Modelagem de ocorrência e densidade de juvenis de Callinectes sapidus (Decapoda, Portunidae) em dois estuários do Rio Grande do Sul, com dados zero-inflacionados

Dados de contagem de juvenis de siri-azul (Callinectes sapidus Rathbun, 1896) coletados em dois estuários do Rio Grande do Sul são objeto do presente estudo. Por se encontrarem zero-inflacionados, esses dados motivaram a formulação de modelos hierárquicos, que quantificam o efeito das covariáveis categóricas mês e local sobre a probabilidade de ocorrência e densidade dessas populações, levando em conta a detecção imperfeita. Foram também desenvolvidos modelos não-hierárquicos para comparação. Uma abordagem Bayesiana foi adotada para a estimação dos parâmetros dos modelos por simulação Monte Carlo com Cadeias de Markov (MCMC). A comparação entre modelos foi feita com o Critério de Informação da Deviância (DIC). Os modelos hierárquicos apresentaram ajustes melhores que os modelos convencionais, mitigaram o problema do excesso de zeros e permitiram analisar simultaneamente as probabilidades de ocorrência e a densidade de juvenis de siri-azul. No estuário da Lagoa dos Patos, a probabilidade de ocorrência de juvenis na Classe 2 aumenta com a distância da desembocadura, enquanto em Tramandaí os pontos intermediários apresentam as maiores probabilidades. Em ambos os estuários a ocorrência é mais provável nos meses de verão e de inverno. A densidade de juvenis da Classe 2 apresenta marcada variação em relação aos meses do ano sendo, em geral, maior no estuário de Tramandaí.

Distinction of males of the Lutzomyia intermedia (Lutz & Neiva, 1912) species complex by ratios between dimensions and by an artificial neural network (Diptera: Psychodidae, Phlebotominae)

The females of the two species of the Lutzomyia intermedia complex can be easily distinguished, but the males of each species are quite similar. The ratios between the extra-genital and the genital structures of L. neivai are larger than those of L. intermedia s. s., according to ANOVA. An artificial neural network was trained with a set of 300 examples, randomly taken from a sample of 358 individuals. The input vectors consisted of several ratios between some structures of each insect. The model was tested on the remaining 58 insects, 56 of which (96.6%) were correctly identified. This ratio of success can be considered remarkable if one takes into account the difficulty of attaining comparable results using traditional statistical techniques.

Toxoplasma gondii: further studies on the subpellicular network

The association of the pellicle with cytoskeletal elements in Toxoplasma gondii allows this parasite to maintain its mechanical integrity and makes possible its gliding motility and cell invasion. The inner membrane complex (IMC) resembles the flattened membrane sacs observed in free-living protozoa and these sacs have been found to associate with cytoskeletal proteins such as articulins. We used immunofluorescence microscopy to characterise the presence and distribution of plateins, a sub-family of articulins, in T. gondii tachyzoites. A dispersed labelling of the whole protozoan body was observed. Electron microscopy of detergent-extracted cells revealed the presence of a network of 10 nm filaments distributed throughout the parasite. These filaments were labelled with anti-platein antibodies. Screening the sequenced T. gondii genome, we obtained the sequence of an IMC predicted protein with 25% identity and 42% similarity to the platein isoform alpha 1 present in Euplotes aediculatus, but with 42% identity and 55% similarity to that found in Euglena gracilis, suggesting strong resemblance to articulins.

Sentinel network for monitoring in vitro susceptibility of Plasmodium falciparum to antimalarial drugs in Colombia: a proof of concept

Drug resistance is one of the principal obstacles blocking worldwide malaria control. In Colombia, malaria remains a major public health concern and drug-resistant parasites have been reported. In vitro drug susceptibility assays are a useful tool for monitoring the emergence and spread of drug-resistant Plasmodium falciparum. The present study was conducted as a proof of concept for an antimalarial drug resistance surveillance network based on in vitro susceptibility testing in Colombia. Sentinel laboratories were set up in three malaria endemic areas. The enzyme linked immunosorbent assay-histidine rich protein 2 and schizont maturation methods were used to assess the susceptibility of fresh P. falciparum isolates to six antimalarial drugs. This study demonstrates that an antimalarial drug resistance surveillance network based on in vitro methods is feasible in the field with the participation of a research institute, local health institutions and universities. It could also serve as a model for a regional surveillance network. Preliminary susceptibility results showed widespread chloroquine resistance, which was consistent with previous reports for the Pacific region. However, high susceptibility to dihydroartemisinin and lumefantrine compounds, currently used for treatment in the country, was also reported. The implementation process identified critical points and opportunities for the improvement of network sustainability strategies.

