O relevo na interpretação da variabilidade espacial dos teores de nutrientes em folha de citros

Foliar diagnosis is a method for assessing the nutritional status of agricultural crops, which helps in the understanding of soil fertility and rationalized application of fertilizers taking into account economic and environmental criteria. The study aimed to use the landrelief as criteria to assist in interpreting the spatial variability of nutrient content of the citrus leaf. The leaves were collected at regular intervals of 50 m, totaling 332 sampling points. Data were analyzed by descriptive statistics, geostatistics and induction of decision tree. With the aid of digital elevation model (MDE) and the profile planaltimetric, the area was divided into three different landrelief and sub-strands. The highest values for nutrients from the leaves of citrus were observed at the top (concave area) segments on a half-slope and lower slope. The nutrients from the citrus leaves showed high values of correlation (above 0.5) with the altitude of the study area. The technique of geostatistics and the induction of decision tree show that the relief is the variable with the greatest potential to interpret the maps of spatial variability of nutrients from the citrus leaves.

Análise dos custos assistenciais de uma operadora de plano de saúde no Brasil

The relevance of rising healthcare costs is a main topic in complementary health companies in Brazil. In 2011, these expenses consumed more than 80% of the monthly health insurance in Brazil. Considering the administrative costs, it is observed that the companies operating in this market work, on average, at the threshold between profit and loss. This paper presents results after an investigation of the welfare costs of a health plan company in Brazil. It was based on the KDD process and explorative Data Mining. A diversity of results is presented, such as data summarization, providing compact descriptions of the data, revealing common features and intrinsic observations. Among the key findings was observed that a small portion of the population is responsible for the most demanding of resources devoted to health care

Análise de Agrupamentos Com Base na Teoria da Informação: Uma Abordagem Representativa

Currently, one of the biggest challenges for the field of data mining is to perform cluster analysis on complex data. Several techniques have been proposed but, in general, they can only achieve good results within specific areas providing no consensus of what would be the best way to group this kind of data. In general, these techniques fail due to non-realistic assumptions about the true probability distribution of the data. Based on this, this thesis proposes a new measure based on Cross Information Potential that uses representative points of the dataset and statistics extracted directly from data to measure the interaction between groups. The proposed approach allows us to use all advantages of this information-theoretic descriptor and solves the limitations imposed on it by its own nature. From this, two cost functions and three algorithms have been proposed to perform cluster analysis. As the use of Information Theory captures the relationship between different patterns, regardless of assumptions about the nature of this relationship, the proposed approach was able to achieve a better performance than the main algorithms in literature. These results apply to the context of synthetic data designed to test the algorithms in specific situations and to real data extracted from problems of different fields

Extração e Representação de Conhecimento de Séries Temporais de Demanda de Energia Elétrica Usando TSKR

The opening of the Brazilian market of electricity and competitiveness between companies in the energy sector make the search for useful information and tools that will assist in decision making activities, increase by the concessionaires. An important source of knowledge for these utilities is the time series of energy demand. The identification of behavior patterns and description of events become important for the planning execution, seeking improvements in service quality and financial benefits. This dissertation presents a methodology based on mining and representation tools of time series, in order to extract knowledge that relate series of electricity demand in various substations connected of a electric utility. The method exploits the relationship of duration, coincidence and partial order of events in multi-dimensionals time series. To represent the knowledge is used the language proposed by Mörchen (2005) called Time Series Knowledge Representation (TSKR). We conducted a case study using time series of energy demand of 8 substations interconnected by a ring system, which feeds the metropolitan area of Goiânia-GO, provided by CELG (Companhia Energética de Goiás), responsible for the service of power distribution in the state of Goiás (Brazil). Using the proposed methodology were extracted three levels of knowledge that describe the behavior of the system studied, representing clearly the system dynamics, becoming a tool to assist planning activities

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

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Uma nova forma de calcular os centros dos Clusters em algoritmos de agrupamento tipo fuzzy c-means

