Mummy! Ball! Fish!: Why English-learning children produce nouns earlier than verbs

When English-learning children begin using words the majority of their early utterances (around 80%) are nouns. Compared to nouns, there is a paucity of verbs or non-verb relational words, such as 'up' meaning 'pick me up'. The primary explanations to account for these differences in use either argue in support of a 'cognitive account', which claims that verbs entail more cognitive complexity than nouns, or they provide evidence challenging this account. In this paper I propose an additional explanation for children's noun/verb asymmetry. Presenting a 'multi-modal account' of word-learning based on children's gesture and word combinations, I show that at the one-word stage English-learning children use gestures to express verb-like elements which leaves their words free to express noun-like elements.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Development of a dietary supplement database

Data describing the composition of dietary supplements are not readily available to the public health community. As a result, intake from dietary supplements is generally not considered in most dietary surveys and, hence, little is known about the significance of supplement intake in relation to total diet or disease risk. To enable a more comprehensive analysis of dietary data, a database of the composition of various dietary supplements has been compiled. Active ingredients of all dietary supplements sold in Australia are included in the Australian Register of Therapeutic Goods (ARTG), maintained by the Therapeutic Goods Administration. Products included in the database were restricted to those vitamin, mineral and other supplements identified in dietary data collected from studies conducted in southeast Queensland and New South Wales (850 supplements). Conversion factors from ingredients compounds to active elements were compiled from standard sources. No account has been made for bioavailability, consistent with current practice for food composition databases. The database can be queried by ARTG identification number, brand, product title, or a variety of other fields. Expected future developments include development of standard formulations for use when supplements are incompletely specified, and expansion of products included for more widespread use.

Expressed sequenced tags and new genes from the cattle tick, Boophilus microplus

We used the expressed sequenced tags (ESTs) approach to study the genome of the cattle tick Boophilus microplus. One hundred and forty-two of our 234 unique ESTs were from genes not previously identified from ticks, mites or any other arachnids. The largest class of identified ESTs (29%) was from genes involved in transcription and translation. Ninety-one ESTs (39% of all ESTs) did not match any sequences in international databases; some of these may be specific to ticks. Thirteen percent of our ESTs were from ribosomal proteins and two ESTs were for genes implicated in resistance to pesticides. (C) 1998 Chapman & Hall Ltd.

Interval breast cancers in an Australian mammographic screening program

Objective: To determine the incidence of interval cancers which occurred in the first 12 months after mammographic screening at a mammographic screening service. Design: Retrospective analysis of data obtained by crossmatching the screening Service and the New South Wales Central Cancer Registry databases. Setting: The Central & Eastern Sydney Service of BreastScreen NSW. Participants: Women aged 40-69 years at first screen, who attended for their first or second screen between 1 March 1988 and 31 December 1992. Main outcome measures: Interval-cancer rates per 10 000 screens and as a proportion of the underlying incidence of breast cancer (as estimated by the underlying rate in the total NSW population). Results: The 12-month interval-cancer incidence per 10 000 screens was 4.17 for the 40-49 years age group (95% confidence interval [CI], 1.35-9.73) and 4.64 for the 50-69 years age group (95% CI, 2.47-7.94). Proportional incidence rates were 30.1% for the 40-49 years age group (95% CI, 9.8-70.3) and 22% for the 50-69 years age group (95% CI, 11.7-37.7). There was no significant difference between the proportional incidence rate for the 50-69 years age group for the Central & Eastern Sydney Service and those of major successful overseas screening trials. Conclusion: Screening quality was acceptable and should result in a significant mortality reduction in the screened population. Given the small number of cancers involved, comparison of interval-cancer statistics of mammographic screening programs with trials requires age-specific or age-adjusted data, and consideration of confidence intervals of both program and trial data.

Unsupervised cell nucleus segmentation with active contours

The task of segmenting cell nuclei from cytoplasm in conventional Papanicolaou (Pap) stained cervical cell images is a classical image analysis problem which may prove to be crucial to the development of successful systems which automate the analysis of Pap smears for detection of cancer of the cervix. Although simple thresholding techniques will extract the nucleus in some cases, accurate unsupervised segmentation of very large image databases is elusive. Conventional active contour models as introduced by Kass, Witkin and Terzopoulos (1988) offer a number of advantages in this application, but suffer from the well-known drawbacks of initialisation and minimisation. Here we show that a Viterbi search-based dual active contour algorithm is able to overcome many of these problems and achieve over 99% accurate segmentation on a database of 20 130 Pap stained cell images. (C) 1998 Elsevier Science B.V. All rights reserved.

