Endogenous atrial natriuretic factor and airway control

ix, 82 leaves : ill. ; 30 cm.This thesis describes two experiments investigating the role of endogenous atrial natriuretic factor (ANF) in the modulation of airway calibre and bronchial hyperresponsiveness in humans under normal physiological conditions. Two approaches are used. One approach examines the diurnal variation of endogenous ANF and its effect on the airway function of asthmatics and non-asthmatics. The second approach analyses a change in bronchoresponsiveness and/or airway calibre in response to manipulation of endogenous ANF levels.Thesis (M.Sc.) -- University of Adelaide, Dept. of Physiology, 1997

An integrated approach to epitope analysis I: Dimensional reduction, visualization and prediction of MHC binding using amino acid principal components and regression approaches

BACKGROUND: Operation of the immune system is multivariate. Reduction of the dimensionality is essential to facilitate understanding of this complex biological system. One multi-dimensional facet of the immune system is the binding of epitopes to the MHC-I and MHC-II molecules by diverse populations of individuals. Prediction of such epitope binding is critical and several immunoinformatic strategies utilizing amino acid substitution matrices have been designed to develop predictive algorithms. Contemporaneously, computational and statistical tools have evolved to handle multivariate and megavariate analysis, but these have not been systematically deployed in prediction of MHC binding. Partial least squares analysis, principal component analysis, and associated regression techniques have become the norm in handling complex datasets in many fields. Over two decades ago Wold and colleagues showed that principal components of amino acids could be used to predict peptide binding to cellular receptors. We have applied this observation to the analysis of MHC binding, and to derivation of predictive methods applicable on a whole proteome scale. RESULTS: We show that amino acid principal components and partial least squares approaches can be utilized to visualize the underlying physicochemical properties of the MHC binding domain by using commercially available software. We further show the application of amino acid principal components to develop both linear partial least squares and non-linear neural network regression prediction algorithms for MHC-I and MHC-II molecules. Several visualization options for the output aid in understanding the underlying physicochemical properties, enable confirmation of earlier work on the relative importance of certain peptide residues to MHC binding, and also provide new insights into differences among MHC molecules. We compared both the linear and non-linear MHC binding prediction tools to several predictive tools currently available on the Internet. CONCLUSIONS: As opposed to the highly constrained user-interaction paradigms of web-server approaches, local computational approaches enable interactive analysis and visualization of complex multidimensional data using robust mathematical tools. Our work shows that prediction tools such as these can be constructed on the widely available JMP(® )platform, can operate in a spreadsheet environment on a desktop computer, and are capable of handling proteome-scale analysis with high throughput

Filling the Gap:Defining a Robust Quality Assurance Model for Work-Based Learning in Higher Education

This paper, which is one of the outcomes of the EU co-financed Erasmus+ Knowledge Alliance project, Integrating Entrepreneurship and Work Experience in Higher Education focusses on the recognition and quality assurance mechanisms related to modes of Work-Based Learning (WBL), in particular placements. When discussing quality assurance, process and content related aspects should be distinguished. Content – development of subject specific and generic competences - can be phrased in terms of whether the evidence – the intended level of learning - is actually offered. A well-defined process for quality enhancement and assurance is perceived as a requirement to build trust and confidence. It checks whether the conditions for learning are up to standard. Both - conditions and level of learning – are key ingredients for recognition. An inventory made by ENQA at the beginning of 2018, shows that very limited work has been done by Quality Assurance Agencies so far to assure the quality of WBL. For this paper the work established by the UK Quality Assurance Agency (QAA) and Agency for the Quality of the Basque University System (Unibasq) has been analysed. This work has been aligned with an analysis of the applicability of the European Standards and Guidelines for Quality Assurance in the European Higher EducationArea (ESG) and the insights offered by the WEXHE project. The paper answers the question which elements are thought necessary to build a robust and reliable quality assurance model for WBL

