Data mining of gene arrays for biomarkers of survival in ovarian cancer

The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two care fully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy

Characterizing the Initial Phase of Epidemic Growth on some Empirical Networks

A key parameter in models for the spread of infectious diseases is the basic reproduction number $R_0$, which is the expected number of secondary cases a typical infected primary case infects during its infectious period in a large mostly susceptible population. In order for this quantity to be meaningful, the initial expected growth of the number of infectious individuals in the large-population limit should be exponential. We investigate to what extent this assumption is valid by performing repeated simulations of epidemics on selected empirical networks, viewing each epidemic as a random process in discrete time. The initial phase of each epidemic is analyzed by fitting the number of infected people at each time step to a generalised growth model, allowing for estimating the shape of the growth. For reference, similar investigations are done on some elementary graphs such as integer lattices in different dimensions and configuration model graphs, for which the early epidemic behaviour is known. We find that for the empirical networks tested in this paper, exponential growth characterizes the early stages of the epidemic, except when the network is restricted by a strong low-dimensional spacial constraint, such as is the case for the two-dimensional square lattice. However, on finite integer lattices of sufficiently high dimension, the early development of epidemics shows exponential growth.Comment: To be included in the conference proceedings for SPAS 2017 (International Conference on Stochastic Processes and Algebraic Structures), October 4-6, 201

Utilizing Astroinformatics to Maximize the Science Return of the Next Generation Virgo Cluster Survey

The Next Generation Virgo Cluster Survey is a 104 square degree survey of the Virgo Cluster, carried out using the MegaPrime camera of the Canada-France-Hawaii telescope, from semesters 2009A-2012A. The survey will provide coverage of this nearby dense environment in the universe to unprecedented depth, providing profound insights into galaxy formation and evolution, including definitive measurements of the properties of galaxies in a dense environment in the local universe, such as the luminosity function. The limiting magnitude of the survey is g_AB = 25.7 (10 sigma point source), and the 2 sigma surface brightness limit is g_AB ~ 29 mag arcsec^-2. The data volume of the survey (approximately 50 terabytes of images), while large by contemporary astronomical standards, is not intractable. This renders the survey amenable to the methods of astroinformatics. The enormous dynamic range of objects, from the giant elliptical galaxy M87 at M(B) = -21.6, to the faintest dwarf ellipticals at M(B) ~ -6, combined with photometry in 5 broad bands (u* g' r' i' z'), and unprecedented depth revealing many previously unseen structures, creates new challenges in object detection and classification. We present results from ongoing work on the survey, including photometric redshifts, Virgo cluster membership, and the implementation of fast data mining algorithms on the infrastructure of the Canadian Astronomy Data Centre, as part of the Canadian Advanced Network for Astronomical Research (CANFAR).Comment: 8 pages, 2 figures. Accepted for the Joint Workshop and Summer School: Astrostatistics and Data Mining in Large Astronomical Databases, La Palma, May 30th - June 3rd 2011. A higher resolution version is available at http://sites.google.com/site/nickballastronomer/publication

The Upper Stratospheric Solar Cycle Ozone Response

The solar cycle (SC) stratospheric ozone response is thought to influence surface weather and climate. To understand the chain of processes and ensure climate models adequately represent them, it is important to detect and quantify an accurate SC ozone response from observations. Chemistry climate models (CCMs) and observations display a range of upper stratosphere (1–10 hPa) zonally averaged spatial responses; this and the recommended data set for comparison remains disputed. Recent data‐merging advancements have led to more robust observational data. Using these data, we show that the observed SC signal exhibits an upper stratosphere U‐shaped spatial structure with lobes emanating from the tropics (5–10 hPa) to high altitudes at midlatitudes (1–3 hPa). We confirm this using two independent chemistry climate models in specified dynamics mode and an idealized timeslice experiment. We recommend the BASICv2 ozone composite to best represent historical upper stratospheric solar variability, and that those based on SBUV alone should not be used

