Supply Chain Practice, Supply Chain Performance Indicators and Competitive Advantage of Australian Beef Enterprises: A Conceptual Framework

This research focuses on an Australian agribusiness supply chain, the Australian Beef Supply Chain. The definition of the Australian Beef Supply Chain is the chain or sequence of all activities from the breeding property to the domestic or overseas consumers. The beef sector in Australia is undergoing rapid change because of globalisation, a highly competitive beef market (local and export), quicker production cycle and delivery times and consequently reduced inventories, a general speed-up of the rate of change in the business environment, the trend toward more outsourcing of activities, and the rapid development of IT. In this business environment, advanced supply chain systems have the potential to provide significant contributions to Australian beef industry performance. A conceptual framework of the research project has been proposed. There are three elements of conceptual framework. Firstly, supply chain practice of Australian beef industry consists of five sub-elements such as strategic supplier partnerships, customer relationships, information sharing, information quality and a lean system. Moreover, there is an antecedent of cooperative behaviour such as trust and commitment influencing supply chain practice and supply chain performance indicators. Secondly, supply chain performance indicators include four sub-elements such as flexibility, efficiency, food quality and responsiveness. Finally, the competitive advantage framework of the Australian beef enterprises consists of price, quality, export sales growth and time to market. As a further step of the research after developing the conceptual framework, the research project focuses the analysis on how the antecedents of the sub-elements of supply chain practice affect supply chain performance in Australian beef enterprises, how trust and commitment in trading partners affect supply chain performance, how attributes such as flexibility, efficiency, food quality and responsiveness influence the sub-elements of competitive advantage. The research project leads on to further work on how Australian beef enterprises measure their supply chain performance and what the major difficulties are arising when implementing supply chain management in the Australian beef industry and what kind of changes can be made to beef supply chains to enhance their performance.Agribusiness,

Farm Management in Australia: The Way Forward

This paper summarises the outcomes of a National Workshop in Farm Management, 5-6 December 2002, organised by the University of Sydney, Faculty of Rural Management, Orange. At this Workshop leading farmers, industry leaders, corporate representatives, academics, researchers and extension officers explored the future of farm management (education, research and consultancy) in Australia. Major outcomes were that farm management practice is proceeding informally to undertake decisions supporting socially and ecologically friendly, sustainable commercial production agriculture. However the risks of lack of integration, a reductionist approach to only on-farm practice, stagnation of academic programs to respond to leading edge industry initiatives, as well as ill-defined boundaries for farm management research were identified. The analysis indicated that formal educational models, research and extension-consultancy frameworks of an holistic nature, and a multiple bottom line perspective, were appropriate avenues for the future development of farm management practice and research. Workshop participants perceived that a farm management strand emphasising business management rather than technology could be a better educational model. Also there was an emphasis in highlighting the importance of linked development and partnership amongst the different players. The Workshop created the conditions for development of networks among industry, education and consultative research.Farm Management,

Have the roles of two functional polymorphisms in breast cancer, R72P in P53 and MDM2-309 in MDM2, become clearer?

Genetic differences between individuals have been predicted to account for disparate outcomes in patients diagnosed with cancer. The search for genetic determinants has been ongoing for a considerable amount of time and it is only now that insights have been gained into which polymorphisms are most likely to be important in determining not only disease likelihood but also outcome. The quest to be able to accurately predict patient outcomes in breast cancer may now be a step closer as increased sample size is leading to more robust statistical analysis and a better understanding of molecular mechanisms of disease are forthcoming

Fine-mapping of the HNF1B multicancer locus identifies candidate variants that mediate endometrial cancer risk.

Common variants in the hepatocyte nuclear factor 1 homeobox B (HNF1B) gene are associated with the risk of Type II diabetes and multiple cancers. Evidence to date indicates that cancer risk may be mediated via genetic or epigenetic effects on HNF1B gene expression. We previously found single-nucleotide polymorphisms (SNPs) at the HNF1B locus to be associated with endometrial cancer, and now report extensive fine-mapping and in silico and laboratory analyses of this locus. Analysis of 1184 genotyped and imputed SNPs in 6608 Caucasian cases and 37 925 controls, and 895 Asian cases and 1968 controls, revealed the best signal of association for SNP rs11263763 (P = 8.4 × 10(-14), odds ratio = 0.86, 95% confidence interval = 0.82-0.89), located within HNF1B intron 1. Haplotype analysis and conditional analyses provide no evidence of further independent endometrial cancer risk variants at this locus. SNP rs11263763 genotype was associated with HNF1B mRNA expression but not with HNF1B methylation in endometrial tumor samples from The Cancer Genome Atlas. Genetic analyses prioritized rs11263763 and four other SNPs in high-to-moderate linkage disequilibrium as the most likely causal SNPs. Three of these SNPs map to the extended HNF1B promoter based on chromatin marks extending from the minimal promoter region. Reporter assays demonstrated that this extended region reduces activity in combination with the minimal HNF1B promoter, and that the minor alleles of rs11263763 or rs8064454 are associated with decreased HNF1B promoter activity. Our findings provide evidence for a single signal associated with endometrial cancer risk at the HNF1B locus, and that risk is likely mediated via altered HNF1B gene expression

