Jmjd3 mediates blood-spinal cord barrier disruption after spinal cord injury by regulating MMP-3 and MMP-9 expressions.

The disruption of the blood-spinal cord barrier (BSCB) by matrix metalloprotease (MMP) activation is a detrimental event that leads to blood cell infiltration, inflammation, and apoptosis, thereby contributing to permanent neurological disability after spinal cord injury (SCI). However, the molecular mechanisms underlying Mmp gene regulation have not been fully elucidated. Here, we demonstrated the critical role of histone H3K27 demethylase Jmjd3 in the regulation of Mmp gene expression and BSCB disruption using in vitro cellular and in vivo animal models. We found that Jmjd3 up-regulation, in cooperation with NF-κB, after SCI is required for Mmp-3 and Mmp-9 gene expressions in injured vascular endothelial cells. In addition, Jmjd3 mRNA depletion inhibited Mmp-3 and Mmp-9 gene expressions and significantly attenuated BSCB permeability and the loss of tight junction proteins. These events further led to improved functional recovery, along with decreased hemorrhage, blood cell infiltration, inflammation, and cell death of neurons and oligodendrocytes after SCI. Thus, our findings suggest that Jmjd3 regulation may serve as a potential therapeutic intervention for preserving BSCB integrity following SCI.

Inference of brain pathway activities for Alzheimer's disease classification.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative and progressive disorder that results in brain malfunctions. Resting-state (RS) functional magnetic resonance imaging (fMRI) techniques have been successfully applied for quantifying brain activities of both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) patients. Region-based approaches are widely utilized to classify patients from cognitively normal subjects (CN). Nevertheless, region-based approaches have a few limitations, reproducibility owing to selection of disease-specific brain regions, and heterogeneity of brain activities during disease progression. For coping with these issues, network-based approaches have been suggested in the field of molecular bioinformatics. In comparison with individual gene-based approaches, they acquired more accurate results in diverse disease classification, and reproducibility was confirmed by replication studies. In our work, we applied a similar methodology integrating brain pathway information into pathway activity inference, and permitting classification of both aMCI and AD patients based on pathway activities rather than single region activities. RESULTS: After aggregating the 59 brain pathways from literature, we estimated brain pathway activities by using exhaustive search algorithms between patients and cognitively normal subjects, and identified discriminatory pathways according to disease progression. We used three different data sets and each data set consists of two different groups. Our results show that the pathway-based approach (AUC = 0.89, 0.9, 0.75) outperformed the region-based approach (AUC = 0.69, 0.8, 0.68). Also, our approach provided enhanced diagnostic power achieving higher accuracy, sensitivity, and specificity (pathway-based approach: accuracy = 83%; sensitivity = 86%; specificity = 78%, region-based approach: accuracy = 74%; sensitivity = 78%; specificity = 76%). CONCLUSIONS: We proposed a novel method inferring brain pathway activities for disease classification. Our approach shows better classification performance than region-based approach in four classification models. We expect that brain pathway-based approach would be helpful for precise classification of brain disorders, and provide new opportunities for uncovering disrupted brain pathways caused by disease. Moreover, discriminatory pathways between patients and cognitively normal subjects may facilitate the interpretation of functional alterations during disease progression.

Photostable dynamic rectification of one-dimensional schottky diode circuits with a ZnO nanowire doped by H during passivation.

For the first time, we demonstrated photostable and dynamic rectification in ZnO nanowire (NW) Schottky diode circuits where two diodes are face-to-face connected in the same ZnO wire. With their properties improved by H-doping from atomic layer deposited Al(2)O(3) passivation, our ZnO NW diode circuits stably operated at a maximum frequency of 100 Hz displaying a good rectification even under the lights. We thus conclude that our results promisingly appoached one-dimensional nanoelectronics.

Predicting disease phenotypes based on the molecular networks with condition-responsive correlation.

Network-based methods using molecular interaction networks integrated with gene expression profiles have been proposed to solve problems, which arose from smaller number of samples compared with the large number of predictors. However, previous network-based methods, which have focused only on expression levels of proteins, nodes in the network through the identification of condition-responsive interactions. We propose a novel network-based classification, which focuses on both nodes with discriminative expression levels and edges with Condition-Responsive Correlations (CRCs) across two phenotypes. We found that modules with condition-responsive interactions provide candidate molecular models for diseases and show improved performances compared conventional gene-centric classification methods.

Building the process-drug-side effect network to discover the relationship between biological processes and side effects.