Snow Survey Results for the Central Alaskan Arctic, Arctic Circle to Arctic Ocean: Spring 2013

Many remote areas of Alaska lack meteorological data; this is especially true for solid precipitation. Researchers at the University of Alaska Fairbanks, Water and Environmental Research Center have been collecting end-of-winter snow cover observations (depth, density, snow water equivalent and ablation) since the year 2000. These observations do not document the total snowfall during the winter, but provide quantitative estimate of cold season precipitation on the ground at winter’s end after sublimation and redistribution by wind. This report provides summary of snow cover data collected during cold season of 2012–2013. There are two main areas of study. One includes drainage areas of the western Sagavanirktok, Kuparuk, Itkillik, Anaktuvuk and Chandler Rivers located north of the continental divide in the Brooks Range. While the number of sites has varied each year, we visited 76 sites in April of 2013 on the North Slope of Alaska. Second study area was established in 2012 in the drainage areas of the Kogoluktuk, Mauneluk, Reed, Alatna, and Koyukuk Rivers south of the Brooks Range. Fifty seven new snow survey sites were visited south of the Brooks Range in April 2013. The cold season of 2012-2013 experienced heavy snowfalls (record amounts since 2000) north of the Brooks Range. This was the first year of data collection south of the Brooks Range, thus no comparison can be made. SWE averaged over entire study area was 13.1 cm in 2013, ranging from 1.2 cm to 35.2 cm. Generally, higher SWEs were found in the western portion of the study area. Ablation was later than normal in spring 2013. Ablation window extended from May 8, 2013 in the far south of the study area to middle June at higher elevations on the north side of the Brooks Range.LIST OF FIGURES ....................................................................................................................... iii LIST OF TABLES ........................................................................................................................ vii DISCLAIMER ............................................................................................................................. viii UNITS, ABBREVIATIONS, AND SYMBOLS ........................................................................... ix ACKNOWLEDGMENTS ...............................................................................................................x ABSTRACT ................................................................................................................................... xi 1. INTRODUCTION .......................................................................................................................1 2. STUDY AREA ............................................................................................................................3 3. SAMPLING METHODS .............................................................................................................5 3.1 Snow Survey ..........................................................................................................................5 3.2 Snow Ablation .......................................................................................................................6 3.2.1 Observations from 1985 to 2012 .................................................................................... 8 3.2.2 Observations from 2013 ................................................................................................. 9 3.3 Snow Depth Sensors ............................................................................................................10 4. ACCURACY OF OBSERVATIONS ........................................................................................12 4.1 Snow Water Equivalent .......................................................................................................12 4.2 Snow Depth Sensors ............................................................................................................13 5. SPATIAL DISTRIBUTION OF SNOW SURVEY SITES.......................................................15 6. SNOW SURVEY DATA AT WATERSHED SCALE .............................................................18 7. SONIC SNOW DEPTH DATA .................................................................................................25 7.1 North of the Brooks Range Divide ......................................................................................25 7.2 South of the Brooks Range Divide ......................................................................................50 8. SURFACE WEATHER ANALYSIS ........................................................................................62 9. SWE CORRECTIONS ..............................................................................................................66 9.1 Snow Depth Increase in the Umiat Study Area ...................................................................66 9.2 Snow Depth Increase in the Ambler Study Area .................................................................67 10. ABLATION DATA .................................................................................................................68 11. SUMMARY .............................................................................................................................71 12. REFERENCES ........................................................................................................................73 APPENDIX A. Snow survey data .................................................................................................75 Appendix A1. Measured snow survey data for the Umiat Study Area, April 18-24, 2013. ...................................................................................................................................... 76 Appendix A2. Adjustment of the snow water equivalent for the Umiat Study Area, spring 2013. ........................................................................................................................... 78 Appendix A3. Measured Snow Survey Data for the Ambler Study Area, April 3‐9, 2013. ...................................................................................................................................... 80 Appendix A4. Adjustment of the snow water equivalent data for the Ambler Study Area, spring 2013. ................................................................................................................. 82 APPENDIX B. Ablation data ........................................................................................................84 Appendix B1a. Snow water equivalent (cm) in the Imnavait Creek basin 85-99 (basin average). ..................................................................................................................... 84 Appendix B1b. Snow water equivalent (cm) in the Imnavait Creek basin 00-13 (basin average). ..................................................................................................................... 85 Appendix B2. Snow water equivalent (cm) at the Upper Kuparuk (UK) site. ..................... 86 Appendix B3. Snow water equivalent (cm) at the Happy Valley (HV) site. ........................ 87 Appendix B4. Snow water equivalent (cm) at the Sagwon (SH) site. .................................. 89 Appendix B5. Snow water equivalent (cm) at the Franklin Bluffs (FR) site........................ 90 Appendix B6. Snow water equivalent (cm) at the Betty Pingo (BP) site. ............................ 92 Appendix B7. Snow water equivalent (cm) at the West Dock (WD) site. ........................... 93 Appendix B8. 2010 Snow water equivalent (cm) at the Atigun, Galbraith Lake and Oil Spill Hill sites. ................................................................................................................. 94 Appendix B9. 2011 and 2013 snow water equivalent (cm) at the Anaktuvuk River, Chandler River, Upper Itkilik River and Lower Itkillik meteorological sites. ..................... 95 Appendix B10. 2013 snow water equivalent (cm) at the Ambler Road Corridor project meteorological sites. ................................................................................................. 9

