47 annual records of allergenic fungi spore: predictive models from the NW Iberian Peninsula.
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An analysis was carried out of the atmospheric representivity of Cladosporium and Alternaria spores in the north-western Iberian Peninsula, registering mean annual concentrations in excess of 300,000 spores/m(3). During the main sporulation period, the highest average daily concentrations corresponded to Cladosporium herbarum type (1,197 spores/m(3)) while the highest daily value was 7,556 spores/m(3) (Cladosporium cladosporioides type). Alternaria only represents between 0.1-1% of the total spores identified. In these spore types, the intraday variation was more acute inland than along the coastline due to oceanic influence. In the predictive models proposed that use the meteorological parameters with which a higher correlation was obtained (mean and maximum temperature) as predictive variables, it was seen that the predicted values did not reveal any significant differences as compared to those observed in 2006, data that was only used for verification purposes. (+info)
Enumerating outdoor aeromycota in suburban West Bengal, India, with reference to respiratory allergy and meteorological factors.
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Aeromycota may act as a reservoir of aeroallergens and upon inhalation may induce IgE-mediated Type I hypersensitivity reaction in pre-sensitized individuals. The total aerospora of an outdoor occupational setting (agricultural farm) in suburban West Bengal was sampled for two years (2002-2004) by a Burkard sampler. Concurrently, the cultivable aeromycota were trapped by an Andersen 2-stage sampler, cultured and tested for allergenic potential by skin prick test. The relationships between various climatic factors (temperature, relative humidity, rainfall and wind speed) and the distribution of aerospora were explored by Spearman correlation test. The antigenic extracts of 15 fungal species belonging to Alternaria, Aspergilli/Penicilli, Cladosporium, Curvularia, Drechslera, and Nigrospora evoked 10.8-54.8% skin reactivity in subjects with clinical history of respiratory allergy. The aerospora with skin sensitizing potential collectively represented a considerable fraction (52.3-58.4%) of the total aeromycota. The airborne concentration of Alternaria spores was higher than its borderline value of 100 spores m(-3) in May and June, whereas Cladosporium spore count exceeded its threshold limit value (3,000 spores m(-3)) in December, suggesting that this particular time of the year poses allergenic risk for individuals sensitive to these aerospora. Daily minimum temperature and rainfall appeared to be the most important meteorological factors to affect the concentration of aerospora in the study area. (+info)
Relationships between climate and year-to-year variability in meningitis outbreaks: a case study in Burkina Faso and Niger.
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A 10-year time-series analysis of respiratory and cardiovascular morbidity in Nicosia, Cyprus: the effect of short-term changes in air pollution and dust storms.
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Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand.
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In many malarious regions malaria transmission roughly coincides with rainy seasons, which provide for more abundant larval habitats. In addition to precipitation, other meteorological and environmental factors may also influence malaria transmission. These factors can be remotely sensed using earth observing environmental satellites and estimated with seasonal climate forecasts. The use of remote sensing usage as an early warning tool for malaria epidemics have been broadly studied in recent years, especially for Africa, where the majority of the world's malaria occurs. Although the Greater Mekong Subregion (GMS), which includes Thailand and the surrounding countries, is an epicenter of multidrug resistant falciparum malaria, the meteorological and environmental factors affecting malaria transmissions in the GMS have not been examined in detail. In this study, the parasitological data used consisted of the monthly malaria epidemiology data at the provincial level compiled by the Thai Ministry of Public Health. Precipitation, temperature, relative humidity, and vegetation index obtained from both climate time series and satellite measurements were used as independent variables to model malaria. We used neural network methods, an artificial-intelligence technique, to model the dependency of malaria transmission on these variables. The average training accuracy of the neural network analysis for three provinces (Kanchanaburi, Mae Hong Son, and Tak) which are among the provinces most endemic for malaria, is 72.8% and the average testing accuracy is 62.9% based on the 1994-1999 data. A more complex neural network architecture resulted in higher training accuracy but also lower testing accuracy. Taking into account of the uncertainty regarding reported malaria cases, we divided the malaria cases into bands (classes) to compute training accuracy. Using the same neural network architecture on the 19 most endemic provinces for years 1994 to 2000, the mean training accuracy weighted by provincial malaria cases was 73%. Prediction of malaria cases for 2001 using neural networks trained for 1994-2000 gave a weighted accuracy of 53%. Because there was a significant decrease (31%) in the number of malaria cases in the 19 provinces from 2000 to 2001, the networks overestimated malaria transmissions. The decrease in transmission was not due to climatic or environmental changes. Thailand is a country with long borders. Migrant populations from the neighboring countries enlarge the human malaria reservoir because these populations have more limited access to health care. This issue also confounds the complexity of modeling malaria based on meteorological and environmental variables alone. In spite of the relatively low resolution of the data and the impact of migrant populations, we have uncovered a reasonably clear dependency of malaria on meteorological and environmental remote sensing variables. When other contextual determinants do not vary significantly, using neural network analysis along with remote sensing variables to predict malaria endemicity should be feasible. (+info)
Recent climate extremes alter alpine lake ecosystems.
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Bacillary dysentery and meteorological factors in northeastern China: a historical review based on classification and regression trees.
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The relationship between the incidence of bacillary dysentery and meteorological factors was investigated. Data on bacillary dysentery incidence in Shenyang from 1990 to 1996 were obtained from Liaoning Provincial Center for Disease Control and Prevention, and meteorological data such as atmospheric pressure, air temperature, precipitation, evaporation, wind speed, and the amount of solar radiation were obtained from Shenyang Meteorological Bureau. Kendall and Spearman correlations were used to analyze the relationship between bacillary dysentery and meteorological factors. The incidence of bacillary dysentery was treated as a response variable, and meteorological factors were treated as predictable variables. Software R 2.3.1 was used to execute the classification and regression trees (CART). The model improved the accuracy of the fitting results. The residual sum square error of the regression tree model was 53.9, while the residual sum square error of the multivariate linear regression model was 107.2. Among all the meteorological indexes, relative humidity, minimum temperature, and pressure one month prior were statistically influential factors in the multivariate regression tree model. CART may be a useful tool for dealing with heterogeneous data, as it can serve as a decision support tool and is notable for its simplicity and ease. (+info)