A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

Distribution of major, minor, and trace metals in lake environments of Antarctica

The concentrations of major, minor and trace metals were measured in water samples collected from five shallow Antarctic lakes (Carezza, Edmonson Point (No 14 and 15a), Inexpressible Island and Tarn Flat) found in Terra Nova Bay (northern Victoria Land, Antarctica) during the Italian Expeditions of 1993-2001. The total concentrations of a large suite of elements (Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Gd, K, La, Li, Mg, Mn, Mo, Na, Nd, Ni, Pb, Pr, Rb, Sc, Si, Sr, Ta, Ti, U, V, Y, W, Zn and Zr) were determined using spectroscopic techniques (ICP-AES, GF-AAS and ICP-MS). The results are similar to those obtained for the freshwater lakes of the Larsemann Hills, East Antarctica, and for the McMurdo Dry Valleys. Principal Component Analysis (PCA) and Cluster Analysis (CA) were performed to identify groups of samples with similar characteristics and to find correlations between the variables. The variability observed within the water samples is closely connected to the sea spray input; hence, it is primarily a consequence of geographical and meteorological factors, such as distance from the ocean and time of year. The trace element levels, in particular those of heavy metals, are very low, suggesting an origin from natural sources rather than from anthropogenic contamination.

Semiochemicals and social signaling in the wild European rabbit in Australia: II. Variations in chemical composition of chin gland secretion across sampling sites

The volatile components of the chin gland secretion of the wild European rabbit, Oryctolagus cuniculus (L.), were investigated with the use of gas chromatography. Studies of the chemical nature of this secretion by previous workers demonstrated that it was important in the maintenance of social structure in this species. This study identified 34 different volatile components that consist primarily of aromatic and aliphatic hydrocarbons. Especially common are a series of alkyl-substituted benzene derivatives that provide most of the compound diversity in the secretion. Samples of chin gland secretion collected from animals at three different geographical locations, separated by more than 100 km, showed significant differences in composition. This work suggests that variation among populations needs to be considered when undertaking semiochemical research. Alternate nonparametric methods are also used for the analysis of chromatographic data.

A serine proteinase homolog venom protein from an endoparasitoid wasp inhibits melanization of the host hemolymph

Activation of prophenoloxidase (proPO) in insects is a defense mechanism against intruding microorganisms and parasites. Pattern recognition molecules induce activation of an enzymatic cascade involving serine proteinases, which leads to the conversion of proPO to active phenoloxidase (PO). Phenolic compounds produced by pPO-activation are toxic to invaders. Here, we describe the isolation of a venom protein from the parasitoid, Cotesia rubecula, injected into the host, Pieris rapae, which is homologous to serine proteinase homologs (SPH). The data presented here indicate that the protein interferes with the proteolytic cascade, which under normal circumstances leads to the activation of proPO and melanin formation. (C) 2003 Elsevier Ltd. All rights reserved.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Phenotypic and functional characterization of pulmonary macrophages subpopulations after intratracheal injection of Paracoccidioides brasiliensis cell wall components

A shift in the activation of pulmonary macrophages characterized by an increase of IL-1, INF-alpha and IL-6 production has been induced in mice infected with Paracoccidioides brasiliensis. It is still unclear whether a functional shift in the resident alveolar macrophage population would be responsible for these observations due to the expression of cell surface molecules. We investigated pulmonary macrophages by flow cytometry from mice treated with P. brasiliensis derivatives by intratracheal route. In vivo labeling with the dye PKH26GL was applied to characterize newly recruited pulmonary macrophages from the bloodstream. Pulmonary macrophages from mice inflamed with P. brasiliensis derivatives showed a high expression of the surface antigens CD11b/CD18 and CD23 among several cellular markers. The expression of these markers indicated a pattern of activation of a subpopulation characterized as CD11b(+) or CD23(+), which was modulated in vitro by IFN-gamma and IL-4. Analysis of monocytes labelled with PKH26GL demonstrated that CD11b(+) cells did infiltrate the lung exhibiting a proinflammatoni pattern of activation, whereas CD23(+) cells were considered to be resident in the lung. These findings may contribute to better understand the pathology of lung inflammation caused by P. brasiliensis infection. (C) 2010 Elsevier GmbH. All rights reserved.

