Learning to drive: from hazard detection to hazard handling

Hazard perception has been found to correlate with crash involvement, and has thus been suggested as the most likely source of any skill gap between novice and experienced drivers. The most commonly used method for measuring hazard perception is to evaluate the perception-reaction time to filmed traffic events. It can be argued that this method lacks ecological validity and may be of limited value in predicting the actions drivers’ will take to hazards encountered. The first two studies of this thesis compare novice and experienced drivers’ performance on a hazard detection test, requiring discrete button press responses, with their behaviour in a more dynamic driving environment, requiring hazard handling ability. Results indicate that the hazard handling test is more successful at identifying experience-related differences in response time to hazards. Hazard detection test scores were strongly related to performance on a driver theory test, implying that traditional hazard perception tests may be focusing more on declarative knowledge of driving than on the procedural knowledge required to successfully avoid hazards while driving. One in five Irish drivers crash within a year of passing their driving test. This suggests that the current driver training system does not fully prepare drivers for the dangers they will encounter. Thus, the third and fourth studies in this thesis focus on the development of two simulator-based training regimes. In the third study participants receive intensive training on the molar elements of driving i.e. speed and distance evaluation. The fourth study focuses on training higher order situation awareness skills, including perception, comprehension and projection. Results indicate significant improvement in aspects of speed, distance and situation awareness across training days. However, neither training programme leads to significant improvements in hazard handling performance, highlighting the difficulties of applying learning to situations not previously encountered.

Histone modifications within the human X centromere region.

Human centromeres are multi-megabase regions of highly ordered arrays of alpha satellite DNA that are separated from chromosome arms by unordered alpha satellite monomers and other repetitive elements. Complexities in assembling such large repetitive regions have limited detailed studies of centromeric chromatin organization. However, a genomic map of the human X centromere has provided new opportunities to explore genomic architecture of a complex locus. We used ChIP to examine the distribution of modified histones within centromere regions of multiple X chromosomes. Methylation of H3 at lysine 4 coincided with DXZ1 higher order alpha satellite, the site of CENP-A localization. Heterochromatic histone modifications were distributed across the 400-500 kb pericentromeric regions. The large arrays of alpha satellite and gamma satellite DNA were enriched for both euchromatic and heterochromatic modifications, implying that some pericentromeric repeats have multiple chromatin characteristics. Partial truncation of the X centromere resulted in reduction in the size of the CENP-A/Cenp-A domain and increased heterochromatic modifications in the flanking pericentromere. Although the deletion removed approximately 1/3 of centromeric DNA, the ratio of CENP-A to alpha satellite array size was maintained in the same proportion, suggesting that a limited, but defined linear region of the centromeric DNA is necessary for kinetochore assembly. Our results indicate that the human X centromere contains multiple types of chromatin, is organized similarly to smaller eukaryotic centromeres, and responds to structural changes by expanding or contracting domains.

Cysteine proteinase-1 and cut protein isoform control dendritic innervation of two distinct sensory fields by a single neuron.

Dendrites often exhibit structural changes in response to local inputs. Although mechanisms that pattern and maintain dendritic arbors are becoming clearer, processes regulating regrowth, during context-dependent plasticity or after injury, remain poorly understood. We found that a class of Drosophila sensory neurons, through complete pruning and regeneration, can elaborate two distinct dendritic trees, innervating independent sensory fields. An expression screen identified Cysteine proteinase-1 (Cp1) as a critical regulator of this process. Unlike known ecdysone effectors, Cp1-mutant ddaC neurons pruned larval dendrites normally but failed to regrow adult dendrites. Cp1 expression was upregulated/concentrated in the nucleus during metamorphosis, controlling production of a truncated Cut homeodomain transcription factor. This truncated Cut, but not the full-length protein, allowed Cp1-mutant ddaC neurons to regenerate higher-order adult dendrites. These results identify a molecular pathway needed for dendrite regrowth after pruning, which allows the same neuron to innervate distinct sensory fields.

Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Specialized motor-driven dusp1 expression in the song systems of multiple lineages of vocal learning birds.

Mechanisms for the evolution of convergent behavioral traits are largely unknown. Vocal learning is one such trait that evolved multiple times and is necessary in humans for the acquisition of spoken language. Among birds, vocal learning is evolved in songbirds, parrots, and hummingbirds. Each time similar forebrain song nuclei specialized for vocal learning and production have evolved. This finding led to the hypothesis that the behavioral and neuroanatomical convergences for vocal learning could be associated with molecular convergence. We previously found that the neural activity-induced gene dual specificity phosphatase 1 (dusp1) was up-regulated in non-vocal circuits, specifically in sensory-input neurons of the thalamus and telencephalon; however, dusp1 was not up-regulated in higher order sensory neurons or motor circuits. Here we show that song motor nuclei are an exception to this pattern. The song nuclei of species from all known vocal learning avian lineages showed motor-driven up-regulation of dusp1 expression induced by singing. There was no detectable motor-driven dusp1 expression throughout the rest of the forebrain after non-vocal motor performance. This pattern contrasts with expression of the commonly studied activity-induced gene egr1, which shows motor-driven expression in song nuclei induced by singing, but also motor-driven expression in adjacent brain regions after non-vocal motor behaviors. In the vocal non-learning avian species, we found no detectable vocalizing-driven dusp1 expression in the forebrain. These findings suggest that independent evolutions of neural systems for vocal learning were accompanied by selection for specialized motor-driven expression of the dusp1 gene in those circuits. This specialized expression of dusp1 could potentially lead to differential regulation of dusp1-modulated molecular cascades in vocal learning circuits.

The past, present, and future of human centromere genomics.

The centromere is the chromosomal locus essential for chromosome inheritance and genome stability. Human centromeres are located at repetitive alpha satellite DNA arrays that compose approximately 5% of the genome. Contiguous alpha satellite DNA sequence is absent from the assembled reference genome, limiting current understanding of centromere organization and function. Here, we review the progress in centromere genomics spanning the discovery of the sequence to its molecular characterization and the work done during the Human Genome Project era to elucidate alpha satellite structure and sequence variation. We discuss exciting recent advances in alpha satellite sequence assembly that have provided important insight into the abundance and complex organization of this sequence on human chromosomes. In light of these new findings, we offer perspectives for future studies of human centromere assembly and function.

A natural extension of the conventional finite volume method into polygonal unstructured meshes for CFD application.

A new general cell-centered solution procedure based upon the conventional control or finite volume (CV or FV) approach has been developed for numerical heat transfer and fluid flow which encompasses both structured and unstructured meshes for any kind of mixed polygon cell. Unlike conventional FV methods for structured and block structured meshes and both FV and FE methods for unstructured meshes, the irregular control volume (ICV) method does not require the shape of the element or cell to be predefined because it simply exploits the concept of fluxes across cell faces. That is, the ICV method enables meshes employing mixtures of triangular, quadrilateral, and any other higher order polygonal cells to be exploited using a single solution procedure. The ICV approach otherwise preserves all the desirable features of conventional FV procedures for a structured mesh; in the current implementation, collocation of variables at cell centers is used with a Rhie and Chow interpolation (to suppress pressure oscillation in the flow field) in the context of the SIMPLE pressure correction solution procedure. In fact all other FV structured mesh-based methods may be perceived as a subset of the ICV formulation. The new ICV formulation is benchmarked using two standard computational fluid dynamics (CFD) problems i.e., the moving lid cavity and the natural convection driven cavity. Both cases were solved with a variety of structured and unstructured meshes, the latter exploiting mixed polygonal cell meshes. The polygonal mesh experiments show a higher degree of accuracy for equivalent meshes (in nodal density terms) using triangular or quadrilateral cells; these results may be interpreted in a manner similar to the CUPID scheme used in structured meshes for reducing numerical diffusion for flows with changing direction.

