Desenvolvimento de uma aplicação para reconhecer significado de palavras homônimas utilizando redes neurais artificiais

Este trabalho propõe desenvolver uma aplicação para auxiliar no entendimento do significado de palavras ambíguas da língua portuguesa, que dependem necessariamente do contexto para serem identificadas. A aplicação foi implementada na linguagem de programação JAVA, utilizando a IDE NetBeans, juntamente com a ferramenta ADReNA, que permitiu modelar e treinar a rede neural artificial backpropagation. Foram desenvolvidas duas versões da aplicação, que se diferenciam pela forma que representam a camada de entrada da rede neural artificial. Testes realizados mostraram que a forma que a rede neural é representada gera influência em seus resultados. Após a realização dos testes, a aplicação se mostrou promissora no reconhecimento de significados de palavras homônimas

Successful isolation of Leishmania infantum from Rhipicephalus sanguineus sensu lato (Acari : Ixodidae) collected from naturally infected dogs

Background: The main transmission route of Leishmania infantum is through the bites of sand flies. However, alternative mechanisms are being investigated, such as through the bites of ticks, which could have epidemiological relevance. The objective of this work was to verify the presence of Leishmania spp. In Rhipicephalus sanguineus sensu lato collected from naturally infected dogs in the Federal District of Brazil. Methods: Ticks were dissected to remove their intestines and salivary glands for DNA extraction and the subsequent amplification of the conserved region of 120 bp of kDNA and 234 bp of the hsp70 gene of Leishmania spp. The amplified kDNA products were digested with endonucleases HaeIII and BstUI and were submitted to DNA sequencing. Isolated Leishmania parasites from these ticks were analyzed by multilocus enzyme electrophoresis, and the DNA obtained from this culture was subjected to microsatellite analyses. Results: Overall, 130 specimens of R. sanguineus were collected from 27 dogs. Leishmania spp. were successfully isolated in culture from five pools of salivary glands and the intestines of ticks collected from four dogs. The amplified kDNA products from the dog blood samples and from the tick cultures, when digested by HaeIII and BstUI, revealed the presence of L. braziliensis and L. infantum. One strain was cultivated and characterized as L. infantum by enzyme electrophoresis. The amplified kDNA products from the blood of one dog showed a sequence homology with L. braziliensis; however, the amplified kDNA from the ticks collected from this dog showed a sequence homology to L. infantum. Conclusion: The results confirm that the specimens of R. sanguineus that feed on dogs naturally infected by L. infantum contain the parasite DNA in their intestines and salivary glands, and viable L. infantum can be successfully isolated from these ectoparasites

Stroke outcome measurements from electronic medical records : cross-sectional study on the effectiveness of neural and nonneural classifiers

Background: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. Objective: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. Methods: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. Results: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. Conclusions: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations

Quando, Onde, Quem, O que ou Por que? Um Modelo Híbrido de Classificação de Perguntas para Sistemas de Question Answering

Sistemas de Question Answering é um campo de pesquisa das áreas de Recuperação de Informações e Processamento de Linguagem Natural que propõe, de forma autônoma, responder perguntas feitas por humanos em linguagem natural. Uma das principais etapas desses sistemas é a classificação de perguntas, em que o sistema busca identificar o tipo de resposta que a pergunta se refere, facilitando a localização de informações específicas em sua base de dados. Comumente, modelos supervisionados de aprendizado de máquina são empregados nesta tarefa, em que o texto da pergunta é representado através de um vetor de características, como Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF) ou word embeddings. Entretanto, a qualidade dos resultados produzidos por esses modelos são dependentes da existência de um grande conjunto de dados anotados para o treinamento, como também recursos computacionais e linguísticos externos. Esses recursos muitas vezes não estão acessíveis, devido a intensos esforços manuais na anotação de conjunto de dados ou pela falta de disponibilidade de recursos de qualidade para línguas não inglesa. Assim, este trabalho propõe uma abordagem híbrida para representação de texto que combina TF-IDF e Word2vec na tarefa de classificação de perguntas para sistemas de QA. Essa abordagem busca prover o tipo de resposta para perguntas em texto, utilizando diferentes tamanhos de conjuntos de treinamento com também sem a utilização de recursos computacionais e linguísticos complexos de serem adquiridos. Os experimentos realizados utilizando as coleções Chave e UIUC traduzida para o português, e variando o tamanho do conjunto de dados para treinamento, mostram estatisticamente que o modelo proposto atinge resultados satisfatório aplicado em diferentes modelos supervisionados.Question Answering Systems is a field of Information Retrieval and Natural Language Processing that automatically answers questions posed by humans in a natural language. One of the main steps of these systems is the Question Classification, where the system tries to identify the type of question (i.e. if it is related to a person, time or a location) facilitate the generation of a precise answer. Machine learning techniques are commonly employed in tasks where the text is represented as a vector of features, such as Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF) or word embeddings. However, the quality of results produced by supervised algorithms is dependent on the existence of a large, domain-dependent training dataset which sometimes is unavailable due to laborintense of manual annotation of datasets or lack of availability of quality resources for non-English languages. In this work, we propose a hybrid model that combines TF-IDF and word embedding to provide the answer type to text questions using small and large training sets. Our experiments using the Chaves and UIUC translated for Portuguese datasets, using several different sizes of training sets, showed statistically that the proposed hybrid model reached promising results applied in different supervised models

