Abstract Self-management has become increasingly important with the growing population living with chronic conditions. Self-Management Support (SMS) provided in healthcare systems is essential for its success. While prior research mainly focuses on Western countries, this paper presents a study of SMS practices for osteoporosis management as part of a Whole Course Management (WCM) program recently implemented in the healthcare system in China, which features a new role called case manager in the hospital dedicated to SMS and related coordination. Based on interviews with 22 participants, including one case manager, two physicians, three nurses, seven patients, and nine caregivers, we highlight the importance of the role of case manager in promoting awareness of osteoporosis and self-management, integrated care coordination, and emotional support. At the same time, it also reveals challenges and promising directions to make SMS more effective, mainly in terms of self-management education, active patient involvement, and coordination among the care network. We ended by reflecting on our findings and discussing implications for SMS and the design of ICTs.

BACKGROUND: Understanding the constituent domains of oncogenes, their origins and their fusions may shed new light about the initiation and the development of cancers. RESULTS: We have developed a computational pipeline for identification of functional domains of human genes, prediction of the origins of these domains and their major fusion events during evolution through integration of existing and new tools of our own. An application of the pipeline to 124 well-characterized human oncogenes has led to the identification of a collection of domains and domain pairs that occur substantially more frequently in oncogenes than in human genes on average. Most of these enriched domains and domain pairs are related to tyrosine kinase activities. In addition, our analyses indicate that a substantial portion of the domain-fusion events of oncogenes took place in metazoans during evolution. CONCLUSION: We expect that the computational pipeline for domain identification, domain origin and domain fusion prediction will prove to be useful for studying other groups of genes.

MOTIVATION: Physical mapping of chromosomes using the maximum likelihood (ML) model is a problem of high computational complexity entailing both discrete optimization to recover the optimal probe order as well as continuous optimization to recover the optimal inter-probe spacings. In this paper, two versions of the genetic algorithm (GA) are proposed, one with heuristic crossover and deterministic replacement and the other with heuristic crossover and stochastic replacement, for the physical mapping problem under the maximum likelihood model. The genetic algorithms are compared with two other discrete optimization approaches, namely simulated annealing (SA) and large-step Markov chains (LSMC), in terms of solution quality and runtime efficiency. RESULTS: The physical mapping algorithms based on the GA, SA and LSMC have been tested using synthetic datasets and real datasets derived from cosmid libraries of the fungus Neurospora crassa. The GA, especially the version with heuristic crossover and stochastic replacement, is shown to consistently outperform the SA-based and LSMC-based physical mapping algorithms in terms of runtime and final solution quality. Experimental results on real datasets and simulated datasets are presented. Further improvements to the GA in the context of physical mapping under the maximum likelihood model are proposed. AVAILABILITY: The software is available upon request from the first author.