Constrained models of evolution lead to improved prediction of functional linkage from correlated gain and loss of genes

Motivation: We compare phylogenetic approaches for inferring functional gene links. The approaches detect independent instances of the correlated gain and loss of pairs of genes from species' genomes. We investigate the effect on results of basing evidence of correlations on two phylogenetic approaches, Dollo parsminony and maximum likelihood (ML). We further examine the effect of constraining the ML model by fixing the rate of gene gain at a low value, rather than estimating it from the data. Results: We detect correlated evolution among a test set of pairs of yeast (Saccharomyces cerevisiae) genes, with a case study of 21 eukaryotic genomes and test data derived from known yeast protein complexes. If the rate at which genes are gained is constrained to be low, ML achieves by far the best results at detecting known functional links. The model then has fewer parameters but it is more realistic by preventing genes from being gained more than once. Availability: BayesTraits by M. Pagel and A. Meade, and a script to configure and repeatedly launch it by D. Barker and M. Pagel, are available at http://www.evolution.reading.ac.uk .

Metallo-organic domino reactions: C-H versus C-C bond breaking

HL and MeL are prepared by condensing benzil dihydrazone with 2-formylpyridine and 2-acetylpyridine, respectively, in 1:2 molar proportions. While in a reaction with [Ru-(C6H6)Cl-2](2), HL yields the cation [Ru(C6H6){5,6-diphenyl-3-(pyridin-2-yl)- 1,2,4-triazine}Cl](+), MeL gives the cation [Ru(C6H6)(MeL)Cl](+). Both the cations are isolated as their hexafluorophosphate salts and characterised by X-ray crystallography. In the case of HL, double domino electrocyclic/elimination reactions are found to occur. The electrocyclic reaction occurs in a C=N-N=C-C=N fragment of HL and the elimination reaction involves breaking of a C-H bond of HL. Density functional calculations on model complexes indicate that the identified electrocyclic reaction is thermochemically as well as kinetically feasible for both HL and MeL in the gas phase. For a double domino reaction, similar to that operative in HL, to occur for MeL, breaking of a C-C bond would be required in the elimination step. Our model calculations show the energy barrier for this elimination step to be much higher (329.1 kJ mol(-1)) for MeL than that for HL (96.3 kJ mol(-1)). Thus, the domino reaction takes place for HL and not for MeL. This accounts for the observed stability of [Ru(C6H6)-(MeL)Cl](+) under the reaction conditions employed.

From dichotic listening to the irrelevant sound effect: a behavioural and neuroimaging analysis of the processing of unattended speech

The assumption that ignoring irrelevant sound in a serial recall situation is identical to ignoring a non-target channel in dichotic listening is challenged. Dichotic listening is open to moderating effects of working memory capacity (Conway et al., 2001) whereas irrelevant sound effects (ISE) are not (Beaman, 2004). A right ear processing bias is apparent in dichotic listening, whereas the bias is to the left ear in the ISE (Hadlington et al., 2004). Positron emission tomography (PET) imaging data (Scott et al., 2004, submitted) show bilateral activation of the superior temporal gyrus (STG) in the presence of intelligible, but ignored, background speech and right hemisphere activation of the STG in the presence of unintelligible background speech. It is suggested that the right STG may be involved in the ISE and a particularly strong left ear effect might occur because of the contralateral connections in audition. It is further suggested that left STG activity is associated with dichotic listening effects and may be influenced by working memory span capacity. The relationship of this functional and neuroanatomical model to known neural correlates of working memory is considered.

Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.

