A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space

This study proposes a visualization processing method for the deformation risk level of underground space. The proposed method is based on a BP-Hopfield-RGB (BHR) composite network. Complex environmental factors are integrated in the BP neural network. Dynamic monitoring data are then automatically classified in the Hopfield network. The deformation risk level is combined with the RGB color space model and is displayed visually in real time, after which experiments are conducted with the use of an ultrasonic omnidirectional sensor device for structural deformation monitoring. The proposed method is also compared with some typical methods using a benchmark dataset. Results show that the BHR composite network visualizes the deformation monitoring process in real time and can dynamically indicate dangerous zones.     

冷冻电镜密度图可视化软件VAT4M

基于冷冻电镜三维密度图的特征分析,研究开发了冷冻电镜密度图可视化软件VAT4M(Visual Analysis Tools for Macro-Molecule Map and Model),实现了对三维冷冻电镜密度图的可视化分析,其主要功能包括密度图和分子模型的可视化、密度图的分割和晶体结构在密度图中的匹配。在可视化方面,在实现等值面可视化方法的同时还实现了体绘制可视化模式。对于结构分析,VAT4M提供了自动分割工具,和所见即所得的分割浏览器。同时,VAT4M还提供了极值吸收法与梯度法相结合的自动匹配方法,而且,对于具有对称性的密度图,可根据一个亚基的匹配结果,迅速得到其准原子模型。最后给出了若干应用实例。实验表明,本平台对冷冻电镜密度数据具有良好的可视与分析功能。     

A Neural Network-Based Optimal Spatial Filter Design Method for Motor Imagery Classification

In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.     

一种快速高效的全基因组甲基化CpG岛富集方法的建立

高甲基化的CpG岛所致基因表观遗传学转录失活已经成为肿瘤表观基因组学研究的重要内容。现在已有很多检测CpG岛甲基化的方法,但由于各自的局限,还没有建立一种能快速在全基因组水平上进行甲基化CpG岛的富集方法。本研究利用甲基化结合蛋白MBD2b具有特异性结合甲基化DNA的特性, 建立了一种基于DNA免疫共沉淀技术的全基因组甲基化CpG岛的富集方法。在大肠杆菌中表达重组的GST-MBD2b蛋白,通过Glutathione Sepharose 4B对重组蛋白进行纯化,制备成亲和层析柱,利用在不同的盐离子强度下甲基化DNA和非甲基化DNA的结合能力不同,对甲基化DNA进行富集。用甲基化酶SssI处理过的DNA片段与非甲基化DNA片段进行富集效率的检测,发现0.5M KCl的浓度是甲基化DNA片段和非甲基化DNA片段得以分开的临界条件。样品的富集效率用Real Time PCR进行检测。结果表明,这种方法能够实现对全基因组甲基化DNA的有效富集且最高的富集倍数可达到100多倍。富集到的甲基化DNA可以进行后续的定量PCR, DNA测序和全基因组芯片的分析等工作,为大规模分析全基因组CpG 岛甲基化的改变奠定了基础。     

一种快速提取组织外泌体的分离富集方法

本文提供一种快速提取组织外泌体的分离富集方法。通过将目标组织用机械法切碎,加入组织消化酶进行组织解离和过滤,将获得的组织细胞悬液依次进行差速离心、超离、尺寸排阻和超滤,实现组织高质量外泌体的富集纯化。组织解离方法比较试验中,采用组织消化酶解离组织得到的蛋白质含量更高,获得的外泌体组织来源的蛋白质污染小。富集小鼠心组织、小鼠肝组织、小鼠肾组织、人结肠癌组织、人乳腺癌组织和动脉粥样硬化组织的外泌体,并对其进行纳米粒径追踪和透射电镜观察。结果显示,外泌体的粒径均在30~150 nm内,结构清晰明确。对小鼠肝组织富集的外泌体进行蛋白质印迹分析。结果显示,阳性蛋白质标志物CD9、ALIX和CD63的表达,TSG101弱表达,阴性蛋白质标志物Calnexin无表达。本方法集合多种分离措施,能够达到分离纯化外泌体的作用,同时简化了分离组织外泌体的步骤,相对于其他方法,全程只需要4~5 h,节省了富集时间,所富集的外泌体纯度高、可溶性杂蛋白质污染小,实用性更加广泛。使用微量组织样本富集的外泌体即可满足后续纳米粒径追踪、蛋白质印迹、透射电镜和转录物组等分析。     

