Building family/general practice research capacity

To promote the range of interventions for building family/general practice (family medicine) research capacity, we describe successful international examples. Such examples of interventions that build research capacity focus on diseases and illness research, as well as process research; monitor the output of research in family/general practice (family medicine); increase the number of family medicine research journals; encourage and enable research skills acquisition (including making it part of professional training); strengthen the academic base; and promote research networks and collaborations. The responsibility for these interventions lies with the government, colleges and academies, and universities. There are exciting and varied methods of building research capacity in family medicine.

Inter-organisational networks, value creation and the process of technology integration in research and development

Traditional methods of R&D management are no longer sufficient for embracing innovations and leveraging complex new technologies to fully integrated positions in established systems. This paper presents the view that the technology integration process is a result of fundamental interactions embedded in inter-organisational activities. Emerging industries, high technology companies and knowledge intensive organisations owe a large part of their viability to complex networks of inter-organisational interactions and relationships. R&D organisations are the gatekeepers in the technology integration process with their initial sanction and motivation to develop technologies providing the first point of entry. Networks rely on the activities of stakeholders to provide the foundations of collaborative R&D activities, business-to-business marketing and strategic alliances. Such complex inter-organisational interactions and relationships influence value creation and organisational goals as stakeholders seek to gain investment opportunities. A theoretical model is developed here that contributes to our understanding of technology integration (adoption) as a dynamic process, which is simultaneously structured and enacted through the activities of stakeholders and organisations in complex inter-organisational networks of sanction and integration.

Introduction to Special issue on the Symposium on the Application of Neural Networks to the Earth Sciences

Papers in this issue of Natural Resources Research are from the “Symposium on the Application of Neural Networks to the Earth Sciences,” held 20–21 August 2002 at NASA Moffet Field, Mountain View, California. The Symposium represents the Seventh International Symposium on Mineral Exploration (ISME-02). It was sponsored by the Mining and Materials Processing Institute of Japan (MMIJ), the US Geological Survey, the Circum-Pacific Council, and NASA. The ISME symposia have been held every two years in order to bring together scientists actively working on diverse quantitative methods applied to the earth sciences. Although the title, International Symposium on Mineral Exploration, suggests exclusive focus on mineral exploration, interests and presentations always have been wide-ranging—talks presented at this symposium are no exception.

The influence of social communications networks on intentions to purchase on the web

Despite the increased offering of online communication channels to support web-based retail systems, there is limited marketing research that investigates how these channels act singly, or in combination with offline channels, to influence an individual's intention to purchase online. If the marketer's strategy is to encourage online transactions, this requires a focus on consumer acceptance of the web-based transaction technology, rather than the purchase of the products per se. The exploratory study reported in this paper examines normative influences from referent groups in an individual's on and offline social communication networks that might affect their intention to use online transaction facilities. The findings suggest that for non-adopters, there is no normative influence from referents in either network. For adopters, one online and one offline referent norm positively influenced this group's intentions to use online transaction facilities. The implications of these findings are discussed together with future research directions.

The composition of attachment networks throughout the adult years

This study assessed the structure of adults' attachment networks, using a questionnaire measure of preferred attachment figures with a large sample of adults (N = 812) representing various ages and life situations. Two broad research questions were addressed. The first question concerned the variety of attachment figures reported by adults and the relative strength of attachment to each, including preferred (primary) attachment figures. The second question concerned the effects of normative life events on attachment networks and the nature of primary attachment figures in different life situations. Overall, the results supported the preeminent role of attachment relationships with romantic partners. However, relationships with mothers, fathers, siblings, children, and friends also met the strict criteria used to define full-blown attachments; further, each of these targets constituted the primary attachment figure for some participants. The structure of the attachment network was related to variables such as age, relationship status, and parental status, attesting to the important role of normative life events. The results have theoretical and applied significance and are related to principles of attachment, caregiving, and socioemotional selectivity.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

Prediction of subcellular localization using sequence-biased recurrent networks

Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.

The changing nature of innovation networks in Taiwan: From imitation to innovation?

Taiwan is embarking on a new phase in its approach to building its national innovative capacity, through building the infrastructure for a biotechnology industry. Rather than acting as a “fast follower” of trends developed elsewhere, Taiwan is seeking to evolve the elements of a national innovation system, including upgrading the role of universities in providing fundamental R&D, in providing incubators for new, knowledge-based firms, in developing new funding models, and in establishing new biotech-focused science parks. This paper reviews the progress achieved to date, and the prospects for this new phase in Taiwan’s transition from imitation to innovation