Comparison of genomic DNA extraction techniques from whole blood samples : a time, cost and quality evaluation study

Genomic DNA obtained from patient whole blood samples is a key element for genomic research. Advantages and disadvantages, in terms of time-efficiency, cost-effectiveness and laboratory requirements, of procedures available to isolate nucleic acids need to be considered before choosing any particular method. These characteristics have not been fully evaluated for some laboratory techniques, such as the salting out method for DNA extraction, which has been excluded from comparison in different studies published to date. We compared three different protocols (a traditional salting out method, a modified salting out method and a commercially available kit method) to determine the most cost-effective and time-efficient method to extract DNA. We extracted genomic DNA from whole blood samples obtained from breast cancer patient volunteers and compared the results of the product obtained in terms of quantity (concentration of DNA extracted and DNA obtained per ml of blood used) and quality (260/280 ratio and polymerase chain reaction product amplification) of the obtained yield. On average, all three methods showed no statistically significant differences between the final result, but when we accounted for time and cost derived for each method, they showed very significant differences. The modified salting out method resulted in a seven- and twofold reduction in cost compared to the commercial kit and traditional salting out method, respectively and reduced time from 3 days to 1 hour compared to the traditional salting out method. This highlights a modified salting out method as a suitable choice to be used in laboratories and research centres, particularly when dealing with a large number of samples.

Flexibility in Food Extraction Techniques in Urban Free-Ranging Bonnet Macaques, Macaca radiata

Non-human primate populations, other than responding appropriately to naturally occurring challenges, also need to cope with anthropogenic factors such as environmental pollution, resource depletion, and habitat destruction. Populations and individuals are likely to show considerable variations in food extraction abilities, with some populations and individuals more efficient than others at exploiting a set of resources. In this study, we examined among urban free-ranging bonnet macaques, Macaca radiata (a) local differences in food extraction abilities, (b) between-individual variation and within-individual consistency in problem-solving success and the underlying problem-solving characteristics, and (c) behavioral patterns associated with higher efficiency in food extraction. When presented with novel food extraction tasks, the urban macaques having more frequent exposure to novel physical objects in their surroundings, extracted food material from PET bottles and also solved another food extraction task (i.e., extracting an orange from a wire mesh box), more often than those living under more natural conditions. Adults solved the tasks more frequently than juveniles, and females more frequently than males. Both solution-technique and problem-solving characteristics varied across individuals but remained consistent within each individual across the successive presentations of PET bottles. The macaques that solved the tasks showed lesser within-individual variation in their food extraction behavior as compared to those that failed to solve the tasks. A few macaques appropriately modified their problem-solving behavior in accordance with the task requirements and solved the modified versions of the tasks without trial-and-error learning. These observations are ecologically relevant - they demonstrate considerable local differences in food extraction abilities, between-individual variation and within-individual consistency in food extraction techniques among free-ranging bonnet macaques, possibly affecting the species' local adaptability and resilience to environmental changes.

Evaluation of information extraction techniques to label extracted data from e-commerce web page

Automatically determining and assigning shared and meaningful text labels to data extracted from an e-Commerce web page is a challenging problem. An e-Commerce web page can display a list of data records, each of which can contain a combination of data items (e.g. product name and price) and explicit labels, which describe some of these data items. Recent advances in extraction techniques have made it much easier to precisely extract individual data items and labels from a web page, however, there are two open problems: 1. assigning an explicit label to a data item, and 2. determining labels for the remaining data items. Furthermore, improvements in the availability and coverage of vocabularies, especially in the context of e-Commerce web sites, means that we now have access to a bank of relevant, meaningful and shared labels which can be assigned to extracted data items. However, there is a need for a technique which will take as input a set of extracted data items and assign automatically to them the most relevant and meaningful labels from a shared vocabulary. We observe that the Information Extraction (IE) community has developed a great number of techniques which solve problems similar to our own. In this work-in-progress paper we propose our intention to theoretically and experimentally evaluate different IE techniques to ascertain which is most suitable to solve this problem.

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.

