44 resultados para Biotechnology programs

em University of Queensland eSpace - Australia


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Our AUTC Biotechnology study (Phases 1 and 2) identified a range of areas that could benefit from a common approach by universities nationally. A national network of biotechnology educators needs to be solidified through more regular communication, biennial meetings, and development of methods for sharing effective teaching practices and industry placement strategies, for example. Our aims in this proposed study are to: a. Revisit the state of undergraduate biotechnology degree programs nationally to determine their rate of change in content, growth or shrinkage in student numbers (as the biotech industry has had its ups and downs in recent years), and sustainability within their institutions in light of career movements of key personnel, tightening budgets, and governmental funding priorities. b. Explore the feasibility of a range of initiatives to benefit university biotechnology education to determine factors such as how practical each one is, how much buy-in could be gained from potentially participating universities and industry counterparts, and how sustainable such efforts are. One of many such initiatives arising in our AUTC Biotech study was a national register of industry placements for final-year students. c. During scoping and feasibility study, to involve our colleagues who are teaching in biotechnology – and contributing disciplines. Their involvement is meant to yield not only meaningful insight into how to strengthen biotechnology teaching and learning but also to generate ‘buy-in’ on any initiatives that result from this effort.

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Mediated physical activity interventions can reach large numbers of people at low cost. Programs delivered through the mail that target the stage of motivational readiness have been shown to increase activity. Communication technology (websites and e-mail) might provide a means for delivering similar programs. Randomized trial conducted between August and October 2001. Participants included staff at an Australian university (n=655; mean AGE=43, standard deviation, 10 years). Participants were randomized to either an 8-week, stage-targeted print program (Print) or 8-week, stage-targeted website (Web) program. The main outcome was change in self-reported physical activity.

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This paper addresses the broader unresolved issues posed by the patenting of genetic materials that are central to dealing with the tension between the patenting and competition schemes, namely distinguishing between what has already been 'discovered' and economically useful innovations (including the thresholds for novelty and non-obviousness), the exclusion of some subject matter from patenting and the restrictions on access to genetic resources to facilitate further innovation. The possible solutions of raising the threshold patenting standards, taking advantage of international intellectual property law developments and compulsory licensing are examined as ways to ameliorate the possibly detrimental consequences of current genetic material patenting practices. (C) 2003 Elsevier B.V. All rights reserved.

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1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial. 2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS. 3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r(2), intercept and slope. Second, the two models' assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites. 4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS- and RIVPACS-type models, if not to replace them.

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Lentil is a self-pollinating diploid (2n = 14 chromosomes) annual cool season legume crop that is produced throughout the world and is highly valued as a high protein food. Several abiotic stresses are important to lentil yields world wide and include drought, heat, salt susceptibility and iron deficiency. The biotic stresses are numerous and include: susceptibility to Ascochyta blight, caused by Ascochyta lentis; Anthracnose, caused by Colletotrichum truncatum; Fusarium wilt, caused by Fusarium oxysporum; Sclerotinia white mold, caused by Sclerotinia sclerotiorum; rust, caused by Uromyces fabae; and numerous aphid transmitted viruses. Lentil is also highly susceptible to several species of Orabanche prevalent in the Mediterranean region, for which there does not appear to be much resistance in the germplasm. Plant breeders and geneticists have addressed these stresses by identifying resistant/tolerant germplasm, determining the genetics involved and the genetic map positions of the resistant genes. To this end progress has been made in mapping the lentil genome and several genetic maps are available that eventually will lead to the development of a consensus map for lentil. Marker density has been limited in the published genetic maps and there is a distinct lack of co-dominant markers that would facilitate comparisons of the available genetic maps and efficient identification of markers closely linked to genes of interest. Molecular breeding of lentil for disease resistance genes using marker assisted selection, particularly for resistance to Ascochyta blight and Anthracnose, is underway in Australia and Canada and promising results have been obtained. Comparative genomics and synteny analyses with closely related legumes promises to further advance the knowledge of the lentil genome and provide lentil breeders with additional genes and selectable markers for use in marker assisted selection. Genomic tools such as macro and micro arrays, reverse genetics and genetic transformation are emerging technologies that may eventually be available for use in lentil crop improvement.

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The refinement calculus provides a framework for the stepwise development of imperative programs from specifications. In this paper we study a refinement calculus for deriving logic programs. Dealing with logic programs rather than imperative programs has the dual advantages that, due to the expressive power of logic programs, the final program is closer to the original specification, and each refinement step can achieve more. Together these reduce the overall number of derivation steps. We present a logic programming language extended with specification constructs (including general predicates, assertions, and types and invariants) to form a wide-spectrum language. General predicates allow non-executable properties to be included in specifications. Assertions, types and invariants make assumptions about the intended inputs of a procedure explicit, and can be used during refinement to optimize the constructed logic program. We provide a semantics for the extended logic programming language and derive a set of refinement laws. Finally we apply these to an example derivation.

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