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Magnetic signals in deep‐sea sediments have the potential to unravel past continental environmental changes, via changes in primary terrigenous magnetic supply, but also record past marine environmental conditions, via in situ formation of secondary magnetic minerals, particularly when complemented by independent proxies. By combining environmagnetic, geochemical, and siliciclastic grain size data, we investigated marine sediment core GL‐1090 (24.92°S, 42.51°W, 2,225 m water depth) aiming to unravel changes in terrigenous sediment input and bottom water conditions during the last ∼184 ka at the western South Atlantic middepth. The Al/Si, Fe/κ and siliciclastic grain size data show that terrigenous sediments at this core location derived from the Plata River (southeastern South America). This material was transported northwards by the Brazilian Coastal Current and their delivery to our core site was modulated by sea‐level oscillations. Periods of low sea‐level were characterized by the input of coarser and more abundant terrigenous sediments. Environmagnetic parameters indicate significant downcore variations in the magnetic domain state, which we interpret as changes in the content of biogenic magnetite following glacial‐interglacial cycles. Coeval negative excursions in magnetic grain size and benthic δ13C suggests that concentrations of single domain magnetite (possibly magnetotactic bacterial magnetite) vary in response to middepth water ventilation. We suggest that reduced ventilation in the middepth western South Atlantic bottom waters during peak glaciations triggered a decrease in the production of biogenic magnetite. Peak glaciations were, in turn, linked with increases in the residence time of North Atlantic Deep Water (or its glacial counterpart). Key Points: Multi‐proxy approach points to the Plata River as the main lithogenic sediment source to tropical SE South American continental margin, modulated by sea‐levelReduced biogenic magnetite formation coeval with negative benthic δ13C anomalies during glaciationsBiogenic magnetite formation hampered by decreased bottom water ventilation during glaciations [ABSTRACT FROM AUTHOR]

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability. [ABSTRACT FROM AUTHOR]

A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America., Competing Interests: The authors have declared that no competing interests exist.

Unlabelled: INTRODUCTION AND SETTING: Our analysis compares the most comprehensive epidemiologic and virologic surveillance data compiled to date for laboratory-confirmed H1N1pdm patients between 1 April 2009 - 31 January 2010 from five temperate countries in the Southern Hemisphere-Argentina, Australia, Chile, New Zealand, and South Africa., Objective: We evaluate transmission dynamics, indicators of severity, and describe the co-circulation of H1N1pdm with seasonal influenza viruses., Results: In the five countries, H1N1pdm became the predominant influenza strain within weeks of initial detection. South Africa was unique, first experiencing a seasonal H3N2 wave, followed by a distinct H1N1pdm wave. Compared with the 2007 and 2008 influenza seasons, the peak of influenza-like illness (ILI) activity in four of the five countries was 3-6 times higher with peak ILI consultation rates ranging from 35/1,000 consultations/week in Australia to 275/100,000 population/week in New Zealand. Transmission was similar in all countries with the reproductive rate ranging from 1.2-1.6. The median age of patients in all countries increased with increasing severity of disease, 4-14% of all hospitalized cases required critical care, and 26-68% of fatal patients were reported to have ≥1 chronic medical condition. Compared with seasonal influenza, there was a notable downward shift in age among severe cases with the highest population-based hospitalization rates among children <5 years old. National population-based mortality rates ranged from 0.8-1.5/100,000., Conclusions: The difficulty experienced in tracking the progress of the pandemic globally, estimating its severity early on, and comparing information across countries argues for improved routine surveillance and standardization of investigative approaches and data reporting methods., (© 2011 Blackwell Publishing Ltd.)

In late April 2009 the emergence of 2009 pandemic influenza A (H1N1pdm) virus was detected in humans. From its detection through July 18th, 2009, confirmed cases of H1N1pdm in the Americas were periodically reported to the Pan American Health Organization (PAHO) by member states. Because the Americas span much of the world's latitudes, this data provides an excellent opportunity to examine variation in H1N1pdm transmission by season. Using reports from PAHO member states from April 26th, 2009 through July 18th, 2009, we characterize the early spread of the H1N1 pandemic in the Americas. For a geographically representative sample of member states we estimate the reproductive number (R) of H1N1pdm over the reporting period. The association between these estimates and latitude, temperature, humidity and population age structure was estimated. Estimates of the peak reproductive number of H1N1pdm ranged from 1.3 (for Panama, Colombia) to 2.1 (for Chile). We found that reproductive number estimates were most associated with latitude in both univariate and multivariate analyses. To the extent that latitude is a proxy for seasonal changes in climate and behavior, this association suggests a strong seasonal component to H1N1pdm transmission. However, the reasons for this seasonality remain unclear.