Equivalência de estímulos após a formação de classes seqüênciais com portadores de necessidades educativas especiais

Estudos anteriores realizados no Laboratório de Psicologia Experimental da UFPA demonstraram que o treino por encadeamento era mais eficiente na formação de classes sequenciais, demonstrando maior precisão dos participantes nos testes que documentaram as propriedades de uma relação ordinal, especialmente no teste de substitutabilidade. Com base nesse estudo e através de um procedimento de encadeamento para formar oito sequências independentes com estímulos visuais, procurou-se avaliar os efeitos de uma história de treino com estímulos usuais e não usuais e se os membros das classes seqüenciais emergentes eram também equivalentes. Participaram deste estudo três indivíduos portadores de necessidades educacionais especiais, alunos da APAE (Associação de Pais e Amigos dos Excepcionais), todos experimentalmente ingênuos. As sessões experimentais foram realizadas em uma sala da APAE, cinco vezes por semana. Foi utilizado um computador com tela sensível ao toque com um programa especialmente desenvolvido para este estudo. O procedimento contou com fases de treino e testes e foi dividido em duas condições: na Condição I foi utilizado o treino por encadeamento de respostas com quatro conjuntos de estímulos usuais seguido de teste de seqüenciação, testes com pares de estímulos não adjacentes, teste de substitutabilidade de estímulos e de equivalência. Na Condição II foi utilizado o mesmo procedimento da Condição I com quatro novos conjuntos de estímulos não usuais. Os resultados mostraram que asseqüências ensinadas apresentaram as propriedades de uma relação de ordinalidade, durante os testes de substitutabilidade de estímulos. Os participantes foram capazes de formar uma nova seqüência a partir do treino de duas seqüências independentes sugerindo assim a emergência de classes seqüenciais, em ambas as condições experimentais, demonstrando que os estímulos usuais podem ter exercido uma função básica de ordinalidade e ter facilitado o responder seqüencial com os novos estímulos. A emergência de relações de equivalência sugerem também que os estímulos são funcionalmente equivalentes. Estes resultados ampliam e estendem os resultados de estudos anteriores para uma outra população e confirmam empiricamente o que vem sendo apontado pela literatura da área.

Use of classification in organizing and searching the web

The traditional characteristics and challenges for organizing and searching information on the World Wide Web are outlined and reviewed. The classification features of two of these methods, such as Google, in the case of automated search engines, and Yahoo! Directory, in the case of subject directories are analyzed. Recent advances in the Semantic Web, particularly the growing application of ontologies and Linked Data are also reviewed. Finally, some problems and prospects related to the use of classification and indexing on the World Wide Web are discussed, emphasizing the need of rethinking the role of classification in the organization of these resources and outlining the possibilities of applying Ranganathan's facet theories of classification.

Identification of Candida Spp. In Patients with Denture Stomatitis: Relationship with Gender, Age, Time of Denture use and Newton’s Classification

Epidemiological researches are important to understand the distribution and etiology of oral diseases. The actual researches that show the relationship between patient ages, denture status and denture stomatitis are scarce. So, the aim of this study was to identify of Candida spp. in patients with Denture Stomatitis (DS) and to correlate with gender, age, time of denture use and Newton’s classification. 204 complete denture patients (46 males and 158 females) were selected. DS was classified according to Newton’s classification and it was related to gender, age and time of denture use. Samples from the palatal mucosa and the surface of the upper denture of patients with DS were evaluated using PCR test for identification of Candida species. T-test, chisquare and Fisher’s exact tests were used for statistical analysis. DS was evidenced in 54.4% of the sample. According to gender 41.3% of the males and 58.3% females had the disease and the differences were statistically significant (p = 0.032). The type of DS was directly influenced by the time of denture use (p<0.001), but it was not significantly related to the age of the participants (p>0.05). C. albicans, C. tropicalis, C. glabrata, C. krusei and C. dubliniensis were identified by PCR test. DS is more prevalent in women and the prevalence of DS was influenced by the time of denture use (years). C. albicans was identified as the most frequent specie in patients with DS.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimation of croplands using indicator kriging and fuzzy classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Ear canal cholesteatoma: meta-analysis of clinical characteristics with update on classification, staging and treatment

We present an update on clinical evaluation, staging, classification and treatment of canal cholesteatoma, including a meta-analysis of clinical data of the last 30 years.

Stratification and Prognostic Relevance of Jass's Molecular Classification of Colorectal Cancer

Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP), microsatellite instability (MSI), KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT) and classifies tumors into five subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: Three hundred two patients were included in this study. Molecular analysis was performed for five CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1), MGMT, MSI, KRAS, and BRAF. Methylation in at least 4 promoters or in one to three promoters was considered CIMP-high and CIMP-low (CIMP-H/L), respectively. Results: CIMP-H, CIMP-L, and CIMP-negative were found in 7.1, 43, and 49.9% cases, respectively. One hundred twenty-three tumors (41%) could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-L, 14 CIMP-H, and two CIMP-negative cases. The 10 year survival rate for CIMP-high patients [22.6% (95%CI: 7-43)] was significantly lower than for CIMP-L or CIMP-negative (p = 0.0295). Only the combined analysis of BRAF and CIMP (negative versus L/H) led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.

Modelling the results of health promotion activities in Switzerland: development of the Swiss Model for Outcome Classification in Health Promotion and Prevention

This paper describes the Model for Outcome Classification in Health Promotion and Prevention adopted by Health Promotion Switzerland (SMOC, Swiss Model for Outcome Classification) and the process of its development. The context and method of model development, and the aim and objectives of the model are outlined. Preliminary experience with application of the model in evaluation planning and situation analysis is reported. On the basis of an extensive literature search, the model is situated within the wider international context of similar efforts to meet the challenge of developing tools to assess systematically the activities of health promotion and prevention.

