Retrieval of Trichostrongylus colubriformis infective larvae from grass contaminated in winter and in spring

The survival of infective larvae (L3) of Trichostrongylus colubriformis was evaluated on Brachiaria, Coast-cross and Aruana forage grasses. Feces of sheep parasitized exclusively by T. colubriformis were deposited in winter and spring on experimental plots whose grasses were cut at two heights: 5 cm and 30 cm. One, two, four, eight, 12 and 16 weeks after depositing the feces, fecal and forage samples were collected for the retrieval and quantification of L3. Retrieval of L3 from feces and forage was negligible in winter due to the dry weather, although a few larvae were retrieved in the last larval collections. However, L3 retrieval from fecal samples was greater in spring, especially two weeks after feces were deposited on 30 cm high grasses. At this time, the L3 retrieval rate from the three forage grasses differed significantly (P < 0.05), with Aruana grass showing the highest average L3 retrieval rate, followed by Coast-cross and Brachiaria. In conclusion, the winter drought proved very unfavorable for the presence of L3 in the environment, and the microclimate of Aruana pastureland was generally the most favorable for the retrieval of infective larvae.

Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.

Aboutness em Análise Documental de textos literários infanto-juvenis: perspectivas para o aprimoramento da representação de conteúdo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Relação entre as habilidades metalinguísticas, leitura e compreensão leitora em escolares com Transtorno do Déficit de Atenção com Hiperatividade

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)