Periodic Neural Activity Induced by Network Complexity

We study a model for neural activity on the small-world topology of Watts and Strogatz and on the scale-free topology of Barab\'asi and Albert. We find that the topology of the network connections may spontaneously induce periodic neural activity, contrasting with chaotic neural activities exhibited by regular topologies. Periodic activity exists only for relatively small networks and occurs with higher probability when the rewiring probability is larger. The average length of the periods increases with the square root of the network size.Comment: 4 pages, 5 figure

Chemical ecology of echinoderms: Impact of environment and diet in metabolomic profile

The phylum Echinodermata constitutes a successful and widespread group comprising Asteroidea, Ophiuroidea, Echinoidea, Holothuroidea and Crinodeia. Nowadays, marine organisms are being given a lot of attention in drug discovery pipelines. In these studies, sponges and nudibranchs are frequently addressed, however an increasing number of works focus their attention in echinoderms. Given the fact that many of the bioactive molecules found in echinoderms are diet-derived, different feeding behavior and surrounding environment plays a critical role in the chemical composition of echinoderms. In this work, the most relevant chemical classes of small molecules present in echinoderms, such as fatty acids, carotenoids and sterols will be addressed. When data is available, the influence of the environment on the chemical profile of these organisms will be discussed.(undefined

Comparative assessment of atmospheric correction of Landsat imagery using Modtran and dark object subtraction.

Remote sensing by spectral imaging of the Earth's surface can be widely used, but only when the atmospheric influence is nullified and the data are reduced to surface reflectance units. The atmospheric correction referred to here is an atmospheric "compensation" or "characterization" in which algorithms are used in remote sensing applications for hyper and multispectral images to correct atmospheric propagation effects in measurements taken by airborne and orbital systems. The remission of atmospheric effects guarantees the identification of biophysical properties of the targets and their isonomic relationship with spectroradiometric databases, thus enabling the application of sophisticated classification methods such as linear Spectral Mixture Analysis models (SMA) and Spectral Indexes. Based on this premise, the objective of this article is to compare the atmospheric correction used in the MODTRAN model with that used in the Dark Object Subtraction (DOS1) and Improved Dark Object Subtraction (DOS2) models in order to verify which approach shows better correspondence with reference spectral libraries. We used spectral data on tropical soils obtained using the spectroradiometer (FieldSpec Full Resolution). Due to the difficulty in obtaining data on atmospheric conditions, especially for tropical regions, and the difficulty in accessing the most reliable correction procedures, corrections are sometimes disregarded or even based on extremely simple methods which may produce radiance and reflectance estimation errors even greater than tho se of the original images. MODTRAN presented the most consistent results, especially with regard to season variation and the presence of haze (low contrast) in some images due to the high aerosol concentration. This kind of atmospheric phenomenon is common in tropical regions, which shows the importance of considering local atmospheric correction parameters based on an atmosphere simulation model. Methods DO S1 and DOS2, in spite of their good performance in some of the analyzed areas, have not been effective in the suppression of effects related to atmospheric absorption. This work is one of the few that considers different test targets in a tropical environment with season variation

Raiva em herbívoros no estado do Pará, Brasil: estudo descritivo (2004 a 2013)

PARC/PROPESP and PAPQ/ PROPESPUniversidade Federal do Pará. Instituto de Medicina Veterinária. Laboratório de Epidemiologia e Geoprocessamento. Castanhal, PA, Brazil.Instituto Federal de Educação do Tocantins. Palmas, TO, Brazil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Laboratório de Epidemiologia e Geoprocessamento. Castanhal, PA, Brazil.Universidade Federal de Mato Grosso. Graduate Program in Health Sciences. Sinop, MT, Brazil.Museu Paraense Emílio Goeldi - Campus de Pesquisa. Programa de Capacitação Institucional. Coordenação Ciências da Terra e Ecologia. Belém, PA, Brazil.Agência de Defesa Agropecuária do Pará. Belém, PA, Brazil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Laboratório de Geoprocessamento. Ananindeua, PA, Brasil.Universidade Federal do Pará. Instituto de Medicina Veterinária. Laboratório de Epidemiologia e Geoprocessamento. Castanhal, PA, Brazil.Rabies is an important zoonosis to public health associated with lethal encephalitis and economic losses. Analysis of its spatial distribution is a meaningful tool in understanding its dispersion, which may contribute to the control and prophylaxis of the disease. This study analyzed the spatial-temporal distribution of rabies outbreaks in livestock in Pará state, Brazil, from 2004 to 2013. We used records of neurological syndromes obtained from the state’s livestock authority (Adepará). The analysis recorded 711 neurological syndromes reports in livestock, of which 32.8% were positive for rabies. In 8% of the neurological syndromes (n=57) was not possible to perform the analysis because of bad-packaging conditions of the samples sent. Outbreaks involved at least 1,179 animals and cattle were the most affected animal species (76.8%). The numbers of reported neurological syndromes and of rabies outbreak shad strong positive correlation and exhibited decreasing linear trend. Spatially, most outbreaks occurred in two mesoregions in Pará (Northeast and Southeast). One of the justifications for this spatial distribution may be related with the distribution of the animals in the state, since these mesoregions are the largest cattle producers in Pará and have most of their territory deforested for pasture implementation