Accuracy and repeatability of wrist joint angles in boxing using an electromagnetic tracking system

© 2019, The Author(s). The hand-wrist region is reported as the most common injury site in boxing. Boxers are at risk due to the amount of wrist motions when impacting training equipment or their opponents, yet we know relatively little about these motions. This paper describes a new method for quantifying wrist motion in boxing using an electromagnetic tracking system. Surrogate testing procedure utilising a polyamide hand and forearm shape, and in vivo testing procedure utilising 29 elite boxers, were used to assess the accuracy and repeatability of the system. 2D kinematic analysis was used to calculate wrist angles using photogrammetry, whilst the data from the electromagnetic tracking system was processed with visual 3D software. The electromagnetic tracking system agreed with the video-based system (paired t tests) in both the surrogate ( 0.9). In the punch testing, for both repeated jab and hook shots, the electromagnetic tracking system showed good reliability (ICCs > 0.8) and substantial reliability (ICCs > 0.6) for flexion–extension and radial-ulnar deviation angles, respectively. The results indicate that wrist kinematics during punching activities can be measured using an electromagnetic tracking system

Mechanisms underlying fatigue: a voxel-based morphometric study of chronic fatigue syndrome

BACKGROUND: Fatigue is a crucial sensation that triggers rest, yet its underlying neuronal mechanisms remain unclear. Intense long-term fatigue is a symptom of chronic fatigue syndrome, which is used as a model to study the mechanisms underlying fatigue. METHODS: Using magnetic resonance imaging, we conducted voxel-based morphometry of 16 patients and 49 age-matched healthy control subjects. RESULTS: We found that patients with chronic fatigue syndrome had reduced gray-matter volume in the bilateral prefrontal cortex. Within these areas, the volume reduction in the right prefrontal cortex paralleled the severity of the fatigue of the subjects. CONCLUSION: These results are consistent with previous reports of an abnormal distribution of acetyl-L-carnitine uptake, which is one of the biochemical markers of chronic fatigue syndrome, in the prefrontal cortex. Thus, the prefrontal cortex might be an important element of the neural system that regulates sensations of fatigue

Genetic and Other Contributions to Alcohol Intake in Rhesus Macaques ( Macaca mulatta )

The etiology of alcoholism and alcohol abuse, like many other complex diseases, is heterogeneous and multifactorial. Numerous studies demonstrate a genetic contribution to variation in the expression of alcohol-related disorders in humans. Over the past decade, nonhuman primates have emerged as a valuable model for some aspects of human alcohol abuse because of their phylogenetic proximity to humans. Long-term, longitudinal studies of rhesus macaques ( Macaca mulatta ) have provided much insight into environmental influences, especially early life experiences, on alcohol consumption and behavior patterns that characterize alcohol intake later in life. It is not known, however, whether there is a genetic component as well to the variation seen in alcohol consumption in rhesus macaques. A significant genetic component to variation in alcohol consumption in rhesus macaques would show for the first time that like humans, for nonhuman primates additive genetic influences are important. Moreover, their use as a model for alcohol-related disorders in humans would have even greater relevance and utility for designing experiments incorporating the expanding molecular genetics field, and allow researchers to investigate the interaction among the known environmental influences and various genotypes. Methods : In this study, we investigate factors contributing to variation in alcohol consumption of 156 rhesus macaques collected over 10 years when subjects were adolescent in age, belonging to a single extended pedigree, with each cohort receiving identical early rearing backgrounds and subsequent treatments. To measure alcohol consumption each animal was provided unfettered simultaneous access both to an aspartame-sweetened 8.4% (v/v) alcohol-water solution, the aspartame-sweetened vehicle, and to water for 1 hour each day during the early afternoon between 13:00 and 15:00 in their home cages for a period of 5 to 7 weeks. We use multiple regression to identify factors that significantly affect alcohol consumption among these animals and a maximum likelihood program (ASReml) that, controlling for the significant factors, estimates the genetic contribution to the variance in alcohol consumption. Results : Multiple regression analysis identified test cohort and rearing environment as contributing to 57 and 2%, respectively, of the total variance in alcohol consumption. Of the remaining 41% of the variance about half (19.8%) was attributable to additive genetic effects using a maximum likelihood program. Conclusion : This study demonstrates that, as in humans, there are additive genetic factors that contribute to variation in alcohol consumption in rhesus macaques, with other nongenetic factors accounting for substantial portions of the variance in alcohol consumption, Our findings show the presence of an additive genetic component and suggest the potential utility of the nonhuman primate as a molecular genetics tool for understanding alcohol abuse and alcoholism.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66182/1/j.1530-0277.2006.00044.x.pd

