POTENTIALLY PATHOGENIC FREE-LIVING AMOEBAE IN SOME FLOOD-AFFECTED AREAS DURING 2011 CHIANG MAI FLOOD

SUMMARYThe survey was carried out to investigate the presence of potentially pathogenic free-living amoebae (FLA) during flood in Chiang Mai, Thailand in 2011. From different crisis flood areas, seven water samples were collected and tested for the presence of amoebae using culture and molecular methods. By monoxenic culture, FLA were detected from all samples at 37 °C incubation. The FLA growing at 37 °C were morphologically identified as Acanthamoeba spp., Naegleria spp. and some unidentified amoebae. Only three samples (42.8%), defined as thermotolerant FLA, continued to grow at 42 °C. By molecular methods, two non-thermotolerant FlA were shown to have 99% identity to Acanthamoeba sp. and 98% identity to Hartmannella vermiformis while the two thermotolerant FLA were identified as Echinamoeba exundans (100% identity) and Hartmannella sp. (99% identity). This first report of the occurrence of FLA in water during the flood disaster will provide information to the public to be aware of potentially pathogenic FLA.

A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil

INTRODUCTION: Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach. METHODS: The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009. RESULTS: SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values. CONCLUSIONS: The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.

Flood pulse and spatial dynamics of mercury in sediments in Puruzinho lake, Brazilian Amazon

The study was conducted in Puruzinho lake (Humaitá, AM) considering seasonal periods of rainy and dry in way to elucidate the flood pulse importance in the deposition, remobilization and distributions of mercury and organic matter in bottom sediments in the Madeira River Basin (Brazilian Amazon). Bottom sediments and soils samples were analyzed for total mercury and organic matter. Mercury concentrations obtained in bottom sediment were 32.20-146.40 ng g-1 and organic matter values were 3.5 - 18.0%. The main region for accumulation of mercury and organic matter was in the central and deepest lake area In the rainy season there was a greater distribution of Hg and organic matter, mainly controlled by means of income of the Madeira river water during flooding, while the predominant process in the dry season was the remobilization of total Hg due to the resuspension of bottom sediments.

The feasibility of forecasting influenza epidemics in Cuba

A large influenza epidemic took place in Havana during the winter of 1988. The epidemiologic surveillance unit of the Pedro Kouri Institute of Tropical Medicine detected the begining of the epidemic wave. The Rvachev-Baroyan mathematical model of the geographic spread of an epidemic was used to forecast this epidemic under routine conditions of the public health system. The expected number of individuals who would attend outpatient services, because of influenza-like illness, was calculated and communicated to the health authorities within enough time to permit the introduction of available control measures. The approximate date of the epidemic peak, the daily expected number of individuals attending medical services, and the approximate time of the end of the epidemic wave were estimated. The prediction error was 12%. The model was sufficienty accurate to warrant its use as a pratical forecasting tool in the Cuban public health system.

The three phases of the ensemble forecasting of niche models: geographic range and shifts in climatically suitable areas of Utetheisa ornatrix (Lepidoptera, Arctiidae)

Species' geographic ranges are usually considered as basic units in macroecology and biogeography, yet it is still difficult to measure them accurately for many reasons. About 20 years ago, researchers started using local data on species' occurrences to estimate broad scale ranges, thereby establishing the niche modeling approach. However, there are still many problems in model evaluation and application, and one of the solutions is to find a consensus solution among models derived from different mathematical and statistical models for niche modeling, climatic projections and variable combination, all of which are sources of uncertainty during niche modeling. In this paper, we discuss this approach of ensemble forecasting and propose that it can be divided into three phases with increasing levels of complexity. Phase I is the simple combination of maps to achieve a consensual and hopefully conservative solution. In Phase II, differences among the maps used are described by multivariate analyses, and Phase III consists of the quantitative evaluation of the relative magnitude of uncertainties from different sources and their mapping. To illustrate these developments, we analyzed the occurrence data of the tiger moth, Utetheisa ornatrix (Lepidoptera, Arctiidae), a Neotropical moth species, and modeled its geographic range in current and future climates.

