PROFILE OF CAREGIVERS OF ELDERLY PEOPLE WITH ALZHEIMER DISEASE ASSOCIATED TO RESILIENCE

This exploratory and descriptive study described sociodemographic and health variables of caregivers of elderly people with Alzheimer's disease, associating care provided with resilience. Participants were 101 caregivers over 18 years old who accompanied older adults in a Primary Care Unit of a Brazilian public hospital in 2009. Questionnaires regarding the profile, the Beck Depression Inventory, and the Resilience Scale were used. Descriptive statistical analysis was performed. Most caregivers were female, without depression, aided by other people in providing care, and had a high degree of resilience. The variables degree of kinship, medical treatment, the use of medication, tiredness, prostration, discouragement, and caregivers' mental health had significant association with resilience. Physical health was significantly associated to experience in care, with 82 elderly people presenting acute cognitive damage. Older adults in the family context can benefit from a more resilient caregiver.

Classificação híbrida: pixel a pixel e baseada em objetos para o monitoramento da condição da superfície dos pavimentos rodoviários

Monitorar a condição de uso de toda a extensão das rodovias brasileiras é tarefa dispendiosa e demorada. Este trabalho trata de novas técnicas que permitem o levantamento da condição da superfície dos pavimentos rodoviários de forma ágil utilizando imagens hiperespectrais de sensor digital aeroembarcado. Nos últimos anos, um número crescente de imagens de alta resolução espacial tem surgido no mercado mundial com o aparecimento dos novos satélites e sensores aeroembarcados de sensoriamento remoto. Propõe-se uma metodologia para identificação dos pavimentos asfálticos e classificação das principais ocorrências dos defeitos na superfície do pavimento. A primeira etapa da metodologia é a identificação da superfície asfáltica na imagem, utilizando uma classificação híbrida baseada inicialmente em pixel e depois refinada por objetos. A segunda etapa da metodologia é a identificação e classificação das ocorrências dos principais defeitos nos pavimentos flexíveis que são observáveis nas imagens de alta resolução espacial. Esta última etapa faz uso intensivo das novas técnicas de classificação de imagens baseadas em objetos. O resultado final é a geração de índices da condição da superfície do pavimento a partir das imagens que possam ser comparados com os indicadores vigentes da condição da superfície do pavimento já normatizados pelos órgãos competentes no país.

IMPLEMENTATION OF PROTECTED AREAS FORCENTRAL CORRIDOR OF ATLANTIC FORESTIN BAHIA: CHALLENGES AND LIMITS

In Brazil, Protected Areas (PAs) are considered the cornerstone for development of national strategies for biodiversity conservation. Considering this point of view we analyzed thirty protected areas belonging to Atlantic Central Corridor of Atlantic Forest in Bahia, aiming to identify and analyze its current level of implementation. Lemos de Sa and Ferreira (2000) methodology which consist of applying a standard scale, where the variation of the level of implementation conforms to a range of 0 to 5 points was used, with appropriate adaptations. After obtaining the data from the implementation level we use the aggregation method of Ward to help visualize the dissimilarity between the protected areas studied. We used an international classification proposed by IUCN (International Union for Conservation of Nature) for that the UCs to be compared with works done in another countries, the UCs considered are in the groups Ia, II, V and VI da IUCN. As results, 50% of protected areas analyzed are reasonably implemented, 40% inadequately implemented, 6.7% are presented only on paper and only 3.3% can be classified as satisfactorily implemented. These areas presents problems in their regularization; deficiency in infrastructure, human and financial resources. Given the results its clear the recurrent fact that conservation areas under study must be effectively implemented and for this to occur environmental policies should be focused on actions to consolidate the goals of conservation strategy.

Quantification of Short and Medium Range Order in Mixed Network Former Glasses of the System GeO2-NaPO3: A Combined NMR and X-ray Photoelectron Spectroscopy Study

Glasses in the system xGeO(2)-(1-x)NaPO3 (0 <= x <= 0.50) were prepared by conventional melting quenching and characterized by thermal analysis, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and P-31 nuclear magnetic resonance (MAS NMR) techniques. The deconvolution of the latter spectra was aided by homonuclear J-resolved and refocused INADEQUATE techniques. The combined analyses of P-31 MAS NMR and O-1s XPS lineshapes, taking charge and mass balance considerations into account, yield the detailed quantitative speciations of the phosphorus, germanium, and oxygen atoms and their respective connectivities. An internally consistent description is possible without invoking the formation of higher-coordinated germanium species in these glasses, in agreement with experimental evidence in the literature. The structure can be regarded, to a first approximation, as a network consisting of P-(2) and P-(3) tetrahedra linked via four-coordinate germanium. As implied by the appearance of P-(3) units, there is a moderate extent of network modifier sharing between phosphate and germanate network formers, as expressed by the formal melt reaction P-(2) + Ge-(4) -> P-(3) + Ge-(3). The equilibrium constant of this reaction is estimated as K = 0.52 +/- 0.11, indicating a preferential attraction of network modifier by the phosphorus component. These conclusions are qualitatively supported by Raman spectroscopy as well as P-31{Na-23} and P-31{Na-23} rotational echo double resonance (REDOR) NMR results. The combined interpretation of O-1s XPS and P-31 MAS NMR spectra shows further that there are clear deviations from a random connectivity scenario: heteroatomic P-O-Ge linkages are favored over homoatomic P-O-P and Ge-O-Ge linkages.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

