Treating liver disease in the avian patient

The liver plays a major role in the body's metabolism and, as such, is subject to a multitude of insults-infectious, toxic, metabolic, nutritional, traumatic, and neoplastic. Consequently, liver disease is not uncommon in avian and other exotic patients. As diagnostic modalities (and our experience in using them and interpreting them) improve, veterinarian are becoming more aware of the presence of (often subclinical) liver disease in their patients, and often of the specific nature of that disease. Through new research, veterinarians also are more able to appreciate the liver's unique function and metabolism and the role it plays in the function of the body as a whole. This understanding has led to a better awareness of how the liver responds to disease, and this has allowed refinements in the treatment of diseased and damaged livers. However, treating liver disease is not just about treating the organ; the patient as a whole must be supported and treated until a successful resolution has been achieved. Treatment therefore must be aimed at supporting the patient, treating the specific condition, and creating an environment that allows the liver to heal and regenerate. This article briefly reviews the anatomy and physiology of the liver and how it responds to insult. Treatment of liver disease then is discussed using the aims described above. (C) 2004 Elsevier Inc. All rights reserved.

Nonalcoholic fatty liver disease: is all the fat bad?

Nonalcoholic fatty liver disease is now a major cause of liver disease in developed countries, largely as a result of an epidemic of obesity, diabetes and sedentary lifestyles. This has resulted in raised clinical awareness and diagnostic refinement. The entity encompasses several histologic patterns from benign steatosis to nonalcoholic steatohepatitis, the latter having a significant risk of progressive fibrosis and the development of cirrhosis. Labor-atory tests and imaging are not able to distinguish steatosis from steatohepatitis, which requires liver biopsy. However following an assessment of several risk factors, patients can be stratified for the potential risk of fibrosis, allowing the rational use of liver biopsy. This review will describe the various patterns of nonalcoholic fatty liver disease and relate this to disease pathogenesis and progression. Strategies for management, including experimental interventions, will be discussed.

Genetic and economic analysis of a targeted marker-assisted wheat breeding strategy

The advent of molecular markers as a tool to aid selection has provided plant breeders with the opportunity to rapidly deliver superior genetic solutions to problems in agricultural production systems. However, a major constraint to the implementation of marker-assisted selection (MAS) in pragmatic breeding programs in the past has been the perceived high relative cost of MAS compared to conventional phenotypic selection. In this paper, computer simulation was used to design a genetically effective and economically efficient marker-assisted breeding strategy aimed at a specific outcome. Under investigation was a strategy involving the integration of both restricted backcrossing and doubled haploid (DH) technology. The point at which molecular markers are applied in a selection strategy can be critical to the effectiveness and cost efficiency of that strategy. The application of molecular markers was considered at three phases in the strategy: allele enrichment in the BC1F1 population, gene selection at the haploid stage and the selection for recurrent parent background of DHs prior to field testing. Overall, incorporating MAS at all three stages was the most effective, in terms of delivering a high frequency of desired outcomes and at combining the selected favourable rust resistance, end use quality and grain yield alleles. However, when costs were included in the model the combination of MAS at the BC1F1 and haploid stage was identified as the optimal strategy. A detailed economic analysis showed that incorporation of marker selection at these two stages not only increased genetic gain over the phenotypic alternative but actually reduced the over all cost by 40%.

Resistance gene analogues in sugarcane and sorghum and their association with quantitative trait loci for rust resistance

Fifty-four different sugarcane resistance gene analogue (RGA) sequences were isolated, characterized, and used to identify molecular markers linked to major disease-resistance loci in sugarcane. Ten RGAs were identified from a sugarcane stem expressed sequence tag (EST) library; the remaining 44 were isolated from sugarcane stem, leaf, and root tissue using primers designed to conserved RGA motifs. The map location of 31 of the RGAs was determined in sugarcane and compared with the location of quantitative trait loci (QTL) for brown rust resistance. After 2 years of phenotyping, 3 RGAs were shown to generate markers that were significantly associated with resistance to this disease. To assist in the understanding of the complex genetic structure of sugarcane, 17 of the 31 RGAs were also mapped in sorghum. Comparative mapping between sugarcane and sorghum revealed syntenic localization of several RGA clusters. The 3 brown rust associated RGAs were shown to map to the same linkage group (LG) in sorghum with 2 mapping to one region and the third to a region previously shown to contain a major rust-resistance QTL in sorghum. These results illustrate the value of using RGAs for the identification of markers linked to disease resistance loci and the value of simultaneous mapping in sugarcane and sorghum.

