Modelling study of the hydrodynamic expansion of a laser ablation plume of lithium in vacuum

This work is concerned with the study of the important physical processes present in a laser ablation lithium plasma plume expanding into vacuum. A numerical model has been developed encompassing three main areas, namely, hydrodynamics, atomic physics and radiative transfer and spectroscopy. A self-similar expansion model has been employed to study the hydrodynamics. A comparison between the isothermal and lsentropic self-similar solutions has been performed over a wide range of experimental initial conditions with varying fluence, laser wavelength and target spot size. The effect of these variations is seen, where one of these models predicts experimental observations more accurately, depending on the initial conditions present. Both models have been modified to include the bulk motion of the plasma by considering the pressure exerted by the expanding plume onto the target. The steady state colhsional radiative model was used to calculate the electron, ion and energy level populations of lithium neutral in the expanding plume and is used as a post-processor to the hydrodynamics model. The validity conditions of various equilibrium models and steady state conditions have been assessed, with particular emphasis on the spatial and temporal regions applicable to local thermodynamic equilibrium (LTE). The information taken from the first two models allows the calculation of the full radiative transfer equation through plasma chords parallel to the target surface. Results from this calculation have been compared with experimental timeintegrated spectra. This model was also used to explain a well-known anomalous line intensity ratio between two strong emission lines m neutral lithium produced in a laser ablation plasma

Reduced glycogen availability is associated with increased AMPKα2 activity, nuclear AMPKα2 protein abundance, and GLUT4 mRNA expression in contracting human skeletal muscle

Glycogen availability can influence glucose transporter 4 (GLUT4) expression in skeletal muscle through unknown mechanisms. The multisubstrate enzyme AMP-activated protein kinase (AMPK) has also been shown to play an important role in the regulation of GLUT4 expression in skeletal muscle. During contraction, AMPK [alpha]2 translocates to the nucleus and the activity of this AMPK isoform is enhanced when skeletal muscle glycogen is low. In this study, we investigated if decreased pre-exercise muscle glycogen levels and increased AMPK [alpha]2 activity reduced the association of AMPK with glycogen and increased AMPK [alpha]2 translocation to the nucleus and GLUT4 mRNA expression following exercise. Seven males performed 60 min of exercise at ~70% [VO.sub.2] peak on 2 occasions: either with normal (control) or low (LG) carbohydrate pre-exercise muscle glycogen content. Muscle samples were obtained by needle biopsy before and after exercise. Low muscle glycogen was associated with elevated AMPK [alpha]2 activity and acetyl-CoA carboxylase [beta] phosphorylation, increased translocation of AMPK [alpha]2 to the nucleus, and increased GLUT4 mRNA. Transfection of primary human myotubes with a constitutively active AMPK adenovirus also stimulated GLUT4 mRNA, providing direct evidence of a role of AMPK in regulating GLUT4 expression. We suggest that increased activation of AMPK [alpha]2 under conditions of low muscle glycogen enhances AMPK [alpha]2 nuclear translocation and increases GLUT4 mRNA expression in response to exercise in human skeletal muscle. <br /

The Economics of Policies and Programs Affecting the Employment of People with Disabilities

Over the last several decades, there has been a movement toward the inclusion of people with disabilities in mainstream social institutions. The 1990 Americans with Disabilities Act (ADA), which supports the full participation of people with disabilities in society and mainstream institutions, illustrates the shift in attitudes toward people with disabilities. Rather than being perceived as having a social or medical problem, individuals with disabilities are increasingly viewed as people with challenges that can be solved if appropriate policies and supports are available for addressing them

Senior Theses: Department of Physical Sciences

1993 Fall Semester Senior Theses for the class Physical Science 471: Petrologic Classification of Igneous and Metamorphic Rocks by Frank Baldridge X -Ray Analysis of Cave Sediments From Pigeon Water Cave of Northeastern Pine Mountain by Billy B. Stapleton The Correlation of Stream-deposited Breccias In Bat Cave, Carter Caves, Kentucky by James Bond Jointing and Faulting in Selected Areas of Eastern Kentucky by Mark A. Blai

