Covariation in life-history traits : differential effects of diet on condition, hormones, behavior, and reproduction in genetic finch morphs

The relative contribution of genetic and environmental factors in determining variation in life-history traits is of central interest to evolutionary biologists, but the physiological mechanisms underlying these traits are still poorly understood. Here we experimentally demonstrate opposing effects of nutritional stress on immune function, endocrine physiology, parental care, and reproduction between red and black head-color morphs of the Gouldian finch (Erythrura gouldiae). Although the body condition of black morphs was largely unaffected by diet manipulation, red birds were highly sensitive to dietary changes, exhibiting considerable within-individual changes in condition and immune function. Consequently, nutritionally stressed red birds delayed breeding, produced smaller broods, and reared fewer and lower-quality foster offspring than black morphs. Differences in offspring quality were largely due to morph-specific differences in parental effort: red morphs reduced parental provisioning, whereas black morphs adaptively elevated their provisioning effort to meet the increased nutritional demands of their foster brood. Nutritionally stressed genetic morphs also exhibited divergent glucocorticoid responses. Black morphs showed reduced corticosterone-binding globulin (CBG) concentrations and increased levels of free corticosterone, whereas red morphs exhibited reduced free corticosterone levels and elevated CBG concentrations. These opposing glucocorticoid responses highlight intrinsic differences in endocrine sensitivities and plasticity between genetic morphs, which may underlie the morph-specific differences in condition, behavior, and reproduction and thus ultimately contribute to the evolution and maintenance of color polymorphism.

Minimal similarity in songs suggests limited exchange between humpback whales (Megaptera novaeangliae) in the southern Indian ocean

Comparing humpback whale song from different breeding assemblages can reveal similarities in song due to acoustically interacting males, and therefore indirectly test whether males from different breeding sites are mixing. Northern Hemisphere song comparisons illustrated that whales within ocean basins share similar songs and are subpopulations within a larger population, whereas whales in different ocean basins are isolated populations and therefore do not share songs. During the 2006 breeding season, recordings were collected in Madagascar and Western Australia, and were compared visually plus aurally. Both regions shared one theme, whereas each region had four and six private themes, respectively. This study had a substantially low number of shared themes. The co-occurrence of one theme was interpreted as an indication of limited exchange between these breeding assemblages, and we speculate that limited song similarity is due to inter-oceanic interactions. Male(s) from an Indian Ocean breeding group could be exposed to novel song when they geographically overlap, and acoustically interact, with males from a different ocean basin. Novel song could induce rapid temporal changes as new song content is incorporated, thereby minimizing song similarities between that breeding group and other Indian Ocean breeding groups that were not exposed to the novel song.

Reasons for referral to bone densitometry in men and women aged 20-49 years : population-based data

Introduction Osteoporosis poses a significant public health problem for ageing Australians. However, approximately 25 % of Australian adults aged 20–49 years have osteopenia, a precursor condition to osteoporosis. Despite this, little is known about bone density testing in this age group.

Methods Reasons for referral to dual energy X-ray absorptiometry (DXA) were examined in 2,264 patients aged 20–49 years, referred in 2001–2010 to the Geelong Bone Densitometry Service, Geelong Hospital, Victoria. Referral reasons were determined from clinical indication codes derived from patient records. Age, sex and bone mineral density (BMD) T scores were ascertained for each patient.

Results The most common reason for referral for women reflected glucocorticoid use, and for men reflected fracture. Compared to women, men were more likely to have been referred because of minimal trauma fracture or low BMD (41.7 versus 27.1 %, p < 0.001). No further differences were identified between the sexes, with similar numbers of referral observed for secondary osteoporosis, and monitoring of drug therapy. At the spine, and for all indications, men had a significantly greater BMD deficit compared to women (all p ≤ 0.002). After age adjustment, men who were tested due to fracture or glucocorticoid reasons had significantly greater BMD at the total hip (p ≤ 0.03). No further associations were seen after age adjustment between referral reason and BMD.

Conclusions Our study presents the first data examining reasons for referral to DXA among Australians aged 20–49 years. Understanding health service utilisation regarding bone health in young adults is fundamental to understanding future risk, informing effective public health messages and raising awareness of osteoporosis.

Hemoglobin genotype has minimal influence on the physiological response of juvenile atlantic cod (Gadus morhua) to environmental challenges

