Development and Validation of Stability-Indicating HPLC Methods for Quantitative Determination of Pravastatin, Fluvastatin, Atorvastatin, and Rosuvastatin in Pharmaceuticals

High-performance liquid-chromatographic (HPLC) methods were validated for determination of pravastatin sodium (PS), fluvastatin sodium (FVS), atorvastatin calcium (ATC), and rosuvastatin calcium (RC) in pharmaceuticals. Two stability-indicating HPLC methods were developed with a small change (10%) in the composition of the organic modifier in the mobile phase. The HPLC method for each statin was validated using isocratic elution. An RP-18 column was used with mobile phases consisting of methanol-water (60:40, v/v, for PS and RC and 70:30, v/v, for FVS and ATC). The pH of each mobile phase was adjusted to 3.0 with orthophosphoric acid, and the flow rate was 1.0mL/min. Calibration plots showed correlation coefficients (r)0.999, which were calculated by the least square method. The detection limit (DL) and quantitation limit (QL) were 1.22 and 3.08 mu g/mL for PS, 2.02 and 6.12 mu g/mL for FVS, 0.44 and 1.34 mu g/mL for ATC, and 1.55 and 4.70 mu g/mL for RC. Intraday and interday relative standard deviations (RSDs) were 2.0%. The methods were applied successfully for quantitative determination of statins in pharmaceuticals.

Multilabeled classification approach to find a plaint source for terpenoids

Recently, we have built a classification model that is capable of assigning a given sesquiterpene lactone (STL) into exactly one tribe of the plant family Asteraceae from which the STL has been isolated. Although many plant species are able to biosynthesize a set of peculiar compounds, the occurrence of the same secondary metabolites in more than one tribe of Asteraceae is frequent. Building on our previous work, in this paper, we explore the possibility of assigning an STL to more than one tribe (class) simultaneously. When an object may belong to more than one class simultaneously, it is called multilabeled. In this work, we present a general overview of the techniques available to examine multilabeled data. The problem of evaluating the performance of a multilabeled classifier is discussed. Two particular multilabeled classification methods-cross-training with support vector machines (ct-SVM) and multilabeled k-nearest neighbors (M-L-kNN)were applied to the classification of the STLs into seven tribes from the plant family Asteraceae. The results are compared to a single-label classification and are analyzed from a chemotaxonomic point of view. The multilabeled approach allowed us to (1) model the reality as closely as possible, (2) improve our understanding of the relationship between the secondary metabolite profiles of different Asteraceae tribes, and (3) significantly decrease the number of plant sources to be considered for finding a certain STL. The presented classification models are useful for the targeted collection of plants with the objective of finding plant sources of natural compounds that are biologically active or possess other specific properties of interest.

Selection bias in gene extraction on the basis of microarray gene-expression data

In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called. 632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.

Influence of strawberry jam color and phenolic compounds on acceptance during storage

The objective of this study was to evaluate the influence of the color and phenolic compounds of strawberry jam on acceptance during storage. Jams were processed, stored for 120 days and evaluated monthly for chromatic characteristics, total phenolic compounds, total anthocyanins (ANT), total ellagic acid (TEA), flavonoids and free ellagic acid (FEA), and sensory acceptance as well. Data were submitted to analysis of variance (ANOVA) and the means were compared by the Least Significant Difference (LSD). Cluster Analysis and Partial Least Square Regression (PLS) were performed to investigate the relationships between instrumental data and acceptance. Contents of ANT, TEA and redness decreased during storage. Other chemical characteristics and sensory acceptance showed a nonlinear behavior. Higher acceptance was observed after 60 days, suggesting a trend of quality improvement followed by decline to the initial levels. The same trend was observed for lightness, non-pigment flavonoids and FEA. According to PLS map, for consumers in cluster 2, acceptance was associated to jams at 60 days and to luminosity, FEA, and non-pigment flavonoids. For cluster 1, a positive association between flavor liking, jam at initial storage, and the contents of TEA and ANT was indicated. Jams at 120 days were positively associated to hue and negatively associated to color liking, for cluster 1. Color and texture were positively correlated to overall liking for cluster 2, whereas for cluster 1, overall acceptance seemed to be more associated to flavor liking. Changes in color and phenolic compounds slightly influenced the acceptance of strawberry jams, but in different ways for consumers clusters.

