The search of a genetic basis for noise-induced hearing loss (NIHL)

Background and aim: Knowledge about the genetic factors responsible for noise-induced hearing loss (NIHL) is still limited. This study investigated whether genetic factors are associated or not to susceptibility to NIHL. Subjects and methods: The family history and genotypes were studied for candidate genes in 107 individuals with NIHL, 44 with other causes of hearing impairment and 104 controls. Mutations frequently found among deaf individuals were investigated (35delG, 167delT in GJB2, Delta(GJB6- D13S1830), Delta(GJB6- D13S1854) in GJB6 and A1555G in MT-RNR1 genes); allelic and genotypic frequencies were also determined at the SNP rs877098 in DFNB1, of deletions of GSTM1 and GSTT1 and sequence variants in both MTRNR1 and MTTS1 genes, as well as mitochondrial haplogroups. Results: When those with NIHL were compared with the control group, a significant increase was detected in the number of relatives affected by hearing impairment, of the genotype corresponding to the presence of both GSTM1 and GSTT1 enzymes and of cases with mitochondrial haplogroup L1. Conclusion: The findings suggest effects of familial history of hearing loss, of GSTT1 and GSTM1 enzymes and of mitochondrial haplogroup L1 on the risk of NIHL. This study also described novel sequence variants of MTRNR1 and MTTS1 genes.

Comparison of two methods for collecting free-living ticks in the Amazonian forest

In the present study, we compared 2 methods for collecting ixodid ticks on the verges of animal trails in a primary Amazon forest area in northern Brazil. (i) Dragging: This method was based on passing a 1-m(2) white flannel over the vegetation and checking the flannel for the presence of caught ticks every 5-10 m. (ii) Visual search: This method consisted of looking for guesting ticks on the tips of leaves of the vegetation bordering animal trails in the forest. A total of 103 adult ticks belonging to 4 Amblyomma species were collected by the visual search method on 5 collecting dates, while only 44 adult ticks belonging to 3 Amblyomma species were collected by dragging on 5 other collecting dates. These values were statistically different (Mann-Whitney Test, P = 0.0472). On the other hand, dragging was more efficient for subadult ticks, since no larva or nymph was collected by visual search, whereas 18 nymphs and 7 larvae were collected by dragging. The visual search method proved to be suitable for collecting adult ticks in the Amazon forest: however, field studies should include a second method, such as dragging in order to maximize the collection of subadult ticks. Indeed, these 2 methods can be performed by a single investigator at the same time, while he/she walks on an animal trail in the forest. (C) 2010 Elsevier GmbH. All rights reserved.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

Efficient bulk-loading on dynamic metric access methods

This paper presents a new technique and two algorithms to bulk-load data into multi-way dynamic metric access methods, based on the covering radius of representative elements employed to organize data in hierarchical data structures. The proposed algorithms are sample-based, and they always build a valid and height-balanced tree. We compare the proposed algorithm with existing ones, showing the behavior to bulk-load data into the Slim-tree metric access method. After having identified the worst case of our first algorithm, we describe adequate counteractions in an elegant way creating the second algorithm. Experiments performed to evaluate their performance show that our bulk-loading methods build trees faster than the sequential insertion method regarding construction time, and that it also significantly improves search performance. (C) 2009 Elsevier B.V. All rights reserved.

A pattern recognition approach to complex networks

Complex networks exist in many areas of science such as biology, neuroscience, engineering, and sociology. The growing development of this area has led to the introduction of several topological and dynamical measurements, which describe and quantify the structure of networks. Such characterization is essential not only for the modeling of real systems but also for the study of dynamic processes that may take place in them. However, it is not easy to use several measurements for the analysis of complex networks, due to the correlation between them and the difficulty of their visualization. To overcome these limitations, we propose an effective and comprehensive approach for the analysis of complex networks, which allows the visualization of several measurements in a few projections that contain the largest data variance and the classification of networks into three levels of detail, vertices, communities, and the global topology. We also demonstrate the efficiency and the universality of the proposed methods in a series of real-world networks in the three levels.

Bond Strength and Etching Pattern of Adhesive Systems to Enamel: Effects of Conditioning Time and Enamel Preparation

Statement of the problem: The performance of self-etch systems on enamel is controversial and seems to be dependent on the application technique and the enamel preparation. Purpose of the Study: To examine the effects of conditioning time and enamel surface preparation on bond strength and etching pattern of adhesive systems to enamel. Materials and Methods: Ninety-six teeth were divided into 16 conditions (N = 6) in function of enamel preparation and conditioning time for bond strength test. The adhesive systems OptiBond FL (Kerr, Orange, CA, USA), OptiBond SOLO Plus (Kerr), Clearfil SE Bond (Kuraray, Osaka, Japan), and Adper Prompt L-Pop (3M ESPE, St. Paul, MN, USA) were applied on unground or ground enamel following the manufacturers` directions or doubling the conditioning time. Cylinders of Filtek Flow (0.5-mm height) were applied to each bonded enamel surface using a Tygon tube (0.7 mm in diameter; Saint-Gobain Corp., Aurora, OH, USA). After storage (24 h/37 degrees C), the specimens were subjected to shear force (0.5 mm/min). The data were treated by a three-way analysis of variance and Tukey`s test (alpha = 0.05). The failure modes of the debonded interfaces and the etching pattern of adhesives were observed using scanning electron microscopy. Results: Only the main factor ""adhesive"" was statistically significant (p < 0.001). The lowest bond strength value was observed for OptiBond FL. The most defined etching pattern was observed for 35% phosphoric acid and for Adper Prompt L-Pop. Mixed failures were observed for all adhesives, but OptiBond FL showed cohesive failures in resin predominantly. Conclusions: The increase in the conditioning time as well as the enamel pretreatment did not provide an increase in the resin-enamel bond strength values for the studied adhesives. CLINICAL SIGNIFICANCE The surface enamel preparation and the conditioning time do not affect the performance of self-etch systems to enamel. (J Esthet Restor Dent 20:322-336, 2008)

