A new conceptual and quantitative approach to exploring and defining potential open-access olfactory information.

All organisms emit odour, providing 'open-access' olfactory information for any receiver with the right sensory apparatus. Characterizing open-access information emitted by groups of organisms, such as plant species, provides the means to answer significant questions about ecological interactions and their evolution. We present a new conceptual framework defining information reliability and a practical method to characterize and recover information from amongst olfactory noise. We quantified odour emissions from two tree species, one focal group and one outgroup, to demonstrate our approach using two new R statistical functions. We explore the consequences of relaxing or tightening criteria defining information and, from thousands of odour combinations, we identify and quantify those few likely to be informative. Our method uses core general principles characterizing information while incorporating knowledge of how receivers detect and discriminate odours. We can now map information in consistency-precision reliability space, explore the concept of information, and test information-noise boundaries, and between cues and signals.

Text messages promoting healthy lifestyle and linked with activity monitors stimulate an immediate increase in physical activity among women after gestational diabetes.

AIMS: To evaluate the immediate effect of text messages promoting healthy lifestyle and supporting parenting on physical activity amongst women with recent gestational diabetes (GDM). METHODS: Analysis of data from a pilot randomised controlled trial of a healthy lifestyle program for women with recent GDM. Intervention subjects received text messages providing motivation, reminders, information and feedback as well as an activity monitor. This sub-study examined step count in the 4 h after receipt of a text message, compared to the same time of day on other days among intervention subjects. RESULTS: Data from 7326 days where step counts were recorded, from 31 women were analysed. The median steps in the 4 h following a text message was 1237 (IQR 18-2240) and it was 1063 (IQR 0-2038) over the same time period on comparison days where there was no message (p < 0.001). The effect was similar whether the messages pertained to physical activity or not. There was no attenuation of the response over 36-38 weeks. CONCLUSIONS: Women with recent GDM increase their step count in the hours following positive and supportive text messages. This suggests that text messaging programs can facilitate healthy lifestyle and diabetes prevention in this population.

A framework for de-identification of free-text data in electronic medical records enabling secondary use.

Clinical free-text data represent a vast, untapped source of rich information. If more accessible for research it would supplement information captured in structured fields. Data need to be de-identified prior to being reused for research. However, a lack of transparency with existing de-identification software tools makes it difficult for data custodians to assess potential risks associated with the release of de-identified clinical free-text data. This case study describes the development of a framework for releasing de-identified clinical free-text data in two local health districts in NSW, Australia. A sample of clinical documents (n = 14 768 965), including progress notes, nursing and medical assessments and discharge summaries, were used for development. An algorithm was designed to identify and mask patient names without damaging data utility. For each note, the algorithm output the (i) note length before and after de-identification, (ii) the number of patient names and (iii) the number of common words. These outputs were used to iteratively refine the algorithm performance. This was followed by manual review of a random subset of records by a health information manager. Notes that were not correctly de-identified were fixed, and performance was reassessed until resolution. All notes in this sample were suitably de-identified using this method. Developing a transparent method for de-identifying clinical free-text data enables informed-decision making by data custodians and the safe re-use of clinical free-text data for research and public benefit.