Network Analyis Approach to Find New Candidate Genes and Pathways Involved in the Pathophysiology of Familial Hypercholesterolemia

Introduction: Familial hypercholesterolaemia (FH) is a common genetic cause of premature coronary heart disease (CHD) due to lifelong elevated plasma low-density lipoprotein (LDL) levels. Worldwide only 40 % of patients (FH+) with a clinical diagnosis of FH carry a mutation in any of the three genes (namely: LDLR, APOB, PCSK 9) that are currently known to be associated to the disease. We guess that the remaining 60 % of the patients (FH-) probably includes a high percentage of individuals with a polygenic form of dyslipidemia or an environmental form of hypercholesterolemia and a small percentage of individuals with mutations in some novel genes, never associated before with dyslipidemias. Here we present the preliminary results of an integrative approach intended to identify new candidate genes and to dissect pathways that can be dysregulated in the disease.

Nitrogen transfer within and between plants through common mycorrhizal networks (CMNs)

Mycorthizae play a critical role in nutrient capture from soils. Arbuscular mycorrhizae (AM) and ectomycorrhizae (EM) are the most important mycorrhizae in agricultural and natural ecosystems. AM and EM fungi use inorganic NH4+ and NO3-, and most EM fungi are capable of using organic nitrogen. The heavier stable isotope N-15 is discriminated against during biogeochemical and biochemical processes. Differences in N-15 (atom%) or delta(15)N (parts per thousand) provide nitrogen movement information in an experimental system. A range of 20 to 50% of one-way N-transfer has been observed from legumes to nonlegumes. Mycorrhizal fungal mycelia can extend from one plant's roots to another plant's roots to form common mycorrhizal networks (CMNs). Individual species, genera, even families of plants can be interconnected by CMNs. They are capable of facilitating nutrient uptake and flux. Nutrients such as carbon, nitrogen and phosphorus and other elements may then move via either AM or EM networks from plant to plant. Both N-15 labeling and N-15 natural abundance techniques have been employed to trace N movement between plants interconnected by AM or EM networks. Fine mesh (25similar to45 mum) has been used to separate root systems and allow only hyphal penetration and linkages but no root contact between plants. In many studies, nitrogen from N-2-fixing mycorrhizal plants transferred to non-N-2-fixing mycorrhizal plants (one-way N-transfer). In a few studies, N is also transferred from non-N-2-fixing mycorrhizal plants to N-2-fixing mycorrhizal plants (two-way N-transfer). There is controversy about whether N-transfer is direct through CMNs, or indirect through the soil. The lack of convincing data underlines the need for creative, careful experimental manipulations. Nitrogen is crucial to productivity in most terrestrial ecosystems, and there are potential benefits of management in soil-plant systems to enhance N-transfer. Thus, two-way N-transfer warrants further investigation with many species and under field conditions.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

Finding common ground in team-based qualitative research using the convergent interviewing method

Research councils, agencies, and researchers recognize the benefits of team-based health research. However, researchers involved in large-scale team-based research projects face multiple challenges as they seek to identify epistemological and ontological common ground. Typically, these challenges occur between quantitative and qualitative researchers but can occur between qualitative researchers, particularly when the project involves multiple disciplinary perspectives. The authors use the convergent interviewing technique in their multidisciplinary research project to overcome these challenges. This technique assists them in developing common epistemological and ontological ground while enabling swift and detailed data collection and analysis. Although convergent interviewing is a relatively new method described primarily in marketing research, it compares and contrasts well with grounded theory and other techniques. The authors argue that this process provides a rigorous method to structure and refine research projects and requires researchers to identify and be accountable for developing a common epistemological and ontological position.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

PROST: A programmable structured peer-to-peer overlay network

We present the idea of a programmable structured P2P architecture. Our proposed system allows the key-based routing infrastructure, which is common to all structured P2P overlays, to be shared by multiple applications. Furthermore, our architecture allows the dynamic and on-demand deployment of new applications and services on top of the shared routing layer.

Improved multi-beam neural network scatterometer forward models

Current methods for retrieving near surface winds from scatterometer observations over the ocean surface require a foward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in ¸mod, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the mid-beam and using a common model for the fore- and aft-beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds.

Improved neural network scatterometer forward models

Current methods for retrieving near-surface winds from scatterometer observations over the ocean surface require a forward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in CMOD4, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the midbeam and using a common model for the fore and aft beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds. Copyright 2001 by the American Geophysical Union.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.

Beta frequency neuronal network activity in the primary motor cortex

In this study I investigated the mechanisms of neuronal network oscillatory activity in rat M1 using pharmacological manipulations and electrical stimulation protocols, employing the in vitro brain slice technique in rat and magnetoencephalography (MEG) in man. Co-application of kainic acid and carbachol generated in vitro beta oscillatory activity in all layers in M1. Analyses indicated that oscillations originated from deep layers and indicated significant involvement of GABAA receptors and gap junctions. A modulatory role of GABAB, NMDA, and dopamine receptors was also evident. Intracellular recordings from fast-spiking (FS) GABAergic inhibitory cells revealed phase-locked action potentials (APs) on every beta cycle. Glutamatergic excitatory regular-spiking (RS) and intrinsically-bursting (IB) cells both received phase locked inhibitory postsynaptic potentials, but did not fire APs on every cycle, suggesting the dynamic involvement of different pools of neurones in the overall population oscillations. Stimulation evoked activity at high frequency (HFS; 125Hz) evoked gamma oscillations and reduced ongoing beta activity. 20Hz stimulation promoted theta or gamma oscillations whilst 4Hz stimulation enhanced beta power at theta frequency. I also investigated the modulation of pathological slow wave (theta and beta) oscillatory activity using magnetoencephalography. Abnormal activity was suppressed by sub-sedative doses of GABAA receptor modulator zolpidem and the observed desynchronising effect correlated well with improved sensorimotor function. These studies indicate a fundamental role for inhibitory neuronal networks in the patterning beta activity and suggest that cortical HFS in PD re-patterns abnormally enhanced M1 network activity by modulating the activity of FS cells. Furthermore, pathological oscillation may be common to many neuropathologies and may be an important future therapeutic target.