The Role of Gamification in Designing an Online Course

Online learning is an area that has expanded greatly over the past few years. Manyteachers and universities are considering how to best provide their learners withquality online lessons. However, creating an online course is not quite as simpleas taking classroom materials and uploading them on a classroom managementsystem. There are several issues for consideration when designing a course: forexample, issues of learner styles, motivation, and feedback. This paper looks atthese issues in light of the relatively new concept of “gamification.” Gamification,quite simply, is taking elements of games and introducing them into non-gamesituations. While the idea of using games in the classroom has been with us aslong as teachers have been teaching and students have been learning, it is still anew phenomenon in turns of research into how game elements can be used in non-game contexts. This article will introduce common game elements such as socialgraphs, levels, points, quests, and avatars to provide examples that help toillustrate how they can be built into an online course

Patterns of Predicted T-Cell Epitopes Associated with Antigenic Drift in Influenza H3N2 Hemagglutinin

Antigenic drift allowing escape from neutralizing antibodies is an important feature of transmission and survival of influenza viruses in host populations. Antigenic drift has been studied in particular detail for influenza A H3N2 and well defined antigenic clusters of this virus documented. We examine how host immunogenetics contributes to determination of the antibody spectrum, and hence the immune pressure bringing about antigenic drift. Using uTOPE™ bioinformatics analysis of predicted MHC binding, based on amino acid physical property principal components, we examined the binding affinity of all 9-mer and 15-mer peptides within the hemagglutinin 1 (HA1) of 447 H3N2 virus isolates to 35 MHC-I and 14 MHC-II alleles. We provide a comprehensive map of predicted MHC-I and MHC-II binding affinity for a broad array of HLA alleles for the H3N2 influenza HA1 protein. Each HLA allele exhibited a characteristic predicted binding pattern. Cluster analysis for each HLA allele shows that patterns based on predicted MHC binding mirror those described based on antibody binding. A single amino acid mutation or position displacement can result in a marked difference in MHC binding and hence potential T-helper function. We assessed the impact of individual amino acid changes in HA1 sequences between 10 virus isolates from 1968–2002, representative of antigenic clusters, to understand the changes in MHC binding over time. Gain and loss of predicted high affinity MHC-II binding sites with cluster transitions were documented. Predicted high affinity MHC-II binding sites were adjacent to antibody binding sites. We conclude that host MHC diversity may have a major determinant role in the antigenic drift of influenza A H3N2