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Atkinson, P.

Publications and source records attributed to Atkinson, P..

2 recordsLinked to original sources

Impact of global change on future Ebola emergence and epidemic potential in Africa

Animal-borne or zoonotic human diseases (e.g., SARS, Rabies) represent major health and economic burdens throughout the world, disproportionately impacting poor communities. In 2013-2016, an outbreak of the Ebola virus disease (EVD), a zoonotic disease spread from animal reservoirs caused by the Zaire Ebola virus (EBOV), infected approximately 30,000 people, causing considerable negative social and economic impacts in an unexpected geographical location(Sierra Leone, Guinea, and Liberia). It is not known whether the spatial distribution of this outbreak and unprecedented severity was precipitated by environmental changes and, if so, which areas might be at risk in the future. To better address the major health and economic impacts of zoonotic diseases we develop a system-dynamics approach to capture the impact of future climate, land use and human population change on Ebola (EVD). We create future risk maps for affected areas and predict between a 1.75-3.2 fold increase in EVD outbreaks per year by 2070. While the best case future scenarios we test saw a reduction in the likelihood of epidemics, other future scenarios with high human population growth and low rates of socioeconomic development saw a fourfold increase in the risk of epidemics occurring and almost 50% increase in the risk of catastrophic epidemics. As well as helping to target where health infrastructure might be further developed or vaccines best deployed, our modelling framework can be used to target global interventions and forecast risk for many other zoonotic diseases.\n\nSignificance StatementDespite the severe health and economic impacts of outbreaks of diseases like SARS or Zika, there has been surprisingly little progress in predicting where and when human infectious disease outbreaks will occur next. By modelling the impacts of future climate, land use and human population change on one particular disease Ebola, we develop future risk maps for the affected areas and predict 1.7-3.2 times as many human Ebola outbreaks per year by 2070, and a 50% increase in the chance that these outbreaks will become epidemics. As well as helping to target where health infrastructure might be further developed or vaccines deployed, our approach can also be used to target actions and predict risk hotspots for many other infectious diseases.

epidemiology

piClusterBusteR: Software For Automated Classification And Characterization Of piRNA Cluster Loci

BackgroundPiwi-interacting RNAs (piRNAs) are sRNAs that have a distinct biogenesis and molecular function from siRNAs and miRNAs. The piRNA pathway is well-conserved and shown to play an important role in the regulatory capacity of germline cells in Metazoans. Significant subsets of piRNAs are generated from discrete genomic loci referred to as piRNA clusters. Given that the contents of piRNA clusters dictate the target specificity of primary piRNAs, and therefore the generation of secondary piRNAs, they are of great significance when considering transcriptional and post-transcriptional regulation on a genomic scale. A quantitative comparison of top piRNA cluster composition can provide further insight into piRNA cluster biogenesis and function.\n\nResultsWe have developed software for general use, piClusterBusteR, which performs nested annotation of piRNA cluster contents to ensure high-quality characterization, provides a quantitative representation of piRNA cluster composition by feature, and makes available annotated and unannotated piRNA cluster sequences that can be utilized for downstream analysis. The data necessary to run piClusterBusteR and the skills necessary to execute this software on any species of interest are not overly burdensome for biological researchers.\n\npiClusterBusteR has been utilized to compare the composition of top piRNA generating loci amongst 13 Metazoan species. Characterization and quantification of cluster composition allows for comparison within piRNA clusters of the same species and between piRNA clusters of different species.\n\nConclusionsWe have developed a tool that accurately, automatically, and efficiently describes the contents of piRNA clusters in any biological system that utilizes the piRNA pathway. The results from piClusterBusteR have provided an in-depth description and comparison of the architecture of top piRNA clusters within and between 13 species, as well as a description of annotated and unannotated sequences from top piRNA cluster loci in these Metazoans.\n\npiClusterBusteR is available for download on GitHub: https://github.com/pschreiner/piClusterBuster

bioinformatics