a high degree of parallelism

Multiple sequence alignment （MSA） is the alignment among more than two molecular biological sequences, which is a fundamental method to analyze evolutionary events such as mutations, insertions, deletions, and re-arrangements. In theory, a dynamic programming algorithm can be employed to produce the optimal MSA. However, this leads to an explosive increase in computing time and memory consumption as the number of sequences increases （Taylor, 1990）. So far, MSA is still regarded as one of the most challenging problems in bioinformatics and computational biology （Chatzou et al., 2016）.

Data Augmentation Based Event Detection

Supervised models for event detection usually require large-scale human-annotated training data,especially neural models.A data augmentation technique is proposed to improve the performance of event detection by generating paraphrase sentences to enrich expressions of the original data.Specifically,based on an existing human-annotated event detection dataset,we first automatically build a paraphrase dataset and label it with a designed event annotation alignment algorithm.To alleviate possible wrong labels in the generated paraphrase dataset,a multi-instance learning(MIL)method is adopted for joint training on both the gold human-annotated data and the generated paraphrase dataset.Experimental results on a widely used dataset ACE2005 show the effectiveness of our approach.

Three-dimensional pharmacophore screening for fentanyl derivatives

Fentanyl is a highly selective u-opioid receptor agonist with high analgesic activity. Three-dimensional pharmacophore models were built from a set of 50 fentanyl derivatives. These were employed to elucidate ligand-receptor interactions using information derived only from the ligand structure to identify new potential lead compounds. The present studies demonstrated that three hydrophobic regions, one positive ionizable region and two hydrogen bond acceptor region sites located on the molecule seem to be essential for analgesic activity. The results of the comparative molecular field analysis model suggested that both steric and electrostatic interactions play important roles. The contributions from steric and electrostatic fields for the model were 0.621 and 0.379, respectively. The pharmacophore model provides crucial information about how well the common features of a subject molecule overlap with the hypothesis model, which is very valuable for designing and optimizing new active structures.

Using Cluster Computers in Bioinformatics Research

In the last ten years, high-performance and massively parallel computing technology comes into a high speed developing phase and is used in all fields. The cluster computer systems are also being widely used for their low cost and high performance. In bioinformatics research, solving a problem with computer usually takes hours even days. To speed up research, high-performance cluster computers are considered to be a good platform. Moving into the new MPP (massively parallel processing) system, the original algorithm should be parallelized in a proper way. In this paper, a new parallelizing method of useful sequence alignment algorithm (Smith-Waterman) is designed based on its optimizing algorithm already exists. The result is gratifying.

一种用于位移场分析的应变模式频域数字图像相关方法

本文提出了一种在频域中考虑应变对位移影响的频域数字图像相关方法.研究并建立了一个在频域中评价参考图像子区与目标图像子区之间匹配的评价标准,并在此基础之上,提出了可用于位移和应变计算的频域迭代算法,其中,Hessian矩阵在迭代过程中保持不变.在Hessian矩阵的形成过程中,参考图像子区中灰度的梯度可以通过快速傅里叶变换进行计算,具有很高的计算效率和精度.同时,一种离散傅里叶变换重采样技术被用于代替传统的插值方法对变形图像子区中亚像素位置的灰度进行重建和更新.为了验证所提方法的有效性和准确性,使用了国际DIC挑战委员会(IDCC)提供的系列图像(2D-Challenge 1.0,样本6)进行位移分析.分析的误差结果表明,在子区大小为41×41像素的条件下,对变形为50%的模拟实验进行了计算,位移分析结果的精度约为0.0032像素(最大绝对值误差),在精度上优于已发表的文献.最后,对硅橡胶试件进行了14.85%的单轴拉伸试验,通过相关系数分布检验了本文所提方法在实际应用中的有效性,并给出了分析的位移和应变分布.

Blind false data injection attacks in smart grids subject to measurement outliers

False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,which eliminate the limitation of grasping measurement Jacobian matrix H in advance,but when there are outliers in measurement data,attack performance is degraded.In this paper,improved BFDIAs are proposed.In off-line phase,lowdimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm(LLTSA)is sent into Continuous Deep Belief Network(CDBN)as training data to learn their probability distribution.In on-line phase,real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs,and outputs are reconstructed by the probability distribution in off-line phase,which eliminates the influence of outliers indirectly.Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.

Facility Layout Based on Sequence Analysis:Design of Flowshops

A computer-aided method to design a hybrid layout--tree-shape planar flowlines is presented. In newtype fiowshop layout, the common machines shared by several flowlines could be located together in functional sections. The approach combines traditional cell formation techniques with sequence alignment algorithms. Firstly, a sequence analysis based cell formation procedure is adopted; then the operation sequences for parts are aligned to maximize machines adjacency in hyperedge representations; finally a tree-shape planar flowline will be obtained for each part family. With the help of a sample of operation sequences obtained from industry, this algorithm is illustrated.

Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

In order to discover the novel anticonvulsant drugs, pharmacophore screening of the anticonvulsant inhibitors was enforced. Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets （GALAHAD） and Comparative Molecular Field Analysis （CoMFA） studies were combined to implement our research. Firstly, multiple models were generated using GALAHAG based on high active molecules. Secondly, several of them were validated using the CoMFA study. Finally, a good values of q2 from training set and promising predictive power from test set were obtained based on one model simutaneously. One model had been selected as the most reasonable pharmacophore model. The results of the CoMFA study based on the model 1 suggested that both steric and electrostatic interactions played important roles.