No abstract

Rabbit muscle and its constituent proteins were investigated by differential scanning calorimetry (d.s.c.). Post-rigor muscle yielded a complex thermogram comprising at least three endothermic transitions with Tmax values of 60, 67 and 80°C. Comparison with the purified proteins or fractions indicated that these transitions corresponded to denaturation of myosin, sarcoplasmic proteins and actin respectively. In addition to these endothermic transitions, pre-rigor muscle produced a single large exotherm of T,,, 54°C. The evidence suggested that this transition was closely linked with the process of contraction.

The authors draw attention to the following eri'ors. f o r which we apologiw: Page 361: Section 2.5. The first line should read: The nitrogen content of samples in d.s.c. siimple pans W H ~ determined wing ii Carlo Erba Model Page 368, second paragraph. This should read: Similarly, Hagerdal and Martens" reported ii heating rate dependcnce of T,,, and A H for myoglobin denaturation. In the current study. rhe supcriniposition of a concentration effect upon the heating rate dependency of the measured denaturation enthalpy of myosin would suggest the

No abstract

Price declines and volume growth of concentrated photovoltaic (CPV) systems are analysed using the learning curve methodology and compared with other forms of solar electricity generation. Logarithmic regression analysis determines a learning rate of 18% for CPV systems with 90% confidence of that rate being between 14 and 22%, which is higher than the learning rates of other solar generation systems (11% for CSP and 12 to 14% for PV). Current CPV system prices are competitive with PV and CSP, which, when combined with the higher learning rate, indicates that CPV is likely to further improve its marketability. A target price of 1 $/W in 2020 could be achieved with a compound growth rate of 67% for the total deployed volume between 2014 and 2020, which would realize a cumulative deployed volume of 7900 MW. Other projections of deployment volumes from commercial sources are converted using the learning rate into future price scenarios, resulting in predicted prices in the range of 1.1 to 1.3 $/W in 2020.