A framework for constructing machine learning models with feature set optimisation for evapotranspiration partitioning

A deeper understanding of the drivers of evapotranspiration and the modelling of its constituent parts (evaporation and transpiration) may be of significant importance to the monitoring and management of water resources globally over the coming decades. In this work a framework was developed to identify the best performing machine learning algorithm from a candidate set, select optimal predictive features and rank features in terms of their importance to predictive accuracy. The experiments conducted in this work used 3 separate feature sets across 4 wetland sites as input into 8 candidate machine learning algorithms, providing 96 sets of experimental configurations. Given this high number of parameters, our results show strong evidence that there is no singularly optimal machine learning algorithm or feature set across all of the wetland sites studied despite their similarities. At each of the sites at least one model was identified that improved on the predictive performance of our baseline. A key finding discovered when examining feature importance is that methane flux, a feature whose relationship with evapotranspiration is not generally examined, may contribute to further biophysical process understanding. This work demonstrates the applicability of a machine learning framework for evapotranspiration partitioning that is independent of domain knowledge, producing improved models for partitioning and identifying new and useful predictive features

Ocular Surface Infection and Antimicrobials

Infection of the ocular surface can have devastating consequences if not appropriately treated with antimicrobials at an early stage [...]

No evidence of direct association between GLUT4 and glycogen in human skeletal muscle

Previous studies have demonstrated that exercise increases whole body and skeletal muscle insulin sensitivity that is linked with increased GLUT4 at the plasma membrane following insulin stimulation and associated with muscle glycogen depletion. To assess the potential direct association between muscle glycogen and GLUT4, seven untrained, male subjects exercised for 60 min at ~75% VO2 peak, with muscle samples obtained by percutaneous needle biopsy immediately before and after exercise. Exercise reduced muscle glycogen content by ~43%. An ultracentrifugation protocol resulted in a ~2-3-fold enriched glycogen fraction from muscle samples for analysis. Total GLUT4 content was unaltered by exercise and we were unable to detect any GLUT4 in glycogen fractions, either with or without amylase treatment. In skinned muscle fiber segments, there was very little, if any, GLUT4 detected in wash solutions, except following exposure to 1% Triton X-100. Amylase treatment of single fibers did not increase GLUT4 in the wash solution and there were no differences in GLUT4 content between fibers obtained before or after exercise for any of the wash treatments. Our results indicate no direct association between GLUT4 and glycogen in human skeletal muscle, before or after exercise, and suggest that alterations in GLUT4 translocation associated with exercise-induced muscle glycogen depletion are mediated via other mechanisms

Generic modelleing of faecal indicator organism concentrations in the UK

To meet European Water Framework Directive requirements, data are needed on faecal indicator organism (FIO) concentrations in rivers to enable the more heavily polluted to be targeted for remedial action. Due to the paucity of FIO data for the UK, especially under high-flow hydrograph event conditions, there is an urgent need by the policy community for generic models that can accurately predict FIO concentrations, thus informing integrated catchment management programmes. This paper reports the development of regression models to predict base- and high-flow faecal coliform (FC) and enterococci (EN) concentrations for 153 monitoring points across 14 UK catchments, using land cover, population (human and livestock density) and other variables that may affect FIO source strength, transport and die-off. Statistically significant models were developed for both FC and EN, with greater explained variance achieved in the high-flow models. Both land cover and, in particular, population variables are significant predictors of FIO concentrations, with r2 maxima for EN of 0.571 and 0.624, respectively. It is argued that the resulting models can be applied, with confidence, to other UK catchments, both to predict FIO concentrations in unmonitored watercourses and evaluate the likely impact of different land use/stocking level and human population change scenarios