An appropriate data set size for digital soil mapping in Erechim, Rio Grande do Sul, Brazil

Digital information generates the possibility of a high degree of redundancy in the data available for fitting predictive models used for Digital Soil Mapping (DSM). Among these models, the Decision Tree (DT) technique has been increasingly applied due to its capacity of dealing with large datasets. The purpose of this study was to evaluate the impact of the data volume used to generate the DT models on the quality of soil maps. An area of 889.33 km² was chosen in the Northern region of the State of Rio Grande do Sul. The soil-landscape relationship was obtained from reambulation of the studied area and the alignment of the units in the 1:50,000 scale topographic mapping. Six predictive covariates linked to the factors soil formation, relief and organisms, together with data sets of 1, 3, 5, 10, 15, 20 and 25 % of the total data volume, were used to generate the predictive DT models in the data mining program Waikato Environment for Knowledge Analysis (WEKA). In this study, sample densities below 5 % resulted in models with lower power of capturing the complexity of the spatial distribution of the soil in the study area. The relation between the data volume to be handled and the predictive capacity of the models was best for samples between 5 and 15 %. For the models based on these sample densities, the collected field data indicated an accuracy of predictive mapping close to 70 %.

Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

A process for mining science & technology documents databases, illustrated for the case of "knowledge discovery and data mining"

This paper presents a process of mining research & development abstract databases to profile current status and to project potential developments for target technologies, The process is called "technology opportunities analysis." This article steps through the process using a sample data set of abstracts from the INSPEC database on the topic o "knowledge discovery and data mining." The paper offers a set of specific indicators suitable for mining such databases to understand innovation prospects. In illustrating the uses of such indicators, it offers some insights into the status of knowledge discovery research*.

Analysis of laboratory intercomparison data: a matter of independence

When laboratory intercomparison exercises are conducted, there is no a priori dependence of the concentration of a certain compound determined in one laboratory to that determined by another(s). The same applies when comparing different methodologies. A existing data set of total mercury readings in fish muscle samples involved in a Brazilian intercomparison exercise was used to show that correlation analysis is the most effective statistical tool in this kind of experiments. Problems associated with alternative analytical tools such as mean or paired 't'-test comparison and regression analysis are discussed.

Occurrence, biological activities and 13C NMR data of amides from Piper (Piperaceae)

This manuscript describes an update review with up to 285 references concerning the occurrence of amides from a variety of species of the genus Piper (Piperaceae). Besides addressing occurrence, this review also describes the biological activities attributed to extracts and pure compounds, a compiled 13C NMR data set, the main correlations between structural and NMR spectroscopic data of these compounds, and employment of hyphened techniques such as LC-MS, GC-MS and NMR for analysis of amides from biological samples and crude Piper extracts.

Using data mining to identify factors that influence the degree of leg injuries in broilers

Locomotor problems prevent the bird to move freely, jeopardizing the welfare and productivity, besides generating injuries on the legs of chickens. The objective of this study was to evaluate the influence of age, use of vitamin D, the asymmetry of limbs and gait score, the degree of leg injuries in broilers, using data mining. The analysis was performed on a data set obtained from a field experiment in which it was used two groups of birds with 30 birds each, a control group and one treated with vitamin D. It was evaluated the gait score, the asymmetry between the right and left toes, and the degree of leg injuries. The Weka ® software was used in data mining. In particular, C4.5 algorithm (also known as J48 in Weka environment) was used for the generation of a decision tree. The results showed that age is the factor that most influences the degree of leg injuries and that the data from assessments of gait score were not reliable to estimate leg weakness in broilers.

