Genetic variation and exercise-induced muscle damage: implications for athletic performance, injury and ageing.

Prolonged unaccustomed exercise involving muscle lengthening (eccentric) actions can result in ultrastructural muscle disruption, impaired excitation-contraction coupling, inflammation and muscle protein degradation. This process is associated with delayed onset muscle soreness and is referred to as exercise-induced muscle damage. Although a certain amount of muscle damage may be necessary for adaptation to occur, excessive damage or inadequate recovery from exercise-induced muscle damage can increase injury risk, particularly in older individuals, who experience more damage and require longer to recover from muscle damaging exercise than younger adults. Furthermore, it is apparent that inter-individual variation exists in the response to exercise-induced muscle damage, and there is evidence that genetic variability may play a key role. Although this area of research is in its infancy, certain gene variations, or polymorphisms have been associated with exercise-induced muscle damage (i.e. individuals with certain genotypes experience greater muscle damage, and require longer recovery, following strenuous exercise). These polymorphisms include ACTN3 (R577X, rs1815739), TNF (-308 G>A, rs1800629), IL6 (-174 G>C, rs1800795), and IGF2 (ApaI, 17200 G>A, rs680). Knowing how someone is likely to respond to a particular type of exercise could help coaches/practitioners individualise the exercise training of their athletes/patients, thus maximising recovery and adaptation, while reducing overload-associated injury risk. The purpose of this review is to provide a critical analysis of the literature concerning gene polymorphisms associated with exercise-induced muscle damage, both in young and older individuals, and to highlight the potential mechanisms underpinning these associations, thus providing a better understanding of exercise-induced muscle damage

Rational Design of Protein Stability: Effect of (2S,4R)-4-Fluoroproline on the Stability and Folding Pathway of Ubiquitin

BACKGROUND: Many strategies have been employed to increase the conformational stability of proteins. The use of 4-substituted proline analogs capable to induce pre-organization in target proteins is an attractive tool to deliver an additional conformational stability without perturbing the overall protein structure. Both, peptides and proteins containing 4-fluorinated proline derivatives can be stabilized by forcing the pyrrolidine ring in its favored puckering conformation. The fluorinated pyrrolidine rings of proline can preferably stabilize either a C(γ)-exo or a C(γ)-endo ring pucker in dependence of proline chirality (4R/4S) in a complex protein structure. To examine whether this rational strategy can be generally used for protein stabilization, we have chosen human ubiquitin as a model protein which contains three proline residues displaying C(γ)-exo puckering. METHODOLOGY/PRINCIPAL FINDINGS: While (2S,4R)-4-fluoroproline ((4R)-FPro) containing ubiquitinin can be expressed in related auxotrophic Escherichia coli strain, all attempts to incorporate (2S,4S)-4-fluoroproline ((4S)-FPro) failed. Our results indicate that (4R)-FPro is favoring the C(γ)-exo conformation present in the wild type structure and stabilizes the protein structure due to a pre-organization effect. This was confirmed by thermal and guanidinium chloride-induced denaturation profile analyses, where we observed an increase in stability of -4.71 kJ·mol(-1) in the case of (4R)-FPro containing ubiquitin ((4R)-FPro-ub) compared to wild type ubiquitin (wt-ub). Expectedly, activity assays revealed that (4R)-FPro-ub retained the full biological activity compared to wt-ub. CONCLUSIONS/SIGNIFICANCE: The results fully confirm the general applicability of incorporating fluoroproline derivatives for improving protein stability. In general, a rational design strategy that enforces the natural occurring proline puckering conformation can be used to stabilize the desired target protein

Meta-Analysis of the Reasoned Action Approach (RAA) to Understanding Health Behaviors

