Stochastic Models Based on Molecular Hybridization Theory for Short Oligonucleotide Microarrays

High density oligonucleotide expression arrays are a widely used tool for the measurement of gene expression on a large scale. Affymetrix GeneChip arrays appear to dominate this market. These arrays use short oligonucleotides to probe for genes in an RNA sample. Due to optical noise, non-specific hybridization, probe-specific effects, and measurement error, ad-hoc measures of expression, that summarize probe intensities, can lead to imprecise and inaccurate results. Various researchers have demonstrated that expression measures based on simple statistical models can provide great improvements over the ad-hoc procedure offered by Affymetrix. Recently, physical models based on molecular hybridization theory, have been proposed as useful tools for prediction of, for example, non-specific hybridization. These physical models show great potential in terms of improving existing expression measures. In this paper we demonstrate that the system producing the measured intensities is too complex to be fully described with these relatively simple physical models and we propose empirically motivated stochastic models that compliment the above mentioned molecular hybridization theory to provide a comprehensive description of the data. We discuss how the proposed model can be used to obtain improved measures of expression useful for the data analysts.

Stochastic search for semiparametric linear regression models

This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar [Ann. Statist. 15(3) (1987) 1131–1154]. The idea is to generate a random finite subset of a parameter space which will automatically contain points which are very close to an unknown true parameter. The motivation for this procedure comes from recent work of Dümbgen et al. [Ann. Statist. 39(2) (2011) 702–730] on regression models with log-concave error distributions.

ICOMF: Towards a Multi-cloud Ecosystem for Dynamic Resource Composition and Scaling

Modern cloud-based applications and infrastructures may include resources and services (components) from multiple cloud providers, are heterogeneous by nature and require adjustment, composition and integration. The specific application requirements can be met with difficulty by the current static predefined cloud integration architectures and models. In this paper, we propose the Intercloud Operations and Management Framework (ICOMF) as part of the more general Intercloud Architecture Framework (ICAF) that provides a basis for building and operating a dynamically manageable multi-provider cloud ecosystem. The proposed ICOMF enables dynamic resource composition and decomposition, with a main focus on translating business models and objectives to cloud services ensembles. Our model is user-centric and focuses on the specific application execution requirements, by leveraging incubating virtualization techniques. From a cloud provider perspective, the ecosystem provides more insight into how to best customize the offerings of virtualized resources.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Evaluation of liver parenchyma and perfusion using dynamic contrast-enhanced computed tomography and contrast-enhanced ultrasonography in captive green iguanas (Iguana iguana) under general anesthesia.

BACKGROUND Contrast-enhanced diagnostic imaging techniques are considered useful in veterinary and human medicine to evaluate liver perfusion and focal hepatic lesions. Although hepatic diseases are a common occurrence in reptile medicine, there is no reference to the use of contrast-enhanced ultrasound (CEUS) and contrast-enhanced computed tomography (CECT) to evaluate the liver in lizards. Therefore, the aim of this study was to evaluate the pattern of change in echogenicity and attenuation of the liver in green iguanas (Iguana iguana) after administration of specific contrast media. RESULTS An increase in liver echogenicity and density was evident during CEUS and CECT, respectively. In CEUS, the mean ± SD (median; range) peak enhancement was 19.9% ± 7.5 (18.3; 11.7-34.6). Time to peak enhancement was 134.0 ± 125.1 (68.4; 59.6-364.5) seconds. During CECT, first visualization of the contrast medium was at 3.6 ± 0.5 (4; 3-4) seconds in the aorta, 10.7 ± 2.2 (10.5; 7-14) seconds in the hepatic arteries, and 15 ± 4.5 (14.5; 10-24) seconds in the liver parenchyma. Time to peak was 14.1 ± 3.4 (13; 11-21) and 31 ± 9.6 (29; 23-45) seconds in the aorta and the liver parenchyma, respectively. CONCLUSION CEUS and dynamic CECT are practical means to determine liver hemodynamics in green iguanas. Distribution of contrast medium in iguana differed from mammals. Specific reference ranges of hepatic perfusion for diagnostic evaluation of the liver in iguanas are necessary since the use of mammalian references may lead the clinician to formulate incorrect diagnostic suspicions.

The Effects of Credit Risk on Dynamic Portfolio Management: A New Computational Approach

The study investigates the role of credit risk in a continuous time stochastic asset allocation model, since the traditional dynamic framework does not provide credit risk flexibility. The general model of the study extends the traditional dynamic efficiency framework by explicitly deriving the optimal value function for the infinite horizon stochastic control problem via a weighted volatility measure of market and credit risk. The model's optimal strategy was then compared to that obtained from a benchmark Markowitz-type dynamic optimization framework to determine which specification adequately reflects the optimal terminal investment returns and strategy under credit and market risks. The paper shows that an investor's optimal terminal return is lower than typically indicated under the traditional mean-variance framework during periods of elevated credit risk. Hence I conclude that, while the traditional dynamic mean-variance approach may indicate the ideal, in the presence of credit-risk it does not accurately reflect the observed optimal returns, terminal wealth and portfolio selection strategies.