Synergistic control of forearm based on accelerometer data and artificial neural networks

In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941). For all other movements, prediction was low (range, 0.0316-0.8302). Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

Lime and fertilizer recommendation system for coconut trees

Fertilizer recommendation to most agricultural crops is based on response curves. Such curves are constructed from field experimental data, obtained for a particular condition and may not be reliable to be applied to other regions. The aim of this study was to develop a Lime and Fertilizer Recommendation System for Coconut Crop based on the nutritional balance. The System considers the expected productivity and plant nutrient use efficiency to estimate nutrient demand, and effective rooting layer, soil nutrient availability, as well as any other nutrient input to estimate the nutrient supply. Comparing the nutrient demand with the nutrient supply the System defines the nutrient balance. If the balance for a given nutrient is negative, lime and, or, fertilization is recommended. On the other hand, if the balance is positive, no lime or fertilizer is needed. For coconut trees, the fertilization regime is divided in three stages: fertilization at the planting spot, band fertilization and fertilization at the production phase. The data set for the development of the System for coconut trees was obtained from the literature. The recommendations generated by the System were compared to those derived from recommendation tables used for coconut crop in Brazil. The main differences between the two procedures were for the P rate applied in the planting hole, which was higher in the proposed System because the tables do not pay heed to the pit volume, whereas the N and K rates were lower. The crop demand for K is very high, and the rates recommended by the System are superior to the table recommendations for the formation and initial production stage. The fertilizer recommendations by the System are higher for the phase of coconut tree growth as compared to the production phase, because greater amount of biomass is produced in the first phase.

Analysis of genetic relatedness between populations of Aedes aegypti from different geographic regions of São Paulo state, Brazil

RAPD markers have been used for the analysis of genetic differentiation of Aedes aegypti, because they allow the study of genetic relationships among populations. The aim of this study was to identify populations in different geographic regions of the São Paulo State in order to understand the infestation pattern of A. aegypti. The dendrogram constructed with the combined data set of the RAPD patterns showed that the mosquitoes were segregated into two major clusters. Mosquitoes from the Western region of the São Paulo State constituted one cluster and the other was composed of mosquitoes from a laboratory strain and from a coastal city, where the largest Latin American port is located. These data are in agreement with the report on the infestation in the São Paulo State. The genetic proximity was greater between mosquitoes whose geographic origin was closer. However, mosquitoes from the coastal city were genetically closer to laboratory-reared mosquitoes than to field-collected mosquitoes from the São Paulo State. The origin of the infestation in this place remains unclear, but certainly it is related to mosquitoes of origins different from those that infested the West and North region of the State in the 80's.

Climatic aspects of the Amazonian Tropical Forest

The purpose of this study is to analyse the climatic aspects of the data collected in a forest site in comparison with conventional data obtained at different sites, such as clearing, rural an urban areas. The results showed that diverse climatic conditions do exist among the sites: the urban site showed higher temperature and lower relative humidity. In addition, evapotranspiration (potential and actual rates) was computed from the forest data set, using the classical Penman-Monteith's equation. The actual evapotranspiration is 30% of the potential value during dry period and seems to be almost constant during the whole year (tipically 2.0 to 2.5 mm day-1).

Multivariate Analysis for Animal Selection in Experimental Research

Background: Several researchers seek methods for the selection of homogeneous groups of animals in experimental studies, a fact justified because homogeneity is an indispensable prerequisite for casualization of treatments. The lack of robust methods that comply with statistical and biological principles is the reason why researchers use empirical or subjective methods, influencing their results. Objective: To develop a multivariate statistical model for the selection of a homogeneous group of animals for experimental research and to elaborate a computational package to use it. Methods: The set of echocardiographic data of 115 male Wistar rats with supravalvular aortic stenosis (AoS) was used as an example of model development. Initially, the data were standardized, and became dimensionless. Then, the variance matrix of the set was submitted to principal components analysis (PCA), aiming at reducing the parametric space and at retaining the relevant variability. That technique established a new Cartesian system into which the animals were allocated, and finally the confidence region (ellipsoid) was built for the profile of the animals’ homogeneous responses. The animals located inside the ellipsoid were considered as belonging to the homogeneous batch; those outside the ellipsoid were considered spurious. Results: The PCA established eight descriptive axes that represented the accumulated variance of the data set in 88.71%. The allocation of the animals in the new system and the construction of the confidence region revealed six spurious animals as compared to the homogeneous batch of 109 animals. Conclusion: The biometric criterion presented proved to be effective, because it considers the animal as a whole, analyzing jointly all parameters measured, in addition to having a small discard rate.