Experience of using a CFD code for estimating the noise generated by gusts along the sun-roof of a car

The problem to be examined here is the fluctuating pressure distribution along the open cavity of the sun-roof at the top of a car compartment due to gusts passing over the sun-roof. The aim of this test is to investigate the capability of a typical commercial CFD package, PHOENICS, in recognising pressure fluctuations occurring in an important automotive industrial problem. In particular to examine the accuracy of transporting pulsatory gusts traveling along the main flow through the use of finite volume methods with higher order schemes in the numercial solutins of the unsteady compressible Navier-Stokes equations. The Helmholtz equation is used to solve the sound distribution inside the car compartment, resulting from the externally induced fluctuations.

On information rates of time-varying fading channels modeled as finite-state Markov channels

We study information rates of time-varying flat-fading channels (FFC) modeled as finite-state Markov channels (FSMC). FSMCs have two main applications for FFCs: modeling channel error bursts and decoding at the receiver. Our main finding in the first application is that receiver observation noise can more adversely affect higher-order FSMCs than lower-order FSMCs, resulting in lower capacities. This is despite the fact that the underlying higher-order FFC and its corresponding FSMC are more predictable. Numerical analysis shows that at low to medium SNR conditions (SNR lsim 12 dB) and at medium to fast normalized fading rates (0.01 lsim fDT lsim 0.10), FSMC information rates are non-increasing functions of memory order. We conclude that BERs obtained by low-order FSMC modeling can provide optimistic results. To explain the capacity behavior, we present a methodology that enables analytical comparison of FSMC capacities with different memory orders. We establish sufficient conditions that predict higher/lower capacity of a reduced-order FSMC, compared to its original high-order FSMC counterpart. Finally, we investigate the achievable information rates in FSMC-based receivers for FFCs. We observe that high-order FSMC modeling at the receiver side results in a negligible information rate increase for normalized fading rates fDT lsim 0.01.

Shark personalities? Repeatability of social network traits in a widely distributed predatory fish. Behavioral Ecology and Sociobiology, 68, 1995-2003.

Interest in animal personalities has generated a burgeoning literature on repeatability in individual traits such as boldness or exploration through time or across different contexts. Yet, repeatability can be influenced by the interactive social strategies of individuals, for example, consistent inter-individual variation in aggression is well documented. Previous work has largely focused on the social aspects of repeatability in animal behaviour by testing individuals in dyadic pairings. Under natural conditions, individuals interact in a heterogeneous polyadic network. However, the extent to which there is repeatability of social traits at this higher order network level remains unknown. Here, we provide the first empirical evidence of consistent and repeatable animal social networks. Using a model species of shark, a taxonomic group in which repeatability in behaviour has yet to be described, we repeatedly quantified the social networks of ten independent shark groups across different habitats, testing repeatability in individual network position under changing environments. To understand better the mechanisms behind repeatable social behaviour, we also explored the coupling between individual preferences for specific group sizes and social network position. We quantify repeatability in sharks by demonstrating that despite changes in aggregation measured at the group level, the social network position of individuals is consistent across treatments. Group size preferences were found to influence the social network position of individuals in small groups but less so for larger groups suggesting network structure, and thus, repeatability was driven by social preference over aggregation tendency.

Triply differential single ionization cross sections in coplanar and non-coplanar geometry for fast heavy ion-atom collisions

We have performed a kinematically complete experiment and calculations on single ionization in 100 MeV/amu C6+ + He collisions. For electrons ejected into the scattering plane (defined by the initial and final projectile momentum vectors) our first- and higher-order calculations are in good agreement with the data. In the plane perpendicular to the scattering plane and containing the initial projectile axis a strong forward-backward asymmetry is observed. In this plane both the first-order and the higher-order calculations do not provide good agreement neither with the data nor amongst each other.

