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Contains fulltext : 247191.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 226024.pdf (Publisher’s version ) (Closed access), Quantitative real-time PCR (qPCR) is increasingly used to detect Pneumocystis jirovecii for the diagnosis of Pneumocystis pneumonia (PCP), but there are differences in the nucleic acids targeted, DNA only versus whole nucleic acid (WNA), and also the target genes for amplification. Through the Fungal PCR Initiative, a working group of the International Society for Human and Animal Mycology, a multicenter and monocenter evaluation of PCP qPCR assays was performed. For the multicenter study, 16 reference laboratories from eight different countries, performing 20 assays analyzed a panel consisting of two negative and three PCP positive samples. Aliquots were prepared by pooling residual material from 20 negative or positive- P. jirovecii bronchoalveolar lavage fluids (BALFs). The positive pool was diluted to obtain three concentrations (pure 1:1; 1:100; and 1:1000 to mimic high, medium, and low fungal loads, respectively). The monocenter study compared five in-house and five commercial qPCR assays testing 19 individual BALFs on the same amplification platform. Across both evaluations and for all fungal loads, targeting WNA and the mitochondrial small sub-unit (mtSSU) provided the earliest Cq values, compared to only targeting DNA and the mitochondrial large subunit, the major surface glycoprotein or the beta-tubulin genes. Thus, reverse transcriptase-qPCR targeting the mtSSU gene could serve as a basis for standardizing the P. jirovecii load, which is essential if qPCR is to be incorporated into clinical care pathways as the reference method, accepting that additional parameters such as amplification platforms still need evaluation.

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Using end-to-end models for ecosystem-based management requires knowledge of the structure, uncertainty and sensitivity of the model. The Norwegian and Barents Seas (NoBa) Atlantis model was implemented for use in ‘what if’ scenarios, combining fisheries management strategies with the influences of climate change and climate variability. Before being used for this purpose, we wanted to evaluate and identify sensitive parameters and whether the species position in the foodweb influenced their sensitivity to parameter perturbation. Perturbing recruitment, mortality, prey consumption and growth by +/- 25% for nine biomass-dominating key species in the Barents Sea, while keeping the physical climate constant, proved the growth rate to be the most sensitive parameter in the model. Their trophic position in the ecosystem (lower trophic level, mid trophic level, top predators) influenced their responses to the perturbations. Top-predators, being generalists, responded mostly to perturbations on their individual life-history parameters. Mid-level species were the most vulnerable to perturbations, not only to their own individual life-history parameters, but also to perturbations on other trophic levels (higher or lower). Perturbations on the lower trophic levels had by far the strongest impact on the system, resulting in biomass changes for nearly all components in the system. Combined perturbations often resulted in non-additive model responses, including both dampened effects and increased impact of combined perturbations. Identifying sensitive parameters and species in end-to-end models will not only provide insights about the structure and functioning of the ecosystem in the model, but also highlight areas where more information and research would be useful - both for model parameterization, but also for constraining or quantifying model uncertainty.

Contains fulltext : 175113.pdf (publisher's version ) (Closed access), A wide array of PCR tests has been developed to aid the diagnosis of invasive aspergillosis (IA), providing technical diversity but limiting standardisation and acceptance. Methodological recommendations for testing blood samples using PCR exist, based on achieving optimal assay sensitivity to help exclude IA. Conversely, when testing more invasive samples (BAL, biopsy, CSF) emphasis is placed on confirming disease, so analytical specificity is paramount. This multicenter study examined the analytical specificity of PCR methods for detecting IA by blind testing a panel of DNA extracted from a various fungal species to explore the range of Aspergillus species that could be detected, but also potential cross reactivity with other fungal species. Positivity rates were calculated and regression analysis was performed to determine any associations between technical specifications and performance. The accuracy of Aspergillus genus specific assays was 71.8%, significantly greater (P < .0001) than assays specific for individual Aspergillus species (47.2%). For genus specific assays the most often missed species were A. lentulus (25.0%), A. versicolor (24.1%), A. terreus (16.1%), A. flavus (15.2%), A. niger (13.4%), and A. fumigatus (6.2%). There was a significant positive association between accuracy and using an Aspergillus genus PCR assay targeting the rRNA genes (P = .0011). Conversely, there was a significant association between rRNA PCR targets and false positivity (P = .0032). To conclude current Aspergillus PCR assays are better suited for detecting A. fumigatus, with inferior detection of most other Aspergillus species. The use of an Aspergillus genus specific PCR assay targeting the rRNA genes is preferential.

