Analysis of the study design and manuscript deficiencies in research articles submitted to Emergency Medicine

Objective: To describe and analyse the study design and manuscript deficiencies in original research articles submitted to Emergency Medicine. Methods: This was a retrospective, analytical study. Articles were enrolled if the reports of the Section Editor and two reviewers were available. Data were extracted from these reports only. Outcome measures were the mean number and nature of the deficiencies and the mean reviewers’ assessment score. Results: Fifty-seven articles were evaluated (28 accepted for publication, 19 rejected, 10 pending revision). The mean (± SD) number of deficiencies was 18.1 ± 6.9, 16.4 ± 6.5 and 18.4 ± 6.7 for all articles, articles accepted for publication and articles rejected, respectively (P = 0.31 between accepted and rejected articles). The mean assessment scores (0–10) were 5.5 ± 1.5, 5.9 ± 1.5 and 4.7 ± 1.4 for all articles, articles accepted for publication and articles rejected, respectively. Accepted articles had a significantly higher assessment score than rejected articles (P = 0.006). For each group, there was a negative correlation between the number of deficiencies and the mean assessment score (P > 0.05). Significantly more rejected articles ‘… did not further our knowledge’ (P = 0.0014) and ‘… did not describe background information adequately’ (P = 0.049). Many rejected articles had ‘… findings that were not clinically or socially significant’ (P = 0.07). Common deficiencies among all articles included ambiguity of the methods (77%) and results (68%), conclusions not warranted by the data (72%), poor referencing (56%), inadequate study design description (51%), unclear tables (49%), an overly long discussion (49%), limitations of the study not described (51%), inadequate definition of terms (49%) and subject selection bias (40%). Conclusions: Researchers should undertake studies that are likely to further our knowledge and be clinically or socially significant. Deficiencies in manuscript preparation are more frequent than mistakes in study design and execution. Specific training or assistance in manuscript preparation is indicated.

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.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

An action research report on the Korean national digital library

This paper presents the results of my action research. I was involved in establishing and running a digital library that was founded by the government of South Korea. The process involved understanding the relationship between the national IT infrastructure and the success factors of the digital library. In building, the national IT infrastructure, a digital library system was implemented; it combines all existing digitized university libraries and can provide overseas information, such as foreign journal articles, instantly and freely to every Korean researcher. An empirical survey was made as a part of the action research; the survey determined user satisfaction in the newly established national digital library. After obtaining the survey results, I suggested that the current way of running the nationwide government-owned digital library should be retained. (C) 2002 Elsevier Science B.V. All rights reserved.

A simulated annealing algorithm for finding consensus sequences

Motivation: A consensus sequence for a family of related sequences is, as the name suggests, a sequence that captures the features common to most members of the family. Consensus sequences are important in various DNA sequencing applications and are a convenient way to characterize a family of molecules. Results: This paper describes a new algorithm for finding a consensus sequence, using the popular optimization method known as simulated annealing. Unlike the conventional approach of finding a consensus sequence by first forming a multiple sequence alignment, this algorithm searches for a sequence that minimises the sum of pairwise distances to each of the input sequences. The resulting consensus sequence can then be used to induce a multiple sequence alignment. The time required by the algorithm scales linearly with the number of input sequences and quadratically with the length of the consensus sequence. We present results demonstrating the high quality of the consensus sequences and alignments produced by the new algorithm. For comparison, we also present similar results obtained using ClustalW. The new algorithm outperforms ClustalW in many cases.