Running behavior and its energy cost in mice selectively bred for high voluntary locomotor activity

Locomotion is central to behavior and intrinsic to many fitnesscritical activities (e.g., migration, foraging), and it competes with other life-history components for energy. However, detailed analyses of how changes in locomotor activity and running behavior affect energy budgets are scarce. We quantified these effects in four replicate lines of house mice that have been selectively bred for high voluntary wheel running (S lines) and in their four nonselected control lines (C lines). We monitored wheel speeds and oxygen consumption for 24-48 h to determine daily energy expenditure (DEE), resting metabolic rate (RMR), locomotor costs, and running behavior (bout characteristics). Daily running distances increased roughly 50%-90% in S lines in response to selection. After we controlled for body mass effects, selection resulted in a 23% increase in DEE in males and a 6% increase in females. Total activity costs (DEE - RMR) accounted for 50%-60% of DEE in both S and C lines and were 29% higher in S males and 5% higher in S females compared with their C counterparts. Energetic costs of increased daily running distances differed between sexes because S females evolved higher running distances by running faster with little change in time spent running, while S males also spent 40% more time running than C males. This increase in time spent running impinged on high energy costs because the majority of running costs stemmed from postural costs (the difference between RMR and the zero-speed intercept of the speed vs. metabolic rate relationship). No statistical differences in these traits were detected between S and C females, suggesting that large changes in locomotor behavior do not necessarily effect overall energy budgets. Running behavior also differed between sexes: within S lines, males ran with more but shorter bouts than females. Our results indicate that selection effects on energy budgets can differ dramatically between sexes and that energetic constraints in S males might partly explain the apparent selection limit for wheel running observed for over 15 generations. © 2009 by The University of Chicago. All rights reserved.

Energy recovery in individuals with knee osteoarthritis.

OBJECTIVE: Pathological gaits have been shown to limit transfer between potential (PE) and kinetic (KE) energy during walking, which can increase locomotor costs. The purpose of this study was to examine whether energy exchange would be limited in people with knee osteoarthritis (OA). METHODS: Ground reaction forces during walking were collected from 93 subjects with symptomatic knee OA (self-selected and fast speeds) and 13 healthy controls (self-selected speed) and used to calculate their center of mass (COM) movements, PE and KE relationships, and energy recovery during a stride. Correlations and linear regressions examined the impact of energy fluctuation phase and amplitude, walking velocity, body mass, self-reported pain, and radiographic severity on recovery. Paired t-tests were run to compare energy recovery between cohorts. RESULTS: Symptomatic knee OA subjects displayed lower energetic recovery during self-selected walking speeds than healthy controls (P = 0.0018). PE and KE phase relationships explained the majority (66%) of variance in recovery. Recovery had a complex relationship with velocity and its change across speeds was significantly influenced by the self-selected walking speed of each subject. Neither radiographic OA scores nor subject self-reported measures demonstrated any relationship with energy recovery. CONCLUSIONS: Knee OA reduces effective exchange of PE and KE, potentially increasing the muscular work required to control movements of the COM. Gait retraining may return subjects to more normal patterns of energy exchange and allow them to reduce fatigue.

Predicting the metabolic energy costs of bipedalism using evolutionary robotics

To understand the evolution of bipedalism among the homnoids in an ecological context we need to be able to estimate theenerrgetic cost of locomotion in fossil forms. Ideally such an estimate would be based entirely on morphology since, except for the rare instances where footprints are preserved, this is hte only primary source of evidence available. In this paper we use evolutionary robotics techniques (genetic algoritms, pattern generators and mechanical modeling) to produce a biomimentic simulation of bipedalism based on human body dimensions. The mechnaical simulation is a seven-segment, two-dimensional model with motive force provided by tension generators representing the major muscle groups acting around the lower-limb joints. Metabolic energy costs are calculated from the muscel model, and bipedal gait is generated using a finite-state pattern generator whose parameters are produced using a genetic algorithm with locomotor economy (maximum distance for a fixed energy cost) as the fitness criterion. The model is validated by comparing the values it generates with those for modern humans. The result (maximum efficiency of 200 J m-1) is within 15% of the experimentally derived value, which is very encouraging and suggests that this is a useful analytic technique for investigating the locomotor behaviour of fossil forms. Initial work suggests that in the future this technique could be used to estimate other locomotor parameters such as top speed. In addition, the animations produced by this technique are qualitatively very convincing, which suggests that this may also be a useful technique for visualizing bipedal locomotion.