What differences are detected by superiority trials or ruled out by noninferiority trials? A cross-sectional study on a random sample of two-hundred two-arms parallel group randomized clinical trials

BACKGROUND: The smallest difference to be detected in superiority trials or the largest difference to be ruled out in noninferiority trials is a key determinant of sample size, but little guidance exists to help researchers in their choice. The objectives were to examine the distribution of differences that researchers aim to detect in clinical trials and to verify that those differences are smaller in noninferiority compared to superiority trials. METHODS: Cross-sectional study based on a random sample of two hundred two-arm, parallel group superiority (100) and noninferiority (100) randomized clinical trials published between 2004 and 2009 in 27 leading medical journals. The main outcome measure was the smallest difference in favor of the new treatment to be detected (superiority trials) or largest unfavorable difference to be ruled out (noninferiority trials) used for sample size computation, expressed as standardized difference in proportions, or standardized difference in means. Student t test and analysis of variance were used. RESULTS: The differences to be detected or ruled out varied considerably from one study to the next; e.g., for superiority trials, the standardized difference in means ranged from 0.007 to 0.87, and the standardized difference in proportions from 0.04 to 1.56. On average, superiority trials were designed to detect larger differences than noninferiority trials (standardized difference in proportions: mean 0.37 versus 0.27, P = 0.001; standardized difference in means: 0.56 versus 0.40, P = 0.006). Standardized differences were lower for mortality than for other outcomes, and lower in cardiovascular trials than in other research areas. CONCLUSIONS: Superiority trials are designed to detect larger differences than noninferiority trials are designed to rule out. The variability between studies is considerable and is partly explained by the type of outcome and the medical context. A more explicit and rational approach to choosing the difference to be detected or to be ruled out in clinical trials may be desirable

What differences are detected by superiority trials or ruled out by noninferiority trials? A cross-sectional study on a random sample of two-hundred two-arms parallel group randomized clinical trials

<p>Abstract</p> <p>Background</p> <p>The smallest difference to be detected in superiority trials or the largest difference to be ruled out in noninferiority trials is a key determinant of sample size, but little guidance exists to help researchers in their choice. The objectives were to examine the distribution of differences that researchers aim to detect in clinical trials and to verify that those differences are smaller in noninferiority compared to superiority trials.</p> <p>Methods</p> <p>Cross-sectional study based on a random sample of two hundred two-arm, parallel group superiority (100) and noninferiority (100) randomized clinical trials published between 2004 and 2009 in 27 leading medical journals. The main outcome measure was the smallest difference in favor of the new treatment to be detected (superiority trials) or largest unfavorable difference to be ruled out (noninferiority trials) used for sample size computation, expressed as standardized difference in proportions, or standardized difference in means. Student t test and analysis of variance were used.</p> <p>Results</p> <p>The differences to be detected or ruled out varied considerably from one study to the next; e.g., for superiority trials, the standardized difference in means ranged from 0.007 to 0.87, and the standardized difference in proportions from 0.04 to 1.56. On average, superiority trials were designed to detect larger differences than noninferiority trials (standardized difference in proportions: mean 0.37 versus 0.27, <it>P </it>= 0.001; standardized difference in means: 0.56 versus 0.40, <it>P </it>= 0.006). Standardized differences were lower for mortality than for other outcomes, and lower in cardiovascular trials than in other research areas.</p> <p>Conclusions</p> <p>Superiority trials are designed to detect larger differences than noninferiority trials are designed to rule out. The variability between studies is considerable and is partly explained by the type of outcome and the medical context. A more explicit and rational approach to choosing the difference to be detected or to be ruled out in clinical trials may be desirable.</p