On the Existence and Characterization of Markovian Equilibrium in Models with Simple Non-Paternalistic Altruism

This paper provides sufficient conditions for existence of Markovian equilibrium in models with non-paternalistic altruism extending to one generation ahead. When utility is non-separable, we show that each equilibrium savings policy correspondence is increasing everywhere and single-valued, except perhaps on a countable number of points. It is also upper hemi-continuous where it is single valued. When utility is separable, we show that the equilibrium is unique, increasing, and continuous, and we provide an algorithm converging uniformly to the equilibrium.

Marine Isotope Stage (MIS) 11 results of model data with Community Climate System Model version 3 including the Dynamic Global Vegetation Model (CCSM3-DGVM) in NetCDF format at global and regional scale

The climate of Marine Isotope Stage (MIS) 11, the interglacial roughly 400,000 years ago, is investigated for four time slices, 416, 410, 400, and 394 ka. The overall picture is that MIS 11 was a relatively warm interglacial in comparison to preindustrial, with Northern Hemisphere (NH) summer temperatures early in MIS 11 (416-410 ka) warmer than preindustrial, though winters were cooler. Later in MIS 11, especially around 400 ka, conditions were cooler in the NH summer, mainly in the high latitudes. Climate changes simulated by the models were mainly driven by insolation changes, with the exception of two local feedbacks that amplify climate changes. Here, the NH high latitudes, where reductions in sea ice cover lead to a winter warming early in MIS 11, as well as the tropics, where monsoon changes lead to stronger climate variations than one would expect on the basis of latitudinal mean insolation change alone, are especially prominent. The results support a northward expansion of trees at the expense of grasses in the high northern latitudes early during MIS 11, especially in northern Asia and North America.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.

Dynamic analysis of road vehicle-bridge systems under turbulent wind by means of Finite Element Models

When an automobile passes over a bridge dynamic effects are produced in vehicle and structure. In addition, the bridge itself moves when exposed to the wind inducing dynamic effects on the vehicle that have to be considered. The main objective of this work is to understand the influence of the different parameters concerning the vehicle, the bridge, the road roughness or the wind in the comfort and safety of the vehicles when crossing bridges. Non linear finite element models are used for structures and multibody dynamic models are employed for vehicles. The interaction between the vehicle and the bridge is considered by contact methods. Road roughness is described by the power spectral density (PSD) proposed by the ISO 8608. To consider that the profiles under right and left wheels are different but not independent, the hypotheses of homogeneity and isotropy are assumed. To generate the wind velocity history along the road the Sandia method is employed. The global problem is solved by means of the finite element method. First the methodology for modelling the interaction is verified in a benchmark. Following, the case of a vehicle running along a rigid road and subjected to the action of the turbulent wind is analyzed and the road roughness is incorporated in a following step. Finally the flexibility of the bridge is added to the model by making the vehicle run over the structure. The application of this methodology will allow to understand the influence of the different parameters in the comfort and safety of road vehicles crossing wind exposed bridges. Those results will help to recommend measures to make the traffic over bridges more reliable without affecting the structural integrity of the viaduct

Models for Lateral Dynamic Interaction of High-Speed Trains and Bridges

In this work a methodology for analysing the lateral coupled behavior of large viaducts and high-speed trains is proposed. The finite element method is used for the structure, multibody techniques are applied for vehicles and the interaction between them is established introducing wheel-rail nonlinear contact forces. This methodology is applied for the analysis of the railway viaduct of the R´ıo Barbantino, which is a very long and tall bridge in the north-west spanish high-speed line.

Generating multimedia presentations that summarize the behavior of dynamic systems using a model-based approach

This article describes a knowledge-based method for generating multimedia descriptions that summarize the behavior of dynamic systems. We designed this method for users who monitor the behavior of a dynamic system with the help of sensor networks and make decisions according to prefixed management goals. Our method generates presentations using different modes such as text in natural language, 2D graphics and 3D animations. The method uses a qualitative representation of the dynamic system based on hierarchies of components and causal influences. The method includes an abstraction generator that uses the system representation to find and aggregate relevant data at an appropriate level of abstraction. In addition, the method includes a hierarchical planner to generate a presentation using a model with dis- course patterns. Our method provides an efficient and flexible solution to generate concise and adapted multimedia presentations that summarize thousands of time series. It is general to be adapted to differ- ent dynamic systems with acceptable knowledge acquisition effort by reusing and adapting intuitive rep- resentations. We validated our method and evaluated its practical utility by developing several models for an application that worked in continuous real time operation for more than 1 year, summarizing sen- sor data of a national hydrologic information system in Spain.