Confiança organizacional e compartilhamento e uso do conhecimento tácito

Este estudo investiga a influência da confiança organizacional no desejo de usar e compartilhar o conhecimento tácito, baseado em hipóteses sobre a relação entre capacidade, benevolência e integridade nesse desejo. A amostra foi formada por 655 militares do Exército, instituição caracterizada por cultura de elevada exigência de confiança individual e organizacional, coletada em três instituições de formação de oficiais. O uso da técnica de modelagem de equações estruturais (partial least square) apresentou resultados que sugerem que esse desejo não é significativamente influenciado pela intensidade da confiança organizacional, definida com base na capacidade, benevolência e integridade dos indivíduos. Esses resultados refutam pesquisas anteriores de Holste e Fields, que destacam a influência do fator afeição no compartilhamento e o fator cognição no uso do conhecimento tácito, indicando a necessidade de compreender melhor os estímulos ao uso e compartilhamento do conhecimento dentro das estruturas organizacionais.

Modelo de data mining para deteção de embolias pulmonares

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Automatic email organization

Introdução Actualmente, as mensagens electrónicas são consideradas um importante meio de comunicação. As mensagens electrónicas – vulgarmente conhecidas como emails – são utilizadas fácil e frequentemente para enviar e receber o mais variado tipo de informação. O seu uso tem diversos fins gerando diariamente um grande número de mensagens e, consequentemente um enorme volume de informação. Este grande volume de informação requer uma constante manipulação das mensagens de forma a manter o conjunto organizado. Tipicamente esta manipulação consiste em organizar as mensagens numa taxonomia. A taxonomia adoptada reflecte os interesses e as preferências particulares do utilizador. Motivação A organização manual de emails é uma actividade morosa e que consome tempo. A optimização deste processo através da implementação de um método automático, tende a melhorar a satisfação do utilizador. Cada vez mais existe a necessidade de encontrar novas soluções para a manipulação de conteúdo digital poupando esforços e custos ao utilizador; esta necessidade, concretamente no âmbito da manipulação de emails, motivou a realização deste trabalho. Hipótese O objectivo principal deste projecto consiste em permitir a organização ad-hoc de emails com um esforço reduzido por parte do utilizador. A metodologia proposta visa organizar os emails num conjunto de categorias, disjuntas, que reflectem as preferências do utilizador. A principal finalidade deste processo é produzir uma organização onde as mensagens sejam classificadas em classes apropriadas requerendo o mínimo número esforço possível por parte do utilizador. Para alcançar os objectivos estipulados, este projecto recorre a técnicas de mineração de texto, em especial categorização automática de texto, e aprendizagem activa. Para reduzir a necessidade de inquirir o utilizador – para etiquetar exemplos de acordo com as categorias desejadas – foi utilizado o algoritmo d-confidence. Processo de organização automática de emails O processo de organizar automaticamente emails é desenvolvido em três fases distintas: indexação, classificação e avaliação. Na primeira fase, fase de indexação, os emails passam por um processo transformativo de limpeza que visa essencialmente gerar uma representação dos emails adequada ao processamento automático. A segunda fase é a fase de classificação. Esta fase recorre ao conjunto de dados resultantes da fase anterior para produzir um modelo de classificação, aplicando-o posteriormente a novos emails. Partindo de uma matriz onde são representados emails, termos e os seus respectivos pesos, e um conjunto de exemplos classificados manualmente, um classificador é gerado a partir de um processo de aprendizagem. O classificador obtido é então aplicado ao conjunto de emails e a classificação de todos os emails é alcançada. O processo de classificação é feito com base num classificador de máquinas de vectores de suporte recorrendo ao algoritmo de aprendizagem activa d-confidence. O algoritmo d-confidence tem como objectivo propor ao utilizador os exemplos mais significativos para etiquetagem. Ao identificar os emails com informação mais relevante para o processo de aprendizagem, diminui-se o número de iterações e consequentemente o esforço exigido por parte dos utilizadores. A terceira e última fase é a fase de avaliação. Nesta fase a performance do processo de classificação e a eficiência do algoritmo d-confidence são avaliadas. O método de avaliação adoptado é o método de validação cruzada denominado 10-fold cross validation. Conclusões O processo de organização automática de emails foi desenvolvido com sucesso, a performance do classificador gerado e do algoritmo d-confidence foi relativamente boa. Em média as categorias apresentam taxas de erro relativamente baixas, a não ser as classes mais genéricas. O esforço exigido pelo utilizador foi reduzido, já que com a utilização do algoritmo d-confidence obteve-se uma taxa de erro próxima do valor final, mesmo com um número de casos etiquetados abaixo daquele que é requerido por um método supervisionado. É importante salientar, que além do processo automático de organização de emails, este projecto foi uma excelente oportunidade para adquirir conhecimento consistente sobre mineração de texto e sobre os processos de classificação automática e recuperação de informação. O estudo de áreas tão interessantes despertou novos interesses que consistem em verdadeiros desafios futuros.

