4D bioinformatics: a new look at the ribosome as an example

We have adapted the Java Molecular Viewer (JMV) to virtual reality display environments, through a number of extensions to the Java 3D code. Phylogenetic information derived from multiple alignments (temporal information) can be overlaid onto molecule structures (spatial information). The number of sequences included in the underlying multiple alignment can be changed instantaneously, resulting in dynamical updates of the displayed information. JMV was also extended to handle an infinite number of objects (molecules) in the same display. The objects can be manipulated in six degrees of freedom simultaneously or independently. We have used the small subunit ribosomal RNA to demonstrate the system (http:// cave.ucalgary.ca), which can be used for any molecule with a resolved structure.     

The hippocampal-VTA loop: controlling the entry of information into long-term memory

In this article we develop the concept that the hippocampus and the midbrain dopaminergic neurons of the ventral tegmental area (VTA) form a functional loop. Activation of the loop begins when the hippocampus detects newly arrived information that is not already stored in its long-term memory. The resulting novelty signal is conveyed through the subiculum, accumbens, and ventral pallidum to the VTA where it contributes (along with salience and goal information) to the novelty-dependent firing of these cells. In the upward arm of the loop, dopamine (DA) is released within the hippocampus; this produces an enhancement of LTP and learning. These findings support a model whereby the hippocampal-VTA loop regulates the entry of information into long-term memory.     

Endocrine modulation and the fragile balance of homeostasis--an overview

Endocrine modulation by natural and synthetic chemicals and the eventually resulting beneficial or adverse effects for human and animal health are controversially debated not only among scientists but particularly in the public. Most information is available on so-called environmental estrogens, however the amount of information on substances interfering with other hormonal axes steadily increases, particularly on those exhibiting (anti)androgenic activities. The aim of this paper is to summarize existing data and to give an overview on the potential pathways leading to interferences of environmental hormones with homeostasis and eventually resulting health effects. Experimental evidence suggests the hypothesis that fetal and neonatal organisms may be at risk if exposed to environmental estrogens. In contrary, it appears as if phytoestrogens, particularly those with selective estrogen receptor modulator- (SERM-)like activities have the potential to be useful in medical application, both as dietary means and as pharmaceuticals. Lacking valid information about the detailed analysis of the molecular mode of action for environmental estrogens, the possibility for an ultimate classification of environmental estrogens in "dangerous endocrine disruptors" and phytoestrogens in "useful pharmaceuticals" cannot be supported conclusively. Nevertheless both activities are likely.     

Soft ionization mass spectral methods for lipid analysis

Chemical ionization (CI), field ionization (FI) and field desorption (FD) are sometimes preferable to electron impact (EI) mass spectrometry as methods for obtaining abundant high-mass ions from lipids. FD often provides mass spectral information which is unobtainable by other methods, and is the best method for obtaining molecular weight information. Fragment ions are observed in the spectra from all the ionization methods, which provide structural information complementing that obtainable from an EI spectrum. Using CI, high-mass ions carrying a large proportion of the total ionization current can be monitored by selected ion monitoring, resulting in enhanced sensitivity for quantitative studies in some cases.     

Mathematical Modeling and Numerical Simulation of Magnetic Susceptibility Artifacts in Magnetic Resonance Imaging

The technique used to recognise information in Magnetic Resonance Imaging (MRI) is based on electromagnetic fields. A linearly varying field (around 10(-2) Tesla per meter) is added to a strong homogeneous magnetic field (order of magnitude of approximately one Tesla). When these fields are disturbed by the presence of a paramagnetic material, in the sample for instance, the resulting image is usually distorted, these distortions being termed artifacts. Our goal is to present a method, assuming the field disturbances are known, to construct the resulting images. A mathematical model of the MRI process is developed. The way the images are distorted in intensity and shape is explained and an algorithm to simulate magnetic susceptibility artifacts is deduced.     