A novel approach to dynamic modelling of polymer extrusion for improved process control

Polymer extrusion is a complex process and the availability of good dynamic models is key for improved system operation. Previous modelling attempts have failed adequately to capture the non-linearities of the process or prove too complex for control applications. This work presents a novel approach to the problem by the modelling of extrusion viscosity and pressure, adopting a grey box modelling technique that combines mechanistic knowledge with empirical data using a genetic algorithm approach. The models are shown to outperform those of a much higher order generated by a conventional black box technique while providing insight into the underlying processes at work within the extruder.

Green function for the diffusion limit of one-dimensional continuous time random walks

It is shown how the fractional probability density diffusion equation for the diffusion limit of one-dimensional continuous time random walks may be derived from a generalized Markovian Chapman-Kolmogorov equation. The non-Markovian behaviour is incorporated into the Markovian Chapman-Kolmogorov equation by postulating a Levy like distribution of waiting times as a kernel. The Chapman-Kolmogorov equation so generalised then takes on the form of a convolution integral. The dependence on the initial conditions typical of a non-Markovian process is treated by adding a time dependent term involving the survival probability to the convolution integral. In the diffusion limit these two assumptions about the past history of the process are sufficient to reproduce anomalous diffusion and relaxation behaviour of the Cole-Cole type. The Green function in the diffusion limit is calculated using the fact that the characteristic function is the Mittag-Leffler function. Fourier inversion of the characteristic function yields the Green function in terms of a Wright function. The moments of the distribution function are evaluated from the Mittag-Leffler function using the properties of characteristic functions and a relation between the powers of the second moment and higher order even moments is derived. (C) 2004 Elsevier B.V. All rights reserved.

The pathobiology of the septin gene family.

Septins are an evolutionarily conserved group of GTP-binding and filament-forming proteins that belong to the large superclass of P-loop GTPases. While originally discovered in yeast as cell division cycle mutants with cytokinesis defects, they are now known to have diverse cellular roles which include polarity determination, cytoskeletal reorganization, membrane dynamics, vesicle trafficking, and exocytosis. Septin proteins form homo- and hetero-oligomeric polymers which can assemble into higher-order filaments. They are also known to interact with components of the cytoskeleton, ie actin and tubulin. The precise role of GTP binding is not clear but a current model suggests that it is associated with conformational changes which alter binding to other proteins. There are at least 12 human septin genes, and although information on expression patterns is limited, most undergo complex alternative splicing with some degree of tissue specificity. Nevertheless, an increasing body of data implicates the septin family in the pathogenesis of diverse disease states including neoplasia, neurodegenerative conditions, and infections. Here the known biochemical properties of mammalian septins are reviewed in the light of the data from yeast and other model organisms. The data implicating septins in human disease are considered and a model linking these data is proposed. It is posited that septins can act as regulatable scaffolds where the stoichiometry of septin associations, modifications, GTP status, and the interactions with other proteins allow the regulation of key cellular processes including polarity determination. Derangements of such septin scaffolds thus explain the role of septins in disease states. Copyright © 2004 Pathological Society of Great Britain and Ireland.

HOS-Based Semi-Blind Spatial Equalization for MIMO Rayleigh Fading Channels

In this paper we concentrate on the direct semi-blind spatial equalizer design for MIMO systems with Rayleigh fading channels. Our aim is to develop an algorithm which can outperform the classical training based method with the same training information used, and avoid the problems of low convergence speed and local minima due to pure blind methods. A general semi-blind cost function is first constructed which incorporates both the training information from the known data and some kind of higher order statistics (HOS) from the unknown sequence. Then, based on the developed cost function, we propose two semi-blind iterative and adaptive algorithms to find the desired spatial equalizer. To further improve the performance and convergence speed of the proposed adaptive method, we propose a technique to find the optimal choice of step size. Simulation results demonstrate the performance of the proposed algorithms and comparable schemes.