Inferences about the global scenario of human T-cell lymphotropic virus type 1 infection using data mining of viral sequences

Human T-cell lymphotropic virus type 1 (HTLV-1) is mainly associated with two diseases: tropical spastic paraparesis/HTLV-1-associated myelopathy (TSP/HAM) and adult T-cell leukaemia/lymphoma. This retrovirus infects five-10 million individuals throughout the world. Previously, we developed a database that annotates sequence data from GenBank and the present study aimed to describe the clinical, molecular and epidemiological scenarios of HTLV-1 infection through the stored sequences in this database. A total of 2,545 registered complete and partial sequences of HTLV-1 were collected and 1,967 (77.3%) of those sequences represented unique isolates. Among these isolates, 93% contained geographic origin information and only 39% were related to any clinical status. A total of 1,091 sequences contained information about the geographic origin and viral subtype and 93% of these sequences were identified as subtype “a”. Ethnicity data are very scarce. Regarding clinical status data, 29% of the sequences were generated from TSP/HAM and 67.8% from healthy carrier individuals. Although the data mining enabled some inferences about specific aspects of HTLV-1 infection to be made, due to the relative scarcity of data of available sequences, it was not possible to delineate a global scenario of HTLV-1 infection.

Prevalence of human papillomavirus infection, distribution of viral types and risk factors in cervical samples from human immunodeficiency virus-positive women attending three human immunodeficiency virus-acquired immune deficiency syndrome reference centres in northeastern Brazil

Human immunodeficiency virus (HIV)-positive patients have a greater prevalence of coinfection with human papillomavirus (HPV) is of high oncogenic risk. Indeed, the presence of the virus favours intraepithelial squamous cell lesion progression and may induce cancer. The aim of this study was to evaluate the prevalence of HPV infection, distribution of HPV types and risk factors among HIV-positive patients. Cervical samples from 450 HIV-positive patients were analysed with regard to oncotic cytology, colposcopy and HPV presence and type by means of polymerase chain reaction and sequencing. The results were analysed by comparing demographic data and data relating to HPV and HIV infection. The prevalence of HPV was 47.5%. Among the HPV-positive samples, 59% included viral types of high oncogenic risk. Multivariate analysis showed an association between HPV infection and the presence of cytological alterations (p = 0.003), age greater than or equal to 35 years (p = 0.002), number of partners greater than three (p = 0.002), CD4+ lymphocyte count < 200/mm3 (p = 0.041) and alcohol abuse (p = 0.004). Although high-risk HPV was present in the majority of the lesions studied, the low frequency of HPV 16 (3.3%), low occurrence of cervical lesions and preserved immunological state in most of the HIV-positive patients were factors that may explain the low occurrence of precancerous cervical lesions in this population.

Spontaneous hepatitis C viral clearance and hepatitis C chronic infection are associated with distinct cytokine profiles in Mexican patients

The mechanisms related to the spontaneous clearance of hepatitis C virus (HCV) have been primarily studied in regions where the infection is endemic. Results of prior studies have been extrapolated to populations with low endemicity, such as Mexico. Herein, we determined the cytokine profiles in serum samples from Mexican patients who spontaneously cleared HCV and patients chronically infected with HCV genotype 1a. Chronic HCV-infected patients displayed increased interleukin (IL)-8 and regulated upon activation, normal T-cell expressed and secreted (CCL-5) secretion, whereas patients who spontaneously cleared HCV showed augmented levels of IL-1 alpha, tumour necrosis factor-alpha, transforming growth factor-beta, monocyte chemoattractant protein-2 (CCL-8), IL-13 and IL-15. Our study suggeststhat cytokine profiles may predict disease outcome during HCV infection.

Viral aetiology of common colds of outpatient children at primary care level and the use of antibiotics

Although antibiotics are ineffective against viral respiratory infections, studies have shown high rates of prescriptions worldwide. We conducted a study in Brazil to determine the viral aetiologies of common colds in children and to describe the use of antibiotics for these patients. Children up to 12 years with common colds were enrolled from March 2008-February 2009 at a primary care level facility and followed by regular telephone calls and medical consultations. A nasopharyngeal wash was obtained at enrollment and studied by direct fluorescence assay and polymerase chain reaction for nine different types of virus. A sample of 134 patients was obtained, median age 2.9 years (0.1-11.2 y). Respiratory viruses were detected in 73.9% (99/134) with a coinfection rate of 30.3% (30/99). Rhinovirus was the most frequent virus (53/134; 39.6%), followed by influenza (33/134; 24.6%) and respiratory syncytial virus (8/134; 13.4%). Antibiotic prescription rate was 39.6% (53/134) and 69.8% (37/53) were considered inappropriate. Patients with influenza infection received antibiotics inappropriately in a greater proportion of cases when compared to respiratory syncytial virus and rhinovirus infections (p = 0.016). The rate of inappropriate use of antibiotics was very high and patients with influenza virus infection were prescribed antibiotics inappropriately in a greater proportion of cases.

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.