Inequitable distribution of general practitioners in Australia: estimating need through the Robin Hood Index

Objective: From Census data, to document the distribution of general practitioners in Australia and to estimate the number of general practitioners needed to achieve an equitable distribution accounting for community health need. Methods: Data on location of general practitioners, population size and crude mortality by statistical division (SD) were obtained from the Australian Bureau of Statistics. The number of patients per general practitioner by SD was calculated and plotted. Using crude mortality to estimate community health need, a ratio of the number of general practitioners per person:mortality was calculated for all Australia and for each SD (the Robin Hood Index). From this, the number of general practitioners needed to achieve equity was calculated. Results: In all, 26,290 general practitioners were identified in 57 SDs. The mean number of people per general practitioner is 707, ranging from 551 to 1887. Capital city SDs have most favourable ratios. The Robin Hood Index for Australia is 1, and ranges from 0.32 (relatively under-served) to 2.46 (relatively over-served). Twelve SDs (21%) including all capital cities and 65% of all Australians, have a Robin Hood Index > 1. To achieve equity per capita 2489 more general practitioners (10% of the current workforce) are needed. To achieve equity by the Robin Hood Index 3351 (13% of the current workforce) are needed. Conclusions: The distribution of general practitioners in Australia is skewed. Nonmetropolitan areas are relatively underserved. Census data and the Robin Hood Index could provide a simple means of identifying areas of need in Australia.

Discovery and characterization of a family of insecticidal neurotoxins with a rare vicinal disulfide bridge

We have isolated a family of insect-selective neurotoxins from the venom of the Australian funnel-web spider that appear to be good candidates for biopesticide engineering. These peptides, which we have named the Janus-faced atracotoxins (J-ACTXs), each contain 36 or 37 residues, with four disulfide bridges, and they show no homology to any sequences in the protein/DNA databases. The three-dimensional structure of one of these toxins reveals an extremely rare vicinal disulfide bridge that we demonstrate to be critical for insecticidal activity. We propose that J-ACTX comprises an ancestral protein fold that we refer to as the disulfide-directed beta-hairpin.

Coupled experimental and thermodynamic modeling studies for metallurgical smelting and coal combustion slag systems

An extensive research program focused on the characterization of various metallurgical complex smelting and coal combustion slags is being undertaken. The research combines both experimental and thermodynamic modeling studies. The approach is illustrated by work on the PbO-ZnO-Al2O3-FeO-Fe2O3-CaO-SiO2 system. Experimental measurements of the liquidus and solidus have been undertaken under oxidizing and reducing conditions using equilibration, quenching, and electron probe X-ray microanalysis. The experimental program has been planned so as to obtain data for thermodynamic model development as well as for pseudo-ternary Liquidus diagrams that can be used directly by process operators. Thermodynamic modeling has been carried out using the computer system FACT, which contains thermodynamic databases with over 5000 compounds and evaluated solution models. The FACT package is used for the calculation of multiphase equilibria in multicomponent systems of industrial interest. A modified quasi-chemical solution model is used for the liquid slag phase. New optimizations have been carried out, which significantly improve the accuracy of the thermodynamic models for lead/zinc smelting and coal combustion processes. Examples of experimentally determined and calculated liquidus diagrams are presented. These examples provide information of direct relevance to various metallurgical smelting and coal combustion processes.

Indicators used to measure the innovation process: defects and possible remedies

Some diverse indicators used to measure the innovation process are considered, They include those with art aggregate, and often national, focus, and rely on data from scientific publications, patents and R&D expenditures, etc. Others have a firm-level perspective, relying primarily on surveys or case studies. Also included are indicators derived from specialized databases, or consensual agreements reached through foresight exercises. There is an obvious need for greater integration of the various approaches to capture move effectively the richness of available data and better reflect the reality of innovation. The focus for such integration could be in the area of technology strategy, which integrates the diverse scientific, technological, and innovation activities of firms within their operating environments; improved capacity to measure it has implications for policy-makers, managers and researchers.