Towards Emotional Interaction: Using Movies to Automatically Learn Users' Emotional States

The HCI community is actively seeking novel methodologies to gain insight into the user’s experience during interaction with both the application and the content. We propose an emotional recognition engine capable of automatically recognizing a set of human emotional states using psychophysiological measures of the autonomous nervous system, including galvanic skin response, respiration, and heart rate. A novel pattern recognition system, based on discriminant analysis and support vector machine classifiers is trained using movies’ scenes selected to induce emotions ranging from the positive to the negative valence dimension, including happiness, anger, disgust, sadness, and fear. In this paper we introduce an emotion recognition system and evaluate its accuracy by presenting the results of an experiment conducted with three physiologic sensors.

A new cloning system based on the OprI lipoprotein for the production of recombinant bacterial cell wall-derived immunogenic formulations

The conjugation of antigens with ligands of pattern recognition receptors (PRR) is emerging as a promising strategy for the modulation of specific immunity. Here, we describe a new Escherichia coli system for the cloning and expression of heterologous antigens in fusion with the OprI lipoprotein, a TLR ligand from the Pseudomonas aeruginosa outer membrane (OM). Analysis of the OprI expressed by this system reveals a triacylated lipid moiety mainly composed by palmitic acid residues. By offering a tight regulation of expression and allowing for antigen purification by metal affinity chromatography, the new system circumvents the major drawbacks of former versions. In addition, the anchoring of OprI to the OM of the host cell is further explored for the production of novel recombinant bacterial cell wall-derived formulations (OM fragments and OM vesicles) with distinct potential for PRR activation. As an example, the African swine fever virus ORF A104R was cloned and the recombinant antigen was obtained in the three formulations. Overall, our results validate a new system suitable for the production of immunogenic formulations that can be used for the development of experimental vaccines and for studies on the modulation of acquired immunity.

Role on serine protease inhibitors in the pathogenisis of Steinernema carpocapsae

Tese de Doutoramento, Biologia (Biologia Celular e Molecular), 18 de Novembro de 2013, Universidade dos Açores.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

On cluster analysis of complex and heterogeneous data

3rd SMTDA Conference Proceedings, 11-14 June 2014, Lisbon Portugal.

Inperlys – independente personal location system

O desenvolvimento de sistemas de localização pedestre com recurso a técnicas de dead reckoning tem mostrado ser uma área em expansão no mundo académico e não só. Existem algumas soluções criadas, no entanto, nem todas as soluções serão facilmente implementadas no mercado, quer seja pelo hardware caro, ou pelo sistema em si, que é desenvolvido tendo em conta um cenário em particular. INPERLYS é um sistema que visa apresentar uma solução de localização pedestre, independentemente do cenário, utilizando recursos que poderão ser facilmente usados. Trata-se de um sistema que utiliza uma técnica de dead reckonig para dar a localização do utilizador. Em cenários outdoor, um receptor GPS fornece a posição do utilizador, fornecendo uma posição absoluta ao sistema. Quando não é possível utilizar o GPS, recorre-se a um sensor MEMS e a uma bússola para se obter posições relativas à última posição válida do GPS. Para interligar todos os sensores foi utilizado o protocolo de comunicações sem fios ZigBee™. A escolha recaiu neste protocolo devido a factores como os seus baixos consumos e o seu baixo custo. Assim o sistema torna-se de uso fácil e confortável para o utilizador, ao contrário de sistemas similares desenvolvidos, que utilizam cabos para interligarem os diferentes componentes do sistema. O sensor MEMS do tipo acelerómetro tem a função de ler a aceleração horizontal, ao nível do pé. Esta aceleração será usada por um algoritmo de reconhecimento do padrão das acelerações para se detectar os passos dados. Após a detecção do passo, a aceleração máxima registada nesse passo é fornecida ao coordenador, para se obter o deslocamento efectuado. Foram efectuados alguns testes para se perceber a eficiência do INPERLYS. Os testes decorreram num percurso plano, efectuados a uma velocidade normal e com passadas normais. Verificou-se que, neste momento, o desempenho do sistema poderá ser melhorado, quer seja a nível de gestão das comunicações, quer a nível do reconhecimento do padrão da aceleração horizontal, essencial para se detectar os passos. No entanto o sistema é capaz de fornecer a posição através do GPS, quando é possível a sua utilização, e é capaz de fornecer a orientação do movimento.

Dependent component analysis: a hyperspectral unmixing algorithm

Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical properties of hyperspectral data. DECA models the abundance fractions as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. The performance of the method is illustrated using simulated and real data.

Signal Subspace Identification in Hyperspectral Linear Mixtures

Chpater in Book Proceedings with Peer Review Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceedings, Part II