Understanding the drivers of dispersal among populations is a central topic in marine ecology and fundamental for spatially explicit management of marine resources. The extensive coast of Northwestern Australia provides an emerging frontier for implementing new genomic tools to comparatively identify patterns of dispersal across diverse and extreme environmental conditions. Here, we focused on the stripey snapper (Lutjanus carponotatus) , which is important to recreational, charter-based and customary fishers throughout the Indo-West Pacific. We collected 1,016 L. carponotatus samples at 51 locations in the coastal waters of Northwestern Australia ranging from the Northern Territory to Shark Bay and adopted a genotype-by-sequencing approach to test whether realized connectivity (via larval dispersal) was related to extreme gradients in coastal hydrodynamics. Hydrodynamic simulations using CONNIE and a more detailed treatment in the Kimberley Bioregion provided null models for comparison. Based on 4,402 polymorphic single nucleotide polymorphism loci shared across all individuals, we demonstrated significant genetic subdivision between the Shark Bay Bioregion in the south and all locations within the remaining, more northern bioregions. More importantly, we identified a zone of admixture spanning a distance of 180 km at the border of the Kimberley and Canning bioregions, including the Buccaneer Archipelago and adjacent waters, which collectively experiences the largest tropical tidal range and some of the fastest tidal currents in the world. Further testing of the generality of this admixture zone in other shallow water species across broader geographic ranges will be critical for our understanding of the population dynamics and genetic structure of marine taxa in our tropical oceans.

Models are increasingly used to support decision-making in the management of natural resources. They can provide system understanding, learning, a platform for stakeholder engagement, projections of system behaviour and an environment for virtual testing of alternative management strategies. However, rarely is a single numerical model suitable for all these purposes. Our experience is that a suite of models of different size, complexity and scope can be more effective and can better address the needs of environmental management projects. Models of different complexity can address different needs, but can also be combined as a flexibly sculpted tool kit – as they require very different development effort they can be deployed at different stages during a project. Using rapidly deployed qualitative, or simple quantitative, models stakeholders can be exposed to models very early in the project, eliciting feedback on appropriate model content and familiarity with the modelling process without affecting the development of more complex, resource intensive, models aimed at answering core management questions. This early and continuous stakeholder exposure to models provides flexibility in addressing specific novel questions as they arise during project development, as well as an opportunity for developing skills and changing both modellers and stakeholders’ attitudes, as is often needed when facing complex problems. Using an example where we used five different model types in an effort to inform policy-making around regional multiple use management in north-western Australia, we describe (i) how each model type can be used, (ii) the different roles the models cover, and (iii) how they fit into a full decision making process and stakeholder engagement. We conclude by summarising the lessons we learnt.

We describe the different types of models we used as part of an effort to inform policy-making aiming at the management of the Ningaloo coast in the Gascoyne region, Western Australia. This provides an overview of how these models interact, the different roles they cover, how they fit into a full decision making process and what we learnt about the stakeholders involved in our project via their use. When modelling is explicitly used to address socio-ecological issues, the key determinant of success is whether the models, their results and recommendations are taken up by stakeholders; such uptake in turn depends on addressing stakeholders’ concerns, on engaging them in the project, on ensuring they feel ownership of the decision process at large, and that they understand and trust the modelling effort. This observation has guided our approach and has resulted in treating ‘building a model’ as the catalyst, rather than the final aim, of the process. In other words, extensive interactions in order to introduce, showcase, discuss and tune the model used for final decision making have represented both a requirement and an opportunity to ensure (i) model relevance, (ii) its acceptance, (iii) that all information available in the stakeholder team was accounted for and (iv) that stakeholders holding different levels of understanding of modelling, what it does and what it can provide to decision-making could develop an informed opinion on its use. To fulfil these roles we developed five broad classes of models: conceptual models, toy-models, single system models, shuttle-models and a full-system model. In conceptual models the main drivers of a system are highlighted for subsequent representation as components of the full-system model. This usually results in a diagram summarising our understanding of how the system works. In toy-models a problem is simplified in such a way that only a handful of components are included. The purpose of these models is mostly educational: we

We describe the different types of models we used as part of an effort to inform policy-making aiming at the management of the Ningaloo coast in the Gascoyne region, Western Australia. This provides an overview of how these models interact, the different roles they cover, how they fit into a full decision making process and what we learnt about the stakeholders involved in our project via their use. When modelling is explicitly used to address socio-ecological issues, the key determinant of success is whether the models, their results and recommendations are taken up by stakeholders; such uptake in turn depends on addressing stakeholders’ concerns, on engaging them in the project, on ensuring they feel ownership of the decision process at large, and that they understand and trust the modelling effort. This observation has guided our approach and has resulted in treating ‘building a model’ as the catalyst, rather than the final aim, of the process. In other words, extensive interactions in order to introduce, showcase, discuss and tune the model used for final decision making have represented both a requirement and an opportunity to ensure (i) model relevance, (ii) its acceptance, (iii) that all information available in the stakeholder team was accounted for and (iv) that stakeholders holding different levels of understanding of modelling, what it does and what it can provide to decision-making could develop an informed opinion on its use. To fulfil these roles we developed five broad classes of models: conceptual models, toy-models, singlesystem models, shuttle-models and a full-system model. In conceptual models the main drivers of a system are highlighted for subsequent representation as components of the full-system model. This usually results in a diagram summarising our understanding of how the system works. In toy-models a problem is simplified in such a way that only a handful of components are included. The purpose of these models is mostly educational: we w