Stochastic resonance in a nonlinear model of a rotating, stratified shear flow, with a simple stochastic inertia-gravity wave parameterization

We report on a numerical study of the impact of short, fast inertia-gravity waves on the large-scale, slowly-evolving flow with which they co-exist. A nonlinear quasi-geostrophic numerical model of a stratified shear flow is used to simulate, at reasonably high resolution, the evolution of a large-scale mode which grows due to baroclinic instability and equilibrates at finite amplitude. Ageostrophic inertia-gravity modes are filtered out of the model by construction, but their effects on the balanced flow are incorporated using a simple stochastic parameterization of the potential vorticity anomalies which they induce. The model simulates a rotating, two-layer annulus laboratory experiment, in which we recently observed systematic inertia-gravity wave generation by an evolving, large-scale flow. We find that the impact of the small-amplitude stochastic contribution to the potential vorticity tendency, on the model balanced flow, is generally small, as expected. In certain circumstances, however, the parameterized fast waves can exert a dominant influence. In a flow which is baroclinically-unstable to a range of zonal wavenumbers, and in which there is a close match between the growth rates of the multiple modes, the stochastic waves can strongly affect wavenumber selection. This is illustrated by a flow in which the parameterized fast modes dramatically re-partition the probability-density function for equilibrated large-scale zonal wavenumber. In a second case study, the stochastic perturbations are shown to force spontaneous wavenumber transitions in the large-scale flow, which do not occur in their absence. These phenomena are due to a stochastic resonance effect. They add to the evidence that deterministic parameterizations in general circulation models, of subgrid-scale processes such as gravity wave drag, cannot always adequately capture the full details of the nonlinear interaction.

A Functional Magnetic Resonance Imaging Predictor of Treatment Response to Venlafaxine in Generalized Anxiety Disorder

Background: Functional magnetic resonance imaging (fMRI) holds promise as a noninvasive means of identifying neural responses that can be used to predict treatment response before beginning a drug trial. Imaging paradigms employing facial expressions as presented stimuli have been shown to activate the amygdala and anterior cingulate cortex (ACC). Here, we sought to determine whether pretreatment amygdala and rostral ACC (rACC) reactivity to facial expressions could predict treatment outcomes in patients with generalized anxiety disorder (GAD).Methods: Fifteen subjects (12 female subjects) with GAD participated in an open-label venlafaxine treatment trial. Functional magnetic resonance imaging responses to facial expressions of emotion collected before subjects began treatment were compared with changes in anxiety following 8 weeks of venlafaxine administration. In addition, the magnitude of fMRI responses of subjects with GAD were compared with that of 15 control subjects (12 female subjects) who did not have GAD and did not receive venlafaxine treatment.Results The magnitude of treatment response was predicted by greater pretreatment reactivity to fearful faces in rACC and lesser reactivity in the amygdala. These individual differences in pretreatment rACC and amygdala reactivity within the GAD group were observed despite the fact that 1) the overall magnitude of pretreatment rACC and amygdala reactivity did not differ between subjects with GAD and control subjects and 2) there was no main effect of treatment on rACC-amygdala reactivity in the GAD group.Conclusions: These findings show that this pattern of rACC-amygdala responsivity could prove useful as a predictor of venlafaxine treatment response in patients with GAD.

A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphisms

Genetic polymorphisms in deoxyribonucleic acid coding regions may have a phenotypic effect on the carrier, e.g. by influencing susceptibility to disease. Detection of deleterious mutations via association studies is hampered by the large number of candidate sites; therefore methods are needed to narrow down the search to the most promising sites. For this, a possible approach is to use structural and sequence-based information of the encoded protein to predict whether a mutation at a particular site is likely to disrupt the functionality of the protein itself. We propose a hierarchical Bayesian multivariate adaptive regression spline (BMARS) model for supervised learning in this context and assess its predictive performance by using data from mutagenesis experiments on lac repressor and lysozyme proteins. In these experiments, about 12 amino-acid substitutions were performed at each native amino-acid position and the effect on protein functionality was assessed. The training data thus consist of repeated observations at each position, which the hierarchical framework is needed to account for. The model is trained on the lac repressor data and tested on the lysozyme mutations and vice versa. In particular, we show that the hierarchical BMARS model, by allowing for the clustered nature of the data, yields lower out-of-sample misclassification rates compared with both a BMARS and a frequen-tist MARS model, a support vector machine classifier and an optimally pruned classification tree.

