Web-GIS models: accomplishing modularity with aspects

Spatial concerns of Web geographical information systems (Web-GIS) are inherently crosscutting and volatile: crosscutting because they affect multiple functionalities of Web-GIS systems, and volatile because their status may change often. If these concerns are not modularized properly, the quality of Web-GIS services, particularly with regard to adaptation and evolution, can be severely compromised. This paper uses aspect-orientation to model crosscutting and volatile spatial concerns. By modeling both types of concerns, crosscutting and volatile, as candidate aspects, one can use dynamic weaving to add or remove them from a system at runtime. The aspect-oriented approach proposed starts with the identification and specification of crosscutting concerns and follows by composing these using modeling aspects using a transformation approach, an aspect-oriented modeling technique. The conflicts that can emerge due to the composition order are also taken into consideration. Finally, this paper proposes a set of reusable GIS crosscutting concerns, documenting them in a concern catalogue.     

Metabolomics of Genetically Modified Crops

Metabolomic-based approaches are increasingly applied to analyse genetically modified organisms (GMOs) making it possible to obtain broader and deeper information on the composition of GMOs compared to that obtained from traditional analytical approaches. The combination in metabolomics of advanced analytical methods and bioinformatics tools provides wide chemical compositional data that contributes to corroborate (or not) the substantial equivalence and occurrence of unintended changes resulting from genetic transformation. This review provides insight into recent progress in metabolomics studies on transgenic crops focusing mainly in papers published in the last decade.     

Mathematical modelling of adult hippocampal neurogenesis: effects of altered stem cell dynamics on cell counts and bromodeoxyuridine-labelled cells

In the adult hippocampus, neurogenesis—the process of generating mature granule cells from adult neural stem cells—occurs throughout the entire lifetime. In order to investigate the involved regulatory mechanisms, knockout (KO) experiments, which modify the dynamic behaviour of this process, were conducted in the past. Evaluating these KOs is a non-trivial task owing to the complicated nature of the hippocampal neurogenic niche. In this study, we model neurogenesis as a multicompartmental system of ordinary differential equations based on experimental data. To analyse the results of KO experiments, we investigate how changes of cell properties, reflected by model parameters, influence the dynamics of cell counts and of the experimentally observed counts of cells labelled by the cell division marker bromodeoxyuridine (BrdU). We find that changing cell proliferation rates or the fraction of self-renewal, reflecting the balance between symmetric and asymmetric cell divisions, may result in multiple time phases in the response of the system, such as an initial increase in cell counts followed by a decrease. Furthermore, these phases may be qualitatively different in cells at different differentiation stages and even between mitotically labelled cells and all cells existing in the system.     

Statistical modeling of dissimilarity increments for d-dimensional data: Application in partitional clustering

This paper addresses the use of high order dissimilarity models in data mining problems. We explore dissimilarities between triplets of nearest neighbors, called dissimilarity increments (DIs). We derive a statistical model of DIs for d-dimensional data (d-DID) assuming that the objects follow a multivariate Gaussian distribution. Empirical evidence shows that the d-DID is well approximated by the particular case d=2. We propose the application of this model in clustering, with a partitional algorithm that uses a merge strategy on Gaussian components. Experimental results, in synthetic and real datasets, show that clustering algorithms using DID usually outperform well known clustering algorithms.     

Design principles for preschool children’s interactive TV

Preschool children have increased access to media via a number of platforms, including digital interactive television. However, the viewing and interaction needs of this user group have not been extensively researched. The project reported in this paper investigates preschoolers’ interactions with a simulated interactive television set-up. The study involved the development of an electronic programme guide prototype and its empirical evaluation. This research was carried out with children in Brazil and in the United Kingdom, aged between three and four. The main issues that arose during the interaction with the prototype application are documented, and a list of design principles is presented to assist in the design of accessible preschool interactive television applications for this age group.     

Applications of an oak extract on Petit Verdot grapevines. Influence on grape and wine volatile compounds

Petit Verdot vineyards were treated at veraison with a commercial aqueous French oak extract in order to determine if the extract’s volatile components can be transferred to grapes and then to wines. Three different formulations (25% (one application), 25% (four applications) and 100%) were tested, together with an eugenol and guaiacol standard solution to better follow their behaviour. The volatile compounds of treated grapes and their wines after alcoholic and malolactic fermentation and after 8 months were analysed by stir bar sorptive extraction and gas chromatography mass spectrometry (SBSE-GC–MS). The results showed that the grapes stored the volatile compounds mainly as non-volatile precursors, and some of these were released after winemaking. In the case of wines, it was possible to distinguish the control versus the ones from vineyard treatments. The different oak extract applications were evident only after alcoholic fermentation sampling, making it very interesting for young wines.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.