Differential activation of the thoracic multifidus and longissimus thoracis during trunk rotation

Study Design. Cross-sectional study. Objective. To develop a technique to measure electromyographic (EMG) activity of deep and superficial paraspinal muscles at different thoracic levels and to investigate activity of these muscles during seated trunk rotation. Summary of Background Data. Few studies have compared activity of deep and superficial paraspinal muscles of the thorax during trunk rotation, and conflicting results have been presented. Conflicting data may result from recording techniques or variation in activity between thoracic regions. Methods. EMG recordings were made from deep (multifidus/ rotatores) and superficial ( longissimus) paraspinal muscles at T5, T8, and T11 using selective intramuscular electrodes. Ten subjects rotated the trunk to end of range in each direction. EMG amplitude was measured in neutral, at end of range, and during four epochs, which represented four quarters of the movement. Results. During trunk rotation in sitting, longissimus EMG either increased with ipsilateral rotation ( T5) or decreased with contralateral rotation ( T5, T8, T11). In contrast, multifidus EMG was more variable and was either active with rotation in both directions ( particularly T5) or with one movement direction. Conclusions. The deep and superficial muscles of the thorax are differentially active, and the patterns of activity differ between the regions of the thorax. Data from this study support the hypothesis that multifidus may have a role in control of segmental motion at T5. Variability in multifidus activity at T8 and T11 suggests that this muscle may also control coupling between rotation and lateral flexion.

Optimising profitability and environmental trade-offs in managing cropping systems using differential evolution

Whilst traditional optimisation techniques based on mathematical programming techniques are in common use, they suffer from their inability to explore the complexity of decision problems addressed using agricultural system models. In these models, the full decision space is usually very large while the solution space is characterized by many local optima. Methods to search such large decision spaces rely on effective sampling of the problem domain. Nevertheless, problem reduction based on insight into agronomic relations and farming practice is necessary to safeguard computational feasibility. Here, we present a global search approach based on an Evolutionary Algorithm (EA). We introduce a multi-objective evaluation technique within this EA framework, linking the optimisation procedure to the APSIM cropping systems model. The approach addresses the issue of system management when faced with a trade-off between economic and ecological consequences.