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

Enhancing Multimodal Silent Speech Interfaces With Feature Selection

In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.

Incremental filter and wrapper approaches for feature discretization

Discrete data representations are necessary, or at least convenient, in many machine learning problems. While feature selection (FS) techniques aim at finding relevant subsets of features, the goal of feature discretization (FD) is to find concise (quantized) data representations, adequate for the learning task at hand. In this paper, we propose two incremental methods for FD. The first method belongs to the filter family, in which the quality of the discretization is assessed by a (supervised or unsupervised) relevance criterion. The second method is a wrapper, where discretized features are assessed using a classifier. Both methods can be coupled with any static (unsupervised or supervised) discretization procedure and can be used to perform FS as pre-processing or post-processing stages. The proposed methods attain efficient representations suitable for binary and multi-class problems with different types of data, being competitive with existing methods. Moreover, using well-known FS methods with the features discretized by our techniques leads to better accuracy than with the features discretized by other methods or with the original features. (C) 2013 Elsevier B.V. All rights reserved.

Propriedades físico-químicas de líquidos iónicos e estudos quimio- informáticos de reactividade química

Dissertação apresentada para a obtenção do Grau de Doutor em Química Especialidade de Química Orgânica Pela Universidade Nova de Lisboa Faculdade de Ciências e Tecnologia

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Interface caligráfica de escrita no ar