Nest plasticity of Cornitermes silvestrii (Isoptera, Termitidae, Syntermitinae) in response to flood pulse in the Pantanal, Mato Grosso, Brazil

Nest plasticity of Cornitermes silvestrii (Isoptera, Termitidae, Syntermitinae) in response to flood pulse in the Pantanal, Mato Grosso, Brazil. The Pantanal is one of the largest wetlands in the world. Since many areas in Pantanal are flooded during part of the year, it is expected that plants and animals would have mechanisms for their survival during the flooded period. This study investigated the existence of differences in nest shape and inquilines of Cornitermes silvestrii in areas influenced by the flood pulse. We measured the volume, height, width, and height/width ratio of 32 nests in flooded areas and 27 in dry areas, and performed an one-way-Anova with the quasi-Poisson distribution to determine if there were differences in the nest measurements between the points. To analyze the relationship of nest inquilines to flood pulse and nest shape, we performed a regression with a Poisson distribution with the inquiline richness and flood pulse, and the above measurements. The nests of C. silvestrii in flooded areas were significantly higher than nests in dry areas, and had a larger height/width ratio. Colonies in periodically flooded areas would probably make a larger effort to extend their nests vertically, to maintain at least some portion of the structure out of the water and prevent the entire colony from being submerged. Neither the size of the nest nor the flood pulses influenced the assemblage of 11 species found in nests of C. silvestrii.

Mitigation of methane and nitrous oxide emissions from flood-irrigated rice by no incorporation of winter crop residues into the soil

Winter cover crops are sources of C and N in flooded rice production systems, but very little is known about the effect of crop residue management and quality on soil methane (CH4) and nitrous oxide (N2O) emissions. This study was conducted in pots in a greenhouse to evaluate the influence of crop residue management (incorporated into the soil or left on the soil surface) and the type of cover-crop residues (ryegrass and serradella) on CH4 and N2O emissions from a flooded Albaqualf soil cultivated with rice (Oryza sativa L.). The closed chamber technique was used for air sampling and the CH4 and N2O concentrations were analyzed by gas chromatography. Soil solution was sampled at two soil depths (2 and 20 cm), simultaneously to air sampling, and the contents of dissolved organic C (DOC), NO3-, NH4+, Mn2+, and Fe2+ were analyzed. Methane and N2O emissions from the soil where crop residues had been left on the surface were lower than from soil with incorporated residues. The type of crop residue had no effect on the CH4 emissions, while higher N2O emissions were observed from serradella (leguminous) than from ryegrass, but only when the residues were left on the soil surface. The more intense soil reduction verified in the deeper soil layer (20 cm), as evidenced by higher contents of reduced metal species (Mn2+ and Fe2+), and the close relationship between CH4 emission and the DOC contents in the deeper layer indicated that the sub-surface layer was the main CH4 source of the flooded soil with incorporated crop residues. The adoption of management strategies in which crop residues are left on the soil surface is crucial to minimize soil CH4 and N2O emissions from irrigated rice fields. In these production systems, CH4 accounts for more than 90 % of the partial global warming potential (CH4+N2O) and, thus, should be the main focus of research.

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.

The succession dynamics of a macroalgal community after a flood disturbance in a tropical stream from São Paulo State, southeastern Brazil

The succession dynamics of a macroalgal community in a tropical stream (20º58' S and 49º25' W) was investigated after disturbance by a sequence of intensive rains. High precipitation levels caused almost complete loss of the macroalgal community attached to the substratum and provided a strong pressure against its immediate re-establishment. After this disturbance, a weekly sampling program from May 1999 to January 2000 was established to investigate macroalgal recolonization. The community changed greatly throughout the succession process. The number of species varied from one to seven per sampling. Global abundance of macroalgal community did not reveal a consistent temporal pattern of variation. In early succession stages, the morphological form of tufts dominated, followed by unbranched filaments. Latter succession stages showed the almost exclusive occurrence of gelatinous forms, including filaments and colonies. The succession trajectory was mediated by phosphorus availability in which community composition followed a scheme of changes in growth forms. However, we believe that deterministic and stochastic processes occur in lotic ecosystems, but they are dependent on the length of time considered in the succession analyses.