Understanding gene function and genetic relationships is elemental in our efforts to better understand biological systems. Here, I describe a reliable high-throughput approach, Systematic Genetic Interaction analysis (SGI), capable of revealing both weak and strong genetic interactions in the nematode Caenorhabditis elegans. I also present evidence that UNC-73 functions cell-autonomously in an UNC-40 pathway to direct muscle arm extension in C. elegans. Previous efforts to systematically describe genetic interactions between redundant genes on a global scale either have focused on core biological processes in protozoans or have surveyed catastrophic interactions in metazoans. I investigated synthetic genetic interactions between eleven ‘query’ mutants in conserved signal transduction pathways and hundreds of ‘target’ genes compromised by RNAi. A network of 1246 genetic interactions was uncovered through an unbiased global analysis of the interaction matrix, establishing the largest metazoan genetic interaction network to date. To investigate how genetic interactions connect genes on a systems-wide level, the SGI network was superimposed with existing networks of physical, genetic, phenotypic and co-expression interactions. Fifty-six putative functional modules were identified within the superimposed network, one of which regulates fat accumulation and is coordinated by bar-1(ga80)/β-catenin interactions. This led to the discovery that SGI interactions link distinct functional modules on a global scale, which is a previously unappreciated level of organization within metazoan systems. In addition, I present evidence that the properties of genetic networks are conserved between C. elegans and S. cerevisiae, but that the connectivity of the interactions within the current networks is not. Although the buffering between functional modules may differ between species, studying these differences may provide insight into the evolution of divergent form and function. In C. elegans the postsynaptic membrane of the neuromuscular junction reaches its destination through an active process of guided cell extension. The worm has 95 body wall muscles (BWMs) that extend projections called 'muscle arms' to motor axons. The muscle arms harbour the postsynaptic elements of neuromuscular junctions. The stereotypical pattern of muscle arm extension was exploited in a forward genetic screen for new genes required for guided cell migration by looking for mutations that caused a reduction in the number of arms that extend to the motor axons. One of the resulting mutants was tr117, which extended half the number of arms compared to wild type animals. Genetic mapping, complementation tests, and sequencing revealed that tr117 was a mutation in unc-73/Trio, which encodes a guanine nucleotide exchange factor. Expression of UNC-73 specifically in the BWMs rescued the muscle arm development defects of unc-73(e936) mutants, indicating that UNC-73 functions cell-autonomously to regulate muscle arm extension. UNC-73::CFP was localized to muscle arm termini in a pattern similar to that of UNC-40/Dcc, which directs muscle arm extension. UNC-73 over-expression suppressed the Madd phenotype of unc-40 null worms and unc-73(e936) suppressed ectopic myopodia induced by UNC-40 over-expression. These results indicate that UNC-73 functions downstream of UNC-40 in a pathway that regulates muscle arm extension.

Despite the growing popularity and expansion of video game interaction techniques and research in the area of hand gesture recognition, the application of hand gesture video game interaction using arm, hand, and finger motion has not been extensively explored. Most current gesture-based approaches to video game interaction neglect the use of the fingers for interaction, but inclusion of the fingers will allow for more natural and unique interaction and merits further research. To implement arm, hand and finger-based interaction for the video game domain, several problems must be solved including gesture recognition, segmentation, hand visualization, and video game interaction that responds to arm, hand, and finger input. Solutions to each of these problems have been implemented. The potential of this interaction style is illustrated through the introduction of an arm, hand, and finger controlled video game system that responds to players' hand gestures. It includes a finger-gesture recognizer as well as a video game system employing various interaction styles. This consists of a first person shooter game, a driving game, and a menu interaction system. Several users interacted with and played these games, and this form of interaction is especially suitable for real time interaction in first-person games. This is perhaps the first implementation of its kind for video game interaction. Based on test results, arm, hand, and finger interaction a viable form of interaction that deserves further research. This implementation bridges the gap between existing gesture interaction methods and more advanced virtual reality techniques. It successfully combines the solutions to each problem mentioned above into a single, working video game system. This type of interaction has proved to be more intuitive than existing gesture controls in many situations and also less complex to implement than a full virtual reality setup. It allows more control by using the hands' natural motion and allows each hand to interact independently. It can also be reliably implemented using today's technology. This implementation is a base system that can be greatly expanded on. Many possibilities for future work can be applied to this form of interaction.

