Author Correction: Control of electronic topology in a strongly correlated electron system

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The lifelong impact of fetal growth restriction on cardiac development

Background: Maternal nutrient restriction (MNR) is a widespread cause of fetal growth restriction (FGR), an independent predictor of heart disease and cardiovascular mortality. Our objective was to examine the developmental and long-term impact of MNR-induced FGR on cardiac structure in a model that closely mimics human development. Methods: A reduction in total caloric intake spanning pregestation through to lactation in guinea pig sows was used to induce FGR. Proliferation, differentiation, and apoptosis of cardiomyocytes were assessed in late-gestation fetal, neonatal, and adult guinea pig hearts. Proteomic analysis and pathway enrichment were performed on fetal hearts. Results: Cardiomyocyte proliferation and the number of mononucleated cells were enhanced in the MNR–FGR fetal and neonatal heart, suggesting a delay in cardiomyocyte differentiation. In fetal hearts of MNR–FGR animals, apoptosis was markedly elevated and the total number of cardiomyocytes reduced, the latter remaining so throughout neonatal and into adult life. A reduction in total cardiomyocyte number in adult MNR–FGR hearts was accompanied by exaggerated hypertrophy and a disorganized architecture. Pathway analysis identified genes related to cell proliferation, differentiation, and survival. Conclusions: FGR influences cardiomyocyte development during critical windows of development, leading to a permanent deficiency in cardiomyocyte number and compensatory hypertrophy in a rodent model that recapitulates human development

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

Scenario Planning Case Studies Using Open Government Data

Part 3: Decision Support Tools and SystemsInternational audienceThe opportunity for improved decision making has been enhanced in recent years through the public availability of a wide variety of information. In Australia, government data is routinely made available and maintained in the http://data.gov.au repository. This is a single point of reference for data that can be reused for purposes beyond that originally considered by the data custodians. Similarly a wealth of citizen information is available from the Australian Bureau of Statistics. Combining this data allows informed decisions to be made through planning scenarios.We present two case studies that demonstrate the utility of data integration and web mapping. As a simple proof of concept the user can explore different scenarios in each case study by indicating the relative weightings to be used for the decision making process. Both case studies are demonstrated as a publicly available interactive map-based website