Temporal variation in the composition of ant assemblages (Hymenoptera, Formicidae) on trees in the Pantanal floodplain, Mato Grosso do Sul, Brazil

Temporal variation in the composition of ant assemblages (Hymenoptera, Formicidae) on trees in the Pantanal floodplain, Mato Grosso do Sul, Brazil. In this paper we investigate how seasonal flooding influences the composition of assemblages of ants foraging on trees in the Pantanal of Mato Grosso do Sul. During the flood in the Pantanal, a large area is covered by floods that are the main forces that regulate the pattern of diversity in these areas. However, the effects of such natural disturbances in the ant communities are poorly known. In this sense, the objective of this study was to evaluate the effect of temporal variation in assemblages of ants foraging on trees in the Pantanal of Miranda. Samples were collected during a year in two adjacent areas, one who suffered flooding during the wet period and another that did not suffer flooding throughout the year. In 10 sites for each evaluated habitat, five pitfall traps were installed at random in trees 25 m apart from each other. In the habitat with flooding, the highest richness was observed during the flooding period, while there was no significant change in richness in the area that does not suffer flooding. The diversity of species between the two evaluated habitats varied significantly during the two seasons. Most ants sampled belong to species that forage and nest in soil. This suggests that during the flood in flooded habitats, ants that did not migrate to higher areas without flooding adopt the strategy to search for resources in the tree canopy.

Inferring epidemic contact structure from phylogenetic trees.

Contact structure is believed to have a large impact on epidemic spreading and consequently using networks to model such contact structure continues to gain interest in epidemiology. However, detailed knowledge of the exact contact structure underlying real epidemics is limited. Here we address the question whether the structure of the contact network leaves a detectable genetic fingerprint in the pathogen population. To this end we compare phylogenies generated by disease outbreaks in simulated populations with different types of contact networks. We find that the shape of these phylogenies strongly depends on contact structure. In particular, measures of tree imbalance allow us to quantify to what extent the contact structure underlying an epidemic deviates from a null model contact network and illustrate this in the case of random mixing. Using a phylogeny from the Swiss HIV epidemic, we show that this epidemic has a significantly more unbalanced tree than would be expected from random mixing.

Stratification of the severity of critically ill patients with classification trees

Background: Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR). Methods: Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%). Results: CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69- 75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)). Conclusion: With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.

Production costs and fruit yield profitability in the initial harvest of custard apple trees

The aim of this study was to estimate the production cost and economic indicators associated with the production and sales of fruits from 20 custard apple progenies during the initial five harvests, in order to identify the harvest season from which custard apple exploitation becomes profitable, as well as the most promising progenies from an economic point of view. The fruit yield data upon which the present work was based were obtained during the period from 2001 to 2005, in an experiment that evaluated 20 custard apple half-sibling progenies, under sprinkler irrigation. The progenies were evaluated in a random block design with five replicates and plots consisting of four plants each. The exploitation of custard apple progenies only showed to be a profitable agribusiness after the fourth year. Before that, only A3 and A4 progenies in the second year, and P3 and P11 in the third year provided profitable incomes. Considering the methodological assumptions imposed concerning the time period analysis and the prices as of July 2007, the most important profitability indicators (operating profit, return index and equilibrium price) evidenced that the A4 progeny is the most recommended, although other progenies are also highlighted, such as FJ1 and FJ2. As already discussed, the progenies showing the highest average yields of five harvests are not always the most economically recommendable ones.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Weed community and growth under the canopy of trees adapted to the brazilian semi-arid region

The objectives of this work were to evaluate the floristic composition and dry biomass of weeds under the canopy of seven perennial species adapted to the Semi-Arid region of Brazil, and correlate these characteristics with growth traits of the perennial species. The following perennial species were evaluated in two experiments (E1 and E2): mesquite (Prosopis juliflora), jucá (Caesalpinia ferrea), white popinac (Leucaena leucocephala), mofumbo (Combretum leprosum), neem (Azadirachata indica), sabiá (Mimosa caesalpiniaefolia) and tamarind (Tamarindus indica). In E1, the seven species were evaluated in a random block design with four replicates and nine plants per plot. In E2, evaluation comprised four species (mesquite, jucá, white popinac, and tamarind) in a random block design with eight replicates and nine plants per plot. A circle with an area of 1.77 m² was established around the trunk of each plant, two years after they were transplanted to the permanent location. The weeds collected within this circle were cut even with the ground, classified and weighed. At this time, plant height, and crown and stem diameters were evaluated in all trees of each plot. In E1 there were no differences between tree species as to weed frequency under their canopies; however, weed growth was smaller under the canopy of sabiá trees. Mesquite and sabiá had the greatest plant height and crown diameter means, but only sabiá had the greatest stem diameter. In E2, the perennial species were not different with regard to weed frequency and growth under their canopies, but mesquite had the greatest growth, as measured by plant height (with significant results for jucá as well) and crown and stem diameter.