Clustering data is a very important task in data mining, image processing and pattern recognition problems. One of the most popular clustering algorithms is the Fuzzy C-Means (FCM). This thesis proposes to implement a new way of calculating the cluster centers in the procedure of FCM algorithm which are called ckMeans, and in some variants of FCM, in particular, here we apply it for those variants that use other distances. The goal of this change is to reduce the number of iterations and processing time of these algorithms without affecting the quality of the partition, or even to improve the number of correct classifications in some cases. Also, we developed an algorithm based on ckMeans to manipulate interval data considering interval membership degrees. This algorithm allows the representation of data without converting interval data into punctual ones, as it happens to other extensions of FCM that deal with interval data. In order to validate the proposed methodologies it was made a comparison between a clustering for ckMeans, K-Means and FCM algorithms (since the algorithm proposed in this paper to calculate the centers is similar to the K-Means) considering three different distances. We used several known databases. In this case, the results of Interval ckMeans were compared with the results of other clustering algorithms when applied to an interval database with minimum and maximum temperature of the month for a given year, referring to 37 cities distributed across continents

Novos métodos determinísticos para gerar centros iniciais dos grupos no algoritmo fuzzy C-Means e variantes

Data clustering is applied to various fields such as data mining, image processing and pattern recognition technique. Clustering algorithms splits a data set into clusters such that elements within the same cluster have a high degree of similarity, while elements belonging to different clusters have a high degree of dissimilarity. The Fuzzy C-Means Algorithm (FCM) is a fuzzy clustering algorithm most used and discussed in the literature. The performance of the FCM is strongly affected by the selection of the initial centers of the clusters. Therefore, the choice of a good set of initial cluster centers is very important for the performance of the algorithm. However, in FCM, the choice of initial centers is made randomly, making it difficult to find a good set. This paper proposes three new methods to obtain initial cluster centers, deterministically, the FCM algorithm, and can also be used in variants of the FCM. In this work these initialization methods were applied in variant ckMeans.With the proposed methods, we intend to obtain a set of initial centers which are close to the real cluster centers. With these new approaches startup if you want to reduce the number of iterations to converge these algorithms and processing time without affecting the quality of the cluster or even improve the quality in some cases. Accordingly, cluster validation indices were used to measure the quality of the clusters obtained by the modified FCM and ckMeans algorithms with the proposed initialization methods when applied to various data sets

Caracterização de dois cDNAs homológos e uma AP endonuclease em cana-de-açúcar

The genome of all organisms is subject to injuries that can be caused by endogenous and environmental factors. If these lesions are not corrected, it can be fixed generating a mutation which can be lethal to the organisms. In order to prevent this, there are different DNA repair mechanisms. These mechanisms are well known in bacteria, yeast, human, but not in plants. Two plant models Oriza sativa and Arabidopsis thaliana had the genome sequenced and due to this some DNA repair genes have been characterized. The aim of this work is to characterized two sugarcane cDNAs that had homology to AP endonuclease: scARP1 and scARP3. In silico has been done with these two sequences and other from plants. It has been observed domain conservation on these sequences, but the cystein at 65 position that is a characteristic from the redox domain in APE1 protein was not so conservated in plants. Phylogenetic relationship showed two branches, one branch with dicots and monocots sequence and the other branch with only monocots sequences. Another approach in order to characterized these two cDNAs was to construct overexpression cassettes (sense and antisense orientation) using the 35S promoter. After that, these cassettes were transferred to the binary vector pPZP211. Furthermore, previously in the laboratory was obtained a plant from nicotiana tabacum containing the overexpression cassette in anti-sense orientation. It has been observed that this plant had a slow development and problems in setting seeds. After some manual crossing, some seeds were obtained (T2) and it was analyzed the T2 segregation. The third approach used in this work was to clone the promoter region from these two cDNAs by PCR walking. The sequences obtained were analyzed using the program PLANTCARE. It was observed in these sequences some motives that may be related to oxidative stress response

EULAR/PRINTO/PRES criteria for Henoch-Schonlein purpura, childhood polyarteritis nodosa, childhood Wegener granulomatosis and childhood Takayasu arteritis: Ankara 2008. Part I: Overall methodology and clinical characterisation

