77 resultados para Normalized Coarse-grained Information Rate (NCIR).


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Gastropod shells consist of two crystal types of calcium carbonate, an outer, prismatic calcite layer and an inner nacreous layer made of aragonite. In cross-section, the nacre of the nacreous layer appears to have a regular brick-like microstructure composed of thin laminae of aragonite crystals, separated by very thin sheets of protein (Lutz and Rhoads, 1980; Nakahara, 1983). In abalone (Genus, Haliotis) and other gastropods, thin layers of non-lamellar pigmented material occur within the nacre and have been termed alternatively, fine lines, growth rings or growth lines (Shepherd et al., 1995). It has been suggested that these pigmented layers are small, prismatic, calcite layers (Shepherd and Avalos-Borja, 1997; Zaremba et al., 1996) but investigations using a Raman laser in Haliotis rubra show that they contain aragonite rather than calcite (Hawkes et al, 1996). Day and Fleming (1992) suggest that the occurrence of pigmented layers is correlated with regular exogenous cues such as reproduction or temperature changes and indeed in some species, pigmented layers in the shell can be used to age abalone (review: Shepherd and Triantafillos, 1997). However, McShane and Smith (1992) suggest that pigmented layers can occur irregularly and therefore may be unreliable indicators of age.

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The explosive growth in biotechnology combined with major advancesin information technology has the potential to radically transformimmunology in the postgenomics era. Not only do we now have readyaccess to vast quantities of existing data, but new data with relevanceto immunology are being accumulated at an exponential rate. Resourcesfor computational immunology include biological databases and methodsfor data extraction, comparison, analysis and interpretation. Publiclyaccessible biological databases of relevance to immunologists numberin the hundreds and are growing daily. The ability to efficientlyextract and analyse information from these databases is vital forefficient immunology research. Most importantly, a new generationof computational immunology tools enables modelling of peptide transportby the transporter associated with antigen processing (TAP), modellingof antibody binding sites, identification of allergenic motifs andmodelling of T-cell receptor serial triggering.

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This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters' responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers' behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief-desire-intention agent architecture. (C) 2002 Elsevier Science Ltd. All rights reserved.

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There has been a debate on whether or not the incidence of schizophrenia varies across time and place. In order to optimise the evidence upon which this debate is based, we have undertaken a systematicsystematic review of the literature. In this paper we provide an overview of the methods of the review and a preliminary analysis of the studies identified to date. Electronic databases (Medline, Psychlnfo, Embase, LILAC) were systematically searched for articles published between January 1965 and December 2001. The search terms were: (schizo* OR psycho*)AND (incidence OR prevalence). References were also identified from review articles, reference list and by writing to authors. To date we have identified 137 papers drawn from 33 nations. 37 papers in language other than English await translation. The currently included papers have generated 1413 different items of rate information data. In order to analyze these data we have undertaken several sequential filters in order to identify (a) non-overlapping data, (b) birth cohort study versus noncohort studies, (c) overall and sex-specific rates, (d) diagnostic criteria, (e) age ranges, (f) epoch of study, and (g) data on migrant or other special interest groups. In addition, we will examine the impact of urbanicity of site, age and/or sex standardization, and quality score on the incidence rates. The various discrete incidence rates will be presented graphically and the impact of various filters on these rates will be inspected using meta-analytic techniques. The use of meta-analysis may help elucidate the epidemiological landscape with respect to the incidence of schizophrenia and aid in the generation of new hypothesis. Acknowledgements: The Stanley Medical Research Institute supported project

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We describe a method by which the decoherence time of a solid-state qubit may be measured. The qubit is coded in the orbital degree of freedom of a single electron bound to a pair of donor impurities in a semiconductor host. The qubit is manipulated by adiabatically varying an external electric field. We show that by measuring the total probability of a successful qubit rotation as a function of the control field parameters, the decoherence rate may be determined. We estimate various system parameters, including the decoherence rates due to electromagnetic fluctuations and acoustic phonons. We find that, for reasonable physical parameters, the experiment is possible with existing technology. In particular, the use of adiabatic control fields implies that the experiment can be performed with control electronics with a time resolution of tens of nanoseconds.

