Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.

MOTIVATION: Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries. RESULTS: We have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients. AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues. CONTACT: yili@umich.edu.

Emergence: a paradigm for robust and scalable distributed applications

Natural distributed systems are adaptive, scalable and fault-tolerant. Emergence science describes how higher-level self-regulatory behaviour arises in natural systems from many participants following simple rulesets. Emergence advocates simple communication models, autonomy and independence, enhancing robustness and self-stabilization. High-quality distributed applications such as autonomic systems must satisfy the appropriate nonfunctional requirements which include scalability, efficiency, robustness, low-latency and stability. However the traditional design of distributed applications, especially in terms of the communication strategies employed, can introduce compromises between these characteristics. This paper discusses ways in which emergence science can be applied to distributed computing, avoiding some of the compromises associated with traditionally-designed applications. To demonstrate the effectiveness of this paradigm, an emergent election algorithm is described and its performance evaluated. The design incorporates nondeterministic behaviour. The resulting algorithm has very low communication complexity, and is simultaneously very stable, scalable and robust.

Molecular biology of cancer: mechanisms, targets, and therapeutics

1: Introduction 2: DNA structure and stability: mutations vs. repair 3: Regulation of gene expression 4: Growth factor signaling and oncogenes 5: The cell cycle 6: Growth inhibition and tumor suppressor genes 7: Apoptosis 8: Stem cells and differentiation 9: Metastasis 10: Infections and inflammation 11: Nutrients, hormones, and gene interactions 12: The Cancer Industry: drug development and clinical trial design 13: Cancer in the future: focus on diagnostics and immunotherapy

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

Models of open populations with space-limited recruitment: extension of theory and application to the barnacle Chthamalus montagui.

1. Barnacles are a good model organism for the study of open populations with space-limited recruitment. These models are applicable to other species with open supply of new individuals and resource limitation. The inclusion of space in models leads to reductions in recruitment with increasing density, and thus predictions of population size and stability are possible. 2. Despite the potential generality of a demographic theory for open space-limited populations, the models currently have a narrow empirical base. In this study, a model for an open population with space-limited recruitment was extended to include size-specific survival and promotions to any size class. The assumptions of this model were tested using data from a pan-European study of the barnacle Chthamalus montagui Southward. Two models were constructed: a 6-month model and a periodic annual model. Predicted equilibria and their stabilities were compared between shores. 3. Tests of model assumptions supported the extension of the theory to include promotions to any size class. Mortality was found to be size-specific and density independent. Studied populations were open, with recruitment proportional to free space. 4. The 6-month model showed a significant interaction between time and location for equilibrium free space. This may have been due to contrasts in the timing of structuring processes (i.e. creating and filling space) between Mediterranean and Atlantic systems. Integration of the 6-month models into a periodic annual model removed the differences in equilibrium-free space between locations. 5. Model predictions show a remarkable similarity between shores at a European scale. Populations were persistent and all solutions were stable. This reflects the apparent absence of density-dependent mortality and a high adult survivorship in C. montagui. As populations are intrinsically stable, observations of fluctuations in density are directly attributable to variations in the environmental forcing of recruitment or mortality

Simulation study of Pd submonolayer films on Au(hkl) and Pt(hkl) and their relationship to underpotential deposition

The structure and stability of palladium adlayers on Au(hkl) and Pt(hkl) were studied at different coverage degrees by means of Monte Carlo simulations using the interatomic potentials of the embedded atom model. In all cases the Pd films were found to grow epitaxially and pseudomorphically with the crystallographic orientation of the substrate. The differences and similarities of the adlayer with the substrate were analyzed.

The evolution of solar systems

The recent detection of extra-solar planets and the increasing ability of modern instruments to image discs around young stars has brought a renewed interest in the formation of solar systems. In this article, I shall briefly review what we know about extra-solar planets and the physical structure of protostellar discs. One of the most powerful means of studying these disc systems is to observe the rotational line emission from molecules which can give information on physics and dynamics. At present, the observations are relatively crude but future instruments should be able to resolve molecular structures in the disc around nearby stars. As a prelude to these observations, I discuss some conceptually simple, although numerically challenging, models of the physical and chemical processes involved in determining the molecular distributions.

Prediction of the formation and stabilities of energetic salts and ionic liquids based on ab initio electronic structure calculations

A computational approach to predict the thermodynamics for forming a variety of imidazolium-based salts and ionic liquids from typical starting materials is described. The gas-phase proton and methyl cation acidities of several protonating and methylating agents, as well as the proton and methyl cation affinities of many important methyl-, nitro-, and cyano- substituted imidazoles, have been calculated reliably by using the computationally feasible DFT (B3LYP) and MP2 (extrapolated to the complete basis set limit) methods. These accurately calculated proton and methyl cation affinities of neutrals and anions are used in conjunction with an empirical approach based on molecular volumes to estimate the lattice enthalpies and entropies of ionic liquids, organic solids, and organic liquids. These quantities were used to construct a thermodynamic cycle for salt formation to reliably predict the ability to synthesize a variety of salts including ones with potentially high energetic densities. An adjustment of the gas phase thermodynamic cycle to account for solid- and liquid-phase chemistries provides the best overall assessment of salt formation and stability. This has been applied to imidazoles (the cation to be formed) with alkyl, nitro, and cyano substituents. The proton and methyl cation donors studied were as follows: HCl, HBr, HI, (HO)(2)SO2, HSO3CF3 (TfOH), and HSO3(C6H4)CH3 (TsOH); CH3Cl, CH3Br, CH3I, (CH3O)(2)SO2, CH3SO3CF3 (TfOCH3) and CH3SO3(C6H4)CH3 (TsOCH3). As substitution of the cation with electron-withdrawing groups increases, the triflate reagents appear to be the best overall choice as protonating and methylating agents. Even stronger alkylating agents should be considered to enhance the chances of synthetic success. When using the enthalpies of reaction for the gas-phase reactants (eq 6) to form a salt, a cutoff value of - 13 kcal mol(-1) or lower (more negative) should be used as the minimum value for predicting whether a salt can be synthesized.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