Familiality and partitioning the variability of femoral bone mineral density in women of child-bearing age

The contributions of polygenic loci and environmental factors to femoral bone mineral density (BMD in g/cm 2 ) variability were estimated in modified family sets consisting of women of child-bearing age. Femoral BMDs were measured in 535 women who were members of 137 family sets consisting minimally of an index, her sister, and unrelated female control. The family set could also include multiple sisters and first cousins. Women included in these family sets were all between 20 and 40 year of age to minimize the cohort effects of maturation and menopause on measures of BMD. BMDs were measured at three femoral sites using dual photon densitometry. Values were regressed on age and Quetelet Index which explained 13–15% of the variability in BMD (dependent on site). Subsequent variance components analysis on the residuals indicated that unmeasured polygenic loci accounted for substantial additional variability: 67% for femoral neck, 58% for Wards triangle, and 45% for trochanter. These results suggest that polygenic loci account for approximately half of the variability in maxmal femoral BMD.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48002/1/223_2004_Article_BF00298785.pd

Five mucosal transcripts of interest in ulcerative colitis identified by quantitative real-time PCR: a prospective study

<p>Abstract</p> <p>Background</p> <p>The cause and pathophysiology of ulcerative colitis are both mainly unknown. We have previously used whole-genome microarray technique on biopsies obtained from patients with ulcerative colitis to identifiy 5 changed mucosal transcripts. The aim of this study was to compare mucosal expressions of these five transcripts in ulcerative colitis patients vs. controls, along with the transcript expression in relation to the clinical ulcerative colitis status.</p> <p>Methods</p> <p>Colonic mucosal specimens from rectum and caecum were taken at ambulatory colonoscopy from ulcerative colitis patients (<it>n </it>= 49) with defined inflammatory activity and disease extension, and from controls (<it>n </it>= 67) without inflammatory bowel disease. The five mucosal transcripts aldolase B, elafin, MST-1, simNIPhom and SLC6A14 were analyzed using quantitative real-time PCR.</p> <p>Results</p> <p>Significant transcript differences in the rectal mucosa for all five transcripts were demonstrated in ulcerative colitis patients compared to controls. The grade of transcript expression was related to the clinical disease activity.</p> <p>Conclusion</p> <p>The five gene transcripts were changed in patients with ulcerative colitis, and were related to the disease activity. The known biological function of some of the transcripts may contribute to the inflammatory features and indicate a possible role of microbes in ulcerative colitis. The findings may also contribute to our pathophysiological understanding of ulcerative colitis.</p

Prospects for a Statistical Theory of LC/TOFMS Data

The critical importance of employing sound statistical arguments when seeking to draw inferences from inexact measurements is well-established throughout the sciences. Yet fundamental statistical methods such as hypothesis testing can currently be applied to only a small subset of the data analytical problems encountered in LC/MS experiments. The means of inference that are more generally employed are based on a variety of heuristic techniques and a largely qualitative understanding of their behavior. In this article, we attempt to move towards a more formalized approach to the analysis of LC/TOFMS data by establishing some of the core concepts required for a detailed mathematical description of the data. Using arguments that are based on the fundamental workings of the instrument, we derive and validate a probability distribution that approximates that of the empirically obtained data and on the basis of which formal statistical tests can be constructed. Unlike many existing statistical models for MS data, the one presented here aims for rigor rather than generality. Consequently, the model is closely tailored to a particular type of TOF mass spectrometer although the general approach carries over to other instrument designs. Looking ahead, we argue that further improvements in our ability to characterize the data mathematically could enable us to address a wide range of data analytical problems in a statistically rigorous manner