An Empirical Analysis of Predictive Machine Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space p is much larger than the number of observations n. Seven gene-expression microarray cancer datasets, where the ratio κ = n/p is less than one, were chosen for evaluation. The statistical and computational challenges inherent with this type of high-dimensional low sample size (HDLSS) data were explored. The capability and performance of a diverse set of machine learning algorithms is presented and compared. The sparsity and collinearity of the data being employed, in conjunction with the complexity of the algorithms studied, demanded rigorous and careful tuning of the hyperparameters and regularization parameters. This necessitated several extensions of cross-validation to be investigated, with the purpose of culminating in the best predictive performance. For the techniques evaluated in this thesis, regularization or kernelization, and often both, produced lower classification error rates than randomized ensemble for all datasets used in this research. However, no one technique evaluated for classifying HDLSS microarray cancer data emerged as the universally best technique for predicting the generalization error.1 From the empirical analysis performed in this thesis, the following fundamentals emerged as being instrumental in consistently resulting in lower error rates when estimating the generalization error in this HDLSS microarray cancer data: • Thoroughly investigate and understand the data • Stratify during all sampling due to the uneven classes and extreme sparsity of this data. • Perform 3 to 5 replicates of stratified cross-validation, implementing an adaptive K-fold, to determine the optimal tuning parameters. • To estimate the generalization error in HDLSS data, replication is paramount. Replicate R=500 or R=1000 times with training and test sets of 2/3 and 1/3, respectively, to get the best generalization error estimate. • Whenever possible, obtain an independent validation dataset. • Seed the data for a fair and unbiased comparison among techniques. • Define a methodology or standard set of process protocols to apply to machine learning research. This would prove very beneficial in ensuring reproducibility and would enable better comparisons among techniques. _____ 1A predominant portion of this research was published in the Serdica Journal of Computing (Volume 8, Number 2, 2014) as proceedings from the 2014 Flint International Statistical Conference at Kettering University, Michigan, USA

Two Dimensional Window Exchange Umbrella Sampling for Transmembrane Helix Assembly

The method of window exchange umbrella sampling molecular dynamics (WEUSMD) with a pre-optimized parameter set was recently used to obtain the most probable conformations and the energetics of transmembrane (TM) helix assembly of a generic TM sequence. When applied to glycophorin A TM domain (GpA-TM) using the restraint potentials along the helix-helix distance, however, tight interfacial packing of GpA-TM resulted in insufficient conformational sampling at short helix-helix separation. This sampling issue is addressed by extending the WEUSMD into two dimensions with the restraint potentials along the helix-helix distance and crossing angle. The two-dimensional WEUSMD results demonstrate that the incomplete sampling in the one-dimensional WEUSMD arises from high barriers along the crossing angle between the GpA-TM helices. Together with the faster convergence in both the assembled conformations and the potential of mean force, the 2D-WEUSMD can be a general and efficient approach in computational studies of TM helix assembly

OPM database and PPM web server: resources for positioning of proteins in membranes

The Orientations of Proteins in Membranes (OPM) database is a curated web resource that provides spatial positions of membrane-bound peptides and proteins of known three-dimensional structure in the lipid bilayer, together with their structural classification, topology and intracellular localization. OPM currently contains more than 1200 transmembrane and peripheral proteins and peptides from approximately 350 organisms that represent approximately 3800 Protein Data Bank entries. Proteins are classified into classes, superfamilies and families and assigned to 21 distinct membrane types. Spatial positions of proteins with respect to the lipid bilayer are optimized by the PPM 2.0 method that accounts for the hydrophobic, hydrogen bonding and electrostatic interactions of the proteins with the anisotropic water-lipid environment described by the dielectric constant and hydrogen-bonding profiles. The OPM database is freely accessible at http://opm.phar.umich.edu. Data can be sorted, searched or retrieved using the hierarchical classification, source organism, localization in different types of membranes. The database offers downloadable coordinates of proteins and peptides with membrane boundaries. A gallery of protein images and several visualization tools are provided. The database is supplemented by the PPM server (http://opm.phar.umich.edu/server.php) which can be used for calculating spatial positions in membranes of newly determined proteins structures or theoretical models

Pichia pastoris versus Saccharomyces cerevisiae:a case study on the recombinant production of human granulocyte-macrophage colony-stimulating factor

BACKGROUND: Recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-CSF) is a glycoprotein that has been approved by the FDA for the treatment of neutropenia and leukemia in combination with chemotherapies. Recombinant hGM-CSF is produced industrially using the baker's yeast, Saccharomyces cerevisiae, by large-scale fermentation. The methylotrophic yeast, Pichia pastoris, has emerged as an alternative host cell system due to its shorter and less immunogenic glycosylation pattern together with higher cell density growth and higher secreted protein yield than S. cerevisiae. In this study, we compared the pipeline from gene to recombinant protein in these two yeasts. RESULTS: Codon optimization in silico for both yeast species showed no difference in frequent codon usage. However, rhGM-CSF expressed from S. cerevisiae BY4742 showed a significant discrepancy in molecular weight from those of P. pastoris X33. Analysis showed purified rhGM-CSF species with molecular weights ranging from 30 to more than 60 kDa. Fed-batch fermentation over 72 h showed that rhGM-CSF was more highly secreted from P. pastoris than S. cerevisiae (285 and 64 mg total secreted protein/L, respectively). Ion exchange chromatography gave higher purity and recovery than hydrophobic interaction chromatography. Purified rhGM-CSF from P. pastoris was 327 times more potent than rhGM-CSF from S. cerevisiae in terms of proliferative stimulating capacity on the hGM-CSF-dependent cell line, TF-1. CONCLUSION: Our data support a view that the methylotrophic yeast P. pastoris is an effective recombinant host for heterologous rhGM-CSF production

Lateral flow test engineering and lessons learned from COVID-19

The acceptability and feasibility of large-scale testing with lateral flow tests (LFTs) for clinical and public health purposes has been demonstrated during the COVID-19 pandemic. LFTs can detect analytes in a variety of samples, providing a rapid read-out, which allows self-testing and decentralized diagnosis. In this Review, we examine the changing LFT landscape with a focus on lessons learned from COVID-19. We discuss the implications of LFTs for decentralized testing of infectious diseases, including diseases of epidemic potential, the ‘silent pandemic’ of antimicrobial resistance, and other acute and chronic infections. Bioengineering approaches will play a key part in increasing the sensitivity and specificity of LFTs, improving sample preparation, incorporating nucleic acid amplification and detection, and enabling multiplexing, digital connection and green manufacturing, with the aim of creating the next generation of high-accuracy, easy-to-use, affordable and digitally connected LFTs. We conclude with recommendations, including the building of a global network of LFT research and development hubs to facilitate and strengthen future diagnostic resilience

Mono- or Double-Site Phosphorylation Distinctly Regulates the Proapoptotic Function of Bax

Bax is the major multidomain proapoptotic molecule that is required for apoptosis. It has been reported that phosphorylation of Bax at serine(S) 163 or S184 activates or inactivates its proapoptotic function, respectively. To uncover the mechanism(s) by which phosphorylation regulates the proapoptotic function of Bax, a series of serine (S)→ alanine/glutamate (A/E) Bax mutants, including S163A, S184A, S163E, S184E, S163E/S184A (EA), S163A/S184E (AE), S163A/S184A (AA) and S163E/S184E (EE), were created to abrogate or mimic, respectively, either single or double-site phosphorylation. The compound Bax mutants (i.e. EA and AE) can flesh out the functional contribution of individual phosphorylation site(s). WT and each of these Bax mutants were overexpressed in Bax−/− MEF or lung cancer H157 cells and the proapoptotic activities were compared. Intriguingly, expression of any of Bax mutants containing the mutation S→A at S184 (i.e. S184A, EA or AA) represents more potent proapoptotic activity as compared to WT Bax in association with increased 6A7 epitope conformational change, mitochondrial localization/insertion and prolonged half-life. In contrast, all Bax mutants containing the mutation S→E at S184 (i.e. S184E, AE or EE) have a mobility-shift and fail to insert into mitochondrial membranes with decreased protein stability and less apoptotic activity. Unexpectedly, mutation either S→A or S→E at S163 site does not significantly affect the proapoptotic activity of Bax. These findings indicate that S184 but not S163 is the major phosphorylation site for functional regulation of Bax's activity. Therefore, manipulation of the phosphorylation status of Bax at S184 may represent a novel strategy for cancer treatment

Changes in weight loss, body composition and cardiovascular disease risk after altering macronutrient distributions during a regular exercise program in obese women

<p>Abstract</p> <p>Background</p> <p>This study's purpose investigated the impact of different macronutrient distributions and varying caloric intakes along with regular exercise for metabolic and physiological changes related to weight loss.</p> <p>Methods</p> <p>One hundred forty-one sedentary, obese women (38.7 ± 8.0 yrs, 163.3 ± 6.9 cm, 93.2 ± 16.5 kg, 35.0 ± 6.2 kg•m^-2, 44.8 ± 4.2% fat) were randomized to either no diet + no exercise control group (CON) a no diet + exercise control (ND), or one of four diet + exercise groups (high-energy diet [HED], very low carbohydrate, high protein diet [VLCHP], low carbohydrate, moderate protein diet [LCMP] and high carbohydrate, low protein [HCLP]) in addition to beginning a 3x•week^-1supervised resistance training program. After 0, 1, 10 and 14 weeks, all participants completed testing sessions which included anthropometric, body composition, energy expenditure, fasting blood samples, aerobic and muscular fitness assessments. Data were analyzed using repeated measures ANOVA with an alpha of 0.05 with LSD post-hoc analysis when appropriate.</p> <p>Results</p> <p>All dieting groups exhibited adequate compliance to their prescribed diet regimen as energy and macronutrient amounts and distributions were close to prescribed amounts. Those groups that followed a diet and exercise program reported significantly greater anthropometric (waist circumference and body mass) and body composition via DXA (fat mass and % fat) changes. Caloric restriction initially reduced energy expenditure, but successfully returned to baseline values after 10 weeks of dieting and exercising. Significant fitness improvements (aerobic capacity and maximal strength) occurred in all exercising groups. No significant changes occurred in lipid panel constituents, but serum insulin and HOMA-IR values decreased in the VLCHP group. Significant reductions in serum leptin occurred in all caloric restriction + exercise groups after 14 weeks, which were unchanged in other non-diet/non-exercise groups.</p> <p>Conclusions</p> <p>Overall and over the entire test period, all diet groups which restricted their caloric intake and exercised experienced similar responses to each other. Regular exercise and modest caloric restriction successfully promoted anthropometric and body composition improvements along with various markers of muscular fitness. Significant increases in relative energy expenditure and reductions in circulating leptin were found in response to all exercise and diet groups. Macronutrient distribution may impact circulating levels of insulin and overall ability to improve strength levels in obese women who follow regular exercise.</p

Crop Updates 2007 - Lupins, Pulses and Oilseeds

This session covers forty eight papers from different authors: 2006 REGIONAL ROUNDUP 1. South east agricultural region, Mark Seymour1 and Jacinta Falconer2, 1Department of Agriculture and Food, 2Cooperative Bulk Handling Group 2. Central agricultural region, Ian Pritchard, Department of Agriculture and Food 3. Great Southern and Lakes region, Rodger Beermier, Department of Agriculture and Food 4. Northern agricultural region, Wayne Parker and Martin Harries, Department of Agriculture and Food LUPINS 5. Development of anthracnose resistant and early flowering albus lupins (Lupinus albus L) in Western Australia, Kedar Adhikari and Geoff Thomas, Department of Agriculture and Food 6. New lupins adapted to the south coast, Peter White, Bevan Buirchell and Mike Baker, Department of Agriculture and Food 7. Lupin species and row spacing interactions by environment, Martin Harries, Peter White, Bob French, Jo Walker, Mike Baker and Laurie Maiolo, Department of Agriculture and Food 8. The interaction of lupin species row spacing and soil type, Martin Harries, Bob French, Laurie Maiolo and Jo Walker, Department of Agriculture and Food 9. The effects of row spacing and crop density on competitiveness of lupins with wild radish, Bob French and Laurie Maiolo, Department of Agriculture and Food 10. The effect of time of sowing and radish weed density on lupin yield, Martin Harries and Jo Walker, Department of Agriculture and Food 11. Interaction of time of sowing and weed management in lupins, Martin Harries and Jo Walker, Department of Agriculture and Food 12. Delayed sowing as a strategy to manage annual ryegrass, Bob French and Laurie Maiolo, Department of Agriculture and Food 13. Is delayed sowing a good strategy for weed management in lupins? Bob French, Department of Agriculture and Food 14. Lupins aren’t lupins when it comes to simazine, Peter White and Leigh Smith, Department of Agriculture and Food 15. Seed yield and anthracnose resistance of Tanjil mutants tolerant to metribuzin, Ping Si1, Bevan Buirchell1,2 and Mark Sweetingham1,2, 1Centre for Legumes in Mediterranean Agriculture, Australia; 2Department of Agriculture and Food 16. The effect of herbicides on nodulation in lupins, Lorne Mills1, Harmohinder Dhammu2 and Beng Tan1, 1Curtin University of Technology and 2Department of Agriculture and Food 17. Effect of fertiliser placements and watering regimes on lupin growth and seed yield in the central grain belt of Western Australia, Qifu Ma1, Zed Rengel1, Bill Bowden2, Ross Brennan2, Reg Lunt2 and Tim Hilder2, 1Soil Science & Plant Nutrition UWA, 2Department of Agriculture and Food 18. Development of a forecasting model for Bean Yellow Mosaic Virus in lupins, T. Maling1,2, A. Diggle1, D. Thackray1,2, R.A.C. Jones2, and K.H.M. Siddique1, 1Centre for Legumes in Mediterranean Agriculture, The University of Western Australia; 2Department of Agriculture and Food 19. Manufacturing of lupin tempe,Vijay Jayasena1,4, Leonardus Kardono2,4, Ken Quail3,4 and Ranil Coorey1,4, 1Curtin University of Technology, Perth, Australia, 2Indonesian Institute of Sciences (LIPI), Indonesia, 3BRI Australia Ltd, Sydney, Australia, 4Grain Foods CRC, Sydney, Australia 20. The impact of lupin based ingredients in ice-cream, Hannah Williams, Lee Sheer Yap and Vijay Jayasena, Curtin University of Technology, Perth WA 21. The acceptability of muffins substituted with varying concentrations of lupin flour, Anthony James, Don Elani Jayawardena and Vijay Jayasena, Curtin University of Technology, PerthWA PULSES 22. Chickpea variety evaluation, Kerry Regan1, Rod Hunter1, Tanveer Khan1,2and Jenny Garlinge1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia 23. Advanced breeding trials of desi chickpea, Khan, T.N.1, Siddique, K.H.M.3, Clarke, H.2, Turner, N.C.2, MacLeod, W.1, Morgan, S.1, and Harris, A.1, 1Department of Agriculture and Food, 2Centre for Legumes in Mediterranean Agriculture, 3TheUniversity of Western Australia 24. Ascochyta resistance in chickpea lines in Crop Variety Testing (CVT) of 2006, Tanveer Khan1 2, Bill MacLeod1, Alan Harris1, Stuart Morgan1and Kerry Regan1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia 25. Yield