The neural response to emotional prosody, as revealed by functional magnetic resonance imaging

Prosody is an important feature of language, comprising intonation, loudness, and tempo. Emotional prosodic processing forms an integral part of our social interactions. The main aim of this study was to use bold contrast fMRI to clarify the normal functional neuroanatomy of emotional prosody, in passive and active contexts. Subjects performed six separate scanning studies, within which two different conditions were contrasted: (1) "pure" emotional prosody versus rest; (2) congruent emotional prosody versus 'neutral' sentences; (3) congruent emotional prosody versus rest; (4) incongruent emotional prosody versus rest; (5) congruent versus incongruent emotional prosody; and (6) an active experiment in which subjects were instructed to either attend to the emotion conveyed by semantic content or that conveyed by tone of voice. Data resulting from these contrasts were analysed using SPM99. Passive listening to emotional prosody consistently activated the lateral temporal lobe (superior and/or middle temporal gyri). This temporal lobe response was relatively right-lateralised with or without semantic information. Both the separate and direct comparisons of congruent and incongruent emotional prosody revealed that subjects used fewer brain regions to process incongruent emotional prosody than congruent. The neural response to attention to semantics, was left lateralised, and recruited an extensive network not activated by attention to emotional prosody. Attention to emotional prosody modulated the response to speech, and induced right-lateralised activity, including the middle temporal gyrus. In confirming the results of lesion and neuropsychological studies, the current study emphasises the importance of the right hemisphere in the processing of emotional prosody, specifically the lateral temporal lobes. (C) 2003 Elsevier Science Ltd. All rights reserved.

Development and experimental identification of a biomechanical model of the trunk for functional electrical stimulation control in paraplegia

Objectives. Theoretic modeling and experimental studies suggest that functional electrical stimulation (FES) can improve trunk balance in spinal cord injured subjects. This can have a positive impact on daily life, increasing the volume of bimanual workspace, improving sitting posture, and wheelchair propulsion. A closed loop controller for the stimulation is desirable, as it can potentially decrease muscle fatigue and offer better rejection to disturbances. This paper proposes a biomechanical model of the human trunk, and a procedure for its identification, to be used for the future development of FES controllers. The advantage over previous models resides in the simplicity of the solution proposed, which makes it possible to identify the model just before a stimulation session ( taking into account the variability of the muscle response to the FES). Materials and Methods. The structure of the model is based on previous research on FES and muscle physiology. Some details could not be inferred from previous studies, and were determined from experimental data. Experiments with a paraplegic volunteer were conducted in order to measure the moments exerted by the trunk-passive tissues and artificially stimulated muscles. Data for model identification and validation also were collected. Results. Using the proposed structure and identification procedure, the model could adequately reproduce the moments exerted during the experiments. The study reveals that the stimulated trunk extensors can exert maximal moment when the trunk is in the upright position. In contrast, previous studies show that able-bodied subjects can exert maximal trunk extension when flexed forward. Conclusions. The proposed model and identification procedure are a successful first step toward the development of a model-based controller for trunk FES. The model also gives information on the trunk in unique conditions, normally not observable in able-bodied subjects (ie, subject only to extensor muscles contraction).

Multiscale evolving complex network model of functional connectivity in neuronal cultures

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological selforganization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory. Functional connectivity networks can be constructed by observing patterns of synchrony that evolve during bursts. To capture this evolution, a modelling approach is adopted using a framework of emergent evolving complex networks and, through taking advantage of the multiple time scales of the system, aims to show the importance of sequential and ordered synchronization in network function.

A functional coefficient model view of the Feldstein–Horioka puzzle

What does the saving–investment (SI) relation really measure and how should the SI relation be measured? These are two of the most discussed issues triggered by the so-called Feldstein–Horioka puzzle. Based on panel data we introduce a new variant of functional coefficient models that allows to separate long and short to medium run parameter dependence. The new modeling framework is applied to uncover the determinants of the SI relation. Macroeconomic state variables such as openness, the age dependency ratio, government current and consumption expenditures are found to affect the SI relation significantly in the long run.