Biology subjects

Oliver, I.

Publications and source records attributed to Oliver, I..

3 recordsLinked to original sources

bioRxiv2026

A fibroblast-CD8+ T cell circuit drives a Type 1 shift in allergic inflammatory memory

Inflammatory memory in barrier tissues often manifests as exaggerated recall responses to previously encountered stimuli. While recall is generally thought to reinforce the Type of inflammation (e.g., Type 1 (T1), Type 2 (T2), or Type 17 (T17)) induced in the primary encounter, whether and how memory responses engage programs beyond the initiating Type is unclear. Here, we adapted well-characterized murine models of T2 atopic dermatitis (AD)-like and T17 psoriasis-like inflammation into repeated challenge paradigms to compare how inflammatory memory is stored and recalled through longitudinal single-cell RNA-sequencing. In T17 inflammation, a network of IL-17-producing {gamma}{delta}T cells, IL-17-responsive keratinocytes and myeloid cells established in primary challenge re-emerged in recall with increased abundance and accelerated kinetics. By contrast, allergic T2 memory drove a particularly exacerbated and durable recall response in which hallmark features of T1 immunity, including interferon (IFN) signaling and cytotoxic lymphocyte recruitment, were unexpectedly layered onto amplified T2 responses. This T2-to-T1 shift was initiated by CD8+ tissue-resident memory T (Trm) cells that expanded with repeated challenge, produced IFN{gamma}, TNF- and IL-13, and closely resembled treatment-refractory Trm cells in AD. Tissue rewiring was supported by Trm-derived T1 cytokines and the IL-4-rich T2 milieu whose synergy on fibroblasts amplified allergic recall via IL-15 and CCL19. By tuning fibroblast IFN responsiveness in vivo, we mechanistically dissect how this fibroblast-CD8+ T cell circuit supports cytotoxic CD8+ T cell responses. In human AD, both arms of allergic recall amplification by this two-cell circuit featuring T1 and T2 effector responses were conserved. Our findings reveal how a stromal-immune circuit drives a clinically relevant T1 immune axis to support allergic inflammatory memory.

immunology↗

bioRxiv2019

Extending site-based observations to predict the spatial patterns of vegetation structure and composition

ContextConservation planning and land management are inherently spatial processes that are most effective when implemented over large areas.\n\nObjectivesOur objectives were to (i) use existing plot data to aggregate species inventories to growth forms and derive indicators of vegetation structure and composition and ii) generate spatially-explicit, continuous, landscape scaled models of these discrete vegetation indicators, accompanied by maps of model uncertainty.\n\nMethodUsing a case study from New South Wales, Australia, we aggregated floristic observations from 7234 sites into growth forms. We trained ensembles of artificial neural networks (ANN) to predict the distribution of these indicators over a broad region covering 11.5 million hectares. Importantly, we show spatially explicit models of uncertainty so that end-users have a tangible and transparent means of assessing models.\n\nResultsOur key findings were firstly, widely available site-based floristic records can be used to derive aggregated indicators of the structure and composition of plant growth forms. Secondly, ANNs are a powerful method to predict continuous patterns in complex, non-linear data (Pearsons correlation coefficient 0.83 (total native vegetation cover) to 0.42 (forb cover)). Thirdly, maps of the standardised residual error give insight into model performance and provide an assessment of model uncertainty in specific locations.\n\nConclusionsSpatially explicit, continuous representations of vegetation composition and structural complexity can add considerable value to conventional maps of vegetation extent or community type. This application has the potential to enhance the capacity for conservation planners, landscape managers and policy-makers to make informed decisions across landscape and regional scales.

ecology↗

bioRxiv2019

Species abundance distributions should underpin ordinal cover-abundance transformations

The cover and abundance of individual plant species have been recorded on ordinal scales for millions of plots world-wide. Many ecological questions can be addressed using these data. However ordinal cover data may need to be transformed to a quantitative form (0 to 100%), especially when scrutinising summed cover of multiple species. Traditional approaches to transforming ordinal data often assume that data are symmetrically distributed. However, skewed abundance patterns are ubiquitous in plant community ecology. A failure to account for this skew will bias plant cover estimates, especially when cover of multiple species are summed. The questions this paper addresses are (i) how can we estimate transformation values for ordinal data that accounts for the underlying right-skewed distribution of plant cover; (ii) do different plant groups require different transformations and (iii) how do our transformations compare to other commonly used transformations within the context of exploring the aggregate properties of vegetation? Using a continuous cover dataset, each occurrence record was mapped to its commensurate ordinal value, in this case, the ubiquitous Braun-Blanquet cover-abundance (BBCA) scale. We fitted a Bayesian hierarchical beta regression to estimate the predicted mean (PM) cover of each of six plant growth forms within different ordinal classes. We illustrate our method using a case study of 2 809 plots containing 95 812 occurrence records with visual estimates of cover for 3 967 species. We compare the model derived estimates to other commonly used transformations. Our model found that PM estimates differed by growth form and that previous methods overestimated cover, especially of smaller growth forms such as forbs and grasses. Our approach reduced the cumulative compounding of errors when transformed cover data were used to explore the aggregate properties of vegetation and was robust when validated against an independent dataset. By accounting for the right-skewed distribution of cover data, our alternate approach for estimating transformation values can be extended to other ordinal scales. A more robust approach to transforming floristic data and aggregating cover estimates can strengthen ecological analyses to support biodiversity conservation and management.

ecology↗