YesBackground: Reasoned action approach (RAA) includes subcomponents of attitude (experiential/instrumental), perceived norm (injunctive/descriptive), and perceived behavioral control (capacity/autonomy) to predict intention and behavior. Purpose: To provide a meta-analysis of the RAA for health behaviors focusing on comparing the pairs of RAA subcomponents and differences between health protection and health-risk behaviors. Methods: The present research reports a meta-analysis of correlational tests of RAA subcomponents, examination of moderators, and combined effects of subcomponents on intention and behavior. Regressions were used to predict intention and behavior based on data from studies measuring all variables. Results: Capacity and experiential attitude had large, and other constructs had small-medium-sized correlations with intention; all constructs except autonomy were significant independent predictors of intention in regressions. Intention, capacity, and experiential attitude had medium-large, and other constructs had small-medium-sized correlations with behavior; intention, capacity, experiential attitude, and descriptive norm were significant independent predictors of behavior in regressions. Conclusions: The RAA subcomponents have utility in predicting and understanding health behaviors

Application of the transmission line method to the solution of the continuous Kalman filter equations of general order

In this paper, the transmission line method (TLM) is applied to the solution of the general, nth order, continuous Kalman filter estimation equations. A comparison is made between this method, the first-order Gear algorithm and the fourth-order Runge-Kutta method, in the estimation of the voltage drop and its derivative across a capacitor in an LCR circuit. An analysis is made of the sensitivity of the algorithms to changing time step and measurement error variance. In most cases, the Runge- Kutta method has the best performance at the expense of computing time. However, in some cases, the new algorithm yields less biased and smoother estimates. The TLM algorithm performs consistently better than the Gear method for the particular problem analysed. The CPU time for the TLM algorithm has also been compared with that required by the Gear and Runge-Kutta methods; the TLM method is found to take approximately 25% of the time required by the Runge-Kutta method to process one measurement. The TLM algorithm appears to present a compromise between accuracy of estimation and computing time. Finally, suggestions are made for further work.link_to_subscribed_fulltex

Solution of the continuous Kalman filter equations using the transmission line method

A novel method is presented for the solution of the one-dimensional continuous Kalman filter equations implemented on a digital computer. The Kalman filter is implemented in an unusual way as a transmission line circuit. Estimated variables are regarded as discrete pulses bouncing to and from the nodes in the transmission line network at each time step. This approach is tested on a problem of voltage estimation in a passive electrical network in the presence of voltmeter errors. The transmission line method is compared with the results that are obtained using the Gear first- and third-order methods, the fourth-order Runge-Kutta method and the application of the discrete Kalman filter. It is found that the transmission line method has a tendency to yield less biased estimates than the other algorithms as well as providing smoother estimates for sampling times approaching the time constant of the circuit.link_to_subscribed_fulltex

Feature selective validation (FSV) for validation of computational electromagnetics (CEM). Part I - The FSV method

A goal for the validation of computational electromagnetics (CEM) is to provide the community with a simple computational method that can be used to predict the assessment of electromagnetic compatibility (EMC) data as it would be undertaken by individuals or teams of engineers. The benefits of being able to do this include quantifying the comparison of data that has hitherto only been assessed qualitatively, to provide the ability to track differences between model iterations, and to provide a means of capturing the variability and range of opinions of groups and teams of workers. The feature selective validation (FSV) technique shows great promise for achieving this goal. This paper presents a detailed analysis of the FSV method, setting it firmly in the context of previous comparison techniques; it suggests the relationship between validation of graphically presented data and the psychology of visual perception. A set of applicability tests to judge the effectiveness of computer-based CEM validation techniques is also proposed. This paper is followed by a detailed comparison with visual assessment, which is presented in Part II.A goal for the validation of computational electromagnetics (CEM) is to provide the community with a simple computational method that can be used to predict the assessment of electromagnetic compatibility (EMC) data as it would be undertaken by individuals or teams of engineers. The benefits of being able to do this include quantifying the comparison of data that has hitherto only been assessed qualitatively, to provide the ability to track differences between model iterations, and to provide a means of capturing the variability and range of opinions of groups and teams of workers. The feature selective validation (FSV) technique shows great promise for achieving this goal. This paper presents a detailed analysis of the FSV method, setting it firmly in the context of previous comparison techniques; it suggests the relationship between validation of graphically presented data and the psychology of visual perception. A set of applicability tests to judge the effectiveness of computer-based CEM validation techniques is also proposed. This paper is followed by a detailed comparison with visual assessment, which is presented in Part II. © 2006 IEEE