Subcellular compartmentalisation of copper, iron, manganese, and zinc in the Parkinson's disease brain

© 2017 The Royal Society of Chemistry. Elevated iron and decreased copper levels are cardinal features of the degenerating substantia nigra pars compacta in the Parkinson's disease brain. Both of these redox-active metals, and fellow transition metals manganese and zinc, are found at high concentrations within the midbrain and participate in a range of unique biological reactions. We examined the total metal content and cellular compartmentalisation of manganese, iron, copper and zinc in the degenerating substantia nigra, disease-affected but non-degenerating fusiform gyrus, and unaffected occipital cortex in the post mortem Parkinson's disease brain compared with age-matched controls. An expected increase in iron and a decrease in copper concentration was isolated to the soluble cellular fraction, encompassing both interstitial and cytosolic metals and metal-binding proteins, rather than the membrane-associated or insoluble fractions. Manganese and zinc levels did not differ between experimental groups. Altered Fe and Cu levels were unrelated to Braak pathological staging in our cases of late-stage (Braak stage V and VI) disease. The data supports our hypothesis that regional alterations in Fe and Cu, and in proteins that utilise these metals, contribute to the regional selectively of neuronal vulnerability in this disorder

Two-loop Corrections to the B to pi Form Factor from QCD Sum Rules on the Light-Cone and |V(ub)|

We calculate the leading-twist O(alphas^2 beta0) corrections to the B to pi transition form factor f+(0) in light-cone sum rules. We find that, as expected, there is a cancellation between the O(alphas^2 beta0) corrections to fB f+(0) and the large corresponding corrections to fB, calculated in QCD sum rules. This suggests the insensitivity of the form factors calculated in the light-cone sum rules approach to this source of radiative corrections. We further obtain an improved determination of the CKM matrix element |V(ub)|, using latest results from BaBar and Belle for f+(0)|V(ub)|.Comment: 18 pages, 3 figure

Cross-species gene expression analysis of species specific differences in the preclinical assessment of pharmaceutical compounds

Animals are frequently used as model systems for determination of safety and efficacy in pharmaceutical research and development. However, significant quantitative and qualitative differences exist between humans and the animal models used in research. This is as a result of genetic variation between human and the laboratory animal. Therefore the development of a system that would allow the assessment of all molecular differences between species after drug exposure would have a significant impact on drug evaluation for toxicity and efficacy. Here we describe a cross-species microarray methodology that identifies and selects orthologous probes after cross-species sequence comparison to develop an orthologous cross-species gene expression analysis tool. The assumptions made by the use of this orthologous gene expression strategy for cross-species extrapolation is that; conserved changes in gene expression equate to conserved pharmacodynamic endpoints. This assumption is supported by the fact that evolution and selection have maintained the structure and function of many biochemical pathways over time, resulting in the conservation of many important processes. We demonstrate this cross-species methodology by investigating species specific differences of the peroxisome proliferatoractivator receptor (PPAR) a response in rat and human

Structural subnetwork evolution across the life-span: rich-club, feeder, seeder

The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and understand the underlying subnetworks which contribute towards these observed holistic connectomic alterations. One organizational principle is the rich-club - a core subnetwork of brain regions that are strongly connected, forming a high-cost, high-capacity backbone that is critical for effective communication in the network. Investigations primarily focus on its alterations with disease and age. Here, we present a systematic analysis of not only the rich-club, but also other subnetworks derived from this backbone - namely feeder and seeder subnetworks. Our analysis is applied to structural connectomes in a normal cohort from a large, publicly available lifespan study. We demonstrate changes in rich-club membership with age alongside a shift in importance from 'peripheral' seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as increased efficiency in the feeder subnetwork and decreased measures of network integration and segregation in the seeder subnetwork. These results demonstrate the different developmental patterns when analyzing the connectome stratified according to its rich-club and the potential of utilizing this subnetwork analysis to reveal the evolution of brain architectural alterations across the life-span

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas