320 research outputs found
Adverse Drug Reactions and Avalanches: Life at the Edge of Chaos
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97209/1/0091270005278056.pd
Sonic diaspora, vibrations and rhythm: thinking through the sounding of the Jamaican dancehall session
The propagation of vibrations may provide a better way of understanding diasporic spread than the conventional focus on the circulation of products (Hall 1980, Appadurai 1986, 1996, Gilroy 1993a, Brah 1996). Jamaican sound systems operate as a broadcast medium and a source of CDs, DVDs and other commercial products (Henriques 2007a). But the dancehall sound system session also propagates a broad spectrum of frequencies diffused through a range of media and activities - described as âsoundingâ (following Smallâs 1998 concept of âmusickingâ). These include the material vibrations of the signature low-pitched auditory frequencies of Reggae as a bass culture (Johnson 1980), at the loudness of âsonic dominanceâ (Henriques 2003). Secondly a session propagates the corporeal vibrations of rituals, dance routines and bass-line âriddimsâ (Veal 2007). Thirdly it propagates the ethereal vibrations (Henriques 2007b), âvibesâ or atmosphere of the sexually charged popular subculture by which the crowd (audience) appreciate each dancehall session as part of the Dancehall scene (Cooper 2004). The paper concludes that thinking though vibrating frequencies makes it easier to appreciate how audiences with no direct or inherited connection with a particular music genre can be energetically infected and affected - to form a sonic diaspora
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 arenât 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. BalanceÂź 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 OâKeefe, 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 â 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 BalanceĂą(Isoxaflutole) and GalleryĂą (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 â 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
Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus.
BACKGROUND: Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk. METHOD: We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/ ), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation. RESULTS: Analysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR)â=â1.09, 95 % confidence interval (CI)â=â1.06-1.12; Pâ=â3âĂâ10(-9)), rs805510 (ORâ=â1.08, 95 % CIâ=â1.04-1.12, Pâ=â2âĂâ10(-5)), and rs1871152 (ORâ=â1.04, 95 % CIâ=â1.02-1.06; Pâ=â2âĂâ10(-4)) identified in the general populations, and rs113824616 (Pâ=â7âĂâ10(-5)) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at Pâ<â0.05 in East Asians, but none of the associations were statistically significant in African descendants. Multiple candidate functional variants are located in putative enhancer sequences. Chromatin interaction data suggested that PTHLH was the likely target gene of these enhancers. Of the six variants with the strongest evidence of potential functionality, rs11049453 was statistically significantly associated with the expression of PTHLH and its nearby gene CCDC91 at Pâ<â0.05. CONCLUSION: This study identified four independent association signals at 12p11 and revealed potentially functional variants, providing additional insights into the underlying biological mechanism(s) for the association observed between variants at 12p11 and breast cancer risk.UK funding includes Cancer Research UK and NIH.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13058-016-0718-
Use of SMS texts for facilitating access to online alcohol interventions: a feasibility study
A41 Use of SMS texts for facilitating access to online alcohol interventions: a feasibility study
In: Addiction Science & Clinical Practice 2017, 12(Suppl 1): A4
Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus
A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 Ă 10-20), ER-negative BC (P=1.1 Ă 10-13), BRCA1-associated BC (P=7.7 Ă 10-16) and triple negative BC (P-diff=2 Ă 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 Ă 10-3) and ABHD8 (P<2 Ă 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3âČ-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk
Search for Hâγγ produced in association with top quarks and constraints on the Yukawa coupling between the top quark and the Higgs boson using data taken at 7 TeV and 8 TeV with the ATLAS detector
A search is performed for Higgs bosons produced in association with top quarks using the diphoton decay mode of the Higgs boson. Selection requirements are optimized separately for leptonic and fully hadronic final states from the top quark decays. The dataset used corresponds to an integrated luminosity of 4.5 fbâ14.5 fbâ1 of protonâproton collisions at a center-of-mass energy of 7 TeV and 20.3 fbâ1 at 8 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. No significant excess over the background prediction is observed and upper limits are set on the ttÂŻH production cross section. The observed exclusion upper limit at 95% confidence level is 6.7 times the predicted Standard Model cross section value. In addition, limits are set on the strength of the Yukawa coupling between the top quark and the Higgs boson, taking into account the dependence of the ttÂŻH and tH cross sections as well as the Hâγγ branching fraction on the Yukawa coupling. Lower and upper limits at 95% confidence level are set at â1.3 and +8.0 times the Yukawa coupling strength in the Standard Model
Operation and performance of the ATLAS semiconductor tracker
The semiconductor tracker is a silicon microstrip detector forming part of the inner tracking system of the ATLAS experiment at the LHC. The operation and performance of the semiconductor tracker during the first years of LHC running are described. More than 99% of the detector modules were operational during this period, with an average intrinsic hit efficiency of (99.74±0.04)%. The evolution of the noise occupancy is discussed, and measurements of the Lorentz angle, Ύ-ray production and energy loss presented. The alignment of the detector is found to be stable at the few-micron level over long periods of time. Radiation damage measurements, which include the evolution of detector leakage currents, are found to be consistent with predictions and are used in the verification of radiation background simulations
Measurement of the cross section of high transverse momentum ZâbbÌ production in protonâproton collisions at âs = 8 TeV with the ATLAS detector
This Letter reports the observation of a high transverse momentum ZâbbÌ signal in protonâproton collisions at âs=8 TeV and the measurement of its production cross section. The data analysed were collected in 2012 with the ATLAS detector at the LHC and correspond to an integrated luminosity of 19.5 fbâÂč. The ZâbbÌ decay is reconstructed from a pair of b -tagged jets, clustered with the anti-ktkt jet algorithm with R=0.4R=0.4, that have low angular separation and form a dijet with pT>200 GeVpT>200 GeV. The signal yield is extracted from a fit to the dijet invariant mass distribution, with the dominant, multi-jet background mass shape estimated by employing a fully data-driven technique that reduces the dependence of the analysis on simulation. The fiducial cross section is determined to be
ÏZâbbÂŻfid=2.02±0.20 (stat.) ±0.25 (syst.)±0.06 (lumi.) pb=2.02±0.33 pb,
in good agreement with next-to-leading-order theoretical predictions
Fiducial and differential cross sections of Higgs boson production measured in the four-lepton decay channel in pp collisions at âs = 8 TeV with the ATLAS detector
Measurements of fiducial and differential cross sections of Higgs boson production in the HâZZâ â 4â decay channel are presented. The cross sections are determined within a fiducial phase space and corrected for detection efficiency and resolution effects. They are based on 20.3 fbâÂč of pp collision data, produced at âs = 8 TeV centre-of-mass energy at the LHC and recorded by the ATLAS detector. The differential measurements are performed in bins of transverse momentum and rapidity of the four-lepton system, the invariant mass of the subleading lepton pair and the decay angle of the leading lepton pair with respect to the beam line in the four-lepton rest frame, as well as the number of jets and the transverse momentum of the leading jet. The measured cross sections are compared to selected theoretical calculations of the Standard Model expectations. No significant deviation from any of the tested predictions is found
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