evaluation of ascochyta blight resistant Kabuli chickpeas, Kerry Regan1and Kadambot Siddique2, 1Department of Agriculture and Food, 2Institute of Agriculture, The University of Western Australia 26. Pulse WA Chickpea Industry Survey 2006, Mark Seymour1, Ian Pritchard1, Wayne Parker1and Alan Meldrum2, 1Department of Agriculture and Food, 2Pulse Australia 27. Genes from the wild as a valuable genetic resource for chickpea improvement, Heather Clarke1, Helen Bowers1and Kadambot Siddique2, 1Centre for Legumes in Mediterranean Agriculture, 2Institute of Agriculture, The University of Western Australia 28. International screening of chickpea for resistance to Botrytis grey mould, B. MacLeod1, Dr T. Khan1, Prof. K.H.M. Siddique2and Dr A. Bakr3, 1Department of Agriculture and Food, 2The University of Western Australia, 3Bangladesh Agricultural Research Institute 29. Balance® in chickpea is safest applied post sowing to a level seed bed, Wayne Parker, Department of Agriculture and Food, 30. Demonstrations of Genesis 510 chickpea, Wayne Parker, Department of Agriculture and Food 31. Field pea 2006, Ian Pritchard, Department of Agriculture and Food 32. Field pea variety evaluation, Kerry Regan1, Rod Hunter1, Tanveer Khan1,2 and Jenny Garlinge1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia 33. Breeding highlights of the Australian Field Pea Improvement Program (AFPIP),Kerry Regan1, Tanveer Khan1,2, Phillip Chambers1, Chris Veitch1, Stuart Morgan1 , Alan Harris1and Tony Leonforte3, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia, 3Department of Primary Industries, Victoria 34. Field pea germplasm enhancement for black spot resistance, Tanveer Khan, Kerry Regan, Stuart Morgan, Alan Harris and Phillip Chambers, Department of Agriculture and Food 35. Validation of Blackspot spore release model and testing moderately resistant field pea line, Mark Seymour, Ian Pritchard, Rodger Beermier, Pam Burgess and Leanne Young, Department of Agriculture and Food 36. Yield losses from sowing field pea seed infected with Pea Seed-borne Mosaic Virus, Brenda Coutts, Donna O’Keefe, Rhonda Pearce, Monica Kehoe and Roger Jones, Department of Agriculture and Food 37. Faba bean in 2006, Mark Seymour, Department of Agriculture and Food 38. Germplasm evaluation – faba bean, Mark Seymour1, Terri Jasper1, Ian Pritchard1, Mike Baker1 and Tim Pope1,2, 1Department of Agriculture and Food, , 2CLIMA, The University of Western Australia 39. Breeding highlights of the Coordinated Improvement Program for Australian Lentils (CIPAL), Kerry Regan1, Chris Veitch1, Phillip Chambers1 and Michael Materne2, 1Department of Agriculture and Food, 2Department of Primary Industries, Victoria 40. Screening pulse lentil germplasm for tolerance to alternate herbicides, Ping Si1, Mike Walsh2 and Mark Sweetingham1,3, 1Centre for Legumes in Mediterranean Agriculture, 2West Australian Herbicide Resistance Initiative, 3Department of Agriculture and Food 41. Genomic synteny in legumes: Application to crop breeding, Phan, H.T.T.1, Ellwood, S.R.1, Hane, J.1, Williams, A.1, Ford, R.2, Thomas, S.3 and Oliver R1, 1Australian Centre of Necrotrophic Plant Pathogens, Murdoch University, 2BioMarka, University of Melbourne, 3NSW Department of Primary Industries 42. Tolerance of lupins, chickpeas and canola to Balanceâ(Isoxaflutole) and Galleryâ (Isoxaben), Leigh Smith and Peter White, Department of Agriculture and Food CANOLA AND OILSEEDS 43. The performance of TT Canola varieties in the National Variety Test (NVT),WA,2006,Katie Robinson, Research Agronomist, Agritech Crop Research 44. Evaluation of Brassica crops for biodiesel in Western Australia, Mohammad Amjad, Graham Walton, Pat Fels and Andy Sutherland, Department of Agriculture and Food 45. Production risk of canola in different rainfall zones in Western Australia, Imma Farré1, Michael Robertson2 and Senthold Asseng3, 1Department of Agriculture and Food, 2CSIRO Sustainable Ecosystems, 3CSIRO Plant Industry 46. Future directions of blackleg management – dynamics of blackleg susceptibility in canola varieties, Ravjit Khangura, Moin Salam and Bill MacLeod, Department of Agriculture and Food 47. Appendix 1: Contributors 48. Appendix 2: List of common acronym