Objectives To report methodology and overall clinical, laboratory and radiographic characteristics for Henoch-Schonlein purpura (HSP), childhood polyarteritis nodosa (c-PAN), c-Wegener granulomatosis (c-WG) and c-Takayasu arteritis (c-TA) classification criteria.Methods The preliminary Vienna 2005 consensus conference, which proposed preliminary criteria for paediatric vasculitides, was followed by a EULAR/PRINTO/PRES-supported validation project divided into three main steps. Step 1: retrospective/prospective web-data collection for HSP, c-PAN, c-WG and c-TA, with age at diagnosis <= 18 years. Step 2: blinded classification by consensus panel of a subgroup of 280 cases (128 difficult cases, 152 randomly selected) enabling expert diagnostic verification. Step 3: Ankara 2008 Consensus Conference and statistical evaluation (sensitivity, specificity, area under the curve, kappa-agreement) using as 'gold standard' the final consensus classification or original treating physician diagnosis.Results A total of 1183/1398 (85%) samples collected were available for analysis: 827 HSP, 150 c-PAN, 60 c-WG, 87 c-TA and 59 c-other. Prevalence, signs/symptoms, laboratory, biopsy and imaging reports were consistent with the clinical picture of the four c-vasculitides. A representative subgroup of 280 patients was blinded to the treating physician diagnosis and classified by a consensus panel, with kappa-agreement of 0.96 for HSP (95% CI 0.84 to 1), 0.88 for c-WG (95% CI 0.76 to 0.99), 0.84 for c-TA (95% CI 0.73 to 0.96) and 0.73 for c-PAN (95% CI 0.62 to 0.84), with an overall. of 0.79 (95% CI 0.73 to 0.84).Conclusion EULAR/PRINTO/PRES propose validated classification criteria for HSP, c-PAN, c-WG and c-TA, with substantial/almost perfect agreement with the final consensus classification or original treating physician diagnosis.

Spatial Distribution of Seropositive Dogs to Leptospira spp. and Evaluation of Leptospirosis Risk Factors Using a Decision Tree

Background: Leptospirosis is an important zoonotic disease associated with poor areas of urban settings of developing countries and early diagnosis and prompt treatment may prevent disease. Although rodents are reportedly considered the main reservoirs of leptospirosis, dogs may develop the disease, may become asymptomatic carriers and may be used as sentinels for disease epidemiology. The use of Geographical Information Systems (GIS) combined with spatial analysis techniques allows the mapping of the disease and the identification and assessment of health risk factors. Besides the use of GIS and spatial analysis, the technique of data mining, decision tree, can provide a great potential to find a pattern in the behavior of the variables that determine the occurrence of leptospirosis. The objective of the present study was to apply Geographical Information Systems and data prospection (decision tree) to evaluate the risk factors for canine leptospirosis in an area of Curitiba, PR.Materials, Methods & Results: The present study was performed on the Vila Pantanal, a urban poor community in the city of Curitiba. A total of 287 dog blood samples were randomly obtained house-by-house in a two-day sampling on January 2010. In addition, a questionnaire was applied to owners at the time of sampling. Geographical coordinates related to each household of tested dog were obtained using a Global Positioning System (GPS) for mapping the spatial distribution of reagent and non-reagent dogs to leptospirosis. For the decision tree, risk factors included results of microagglutination test (MAT) from the serum of dogs, previous disease on the household, contact with rats or other dogs, dog breed, outdoors access, feeding, trash around house or backyard, open sewer proximity and flooding. A total of 189 samples (about 2/3 of overall samples) were randomly selected for the training file and consequent decision rules. The remained 98 samples were used for the testing file. The seroprevalence showed a pattern of spatial distribution that involved all the Pantanal area, without agglomeration of reagent animals. In relation to data mining, from 189 samples used in decision tree, a total of 165 (87.3%) animal samples were correctly classified, generating a Kappa index of 0.413. A total of 154 out of 159 (96.8%) samples were considered non-reagent and were correctly classified and only 5/159 (3.2%) were wrongly identified. on the other hand, only 11 (36.7%) reagent samples were correctly classified, with 19 (63.3%) samples failing diagnosis.Discussion: The spatial distribution that involved all the Pantanal area showed that all the animals in the area are at risk of contamination by Leptospira spp. Although most samples had been classified correctly by the decision tree, a degree of difficulty of separability related to seropositive animals was observed, with only 36.7% of the samples classified correctly. This can occur due to the fact of seronegative animals number is superior to the number of seropositive ones, taking the differences in the pattern of variable behavior. The data mining helped to evaluate the most important risk factors for leptospirosis in an urban poor community of Curitiba. The variables selected by decision tree reflected the important factors about the existence of the disease (default of sewer, presence of rats and rubbish and dogs with free access to street). The analyses showed the multifactorial character of the epidemiology of canine leptospirosis.