Logistic regression analysis of two-phase studies using generalized method of moments

Two-phase designs can reduce the cost of epidemiological studies by limiting the ascertainment of expensive covariates or/and exposures to an efficiently selected subset (phase-II) of a larger (phase-I) study. Efficient analysis of the resulting data set combining disparate information from phase-I and phase-II, however, can be complex. Most of the existing methods, including semiparametric maximum-likelihood estimator, require the information in phase-I to be summarized into a fixed number of strata. In this paper, we describe a novel method for the analysis of two-phase studies where information from phase-I is summarized by parameters associated with a reduced logistic regression model of the disease outcome on available covariates. We then setup estimating equations for parameters associated with the desired extended logistic regression model, based on information on the reduced model parameters from phase-I and complete data available at phase-II after accounting for nonrandom sampling design. We use generalized method of moments to solve overly identified estimating equations and develop the resulting asymptotic theory for the proposed estimator. Simulation studies show that the use of reduced parametric models, as opposed to summarizing data into strata, can lead to more efficient utilization of phase-I data. An application of the proposed method is illustrated using the data from the U.S. National Wilms Tumor Study.     

A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables

The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering.     

Enrichment of land-cover polygons with eco-climatic information derived from MODIS NDVI imagery

Aim The FAO land‐cover classification system (LCCS) represents an innovative approach to standardizing and harmonizing land‐cover classifications based on remote sensing data. The thematic information considered by the LCCS, however, is intrinsically related to vegetation physiognomy and does not report important eco‐climatic features. Our aim is to develop a methodology to enrich LCCS maps with information on vegetation productivity and phenology derived from Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. Location The LCCS has recently been applied in East Africa by the Africover project. The proposed methodology is developed and tested in Tanzania using MODIS NDVI data for a 5‐year period (2001–05). Methods Annual NDVI profiles of Africover polygons were extracted from MODIS imagery. These profiles, composed of 23 NDVI values per year, were averaged over the study period, purified for possible land‐cover errors and converted into a more manageable format composed of 24 half‐month values. The resulting NDVI profiles were first analysed visually and then evaluated statistically against rainfall measurements taken at 12 Tanzanian stations. The steps involved were as follows: NDVI values were aggregated on a monthly basis and represented with a one‐digit integer to obtain an extended code; a subset of parameters describing vegetation development and phenology was identified, thus obtaining a restricted codification; and finally, the information loss resulting from both the extended and restricted codification was evaluated with respect to the original NDVI profiles. Results NDVI profiles of different Africover classes can differ in mean values but tend to have a similar shape, linked to the seasonality of local vegetation. Both NDVI annual averages and seasonal variations are strictly dependent on rainfall patterns, particularly in arid zones. The tested codifications effectively summarize the eco‐climatic information contained in the polygon NDVI profiles, with the extended and restricted codifications retaining > 90% and 80% of such information, respectively. Main conclusions The proposed methodology is capable of enriching LCCS polygons with eco‐climatic information derived from MODIS NDVI data. Such information is related to vegetation development and seasonality, and can be efficiently condensed at various levels of detail.     

Codominant analysis of banding data from a dominant marker system by normal mixtures

Amplified fragment length polymorphisms (AFLPs) currently are among the most widely used marker systems. In many studies, AFLPs are analyzed on the basis of the presence or absence of a band on an electrophoretic gel. As a result, dominant homozygous individuals are not distinguished from heterozygous individuals, resulting in a considerable loss of information. This article shows how codominant information can be obtained if the amount of PCR products is quantified. Due to measurement variation, genotyping on the basis of such information is not error-free. We propose use of normal mixture distributions to determine the most likely genotype, given the data. The method is exemplified using AFLP data from sugar beet.     

Labeling to manage marketing of GM foods

Biotechnology has the potential to introduce new food safety risks, liabilities and benefits, and although privately managed supply chains (involving proactive management of the production of branded products) are effective at providing, managing and communicating adequate information about products with well understood risks, products with uncertain risks pose a greater challenge. The demand for increased product information regarding genetically modified content, in particular, places new constraints on food supply chains, frequently resulting in communication failures. Here we assess and reject mandatory labeling as an appropriate response.     

