Jonathan M. Garibaldi

Real-Time Correlation-Based Stereo Vision with Reduced Border Errors

This paper describes a real-time stereo vision system that is required to support high-level object based tasks in a tele-operated environment. Stereo vision is computationally expensive, due to having to find corresponding pixels. Correlation is a fast, standard way to solve the correspondence problem. This paper analyses the behaviour of correlation based stereo to find ways to improve its quality while maintaining its real-time suitability. Three methods are suggested. Two of them aim to improve the disparity image especially at depth discontinuities, while one targets the identification of possible errors in general. Results are given on real stereo images with ground truth. A comparison with five standard correlation methods is provided. All proposed algorithms are described in detail and performance issues and optimisation are discussed. Finally, performance results of individual parts of the stereo algorithm are shown, including rectification, filtering andcorrelation using all proposed methods. The implemented system shows that errors of simple stereo correlation, especially in object border regions, can be reduced in real-time using non-specialised computer hardware.

Global Histone Modifications in Breast Cancer Correlate with Tumor Phenotypes, Prognostic Factors, and Patient Outcome

Post-translational histone modifications are known to be altered in cancer cells, and loss of selected histone acetylation and methylation marks has recently been shown to predict patient outcome in human carcinoma. Immunohistochemistry was used to detect a series of histone lysine acetylation (H3K9ac, H3K18ac, H4K12ac, and H4K16ac), lysine methylation (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) marks in a well-characterized series of human breast carcinomas (n = 880). Tissue staining intensities were assessed using blinded semiquantitative scoring. Validation studies were done using immunofluorescence staining and Western blotting. Our analyses revealed low or absent H4K16ac in the majority of breast cancer cases (78.9%), suggesting that this alteration may represent an early sign of breast cancer. There was a highly significant correlation between histone modifications status, tumor biomarker phenotype, and clinical outcome, where high relative levels of global histone acetylation and methylation were associated with a favorable prognosis and detected almost exclusively in luminal-like breast tumors (93%). Moderate to low levels of lysine acetylation (H3K9ac, H3K18ac, and H4K12ac), lysine (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) were observed in carcinomas of poorer prognostic subtypes, including basal carcinomas and HER-2-positive tumors. Clustering analysis identified three groups of histone displaying distinct pattern in breast cancer, which have distinct relationships to known prognostic factors and clinical outcome. This study identifies the presence of variations in global levels of histone marks in different grades, morphologic types, and phenotype classes of invasive breast cancer and shows that these differences have clinical significance.

Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism

Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organs apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90° gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40° to the horizontal. We hypothesize roots use a “tipping point” mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.

Umbilical cord blood gas analysis at delivery: a time for quality data

Objectives To address the practical problems of routine umbilical cord blood sampling, to determine the ranges for pH, pCO2 and base deficit and to examine the relationships of these parameters between cord vessels.

A multicentre comparative study of 17 experts and an intelligent computer system for managing labour using the cardiotocogram

Objectives To investigate 1. whether an intelligent computer system could obtain a performance in labour management comparable with experts when using cardiotocograms (CTGs), patient information, and fetal blood sampling and 2. whether experts could be consistent and agree in their management of labour.

Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review

This paper gives a comprehensive review of the application of metaheuristics to optimization problems in systems biology, mainly focusing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the metaheuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various metaheuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying metaheuristics to the systems biology modeling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

BackgroundStatistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.ResultsWe present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining.ConclusionArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

A multi-agent infrastructure and a service level agreement negotiation protocol for robust scheduling in grid computing

In this paper we propose a new infrastructure for efficient job scheduling on the Grid using multi-agent systems and a Service Level Agreement (SLA) negotiation protocol based on the Contract Net Protocol. The agent-based Grid scheduling system involves user agents, local scheduler agents, and super scheduler agents. User agents submit jobs to Grid compute resources. Local scheduler agents schedule jobs on compute resources. Super scheduler agents act as mediators between the local scheduler and the user agents to schedule the jobs at the global level of the Grid. The SLA negotiation protocol is a hierarchical bidding mechanism involving meta-SLA negotiation between the user agents and the super scheduler agents; and sub-SLA negotiation between the super scheduler agents and the local scheduler agents. In this protocol the agents exchange SLA-announcements, SLA-bids, and SLA-awards to negotiate the schedule of jobs on Grid compute resources. In the presence of uncertainties a re-negotiation mechanism is proposed to re-negotiate the SLAs in failure.

Application of simulated annealing fuzzy model tuning to umbilical cord acid-base interpretation

Fuzzy logic and fuzzy set theory provide an important framework for representing and managing imprecision and uncertainty in medical expert systems, but the need remains to optimize such systems to enhance performance. The paper presents a general technique for optimizing fuzzy models in fuzzy expert systems (FESs) by simulated annealing (SA) and N-dimensional hill climbing simplex method. The application of the technique to a FES for the interpretation of the acid-base balance of blood in the umbilical cord of newborn infants is presented. The Spearman rank order correlation statistic was used to assess and to compare the performance of a commercially available crisp expert system, an initial FES, and a tuned FES with experienced clinicians. Results showed that without tuning, the performance of the crisp system was significantly better (correlation of 0.80) than the FES (correlation of 0.67). The performance of the tuned FES was better than the crisp system and effectively indistinguishable from the clinicians (correlation of 0.93) on training data and was the best of the expert systems on validation data. Unlike most applications of fuzzy logic where all fuzzy sets have normalized heights of unity, in this application it was found that a reduction in the height of some fuzzy sets was effective in enhancing performance. This suggests that the height of fuzzy sets may be a generally useful parameter in tuning FESs.

Effect of type-2 fuzzy membership function shape on modelling variation in human decision making

This paper explains how the shape of type-2 fuzzy membership functions can be used to model the variation in human decision making. An interval type-2 fuzzy logic system (FLS) is developed for umbilical acid-base assessment. The influence of the shape of the membership functions on the variation in decision making of the fuzzy logic system is studied using the interval outputs. Three different methods are used to create interval type-2 membership functions. The centre points of the primary membership functions are shifted, the widths are shifted, and a uniform band is introduced around the original type-1 membership functions. It is shown that there is a direct relationship between the variation in decision making and the uncertainty introduced to the membership functions.