To determine the effects of strength training (ST) on bone mineral density (BMD) and bone remodeling, 18 previously inactive untrained males [mean age 59 +/- 2 (SE) yr] were studied before and after 16 wk of either ST (n = 11) or no exercise (inactive controls; n = 7). Total, spinal (L2-L4), and femoral neck BMD were measured in nine training and seven control subjects before and after the experimental period. Serum concentrations of osteocalcin, skeletal alkaline phosphatase isoenzyme, and tartrate-resistant acid phosphatase were measured before, during, and after the experimental program in all subjects. Training increased muscular strength by an average of 45 +/- 3% (P < 0.001) on a three-repetition maximum test and by 32 +/- 4% (P < 0.001) on an isokinetic test of the knee extensors performed at 60 degrees/s. BMD increased in the femoral neck by 3.8 +/- 1.0% (0.900 +/- 0.05 vs. 0.933 +/- 0.05 g/cm2, P < 0.05) and in the lumbar spine by 2.0 +/- 0.9% (1.180 +/- 0.06 vs. 1.203 +/- 0.06 g/cm2, P < 0.05). However, changes in lumbar spine BMD were not significantly different from those in the control group. There was no significant change in total body BMD. Osteocalcin increased by 19 +/- 6% after 12 wk of training (P < 0.05) and remained significantly elevated after 16 wk of training (P < 0.05). There was a 26 +/- 11% increase in skeletal alkaline phosphatase isoenzyme levels (P < 0.05) after 16 wk of training.(ABSTRACT TRUNCATED AT 250 WORDS)
We have developed a portable microprocessor-bared device which samples and stores the heart and stride rate signals. While running, the user can receive instantaneous voice feedback of his heart rate and stride rate. The device can be connected in series between a pair of stereo headphones and a porrable radio, allowing the runner to listen to music while the device is not in speech output mode.
Applied engineers will happily place this book adjacent to their 'Numerical Recipes' volume. Masters' fills this volume with methodology based on many years of experience. The title is misleading, implying that neural networks are to be used for signal and image processing. The book is not about neural networks, but about how 'features' can be extracted from raw data and formatted for neural networks. The methodology described is sound signal processing and can be applied to a variety of processing tasks. However, many readers will deride this volume as Masters provides little theoretical or mathematical background. The volume is filled with applied tips and examples, and the C++ algorithms on the accompanying disk are skillfully written. The book has a very specific agenda, despite its title, and it does a good job outlining complex topics with minimal background. The book is very well suited for the investigator that needs to implement a neural network solution quickly and with minimal grasp of the material. Despite its faults, I would definitely recommend it to any engineer looking for applied tools for implementing neural networks to solve a signal processing task.
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