Integrating the roles of information and competitive ability on the spatial distribution of social foragers

Understanding and predicting the spatial distribution of social foragers among patchily distributed resources is a problem that has been addressed with numerous approaches over the 30 yr since the ideal free distribution (IFD) was first introduced. The two main approaches involve perceptual constraints and unequal competitors. Here we present a model of social foragers choosing among resource patches. Each forager makes a probabilistic choice on the basis of the information acquired through past foraging experiences. Food acquisition is determined by the forager's competitive ability. This model predicts that perceptual constraints have a greater influence on the spatial distribution of foragers than unequal competitive abilities but that competitive ability plays an important role in determining an individual's information state and behavior. Better competitors have access to more information; consequently, we find that competitive abilities and perceptual constraints are integrated through the social environment occupied by individual foragers. Relative competitive abilities influence the forager's information state, and the ability to use information determines the resulting spatial distribution.     

Web-Based Data, Document, and Knowledge Management in Restoration Projects

This paper gives an introduction to composition, creation, and management of knowledge using a long‐term research project as an example. The concept and first experiences for a web‐based knowledge management system (KMS) of the regeneration process of the stream Ilm (Thuringia, Germany) are presented. From 1996 until 2005, this restoration ecology project investigated whether the river ecosystem can attain its natural state again and how the regeneration can be influenced. The web‐based content management system iZone, with an emphasis on information retrieval and feedback, was used to support the process of storage, retrieval, and visualization of existing information, several Ph.D. studies, and expert knowledge in the concluding phase of the research project. The resulting KMS at the moment stores 90 entries from 35 project scientists.     

Some implications of the short-term synaptic plasticity for neuronal computation: a model study

Spikes arriving at the synaptic connection produce short-term plastic changes of the synaptic efficacy. Model experiments have shown that paired-pulse facilitation attaining its maximum after a specific interval between a pair of arriving spikes might turn a “weak” plastic synapse attached to an integrate-and-fire neuron to a frequency-tuned device. Resulting computational capabilities create biologically plausible mechanisms of information processing relating to: (i) real-time identification of temporal patterns in a stream of random spiking activity (a recognition problem); and (ii) codetermination of the specific activity routing among neurons (an addressing problem) resulting in definite spatio-temporal patterns of the output activity (an input-output pattern problem).     

C-ME: a 3D community-based, real-time collaboration tool for scientific research and training

The need for effective collaboration tools is growing as multidisciplinary proteome-wide projects and distributed research teams become more common. The resulting data is often quite disparate, stored in separate locations, and not contextually related. Collaborative Molecular Modeling Environment (C-ME) is an interactive community-based collaboration system that allows researchers to organize information, visualize data on a two-dimensional (2-D) or three-dimensional (3-D) basis, and share and manage that information with collaborators in real time. C-ME stores the information in industry-standard databases that are immediately accessible by appropriate permission within the computer network directory service or anonymously across the internet through the C-ME application or through a web browser. The system addresses two important aspects of collaboration: context and information management. C-ME allows a researcher to use a 3-D atomic structure model or a 2-D image as a contextual basis on which to attach and share annotations to specific atoms or molecules or to specific regions of a 2-D image. These annotations provide additional information about the atomic structure or image data that can then be evaluated, amended or added to by other project members.     

The language of SH2 domain interactions defines phosphotyrosine-mediated signal transduction

Natural languages arise in an unpremeditated fashion resulting in words and syntax as individual units of information content that combine in a manner that is both complex and contextual, yet intuitive to a native reader. In an analogous manner, protein interaction domains such as the Src Homology 2 (SH2) domain recognize and "read" the information contained within their cognate peptide ligands to determine highly selective protein-protein interactions that underpin much of cellular signal transduction. Herein, we discuss how contextual sequence information, which combines the use of permissive and non-permissive residues within a parent motif, is a defining feature of selective interactions across SH2 domains. Within a system that reads phosphotyrosine modifications this provides crucial information to distinguish preferred interactions. This review provides a structural and biochemical overview of SH2 domain binding to phosphotyrosine-containing peptide motifs and discusses how the diverse set of SH2 domains is able to differentiate phosphotyrosine ligands.     

