Magnetic resonance imaging and electromyography as indexes of muscle function.

Electromyography (EMG) is commonly used to determine the electrical activity of skeletal muscle during contraction. To date, independent verification of the relationship between muscle use and EMG has not been provided. It has recently been shown that relaxation- (e.g., T2) weighted magnetic resonance images (MRI) of skeletal muscle demonstrate exercise-induced contrast enhancement that is graded with exercise intensity. This study was conducted to test the hypothesis that exercise-induced magnetic resonance (MR) contrast shifts would relate to EMG amplitude if both measures reflect muscle use during exercise. Both MRI and EMG data were collected for separate eccentric (ECC) and concentric (CON) exercise of increasing intensity to take advantage of the fact that the rate of increase and amplitude of EMG activity are markedly greater for CON muscle actions. Seven subjects 30 +/- 2 (SE) yr old performed five sets of 10 CON or ECC arm curls with each of four resistances representing 40, 60, 80, and 100% of their 10 repetition maximum for CON curls. There was 1.5 min between sets and 30 min between bouts (5 sets of 10 actions at each relative resistance). Multiple echo, transaxial T2-weighted MR images (1.5 T, TR/TE 2,000/30) were collected from a 7-cm region in the middle of the arm before exercise and immediately after each bout. Surface EMG signals were collected from both heads of the biceps brachii and the long head of the triceps brachii muscles. CON and ECC actions resulted in increased integrated EMG (IEMG) and T2 values that were strongly related (r = 0.99, P < 0.05) with relative resistance.(ABSTRACT TRUNCATED AT 250 WORDS)     

Influenza A Gradual and Epochal Evolution: Insights from Simple Models

The recurrence of influenza A epidemics has originally been explained by a “continuous antigenic drift” scenario. Recently, it has been shown that if genetic drift is gradual, the evolution of influenza A main antigen, the haemagglutinin, is punctuated. As a consequence, it has been suggested that influenza A dynamics at the population level should be approximated by a serial model. Here, simple models are used to test whether a serial model requires gradual antigenic drift within groups of strains with the same antigenic properties (antigenic clusters). We compare the effect of status based and history based frameworks and the influence of reduced susceptibility and infectivity assumptions on the transient dynamics of antigenic clusters. Our results reveal that the replacement of a resident antigenic cluster by a mutant cluster, as observed in data, is reproduced only by the status based model integrating the reduced infectivity assumption. This combination of assumptions is useful to overcome the otherwise extremely high model dimensionality of models incorporating many strains, but relies on a biological hypothesis not obviously satisfied. Our findings finally suggest the dynamical importance of gradual antigenic drift even in the presence of punctuated immune escape. A more regular renewal of susceptible pool than the one implemented in a serial model should be part of a minimal theory for influenza at the population level.     

Functional rarefaction: estimating functional diversity from field data

Studies in biodiversity-ecosystem function and conservation biology have led to the development of diversity indices that take species' functional differences into account. We identify two broad classes of indices: those that monotonically increase with species richness (MSR indices) and those that weight the contribution of each species by abundance or occurrence (weighted indices). We argue that weighted indices are easier to estimate without bias but tend to ignore information provided by rare species. Conversely, MSR indices fully incorporate information provided by rare species but are nearly always underestimated when communities are not exhaustively surveyed. This is because of the well-studied fact that additional sampling of a community may reveal previously undiscovered species. We use the rarefaction technique from species richness studies to address sample-size-induced bias when estimating functional diversity indices. Rarefaction transforms any given MSR index into a family of unbiased weighted indices, each with a different level of sensitivity to rare species. Thus rarefaction simultaneously solves the problem of bias and the problem of sensitivity to rare species. We present formulae and algorithms for conducting a functional rarefaction analysis of the two most widely cited MSR indices: functional attribute diversity (FAD) and Petchey and Gaston's functional diversity (FD). These formulae also demonstrate a relationship between three seemingly unrelated functional diversity indices: FAD, FD and Rao's quadratic entropy. Statistical theory is also provided in order to prove that all desirable statistical properties of species richness rarefaction are preserved for functional rarefaction.