Hemoglobin (Hb) polymorphism in cod is associated with temperature‐related differences in biogeographical distribution, and several authors have suggested that functional characteristics of the various hemoglobin isoforms (HbIs) directly influence phenotypic traits such as growth rate. However, no study has directly examined whether Hb genotype translates into physiological differences at the whole animal level. Thus, we generated a family of juvenile Atlantic cod consisting of all three main Hb genotypes (HbI‐1/1, HbI‐2/2, and HbI‐1/2) by crossing a single pair of heterozygous parents, and we compared their metabolic and cortisol responses to an acute thermal challenge (10°C to their critical thermal maximum [CTM] or 22°C, respectively) and tolerance of graded hypoxia. There were no differences in routine metabolism (at 10°C), maximum metabolic rate, metabolic scope, CTM (overall mean 22.9° ± 0.2°C), or resting and poststress plasma cortisol levels among Hb genotypes. Further, although the HbI‐1/1 fish grew more (by 15%–30% during the first 9 mo) when reared at 10° ± 1°C and had a slightly enhanced hypoxia tolerance at 10°C (e.g., the critical O2 levels for HbI‐1/1, HbI‐2/2, and HbI‐1/2 cod were 35.56% ± 1.24%, and 40.20% ± 1.99% air saturation, respectively), these results are contradictory to expectations based on HbI functional properties. Thus, our findings (1) do not support previous assumptions that growth rate differences among cod Hb genotypes result from a more efficient use of the oxygen supply—that is, reduced standard metabolic rates and/or increased metabolic capacity—and (2) suggest that in juvenile cod, there is no selective advantage to having a particular Hb genotype with regards to the capacity to withstand ecologically relevant environmental challenges.

A mutation-based evolving neural network model and its application to condition monitoring

Data analysis using intelligent systems is a key solution to many industrial problems. In this paper, a mutation-based evolving artificial neural network, which is based on an integration of the Fuzzy ARTMAP (FAM) neural network and evolutionary programming (EP), is proposed. The proposed FAMEP model is applied to detect and classify possible faults from a number of sensory signals of a circulating water system in a power generation plant. The efficiency of FAM-EP is assessed and compared with that of the original FAM network in terms of classification accuracy as well as network complexity. In addition, the bootstrap method is used to quantify the performance statistically. The results positively demonstrate the usefulness of FAM-EP in tackling data classification problems.

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

Stochastic models of road geometry and wind condition for vehicle energy management and control

Modeling and simulation is commonly used to improve vehicle performance, to optimize vehicle system design, and to reduce vehicle development time. Vehicle performances can be affected by environmental conditions and driver behavior factors, which are often uncertain and immeasurable. To incorporate the role of environmental conditions in the modeling and simulation of vehicle systems, both real and artificial data are used. Often, real data are unavailable or inadequate for extensive investigations. Hence, it is important to be able to construct artificial environmental data whose characteristics resemble those of the real data for modeling and simulation purposes. However, to produce credible vehicle simulation results, the simulated environment must be realistic and validated using accepted practices. This paper proposes a stochastic model that is capable of creating artificial environmental factors such as road geometry and wind conditions. In addition, road geometric design principles are employed to modify the created road data, making it consistent with the real-road geometry. Two sets of real-road geometry and wind condition data are employed to propose probability models. To justify the distribution goodness of fit, Pearson's chi-square and correlation statistics have been used. Finally, the stochastic models of road geometry and wind conditions (SMRWs) are developed to produce realistic road and wind data. SMRW can be used to predict vehicle performance, energy management, and control strategies over multiple driving cycles and to assist in developing fuel-efficient vehicles.

Little evidence that condition, stress indicators, sex ratio, or homozygosity are related to landscape or habitat attributes in declining woodland birds

Habitat loss, fragmentation and degradation are drivers of major declines in biodiversity and species extinctions. The actual causes of species population declines following habitat change are more difficult to discern and there is typically high covariation among the measures used to infer the causes of decline. The causes of decline may act directly on individual fitness and survival, or through disruption of population processes. We examined the relationships among configuration, extent and status of native vegetation and three commonly used indicators of individual body condition and chronic stress (haemoglobin level, haematocrit, residual body mass condition index) in 13 species of woodland-dependent birds in south-eastern Australia. We also examined two measures of changes to population processes (sex ratio and individual homozygosity) in ten species and alleic richness in five species. We found little support for relationships between site or landscape characteristics and individual or population response variables, notwithstanding that our simulations showed we had sufficient power to detect relatively small effects. We discuss possible causes of the absence of detectable habitat effects in this system and the implications for the usefulness of individual body condition and easily measured haematological indices as indicators of the response of avian populations to habitat change. © 2012 The Authors.

Reliable knowledge discovery with a minimal causal model inducer

This paper presents a Minimal Causal Model Inducer that can be used for the reliable knowledge discovery. The minimal-model semantics of causal discovery is an essential concept for the identification of a best fitting model in the sense of satisfactory consistent with the given data and be the simpler, less expressive model. Consistency is one of major measures of reliability in knowledge discovery. Therefore to develop an algorithm being able to derive a minimal model is an interesting topic in the are of reliable knowledge discovery. various causal induction algorithms and tools developed so far can not guarantee that the derived model is minimal and consistent. It was proved the MML induction approach introduced by Wallace, Keven and Honghua Dai is a minimal causal model learner. In this paper, we further prove that the developed minimal causal model learner is reliable in the sense of satisfactory consistency. The experimental results obtained from the tests on a number of both artificial and real models provided in this paper confirm this theoretical result.