Systematics and Population Level Analysis of Anopheles darlingi

A new phylogenetic analysis of the Nyssorhynchus subgenus (Danoff-Burg and Conn, unpub. data) using six data sets {morphological (all life stages); scanning electron micrographs of eggs; nuclear ITS2 sequences; mitochondrial COII, ND2 and ND6 sequences} revealed different topologies when each data set was analyzed separately but no heterogeneity between the data sets using the arn test. Consequently, the most accurate estimate of the phylogeny was obtained when all the data were combined. This new phylogeny supports a monophyletic Nyssorhynchus subgenus but both previously recognized sections in the subgenus (Albimanus and Argyritarsis) were demonstrated to be paraphyletic relative to each other and four of the seven clades included species previously placed in both sections. One of these clades includes both Anopheles darlingi and An. albimanus, suggesting that the ability to vector malaria effectively may have originated once in this subgenus. Both a conserved (315 bp) and a variable (425 bp) region of the mitochondrial COI gene from 15 populations of An. darlingi from Belize, Bolivia, Brazil, French Guiana, Peru and Venezuela were used to examine the evolutionary history of this species and to test several analytical assumptions. Results demonstrated (1) parsimony analysis is equally informative compared to distance analysis using NJ; (2) clades or clusters are more strongly supported when these two regions are combined compared to either region separately; (3) evidence (in the form of remnants of older haplotype lineages) for two colonization events; and (4) significant genetic divergence within the population from Peixoto de Azevedo (State of Mato Grosso, Brazil). The oldest lineage includes populations from Peixoto, Boa Vista (State of Roraima) and Dourado (State of São Paulo).

Case-control study of factors associated with chronic Chagas heart disease in patients over 50 years of age

A case-control study on chronic Chagas heart disease (CCHD) was carried out between 1997 and 2005. Ninety patients over 50 years of age were examined for factors related to (CCHD). Fourty-six patients (51.1%) with Chagas heart disease (anomalous ECG) were assigned to the case group and 44 (48.9%) were included in the control group as carriers of undetermined forms of chronic disease. Social, demographic (age, gender, skin color, area of origin), epidemiological (permanence within an endemic zone, family history of Chagas heart disease or sudden death, physical strain, alcoholism, and smoking), and clinical (systemic hypertension) variables were analyzed. The data set was assessed through single-variable and multivariate analysis. The two factors independently associated with heart disease were age - presence of heart disease being three times higher in patients over 60 years of age (odds ratio, OR: 2.89; confidence interval of 95%: 1.09-7.61) - and family history of Chagas heart disease (OR: 2.833, CI 95%: 1.11-7.23). Systemic hypertension and gender did not prove to hold any association with heart disease, as neither did skin color, but this variable showed low statistical power due to reduced sample size.

Drosophilids (Diptera) from an Atlantic Forest Area in Santa Catarina, Southern Brazil

The present work aims at knowing the faunal composition of drosophilids in forest areas of southern Brazil. Besides, estimation of species richness for this fauna is briefly discussed. The sampling were carried out in three well-preserved areas of the Atlantic Rain Forest in the State of Santa Catarina. In this study, 136,931 specimens were captured and 96.6% of them were identified in the specific level. The observed species richness (153 species) is the largest that has been registered in faunal inventories conducted in Brazil. Sixty-three of the captured species did not fit to the available descriptions, and we believe that most of them are non-described species. The incidence-based estimators tended to give rise to the largest richness estimates while the abundance based give rise to the smallest ones. Such estimators suggest the presence from 172.28 to 220.65 species in the studied area. Based on these values, from 69.35 to 88.81% of the expected species richness were sampled. We suggest that the large richness recorded in this study is a consequence of the large sampling effort, the capture method, recent advances in the taxonomy of drosophilids, the high preservation level and the large extension of the sampled fragment and the high complexity of the Atlantic Rain forest. Finally, our data set suggest that the employment of estimators of richness for drosophilid assemblages is useful but it requires caution.