A heuristic model for the simulation of the deformation of elastic and spongy material for virtual reality applications

This paper presents a practical algorithm for the simulation of interactive deformation in a 3D polygonal mesh model. The algorithm combines the conventional simulation of deformation using a spring-mass-damping model, solved by explicit numerical integration, with a set of heuristics to describe certain features of the transient behaviour, to increase the speed and stability of solution. In particular, this algorithm was designed to be used in the simulation of synthetic environments where it is necessary to model realistically, in real time, the effect on non-rigid surfaces being touched, pushed, pulled or squashed. Such objects can be solid or hollow, and have plastic, elastic or fabric-like properties. The algorithm is presented in an integrated form including collision detection and adaptive refinement so that it may be used in a self-contained way as part of a simulation loop to include human interface devices that capture data and render a realistic stereoscopic image in real time. The algorithm is designed to be used with polygonal mesh models representing complex topology, such as the human anatomy in a virtual-surgery training simulator. The paper evaluates the model behaviour qualitatively and then concludes with some examples of the use of the algorithm.

Cation complexation by chemically modified calixarenes. 11. Complexation and extraction of alkali cations by calix[5]- and -[6]arene ketones. Crystal and molecular structures of calix[5]arene ketones and Na+ and Rb+ complexes

A series of four calix[5]arenes and three calix[6]arenes (R-calixarene-OCH2COR1) (R = H or Bu-t) with alkyl ketone residues (R-1 = Me or Bu-t) on the lower rim have been synthesized, and their affinity for complexation of alkali cations has been assessed through phase-transfer experiments and stability constant measurements. The conformations of these ketones have been probed by H-1 NMR and X-ray diffraction analysis, and by molecular mechanics calculations. Pentamer 3 (R R-1 = Bu-t) possesses a symmetrical cone conformation in solution and a very distorted cone conformation in the solid state. Pentamer 5 (R = H, R-1 = Bu-t) exists in a distorted 1,2-alternate conformation in the solid state, but in solution two slowly interconverting conformations, one a cone and the other presumed to be 1,2-alternate, can be detected. X-ray structure analysis of the sodium and rubidium perchlorate complexes of 3 reveal the cations deeply encapsulated by the ethereal and carbonyl oxygen atoms in distorted cone conformations which can be accurately reproduced by molecular mechanics calculations. The phase-transfer and stability constant data reveal that the extent of complexation depends on calixarene size and the nature of the alkyl residues adjacent to the ketonic carbonyls with tert-butyl much more efficacious than methyl.

Manipulating Interaction Strengths and the Consequences for Trivariate Patterns in a Marine Food Web

We are experiencing a global extinction crisis as a result of climate change and human-induced alteration of natural habitats, with large predators at high trophic levels in food webs being particularly vulnerable. Unfortunately, there is a scarcity of food web data that can be used to assess how species extinctions alter the structure and stability of temporally and spatially replicated networks. We established a series of large experimental mesocosms in a shallow subtidal benthic marine system and constructed food webs for each replicate. After 6 months of community assembly, we removed large predators from the core communities of 20 experimental food webs, based on the strength of their trophic interactions, and monitored the changes in the networks' structure and stability over an 8-month period. Our analyses revealed the importance of allometric relationships and size-structuring in natural communities as a means of preserving food web structure and sustainability, despite significant changes in the diversity, stability and productivity of the system.

Macroecological patterns and niche structure in a new marine food web

The integration of detailed information on feeding interactions with measures of abundance and body mass of individuals provides a powerful platform for understanding ecosystem organisation. Metabolism and, by proxy, body mass constrain the flux, turnover and storage of energy and biomass in food webs. Here, we present the first food web data for Lough Hyne, a species rich Irish Sea Lough. Through the application of individual-and size-based analysis of the abundance-body mass relationship, we tested predictions derived from the metabolic theory of ecology. We found that individual body mass constrained the flux of biomass and determined its distribution within the food web. Body mass was also an important determinant of diet width and niche overlap, and predator diets were nested hierarchically, such that diet width increased with body mass. We applied a novel measure of predator-prey biomass flux which revealed that most interactions in Lough Hyne were weak, whereas only a few were strong. Further, the patterning of interaction strength between prey sharing a common predator revealed that strong interactions were nearly always coupled with weak interactions. Our findings illustrate that important insights into the organisation, structure and stability of ecosystems can be achieved through the theoretical exploration of detailed empirical data.

Devolution and the politics of development in Northern Ireland

The reintroduction of devolution in Northern Ireland is widely interpreted as the working out of the Belfast Agreement (1998) which aimed to embed political consensus in shared institutions of the state. However, such analysis tends to be limited with regard to wider political economy readings of the devolution project and historic struggles to find an appropriate institutional fix to manage different
forms of crisis. Peace and stability have, it is argued, permitted Northern Ireland's reentry to global markets and circuits of capital with new governance structures being assembled to reconfigure `post- conflict' economic space. We argue that the onset of devolution has promoted a mix between ethnosectarian resource competition and a constantly expanding neoliberal model of governance.
Devolved neoliberal structures that sustain social polarisation may perpetuate strategies of resistance that could cut across and challenge ethnosectarian politics and deepening social segregation.