Na atualidade, está a emergir um novo paradigma de interação, designado por Natural User Interface (NUI) para reconhecimento de gestos produzidos com o corpo do utilizador. O dispositivo de interação Microsoft Kinect foi inicialmente concebido para controlo de videojogos, para a consola Xbox360. Este dispositivo demonstra ser uma aposta viável para explorar outras áreas, como a do apoio ao processo de ensino e de aprendizagem para crianças do ensino básico. O protótipo desenvolvido visa definir um modo de interação baseado no desenho de letras no ar, e realizar a interpretação dos símbolos desenhados, usando os reconhecedores de padrões Kernel Discriminant Analysis (KDA), Support Vector Machines (SVM) e $N. O desenvolvimento deste projeto baseou-se no estudo dos diferentes dispositivos NUI disponíveis no mercado, bibliotecas de desenvolvimento NUI para este tipo de dispositivos e algoritmos de reconhecimento de padrões. Com base nos dois elementos iniciais, foi possível obter uma visão mais concreta de qual o hardware e software disponíveis indicados à persecução do objetivo pretendido. O reconhecimento de padrões constitui um tema bastante extenso e complexo, de modo que foi necessária a seleção de um conjunto limitado deste tipo de algoritmos, realizando os respetivos testes por forma a determinar qual o que melhor se adequava ao objetivo pretendido. Aplicando as mesmas condições aos três algoritmos de reconhecimento de padrões permitiu avaliar as suas capacidades e determinar o $N como o que apresentou maior eficácia no reconhecimento. Por último, tentou-se averiguar a viabilidade do protótipo desenvolvido, tendo sido testado num universo de elementos de duas faixas etárias para determinar a capacidade de adaptação e aprendizagem destes dois grupos. Neste estudo, constatou-se um melhor desempenho inicial ao modo de interação do grupo de idade mais avançada. Contudo, o grupo mais jovem foi revelando uma evolutiva capacidade de adaptação a este modo de interação melhorando progressivamente os resultados.

Monitoring the ex-vivo expansion of human mesenchymal stem/stromal cells in xeno-free microcarrier-based reactor systems by MIR spectroscopy

Human mesenchymal stem/stromal cells (MSCs) have received considerable attention in the field of cell-based therapies due to their high differentiation potential and ability to modulate immune responses. However, since these cells can only be isolated in very low quantities, successful realization of these therapies requires MSCs ex-vivo expansion to achieve relevant cell doses. The metabolic activity is one of the parameters often monitored during MSCs cultivation by using expensive multi-analytical methods, some of them time-consuming. The present work evaluates the use of mid-infrared (MIR) spectroscopy, through rapid and economic high-throughput analyses associated to multivariate data analysis, to monitor three different MSCs cultivation runs conducted in spinner flasks, under xeno-free culture conditions, which differ in the type of microcarriers used and the culture feeding strategy applied. After evaluating diverse spectral preprocessing techniques, the optimized partial least square (PLS) regression models based on the MIR spectra to estimate the glucose, lactate and ammonia concentrations yielded high coefficients of determination (R2 ≥ 0.98, ≥0.98, and ≥0.94, respectively) and low prediction errors (RMSECV ≤ 4.7%, ≤4.4% and ≤5.7%, respectively). Besides PLS models valid for specific expansion protocols, a robust model simultaneously valid for the three processes was also built for predicting glucose, lactate and ammonia, yielding a R2 of 0.95, 0.97 and 0.86, and a RMSECV of 0.33, 0.57, and 0.09 mM, respectively. Therefore, MIR spectroscopy combined with multivariate data analysis represents a promising tool for both optimization and control of MSCs expansion processes.

Comparative analysis of near and mid-infrared spectroscopy to monitor recombinant cyprosin production

Infrared spectroscopy, either in the near and mid (NIR/MIR) region of the spectra, has gained great acceptance in the industry for bioprocess monitoring according to Process Analytical Technology, due to its rapid, economic, high sensitivity mode of application and versatility. Due to the relevance of cyprosin (mostly for dairy industry), and as NIR and MIR spectroscopy presents specific characteristics that ultimately may complement each other, in the present work these techniques were compared to monitor and characterize by in situ and by at-line high-throughput analysis, respectively, recombinant cyprosin production by Saccharomyces cerevisiae. Partial least-square regression models, relating NIR and MIR-spectral features with biomass, cyprosin activity, specific activity, glucose, galactose, ethanol and acetate concentration were developed, all presenting, in general, high regression coefficients and low prediction errors. In the case of biomass and glucose slight better models were achieved by in situ NIR spectroscopic analysis, while for cyprosin activity and specific activity slight better models were achieved by at-line MIR spectroscopic analysis. Therefore both techniques enabled to monitor the highly dynamic cyprosin production bioprocess, promoting by this way more efficient platforms for the bioprocess optimization and control.