A process for mining science & technology documents databases, illustrated for the case of "knowledge discovery and data mining"

This paper presents a process of mining research & development abstract databases to profile current status and to project potential developments for target technologies, The process is called "technology opportunities analysis." This article steps through the process using a sample data set of abstracts from the INSPEC database on the topic o "knowledge discovery and data mining." The paper offers a set of specific indicators suitable for mining such databases to understand innovation prospects. In illustrating the uses of such indicators, it offers some insights into the status of knowledge discovery research*.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Characterization of new Schistosoma mansoni microsatellite loci in sequences obtained from public DNA databases and microsatellite enriched genomic libraries

In the last decade microsatellites have become one of the most useful genetic markers used in a large number of organisms due to their abundance and high level of polymorphism. Microsatellites have been used for individual identification, paternity tests, forensic studies and population genetics. Data on microsatellite abundance comes preferentially from microsatellite enriched libraries and DNA sequence databases. We have conducted a search in GenBank of more than 16,000 Schistosoma mansoni ESTs and 42,000 BAC sequences. In addition, we obtained 300 sequences from CA and AT microsatellite enriched genomic libraries. The sequences were searched for simple repeats using the RepeatMasker software. Of 16,022 ESTs, we detected 481 (3%) sequences that contained 622 microsatellites (434 perfect, 164 imperfect and 24 compounds). Of the 481 ESTs, 194 were grouped in 63 clusters containing 2 to 15 ESTs per cluster. Polymorphisms were observed in 16 clusters. The 287 remaining ESTs were orphan sequences. Of the 42,017 BAC end sequences, 1,598 (3.8%) contained microsatellites (2,335 perfect, 287 imperfect and 79 compounds). The 1,598 BAC end sequences 80 were grouped into 17 clusters containing 3 to 17 BAC end sequences per cluster. Microsatellites were present in 67 out of 300 sequences from microsatellite enriched libraries (55 perfect, 38 imperfect and 15 compounds). From all of the observed loci 55 were selected for having the longest perfect repeats and flanking regions that allowed the design of primers for PCR amplification. Additionally we describe two new polymorphic microsatellite loci.

Inferences about the global scenario of human T-cell lymphotropic virus type 1 infection using data mining of viral sequences

Human T-cell lymphotropic virus type 1 (HTLV-1) is mainly associated with two diseases: tropical spastic paraparesis/HTLV-1-associated myelopathy (TSP/HAM) and adult T-cell leukaemia/lymphoma. This retrovirus infects five-10 million individuals throughout the world. Previously, we developed a database that annotates sequence data from GenBank and the present study aimed to describe the clinical, molecular and epidemiological scenarios of HTLV-1 infection through the stored sequences in this database. A total of 2,545 registered complete and partial sequences of HTLV-1 were collected and 1,967 (77.3%) of those sequences represented unique isolates. Among these isolates, 93% contained geographic origin information and only 39% were related to any clinical status. A total of 1,091 sequences contained information about the geographic origin and viral subtype and 93% of these sequences were identified as subtype “a”. Ethnicity data are very scarce. Regarding clinical status data, 29% of the sequences were generated from TSP/HAM and 67.8% from healthy carrier individuals. Although the data mining enabled some inferences about specific aspects of HTLV-1 infection to be made, due to the relative scarcity of data of available sequences, it was not possible to delineate a global scenario of HTLV-1 infection.

Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat® software was used, and different data mining algorithms were applied using Weka® software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.

Using data mining to identify factors that influence the degree of leg injuries in broilers

Locomotor problems prevent the bird to move freely, jeopardizing the welfare and productivity, besides generating injuries on the legs of chickens. The objective of this study was to evaluate the influence of age, use of vitamin D, the asymmetry of limbs and gait score, the degree of leg injuries in broilers, using data mining. The analysis was performed on a data set obtained from a field experiment in which it was used two groups of birds with 30 birds each, a control group and one treated with vitamin D. It was evaluated the gait score, the asymmetry between the right and left toes, and the degree of leg injuries. The Weka ® software was used in data mining. In particular, C4.5 algorithm (also known as J48 in Weka environment) was used for the generation of a decision tree. The results showed that age is the factor that most influences the degree of leg injuries and that the data from assessments of gait score were not reliable to estimate leg weakness in broilers.

