A DNA fingerprinting procedure for ultra high-throughput genetic analysis of insects

Existing procedures for the generation of polymorphic DNA markers are not optimal for insect studies in which the organisms are often tiny and background molecular Information is often non-existent. We have used a new high throughput DNA marker generation protocol called randomly amplified DNA fingerprints (RAF) to analyse the genetic variability In three separate strains of the stored grain pest, Rhyzopertha dominica. This protocol is quick, robust and reliable even though it requires minimal sample preparation, minute amounts of DNA and no prior molecular analysis of the organism. Arbitrarily selected oligonucleotide primers routinely produced similar to 50 scoreable polymorphic DNA markers, between individuals of three Independent field isolates of R. dominica. Multivariate cluster analysis using forty-nine arbitrarily selected polymorphisms generated from a single primer reliably separated individuals into three clades corresponding to their geographical origin. The resulting clades were quite distinct, with an average genetic difference of 37.5 +/- 6.0% between clades and of 21.0 +/- 7.1% between individuals within clades. As a prelude to future gene mapping efforts, we have also assessed the performance of RAF under conditions commonly used in gene mapping. In this analysis, fingerprints from pooled DNA samples accurately and reproducibly reflected RAF profiles obtained from Individual DNA samples that had been combined to create the bulked samples.

Improving pain management by nurses: A pilot peer intervention program

Significant pain continues to be reported by many hospitalized patients despite the numerous and varied educational programs developed and implemented to improve pain management. A theoretically based Peer Intervention Program was designed from a predictive model to address nurses' beliefs, attitudes, subjective norms, self-efficacy, perceived control and intentions in the management of pain with p.r.n. (as required) narcotic analgesia. The pilot study of this program utilized a quasi-experimental pre-post test design with a patient intervention, nurse and patient intervention and control conditions consisting of 24, 18 and 19 nurses, respectively. One week after the intervention, significant differences were found between the nurse and patient condition and the two other conditions in beliefs, self-efficacy, perceived control, positive trend in attitudes, subjective norms and intentions. The most positive aspects of the program were supportive interactive discussions with peers and an awareness and understanding of beliefs and attitudes and their roles in behavior.

Identification of causal relationships among traits related to drought resistance in Stylosanthes scabra using QTL analysis

Previous studies have shown that a negative relationship exists between transpiration efficiency (TE) and carbon isotope discrimination (Delta) and between TE and specific leaf area (SLA) in Stylosanthes scabra, A glasshouse experiment was conducted to confirm these relationships in an F-2 population and to study the causal nature of these relationships through quantitative trait loci (QTL) analysis, One hundred and twenty F-2 genotypes from a cross between two genotypes within S. scabra were used. Three replications for each genotype were maintained through vegetative propagation, Water stress was imposed by maintaining plants at 40% of field capacity for about 45 d. To facilitate QTL analysis, a genetic linkage map consisting of 151 RAPD markers was developed, Results from this study show that Delta was significantly and negatively correlated with TE and biomass production. Similarly, SLA showed significant negative correlation with TE and biomass production, Most of the QTL for TE and Delta were present on linkage groups 5 and 11. Similarly, QTL for SLA, transpiration and biomass productivity traits were clustered on linkage groups 13 and 24, One unlinked marker was also associated with these traits, There were several markers coincident between different traits, At all the coincident QTL, the direction of QTL effects was consistent with phenotypic data, At the coincident markers between TE and Delta, high alleles of TE were associated with low alleles of Delta. Similarly, low alleles of SLA were associated with high alleles of biomass productivity traits and transpiration. At the coincident markers between trans-4-hydroxy-N-methyl proline (MHP) and relative water content (RWC), low alleles of MHP were associated with high alleles of RWC, This study suggests the causal nature of the relationship between TE and Delta. Phenotypic data and QTL, data show that SLA was more closely associated with biomass production than with TE, This study also shows that a cause-effect relationship may exist between SLA and biomass production.

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.

Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Occult Axillary Lymph Node Metastases in Breast Cancer Do Matter: Results of 10-Year Survival Analysis

Axillary lymph node status is one of the most powerful prognostic factors for patients with breast cancer and is often critical in stratifying patients into adjuvant treatment regimens. In 203 apparently node-negative cases of breast cancer, a combination of immunohistochemical staining and step-sectioning identified occult metastases in 25% of cases. Ten-year follow-up information is available for these patients. Histologic features of the primary tumor and immunohistochemical staining for estrogen receptor, progesterone receptor, Her-2, and p53 were also evaluated. With multivariate analysis, both occult metastases and higher histologic grade of the primary tumor were independent predictors of disease-free survival. Histologic grade was the only significant independent predictor of overall survival. Estrogen receptor, progesterone receptor, Her-2, and p53 status did not predict the presence of metastases or survival when all tumor types were considered together. Metastases >0.5 mm significantly predicted a poorer disease-free survival when invasive ductal carcinomas were considered alone. Histologic grade was significantly associated with disease-free survival in the premenopausal and perimenopausal patients but not in the postmenopausal patients. The presence of occult metastases approached significance for overall survival in the premenopausal and perimenopausal patients but not in the postmenopausal patients.

Aggregated personality, climate and demographic factors as predictors of departmental shrinkage

The present study investigated how demographic, personality, and climate variables act to predict departmental theft. Participants in the current field survey were 153 employees from 17 departments across two stores. The results of confirmatory factor analyses supported the construct validity of the Big Five Inventory (John, Donahue, & Kentle, 1991) and the Occupational Climate Questionnaire (Furnham & Gunter, 1997) in UK work settings. The results of regression analysis indicate that the variability in departmental theft is accountable in terms of a linear combination of demographic, personality, and climate factors. We concluded that an expanded theoretical perspective (utilizing demographic, personality, and climate variables) explained more variance than might otherwise be expected from any single perspective. Indeed, climate, personality, and demographic variables operated legitimately at the departmental level. Finally, we explained aggregated personality as a form of social interaction which is the by-product of individual differences.

Patterns of resistance to angular leaf spot, anthracnose and common bacterial blight in common bean germplasm

Diseases and insect pests are major causes of low yields of common bean (Phaseolus vulgaris L.) in Latin America and Africa. Anthracnose, angular leaf spot and common bacterial blight are widespread foliar diseases of common bean that also infect pods and seeds. One thousand and eighty-two accessions from a common bean core collection from the primary centres of origin were investigated for reaction to these three diseases. Angular leaf spot and common bacterial blight were evaluated in the field at Santander de Quilichao, Colombia, and anthracnose was evaluated in a screenhouse in Popayan, Colombia. By using the 15-group level from a hierarchical clustering procedure, it was found that 7 groups were formed with mainly Andean common bean accessions (Andean gene pool), 7 groups with mainly Middle American accessions (Middle American gene pool), while 1 group contained mixed accessions. Consistent with the theory of co-evolution, it was generally observed that accessions from the Andean gene pool were resistant to Middle American pathogen isolates causing anthracnoxe, while the Middle American accessions were resistant to pathogen isolates from the Andes. Different combinations of resistance patterns were found, and breeders can use this information to select a specific group of accessions on the basis of their need.

A new species of Phyllurus (Lacertilia : Gekkonidae) and a revised phylogeny and key for the Australian leaf-tailed geckos

Phyllurus gulbaru, sp. nov., is a highly distinct species of leaf-tailed gecko restricted to rocky rainforest of Pattersons Gorge, north-west of Townsville. The possession of a cylindrical, non-depressed, tapering original and regenerated tail separates P. gulbaru from all congeners except P. caudiannulatus. From this species P. gulbaru is separated by having a partially divided, as opposed to fully divided, rostral scale. Furthermore, the very small spinose body tubercles of P. gulbaru are in marked contrast to the large spinose body scales of P. caudiannulatus. An analysis of 729 bp of mitochondrial 12S rRNA and cytochrome b genes reveals P. gulbaru to be a deeply divergent lineage with closer affinities to mid-east Queensland congeners than the geographically neighbouring P. amnicola on Mt Elliot. In conservation terms, P. gulbaru is clearly at risk. Field surveys of Pattersons Gorge and the adjacent ranges indicate that this species is restricted to a very small area of highly fragmented habitat, of which only a small proportion receives a degree of protection in State forest. Further, there is ongoing, unchecked destruction of dry rainforest habitat by fire. Under current IUCN criteria, P. gulbaru warrants an Endangered ( B1, 2) listing.