BACKGROUND: Side effects are unwanted responses to drug treatment and are important resources for human phenotype information. The recent development of a database on side effects, the side effect resource (SIDER), is a first step in documenting the relationship between drugs and their side effects. It is, however, insufficient to simply find the association of drugs with biological processes; that relationship is crucial because drugs that influence biological processes can have an impact on phenotype. Therefore, knowing which processes respond to drugs that influence the phenotype will enable more effective and systematic study of the effect of drugs on phenotype. To the best of our knowledge, the relationship between biological processes and side effects of drugs has not yet been systematically researched. METHODS: We propose 3 steps for systematically searching relationships between drugs and biological processes: enrichment scores (ES) calculations, t-score calculation, and threshold-based filtering. Subsequently, the side effect-related biological processes are found by merging the drug-biological process network and the drug-side effect network. Evaluation is conducted in 2 ways: first, by discerning the number of biological processes discovered by our method that co-occur with Gene Ontology (GO) terms in relation to effects extracted from PubMed records using a text-mining technique and second, determining whether there is improvement in performance by limiting response processes by drugs sharing the same side effect to frequent ones alone. RESULTS: The multi-level network (the process-drug-side effect network) was built by merging the drug-biological process network and the drug-side effect network. We generated a network of 74 drugs-168 side effects-2209 biological process relation resources. The preliminary results showed that the process-drug-side effect network was able to find meaningful relationships between biological processes and side effects in an efficient manner. CONCLUSIONS: We propose a novel process-drug-side effect network for discovering the relationship between biological processes and side effects. By exploring the relationship between drugs and phenotypes through a multi-level network, the mechanisms underlying the effect of specific drugs on the human body may be understood.

Repression of the miR-143/145 cluster by oncogenic Ras initiates a tumor-promoting feed-forward pathway.

Although activating mutations in RAS oncogenes are known to result in aberrant signaling through multiple pathways, the role of microRNAs (miRNAs) in the Ras oncogenic program remains poorly characterized. Here we demonstrate that Ras activation leads to repression of the miR-143/145 cluster in cells of human, murine, and zebrafish origin. Loss of miR-143/145 expression is observed frequently in KRAS mutant pancreatic cancers, and restoration of these miRNAs abrogates tumorigenesis. miR-143/145 down-regulation requires the Ras-responsive element-binding protein (RREB1), which represses the miR-143/145 promoter. Additionally, KRAS and RREB1 are targets of miR-143/miR-145, revealing a feed-forward mechanism that potentiates Ras signaling.

Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets.

MOTIVATION: Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. RESULTS: We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology. AVAILABILITY: Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/~ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vapor-phase molecular layer deposition of self-assembled multilayers for organic thin-film transistor.

We report a vapor-phase molecular layer deposition (MLD) of self-assembled multilayer thin films for organic thin-film transistor. In the present MLD process, alkylsiloxane self-assembled multilayers (SAMs) were grown under vacuum by repeated sequential adsorptions of C=C-terminated alkylsilane and aluminum hydroxide with ozone activation. The MLD method is a self-controlled layer-by-layer growth process, and is perfectly compatible with the atomic layer deposition (ALD) method. The SAMs films prepared exhibited good mechanical flexibility and stability, excellent insulating properties, and relatively high dielectric capacitances of 374 nF/cm2 with a high dielectric strength of 4 MV/cm. They were then used as a 12 nm-thick dielectric for pentacene-based thin-film transistors (TFTs), which showed a maximum field effect mobility of 0.57 cm2/V s, operating at -4 V with an on/off current ratio of approximately 10(3).

Inactivation of H 1 N 1 viruses exposed to acidic ozone water.

The inactivation of H 1 N 1 viruses upon exposure to acidic ozone water was investigated using chicken allantoic fluids of different dilutions, p H values, and initial ozone concentrations. The inactivation effect of the acidic ozone water was found to be stronger than the inactivation effect of the ozone water combined with the degree of acidity, indicating a synergic effect of acidity on ozone decay in water. It is also shown that acidic ozone water with a p H value of 4 or less is very effective means of virus inactivation if provided in conjunction with an ozone concentration of 20 mg/l or higher.

Rapid vapor-phase fabrication of organic-inorganic hybrid superlattices with monolayer precision.

We report a new layer-by-layer growth method of self-assembled organic multilayer thin films based on gas-phase reactions. In the present molecular layer deposition (MLD) process, alkylsiloxane self-assembled multilayers (SAMs) were grown under vacuum by repeated sequential adsorptions of C=C-terminated alkylsilane and titanium hydroxide. The MLD method is a self- limiting layer-by-layer growth process, and is perfectly compatible with the atomic layer deposition (ALD) method. The SAMs films prepared exhibited good thermal and mechanical stability, and various unique electrical properties. The MLD method, combined with ALD, was applied to the preparation of organic-inorganic hybrid nanolaminate films in the ALD chamber. The organic-inorganic hybrid superlattices were then used as active mediums for two-terminal electrical bistable devices. The advantages of the MLD method with ALD include accurate control of film thickness, large-scale uniformity, highly conformal layering, sharp interfaces, and a vast library of possible materials. The MLD method with ALD is an ideal fabrication technique for various organic-inorganic hybrid superlattices.

