Biology subjects

Lowe, A. J.

Publications and source records attributed to Lowe, A. J..

6 recordsLinked to original sources

bioRxiv2021

An open MRI dataset for multiscale neuroscience

AO_SCPLOWBSTRACTC_SCPLOWMultimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54{+/-}5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from i) resting-state functional imaging, ii) diffusion tractography, iii) microstructure covariance analysis, and iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and macroscale function. MICA-MICs is available on the Canadian Open Neuroscience Platforms data portal (https://portal.conp.ca).

neuroscience↗

bioRxiv2021

School yard biodiversity determines short-term recovery of disturbed skin microbiota in children

The planting and conservation of biodiverse habitat in urban contexts has been proposed as a public health intervention aimed at reducing the prevalence of non-communicable diseases via microbiome rewilding (Mills et al. 2017; Mills et al. 2019). However, our understanding of the effect of urban biodiversity interventions on the human microbiota remains limited, especially on the skin (Hui et al. 2019; Roslund et al. 2020) and in the context of permanent green spaces (Lehtimaki et al. 2018; Selway et al. 2020). Here, we test the short-term response of experimentally disturbed bacterial communities on the skin of healthy children exposed to different school environments - either a classroom, a sports field, or a biodiverse forest - to understand how exposures to different types of biodiversity may influence skin microbiota. Children exposed to the forest had significantly increased skin microbiota diversity when compared to pre-exposure, an effect that increased over three days suggesting long-term effects. The microbiota on children exposed to the forest had the largest structural and compositional community change compared to children exposed to sports fields, which in turn was larger than those who remained in classrooms. Children exposed to sports fields and forests also acquired new core bacteria after exposure to green spaces, potentially buffering against disturbances to the skin microbiotas diversity, while individuals who remained in the classroom lost microbes throughout the experiment. Overall, we conclude that urban green spaces can have an enriching influence on the diversity of skin microbiota, including core members shared between all children. These findings have important implications for the design and construction of new school yards and public spaces with respect to biodiversity, health, and human microbiota.

ecology↗

bioRxiv2021

Functional architecture of the aging brain

The intrinsic functional organization of the brain changes into older adulthood. Age differences are observed at multiple spatial scales, from global reductions in modularity and segregation of distributed brain systems, to network-specific patterns of dedifferentiation. Whether dedifferentiation reflects an inevitable, global shift in brain function with age, circumscribed, experience dependent changes, or both, is uncertain. We employed a multi-method strategy to interrogate dedifferentiation at multiple spatial scales. Multi-echo (ME) resting-state fMRI was collected in younger (n=181) and older (n=120) healthy adults. Cortical parcellation sensitive to individual variation was implemented for precision functional mapping of each participant, while preserving group-level parcel and network labels. ME-fMRI processing and gradient mapping identified global and macroscale network differences. Multivariate functional connectivity methods tested for microscale, edge-level differences. Older adults had lower BOLD signal dimensionality, consistent with global network dedifferentiation. Gradients were largely age-invariant. Edge-level analyses revealed discrete, network-specific dedifferentiation patterns in older adults. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal control network regions showed greater connectivity; and the dorsal attention network was more integrated with heteromodal regions. These findings highlight the importance of multi-scale, multi-method approaches to characterize the architecture of functional brain aging.

neuroscience↗

bioRxiv2021

Rare genera differentiate urban green space soil bacterial communities in three cities across the world

Vegetation complexity is potentially important for urban green space designs aimed at fostering microbial biodiversity to benefit human health. Exposure to urban microbial biodiversity may influence human health outcomes via immune training and regulation. In this context, improving human exposure to microbiota via biodiversity-centric urban green space designs is an underused opportunity. There is currently little knowledge on the association between vegetation complexity (i.e., diversity and structure) and soil microbiota of urban green spaces. Here, we investigated the association between vegetation complexity and soil bacteria in urban green spaces in Bournemouth, UK; Haikou, China; and the City of Playford, Australia by sequencing the 16S rRNA V4 gene region of soil samples and assessing bacterial diversity. We characterized these green spaces as having low or high vegetation complexity and explored whether these two broad categories contained similar bacterial community compositions and diversity around the world. Within cities, we observed significantly different alpha and beta diversities between vegetation complexities; however, these results varied between cities. Rare genera (< 1 % relative abundance individually, on average 35 % relative abundance when pooled) were most likely to be significantly different in sequence abundance between vegetation complexities and therefore explained much of the differences in microbial communities observed. Overall, general associations exist between soil bacterial communities and vegetation complexity, although these are not consistent between cities. Therefore, more in-depth work is required to be done locally to derive practical actions to assist the conservation and restoration of microbial communities in urban areas.

