Characterization of a Si(Li) Compton polarimeter for the hard x-ray regime, using synchrotron radiation.

BACKGROUND: Buruli ulcer (BU), caused by Mycobacterium ulcerans (M. ulcerans), is a necrotizing skin disease found in more than 30 countries worldwide. BU incidence is highest in West Africa; however, cases have substantially increased in coastal regions of southern Australia over the past 30 years. Although the mode of transmission remains uncertain, the spatial pattern of BU emergence in recent years seems to suggest that there is an environmental niche for M. ulcerans and BU prevalence. METHODOLOGY/PRINCIPAL FINDINGS: Network analysis was applied to BU cases in Victoria, Australia, from 1981-2008. Results revealed a non-random spatio-temporal pattern at the regional scale as well as a stable and efficient BU disease network, indicating that deterministic factors influence the occurrence of this disease. Monthly BU incidence reported by locality was analyzed with landscape and climate data using a multilevel Poisson regression approach. The results suggest the highest BU risk areas occur at low elevations with forested land cover, similar to previous studies of BU risk in West Africa. Additionally, climate conditions as far as 1.5 years in advance appear to impact disease incidence. Warmer and wetter conditions 18-19 months prior to case emergence, followed by a dry period approximately 5 months prior to case emergence seem to favor the occurrence of BU. CONCLUSIONS/SIGNIFICANCE: The BU network structure in Victoria, Australia, suggests external environmental factors favor M. ulcerans transmission and, therefore, BU incidence. A unique combination of environmental conditions, including land cover type, temperature and a wet-dry sequence, may produce habitat characteristics that support M. ulcerans transmission and BU prevalence. These findings imply that future BU research efforts on transmission mechanisms should focus on potential vectors/reservoirs found in those environmental niches. Further, this study is the first to quantitatively estimate environmental lag times associated with BU outbreaks, providing insights for future transmission investigations

Climate and Landscape Factors Associated with Buruli Ulcer Incidence in Victoria, Australia

Background Buruli ulcer (BU), caused by Mycobacterium ulcerans (M. ulcerans), is a necrotizing skin disease found in more than 30 countries worldwide. BU incidence is highest in West Africa; however, cases have substantially increased in coastal regions of southern Australia over the past 30 years. Although the mode of transmission remains uncertain, the spatial pattern of BU emergence in recent years seems to suggest that there is an environmental niche for M. ulcerans and BU prevalence. Methodology/Principal Findings Network analysis was applied to BU cases in Victoria, Australia, from 1981–2008. Results revealed a non-random spatio-temporal pattern at the regional scale as well as a stable and efficient BU disease network, indicating that deterministic factors influence the occurrence of this disease. Monthly BU incidence reported by locality was analyzed with landscape and climate data using a multilevel Poisson regression approach. The results suggest the highest BU risk areas occur at low elevations with forested land cover, similar to previous studies of BU risk in West Africa. Additionally, climate conditions as far as 1.5 years in advance appear to impact disease incidence. Warmer and wetter conditions 18–19 months prior to case emergence, followed by a dry period approximately 5 months prior to case emergence seem to favor the occurrence of BU. Conclusions/Significance The BU network structure in Victoria, Australia, suggests external environmental factors favor M. ulcerans transmission and, therefore, BU incidence. A unique combination of environmental conditions, including land cover type, temperature and a wet-dry sequence, may produce habitat characteristics that support M. ulcerans transmission and BU prevalence. These findings imply that future BU research efforts on transmission mechanisms should focus on potential vectors/reservoirs found in those environmental niches. Further, this study is the first to quantitatively estimate environmental lag times associated with BU outbreaks, providing insights for future transmission investigations.This project was supported by the World Health Organization and the National Institutes of Health and Fogarty International Center (NIH - R01TW007550). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Fogarty International Center or the National Institutes of Health. R.W. Merritt is gratefully acknowledged for supporting this research as part of NIH grant R01TW007550

Complex temporal climate signals drive the emergence of human water-borne disease

Predominantly occurring in developing parts of the world, Buruli ulcer is a severely disabling mycobacterium infection which often leads to extensive necrosis of the skin. While the exact route of transmission remains uncertain, like many tropical diseases, associations with climate have been previously observed and could help identify the causative agent's ecological niche. In this paper, links between changes in rainfall and outbreaks of Buruli ulcer in French Guiana, an ultraperipheral European territory in the northeast of South America, were identified using a combination of statistical tests based on singular spectrum analysis, empirical mode decomposition and cross-wavelet coherence analysis. From this, it was possible to postulate for the first time that outbreaks of Buruli ulcer can be triggered by combinations of rainfall patterns occurring on a long (i.e., several years) and short (i.e., seasonal) temporal scale, in addition to stochastic events driven by the El Nino-Southern Oscillation that may disrupt or interact with these patterns. Long-term forecasting of rainfall trends further suggests the possibility of an upcoming outbreak of Buruli ulcer in French Guiana

The AIC values of models consisting of the base landscape model plus individual climate variables.

<p>AIC = Akaike's Information Criterion.</p><p>Std. Dev. = Standard Deviation.</p><p>The base landscape model consisted of Proportion of Forest and Mean Elevation.</p><p>The notation “Predictor variable @ T – [number]” refers to the given <i>variable</i> at the specified <i>number</i> of months prior to BU case incidence.</p

Calculation of the clustering coefficient (<i>C</i>).

<p>The links between node <i>i</i> and other nodes in the network defines the neighborhood of node <i>i</i> (A). Node <i>i</i> has 8 links, or “neighbors”, denoted as <i>k_i</i>. To calculate the clustering coefficient for node <i>i</i> (<i>C_i</i>), we find the number of possible unique connections between the neighbors (<i>k_i</i>) using the formula [<i>k_i</i>(<i>k_i</i>−1)/2], or in this case (8*7)/2 = 28. We then find the number of actual connections (<i>Δ</i>_i) between neighbors, in this case 5 (B). <i>C_i</i> is simply defined as the actual links (<i>Δ</i>_i) divided by the possible links [<i>k_i</i>(<i>k_i</i>−1)/2] in the neighborhood of node <i>i</i>. Therefore, <i>C_i</i> = 2<i>Δ</i>_i/(<i>k_i</i>−1)<i>k_i</i> = 0.178 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051074#pone.0051074-Watts1" target="_blank">[40]</a>.</p

Buruli ulcer incidence in Australia from 1939–2008.

<p>The value for each state represents the percentage of total reported Buruli ulcer cases for Australia during this time period.</p

Candidate predictor variables for model development.

<p>Std. Dev. = Standard Deviation.</p><p>A predictor level of 1 means the variable is time-variant and 2 means the variable is time-invariant. The correlation coefficient (R) between total cases per locality and the level 2 predictor variables is shown in the last column.</p

The “actual” Victoria BU disease network from 1981–2008.

<p>The centroids of each locality represent the nodes of the network (the black triangles) and the links between consecutive BU cases are represented by lines connecting the nodes.</p

Statistical modeling flowchart.

<p>The flowchart shows the multiple stages of model development and variable selection. Variable definitions: “For” = Proportion of Forest, “Urb” = Proportion of Urban, “Min E” = Minimum Elevation, “Mean E” = Mean Elevation, “TP” = Total Precipitation, “SDP” = Standard Deviation of Precipitation, “Mx” = Maximum Temperatures, and “Mn” = Minimum Temperatures. For the climate variables, the notation “T – [number]” refers to the given <i>variable</i> at the specified <i>number</i> of months prior to BU case incidence.</p