Overrepresentation of Glutamate Signaling in Alzheimer's Disease: Network-Based Pathway Enrichment Using Meta-Analysis of Genome-Wide Association Studies

Genome-wide association studies (GWAS) have successfully identified several risk loci for Alzheimer''s disease (AD). Nonetheless, these loci do not explain the entire susceptibility of the disease, suggesting that other genetic contributions remain to be identified. Here, we performed a meta-analysis combining data of 4,569 individuals (2,540 cases and 2,029 healthy controls) derived from three publicly available GWAS in AD and replicated a broad genomic region (>248,000 bp) associated with the disease near the APOE/TOMM40 locus in chromosome 19. To detect minor effect size contributions that could help to explain the remaining genetic risk, we conducted network-based pathway analyses either by extracting gene-wise p-values (GW), defined as the single strongest association signal within a gene, or calculated a more stringent gene-based association p-value using the extended Simes (GATES) procedure. Comparison of these strategies revealed that ontological sub-networks (SNs) involved in glutamate signaling were significantly overrepresented in AD (p<2.7×10⁻¹¹, p<1.9×10⁻¹¹; GW and GATES, respectively). Notably, glutamate signaling SNs were also found to be significantly overrepresented (p<5.1×10⁻⁸) in the Alzheimer''s disease Neuroimaging Initiative (ADNI) study, which was used as a targeted replication sample. Interestingly, components of the glutamate signaling SNs are coordinately expressed in disease-related tissues, which are tightly related to known pathological hallmarks of AD. Our findings suggest that genetic variation within glutamate signaling contributes to the remaining genetic risk of AD and support the notion that functional biological networks should be targeted in future therapies aimed to prevent or treat this devastating neurological disorder.     

Climate Dynamics: A Network-Based Approach for the Analysis of Global Precipitation

Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941–2010). The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function.The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only.Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that extreme events can enhance high precipitation gradients, leading to a systematic absence of long-range patterns.     

DNA足纹步移作图法

一种以PCR介导的、可对任意长度靶DNA片段上的核蛋白结合位点进行DNA足纹作图分析的新方法.原理是：采用被随机降解靶DNA分子作为模板,用标记的跨越整个模板的足够多条特异性引物进行单链扩增.首先,利用某种化学试剂或酶如DNaseⅠ对已与蛋白质结合或未结合的双链靶DNA进行随机降解,在一定条件下使每一个DNA分子恰好只有一个位点被切割.然后,这些被随机降解DNA分子即可作为模板,用同位素标记的跨越整个模板的足够多条特异性引物（正向或反向）进行单链扩增.最后,扩增的单链产物通过变性聚丙烯酰胺凝胶电泳和放射自显影形成DNA片段梯队,而被蛋白质保护的位点则在DNA片段梯队中形成位置缺口,从而确定DNA与蛋白质相结合的精确位点.该方法被应用对人干细胞因子基因5′旁侧－1190～－273区域的DNA足纹部分作图.     

Self-Contained Gene-Set Analysis of Expression Data: An Evaluation of Existing and Novel Methods

Gene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained. Benefits of self-contained methods include that they can be used for genome-wide, candidate gene, or pathway studies, and have been reported to be more powerful than competitive methods. We therefore investigated ten self-contained methods that can be used for continuous, discrete and time-to-event phenotypes. To assess the power and type I error rate for the various previously proposed and novel approaches, an extensive simulation study was completed in which the scenarios varied according to: number of genes in a gene set, number of genes associated with the phenotype, effect sizes, correlation between expression of genes within a gene set, and the sample size. In addition to the simulated data, the various methods were applied to a pharmacogenomic study of the drug gemcitabine. Simulation results demonstrated that overall Fisher''s method and the global model with random effects have the highest power for a wide range of scenarios, while the analysis based on the first principal component and Kolmogorov-Smirnov test tended to have lowest power. The methods investigated here are likely to play an important role in identifying pathways that contribute to complex traits.     