Quickbird satellite imagery for riparian management : characterizing riparian filter strips and detecting concentrated flow in an agricultural watershed

Riparian ecology plays an important part in the filtration of sediments from upland agricultural lands. The focus of this work makes use of multispectral high spatial resolution remote sensing imagery (Quickbird by Digital Globe) and geographic information systems (GIS) to characterize significant riparian attributes in the USDA’s experimental watershed, Goodwin Creek, located in northern Mississippi. Significant riparian filter characteristics include the width of the strip, vegetation properties, soil properties, topography, and upland land use practices. The land use and vegetation classes are extracted from the remotely sensed image with a supervised maximum likelihood classification algorithm. Accuracy assessments resulted in an acceptable overall accuracy of 84 percent. In addition to sensing riparian vegetation characteristics, this work addresses the issue of concentrated flow bypassing a riparian filter. Results indicate that Quickbird multispectral remote sensing and GIS data are capable of determining riparian impact on filtering sediment. Quickbird imagery is a practical solution for land managers to monitor the effectiveness of riparian filtration in an agricultural watershed.

Interrelation of peri-operative morbidity and ASA class assignment in patients undergoing gynaecological surgery

OBJECTIVE: The aim of this study was to estimate intra- and post-operative risk using the American Society of Anaesthesiologists (ASA) classification which is an important predictor of an intervention and of the entire operating programme. STUDY DESIGN: In this retrospective study, 4435 consecutive patients undergoing elective and emergency surgery at the Gynaecological Clinic of the University Hospital of Zurich were included. The ASA classification for pre-operative risk assessment was determined by an anaesthesiologist after a thorough physical examination. We observed several pre-, intra- and post-operative parameters, such as age, body-mass-index, duration of anaesthesia, duration of surgery, blood loss, duration of post-operative stay, complicated post-operative course, morbidity and mortality. The investigation of different risk factors was achieved by a multiple linear regression model for log-transformed duration of hospitalisation. RESULTS: Age and obesity were responsible for a higher ASA classification. ASA grade correlates with the duration of anaesthesia and the duration of the surgery itself. There was a significant difference in blood loss between ASA grades I (113+/-195 ml) and III (222+/-470 ml) and between classes II (176+/-432 ml) and III. The duration of post-operative hospitalisation could also be correlated with ASA class. ASA class I=1.7+/-3.0 days, ASA class II=3.6+/-4.3 days, ASA class III=6.8+/-8.2 days, and ASA class IV=6.2+/-3.9 days. The mean post-operative in-hospital stay was 2.5+/-4.0 days without complications, and 8.7+/-6.7 days with post-operative complications. Multiple linear regression model showed that not only the ASA classification contained an important information for the duration of hospitalisation. Parameters such as age, class of diagnosis, post-operative complications, etc. also have an influence on the duration of hospitalisation. CONCLUSION: This study shows that the ASA classification can be used as a good and early available predictor for the planning of an intervention in gynaecological surgery. The ASA classification helps the surgeon to assess the peri-operative risk profile of which important information can be derived for the planning of the operation programme.

Classification of platelet concentrates (Platelet-Rich Plasma-PRP, Platelet-Rich Fibrin-PRF) for topical and infiltrative use in orthopedic and sports medicine: current consensus, clinical implications and perspectives.

Platelet concentrates for topical and infiltrative use - commonly termed Platetet-Rich Plasma (PRP) or Platelet-Rich Fibrin (PRF) - are used or tested as surgical adjuvants or regenerative medicine preparations in most medical fields, particularly in sports medicine and orthopaedic surgery. Even if these products offer interesting therapeutic perspectives, their clinical relevance is largely debated, as the literature on the topic is often confused and contradictory. The long history of these products was always associated with confusions, mostly related to the lack of consensual terminology, characterization and classification of the many products that were tested in the last 40 years. The current consensus is based on a simple classification system dividing the many products in 4 main families, based on their fibrin architecture and cell content: Pure Platelet-Rich Plasma (P-PRP), such as the PRGF-Endoret technique; Leukocyte- and Platelet-Rich Plasma (LPRP), such as Biomet GPS system; Pure Platelet-Rich Fibrin (P-PRF), such as Fibrinet; Leukocyte- and Platelet-Rich Fibrin (L-PRF), such as Intra-Spin L-PRF. The 4 main families of products present different biological signatures and mechanisms, and obvious differences for clinical applications. This classification serves as a basis for further investigations of the effects of these products. Perspectives of evolutions of this classification and terminology are also discussed, particularly concerning the impact of the cell content, preservation and activation on these products in sports medicine and orthopaedics.

Molecular Classification of Ependymal Tumors across All CNS Compartments, Histopathological Grades, and Age Groups

Ependymal tumors across age groups are currently classified and graded solely by histopathology. It is, however, commonly accepted that this classification scheme has limited clinical utility based on its lack of reproducibility in predicting patients' outcome. We aimed at establishing a uniform molecular classification using DNA methylation profiling. Nine molecular subgroups were identified in a large cohort of 500 tumors, 3 in each anatomical compartment of the CNS, spine, posterior fossa, supratentorial. Two supratentorial subgroups are characterized by prototypic fusion genes involving RELA and YAP1, respectively. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification and thus might serve as a basis for the next World Health Organization classification of CNS tumors.