DIA1R Is an X-Linked Gene Related to Deleted In Autism-1

Background: Autism spectrum disorders (ASDs) are frequently occurring disorders diagnosed by deficits in three core functional areas: social skills, communication, and behaviours and/or interests. Mental retardation frequently accompanies the most severe forms of ASDs, while overall ASDs are more commonly diagnosed in males. Most ASDs have a genetic origin and one gene recently implicated in the etiology of autism is the Deleted-In-Autism-1 (DIA1) gene. Methodology/Principal Findings: Using a bioinformatics-based approach, we have identified a human gene closely related to DIA1, we term DIA1R (DIA1-Related). While DIA1 is autosomal (chromosome 3, position 3q24), DIA1R localizes to the X chromosome at position Xp11.3 and is known to escape X-inactivation. The gene products are of similar size, with DIA1 encoding 430, and DIA1R 433, residues. At the amino acid level, DIA1 and DIA1R are 62 % similar overall (28 % identical), and both encode signal peptides for targeting to the secretory pathway. Both genes are ubiquitously expressed, including in fetal and adult brain tissue. Conclusions/Significance: Examination of published literature revealed point mutations in DIA1R are associated with X-linked mental retardation (XLMR) and DIA1R deletion is associated with syndromes with ASD-like traits and/or XLMR. Together, these results support a model where the DIA1 and DIA1R gene products regulate molecular traffic through the cellular secretory pathway or affect the function of secreted factors, and functional deficits cause disorders with ASD-lik

