Irene Oliveira

Use of MALDI-TOF mass spectrometry fingerprinting to characterize Enterococcus spp. and Escherichia coli isolates ☆

UNLABELLED Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a faster and more accurate method to identify intact bacteria than conventional microbiology and/or molecular biology methods. The MALDI-TOF MS method is potentially applicable in diagnostic laboratories to characterize commensal bacterial species, some of which are major pathogens, from human or animal gastrointestinal tracts. The aim of this study was to analyze at the cluster and statistical level the capacity of MALDI-TOF MS to distinguish between previously characterized enterococci and Escherichia coli isolated from wild birds of the Azores archipelago. Soluble proteins were extracted from intact cell cultures of 60 isolates of Enterococcus spp. and 60 isolates of E. coli by an expedient method. MALDI-TOF MS was used to obtain 1200 mass spectra that were statistically analyzed and compared. A total of 215 distinct mass-to-charge (m/z) peaks were obtained, including a peak at m/z 4428±3, which is exclusively found in spectra from Enterococcus isolates, and peaks at m/z 5379±3 and m/z 6253±3, which are only detected in spectra from E. coli isolates. By processing mass spectra and analyzing them statistically with MassUp software, including principal component analysis (PCA) and clustering, it was possible to correctly distinguish between isolates of Enterococcus and Escherichia genera. The results of the proteomic analysis confirm that these tools could be used to characterize whole bacterial cells. In the future, with an optimized protocol for obtaining plasmid-associated proteins and the further development of bioinformatics methods, it is likely that mass peak characteristic of antimicrobial resistance will be detected, increasing the potential usefulness of MALDI-TOF in routine clinical assays. BIOLOGICAL SIGNIFICANCE This study highlights the importance of MALDI-TOF MS in the rapid and reliable identification of bacteria by whole-cell analysis. The mass spectrometry approach performed in this study further contributes for the microbial biomarker discovery culminating in a preferable bacteria identification and antimicrobial resistance tool for the future of clinical microbiology. This article is part of a Special Issue entitled: HUPO 2014.

A Data Mining Approach for Multivariate Outlier Detection in Postprocessing of Multitemporal InSAR Results

Displacement maps from multitemporal InSAR (MTI) are usually noisy and fragmented. Thresholding on ensemble coherence is a common practice for identifying radar scatterers that are less affected by decorrelation noise. Thresholding on coherence might, however, cause loss of information over the areas undergoing more complex deformation scenarios. If the discrepancies in the areas of moderate coherence share similar behavior, it appears important to take into account their spatial correlation for correct inference. The information over low-coherent areas might then be used in a similar way the coherence is used in thematic mapping applications such as change detection. We propose an approach based on data mining and statistical procedures for mitigating the impact of outliers in MTI results. Our approach allows for minimization of outliers in final results while preserving spatial and statistical dependence among observations. Tests from monitoring slope failures and undermined areas performed in this work have shown that this is beneficial: 1) for better evaluation of low-coherent scatterers that are commonly discarded by the standard thresholding procedure, 2) for tackling outlying observations with extremes in any variable, 3) for improving spatial densities of standard persistent scatterers, 4) for the evaluation of areas undergoing more complex deformation scenarios, and 5) for the visualization purposes.

A data mining approach for multivariate outlier detection in heterogeneous 2D point clouds: An application to post-processing of multi-temporal InSAR results

Thresholding on coherence is a common practice for identifying the surface scatterers that are less affected by decorrelation noise during post-processing and visualisation of the results from multi-temporal InSAR techniques. Simple selection of the points with coherence greater than a specific value is, however, challenged by the presence of spatial dependence among observations. If the discrepancies in the areas of moderate coherence share similar behaviour, it appears important to take into account their spatial correlation for correct inference. Low coherence areas thus could serve as clear indicators of measurement noise or imperfections in mathematical models. Once exhibiting properties of statistical similarity, they allow for detection of observations that could be considered as outliers and trimmed from the dataset. In this paper we propose an approach based on renowned data mining and exploratory data analysis procedures for mitigating the impact of outlying observations in the final results.

SME Managers’ Most Important Entrepreneurship and Business Competences

The requirements of an increasing globalized and competitive economy lead managers to search for training solutions which can rapidly bridge the gap of their lacking skills, knowledge or competences. To assert with adequate training programs, in particular for SMEs managers, a study was conducted in six European countries with the objective of identifying the most relevant competences they need to fulfill. A literature review and several interviews with business associations’ executives resulted in a list of 34 competences which were organized in four categories: personal, team management, business and technical. These competencies were put at trial through a survey conducted among 154 SMEs managers who had to evaluate each proposed competence with the attribution of a relevance degree. Although we show that SME managers should be well prepared in a rich set of complementary areas to perform their job, it is clear that some of the competences are crucial for them to tackle today’s challenges. This paper presents a ranking of the competences by importance as perceived by managers. These findings can help training institutions wishing to design new training programs which more in line with managers’ needs.