SFBC MIMO energy efficiency improvements of common packet schedulers for the long term evolution downlink

It is desirable that energy performance improvement is not realized at the expense of other network performance parameters. This paper investigates the trade off between energy efficiency, spectral efficiency and user QoS performance for a multi-cell multi-user radio access network. Specifically, the energy consumption ratio (ECR) and the spectral efficiency of several common frequency domain packet schedulers in a cellular E-UTRAN downlink are compared for both the SISO transmission mode and the 2x2 Alamouti Space Frequency Block Code (SFBC) MIMO transmission mode. It is well known that the 2x2 SFBC MIMO transmission mode is more spectrally efficient compared to the SISO transmission mode, however, the relationship between energy efficiency and spectral efficiency is undecided. It is shown that, for the E-UTRAN downlink with fixed transmission power, spectral efficiency improvement results into energy efficiency improvement. The effect of SFBC MIMO versus SISO on the user QoS performance is also studied. © 2011 IEEE.

Aβ(1-42) induced metabolic effects in a stem cell derived neuron and astrocyte network.

Alzheimer’s Disease (AD) is the most common form of dementia currently affecting more than 35 million people worldwide. Hypometabolism is a major feature of AD and appears decades before cognitive decline and pathological lesions. This has a detrimental impact on the brain which has a high energy demand. Current models of AD fail to mimic all the features of the disease, which has an impact on the development of new therapies. Human stem cell derived models of the brain have attracted a lot of attention in recent years as a tool to study neurodegenerative diseases. In this thesis, neurons and astrocytes derived from the human embryonal carcinoma cell line (NT2/D1) were utilised to determine the metabolic coupling between neurons and astrocytes with regards to responses to hypoglycaemia, neuromodulators and increase in neuronal activity. This model was then used to investigate the effects of Aß(1-42) on the metabolism of these NT2-derived co-cultures as well as pure astrocytes. Additionally primary cortical mixed neuronal and glial cultures were utilised to compare this model to a widely accepted in vitro model used in Alzheimer’s disease research. Co-cultures were found to respond to Aß(1-42) in similar way to human and in vivo models. Hypometabolism was characterised by changes in glucose metabolism, as well as lactate, pyruvate and glycogen. This led to a significant decrease in ATP and the ratio of NAD+/NADH. These results together with an increase in calcium oscillations and a decrease in GSH/GSSG ratio, suggests Aß-induces metabolic and oxidative stress. This situation could have detrimental effects in the brain which has a high energy demand, especially in terms of memory formation and antioxidant capacity.

Gene network and proteomic analyses of cardiac responses to pathological and physiological stress

Background—The molecular mechanisms underlying similarities and differences between physiological and pathological left ventricular hypertrophy (LVH) are of intense interest. Most previous work involved targeted analysis of individual signaling pathways or screening of transcriptomic profiles. We developed a network biology approach using genomic and proteomic data to study the molecular patterns that distinguish pathological and physiological LVH. Methods and Results—A network-based analysis using graph theory methods was undertaken on 127 genome-wide expression arrays of in vivo murine LVH. This revealed phenotype-specific pathological and physiological gene coexpression networks. Despite >1650 common genes in the 2 networks, network structure is significantly different. This is largely because of rewiring of genes that are differentially coexpressed in the 2 networks; this novel concept of differential wiring was further validated experimentally. Functional analysis of the rewired network revealed several distinct cellular pathways and gene sets. Deeper exploration was undertaken by targeted proteomic analysis of mitochondrial, myofilament, and extracellular subproteomes in pathological LVH. A notable finding was that mRNA–protein correlation was greater at the cellular pathway level than for individual loci. Conclusions—This first combined gene network and proteomic analysis of LVH reveals novel insights into the integrated pathomechanisms that distinguish pathological versus physiological phenotypes. In particular, we identify differential gene wiring as a major distinguishing feature of these phenotypes. This approach provides a platform for the investigation of potentially novel pathways in LVH and offers a freely accessible protocol (http://sites.google.com/site/cardionetworks) for similar analyses in other cardiovascular diseases.

Network learning:an empirically-derived conceptual model of learning by groups of organizations

Building on a previous conceptual article, we present an empirically derived model of network learning - learning by a group of organizations as a group. Based on a qualitative, longitudinal, multiple-method empirical investigation, five episodes of network learning were identified. Treating each episode as a discrete analytic case, through cross-case comparison, a model of network learning is developed which reflects the common, critical features of the episodes. The model comprises three conceptual themes relating to learning outcomes, and three conceptual themes of learning process. Although closely related to conceptualizations that emphasize the social and political character of organizational learning, the model of network learning is derived from, and specifically for, more extensive networks in which relations among numerous actors may be arms-length or collaborative, and may be expected to change over time.