Computational immunology: The coming of age

The explosive growth in biotechnology combined with major advancesin information technology has the potential to radically transformimmunology in the postgenomics era. Not only do we now have readyaccess to vast quantities of existing data, but new data with relevanceto immunology are being accumulated at an exponential rate. Resourcesfor computational immunology include biological databases and methodsfor data extraction, comparison, analysis and interpretation. Publiclyaccessible biological databases of relevance to immunologists numberin the hundreds and are growing daily. The ability to efficientlyextract and analyse information from these databases is vital forefficient immunology research. Most importantly, a new generationof computational immunology tools enables modelling of peptide transportby the transporter associated with antigen processing (TAP), modellingof antibody binding sites, identification of allergenic motifs andmodelling of T-cell receptor serial triggering.

The incidence of schizophrenia: A systematic review

There has been a debate on whether or not the incidence of schizophrenia varies across time and place. In order to optimise the evidence upon which this debate is based, we have undertaken a systematicsystematic review of the literature. In this paper we provide an overview of the methods of the review and a preliminary analysis of the studies identified to date. Electronic databases (Medline, Psychlnfo, Embase, LILAC) were systematically searched for articles published between January 1965 and December 2001. The search terms were: (schizo* OR psycho*)AND (incidence OR prevalence). References were also identified from review articles, reference list and by writing to authors. To date we have identified 137 papers drawn from 33 nations. 37 papers in language other than English await translation. The currently included papers have generated 1413 different items of rate information data. In order to analyze these data we have undertaken several sequential filters in order to identify (a) non-overlapping data, (b) birth cohort study versus noncohort studies, (c) overall and sex-specific rates, (d) diagnostic criteria, (e) age ranges, (f) epoch of study, and (g) data on migrant or other special interest groups. In addition, we will examine the impact of urbanicity of site, age and/or sex standardization, and quality score on the incidence rates. The various discrete incidence rates will be presented graphically and the impact of various filters on these rates will be inspected using meta-analytic techniques. The use of meta-analysis may help elucidate the epidemiological landscape with respect to the incidence of schizophrenia and aid in the generation of new hypothesis. Acknowledgements: The Stanley Medical Research Institute supported project

Bioinformatics for characterisation of allergens, allergenicity and allergic crossreactivity

Allergies are a major cause of chronic ill health in industrialised countries with the incidence of reported cases steadily increasing. This Research Focus details how bioinformatics is transforming the field of allergy through providing databases for management of allergen data, algorithms for characterisation of allergic crossreactivity, structural motifs and B- and T-cell epitopes, tools for prediction of allergenicity and techniques for genomic and proteomic analysis of allergens.

Identification of a minimal promoter sequence for the human N-acetyltransferase Type I gene that binds AP-1 (activator protein 1) and YY-1 (Yin and Yang 1)

Human N-acetyltransferase Type I (NAT1) catalyses the acetylation of many aromatic amine and hydrazine compounds and it has been implicated in the catabolism of folic acid. The enzyme is widely expressed in the body, although there are considerable differences in the level of activity between tissues. A search of the mRNA databases revealed the presence of several NAT1 transcripts in human tissue that appear to be derived from different promoters. Because little is known about NAT1 gene regulation, the present study was undertaken to characterize one of the putative promoter sequences of the NAT1 gene located just upstream of the coding region. We show with reverse-transcriptase PCR that mRNA transcribed from this promoter (Promoter 1) is present in a variety of human cell-lines, but not in quiescent peripheral blood mononuclear cells. Using deletion mutant constructs, we identified a 20 bp sequence located 245 bases upstream of the translation start site which was sufficient for basal NAT1 expression. It comprised an AP-1 (activator protein 1)-binding site, flanked on either side by a TCATT motif. Mutational analysis showed that the AP-1 site and the 3' TCATT sequence were necessary for gene expression, whereas the 5' TCATT appeared to attenuate promoter activity. Electromobility shift assays revealed two specific bands made up by complexes of c-Fos/Fra, c-Jun, YY-1 (Yin and Yang 1) and possibly Oct-1. PMA treatment enhanced expression from the NAT1 promoter via the AP-1-binding site. Furthermore, in peripheral blood mononuclear cells, PMA increased endogenous NAT1 activity and induced mRNA expression from Promoter I, suggesting that it is functional in vivo.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.