Heterogeneous mantle source and magma differentiation of quaternary arc-like volcanic rocks from Tengchong, SE margin of the Tibetan Plateau

The Tengchong volcanic field north of the Burma arc comprises numerous Quaternary volcanoes in the southeastern margin of the Tibetan Plateau. The volcanic rocks are grouped into four units (1-4) from the oldest to youngest. Units 1, 3 and 4 are composed of olivine trachybasalt, basaltic trachyandesite and trachyandesite, and Unit 2 consists of hornblende dacite. The rocks of Units 1, 3, and 4 form a generally alkaline suite in which the rocks plot along generally linear trends on Harker diagrams with only slight offset from unit to unit. They contain olivine phenocrysts with Fo values ranging from 65 to 85 mol% and have Cr-spinel with Cr# ranging from 23 to 35. All the rocks have chondrite-normalized REE patterns enriched in LREE and primitive mantle-normalized trace element patterns depleted in Ti, Nb and Ta, but they are rich in Th, Ti and P relative to typical arc volcanics. Despite minor crustal contamination, 87Sr/ 86Sr ratios (0.706-0.709), εNd values (-3.2 to -8.7), and εHf values (+4.8 to -6.4) indicate a highly heterogeneous mantle source. The Pb isotopic ratios of the lavas ( 206Pb/ 204Pb = 18.02-18.30) clearly show an EMI-type mantle source. The underlying mantle source was previously modified by subduction of the Neo-Tethyan oceanic and Indian continental lithosphere. The present heterogeneous mantle source is interpreted to have formed by variable additions of fluids and sediments derived from the subducted Indian Oceanic lithosphere, probably the Ninety East Ridge. Magma generation and emplacement was facilitated by transtensional NS-trending strike-slip faulting. © 2011 The Author(s).published_or_final_versionSpringer Open Choice, 28 May 201

Identifying rodent olfactory bulb structures with micro-DTI

Conference Theme: Personalized Healthcare Through TechnologyOlfactory bulb (OB) is one of the most developed systems in rodent models with complex neuronal organization and anatomical structures. MR diffusion tensor imaging (DTI) is a non-invasive technique to probe tissue microstructures by examining the diffusion characteristics of water molecules. This paper presents how different OB layers can be identified and quantitatively characterized by micro-DTI using a specially constructed micro-imaging radio frequency (RF) coil. High spatial resolution and high signal to noise ratio (SNR) DTI images of ex vivo rat OBs were obtained. Distinct contrasts were observed between various olfactory bulb layers in trace map, fractional anisotropy (FA) map and FA color map, all in consistence with the known OB neuroanatomy. These experimental results demonstrate the utility of micro-DTI in investigation of complex OB organization. © 2008 IEEE.published_or_final_versio

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

Extracts of Feijoa Inhibit Toll-Like Receptor 2 Signaling and Activate Autophagy Implicating a Role in Dietary Control of IBD

Inflammatory bowel disease (IBD) is a heterogeneous chronic inflammatory disease affecting the gut with limited treatment success for its sufferers. This suggests the need for better understanding of the different subtypes of the disease as well as nutritional interventions to compliment current treatments. In this study we assess the ability of a hydrophilic feijoa fraction (F3) to modulate autophagy a process known to regulate inflammation, via TLR2 using IBD cell lines

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Mitochondrial Uncoupling Inhibits p53 Mitochondrial Translocation in TPA-Challenged Skin Epidermal JB6 Cells

The tumor suppressor p53 is known to be able to trigger apoptosis in response to DNA damage, oncogene activation, and certain chemotherapeutic drugs. In addition to its transcriptional activation, a fraction of p53 translocates to mitochondria at the very early stage of apoptosis, which eventually contributes to the loss of mitochondrial membrane potential, generation of reactive oxygen species (ROS), cytochrome c release, and caspase activation. However, the mitochondrial events that affect p53 translocation are still unclear. Since mitochondrial uncoupling has been suggested to contribute to cancer development, herein, we studied whether p53 mitochondrial translocation and subsequent apoptosis were affected by mitochondrial uncoupling using chemical protonophores, and further verified the results using a siRNA approach in murine skin epidermal JB6 cells. Our results showed that mitochondrial uncoupling blocked p53 mitochondrial translocation induced by 12-O-tetradecanoylphorbol 13-acetate (TPA), a known tumor promoter to induce p53-mediated apoptosis in skin carcinogenesis. This blocking effect, in turn, led to preservation of mitochondrial functions, and eventually suppression of caspase activity and apoptosis. Moreover, uncoupling protein 2 (UCP2), a potential suppressor of ROS in mitochondria, is important for TPA-induced cell transformation in JB6 cells. UCP2 knock down cells showed enhanced p53 mitochondrial translocation, and were less prone to form colonies in soft agar after TPA treatment. Altogether, our data suggest that mitochondrial uncoupling may serve as an important regulator of p53 mitochondrial translocation and p53-mediated apoptosis during early tumor promotion. Therefore, targeting mitochondrial uncoupling may be considered as a novel treatment strategy for cancer

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Matrix metalloproteinase-9 activity and a downregulated Hedgehog pathway impair blood-brain barrier function in an <i>in vitro</i> model of CNS tuberculosis

Central nervous system tuberculosis (CNS TB) has a high mortality and morbidity associated with severe inflammation. The blood-brain barrier (BBB) protects the brain from inflammation but the mechanisms causing BBB damage in CNS TB are uncharacterized. We demonstrate that Mycobacterium tuberculosis (Mtb) causes breakdown of type IV collagen and decreases tight junction protein (TJP) expression in a co-culture model of the BBB. This increases permeability, surface expression of endothelial adhesion molecules and leukocyte transmigration. TJP breakdown was driven by Mtb-dependent secretion of matrix metalloproteinase (MMP)-9. TJP expression is regulated by Sonic hedgehog (Shh) through transcription factor Gli-1. In our model, the hedgehog pathway was downregulated by Mtb-stimulation, but Shh levels in astrocytes were unchanged. However, Scube2, a glycoprotein regulating astrocyte Shh release was decreased, inhibiting Shh delivery to brain endothelial cells. Activation of the hedgehog pathway by addition of a Smoothened agonist or by addition of exogenous Shh, or neutralizing MMP-9 activity, decreased permeability and increased TJP expression in the Mtb-stimulated BBB co-cultures. In summary, the BBB is disrupted by downregulation of the Shh pathway and breakdown of TJPs, secondary to increased MMP-9 activity which suggests that these pathways are potential novel targets for host directed therapy in CNS TB

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response