Spatiotemporal Pattern Formation in the Oscillatory Electro-Oxidation of Sulfide on a Platinum Disk

Spatiotemporal pattern formation in the electrocatalytic oxidation of sulfide on a platinum disk is investigated using electrochemical methods and a charge-coupled device (CCD) camera simultaneously. The system is characterized by different oscillatory regions spread over a wide potential range. An additional series resistor and a large electrode area facilitate observation of multiple regions of kinetic instabilities along the current/potential curve. Spatiotemporal patterns on the working electrode, such as fronts, pulses, spirals, twinkling eyes, labyrinthine stripes, and alternating synchronized deposition and dissolution, are observed at different operating conditions of series resistance and sweep rate.

A chemometric study of the 5-HT1A receptor affinities presented by arylpiperazine compounds

Arylpiperazine compounds are promising 5-HT1A receptor ligands that can contribute for accelerating the onset of therapeutic effect of selective serotonin reuptake inhibitors. In the present work, the chemometric methods HCA, PCA, KNN, SIMCA and PLS were employed in order to obtain SAR and QSAR models relating the structures of arylpiperazine compounds to their 5-HT1A receptor affinities. A training set of 52 compounds was used to construct the models and the best ones were obtained with nine topological descriptors. The classification and regression models were externally validated by means of predictions for a test set of 14 compounds and have presented good quality, as verified by the correctness of classifications, in the case of pattern recognition studies, and b, the high correlation coefficients (q(2) = 0.76, r(2) = 0.83) and small prediction errors for the PLS regression. Since the results are in good agreement with previous SAR studies, we can suggest that these findings can help in the search for 5-HT1A receptor ligands that are able to improve antidepressant treatment. (c) 2007 Elsevier Masson SAS. All rights reserved.

Teaching grammar: A study of the common English grammar errors and grammar teaching methods that can be relevant for Swedish upper secondary schools

Grammar has always been an important part of language learning. Based on various theories, such as the universal grammar theory (Chomsky, 1959) and, the input theory (Krashen, 1970), the explicit and implicit teaching methods have been developed. Research shows that both methods may have some benefits and disadvantages. The attitude towards English grammar teaching methods in schools has also changed and nowadays grammar teaching methods and learning strategies, as a part of language mastery, are one of the discussion topics among linguists. This study focuses on teacher and learner experiences and beliefs about teaching English grammar and difficulties learners may face. The aim of the study is to conduct a literature review and to find out what scientific knowledge exists concerning the previously named topics. Along with this, the relevant steering documents are investigated focusing on grammar teaching at Swedish upper secondary schools. The universal grammar theory of Chomsky as well as Krashen’s input hypotheses provide the theoretical background for the current study. The study has been conducted applying qualitative and quantitative methods. The systematic search in four databases LIBRIS, ERIK, LLBA and Google Scholar were used for collecting relevant publications. The result shows that scientists’ publications name different grammar areas that are perceived as problematic for learners all over the world. The most common explanation of these difficulties is the influence of learner L1. Research presents teachers’ and learners’ beliefs to the benefits of grammar teaching methods. An effective combination of teaching methods needs to be done to fit learners’ expectations and individual needs. Together, they will contribute to the achieving of higher language proficiency levels and, therefore, they can be successfully applied at Swedish upper secondary schools.

Spontaneous Interblink Time Distributions in Patients with Graves' Orbitopathy and Normal Subjects

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Methods for evaluating the physiological potential of scarlet eggplant (Solanum aethiopicum) seeds

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Contribuições a técnicas de agrupamento e visualização de dados multivariados utilizando mapas auto-organizáveis

Self-organizing maps (SOM) are artificial neural networks widely used in the data mining field, mainly because they constitute a dimensionality reduction technique given the fixed grid of neurons associated with the network. In order to properly the partition and visualize the SOM network, the various methods available in the literature must be applied in a post-processing stage, that consists of inferring, through its neurons, relevant characteristics of the data set. In general, such processing applied to the network neurons, instead of the entire database, reduces the computational costs due to vector quantization. This work proposes a post-processing of the SOM neurons in the input and output spaces, combining visualization techniques with algorithms based on gravitational forces and the search for the shortest path with the greatest reward. Such methods take into account the connection strength between neighbouring neurons and characteristics of pattern density and distances among neurons, both associated with the position that the neurons occupy in the data space after training the network. Thus, the goal consists of defining more clearly the arrangement of the clusters present in the data. Experiments were carried out so as to evaluate the proposed methods using various artificially generated data sets, as well as real world data sets. The results obtained were compared with those from a number of well-known methods existent in the literature

Drug resistance in Mycobacterium tuberculosis clinical isolates from Brazil: Phenotypic and genotypic methods

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)