Nematic liquid crystal dynamics under applied electric fields

In this paper we investigate the dynamics of liquid crystal textures in a two-dimensional nematic under applied electric fields, using numerical simulations performed using a publicly available LIquid CRystal Algorithm (LICRA) developed by the authors. We consider both positive and negative dielectric anisotropies and two different possibilities for the orientation of the electric field (parallel and perpendicular to the two-dimensional lattice). We determine the effect of an applied electric field pulse on the evolution of the characteristic length scale and other properties of the liquid crystal texture network. In particular, we show that different types of defects are produced after the electric field is switched on, depending on the orientation of the electric field and the sign of the dielectric anisotropy.Comment: 7 pages, 12 figure

Thermoconditional modulation of the pleiotropic sensitivity phenotype by the Saccharomyces cerevisiae PRP19 mutant allele pso4-1

The conditionally-lethal pso4-1 mutant allele of the spliceosomal-associated PRP19 gene allowed us to study this gene’s influence on pre-mRNA processing, DNA repair and sporulation. Phenotypes related to intron-containing genes were correlated to temperature. Splicing reporter systems and RT–PCR showed splicing efficiency in pso4-1 to be inversely correlated to growth temperature. A single amino acid substitution, replacing leucine with serine, was identified within the N-terminal region of the pso4-1 allele and was shown to affect the interacting properties of Pso4-1p. Amongst 24 interacting clones isolated in a two-hybrid screening, seven could be identified as parts of the RAD2, RLF2 and DBR1 genes. RAD2 encodes an endonuclease indispensable for nucleotide excision repair (NER), RLF2 encodes the major subunit of the chromatin assembly factor I, whose deletion results in sensitivity to UVC radiation, while DBR1 encodes the lariat RNA splicing debranching enzyme, which degrades intron lariat structures during splicing. Characterization of mutagen-sensitive phenotypes of rad2{Delta}, rlf2{Delta} and pso4-1 single and double mutant strains showed enhanced sensitivity for the rad2{Delta} pso4-1 and rlf2{Delta} pso4-1 double mutants, suggesting a functional interference of these proteins in DNA repair processes in Saccharomyces cerevisiae

Podridão radicular de fitóftora em soja.

bitstream/CNPT-2010/40697/1/p-do79.pd

Estratégia para a obtenção de cultivares de arroz com resistência genética à bicheira-da-raiz.

bitstream/item/27675/1/GrupoCultivar.pd

Tecnologias apropriadas para o desenvolvimento sustentado da bovinocultura de corte no Pantanal.

O sistema tradicional de producao; A cadeia produtiva da pecuaria de corte no Pantanal; Distribuicao e evolucao dos principais aspectos agropecuarios nas sub-regioes do Pantanal; Introducao de tecnologias na criacao de bovinos de corte no Pantanal.bitstream/item/37741/1/DOC24.pd

Implantação de campos de matrizes de cajueiro.

bitstream/item/114589/1/MATRIZES-CAJUEIRO.pd

Exploring the use of classification uncertainty to improve classification accuracy

Moraes, D., Benevides, P., Moreira, F. D., Costa, H., & Caetano, M. (2021). Exploring the use of classification uncertainty to improve classification accuracy. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B3-2021, XXIV ISPRS Congress (2021 edition), (pp. 81-86). https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-81-2021Supervised classification of remotely sensed images has been widely used to map land cover and land use. Since the performance of supervised methods depends on the quality of the training data, it is essential to develop methods to generate an enhanced training dataset. Active learning represents an alternative for such purpose as it proposes to create a dataset of optimized samples, normally collected based on classification uncertainty. However, it is heavily dependent on human interaction, since the user has to label selected samples over a number of iterations. In this paper, we explore the use of uncertainty to improve classification accuracy through a single iteration. We conducted experiments in a region of Portugal (Trás-os-Montes), using multioral Sentinel-2 images. The proposed approach consisted in computing the classification uncertainty of a Random Forest to collect additional training data from areas of high uncertainty and perform a new classification. An accuracy assessment was performed to compare the overall accuracy of the initial and new classifications. The results exhibited an increase in accuracy, though considered not statistically significant. Obstacles related to labelling additional sampling units resulted in a lack of additional training data for various classes, which might have limited the accuracy improvement. Additionally, an uneven proportion of additional training sampling units per class and the collection of new sample data from a limited number of uncertainty regions might also have prevented a higher increase in accuracy. Nevertheless, visual inspection of the maps revealed that the new classification reduced the confusion between some classes.publishe