Quando, Onde, Quem, O que ou Por que? Um Modelo Híbrido de Classificação de Perguntas para Sistemas de Question Answering

Sistemas de Question Answering é um campo de pesquisa das áreas de Recuperação de Informações e Processamento de Linguagem Natural que propõe, de forma autônoma, responder perguntas feitas por humanos em linguagem natural. Uma das principais etapas desses sistemas é a classificação de perguntas, em que o sistema busca identificar o tipo de resposta que a pergunta se refere, facilitando a localização de informações específicas em sua base de dados. Comumente, modelos supervisionados de aprendizado de máquina são empregados nesta tarefa, em que o texto da pergunta é representado através de um vetor de características, como Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF) ou word embeddings. Entretanto, a qualidade dos resultados produzidos por esses modelos são dependentes da existência de um grande conjunto de dados anotados para o treinamento, como também recursos computacionais e linguísticos externos. Esses recursos muitas vezes não estão acessíveis, devido a intensos esforços manuais na anotação de conjunto de dados ou pela falta de disponibilidade de recursos de qualidade para línguas não inglesa. Assim, este trabalho propõe uma abordagem híbrida para representação de texto que combina TF-IDF e Word2vec na tarefa de classificação de perguntas para sistemas de QA. Essa abordagem busca prover o tipo de resposta para perguntas em texto, utilizando diferentes tamanhos de conjuntos de treinamento com também sem a utilização de recursos computacionais e linguísticos complexos de serem adquiridos. Os experimentos realizados utilizando as coleções Chave e UIUC traduzida para o português, e variando o tamanho do conjunto de dados para treinamento, mostram estatisticamente que o modelo proposto atinge resultados satisfatório aplicado em diferentes modelos supervisionados.Question Answering Systems is a field of Information Retrieval and Natural Language Processing that automatically answers questions posed by humans in a natural language. One of the main steps of these systems is the Question Classification, where the system tries to identify the type of question (i.e. if it is related to a person, time or a location) facilitate the generation of a precise answer. Machine learning techniques are commonly employed in tasks where the text is represented as a vector of features, such as Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF) or word embeddings. However, the quality of results produced by supervised algorithms is dependent on the existence of a large, domain-dependent training dataset which sometimes is unavailable due to laborintense of manual annotation of datasets or lack of availability of quality resources for non-English languages. In this work, we propose a hybrid model that combines TF-IDF and word embedding to provide the answer type to text questions using small and large training sets. Our experiments using the Chaves and UIUC translated for Portuguese datasets, using several different sizes of training sets, showed statistically that the proposed hybrid model reached promising results applied in different supervised models

Occurrence of multiple genotype infection caused by Leishmania infantum in naturally infected dogs.