Crop Updates 2006 - Lupins and Pulses

This session covers sixty six papers from different authors: 2005 LUPIN AND PULSE INDUSTRY HIGHLIGHTS 1. Lupin Peter White, Department of Agriculture 2. Pulses Mark Seymour, Department of Agriculture 3. Monthly rainfall at experimental sites in 2005 4. Acknowledgements Amelia McLarty EDITOR 5. Contributors 6. Background Peter White, Department of Agriculture 2005 REGIONAL ROUNDUP 7. Northern agricultural region Wayne Parker, Department of Agriculture 8. Central agricultural region Ian Pritchard and Bob French, Department of Agriculture 9. Great southern and lakes Rodger Beermier, Department of Agriculture 10. South east region Mark Seymour, Department of Agriculture LUPIN AND PULSE PRODUCTION AGRONOMY AND GENETIC IMPROVEMENT 11. Lupin Peter White, Department of Agriculture 12. Narrow-leafed lupin breeding Bevan Buirchell, Department of Agriculture 13. Progress in the development of pearl lupin (Lupinus mutabilis) for Australian agriculture, Mark Sweetingham1,2, Jon Clements1, Geoff Thomas2, Roger Jones1, Sofia Sipsas1, John Quealy2, Leigh Smith1 and Gordon Francis1 1CLIMA, The University of Western Australia 2Department of Agriculture 14. Molecular genetic markers and lupin breeding, Huaan Yang, Jeffrey Boersma, Bevan Buirchell, Department of Agriculture 15. Construction of a genetic linkage map using MFLP, and identification of molecular markers linked to domestication genes in narrow-leafed lupin (Lupinus augustiflolius L) Jeffrey Boersma1,2, Margaret Pallotta3, Bevan Buirchell1, Chengdao Li1, Krishnapillai Sivasithamparam2 and Huaan Yang1 1Department of Agriculture, 2The University of Western Australia, 3Australian Centre for Plant Functional Genomics, South Australia 16. The first gene-based map of narrow-leafed lupin – location of domestication genes and conserved synteny with Medicago truncatula, M. Nelson1, H. Phan2, S. Ellwood2, P. Moolhuijzen3, M. Bellgard3, J. Hane2, A. Williams2, J. Fos‑Nyarko4, B. Wolko5, M. Książkiewicz5, M. Cakir4, M. Jones4, M. Scobie4, C. O’Lone1, S.J. Barker1, R. Oliver2, and W. Cowling1 1School of Plant Biology, The University of Western Australia, 2Australian Centre for Necrotrophic Fungal Pathogens, Murdoch University, 3Centre for Bioinformatics and Biological Computing, Murdoch University, 4School of Biological Sciences and Biotechnology, SABC, Murdoch University,5Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland 17. How does lupin optimum density change row spacing? Bob French and Laurie Maiolo, Department of Agriculture 18. Wide row spacing and seeding rate of lupins with conventional and precision seeding machines Martin Harries, Jo Walker and Murray Blyth, Department of Agriculture 19. Influence of row spacing and plant density on lupin competition with annual ryegrass, Martin Harries, Jo Walker and Murray Blyth, Department of Agriculture 20. Effect of timing and speed of inter-row cultivation on lupins, Martin Harries, Jo Walker and Steve Cosh, Department of Agriculture 21. The interaction of atrazine herbicide rate and row spacing on lupin seedling survival, Martin Harries and Jo Walker Department of Agriculture 22. The banding of herbicides on lupin row crops, Martin Harries, Jo Walker and Murray Blyth, Department of Agriculture 23. Large plot testing of herbicide tolerance of new lupin lines, Wayne Parker, Department of Agriculture 24. Effect of seed source and simazine rate of seedling emergence and growth, Peter White and Greg Shea, Department of Agriculture 25. The effect of lupin row spacing and seeding rate on a following wheat crop, Martin Harries, Jo Walker and Dirranie Kirby, Department of Agriculture 26. Response of crop lupin species to row spacing, Leigh Smith1, Kedar Adhikari1, Jon Clements2 and Patrizia Guantini3, 1Department of Agriculture, 2CLIMA, The University of Western Australia, 3University of Florence, Italy 27. Response of Lupinus mutabilis to lime application and over watering, Peter White, Leigh Smith and Mark Sweetingham, Department of Agriculture 28. Impact of anthracnose on yield of Andromeda lupins, Geoff Thomas, Kedar Adhikari and Katie Bell, Department of Agriculture 29. Survey of lupin root health (in major production areas), Geoff Thomas, Ken Adcock, Katie Bell, Ciara Beard and Anne Smith, Department of Agriculture 30. Development of a generic forecasting and decision support system for diseases in the Western Australian wheatbelt, Tim Maling1, Art Diggle1,2, Debbie Thackray1, Kadambot Siddique1 and Roger Jones1,2 1CLIMA, The University of Western Australia, 2Department of Agriculture 31.Tanjil mutants highly tolerant to metribuzin, Ping Si1, Mark Sweetingham1,2, Bevan Buirchell1,2 and Huaan Yang l,2 1CLIMA, The University of Western Australia, 2Department of Agriculture 32. Precipitation pH vs. yield and functional properties of lupin protein isolate, Vijay Jayasena1, Hui Jun Chih1 and Ken Dods2 1Curtin University of Technology, 2Chemistry Centre 33. Lupin protein isolation with the use of salts, Vijay Jayasena1, Florence Kartawinata1,Ranil Coorey1 and Ken Dods2 1Curtin University of Technology, 2Chemistry Centre 34. Field pea, Mark Seymour, Department of Agriculture 35. Breeding highlights Kerry Regan1,2, Tanveer Khan1,2, Stuart Morgan1 and Phillip Chambers1 1Department of Agriculture, 2CLIMA, The University of Western Australia 36. Variety evaluation, Kerry Regan1,2, Tanveer Khan1,2, Jenny Garlinge1 