Data mining techniques for identification of spectrally homogeneous areas using NDVI temporal profiles of soybean crop

The aim of this study was to group temporal profiles of 10-day composites NDVI product by similarity, which was obtained by the SPOT Vegetation sensor, for municipalities with high soybean production in the state of Paraná, Brazil, in the 2005/2006 cropping season. Data mining is a valuable tool that allows extracting knowledge from a database, identifying valid, new, potentially useful and understandable patterns. Therefore, it was used the methods for clusters generation by means of the algorithms K-Means, MAXVER and DBSCAN, implemented in the WEKA software package. Clusters were created based on the average temporal profiles of NDVI of the 277 municipalities with high soybean production in the state and the best results were found with the K-Means algorithm, grouping the municipalities into six clusters, considering the period from the beginning of October until the end of March, which is equivalent to the crop vegetative cycle. Half of the generated clusters presented spectro-temporal pattern, a characteristic of soybeans and were mostly under the soybean belt in the state of Paraná, which shows good results that were obtained with the proposed methodology as for identification of homogeneous areas. These results will be useful for the creation of regional soybean "masks" to estimate the planted area for this crop.

Vocalization data mining for estimating swine stress conditions

This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.

Gestão do conhecimento usando data mining: estudo de caso na Universidade Federal de Lavras

A gestão do conhecimento abrange toda a forma de gerar, armazenar, distribuir e utilizar o conhecimento, tornando necessária a utilização de tecnologias de informação para facilitar esse processo, devido ao grande aumento no volume de dados. A descoberta de conhecimento em banco de dados é uma metodologia que tenta solucionar esse problema e o data mining é uma técnica que faz parte dessa metodologia. Este artigo desenvolve, aplica e analisa uma ferramenta de data mining, para extrair conhecimento referente à produção científica das pessoas envolvidas com a pesquisa na Universidade Federal de Lavras. A metodologia utilizada envolveu a pesquisa bibliográfica, a pesquisa documental e o método do estudo de caso. As limitações encontradas na análise dos resultados indicam que ainda é preciso padronizar o modo do preenchimento dos currículos Lattes para refinar as análises e, com isso, estabelecer indicadores. A contribuição foi gerar um banco de dados estruturado, que faz parte de um processo maior de desenvolvimento de indicadores de ciência e tecnologia, para auxiliar na elaboração de novas políticas de gestão científica e tecnológica e aperfeiçoamento do sistema de ensino superior brasileiro.

Redescription of Prosthenhystera obesa (Diesing, 1850) (Callodistomidae, Digenea) with New Host Records and Data on Morphological Variability

Prosthenhystera obesa (Diesing,1850) Travassos, 1922 from the gall bladder of Astyanax bimaculatus, Caranx gibbosus, Galeocharax humeralis, Leporinus copelandii, Pimelodus fur, Pseudopimelodus roosevelti, Salminus brevidens, Salminus maxillosus and from the new hosts, Cynopotamus amazonum and Triurobrycon lundii is redescribed, demonstrating a large morphological variation, mainly in body and testes size and shape. New hosts harbouring immature specimens of P. obesa are presented: Brycon sp., Leporellus vittatus, Pachyurus squamipinnis, Pimelodus clarias, Pseudoplatystoma corruscans and Salminus hilarii. Scanning electron microscopy micrographies, original figures and measurements of adult and immature specimens from different Brazilian hosts and localities are presented

Tannery and coal mining waste disposal on soil

Tannery residues and coal mine waste are heavily polluting sources in Brazil, mainly in the Southern States of Rio Grande do Sul and Santa Catarina. In order to study the effects of residues of chrome leather tanning (sludge and leather shavings) and coal waste on soybean and maize crops, a field experiment is in progress since 1996, at the Federal University of Rio Grande do Sul Experimental Station, county of Eldorado do Sul, Brazil. The residues were applied twice (growing seasons 1996/97 and 1999/00). The amounts of tannery residues were applied according to their neutralizing value, at rates of up to 86.8 t ha-1, supplying from 671 to 1.342 kg ha-1 Cr(III); coal waste was applied at a total rate of 164 t ha-1. Crop yield and dry matter production were evaluated, as well as the nutrients (N, P, K, Ca, Mg, Cu and Zn) and Cr contents. Crop yields with tannery sludge application were similar to those obtained with N and lime supplied with mineral amendments. Plant Cr absorption did not increase significantly with the residue application. Tannery sludge can be used also to neutralize the high acidity developed in the soil by coal mine waste.

