QUALITATIVE BIOMECHANICS FOR COACHING

Session Information: Coaches must apply principles of biomechanics in their qualitative judgments of the technique used by athletes. These judgments can have a major influence on performance and injury risk. This session will focus on the most effective use of qualitative biomechanical analyses and video replay software. Several scholars who have experience teaching qualitative biomechanical analysis to future coaches will present, followed by a question and answer session. Schedule of Presentations: Dr. Knudson will introduce the session and provide a brief overview of qualitative biomechanical analysis. 11:00 – 11:15 Dr. Alderson will present sport injury models as they apply to assessment, intervention and rehabilitation of common injuries in cricket, tennis and running. Relevant qualitative and quantitative 2D features of SiliconCoach that can be utilised by a coach to potentially reduce injury incidence will be presented. 11:15 – 11:45 Dr. Bahamonde will present how qualitative analysis can be used to teach biomechanics concepts to physical education and coaching students. Movement examples from tennis, soccer and track field and meaningful features of Hu-m-an software will be illustrated. Hu-m-an is unique in that it was developed with a specific teaching-learning focus. 11:45 – 12:15 Dr. Bird will present biomechanical core concepts as a “common language” to evaluate and improve all human movements. The core concepts are visually observable, but meaningful features of Dartfish will be illustrated to enhance what is seen by both the coach and the mover. Movement examples from golf, resistance training, basketball, and other sports will be presented. 12:15 – 12:45 Discussion: 12:45 – 13:00 This will provide an opportunity for delegates to ask specific questions relating to any of the presenters

Probing the interiors of the ice giants: Shock compression of water to 700 GPa and 3.8 g/ccm

Recently there has been tremendous increase in the number of identified extra-solar planetary systems. Our understanding of their formation is tied to exoplanet internal structure models, which rely upon equations of state of light elements and compounds like water. Here we present shock compression data for water with unprecedented accuracy that shows water equations of state commonly used in planetary modeling significantly overestimate the compressibility at conditions relevant to planetary interiors. Furthermore, we show its behavior at these conditions, including reflectivity and isentropic response, is well described by a recent first-principles based equation of state. These findings advocate this water model be used as the standard for modeling Neptune, Uranus, and "hot Neptune" exoplanets, and should improve our understanding of these types of planets.Comment: Accepted to Phys. Rev. Lett.; supplementary material attached including 2 figures and 2 tables; to view attachments, please download and extract the gzipped tar source file listed under "Other formats

Warming Up Density Functional Theory

Density functional theory (DFT) has become the most popular approach to electronic structure across disciplines, especially in material and chemical sciences. Last year, at least 30,000 papers used DFT to make useful predictions or give insight into an enormous diversity of scientific problems, ranging from battery development to solar cell efficiency and far beyond. The success of this field has been driven by usefully accurate approximations based on known exact conditions and careful testing and validation. In the last decade, applications of DFT in a new area, warm dense matter, have exploded. DFT is revolutionizing simulations of warm dense matter including applications in controlled fusion, planetary interiors, and other areas of high energy density physics. Over the past decade or so, molecular dynamics calculations driven by modern density functional theory have played a crucial role in bringing chemical realism to these applications, often (but not always) with excellent agreement with experiment. This chapter summarizes recent work from our group on density functional theory at non-zero temperatures, which we call thermal DFT. We explain the relevance of this work in the context of warm dense matter, and the importance of quantum chemistry to this regime. We illustrate many basic concepts on a simple model system, the asymmetric Hubbard dimer

Calculation of a Deuterium Double Shock Hugoniot from Ab initio Simulations

We calculate the equation of state of dense deuterium with two ab initio simulations techniques, path integral Monte Carlo and density functional theory molecular dynamics, in the density range of 0.67 < rho < 1.60 g/cc. We derive the double shock Hugoniot and compare with the recent laser-driven double shock wave experiments by Mostovych et al. [1]. We find excellent agreement between the two types of microscopic simulations but a significant discrepancy with the laser-driven shock measurements.Comment: accept for publication in Phys. Rev. Lett., Nov. 2001, 4 pages, 4 figure

BAX Is Required for Neuronal Death after Trophic Factor Deprivation and during Development

AbstractMembers of the BCL2-related family of proteins either promote or repress programmed cell death. BAX, a death-promoting member, heterodimerizes with multiple death-repressing molecules, suggesting that it could prove critical to cell death. We tested whether Bax is required for neuronal death by trophic factor deprivation and during development. Neonatal sympathetic neurons and facial motor neurons from Bax-deficient mice survived nerve growth factor deprivation and disconnection from their targets by axotomy, respectively. These salvaged neurons displayed remarkable soma atrophy and reduced elaboration of neurites; yet they responded to readdition of trophic factor with soma hypertrophy and enhanced neurite outgrowth. Bax-deficient superior cervical ganglia and facial nuclei possessed increased numbers of neurons. Our observations demonstrate that trophic factor deprivation–induced death of sympathetic and motor neurons depends on Bax

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis

IntroductionAdvances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome.MethodsMultivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality.ResultsWe identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.ConclusionsHere we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients

The Equation of State and the Hugoniot of Laser Shock-Compressed Deuterium

The equation of state and the shock Hugoniot of deuterium are calculated using a first-principles approach, for the conditions of the recent shock experiments. We use density functional theory within a classical mapping of the quantum fluids [ Phys. Rev. Letters, {\bf 84}, 959 (2000) ]. The calculated Hugoniot is close to the Path-Integral Monte Carlo (PIMC) result. We also consider the {\it quasi-equilibrium} two-temperature case where the Deuterons are hotter than the electrons; the resulting quasi-equilibrium Hugoniot mimics the laser-shock data. The increased compressibility arises from hot $D^+-e$ pairs occuring close to the zero of the electron chemical potential.Comment: Four pages; One Revtex manuscript, two postscipt figures; submitted to PR

Hydrogen-Helium Mixtures at High Pressure

The properties of hydrogen-helium mixtures at high pressure are crucial to address important questions about the interior of Giant planets e.g. whether Jupiter has a rocky core and did it emerge via core accretion? Using path integral Monte Carlo simulations, we study the properties of these mixtures as a function of temperature, density and composition. The equation of state is calculated and compared to chemical models. We probe the accuracy of the ideal mixing approximation commonly used in such models. Finally, we discuss the structure of the liquid in terms of pair correlation functions.Comment: Proceedings article of the 5th Conference on Cryocrystals and Quantum Crystals in Wroclaw, Poland, submitted to J. Low. Temp. Phys. (2004

Accumulation of driver and passenger mutations during tumor progression

Major efforts to sequence cancer genomes are now occurring throughout the world. Though the emerging data from these studies are illuminating, their reconciliation with epidemiologic and clinical observations poses a major challenge. In the current study, we provide a novel mathematical model that begins to address this challenge. We model tumors as a discrete time branching process that starts with a single driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of clonal expansion. Using the model, we observe tremendous variation in the rate of tumor development - providing an understanding of the heterogeneity in tumor sizes and development times that have been observed by epidemiologists and clinicians. Furthermore, the model provides a simple formula for the number of driver mutations as a function of the total number of mutations in the tumor. Finally, when applied to recent experimental data, the model allows us to calculate, for the first time, the actual selective advantage provided by typical somatic mutations in human tumors in situ. This selective advantage is surprisingly small, 0.005 +- 0.0005, and has major implications for experimental cancer research