Characterization of plasmids in a human clinical strain of Lactococcus garvieae

The present work describes the molecular characterization of five circular plasmids found in the human clinical strain Lactococcus garvieae 21881. The plasmids were designated pGL1-pGL5, with molecular sizes of 4,536 bp, 4,572 bp, 12,948 bp, 14,006 bp and 68,798 bp, respectively. Based on detailed sequence analysis, some of these plasmids appear to be mosaics composed of DNA obtained by modular exchange between different species of lactic acid bacteria. Based on sequence data and the derived presence of certain genes and proteins, the plasmid pGL2 appears to replicate via a rolling-circle mechanism, while the other four plasmids appear to belong to the group of lactococcal theta-type replicons. The plasmids pGL1, pGL2 and pGL5 encode putative proteins related with bacteriocin synthesis and bacteriocin secretion and immunity. The plasmid pGL5 harbors genes (txn, orf5 and orf25) encoding proteins that could be considered putative virulence factors. The gene txn encodes a protein with an enzymatic domain corresponding to the family actin-ADP-ribosyltransferases toxins, which are known to play a key role in pathogenesis of a variety of bacterial pathogens. The genes orf5 and orf25 encode two putative surface proteins containing the cell wall-sorting motif LPXTG, with mucin-binding and collagen-binding protein domains, respectively. These proteins could be involved in the adherence of L. garvieae to mucus from the intestine, facilitating further interaction with intestinal epithelial cells and to collagenous tissues such as the collagen-rich heart valves. To our knowledge, this is the first report on the characterization of plasmids in a human clinical strain of this pathogen.

Baran: an interaction-centred user monitoring framework

User Quality of Experience (QoE) is a subjective entity and difficult to measure. One important aspect of it, User Experience (UX), corresponds to the sensory and emotional state of a user. For a user interacting through a User Interface (UI), precise information on how they are using the UI can contribute to understanding their UX, and thereby understanding their QoE. As well as a user’s use of the UI such as clicking, scrolling, touching, or selecting, other real-time digital information about the user such as from smart phone sensors (e.g. accelerometer, light level) and physiological sensors (e.g. heart rate, ECG, EEG) could contribute to understanding UX. Baran is a framework that is designed to capture, record, manage and analyse the User Digital Imprint (UDI) which, is the data structure containing all user context information. Baran simplifies the process of collecting experimental information in Human and Computer Interaction (HCI) studies, by recording comprehensive real-time data for any UI experiment, and making the data available as a standard UDI data structure. This paper presents an overview of the Baran framework, and provides an example of its use to record user interaction and perform some basic analysis of the interaction.

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.

MEF2C: expression, regulation and interaction with target genes in health and diseases

Tese de doutoramento, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2015

Spatial, environmental and human influences on the distribution of otter (Lutra lutra) in the Spanish provinces

In a previous survey of otters ( Lutra lutra L. 1758) in Spain, different causes were invoked to explain the frequency of the species in each province. To find common causes of the distribution of the otter in Spain, we recorded a number of spatial, environmental and human variables in each Spanish province. We then performed a stepwise linear multiple regression of the proportion of positive sites of otter in the Spanish provinces separately on each of the three groups of variables. Geographic longitude, January air humidity, soil permeability and highway density were the variables selected. A linear regression of the proportion of otter presence on these variables explained 62.4% of the variance. We then used the selected variables in a partial regression analysis to specify which proportions of the variation are explained exclusively by spatial, environmental and human factors, and which proportions are attributable to interactions between these components. Pure environmental effects accounted for only 5.5% of the variation, while pure spatial and pure human effects explained 18% and 9.7%, respectively. Shared variation among the components totalled 29.2%, of which 10.9% was explained by the interaction between environmental and spatial factors. Human factors explained globally less variance than spatial and environmental ones, but the pure human influence was higher than the pure environmental one. We concluded that most of the variation in the proportion of occurrences of otter in Spanish provinces is spatially structured, and that environmental factors have more influence on otter presence than human ones; however, the human influence on otter distribution is less structured in space, and thus can be more disruptive. This effect of large infrastructures on wild populations must be taken into account when planning large-scale conservation policies