Next-generation sequencing of the Chinese hamster ovary microRNA transcriptome: Identification, annotation and profiling of microRNAs as targets for cellular engineering

Chinese hamster ovary (CHO) cells are the predominant cell factory for the production of recombinant therapeutic proteins. Nevertheless, the lack in publicly available sequence information is severely limiting advances in CHO cell biology, including the exploration of microRNAs (miRNA) as tools for CHO cell characterization and engineering. In an effort to identify and annotate both conserved and novel CHO miRNAs in the absence of a Chinese hamster genome, we deep-sequenced small RNA fractions of 6 biotechnologically relevant cell lines and mapped the resulting reads to an artificial reference sequence consisting of all known miRNA hairpins. Read alignment patterns and read count ratios of 5' and 3' mature miRNAs were obtained and used for an independent classification into miR/miR* and 5p/3p miRNA pairs and discrimination of miRNAs from other non-coding RNAs, resulting in the annotation of 387 mature CHO miRNAs. The quantitative content of next-generation sequencing data was analyzed and confirmed using qPCR, to find that miRNAs are markers of cell status. Finally, cDNA sequencing of 26 validated targets of miR-17-92 suggests conserved functions for miRNAs in CHO cells, which together with the now publicly available sequence information sets the stage for developing novel RNAi tools for CHO cell engineering.     

Mathematical Modeling and Numerical Simulation of Magnetic Susceptibility Artifacts in Magnetic Resonance IMAGING*

Abstract

The technique used to recognise information in Magnetic Resonance Imaging (MRI) is based on electromagnetic fields. A linearly varying field (around 10^?2 Tesla per meter) is added to a strong homogeneous magnetic field (order of magnitude of approximately one Tesla). When these fields are disturbed by the presence of a paramagnetic material, in the sample for instance, the resulting image is usually distorted, these distortions being termed artifacts. Our goal is to present a method, assuming the field disturbances are known, to construct the resulting images. A mathematical model of the MRI process is developed. The way the images are distorted in intensity and shape is explained and an algorithm to simulate magnetic susceptibility artifacts is deduced.     

Hilbert phase entropy imaging of fMRI time series

Functional magnetic resonance imaging (fMRI) data-processing methods in the time domain include correlation analysis and the general linear model, among others. Virtually, many fMRI processing strategies utilise temporal information and ignore or pay little attention to phase information, resulting in an unnecessary loss of efficiency. We proposed a novel method named Hilbert phase entropy imaging (HPEI) that used the discrete Hilbert transform of the magnitude time series to detect brain functional activation. The data from two simulation studies and two in vivo fMRI studies that both contained block-design and event-related experiments revealed that the HPEI method enabled the effective detection of brain functional activation and the distinction of different response patterns. Our results demonstrate that this method is useful as a complementary analysis, but hypothesis-constrained, in revealing additional information regarding the complex nature of fMRI time series.     

Simultaneous fine mapping of multiple closely linked quantitative trait Loci using combined linkage disequilibrium and linkage with a general pedigree

Within a small region (e.g., <10 cM), there can be multiple quantitative trait loci (QTL) underlying phenotypes of a trait. Simultaneous fine mapping of closely linked QTL needs an efficient tool to remove confounded shade effects among QTL within such a small region. We propose a variance component method using combined linkage disequilibrium (LD) and linkage information and a reversible jump Markov chain Monte Carlo (MCMC) sampling for model selection. QTL identity-by-descent (IBD) coefficients between individuals are estimated by a hybrid MCMC combining the random walk and the meiosis Gibbs sampler. These coefficients are used in a mixed linear model and an empirical Bayesian procedure combines residual maximum likelihood (REML) to estimate QTL effects and a reversible jump MCMC that samples the number of QTL and the posterior QTL intensities across the tested region. Note that two MCMC processes are used, i.e., an (internal) MCMC for IBD estimation and an (external) MCMC for model selection. In a simulation study, the use of the multiple-QTL model clearly removes the shade effects between three closely linked QTL located at 1.125, 3.875, and 7.875 cM across the region of 10 cM, using 40 markers at 0.25-cM intervals. It is shown that the use of combined LD and linkage information gives much more useful information compared to using linkage information alone for both single- and multiple-QTL analyses. When using a lower marker density (11 markers at 1-cM intervals), the signal of the second QTL can disappear. Extreme values of past effective size (resulting in extreme levels of LD) decrease the mapping accuracy.