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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

A Genetic Linkage Map for Cattle

We report the most extensive physically anchored linkage map for cattle produced to date. Three-hundred thirteen genetic markers ordered in 30 linkage groups, anchored to 24 autosomal chromosomes (n = 29), the X and Y chromosomes, four unanchored syntenic groups and two unassigned linkage groups spanning 2464 cM of the bovine genome are summarized. The map also assigns 19 type I loci to specific chromosomes and/or syntenic groups and four cosmid clones containing informative microsatellites to chromosomes 13, 25 and 29 anchoring syntenic groups U11, U7 and U8, respectively. This map provides the skeletal framework prerequisite to development of a comprehensive genetic map for cattle and analysis of economic trait loci (ETL).     

Acanthamoeba-Campylobacter Coculture as a Novel Method for Enrichment of Campylobacter Species

In this study, we present a novel method to isolate and enrich low concentrations of Campylobacter pathogens. This method, Acanthamoeba-Campylobacter coculture (ACC), is based on the intracellular survival and multiplication of Campylobacter species in the free-living protozoan Acanthamoeba polyphaga. Four of the Campylobacter species relevant to humans and livestock, Campylobacter jejuni, C. coli, C. lari, and C. hyointestinalis, were effectively enriched by the coculture method, with growth rates comparable to those observed in other Campylobacter enrichment media. Studying six strains of C. jejuni isolated from different sources, we found that all of the strains could be enriched from an inoculum of fewer than 10 bacteria. The sensitivity of the ACC method was not negatively affected by the use of Campylobacter-selective antibiotics in the culture medium, but these were effective in suppressing the growth of seven different bacterial species added at a concentration of 10⁴ CFU/ml of each species as deliberate contamination. The ACC method has advantages over other enrichment methods as it is not dependent on a microaerobic milieu and does not require the use of blood or other oxygen-quenching agents. Our study found the ACC method to be a promising tool for the enrichment of Campylobacter species, particularly from water samples with low bacterial concentrations.     

A New Method of the Visualization of the Double-Stranded Mitochondrial and Nuclear DNA

The study describes the method of a sensitive detection of double-stranded DNA molecules in situ. It is based on the oxidative attack on the deoxyribose moiety by copper(I) in the presence of oxygen. We have shown previously that the oxidative attack leads to the formation of frequent gaps in DNA. Here we have demonstrated that the gaps can be utilized as the origins for an efficient synthesis of complementary labeled strands by DNA polymerase I and that such enzymatic detection of the double-stranded DNA is a sensitive approach enabling in-situ detection of both the nuclear and mitochondrial genomes in formaldehyde-fixed human cells.     

基于免疫磁珠富集粪便脱落细胞的大肠癌筛选法

大肠癌是目前发病率、致死率均较高的恶性肿瘤,严重危害人民健康生活.现有的临床检测技术均存在一定的局限性,因而限制了其临床应用的范围.与常规临床大肠癌检测相比,粪便脱落细胞检测法具有高特异性、高灵敏性且容易被受试者接受等显著优点.作为最新型的脱落细胞检测法一免疫磁珠富集粪便脱落细胞检测法除具有一般脱落细胞检测法的优点外,还具有细胞回收效率高、肿瘤细胞特异性高等优点.本文阐述了脱落细胞检测大肠癌的原理和基本方法,着重介绍了免疫磁珠富集脱落细胞的原理和操作过程以及针对脱落细胞进行大肠癌检测的多种方法,并对全自动、快速、高通量的自动化脱落细胞检测仪器应用于脱落细胞检测进行了展望.     

Network-Based Relating Pharmacological and Genomic Spaces for Drug Target Identification

Background

Identifying drug targets is a critical step in pharmacology. Drug phenotypic and chemical indexes are two important indicators in this field. However, in previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome.

Methodology/Principal Findings

Based on the correlations observed in pharmacological and genomic spaces, we develop a computational framework, drugCIPHER, to infer drug-target interactions in a genome-wide scale. Three linear regression models are proposed, which respectively relate drug therapeutic similarity, chemical similarity and their combination to the relevance of the targets on the basis of a protein-protein interaction network. Typically, the model integrating both drug therapeutic similarity and chemical similarity, drugCIPHER-MS, achieved an area under the Receiver Operating Characteristic (ROC) curve of 0.988 in the training set and 0.935 in the test set. Based on drugCIPHER-MS, a genome-wide map of drug biological fingerprints for 726 drugs is constructed, within which unexpected drug-drug relations emerged in 501 cases, implying possible novel applications or side effects.

Conclusions/Significance

Our findings demonstrate that the integration of phenotypic and chemical indexes in pharmacological space and protein-protein interactions in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.