Insect-associated fungi from naturally mycosed vine mealybug Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae)

ABSTRACT Vine mealybug, Planococcus ficus, is a major pest of grapevine, which is present in at least 39 countries. According to American Vineyard Foundation, P. ficus is in the top ranks among major insect-pests of grapevine. It is the ‘top priority concerns’ by grape growers and a ‘threat to the sustainability of wine industry’ demanding a ‘high priority research’. In Douro vineyards, it is considered as an occasional insect-pest; however, its importance is increasing in some localities. The present study investigates the occurrences of P. ficus-associated fungi. Vine mealybugs were observed in two of the four surveyed farms. Out of the 183 collected mealybugs, 58 were dead of which 25 had symptoms of mycosis and 13 were parasitised. Subculturing cadavers and subsequent pathogenicity test yielded 22 entomopathogenic fungi (EPF) including yeasts. The yeast Meyerozyma (=Pichia) guilliermondii, and the EPF Sarocladium kiliense and Purpureocillium lilacinum were the most abundant, i.e. representing 18.18% (N = 4), 13.64% (N = 3) and 13.64% (N = 3) of the isolates, respectively. Considering biological affinities, fungal families Nectriaceae and Microascaceae had the most similar count-data profiles. To our knowledge, this work reports the first isolations of EPF from vine mealybug worldwide; and Pseudocosmospora rogersonii in Europe and as EPF worldwide. The mortality rate originated by mycoses on P. ficus was significantly higher than by its parasitoids, suggesting that fungi as P. ficus biocontrol agents are relatively more important than considered before. Overall, this report provides new insights into the development of mycoinsecticides and conservation biocontrol strategies for P. ficus pest management.

Butterfly Species Richness and Diversity on Tourism Trails of Northeast Portugal

Abstract  Butterfly species can be sensitive to ecosystem disturbance and, therefore, suitable to be used as indicators of habitat quality. We determined species richness and diversity of butterfly species along five tourist trails in the northeast region of Portugal. These trails were in different landscape structures, varying from urban areas to areas extensively managed for agriculture (i.e., vineyards, meadows) to natural areas (i.e., grasslands, rivers, forests). A total of 522 butterflies representing 45 species belonging to 34 genera and 5 families of Lepidoptera were recorded. Of the taxonomic families represented in the survey, the Nymphalidae were most numerous (362 specimens, 22 species) followed by Pieridae (86 specimens, 11 species) and Lycaenidae (58 specimens, 8 species). Four species have a conservation status, an indicator of the risk of extinction they face at present or in the near future [Euphydryas aurinia (Rottemburg, 1775), Phengaris alcon (Denis & Schiffermüller, 1775), Hipparchia semele (L., 1758) and Melanargia lachesis (Hübner, 1790)], and these represent 6.9% of the total species identified. Among the five trails, diversity parameters varied with high values of species richness and diversity, low dominance of species, and moderate evenness of distribution. Additionally, butterfly species comparison among the trails revealed that Alvão and Vale do Corgo trails have most of the species in common, especially from Pieridae and Nymphalidae, while the Marão trail has more species associated exclusively to this trail. These results were also supported by hierarchical clustering performed with an average linkage aggregation method using Jaccard distance and by comparison between proportions of butterflies among trails within each family.

Independent Component Analysis for Extended Time Series in Climate Data

Various techniques of multivariate data analysis have been proposed to study time series, including the multi-channel singular spectrum analysis (MSSA). This technique is a principal component analysis (PCA) of the extended matrix of initial lagged series, also called extended empirical orthogonal function (EEOF) analysis in a climatological context. This work uses independent component analysis (ICA) as an alternative to the MSSA method, when studying the extended time series matrix. Often, ICA is more appropriate than PCA to analyse time series, since the extraction of independent components (ICs) involves higher-order statistics whereas PCA only uses the second-order statistics to obtain the principal components (PCs), which are not correlated and are not necessarily independent. An example of time series for meteorological data and some comparative results between the techniques under study are given. Different methods of ordering ICs are also presented, including a new one, which may influence the quality of the reconstruction of the original data.