High- and low-volume resistance training similarly enhances functional performance in older adults

It is well known that resistance training improves muscle strength in older adults and may enhance or preserve functional performance. However, it is unclear if the volume of work undertaken in the elderly alters the response in functional performance. PURPOSE: To investigate the effect of a high- versus low-volume resistance training program on functional performance in older adults. METHODS: Thirty-two healthy men and women aged 65-78 years were randomly assigned to either a single-set (SS, n = 16) or 3-set (MS, n = 16) progressive resistance training program for 20 weeks. Groups trained 2 days per week using machine weights at 8 repetitions maximum (8-RM) for 7 upper and lower body exercises. Muscle strength was assessed by the 1-RM and functional performance by a battery of tests (repeated chair rise, usual and fast 6-m walk, 6-m backwards walk, floor rise to standing, stair climb, and 400-m walk time). RESULTS: Twenty-eight subjects completed the study. There was no difference between groups at baseline in muscle strength or functional performance. Whole body muscle strength significantly increased in both groups with greater gains in the 3-set group (MS 32.9 ± 3.1%; SS 18.6 ± 2.7%, mean ± SE; P < 0.01). Significant improvement (time effect, P < 0.01) occurred for both groups in the chair rise (MS 13.6 ± 3.2%; SS 10.2 ± 3.0%), 6-m backwards walk (MS 14.9 ± 3.3%; SS 14.3 ± 4.2%), stair climb (MS 6.4 ± 2.8%; SS 7.7 ± 3.1%) and 400-m walk (MS 7.4 ± 1.4%; SS 3.9 ± 1.2%). There were no interaction (group × time) effects for functional performance and no differences by sex. CONCLUSION: Resistance training that utilizes either a singleset or 3-set regimen may significantly and similarly improve functional performance in community-dwelling older adults. Enhancement of functional performance may prolong independence and improve quality of life. ©2004The American College of Sports Medicine

Evaluation and comparison of mammalian subcellular localization prediction methods

Background: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results: In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion: No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.