Speed estimation from a tri-axial accelerometer using neural networks.

We propose a speed estimation method with human body accelerations measured on the chest by a tri-axial accelerometer. To estimate the speed we segmented the acceleration signal into strides measuring stride time, and applied two neural networks into the patterns parameterized from each stride calculating stride length. The first neural network determines whether the subject walks or runs, and the second neural network with different node interactions according to the subject's status estimates stride length. Walking or running speed is calculated with the estimated stride length divided by the measured stride time. The neural networks were trained by patterns obtained from 15 subjects and then validated by 2 untrained subjects' patterns. The result shows good agreement between actual and estimated speeds presenting the linear correlation coefficient r=0.9874. We also applied the method to the real field and track data.

Density-induced support vector data description.

The purpose of data description is to give a compact description of the target data that represents most of its characteristics. In a support vector data description (SVDD), the compact description of target data is given in a hyperspherical model, which is determined by a small portion of data called support vectors. Despite the usefulness of the conventional SVDD, however, it may not identify the optimal solution of target description especially when the support vectors do not have the overall characteristics of the target data. To address the issue in SVDD methodology, we propose a new SVDD by introducing new distance measurements based on the notion of a relative density degree for each data point in order to reflect the distribution of a given data set. Moreover, for a real application, we extend the proposed method for the protein localization prediction problem which is a multiclass and multilabel problem. Experiments with various real data sets show promising results.

Brain transplantation of immortalized human neural stem cells promotes functional recovery in mouse intracerebral hemorrhage stroke model.

We have generated stable, immortalized cell lines of human NSCs from primary human fetal telencephalon cultures via a retroviral vector encoding v-myc. HB1.F3, one of the human NSC lines, expresses a normal human karyotype of 46, XX, and nestin, a cell type-specific marker for NSCs. F3 has the ability to proliferate continuously and differentiate into cells of neuronal and glial lineage. The HB1.F3 human NSC line was used for cell therapy in a mouse model of intracerebral hemorrhage (ICH) stroke. Experimental ICH was induced in adult mice by intrastriatal administration of bacterial collagenase; 1 week after surgery, the rats were randomly divided into two groups so as to receive intracerebrally either human NSCs labeled with beta-galactosidase (n = 31) or phosphate-buffered saline (PBS) (n = 30). Transplanted NSCs were detected by 5-bromo-4-chloro-3-indolyl-beta-d-galactoside histochemistry or double labeling with beta-galactosidase (beta-gal) and mitogen-activated protein (MAP)2, neurofilaments (both for neurons), or glial fibrillary acidic protein (GFAP) (for astrocytes). Behavior of the animals was evaluated for period up to 8 weeks using modified Rotarod tests and a limb placing test. Transplanted human NSCs were identified in the perihematomal areas and differentiated into neurons (beta-gal/MAP2(+) and beta-gal/NF(+)) or astrocytes (beta-gal/GFAP(+)). The NSC-transplanted group showed markedly improved functional performance on the Rotarod test and limb placing after 2-8 weeks compared with the control PBS group (p < .001). These results indicate that the stable immortalized human NSCs are a valuable source of cells for cell replacement and gene transfer for the treatment of ICH and other human neurological disorders. Disclosure of potential conflicts of interest is found at the end of this article.

Towards clustering of incomplete microarray data without the use of imputation.

MOTIVATION: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process. RESULTS: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications.

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene-patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene-patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at http://www.patome.org/; the information is updated bimonthly.

H-CORE: enabling genome-scale Bayesian analysis of biological systems without prior knowledge.

The Bayesian network is a popular tool for describing relationships between data entities by representing probabilistic (in)dependencies with a directed acyclic graph (DAG) structure. Relationships have been inferred between biological entities using the Bayesian network model with high-throughput data from biological systems in diverse fields. However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. For this reason, most previous approaches limit the number of target entities or use additional knowledge to restrict the search space. In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those clusters, with the aim of overcoming the scalability problem and thus making it possible to perform genome-scale Bayesian network analysis without additional biological knowledge. We use simulations to show that H-CORE is much faster than the widely used sparse candidate method, whilst being of comparable quality. We have also applied H-CORE to retrieving gene-to-gene relationships in a biological system (The 'Rosetta compendium'). By evaluating learned information through literature mining, we demonstrate that H-CORE enables the genome-scale Bayesian analysis of biological systems without any prior knowledge.