A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis

Background: Several lines of evidence suggest that transcription factors are involved in the pathogenesis of Multiple Sclerosis (MS) but complete mapping of the whole network has been elusive. One of the reasons is that there are several clinical subtypes of MS and transcription factors that may be involved in one subtype may not be in others. We investigate the possibility that this network could be mapped using microarray technologies and contemporary bioinformatics methods on a dataset derived from whole blood in 99 untreated MS patients (36 Relapse Remitting MS, 43 Primary Progressive MS, and 20 Secondary Progressive MS) and 45 age-matched healthy controls. Methodology/Principal Findings: We have used two different analytical methodologies: a non-standard differential expression analysis and a differential co-expression analysis, which have converged on a significant number of regulatory motifs that are statistically overrepresented in genes that are either differentially expressed (or differentially co-expressed) in cases and controls (e.g., V$KROX_Q6, p-value ,3.31E-6; V$CREBP1_Q2, p-value ,9.93E-6, V$YY1_02, p-value ,1.65E-5). Conclusions/Significance: Our analysis uncovered a network of transcription factors that potentially dysregulate several genes in MS or one or more of its disease subtypes. The most significant transcription factor motifs were for the Early Growth Response EGR/KROX family, ATF2, YY1 (Yin and Yang 1), E2F-1/DP-1 and E2F-4/DP-2 heterodimers, SOX5, and CREB and ATF families. These transcription factors are involved in early T-lymphocyte specification and commitment as well as in oligodendrocyte dedifferentiation and development, both pathways that have significant biological plausibility in MS causation

Global circulation patterns of seasonal influenza viruses vary with antigenic drift.

Understanding the spatiotemporal patterns of emergence and circulation of new human seasonal influenza virus variants is a key scientific and public health challenge. The global circulation patterns of influenza A/H3N2 viruses are well characterized, but the patterns of A/H1N1 and B viruses have remained largely unexplored. Here we show that the global circulation patterns of A/H1N1 (up to 2009), B/Victoria, and B/Yamagata viruses differ substantially from those of A/H3N2 viruses, on the basis of analyses of 9,604 haemagglutinin sequences of human seasonal influenza viruses from 2000 to 2012. Whereas genetic variants of A/H3N2 viruses did not persist locally between epidemics and were reseeded from East and Southeast Asia, genetic variants of A/H1N1 and B viruses persisted across several seasons and exhibited complex global dynamics with East and Southeast Asia playing a limited role in disseminating new variants. The less frequent global movement of influenza A/H1N1 and B viruses coincided with slower rates of antigenic evolution, lower ages of infection, and smaller, less frequent epidemics compared to A/H3N2 viruses. Detailed epidemic models support differences in age of infection, combined with the less frequent travel of children, as probable drivers of the differences in the patterns of global circulation, suggesting a complex interaction between virus evolution, epidemiology, and human behaviour.T.B. was supported by a Newton International Fellowship from the Royal Society and through NIH U54 GM111274. S.R. was supported by MRC (UK, Project MR/J008761/1), Wellcome Trust (UK, Project 093488/Z/10/Z), Fogarty International Centre (USA, R01 TW008246‐01), DHS (USA, RAPIDD program), NIGMS (USA, MIDAS U01 GM110721‐01) and NIHR (UK, Health Protection Research Unit funding). The Melbourne WHO Collaborating Centre for Reference and Research on Influenza was supported by the Australian Government Department of Health and thanks N. Komadina and Y.‐M. Deng. The Atlanta WHO Collaborating Center for Surveillance, Epidemiology and Control of Influenza was supported by the U.S. Department of 13 Health and Human Services. NIV thanks A.C. Mishra, M. Chawla‐Sarkar, A.M. Abraham, D. Biswas, S. Shrikhande, AnuKumar B, and A. Jain. Influenza surveillance in India was expanded, in part, through US Cooperative Agreements (5U50C1024407 and U51IP000333) and by the Indian Council of Medical Research. M.A.S. was supported through NSF DMS 1264153 and NIH R01 AI 107034. Work of the WHO Collaborating Centre for Reference and Research on Influenza at the MRC National Institute for Medical Research was supported by U117512723. P.L., A.R. & M.A.S were supported by EU Seventh Framework Programme [FP7/2007‐2013] under Grant Agreement no. 278433-­‐PREDEMICS and ERC Grant agreement no. 260864. C.A.R. was supported by a University Research Fellowship from the Royal Society.This is the author accepted manuscript. It is currently under infinite embargo pending publication of the final version