A microsatellite-based, gene-rich linkage map for the AA genome of Arachis (Fabaceae)

Cultivated peanut (Arachis hypogaea) is an important crop, widely grown in tropical and subtropical regions of the world. It is highly susceptible to several biotic and abiotic stresses to which wild species are resistant. As a first step towards the introgression of these resistance genes into cultivated peanut, a linkage map based on microsatellite markers was constructed, using an F-2 population obtained from a cross between two diploid wild species with AA genome (A. duranensis and A. stenosperma). A total of 271 new microsatellite markers were developed in the present study from SSR-enriched genomic libraries, expressed sequence tags (ESTs), and by data-mining sequences available in GenBank. of these, 66 were polymorphic for cultivated peanut. The 271 new markers plus another 162 published for peanut were screened against both progenitors and 204 of these (47.1%) were polymorphic, with 170 codominant and 34 dominant markers. The 80 codominant markers segregating 1:2:1 (P < 0.05) were initially used to establish the linkage groups. Distorted and dominant markers were subsequently included in the map. The resulting linkage map consists of 11 linkage groups covering 1,230.89 cM of total map distance, with an average distance of 7.24 cM between markers. This is the first microsatellite-based map published for Arachis, and the first map based on sequences that are all currently publicly available. Because most markers used were derived from ESTs and genomic libraries made using methylation-sensitive restriction enzymes, about one-third of the mapped markers are genic. Linkage group ordering is being validated in other mapping populations, with the aim of constructing a transferable reference map for Arachis.

14-3-3: Defense and regulatory proteins coding by Eucalyptus genome

The data mining of Eucalyptus ESTs genome finds four clusters (EGCEST2257E11.g, EGBGRT3213F11.g, and EGCCFB1223H11.g) from highly conservative 14-3-3 protein family which modulates a wide variety of cellular processes. Multiple alignments were built from twenty four sequences of 14-3-3 proteins searched into the GenBank databases and into the four pools of Eucalyptus genome programs. The alignment has shown two regions highly conservative on the sequences corresponding to the motifs of protein phosphorylation and nine highly conservative regions on the sequence corresponding to the linkage regions of alpha helices structure based on three dimensional of dimer functional structure. The differences of amino acid into the structural and functional domains of 14-3-3 plant protein were identified and can explain the functional diversity of different isoforms. The phylogenic protein trees were built by the maximum parsimony and neighborjoining procedures of Clustal X alignments and PAUP software for phylogenic analysis.

Knowledge extraction using visualization of hemoglobin parameters to identify thalassemia

The analysis of large amounts of data is better performed by humans when represented in a graphical format. Therefore, a new research area called the Visual Data Mining is being developed endeavoring to use the number crunching power of computers to prepare data for visualization, allied to the ability of humans to interpret data presented graphically.This work presents the results of applying a visual data mining tool, called FastMapDB to detect the behavioral pattern exhibited by a dataset of clinical information about hemoglobinopathies known as thalassemia. FastMapDB is a visual data mining tool that get tabular data stored in a relational database such as dates, numbers and texts, and by considering them as points in a multidimensional space, maps them to a three-dimensional space. The intuitive three-dimensional representation of objects enables a data analyst to see the behavior of the characteristics from abnormal forms of hemoglobin, highlighting the differences when compared to data from a group without alteration.