Both genome-wide genetic and epigenetic alterations are fundamentally important for the development of cancers, but the interdependence of these aberrations is poorly understood. Glioblastomas and other cancers with the CpG island methylator phenotype (CIMP) constitute a subset of tumours with extensive epigenomic aberrations and a distinct biology1–3. Glioma CIMP (G-CIMP) is a powerful determinant of tumour pathogenicity, but the molecular basis of G-CIMP remains unresolved. Here we show that mutation of a single gene, isocitrate dehydrogenase 1 (IDH1), establishes G-CIMP by remodelling the methylome. This remodelling results in reorganization of the methylome and transcriptome. Examination of the epigenome of a large set of intermediate-grade gliomas demonstrates a distinct G-CIMP phenotype that is highly dependent on the presence of IDH mutation. Introduction of mutant IDH1 into primary human astrocytes alters specific histone marks, induces extensive DNA hypermethylation, and reshapes the methylome in a fashion that mirrors the changes observed in G-CIMP-positive lower-grade gliomas. Furthermore, the epigenomic alterations resulting from mutant IDH1 activate key gene expression programs, characterize G-CIMP-positive proneural glioblastomas but not other glioblastomas, and are predictive of improved survival. Our findings demonstrate that IDH mutation is the molecular basis of CIMP in gliomas, provide a framework for understanding oncogenesis in these gliomas, and highlight the interplay between genomic and epigenomic changes in human cancers.

Cyclophilin D (CypD, encoded by Ppif) is an integral part of the mitochondrial permeability transition pore, whose opening leads to cell death. Here we show that interaction of CypD with mitochondrial amyloid-β protein (Aβ) potentiates mitochondrial, neuronal and synaptic stress. The CypD-deficient cortical mitochondria are resistant to Aβ- and Ca2+-induced mitochondrial swelling and permeability transition. Additionally, they have an increased calcium buffering capacity and generate fewer mitochondrial reactive oxygen species. Furthermore, the absence of CypD protects neurons from Aβ- and oxidative stress-induced cell death. Notably, CypD deficiency substantially improves learning and memory and synaptic function in an Alzheimer's disease mouse model and alleviates Aβ-mediated reduction of long-term potentiation. Thus, the CypD-mediated mitochondrial permeability transition pore is directly linked to the cellular and synaptic perturbations observed in the pathogenesis of Alzheimer's disease. Blockade of CypD may be a therapeutic strategy in Alzheimer's disease.

Machine learning offers the potential for effective and efficient classification of remotely sensed imagery. The strengths of machine learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. Nevertheless, implementing a machine-learning classification is not straightforward, and the literature provides conflicting advice regarding many key issues. This article therefore provides an overview of machine learning from an applied perspective. We focus on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN). Issues considered include the choice of algorithm, training data requirements, user-defined parameter selection and optimization, feature space impacts and reduction, and computational costs. We illustrate these issues through applying machine-learning classification to two publically available remotely sensed data sets.

Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by di fferent physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and e fficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solarwind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a blockadaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional blockadaptive grid. We describe the algorithms of BATL and demonstrate its e fficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical schemes. Depending on the application, we find that di fferent time stepping methods are optimal. Several of the time integration schemes exploit the block-based granularity of the grid structure. The framework and the adaptive algorithms enable physics based space weather modeling and even forecasting.https://ntrs.nasa.gov/search.jsp?R=20110005631 2018-05-12T08:50:45+00:00Z

Objective To report contemporary estimates of the prevalence of hip-related osteoarthritis (OA) outcomes in African-Americans and Caucasians aged ≥ 45 years. Methods Weighted prevalence estimates and their corresponding 95% confidence intervals for hip symptoms, radiographic hip OA, symptomatic hip OA, and severe radiographic hip OA were calculated using SUDAAN® for age, race, and sex subgroups among 3,068 participants (33% African-Americans, 38% men) in the baseline examination (1991–1997) of The Johnston County Osteoarthritis Project, a population-based study of OA in North Carolina. Radiographic hip OA was defined as Kellgren-Lawrence radiographic grade ≥ 2, moderate/severe radiographic hip OA as grades 3 and 4, and symptomatic hip OA as hip symptoms in a hip with radiographic OA. Results Hip symptoms were present in 36%; 28% had radiographic hip OA; nearly 10% had symptomatic hip OA; and 2.5% had moderate/severe radiographic hip OA. Prevalence of all 4 outcomes was higher in older individuals; most outcomes were higher for women and African-Americans. Conclusion These hip-related outcomes were common in this population, and African-Americans did not have a lower prevalence as previous studies have suggested. Increasing public and health system awareness of the relatively high prevalence of these outcomes, which can be disabling, may help to decrease their impact and ultimately prevent them.