Characterization of two polymorphisms in the leukotriene C4 synthase gene in an Australian population of subjects with mild, moderate, and severe asthma

Background: The cysteinyl-leukotrienes (cys-LTs) are proinflammatory mediators that are important in the pathophysiology of asthma. LTC4 synthase is a key enzyme in the cys-LT biosynthetic pathway, and studies in small populations have suggested that a promoter polymorphism (A(-444)C) in the gene might be associated with asthma severity and aspirin intolerance. Objective: We sought to screen the LTC4 synthase gene for polymorphisms and to determine whether there is an association between these polymorphisms and asthma severity or aspirin sensitivity in a large, well-phenotyped population and to determine whether this polymorphism is functionally relevant. Methods: The coding regions of the LTC4 synthase gene were screened for polymorphisms and the A(-444)C polymorphism was analyzed in a large Australian white adult population of mild (n = 282), moderate (n = 236), and severe asthmatic subjects (n = 86) and nonasthmatic subjects (n = 458), as well as in aspirin-intolerant asthmatic subjects (n = 67). The functional activity of the promoter polymorphism was investigated by transient transfection of HL-60 cells with a promoter construct. Results: A new polymorphism was identified in intron 1 of the gene (IVS1-10c>a) but was not associated with asthma. Association studies showed that the A(-444)C polymorphism was weakly associated with asthma per se, but there was no association between the C-444 allele and chronic asthma severity or aspirin intolerance. A meta-analysis of all the genetic studies conducted to date found significant between-study heterogeneity in C-444 allele frequencies within different clinical subgroups. In vitro functional studies showed no significant differences in transcription efficiency between constructs containing the A(-444) allele or the C-444 allele. Conclusions: Our data confirm that, independent of transcriptional activity, the C-444 allele in the LTC4 synthase gene is weakly associated with the asthma phenotype, but it is not related to disease severity or aspirin intolerance.

Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings

The study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and natural selection and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by R. A. Fisher in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the expected proportion of genes shared between different types of relatives, and explicit, often controversial and untestable models of genetic and non-genetic causes of family resemblance. With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied from 0.374 to 0.617 (mean 0.498, standard deviation 0.036). The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The maximum likelihood estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within- family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.

Analysing diversity in sugarcane resistance gene analogues

As resistance genes have been shown to contain conserved motifs and cluster in many plant genomes, the identification of resistance gene analogues can be used as a strategy for both the discovery of DNA markers linked to disease resistance loci and the map-based cloning of disease resistance genes. Sugarcane suffers from many important diseases and an analysis of resistance gene analogues offers a means to identify DNA markers linked to resistance loci. However, sugarcane has the most complex genome of any crop plant and initially it is important to understand the extent of resistance gene analogue diversity in the sugarcane genome before genetic analysis. We review herein how more than 100 expressed sequence tags with homology to different resistance genes have been identified in sugarcane with many mapped as single-dose restriction fragment length polymorphism markers. Importantly, some of these resistance gene analogues have been shown to be linked to disease resistance genes or disease quantitative trait loci. In an attempt to more efficiently analyse additional resistance gene analogues in sugarcane, we report on experiments aimed at investigating the molecular diversity of several resistance gene analogue families using a modified form of a technique termed Ecotilling. Using Ecotilling, we were able to rapidly detect single nucleotide polymorphisms in fragments amplified by PCR from four different resistance gene analogue families, SoRP1D, SoPTO, SoXa21 and SoHs1pro-1. An analysis of a diverse set of sugarcane varieties, including modern sugarcane cultivars and several S. officinarum and S. spontaneum clones, indicated that all amplicons, apart from SoHs1pro-1, contained significant polymorphism within the gene region studied. However, a comparison among these sugarcane clones, including between the parents of two sugarcane mapping populations, indicated that most polymorphisms were multi-dose, not single-dose, preventing their genetic map location or association with disease susceptibility or resistance from being determined.

Empirical Evaluation of the Genetic Similarity of Samples From Twin Registries in Australia and the Netherlands Using 359 STRP Markers

One way to achieve the large sample sizes required for genetic studies of complex traits is to combine samples collected by different groups. It is not often clear, however, whether this practice is reasonable from a genetic perspective. To assess the comparability of samples from the Australian and the Netherlands twin studies, we estimated F,, (the proportion of total genetic variability attributable to genetic differences between cohorts) based on 359 short tandem repeat polymorphisms in 1068 individuals. IF,, was estimated to be 0.30% between the Australian and the Netherlands cohorts, a smaller value than between many European groups. We conclude that it is reasonable to combine the Australian and the Netherlands samples for joint genetic analyses.