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The increased use of trickle or drip irrigation is seen as one way of helping to improve the sustainability of irrigation systems around the world. However, soil water and solute transport properties and soil profile characteristics are often not adequately incorporated in the design and management of trickle systems. In this paper, we describe results of a simulation study designed to highlight the impacts of soil properties on water and solute transport from buried trickle emitters. The analysis addresses the influence of soil hydraulic properties, soil layering, trickle discharge rate, irrigation frequency, and timing of nutrient application on wetting patterns and solute distribution. We show that (1) trickle irrigation can improve plant water availability in medium and low permeability fine-textured soils, providing that design and management are adapted to account for their soil hydraulic properties, (2) in highly permeable coarse-textured soils, water and nutrients move quickly downwards from the emitter, making it difficult to wet the near surface zone if emitters are buried too deep, and (3) changing the fertigation strategy for highly permeable coarse-textured soils to apply nutrients at the beginning of an irrigation cycle can maintain larger amounts of nutrient near to and above the emitter, thereby making them less susceptible to leaching losses. The results demonstrate the need to account for differences in soil hydraulic properties and solute transport when designing irrigation and fertigation management strategies. Failure to do this will result in inefficient systems and lost opportunities for reducing the negative environmental impacts of irrigation.

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The assumption in analytical solutions for flow from surface and buried point sources of an average water content, (θ) over bar, behind the wetting front is examined. Some recent work has shown that this assumption fitted some field data well. Here we calculated (θ) over bar using a steady state solution based on the work by Raats [1971] and an exponential dependence of the diffusivity upon the water content. This is compared with a constant value of (θ) over bar calculated from an assumption of a hydraulic conductivity at the wetting front of 1 mm day(-1) and the water content at saturation. This comparison was made for a wide range of soils. The constant (θ) over bar generally underestimated (θ) over bar at small wetted radii and overestimated (θ) over bar at large radii. The crossover point between under and overestimation changed with both soil properties and flow rate. The largest variance occurred for coarser texture soils at low-flow rates. At high-flow rates in finer-textured soils the use of a constant (θ) over bar results in underestimation of the time for the wetting front to reach a particular radius. The value of (θ) over bar is related to the time at which the wetting front reaches a given radius. In coarse-textured soils the use of a constant value of (θ) over bar can result in an error of the time when the wetting front reaches a particular radius, as large as 80% at low-flow rates and large radii.

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Background: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term patholog to mean a homolog of a human disease-related gene encoding a product ( transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. Results: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity ( 70 - 85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool ( FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic ( 53%), hereditary ( 24%), immunological ( 5%), cardio-vascular (4%), or other (14%), disorders. Conclusions: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets.

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A two-component survival mixture model is proposed to analyse a set of ischaemic stroke-specific mortality data. The survival experience of stroke patients after index stroke may be described by a subpopulation of patients in the acute condition and another subpopulation of patients in the chronic phase. To adjust for the inherent correlation of observations due to random hospital effects, a mixture model of two survival functions with random effects is formulated. Assuming a Weibull hazard in both components, an EM algorithm is developed for the estimation of fixed effect parameters and variance components. A simulation study is conducted to assess the performance of the two-component survival mixture model estimators. Simulation results confirm the applicability of the proposed model in a small sample setting. Copyright (C) 2004 John Wiley Sons, Ltd.

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This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood and adult-onset schizophrenia, bipolar disorder, attention-deficit/ hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages. (C) 2004 Published by Elsevier Inc.

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High removal rate (up to 16.6 mm(3)/s per mm) grinding of alumina and alumina-titania was investigated with respect to material removal and basic grinding parameters using a resin-bond 160 mu m grit diamond wheel at the speeds of 40 and 160 m/s, respectively. The results show that the material removal for the single-phase polycrystalline alumina and the two-phase alumina-titania composite revealed identical mechanisms of microfracture and grain dislodgement under the grinding conditioned selected. There were no distinct differences in surface roughness and morphology for both materials ground at either conventional or high speed. An increase in material removal rate did not necessarily worsen the surface toughness for the two materials at both speeds. Also the grinding forces for the two ceramics demonstrated similar characteristics at any grinding speeds and specific removal rates. Both normal and tangential grinding forces and their force ratios at the high speed were lower than those at the conventional speed, regardless of removal rates. An increase in specific removal rate caused more rapid increases in normal and tangential forces obtained at the conventional grinding speed than those at the high speed. Furthermore, it is found that the high speed grinding at all the removal rates exerted a great amount of coolant-induced normal forces in grinding zone, which were 4-6 times higher than the pure normal grinding forces. (c) 2004 Elsevier Ltd. All rights reserved.