ecology↗

bioRxiv2020

Using generalised dissimilarity modelling and targeted field surveys to gap-fill an ecosystem surveillance network

O_LIWhen considering which sites or land parcels complement existing conservation or monitoring networks, there are many strategies for optimising ecological coverage in the absence of ground observations. However, such optimisation is often implemented theoretically in conservation prioritisation frameworks and real-world implementation is rarely assessed, particularly for networks of monitoring sites. C_LIO_LIWe assessed the performance of adding new survey sites informed by predictive modelling in gap-filling the ecological coverage of the Terrestrial Ecosystem Research Networks (TERN) continental network of ecosystem surveillance plots, Ausplots. Using plant cover observations from 531 sites, we constructed a generalised dissimilarity model (GDM) in which species composition was predicted by environmental parameters. We combined predicted nearest-neighbour ecological distances for locations across Australia with practical considerations to select regions for gap-filling surveys of 181 new plots across 18 trips. We tracked the drop in mean nearest-neighbour distances in GDM space, and increases in the actual sampling of ecological space through cumulative multivariate dispersion. C_LIO_LIGDM explained 34% of deviance in species compositional turnover and retained geographic distance, soil P, aridity, actual evapotranspiration and rainfall seasonality among 17 significant predictors. C_LIO_LIKey bioregions highlighted as gaps included Cape York Peninsula, Brigalow Belt South, South Eastern Queensland, Gascoyne and Dampierland. C_LIO_LIWe targeted identified gap regions for surveys in addition to opportunistic or project-based gap-filling over two years. Approximately 20% of the land area of Australia received increased servicing of biological representation, corresponding to a drop in mean nearest-neighbour ecological distances from 0.38 to 0.33 in units of compositional dissimilarity. The gain in sampled ecological space was 172% that from the previous 181 plots. Notable gaps were filled in northern and south-east Queensland, north-east New South Wales and northern Western Australia. C_LIO_LIBiological scaling of environmental variables through GDM supports practical sampling decisions for ecosystem monitoring networks. Optimising putative survey locations via ecological distance to a nearest neighbour rather than to all existing sites is useful when the aim is to increase representation of habitats rather than sampling evenness per se. Iterations between modelled gaps and field campaigns provide a pragmatic compromise between theoretical optima and real-world decision-making. C_LI

ecology↗

bioRxiv2020

Stocktaking the environmental coverage of a continental ecosystem observation network

Field-based sampling of terrestrial habitats at continental scales is required to build ecosystem observation networks. However, a key challenge for detecting change in ecosystem composition, structure and function is to obtain a representative sample of habitats. Representative sampling contributes to ecological validity when analysing large spatial surveys, but field resources are limited and representativeness may differ markedly from purely practical sampling strategies to statistically rigorous ones. Here, we report a post hoc assessment of the coverage of environmental gradients as a surrogate for ecological coverage by a continental-scale survey of the Australian Terrestrial Ecosystem Research Network (TERN). TERNs surveillance program maintains a network of ecosystem observation plots that were init ially established in the rangelands through a stratification method (clustering of bioregions by environment) and Ausplots methodology. Subsequent site selection comprised gap filling combined with opportunistic sampling. Firstly, we confirmed that environmental coverage has been a good surrogate for ecological coverage. The cumulative sampling of environments and plant species composition over time were strongly correlated (based on mean multivariate dispersion; r = 0.93). We then compared the environmental sampling of Ausplots to 100,000 background points and a set of retrospective (virtual) sampling schemes: systematic grid, simple random, stratified random, and generalised random-tessellation stratified (GRTS). Differences were assessed according to sampling densities along environmental gradients, and multivariate dispersion (environmental space represented via multi-dimensional scaling). Ausplots outperformed systematic grid, simple random and GRTS in coverage of environmental space (Tukey HSD of mean dispersion, p < .001). GRTS site selection obtained similar coverage to Ausplots when employing the same bioregional stratification. Stratification by climatic zones generated the highest environmental coverage (p < .001), but the resulting sampling densities over-represented mesic coastal habitats. The Ausplots stratification by bioregions implemented under practical constraints represented complex environments well compared to statistically oriented or spatially even samples. However, potential statistical inference and power also depend on spatial and temporal replication, unbiased site selection, and accurate field measurements relative to the magnitude of change. A key conclusion is that environmental, rather than spatial, stratification is required to maximise ecological coverage across continental ecosystem observation networks.

ecology↗