Effect of training with and without a load on military fitness tests and marksmanship.

The purpose of this study was to determine whether military-style training performed while carrying a weighted vest and backpack (Load condition) resulted in superior training adaptations (specifically, changes in military fitness and marksmanship) than did more conventional training (No-Load condition). A total of 33 college-aged men and women (16 Load, 17 No-Load) completed all testing and 9 weeks of training (1 h·d, 4 d·wk). No-Load training consisted of military calisthenics, sprints, agility drills, and running. Load training was similar except that running was replaced with stair climbing, and Load increased across the 9 weeks to 20 kg for women and 30 kg for men. Pretraining and posttraining, all subjects performed an uphill treadmill test with full load to determine peak oxygen consumption (VO(2)peak), the marine physical fitness test (PFT) and combat fitness test (CFT) without load, other fitness tests, and an indoor marksmanship test using a laser-fitted carbine. The marksmanship test was performed with full load and done before and immediately after a 200-m shuttle run performed in 60 seconds. Both groups significantly improved their VO(2)peak, PFT, and CFT scores by similar amounts. Pretraining, shooting score decreased significantly after the 200-m run and then rapidly recovered, with no difference between groups. A similar, but nonsignificant, pattern in shooting scores was seen in both groups posttraining. In conclusion, loaded training did not produce measurable advantages compared with unloaded training in this population. A strenuous anaerobic challenge caused a temporary reduction in marksmanship.

Effects of training on physical performance wearing personal protective equipment.

We evaluated the effects of wearing a weighted vest during 6 weeks of military-style training. Forty-three subjects were randomly assigned to a control group or a vest group (carrying 4-5 kg for 2 weeks, and 8-10 kg for 4 weeks), with 37 completing the study (17 vest, 20 control). Both groups performed stair climbing in addition to standard Marine Corps training for 1 hour, four times per week. Pre- and post-tests were performed while wearing military personal protective equipment, with the exception of the Marine Physical Readiness Test (PRT). Both groups significantly improved PRT scores (8.4% 3-mile run, 28-38% calisthenics) and an agility drill (4.4%). Significant improvements in uphill treadmill performance (6.8% vest, 3.0% control) and maximal oxygen consumption (10.7% vest, 6.8% control) were approximately twice as much in the vest versus control group, although these differences did not reach significance (p = 0.16 and 0.13, respectively).

Studying the effects of correlation on protein selection in proteomics data.

Recently, Efron (2007) provided methods for assessing the effect of correlation on false discovery rate (FDR) in large-scale testing problems in the context of microarray data. Although FDR procedure does not require independence of the tests, existence of correlation grossly under- or overestimates the number of critical genes. Here, we briefly review Efron's method and apply it to a relatively smaller spectrometry proteomics data. We show that even here the correlation can affect the FDR values and the number of proteins declared as critical.

Computational protein biomarker prediction: a case study for prostate cancer.

BACKGROUND: Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates. RESULTS: Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably. CONCLUSIONS: Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.