Genetic polymorphisms in natural Leishmania populations have been reported in endemic areas. Microsatellite typing is a useful tool to elucidate the genetic variability of parasite strains, due to its capability for high-resolution mapping of genomic targets. The present study employed multilocus microsatellite typing (MLMT) to explore the genotypic composition of Leishmania infantum in naturally infected dogs by genotyping parasites infecting different tissues with or without in vitro expansion. Eighty-six samples were collected from 46 animals in an endemic region of visceral leishmaniasis (VL). MLMT was performed for 38 spleen samples and 48 L. infantum cultures isolated from different tissues. Of the 86 samples, 23 were effectively genotyped by MLMT, identifying nine multilocus genotypes (MLG; referred to as MLG A-I). MLGs A, B and C were detected in more than one type of tissue and in more than one sample. Conversely, MLG D-I were uniquely detected in one sample each. The results showed that multiple genotype infections occur within a single host and tissue. Paired sample analysis revealed the presence of different MLMT alleles in 14 dogs, while the same MLG allele was present in 15 animals. STRUCTURE analysis demonstrated the presence of two populations; 13 samples displayed a similar admixture of both ancestral populations, and these were not assigned to any population. Only samples for which Q ≥ 0.70 after CLUMPP alignment were considered to be part of Population 1 (POP1) or Population 2 (POP2). POP2 comprised the majority of samples (n = 54) compared to POP1 (n = 19). This study presents evidence of multiple genotype infections (caused by L. infantum) in dogs in an area with high VL transmission. Further investigations must be undertaken to determine the effects of multiple infection on the host immune response and disease dynamics and treatment

Polymorphisms and ambiguous sites present in DNA sequences of Leishmania clones: Looking closer

Made available in DSpace on 2015-06-08T14:01:51Z (GMT). No. of bitstreams: 2 license.txt: 1914 bytes, checksum: 7d48279ffeed55da8dfe2f8e81f3b81f (MD5) mariana_boite2etal_IOC_2014.pdf: 563268 bytes, checksum: fc1be1fe16dbb897960829cea0f2687f (MD5) Previous issue date: 2014Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Pesquisa em Leishmaniose. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Pesquisa em Leishmaniose. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Pesquisa em Leishmaniose. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Pesquisa em Leishmaniose. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Pesquisa em Leishmaniose. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Pesquisa em Leishmaniose. Rio de Janeiro, RJ, Brasil.In genetic studies of Leishmania parasites, co-dominant markers are chosen for their ability to detect heterozygous polymorphisms, to infer the occurrence of inbreeding and to resolve genetic variability. The majority of DNA sequence based reports perform conventional dye terminator cycle sequencing where perfectly ambiguous sites or double peaks in the chromatogram are interpreted as heterozygous strains. However, molecular peculiarities of the parasite such as aneuploidy, mixed populations and homologous recombination advise that data from regular DNA sequence analysis should be carefully evaluated. We report here a closer look at ambiguous sites observed in 6pgd DNA sequences obtained for a multilocus sequence analysis project on Leishmania (Viannia) strains. After comparing 286 DNA sequences from biological and molecular clones of six L. (Viannia) strains we could distinguish events that contribute to genetic variation in Leishmania (recombination, mutation, chromosomal mosaics). Also, the results suggest how diversity might not be completely revealed through regular DNA sequence analysis and demonstrate the importance for molecular epidemiology research to be aware of such possibilities while choosing samples for studies

Piptadenia gonoacantha-based natural dermocosmetic: a clinical trial

The use of phytotherapy expands the possibility of therapeutic resources for the population, often offering reduced costs when compared to the pharmaceutical industry. In this perspective, the JACBIO® dermocosmetic ointment revealed, in non-clinical trials, its antibacterial and healing potential, with a great stimulating effect in increasing the production of images. This work aimed to carry out the clinical phase study on dermal toxicity, in serious humans, by applying JACBIO®, based on extracts from the leaves of Piptadenia gonoacantha (Pau Jacaré). The phase I randomized clinical trial was carried out with 28 clinically healthy patients at a public university in Minas Gerais, with no period from August to December 2018. The toxicological trial was developed with the intervention group that received a JACBIO® dermatological ointment and the Placebo group. From the experimental protocol, participants were followed for four weeks. An analysis between the ointment and placebo groups, without reference to anticholinergic and cardiovascular events, showed no statistically significant difference. Likewise, there was no difference in laboratory results performed before and after treatment, both for the placebo group and for the intervention group. A low toxicity of the product indicates that this adjustment is safe and serves as a basis for phase II clinical trials in patients with lesions