Networks in Argentine agriculture: a multiple-case study approach

Argentina is among the four largest producers of soybeans, sunflower, corn, and wheat, among other agricultural products. Institutional and policy changes during the 1990s fostered the development of Argentine agriculture and the introduction of innovative process and product technologies (no-till, agrochemicals, GMO, GPS) and new investments in modern, large-scale sunflower and soybean processing plants. In addition to technological changes, a "quiet revolution" occurred in the way agricultural production was carried out and organized: from self-production or ownership agriculture to a contract-based agriculture. The objective of this paper is to explore and describe the emergence of networks in the Argentine crop production sector. The paper presents and describes four cases that currently represent about 50% of total grain and oilseed production in Argentina: "informal hybrid form", "agricultural trust fund", "investor-oriented corporate structure", and "network of networks". In all cases, hybrid forms involve a group of actors linked by common objectives, mainly to gain scale, share resources, and improve the profitability of the business. Informal contracts seem to be the most common way of organizing the agriculture process, but using short-term contracts and sequential interfirm collaboration. Networks of networks involve long-term relationships and social development, and reciprocal interfirm collaboration. Agricultural trust fund and investor-oriented corporate structures have combined interfirm collaboration and medium-term relationships. These organizational forms are highly flexible and show a great capacity to adapt to challenges; they are competitive because they enjoy aligned incentives, flexibility, and adaptability.

The network social support experience of people involved in home care

Objective: To build a theoretical model to configure the network social support experience of people involved in home care. Method: A quantitative approach research, utilizing the Grounded Theory method. The simultaneous data collection and analysis allowed the interpretation of the phenomenon meaning The network social support of people involved in home care. Results: The population passive posture in building their well-being was highlighted. The need of a shared responsibility between the involved parts, population and State is recognized. Conclusion: It is suggested for nurses to be stimulated to amplify home care to attend the demands of caregivers; and to elaborate new studies with different populations, to validate or complement the proposed theoretical model.


Root abundance of maize in conventionally-tilled and zero-tilled soils of Argentina

Maize root growth is negatively affected by compacted layers in the surface (e.g. agricultural traffic) and subsoil layers (e.g. claypans). Both kinds of soil mechanical impedances often coexist in maize fields, but the combined effects on root growth have seldom been studied. Soil physical properties and maize root abundance were determined in three different soils of the Rolling Pampa of Argentina, in conventionally-tilled (CT) and zero-tilled (ZT) fields cultivated with maize. In the soil with a light Bt horizon (loamy Typic Argiudoll, Chivilcoy site), induced plough pans were detected in CT plots at a depth of 0-0.12 m through significant increases in bulk density (1.15 to 1.27 Mg m-3) and cone (tip angle of 60 º) penetrometer resistance (7.18 to 9.37 MPa in summer from ZT to CT, respectively). This caused a reduction in maize root abundance of 40-80 % in CT compared to ZT plots below the induced pans. Two of the studied soils had hard-structured Bt horizons (clay pans), but in only one of them (silty clay loam Abruptic Argiudoll, Villa Lía site) the expected penetrometer resistance increases (up to 9 MPa) were observed with depth. In the other clay pan soil (silty clay loam Vertic Argiudoll, Pérez Millán site), penetrometer resistance did not increase with depth but reached 14.5 MPa at 0.075 and 0.2 m depth in CT and ZT plots, respectively. However, maize root abundance was stratified in the first 0.2 m at the Villa Lía and Pérez Millán sites. There, the hard Bt horizons did not represent an absolute but a relative mechanical impedance to maize roots, by the observed root clumping through desiccation cracks.

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

Oxygen consumption of Litopenaeus vannamei juveniles in heterotrophic medium with zero water exchange

This work aimed at determining the dissolved oxygen consumption rate of Litopenaeus vannamei juveniles maintained in a microbial biofloc raceway system at high density with no aeration. Three 4 L bottles were filled for each treatment, sealed hermetically, and placed in an enclosed greenhouse raceway system. Four shrimp (13.2±1.42 g) were assigned to two sets of the bottles, which underwent the following treatments: light conditions with no shrimp; dark conditions with no shrimp; light conditions with shrimp; and dark conditions with shrimp. Dissolved oxygen content was measured every 10 min for 30 min. A quadratic behavior was observed in dissolved oxygen concentration over time. Significant differences for oxigen consumption were observed only at 10 and 20 min between shrimp maintained in the dark and those under light conditions. At 10 min, a higher value was observed in shrimp maintained under light, and at 20 min, in the dark. Significant differences between 10 and 20 min and between 10 and 30 min were observed when oxygen consumption was analyzed over time in the presence of light. Under dark conditions there were significant differences only between 20 and 30 min. Lethal oxygen concentration (0.65 mg L-1) would be reached in less than one hour either under light or dark conditions with no aeration.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.