The Brazilian Network for HIV-1 Genotyping External Quality Control Assurance Programme

The Brazilian network for genotyping is composed of 21 laboratories that perform and analyze genotyping tests for all HIV-infected patients within the public system, performing approximately 25,000 tests per year. We assessed the interlaboratory and intralaboratory reproducibility of genotyping systems by creating and implementing a local external quality control evaluation. Plasma samples from HIV-1-infected individuals (with low and intermediate viral loads) or RNA viral constructs with specific mutations were used. This evaluation included analyses of sensitivity and specificity of the tests based on qualitative and quantitative criteria, which scored laboratory performance on a 100-point system. Five evaluations were performed from 2003 to 2008, with 64% of laboratories scoring over 80 points in 2003, 81% doing so in 2005, 56% in 2006, 91% in 2007, and 90% in 2008 (Kruskal-Wallis, p = 0.003). Increased performance was aided by retraining laboratories that had specific deficiencies. The results emphasize the importance of investing in laboratory training and interpretation of DNA sequencing results, especially in developing countries where public (or scarce) resources are used to manage the AIDS epidemic.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Multicentric Carpotarsal Osteolysis Is Caused by Mutations Clustering in the Amino-Terminal Transcriptional Activation Domain of MAFB

Multicentric carpotarsal osteolysis (MCTO) is a rare skeletal dysplasia characterized by aggressive osteolysis, particularly affecting the carpal and tarsal bones, and is frequently associated with progressive renal failure. Using exome capture and next-generation sequencing in five unrelated simplex cases of MCTO, we identified previously unreported missense mutations clustering within a 51 base pair region of the single exon of MAFB, validated by Sanger sequencing. A further six unrelated simplex cases with MCTO were also heterozygous for previously unreported mutations within this same region, as were affected members of two families with autosomal-dominant MCTO. MAFB encodes a transcription factor that negatively regulates RANKL-induced osteoclastogenesis and is essential for normal renal development. Identification of this gene paves the way for development of novel therapeutic approaches for this crippling disease and provides insight into normal bone and kidney development.

Automatic aspect discrimination in data clustering

The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

CLASSIFICATION OF THE VIGOR OF SEEDS OF BEAN-KID BY THE RESPIRATORY ACTIVITY

Several tests to assess the vigor of seed lots are used by producing companies for internal quality control. The respiratory activity test determined in the Pettenkofer apparatus has potential to be used for this purpose. Therefore, this study aimed to analyze and compare the use of respiratory activity measured in the Pettenkofer apparatus with standard tests to assess the vigor, and classify seed lots of bean-kid in high, medium and low vigor. The respiratory activity of three lots of bean-kid seeds were related to the following tests: germination, first germination count, electrical conductivity, length of shoots and roots, and dry weight of seedlings shoots and roots. The results of germination tests, germination first count, seedling shoot and root length, seedling shoot and root dry mass, electrical conductivity and determination of respiratory activity the seeds, allowed the classification of seeds lots of bean-kid in levels of different vigor. It is concluded that the respiratory activity measured in the Pettenkofer apparatus is efficient for the classification of seed lots of bean-kid according to vigor, being a fast, effective and low cost procedure.

Identification of protein expression signatures in gastric carcinomas using clustering analysis

Background and Aim: The identification of gastric carcinomas (GC) has traditionally been based on histomorphology. Recently, DNA microarrays have successfully been used to identify tumors through clustering of the expression profiles. Random forest clustering is widely used for tissue microarrays and other immunohistochemical data, because it handles highly-skewed tumor marker expressions well, and weighs the contribution of each marker according to its relatedness with other tumor markers. In the present study, we e identified biologically- and clinically-meaningful groups of GC by hierarchical clustering analysis of immunohistochemical protein expression. Methods: We selected 28 proteins (p16, p27, p21, cyclin D1, cyclin A, cyclin B1, pRb, p53, c-met, c-erbB-2, vascular endothelial growth factor, transforming growth factor [TGF]-beta I, TGF-beta II, MutS homolog-2, bcl-2, bax, bak, bcl-x, adenomatous polyposis coli, clathrin, E-cadherin, beta-catenin, mucin (MUC) 1, MUC2, MUC5AC, MUC6, matrix metalloproteinase [ MMP]-2, and MMP-9) to be investigated by immunohistochemistry in 482 GC. The analyses of the data were done using a random forest-clustering method. Results: Proteins related to cell cycle, growth factor, cell motility, cell adhesion, apoptosis, and matrix remodeling were highly expressed in GC. We identified protein expressions associated with poor survival in diffuse-type GC. Conclusions: Based on the expression analysis of 28 proteins, we identified two groups of GC that could not be explained by any clinicopathological variables, and a subgroup of long-surviving diffuse-type GC patients with a distinct molecular profile. These results provide not only a new molecular basis for understanding the biological properties of GC, but also better prediction of survival than the classic pathological grouping.

Functional clustering of time series gene expression data by Granger causality

Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results: In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions: This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

A review of the classification of rabies virus lineages maintained by insectivorous bats in Brazil

Trabalho apresentado à 23ª Revista de Educação Continuada em Medicina Veterinária e Zootecnia, 2012, São Paulo