On-line writing: a link to learning in a teacher education program

This paper examines the use of on-line discussion as a medium for learning in a pre-service teacher education program. As part of an Education Studies course student teachers engaged in a discussion of issues related to technology and equity in schools. The design of the task and the subsequent analysis of the on-line text were part of a research project investigating whether and how communications technology can be used to integrate and extend the learning of teacher education students. The main argument developed in the paper is that through the on-line activity distinctive sets of writing practices were created. These practices enabled students to make connections between the often disparate parts of teacher education programs-theory and practice, campus and school, research and experience. (C) 2002 Elsevier Science Ltd. All rights reserved.

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts. Copyright (K) 2003 Royal Meteorological Society.

Climate change and biotic invasions: a case history of a tropical woody vine

The impacts of climate change in the potential distribution and relative abundance of a C3 shrubby vine, Cryptostegia grandiflora, were investigated using the CLIMEX modelling package. Based upon its current naturalised distribution, C. grandiflora appears to occupy only a small fraction of its potential distribution in Australia under current climatic conditions; mostly in apparently sub-optimal habitat. The potential distribution of C. grandiflora is sensitive towards changes in climate and atmospheric chemistry in the expected range of this century, particularly those that result in increased temperature and water use efficiency. Climate change is likely to increase the potential distribution and abundance of the plant, further increasing the area at risk of invasion, and threatening the viability of current control strategies markedly. By identifying areas at risk of invasion, and vulnerabilities of control strategies, this analysis demonstrates the utility of climate models for providing information suitable to help formulate large-scale, long-term strategic plans for controlling biotic invasions. The effects of climate change upon the potential distribution of C. grandiflora are sufficiently great that strategic control plans for biotic invasions should routinely include their consideration. Whilst the effect of climate change upon the efficacy of introduced biological control agents remain unknown, their possible effect in the potential distribution of C. grandiflora will likely depend not only upon their effects on the population dynamics of C. grandiflora, but also on the gradient of climatic suitability adjacent to each segment of the range boundary.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.

A yield architecture framework to explain adaptation of pearl millet to environmental stress

Functional knowledge of the physiological basis of crop adaptation to stress is a prerequisite for exploiting specific adaptation to stress environments in breeding programs. This paper presents an analysis of yield components for pearl millet, to explain the specific adaptation of local landraces to stress environments in Rajasthan, India. Six genotypes, ranging from high-tillering traditional landraces to low-tillering open-pollinated modern cultivars, were grown in 20 experiments, covering a range of nonstress and drought stress patterns. In each experiment, yield components (particle number, grain number, 100 grain mass) were measured separately for main shoots, basal tillers, and nodal tillers. Under optimum conditions, landraces had a significantly lower grain yield than the cultivars, but no significant differences were observed at yield levels around 1 ton ha(-1). This genotype x environment interaction for grain yield was due to a difference in yield strategy, where landraces aimed at minimising the risk of a crop failure under stress conditions, and modem cultivars aimed at maximising yield potential under optimum conditions. A key aspect of the adaptation of landraces was the small size of the main shoot panicle, as it minimised (1) the loss of productive tillers during stem elongation; (2) the delay in anthesis if mid-season drought occurs; and (3) the reduction in panicle productivity of the basal tillers under stress. In addition, a low investment in structural panicle weight, relative to vegetative crop growth rate, promoted the production of nodal tillers, providing a mechanism to compensate for reduced basal tiller productivity if stress occurred around anthesis. A low maximum 100 grain mass also ensured individual grain mass was little affected by environmental conditions. The strategy of the high-tillering landraces carries a yield penalty under optimum conditions, but is expected to minimise the risk of a crop failure, particularly if mid-season drought stress occurs. The yield architecture of low-tillering varieties, by contrast, will be suited to end-of-season drought stress, provided anthesis is early. Application of the above adaptation mechanisms into a breeding program could enable the identification of plant types that match the prevalent stress patterns in the target environments. (C) 2003 E.J. van Oosterom. Published by Elsevier Science B.V. All rights reserved.