Juvenile dynamics of the endemic and rare Enterolobium glaziovii Benth. (Mimosaceae) around reproductive trees in the Atlantic forest, Brazil

Studies on the regeneration and seedling mortality of rare tree species are important, but scarce. The aim of this study was to investigate the annual variation in recruitment, growth and mortality of juveniles of Enterolobium glaziovii Benth., a rare tree species from the Brazilian Atlantic Rain Forest. All seedlings and juveniles around four reproductive trees were labeled and their fate was followed from 1996 to 1999. There were no annual differences in juveniles' recruitment below and beyond the parental crown, but juveniles' survival and growth were lower below than beyond of the parental tree crowns. Small individuals (< 15 cm tall) showed the greatest mortality and the lowest growth, followed by medium (from 15 to 50 cm tall) and large ones (> 50 cm tall). Large juveniles were more widely dispersed from the conspecific parental tree than were medium and small ones. This suggests that distance dependent mortality of juveniles mediated by the parental tree is an important cause of spacing shifts associated with the growth of small individuals of E. glaziovii into large ones. Widely dispersed juveniles may escape the high mortality associated with pathogens, herbivores or seed predators concentrated around adult conspecifics. The negative influence of the parental tree on its juveniles may explain the sparse distribution of its adults in the forest.

Taking the LEED? Analyzing spatial variations in market penetration rates of eco-labeled properties

This paper investigates the impact of policies to promote the adoption of LEED-certified buildings across CBSA in the United States. Drawing upon a unique database that combines data from a large number of sources and using a number of regression procedures, the determinants of the proportion LEED-certified space for more than 170 CBSA in the US is modeled. LEED-certified space still accounts for a relatively small proportion of commercial stock in all markets. The average proportion is less than 1%. There is no conclusive evidence of a positive impact of policy intervention on the levels of LEED-certified space. However, after accounting for bias introduced by non-random assignment of policies, we find preliminary evidence of a positive impact of city-level green building incentives. There is a significant positive association between market size and indicators of economic vitality on proportions of LEED-certified space.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

A scalable expressive ensemble learning using Random Prism: a MapReduce approach

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system.

Carbon-13 turnover in 'roxo de valinhos' fig trees

The aim of this study was to assess carbon-13 turnover in different organs of the fig tree, 'Roxo de Valinhos' cultivar. The experiment was carried out in an orchard at School of Agronomical Sciences, FCA/UNESP, Botucatu Campus, State of São Paulo, Brazil. The main photosynthetically active leaf was previously determined based on gas exchanges by means of an open portable photosynthesis system, IRGA. That leaf was placed in a chamber where the enriched gas injection occurred. The leaf enrichment time was 30 minutes. Treatments were constituted of seven fig trees removed from the soil after: 6; 24; 48; 72; 120; 168 and 360 hours of enrichment using (13)C, and their parts were sectioned into: apical bud, young leaves, adult leaves (photosynthetically active), lateral sprouts, fruits, and branch. The results allowed the establishment of the carbon-13 metabolism sequence in the studied parts: Young leaves > Fruits > Sprouts > Adult leaves > Apical bud > branch > Labeled leaf. 'Roxo de Valinhos' fig trees, had (13)C turnover of 24 hours and carbon-13 half-time shorter than 11 hours.

Extraction of Building Roof Contours From LiDAR Data Using a Markov-Random-Field-Based Approach

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Molecular characterization and relatedness of Haematobia irritans (horn fly) populations, by RAPD-PCR

Haematobia irritans is a hematophagous parasite of cattle that causes significant economic losses in many parts of the world, including Brazil. In the present work, one American and four Brazilian populations of this species were studied by Random Amplified Polymorpht DNA (RAPD) to assess basically genetic variability within and between populations. Ten different decamer random primers were employed in the genomic DNA amplification, yielding 117 fragments in the five H.. irritans populations. In Drosophila prosaltans, used as an outgroup, 81 fragments were produced. Forty-three of these fragments were shared by both species. Among the H. irritans samples, that from Rio Branco (Acre State, Brazil) produced the smallest numbers of fragments and polymorphic bands. This high genetic homogenity may be ascribed to its geographic origin (in the Northwest of Brazil), which causes high isolation and low gene flow, unlike the other Brazilian populations, from the South Central region, in which cattle trade is very intensive. Marker fragments (exclusive bands) detected in every sample enabled the population origin to be characterized, but they are also potentially useful for further approaches such as the putative origin of Brazilian populations from North America. Similarity indices [Nei & Li, 1979, Proc. Natl. Acad. Sci. USA 76: 5269-5273] and phylogenetic trees, rooted by using the outgroup and produced by the Phylogenetic Analysis using Parsimony (PAUP 4.0-Swofford, 2001) program showed the closest relationships between flies from Sao Jose do Rio Preto and Turiuba (both from São Paulo State, Brazil) while flies from the geographically distant Rio Branco showed the greatest differentiation relative to the others.