A Comparative Study of Wavelet Based Feature Extraction Techniques in Recognizing Isolated Spoken Words

Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. This work includes two speech recognition methods. First one is a hybrid approach with Discrete Wavelet Transforms and Artificial Neural Networks and the second method uses a combination of Wavelet Packet Decomposition and Artificial Neural Networks. Features are extracted by using Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results obtained show the efficiency of these techniques in recognizing speech

A comparison of automated keyphrase extraction techniques and of automatic evaluation vs. human evaluation

Keyphrases are added to documents to help identify the areas of interest they contain. However, in a significant proportion of papers author selected keyphrases are not appropriate for the document they accompany: for instance, they can be classificatory rather than explanatory, or they are not updated when the focus of the paper changes. As such, automated methods for improving the use of keyphrases are needed, and various methods have been published. However, each method was evaluated using a different corpus, typically one relevant to the field of study of the method’s authors. This not only makes it difficult to incorporate the useful elements of algorithms in future work, but also makes comparing the results of each method inefficient and ineffective. This paper describes the work undertaken to compare five methods across a common baseline of corpora. The methods chosen were Term Frequency, Inverse Document Frequency, the C-Value, the NC-Value, and a Synonym based approach. These methods were analysed to evaluate performance and quality of results, and to provide a future benchmark. It is shown that Term Frequency and Inverse Document Frequency were the best algorithms, with the Synonym approach following them. Following these findings, a study was undertaken into the value of using human evaluators to judge the outputs. The Synonym method was compared to the original author keyphrases of the Reuters’ News Corpus. The findings show that authors of Reuters’ news articles provide good keyphrases but that more often than not they do not provide any keyphrases.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Solid phase microextraction as a reliable alternative to conventional extraction techniques to evaluate the pattern of hydrolytically released components in Vitis vinifera L. grapes

In present research, headspace solid-phase microextraction (HS-SPME) followed by gas chromatography–mass spectrometry (GC–qMS), was evaluated as a reliable and improved alternative to the commonly used liquid–liquid extraction (LLE) technique for the establishment of the pattern of hydrolytically released components of 7 Vitis vinifera L. grape varieties, commonly used to produce the world-famous Madeira wine. Since there is no data available on their glycosidic fractions, at a first step, two hydrolyse procedures, acid and enzymatic, were carried out using Boal grapes as matrix. Several parameters susceptible of influencing the hydrolytic process were studied. The best results, expressed as GC peak area, number of identified components and reproducibility, were obtained using ProZym M with b-glucosidase activity at 35 °C for 42 h. For the extraction of hydrolytically released components, HS-SPME technique was evaluated as a reliable and improved alternative to the conventional extraction technique, LLE (ethyl acetate). HS-SPME using DVB/CAR/PDMS as coating fiber displayed an extraction capacity two fold higher than LLE (ethyl acetate). The hydrolyzed fraction was mainly characterized by the occurrence of aliphatic and aromatic alcohols, followed by acids, esters, carbonyl compounds, terpenoids, and volatile phenols. Concerning to terpenoids its contribution to the total hydrolyzed fraction is highest for Malvasia Cândida (23%) and Malvasia Roxa (13%), and their presence according previous studies, even at low concentration, is important from a sensorial point of view (can impart floral notes to the wines), due to their low odor threshold (μg/L). According to the obtained data by principal component analysis (PCA), the sensorial properties of Madeira wines produced by Malvasia Cândida and Malvasia Roxa could be improved by hydrolysis procedure, since their hydrolyzed fraction is mainly characterized by terpenoids (e.g. linalool, geraniol) which are responsible for floral notes. Bual and Sercial grapes are characterized by aromatic alcohols (e.g. benzyl alcohol, 2-phenylethyl alcohol), so an improvement in sensorial characteristics (citrus, sweet and floral odors) of the corresponding wines, as result of hydrolytic process, is expected.

Ecohydrological separation in wet, low energy northern environments? A preliminary assessment using different soil water extraction techniques

Funded by European Research Council ERC. Grant Number: project GA 335910 VEWA

Ecohydrological separation in wet, low energy northern environments? A preliminary assessment using different soil water extraction techniques

Funded by European Research Council ERC. Grant Number: project GA 335910 VEWA

Ecotoxicidade aguda, crônica e genotoxicidade de solo contaminado por óleo lubrificante usado e biorremediado para Eisenia andrei.