GIV: A Tool for Genomic Islands Visualization

A Genomic Islands (GI) is a chunk of DNA sequence in a genome whose origin can be traced back to other organisms or viruses. The detection of GIs plays an indispensable role in biomedical research, due to the fact that GIs are highly related to special functionalities such as disease-causing GIs - pathogenicity islands. It is also very important to visualize genomic islands, as well as the supporting features corresponding to the genomic islands in the genome. We have developed a program, Genomic Island Visualization (GIV), which displays the locations of genomic islands in a genome, as well as the corresponding supportive feature information for GIs. GIV was implemented in C++, and was compiled and executed on Linux/Unix operating systems.

Availability

GIV is freely available for non-commercial use at http://www5.esu.edu/cpsc/bioinfo/software/GIV     

A Method for Visualization of Incoming Adenovirus Chromatin Complexes in Fixed and Living Cells

Inside the adenovirus virion, the genome forms a chromatin-like structure with viral basic core proteins. Core protein VII is the major DNA binding protein and was shown to remain associated with viral genomes upon virus entry even after nuclear delivery. It has been suggested that protein VII plays a regulatory role in viral gene expression and is a functional component of viral chromatin complexes in host cells. As such, protein VII could be used as a maker to track adenoviral chromatin complexes in vivo. In this study, we characterize a new monoclonal antibody against protein VII that stains incoming viral chromatin complexes following nuclear import. Furthermore, we describe the development of a novel imaging system that uses Template Activating Factor-I (TAF-I/SET), a cellular chromatin protein tightly bound to protein VII upon infection. This setup allows us not only to rapidly visualize protein VII foci in fixed cells but also to monitor their movement in living cells. These powerful tools can provide novel insights into the spatio-temporal regulation of incoming adenoviral chromatin complexes.     

基于基因表达变异性的通路富集方法研究

当前的通路富集方法主要是基于基因的表达差异,很少有方法从通路变异性(方差)角度对其富集分析．我们注意到用合适的统计量描述通路的变异性时,在疾病表型下一些通路的变异性有明显的上升或者下降．因此本研究假设：通路变异性程度在不同表型中存在差异．本文设计了14种描述通路变异性的统计量与检验方法,检测不同表型下变异性有差异的通路即富集通路,并将富集结果与文献检索结果进行比较,同时,分析不同芯片预处理方法对数据和结果的影响．研究结果表明：5种预处理方法中,多阵列对数健壮算法(RMA)是数据预处理的最优方法;不同表型下通路的变异性程度存在差异;根据文献检索的通路结果,14种基于变异性的通路富集方法中,以通路中各基因欧氏距离的方差做统计量进行permutation检验(方法11)能有效识别显著通路,其富集结果优于基因集富集分析(GSEA)．综上所述,基于通路变异性的通路富集策略具有可行性,不仅对通路富集分析有一定的理论指导意义,而且为人类疾病研究提供新的视角．     