Inteligência obtida pela aplicação de data mining em base de teses francesas sobre o Brasil

O assunto Brasil foi analisado na base de teses francesas DocThèses, compreendendo os anos de 1969 a 1999. Utilizou-se a técnica de Data Mining como ferramenta para obter inteligência e conhecimento. O software utilizado para a limpeza da base DocThèses foi o Infotrans, e, para a preparação dos dados, empregou-se o Dataview. Os resultados da análise foram ilustrados com a aplicação dos pressupostos da Lei de Zipf, classificando-se as informações em trivial, interessante e ruído, conforme a distribuição de freqüência. Conclui-se que a técnica do Data Mining associada a softwares especialistas é uma poderosa aliada no emprego de inteligência no processo decisório em todos os níveis, inclusive o nível macro, pois oferece subsídios para a consolidação, investimento e desenvolvimento de ações e políticas.

Breast imaging reporting and data system (BI-RADS™): como tem sido utilizado?

O Breast Imaging Reporting and Data System (BI-RADS™), do American College of hRadiology, foi concebido para padronizar o laudo mamográfico e reduzir os fatores de confusão na descrição e interpretação das imagens, além de facilitar o monitoramento do resultado final. OBJETIVO: Identificar a maneira como vem sendo utilizado o BI-RADS™, gerando informações que possam auxiliar o Colégio Brasileiro de Radiologia a desenvolver estratégias para aperfeiçoar o seu uso. MATERIAIS E MÉTODOS: Os dados foram coletados na cidade de Goiânia, GO. Foram solicitados os exames de mamografia anteriores a todas as mulheres que se dirigiram ao serviço para realização de mamografia entre janeiro/2003 e junho/2003. Foram incluídos na análise exames anteriores, realizados entre 1/7/2001 e 30/6/2003. RESULTADOS: Foram coletados 104 laudos anteriores, emitidos por 40 radiologistas de 33 diferentes serviços. Dos 104 laudos, 77% (n = 80) utilizavam o BI-RADS™. Destes, apenas 15% (n = 12) eram concisos, nenhum utilizava a estrutura e organização recomendadas pelo sistema, 98,75% (n = 79) não respeitavam o léxico e 65% (n = 51) não faziam recomendação de conduta. CONCLUSÃO: O BI-RADS™, apesar de bastante utilizado, não foi reconhecido como sistema para padronização dos laudos. Foi usado quase exclusivamente como forma de classificação final dos exames.

Breast Imaging Reporting and Data System - BI-RADS®: valor preditivo positivo das categorias 3, 4 e 5. revisão sistemática da literatura

OBJETIVO: Avaliar artigos, na literatura, que verificam o valor preditivo positivo das categorias 3, 4 e 5 do Breast Imaging Reporting and Data System (BI-RADS®). MATERIAIS E MÉTODOS: Foi realizada pesquisa na base de dados Medline utilizando os termos "predictive value" e "BI-RADS". Foram incluídos 11 artigos nesta revisão. RESULTADOS: O valor preditivo positivo das categorias 3, 4 e 5 variou entre 0% e 8%, 4% e 62%, 54% e 100%, respectivamente. Três artigos avaliaram, concomitantemente, os critérios morfológicos das lesões que apresentaram maior valor preditivo positivo na mamografia, sendo nódulo espiculado o critério com maior valor preditivo positivo. CONCLUSÃO: Houve grande variabilidade do valor preditivo positivo das categorias 3, 4 e 5 do BI-RADS® em todos os estudos, porém foram identificadas diferenças metodológicas que limitaram a comparação desses estudos.