O objetivo do presente deste trabalho foi avaliar a toxicidade aguda, crônica e a genotoxicidade sobre E. andrei causadas por solo recém-contaminado com óleo lubrificante usado e após biorremediação por diferentes estratégias, após 22 meses, e paralelamente ao estudo de ecotoxicidade, foi conduzida uma investigação comparativa de três métodos de extração de HTP e HPA de solos para análise cromatográfica. A comparação das técnicas de extração evidenciou que para HTP, a técnica de extração acelerada por solvente-ASE foi a que melhor recuperou n-alcanos; já para as frações HRP e MCNR as técnicas soxhlet e micro-ondas-MARS não apresentaram diferenças significativas e foram melhores que ASE. Para HPA, a técnica de extração por soxhlet foi a que apresentou melhor recuperação em todos os solos. O teste de mortalidade apresentou, aos 14 dias, taxas crescentes de mortalidade de 10 6%, 20 0%, 73 25%, 93 12% e 100 0% para amostras de CONT (solo controle, sem contaminação artificial), BIOS (solo contaminado com 5% de OLU e biorremediado por bioestimulo), BIOA1 (solo contaminado com 5% de OLU e biorremediado por bioestimulo + bioaumento com adição de 10% de RSU maturado), e BIOA2 (solo contaminado com 5% de OLU e biorremediado por bioestimulo + bioaumento com adição de 10% de RSU semi-maturado) e OLU (solo contaminado com 5% de OLU), respectivamente. Aos 28 dias, entretanto, BIOS e OLU apresentaram taxas de mortalidade de 97 % 6 % e de 100 % 0 % respectivamente, valores estes significativamente superiores ao CONT. Foram observadas deformações anatômicas nos indivíduos mantidos em BIOS e OLU, assim como diminuição da biomassa em todas as amostras, evidenciando efeitos crônicos. O teste de reprodução, aos 28 dias, foram observadas grandes quantidades de indivíduos jovens nos solos biorremediados e recém-contaminado. No entanto, aos 56 dias houve uma diminuição dessas formas e o controle (CONT) exibiu uma quantidade maior de formas juvenis. O teste de densidade e viabilidade celular mostrou ser indicador sensível para toxicidade crônica apresentando queda nos solos BIOS e OLU em relação ao CONT com diferenças significativas (p <0.05). Não foram observados micronúcleos nos solos em estudo. Tal observação reforça a necessidade de testes de ecotoxicidade para avaliar a real eficácia de tecnologias de tratamento.

Ecotoxicidade aguda, crônica e genotoxicidade de solo contaminado por óleo lubrificante usado e biorremediado para Eisenia andrei.

O objetivo do presente deste trabalho foi avaliar a toxicidade aguda, crônica e a genotoxicidade sobre E. andrei causadas por solo recém-contaminado com óleo lubrificante usado e após biorremediação por diferentes estratégias, após 22 meses, e paralelamente ao estudo de ecotoxicidade, foi conduzida uma investigação comparativa de três métodos de extração de HTP e HPA de solos para análise cromatográfica. A comparação das técnicas de extração evidenciou que para HTP, a técnica de extração acelerada por solvente-ASE foi a que melhor recuperou n-alcanos; já para as frações HRP e MCNR as técnicas soxhlet e micro-ondas-MARS não apresentaram diferenças significativas e foram melhores que ASE. Para HPA, a técnica de extração por soxhlet foi a que apresentou melhor recuperação em todos os solos. O teste de mortalidade apresentou, aos 14 dias, taxas crescentes de mortalidade de 10 6%, 20 0%, 73 25%, 93 12% e 100 0% para amostras de CONT (solo controle, sem contaminação artificial), BIOS (solo contaminado com 5% de OLU e biorremediado por bioestimulo), BIOA1 (solo contaminado com 5% de OLU e biorremediado por bioestimulo + bioaumento com adição de 10% de RSU maturado), e BIOA2 (solo contaminado com 5% de OLU e biorremediado por bioestimulo + bioaumento com adição de 10% de RSU semi-maturado) e OLU (solo contaminado com 5% de OLU), respectivamente. Aos 28 dias, entretanto, BIOS e OLU apresentaram taxas de mortalidade de 97 % 6 % e de 100 % 0 % respectivamente, valores estes significativamente superiores ao CONT. Foram observadas deformações anatômicas nos indivíduos mantidos em BIOS e OLU, assim como diminuição da biomassa em todas as amostras, evidenciando efeitos crônicos. O teste de reprodução, aos 28 dias, foram observadas grandes quantidades de indivíduos jovens nos solos biorremediados e recém-contaminado. No entanto, aos 56 dias houve uma diminuição dessas formas e o controle (CONT) exibiu uma quantidade maior de formas juvenis. O teste de densidade e viabilidade celular mostrou ser indicador sensível para toxicidade crônica apresentando queda nos solos BIOS e OLU em relação ao CONT com diferenças significativas (p <0.05). Não foram observados micronúcleos nos solos em estudo. Tal observação reforça a necessidade de testes de ecotoxicidade para avaliar a real eficácia de tecnologias de tratamento.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.