Interaction between Candida albicans and Pseudomonas aeruginosa

Fungal pathogen Candida albicans causes serious nosocomial infections in patients, in part, due to formation of drug-resistant biofilms. Protein kinases (PK) and transcription factors (TF) mediate signal transduction and transcription of proteins involved in biofilm development. To discover biofilm-related PKs, a collection of 63 C. albicans PK mutants was screened twice independently with microtiter plate-based biofilm assay (XTT). Thirty-eight (60%) mutants showed different degrees of biofilm impairment with the poor biofilm formers additionally possessing filamentation defects. Most of these genes were already known to encode proteins associated with Candida morphology and biofilms but VPS15, PKH3, PGA43, IME2 and CEX1, were firstly associated with both processes in this study. Previous studies of Holcombe et al. (2010) had shown that bacterial pathogen, Pseudomonas aeruginosa can impair C. albicans filamentation and biofilm development. To investigate their interaction, the good biofilm former PK mutants of C. albicans were assessed for their response to P. aeruginosa supernatants derived from two strains, wildtype PAO1 and homoserine lactone (HSL)-free mutant ΔQS, without finding any nonresponsive mutants. This suggested that none of the PKs in this study was implicated in Candida-Pseudomonas signaling. To screen promoter sequences for overrepresented TFs across C. albicans gene sets significantly up/downregulated in presence of bacterial supernatants from Holcombe et al. (2010) study, TFbsST database was created online. The TFbsST database integrates experimentally verified TFs of Candida to analyse promoter sequences for TF binding sites. In silico studies predicted that Efg1p was overrepresented in C. albicans and C. parapsilosis RBT family genes.

User interaction monitoring and analysis framework

An overview is given of a user interaction monitoring and analysis framework called BaranC. Monitoring and analysing human-digital interaction is an essential part of developing a user model as the basis for investigating user experience. The primary human-digital interaction, such as on a laptop or smartphone, is best understood and modelled in the wider context of the user and their environment. The BaranC framework provides monitoring and analysis capabilities that not only records all user interaction with a digital device (e.g. smartphone), but also collects all available context data (such as from sensors in the digital device itself, a fitness band or a smart appliances). The data collected by BaranC is recorded as a User Digital Imprint (UDI) which is, in effect, the user model and provides the basis for data analysis. BaranC provides functionality that is useful for user experience studies, user interface design evaluation, and providing user assistance services. An important concern for personal data is privacy, and the framework gives the user full control over the monitoring, storing and sharing of their data.

A service-oriented user interaction analysis framework supporting adaptive applications

A comprehensive user model, built by monitoring a user's current use of applications, can be an excellent starting point for building adaptive user-centred applications. The BaranC framework monitors all user interaction with a digital device (e.g. smartphone), and also collects all available context data (such as from sensors in the digital device itself, in a smart watch, or in smart appliances) in order to build a full model of user application behaviour. The model built from the collected data, called the UDI (User Digital Imprint), is further augmented by analysis services, for example, a service to produce activity profiles from smartphone sensor data. The enhanced UDI model can then be the basis for building an appropriate adaptive application that is user-centred as it is based on an individual user model. As BaranC supports continuous user monitoring, an application can be dynamically adaptive in real-time to the current context (e.g. time, location or activity). Furthermore, since BaranC is continuously augmenting the user model with more monitored data, over time the user model changes, and the adaptive application can adapt gradually over time to changing user behaviour patterns. BaranC has been implemented as a service-oriented framework where the collection of data for the UDI and all sharing of the UDI data are kept strictly under the user's control. In addition, being service-oriented allows (with the user's permission) its monitoring and analysis services to be easily used by 3rd parties in order to provide 3rd party adaptive assistant services. An example 3rd party service demonstrator, built on top of BaranC, proactively assists a user by dynamic predication, based on the current context, what apps and contacts the user is likely to need. BaranC introduces an innovative user-controlled unified service model of monitoring and use of personal digital activity data in order to provide adaptive user-centred applications. This aims to improve on the current situation where the diversity of adaptive applications results in a proliferation of applications monitoring and using personal data, resulting in a lack of clarity, a dispersal of data, and a diminution of user control.