The purpose of this study was to devise a simple mechanical power formula for both peak and average power using a countermovement jump and reach test for both college male and female athletes. Forty-nine female and 69 male athletes were measured for height, weight, thigh circumference, thigh skinfold, upper leg length, and lower leg length. The athletes performed a countermovement jump and reach test off of a force platform. A Vertec jumping apparatus was used to measure vertical jump height and the force platform was used to acquire force/time data to determine actual peak and average power output. Eight anthropometric measurements, vertical jump height, and gender were the variables presented to develop the equations. A stepwise multiple regression statistical procedure was used to develop the prediction equations. Vertical jump height, mass, and body height were the significant (p<.05) variables loaded into both peak and average mechanical power prediction equations. Gender was not significant (p>.05) and, therefore, not loaded into either equation. Predicted peak power and actual peak power values were 4,707 t 1,511 and 4,687 ± 1,612 watts, respectively. Predicted averagepower and actual average power values were 2,547 ± 760 and 2,463 ± 753 watts, respectively. The following best model regression-derived equations produced R2 values of .91 for peak power and .82 for average power:Peak Power (W) = 78.47 • VJ (cm) + 60.57 • Mass (kg) - 15.31 • Ht (cm) - 1,308 Average Power (W) = 41.41 • VJ (cm) + 31.18 • Mass (kg) - 13.86 • Ht (cm) + 431 Results of this study conclude that the two regression equations are good predictors of peak and average mechanical power output. / School of Physical Education

Background Hand-arm vibration (HAV) is an occupational hazard which, cumulatively, leads to hand-arm vibration syndrome (HAVS). Detection and reduction of HAV can help prevent the disease or slow down its progress. Unfortunately, assessment of HAV through direct measurement is difficult due to the high cost of measurement equipment, interruption of work performance, and long travel cost to worksites. An alternative assessment method is through development of an exposure prediction model to identify workplace, tool, and task characteristics which significantly predict HAV exposure. Purpose The purpose of this study is twofold: 1) to determine the extent and nature of previously published scientific journal articles on exposure prediction modeling of HAV through performing a systematic review; and 2) to develop a new exposure prediction model using administrative data to find significant HAV predictors. Methods A systematic review of relevant studies involved humans aged 18 or over, applicable to occupational setting, with vibration measured using tri-axial accelerometer and statistical modeling of the effects of occupational characteristics on measure HAV were identified. Five online bibliographic databases (Medline, CINAHL, Web of Science, Scopus, and EMBASE) were searched using a combined word list of terms under three categories: “occupational diseases”; “Hand-arm vibration”; and “Statistical Modelling”. Two multiple linear regression models predicting average hand-arm vibration exposure over 8 hour day, A(8), were built using enforcement data collected by Ministry of Labour Relations and Workplace Safety Saskatchewan using standard model building method. In addition, GEE was used to account for repeated data collection within workers and worksites. Results In the first manuscript, 7 articles found were deemed relevant. Four studies built regression models, while three used ANOVA to find significant HAV predictors. Examples of significant HAV predictors included: tool age, tool weight, participant, and operating power; the proportion of HAV variance explained varied from 46 to 90%. In the second manuscript, the models based on administrative data explained 16% to 27% of A(8) variance. The included variables were tool power source, vibration control, and job type for the parsimonious model; the comprehensive model has the same variables as parsimonious along with accelerometer attachment method and tool brand. Conclusion HAV prediction through exposure prediction modeling is a relatively new method for assessing occupational HAV. It is feasible to find HAV predictors using low-cost administrative data, and variables such as tool power source, job type, and vibration control make promising predictors. However, the variance explained will be lower than using data collected for research purposes. The significant predictors found in the systematic review can be considered when installing protective measures in the future. The significant predictors found and procedures used from the modeling study can be considered for future HAV exposure prediction modeling studies.