Method for Enrichment of Rhodopseudomonas sphaeroides from Fresh Water

An inulinase was highly purified from the culture broth of Penicillium purpurogenum by chromatographies on DEAE-Sepharose CL-6B, Toyopearl HW-65, and Bio-Gel P-100. The enzyme was homogeneous by disc electrophoretic analysis. The molecular weight was 6.4 × 10⁴ by SDS-disc electrophoresis and gel filtration on Bio-Gel P-150. The isoelectric point was pH 3.6 by isoelectric focusing. The enzyme hydrolyzed inulin rapidly, but did not affect sucrose. By paper chromatography analysis, the major products from inulin were tri-, tetra-, penta-, and hexa-saccharides. The substrate specificity of the enzyme on hydrolyses of fructo-oligosaccharides[1^F(1-β-d-fructofuranosyl)n sucrose (n = 1 to 6 and n (average of polymerization degree) = 8)] were examined. The Km values and relative maximum velocities for the hydrolyses of inulin and fructo-oligosaccharides (GF_n, n = 2 to 7 and n = 9) were as follows: inulin, (DP = 35) 0.21 mM and 100; GF₉, 0.24 mM and 86.5; GF₇, 0.33 mM and 132; GF₆, 0.85 mM and 71.2; GF₅, 3.8 mM and 25.4; GF₄, 2.8 mM and 28.8; GF₃, (nystose) 16 mM and 0.8; GF₂ (1-kestose), 8.4 mM and 0.2. The molecular activities for the hydrolyses of fructo-oligosaccharides (GF_n, n = 2 to 6) were increased depending on the degree of polymerization of fructosyl residues, and were nearly constant if the polymerization degree was over seven. These results strongly suggested that the endo-type inulinase from Penicillium purpurogenum had a subsite structure consisting of at least seven subsites.     

A Simple Clearing Method for Improved Visualization of lacZ Expression in Fish Larvae

The protocol described in this report provides a simple and reliable method for improved visualization of lacZ expression upon X-gal staining. The simple treatments of KOH-H2O2 and glycerol removed all pigment from the whole body and made the demelanized fry transparent without any structural damage. Based on this method, the exogenous expression of lacZ could be easily identified and distinguished from endogenous background activity in pigmented transgenic fry.     

Structural Design Principles of Complex Bird Songs: A Network-Based Approach

Bird songs are acoustic communication signals primarily used in male-male aggression and in male-female attraction. These are often monotonous patterns composed of a few phrases, yet some birds have extremely complex songs with a large phrase repertoire, organized in non-random fashion with discernible patterns. Since structure is typically associated with function, the structures of complex bird songs provide important clues to the evolution of animal communication systems. Here we propose an efficient network-based approach to explore structural design principles of complex bird songs, in which the song networks–transition relationships among different phrases and the related structural measures–are employed. We demonstrate how this approach works with an example using California Thrasher songs, which are sequences of highly varied phrases delivered in succession over several minutes. These songs display two distinct features: a large phrase repertoire with a ‘small-world’ architecture, in which subsets of phrases are highly grouped and linked with a short average path length; and a balanced transition diversity amongst phrases, in which deterministic and non-deterministic transition patterns are moderately mixed. We explore the robustness of this approach with variations in sample size and the amount of noise. Our approach enables a more quantitative study of global and local structural properties of complex bird songs than has been possible to date.     

Partition Enrichment of Nucleotide Sequences (PINS) - A Generally Applicable,Sequence Based Method for Enrichment of Complex DNA Samples

The dwindling cost of DNA sequencing is driving transformative changes in various biological disciplines including medicine, thus resulting in an increased need for routine sequencing. Preparation of samples suitable for sequencing is the starting point of any practical application, but enrichment of the target sequence over background DNA is often laborious and of limited sensitivity thereby limiting the usefulness of sequencing. The present paper describes a new method, Probability directed Isolation of Nucleic acid Sequences (PINS), for enrichment of DNA, enabling the sequencing of a large DNA region surrounding a small known sequence. A 275,000 fold enrichment of a target DNA sample containing integrated human papilloma virus is demonstrated. Specifically, a sample containing 0.0028 copies of target sequence per ng of total DNA was enriched to 786 copies per ng. The starting concentration of 0.0028 target copies per ng corresponds to one copy of target in a background of 100,000 complete human genomes. The enriched sample was subsequently amplified using rapid genome walking and the resulting DNA sequence revealed not only the sequence of a the truncated virus, but also 1026 base pairs 5′ and 50 base pairs 3′ to the integration site in chromosome 8. The demonstrated enrichment method is extremely sensitive and selective and requires only minimal knowledge of the sequence to be enriched and will therefore enable sequencing where the target concentration relative to background is too low to allow the use of other sample preparation methods or where significant parts of the target sequence is unknown.     

A Network-Based Classification Model for Deriving Novel Drug-Disease Associations and Assessing Their Molecular Actions

The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer’s disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer’s disease.