and Rod Hunter1 1Department of Agriculture, 2CLIMA, The University of Western Australia 37. Days to flowering of field pea varieties throughout WA Mark Seymour1, Ian Pritchard1, Rodger Beermier1, Pam Burgess1 and Dr Eric Armstrong2 Department of Agriculture, 2NSW Department of Primary Industries, Wagga Wagga 38. Semi-leafless field peas yield more, with less ryegrass seed set, in narrow rows, Glen Riethmuller, Department of Agriculture 39. Swathing, stripping and other innovative ways to harvest field peas, Mark Seymour, Ian Pritchard, Rodger Beermier and Pam Burgess, Department of Agriculture 40. Pulse demonstrations, Ian Pritchard, Wayne Parker, Greg Shea, Department of Agriculture 41. Field pea extension – focus on field peas 2005, Ian Pritchard, Department of Agriculture 42. Field pea blackspot disease in 2005: Prediction versus reality, Moin Salam, Jean Galloway, Pip Payne, Bill MacLeod and Art Diggle, Department of Agriculture 43. Pea seed-borne mosaic virus in pulses: Screening for seed quality defects and virus resistance, Rohan Prince, Brenda Coutts and Roger Jones, Department of Agriculture, and CLIMA, The University of Western Australia 44. Yield losses from sowing field peas infected with pea seed-borne mosaic virus, Rohan Prince, Brenda Coutts and Roger Jones, Department of Agriculture, and CLIMA, The University of Western Australia 45. Desi chickpea, Wayne Parker, Department of Agriculture 46. Breeding highlights, Tanveer Khan 1,2, Pooran Gaur3, Kadambot Siddique2, Heather Clarke2, Stuart Morgan1and Alan Harris1, 1Department of Agriculture2CLIMA, The University of Western Australia, 3International Crop Research Institute for Semi Arid Tropics (ICRISAT), India 47. National chickpea improvement program, Kerry Regan1, Ted Knights2 and Kristy Hobson3,1Department of Agriculture, 2Agriculture New South Wales 3Department of Primary Industries, Victoria 48. Chickpea breeding lines in CVT exhibit excellent ascochyta blight resistance, Tanveer Khan1,2, Alan Harris1, Stuart Morgan1 and Kerry Regan1,2, 1Department of Agriculture, 2CLIMA, The University of Western Australia 49. Variety evaluation, Kerry Regan1,2, Tanveer Khan1,2, Jenny Garlinge2 and Rod Hunter2, 1CLIMA, The University of Western Australia 2Department of Agriculture 50. Desi chickpeas for the wheatbelt, Wayne Parker and Ian Pritchard, Department of Agriculture 51. Large scale demonstration of new chickpea varieties, Wayne Parker, MurrayBlyth, Steve Cosh, Dirranie Kirby and Chris Matthews, Department of Agriculture 52. Ascochyta management with new chickpeas, Martin Harries, Bill MacLeod, Murray Blyth and Jo Walker, Department of Agriculture 53. Management of ascochyta blight in improved chickpea varieties, Bill MacLeod1, Colin Hanbury2, Pip Payne1, Martin Harries1, Murray Blyth1, Tanveer Khan1,2, Kadambot Siddique2, 1Department of Agriculture, 2CLIMA, The University of Western Australia 54. Botrytis grey mould of chickpea, Bill MacLeod, Department of Agriculture 55. Kabuli chickpea, Kerry Regan, Department of Agriculture, and CLIMA, The University of Western Australia 56. New ascochyta blight resistant, high quality kabuli chickpea varieties, Kerry Regan1,2, Kadambot Siddique2, Tim Pope2 and Mike Baker1, 1Department of Agriculture, 2CLIMA, The University of Western Australia 57. Crop production and disease management of Almaz and Nafice, Kerry Regan and Bill MacLeod, Department of Agriculture, and CLIMA, The University of Western Australia 58. Faba bean,Mark Seymour, Department of Agriculture 59. Germplasm evaluation – faba bean, Mark Seymour1, Tim Pope2, Peter White1, Martin Harries1, Murray Blyth1, Rodger Beermier1, Pam Burgess1 and Leanne Young1,1Department of Agriculture, 2CLIMA, The University of Western Australia 60. Factors affecting seed coat colour of faba bean during storage, Syed Muhammad Nasar-Abbas1, Julie Plummer1, Kadambot Siddique2, Peter White 3, D. Harris4 and Ken Dods4.1The University of Western Australia, 2CLIMA, The University of Western Australia, 3Department of Agriculture, 4Chemistry Centre 61. Lentil,Kerry Regan, Department of Agriculture, and CLIMA, The University of Western Australia 62. Variety and germplasm evaluation, Kerry Regan1,2, Tim Pope2, Leanne Young1, Phill Chambers1, Alan Harris1, Wayne Parker1 and Michael Materne3, 1Department of Agriculture 2CLIMA, The University of Western Australia, 3Department of Primary Industries, Victoria Pulse species 63. Land suitability for production of different crop species in Western Australia, Peter White, Dennis van Gool, and Mike Baker, Department of Agriculture 64. Genomic synteny in legumes: Application to crop breeding, Huyen Phan1, Simon Ellwood1, J. Hane1, Angela Williams1, R. Ford2, S. Thomas3 and Richard Oliver1,1Australian Centre of Necrotrophic Plant Pathogens, Murdoch University 2BioMarka, School of Agriculture and Food Systems, ILFR, University of Melbourne 3NSW Department of Primary Industries 65. ALOSCA – Development of a dry flow legume seed inoculant, Rory Coffey and Chris Poole, ALOSCA Technologies Pty Ltd 66. Genetic dissection of resistance to fungal necrotrophs in Medicago truncatula, Simon Ellwood1, Theo Pfaff1, Judith Lichtenzveig12, Lars Kamphuis1, Nola D\u27Souza1, Angela Williams1, Emma Groves1, Karam Singh2 and Richard Oliver1 1Australian Centre of Necrotrophic Plant Pathogens, Murdoch University, 2CSIRO Plant Industry APPENDIX I: LIST OF COMMON ACRONYM