Understanding gene function and genetic relationships is elemental in our efforts to better understand biological systems. Here, I describe a reliable high-throughput approach, Systematic Genetic Interaction analysis (SGI), capable of revealing both weak and strong genetic interactions in the nematode Caenorhabditis elegans. I also present evidence that UNC-73 functions cell-autonomously in an UNC-40 pathway to direct muscle arm extension in C. elegans. Previous efforts to systematically describe genetic interactions between redundant genes on a global scale either have focused on core biological processes in protozoans or have surveyed catastrophic interactions in metazoans. I investigated synthetic genetic interactions between eleven ‘query’ mutants in conserved signal transduction pathways and hundreds of ‘target’ genes compromised by RNAi. A network of 1246 genetic interactions was uncovered through an unbiased global analysis of the interaction matrix, establishing the largest metazoan genetic interaction network to date. To investigate how genetic interactions connect genes on a systems-wide level, the SGI network was superimposed with existing networks of physical, genetic, phenotypic and co-expression interactions. Fifty-six putative functional modules were identified within the superimposed network, one of which regulates fat accumulation and is coordinated by bar-1(ga80)/β-catenin interactions. This led to the discovery that SGI interactions link distinct functional modules on a global scale, which is a previously unappreciated level of organization within metazoan systems. In addition, I present evidence that the properties of genetic networks are conserved between C. elegans and S. cerevisiae, but that the connectivity of the interactions within the current networks is not. Although the buffering between functional modules may differ between species, studying these differences may provide insight into the evolution of divergent form and function. In C. elegans the postsynaptic membrane of the neuromuscular junction reaches its destination through an active process of guided cell extension. The worm has 95 body wall muscles (BWMs) that extend projections called 'muscle arms' to motor axons. The muscle arms harbour the postsynaptic elements of neuromuscular junctions. The stereotypical pattern of muscle arm extension was exploited in a forward genetic screen for new genes required for guided cell migration by looking for mutations that caused a reduction in the number of arms that extend to the motor axons. One of the resulting mutants was tr117, which extended half the number of arms compared to wild type animals. Genetic mapping, complementation tests, and sequencing revealed that tr117 was a mutation in unc-73/Trio, which encodes a guanine nucleotide exchange factor. Expression of UNC-73 specifically in the BWMs rescued the muscle arm development defects of unc-73(e936) mutants, indicating that UNC-73 functions cell-autonomously to regulate muscle arm extension. UNC-73::CFP was localized to muscle arm termini in a pattern similar to that of UNC-40/Dcc, which directs muscle arm extension. UNC-73 over-expression suppressed the Madd phenotype of unc-40 null worms and unc-73(e936) suppressed ectopic myopodia induced by UNC-40 over-expression. These results indicate that UNC-73 functions downstream of UNC-40 in a pathway that regulates muscle arm extension.

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[Truncated abstract] In cricket, the bowler propels a ball using a straight arm (permitted minimal extension) in an overhead fashion to a batsman situated approximately 20m away, who attempts to strike the ball in order to score runs for their team. Cricket bowling can be generalised by two types of bowlers; fast bowlers, who primarily use high ball speed, and spin bowlers that attempt to impart spin on the ball causing it to bounce in different directions. There has been numerous studies investigating the kinematics of fast bowling in cricket, but there is a paucity of objective literature on the spin bowling action due to the complex rotations of the upper limb necessary to develop ball velocity and rotation. One primary reason is that three dimensional (3D) analysis of upper limb movement is difficult due to the high degrees of freedom and ranges of motion of the associated joints. Furthermore, existing methods do not allow measurement of the kinematics of this highly dynamic task to be performed in an ecologically sound environment. The complexity is further compounded as the upper limb does not perform regular cyclical movements like the lower limb does with gait. Therefore, this makes it difficult to determine what

Thesis (Ph. D.)--Indiana University, 2005. / Includes bibliographical references (leaves 220-228). Also available online (PDF file) by a subscription to the set or by purchasing the individual file.

Thesis (Ph.D.)--Indiana University, School of Health, Physical Education, and Recreation, 2005. / Includes bibliographical references (leaves 220-228).

Thesis (Ph. D.)--University of Oregon, 2008. / Typescript. Includes vita and abstract. "This dissertation includes unpublished co-authored material"--P. v. Includes bibliographical references (leaves 119-128). Also available online in Scholars' Bank; and in ProQuest, free to University of Oregon users.