Bias, precision and heritability of self-reported and clinically measured height in Australian twins

Many studies of quantitative and disease traits in human genetics rely upon self-reported measures. Such measures are based on questionnaires or interviews and are often cheaper and more readily available than alternatives. However, the precision and potential bias cannot usually be assessed. Here we report a detailed quantitative genetic analysis of stature. We characterise the degree of measurement error by utilising a large sample of Australian twin pairs (857 MZ, 815 DZ) with both clinical and self-reported measures of height. Self-report height measurements are shown to be more variable than clinical measures. This has led to lowered estimates of heritability in many previous studies of stature. In our twin sample the heritability estimate for clinical height exceeded 90%. Repeated measures analysis shows that 2-3 times as many self-report measures are required to recover heritability estimates similar to those obtained from clinical measures. Bivariate genetic repeated measures analysis of self-report and clinical height measures showed an additive genetic correlation > 0.98. We show that the accuracy of self-report height is upwardly biased in older individuals and in individuals of short stature. By comparing clinical and self-report measures we also showed that there was a genetic component to females systematically reporting their height incorrectly; this phenomenon appeared to not be present in males. The results from the measurement error analysis were subsequently used to assess the effects of error on the power to detect linkage in a genome scan. Moderate reduction in error (through the use of accurate clinical or multiple self-report measures) increased the effective sample size by 22%; elimination of measurement error led to increases in effective sample size of 41%.

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.

The dynamics of a genetic algorithm under stabilizing selection

A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest than the test cases they consider. In this paper, the technique is applied to the subset sum problem, which is a combinatorial optimization problem with a strongly non-linear energy (fitness) function and many local minima under single spin flip dynamics. It is a problem which exhibits an interesting dynamics, reminiscent of stabilizing selection in population biology. The dynamics are solved under certain simplifying assumptions and are reduced to a set of difference equations for a small number of relevant quantities. The quantities used are the population's cumulants, which describe its shape, and the mean correlation within the population, which measures the microscopic similarity of population members. Including the mean correlation allows a better description of the population than the cumulants alone would provide and represents a new and important extension of the technique. The formalism includes finite population effects and describes problems of realistic size. The theory is shown to agree closely to simulations of a real genetic algorithm and the mean best energy is accurately predicted.

What determines the molecular composition of abnormal protein aggregates in neurodegenerative disease?

Abnormal protein aggregates, in the form of either extracellular plaques or intracellular inclusions, are an important pathological feature of the majority of neurodegenerative disorders. The major molecular constituents of these lesions, viz., beta-amyloid (Abeta), tau, and alpha-synuclein, have played a defining role in the diagnosis and classification of disease and in studies of pathogenesis. The molecular composition of a protein aggregate, however, is often complex and could be the direct or indirect consequence of a pathogenic gene mutation, be the result of cell degeneration, or reflect the acquisition of new substances by diffusion and molecular binding to existing proteins. This review examines the molecular composition of the major protein aggregates found in the neurodegenerative diseases including the Abeta and prion protein (PrP) plaques found in Alzheimer's disease (AD) and prion disease, respectively, and the cellular inclusions found in the tauopathies and synucleinopathies. The data suggest that the molecular constituents of a protein aggregate do not directly cause cell death but are largely the consequence of cell degeneration or are acquired during the disease process. These findings are discussed in relation to diagnosis and to studies of to disease pathogenesis.

PCB assembly line assignment:a genetic algorithm approach

Purpose – This paper sets out to study a production-planning problem for printed circuit board (PCB) assembly. A PCB assembly company may have a number of assembly lines for production of several product types in large volume. Design/methodology/approach – Pure integer linear programming models are formulated for assigning the product types to assembly lines, which is the line assignment problem, with the objective of minimizing the total production cost. In this approach, unrealistic assignment, which was suffered by previous researchers, is avoided by incorporating several constraints into the model. In this paper, a genetic algorithm is developed to solve the line assignment problem. Findings – The procedure of the genetic algorithm to the problem and a numerical example for illustrating the models are provided. It is also proved that the algorithm is effective and efficient in dealing with the problem. Originality/value – This paper studies the line assignment problem arising in a PCB manufacturing company in which the production volume is high.

A hybrid genetic algorithm for sequencing PCB component placement

A chip shooter machine for electronic components assembly has a movable feeder carrier holding components, a movable X-Y table carrying a printed circuit board (PCB), and a rotary turret having multiple assembly heads. This paper presents a hybrid genetic algorithm to optimize the sequence of component placements for a chip shooter machine. The objective of the problem is to minimize the total traveling distance of the X-Y table or the board. The genetic algorithm developed in the paper hybridizes the nearest neighbor heuristic, and an iterated swap procedure, which is a new improved heuristic. We have compared the performance of the hybrid genetic algorithm with that of the approach proposed by other researchers and have demonstrated our algorithm is superior in terms of the distance traveled by the X-Y table or the board.

A hybrid genetic algorithm for component sequencing and feeder arrangement

This paper presents a hybrid genetic algorithm to optimize the sequence of component placements on a printed circuit board and the arrangement of component types to feeders simultaneously for a pick-and-place machine with multiple stationary feeders, a fixed board table and a movable placement head. The objective of the problem is to minimize the total travelling distance, or the travelling time, of the placement head. The genetic algorithm developed in the paper hybrisizes different search heuristics including the nearest neighbor heuristic, the 2-opt heuristic, and an iterated swap procedure, which is a new improving heuristic. Compared with the results obtained by other researchers, the performance of the hybrid genetic algorithm is superior to others in terms of the distance travelled by the placement head.