Exposure to pesticides, ill health and averting behaviour : costs and determining the relationship

Farmers' exposure to pesticides is high in developing countries. As a result many farmers suffer from ill-health, both short and long term. Deaths are not uncommon. This paper addresses this issue. Field survey data from Sri Lanka are used to estimate farmers' expenditure on defensive behavior (DE) and to determine factors that influence DE. The avertive behavior approach is used to estimate costs. Tobit regression analysis is used to determine factors that influence DE. Field survey data show that farmers' expenditures on DE are low. This is inversely related to high incidence of ill health among farmers using pesticides.

The impact of planning delays and other holding costs on housing affordability

Housing affordability is gaining increasing prominence in the Australian socioeconomic landscape, despite strong economic growth and prosperity. It is a major consideration for any new development. However, it is multi-dimensional, has many facets, is complex and interwoven. One factor widely held to impact housing affordability is holding costs. Although it is only one contributor, the nature and extent of its impact requires clarification. It is certainly more multifarious than simple calculation of the interest or opportunity cost of land holding. For example, preliminary analysis suggests that even small shifts in the regulatory assessment period can significantly affect housing affordability. Other costs associated with “holding” also impact housing affordability, however these costs cannot always be easily identified. Nevertheless it can be said that ultimately the real impact is felt by those whom can least afford it - new home buyers whom can be relatively easily pushed into the realms of un-affordability.

Final : report assessing risk and variation in maintenance and rehabilitation costs for road network

This report presents the results of research projects conducted by The Australian Cooperative Research Centre for Construction Innovation, Queensland University of Technology, RMIT University, Queensland Government Department of Main Roads and Queensland Department of Public Works. The research projects aimed at developing a methodology for assessing variation and risk in investment in road network, including the application of the method in assessing road network performance and maintenance and rehabilitation costs for short- and long-term future investment.

Report : review of the literature : maintenance and rehabilitation costs for roads (Risk-based Analysis)

Realistic estimates of short- and long-term (strategic) budgets for maintenance and rehabilitation of road assessment management should consider the stochastic characteristics of asset conditions of the road networks so that the overall variability of road asset data conditions is taken into account. The probability theory has been used for assessing life-cycle costs for bridge infrastructures by Kong and Frangopol (2003), Zayed et.al. (2002), Kong and Frangopol (2003), Liu and Frangopol (2004), Noortwijk and Frangopol (2004), Novick (1993). Salem 2003 cited the importance of the collection and analysis of existing data on total costs for all life-cycle phases of existing infrastructure, including bridges, road etc., and the use of realistic methods for calculating the probable useful life of these infrastructures (Salem et. al. 2003). Zayed et. al. (2002) reported conflicting results in life-cycle cost analysis using deterministic and stochastic methods. Frangopol et. al. 2001 suggested that additional research was required to develop better life-cycle models and tools to quantify risks, and benefits associated with infrastructures. It is evident from the review of the literature that there is very limited information on the methodology that uses the stochastic characteristics of asset condition data for assessing budgets/costs for road maintenance and rehabilitation (Abaza 2002, Salem et. al. 2003, Zhao, et. al. 2004). Due to this limited information in the research literature, this report will describe and summarise the methodologies presented by each publication and also suggest a methodology for the current research project funded under the Cooperative Research Centre for Construction Innovation CRC CI project no 2003-029-C.

Report : development of a methodology for assessing variation in maintenance and rehabilitation costs

In the previous research CRC CI 2001-010-C “Investment Decision Framework for Infrastructure Asset Management”, a method for assessing variation in cost estimates for road maintenance and rehabilitation was developed. The variability of pavement strength collected from a 92km national highway was used in the analysis to demonstrate the concept. Further analysis was conducted to identify critical input parameters that significantly affect the prediction of road deterioration. In addition to pavement strength, rut depth, annual traffic loading and initial roughness were found to be critical input parameters for road deterioration. This report presents a method developed to incorporate other critical parameters in the analysis, such as unit costs, which are suspected to contribute to a certain degree to cost estimate variation. Thus, the variability of unit costs will be incorporated in this analysis. Bruce Highway located in the tropical east coast of Queensland has been identified to be the network for the analysis. This report presents a step by step methodology for assessing variation in road maintenance and rehabilitation cost estimates.