Publications by authors named "P J De Barro"

Loss of effective antibiotics through antimicrobial resistance (AMR) is one of the greatest threats to human health. By 2050, the annual death rate resulting from AMR infections is predicted to have climbed from 1.27 million per annum in 2019, up to 10 million per annum.

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Management of agricultural pests requires an understanding of pest species diversity, their interactions with beneficial insects and spatial-temporal patterns of pest abundance. Invasive and agriculturally important insect pests can build up very high populations, especially in cropping landscapes. Traditionally, sampling effort for species identification involves small sample sizes and is labour intensive.

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The spread of antimicrobial resistance (AMR) is a rapidly growing threat to humankind on both regional and global scales. As countries worldwide prepare to embrace a One Health approach to AMR management, which is one that recognizes the interconnectivity between human, animal, and environmental health, increasing attention is being paid to identifying and monitoring key contributing factors and critical control points. Presently, AMR sensing technologies have significantly progressed phenotypic antimicrobial susceptibility testing (AST) and genotypic antimicrobial resistance gene (ARG) detection in human healthcare.

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Releasing sterile or incompatible male insects is a proven method of population management in agricultural systems with the potential to revolutionize mosquito control. Through a collaborative venture with the "Debug" Verily Life Sciences team, we assessed the incompatible insect technique (IIT) with the mosquito vector in northern Australia in a replicated treatment control field trial. Backcrossing a US strain of carrying AlbB from with a local strain, we generated a AlbB2-F4 strain incompatible with both the wild-type (no ) and Mel- now extant in North Queensland.

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The Bemisia cassava whitefly complex includes species that cause severe crop damage through vectoring cassava viruses in eastern Africa. Currently, this whitefly complex is divided into species and subgroups (SG) based on very limited molecular markers that do not allow clear definition of species and population structure. Based on 14,358 genome-wide SNPs from 62 Bemisia cassava whitefly individuals belonging to sub-Saharan African species (SSA1, SSA2 and SSA4), and using a well-curated mtCOI gene database, we show clear incongruities in previous taxonomic approaches underpinned by effects from pseudogenes.

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