Dynamic magnetic resonance spectroscopy of phosphate energetics during muscle exercise and recovery

Entailing of phosphorus exchanges in most bio-chemicals as a key factor in disease, increases researcher’s interest to develop the technologies capable of detecting this metabolite. Phosphorus magnetic resonance spectroscopy is able to detect key metabolites in a non-invasive manner. Particularly, it offers the ability to measure the dynamic rate of phosphocreatine(PCr) degeneration through the exercise and recovery. This metric as a valid indication of mitochondrial oxidative metabolism in muscle, differentiate between normal and pathological state. To do magnetic resonance imaging and spectroscopy, clinical research tools provide a wide variety of anatomical and functional contrasts, however they are typically restricted to the tissues containing water or hydrogen atoms and they are still blind to the biochemicals of other atoms of interests. Through this project we intended to obtain the phosphorus spectrum in human body – specificadenerativelly in muscle – using 31P spectroscopy. To do so a double loop RF surface coil, tuned to phosphorus frequency, is designed and fabricated using bench work facilities and then validated through in vitro spectroscopy using 3 Tesla Siemens scanner. We acquired in vitro as well as in vivo phosphorus spectrum in a 100 mM potassium phosphate phantom and human calf muscle in rest-exercise-recovery phase in a 3T MR scanner. The spectrum demonstrates the main constituent in high-energy phosphate metabolism. We also observed the dynamic variation of PCr for five young healthy subjects who performed planter flexions using resistance band during exercise and recovery. The took steps in this project pave the way for future application of spectroscopic quantification of phosphate metabolism in patients affected by carotid artery disease as well as in age-matched control subjects.

A Coupled 2 × 2D Babcock–Leighton Solar Dynamo Model. I. Surface Magnetic Flux Evolution

The need for reliable predictions of the solar activity cycle motivates the development of dynamo models incorporating a representation of surface processes sufficiently detailed to allow assimilation of magnetographic data. In this series of papers we present one such dynamo model, and document its behavior and properties. This first paper focuses on one of the model's key components, namely surface magnetic flux evolution. Using a genetic algorithm, we obtain best-fit parameters of the transport model by least-squares minimization of the differences between the associated synthetic synoptic magnetogram and real magnetographic data for activity cycle 21. Our fitting procedure also returns Monte Carlo-like error estimates. We show that the range of acceptable surface meridional flow profiles is in good agreement with Doppler measurements, even though the latter are not used in the fitting process. Using a synthetic database of bipolar magnetic region (BMR) emergences reproducing the statistical properties of observed emergences, we also ascertain the sensitivity of global cycle properties, such as the strength of the dipole moment and timing of polarity reversal, to distinct realizations of BMR emergence, and on this basis argue that this stochasticity represents a primary source of uncertainty for predicting solar cycle characteristics.