Adolescent support networks in a health care context: the interface between health, family and education

AbstractOBJECTIVEAnalyze adolescents' perceptions about support networks and their health needs.METHODAnalytical and interpretive study using focus groups conducted in municipal state schools in Fortaleza, in the State of Ceará during the first semester of 2012. The sample comprised 36 male and female adolescents aged between 13 and 16 years attending the ninth grade of the second phase of elementary school.RESULTSThematic analysis revealed that the health care support network and interaction between health professionals, education professionals and family members was insufficient, constituting a lack of an integrated network to enable and provide support for health promotion.CONCLUSIONCoordination between education, health and family services has the potential to act as a support network to help meet adolescents' healthcare needs and demands.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Sequence similarity between the viral cp gene and the transgene in transgenic papayas

The Papaya ringspot virus (PRSV) coat protein transgene present in 'Rainbow' and 'SunUp' papayas disclose high sequence similarity (>89%) to the cp gene from PRSV BR and TH. Despite this, both isolates are able to break down the resistance in 'Rainbow', while only the latter is able to do so in 'SunUp'. The objective of this work was to evaluate the degree of sequence similarity between the cp gene in the challenge isolate and the cp transgene in transgenic papayas resistant to PRSV. The production of a hybrid virus containing the genome backbone of PRSV HA up to the Apa I site in the NIb gene, and downstream from there, the sequence of PRSV TH was undertaken. This hybrid virus, PRSV HA/TH, was obtained and used to challenge 'Rainbow', 'SunUp', and an R2 population derived from line 63-1, all resistant to PRSV HA. PRSV HA/TH broke down the resistance in both papaya varieties and in the 63-1 population, demonstrating that sequence similarity is a major factor in the mechanism of resistance used by transgenic papayas expressing the cp gene. A comparative analysis of the cp gene present in line 55-1 and 63-1-derived transgenic plants and in PRSV HA, BR, and TH was also performed.

Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks

This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

Garlic viral complex: identification of Potyviruses and Carlavirus in Central Brazil

Garlic viruses often occur in complex infections in nature. In this study, a garlic virus complex, collected in fields in Brazil, was purified. RT-PCR was performed using specific primers designed from the consensus regions of the coat protein genes of Onion yellow dwarf virus, a garlic strain (OYDV-G) and Leek yellow stripe virus (LYSV). cDNA of Garlic common latent virus (GCLV) was synthesized using oligo-dT and random primers. By these procedures individual garlic virus genomes were isolated and sequenced. The nucleotide sequence analysis associated with serological data reveals the presence of two Potyvirus OYDV-G and LYSV, and GCLV, a Carlavirus, simultaneously infecting garlic plants. Deduced amino acid sequences of the Brazilian isolates were compared with related viruses reported in different geographical regions of the world. The analysis showed closed relations considering the Brazilian isolates of OYDV-G and GCLV, and large divergence considering LYSV isolate. The detection of these virus species was confirmed by specific reactions observed when coat protein genes of the Brazilian isolates were used as probes in dot-blot and Southern blot hybridization assays. In field natural viral re-infection of virus-free garlic was evaluated.

Description of epidemics of coffee rust with neural networks

The objective of this paper was to evaluate the potential of neural networks (NN) as an alternative method to the basic epidemiological approach to describe epidemics of coffee rust. The NN was developed from the intensities of coffee (Coffea arabica) rust along with the climatic variables collected in Lavras-MG between 13 February 1998 and 20 April 2001. The NN was built with climatic variables that were either selected in a stepwise regression analysis or by the Braincel® system, software for NN building. Fifty-nine networks and 26 regression models were tested. The best models were selected based on small values of the mean square deviation (MSD) and of the mean prediction error (MPE). For the regression models, the highest coefficients of determination (R²) were used. The best model developed with neural networks had an MSD of 4.36 and an MPE of 2.43%. This model used the variables of minimum temperature, production, relative humidity of the air, and irradiance 30 days before the evaluation of disease. The best regression model was developed from 29 selected climatic variables in the network. The summary statistics for this model were: MPE=6.58%, MSE=4.36, and R²=0.80. The elaborated neural networks from a time series also were evaluated to describe the epidemic. The incidence of coffee rust at four previous fortnights resulted in a model with MPE=4.72% and an MSD=3.95.

Influence of distance, geometry and number of control stations on quality of local geodetic networks for the purpose of georeferencing of rural properties

The implementation of local geodetic networks for georeferencing of rural properties has become a requirement after publication of the Georeferencing Technical Standard by INCRA. According to this standard, the maximum distance of baselines to GNSS L1 receivers is of 20 km. Besides the length of the baseline, the geometry and the number of geodetic control stations are other factors to be considered in the implementation of geodetic networks. Thus, this research aimed to examine the influence of baseline lengths higher than the regulated limit of 20 km, the geometry and the number of control stations on quality of local geodetic networks for georeferencing, and also to demonstrate the importance of using specific tests to evaluate the solution of ambiguities and on the quality of the adjustment. The results indicated that the increasing number of control stations has improved the quality of the network, the geometry has not influenced on the quality and the baseline length has influenced on the quality; however, lengths higher than 20 km has not interrupted the implementation, with GPS L1 receiver, of the local geodetic network for the purpose of georeferencing. Also, the use of different statistical tests, both for the evaluation of the resolution of ambiguities and for the adjustment, have enabled greater clearness in analyzing the results, which allow that unsuitable observations may be eliminated.

Application of artificial neural networks as an alternative to volumetric water balance in drip irrigation management in watermelon crop

Precision irrigation seeks to establish strategies which achieve an efficient ratio between the volume of water used (reduction in input) and the productivity obtained (increase in production). There are several studies in the literature on strategies for achieving this efficiency, such as those dealing with the method of volumetric water balance (VWB). However, it is also of great practical and economic interest to set up versatile implementations of irrigation strategies that: (i) maintain the performance obtained with other implementations, (ii) rely on few computational resources, (iii) adapt well to field conditions, and (iv) allow easy modification of the irrigation strategy. In this study, such characteristics are achieved when using an Artificial Neural Network (ANN) to determine the period of irrigation for a watermelon crop in the Irrigation Perimeter of the Lower Acaraú, in the state of Ceará, Brazil. The Volumetric Water Balance was taken as the standard for comparing the management carried out with the proposed implementation of ANN. The statistical analysis demonstrates the effectiveness of the proposed management, which is able to replace VWB as a strategy in automation.