Biology subjects

Bell, P.

Publications and source records attributed to Bell, P..

4 recordsLinked to original sources

bioRxiv2025

Biofouling on aquaculture mesh: disentangling seasonal and environmental effects with eDNA

Biofouling presents ecological and economic challenges for marine industries, yet patterns of community development in relevant environments remain underexplored. In this study, we examined seasonal (winter vs summer) and spatial (sheltered vs exposed) patterns in eukaryotic biofouling across six coastal sites in New Zealand. Using standardised mesh substrates and environmental DNA (eDNA) metabarcoding with the 18S rRNA gene, we characterised over 10,000 Amplicon Sequence Variants (ASVs) across 216 samples, revealing 541 species from 249 taxonomic classes. Exposed sites exhibited marked seasonal contrasts: winter communities were dominated by hydroids (e.g., Coryne eximia), while summer samples shifted to amphipod-rich assemblages (e.g., Jassa slatteryi). Sheltered sites showed more stable biomass and diversity but greater site-level variability, especially in summer. Temperature and wind fetch emerged as key environmental drivers, with larger seasonal fluctuations observed under high-exposure conditions. Notably, several dominant species were non-native, underscoring the importance of early detection. These findings highlight the value of molecular monitoring for predicting biofouling risks and informing site-specific antifouling strategies in dynamic marine environments.

ecology↗

bioRxiv2023

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning based segmentation methods demonstrating human level performance have emerged. However, limited work has been done in developing such generalist methods within the label-free unsupervised context. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane markers to construct an a posteriori probability estimate of each pixel belonging to the nucleus, cell membrane, or background. It uses this estimate to segment each cell into its nuclear and cell-membrane compartments. We show that UNSEG is more internally consistent and better at generalizing to the complexity of tissue morphology than current deep learning methods. This allows UNSEG to unambiguously identify the cytoplasmic compartment of a cell, which we employ to demonstrate its use in an exemplar biological scenario. Within the UNSEG framework, we also introduce a new perturbed watershed algorithm capable of stably and automatically segmenting a cluster of cell nuclei into individual cell nuclei that increases the accuracy of classical watershed. Perturbed watershed can also be used as a standalone algorithm that researchers can incorporate within their supervised or unsupervised learning approaches to extend classical watershed, particularly in the multiplexed imaging context. Finally, as part of developing UNSEG, we have generated a high-quality annotated gastrointestinal tissue (GIT) dataset, which we anticipate will be useful for the broader research community. We demonstrate the efficacy of UNSEG on the GIT dataset, publicly available datasets, and on a range of practical scenarios. In these contexts, we also discuss the possibility of bias inherent in quantification of segmentation accuracy based on F1 score. Segmentation, despite its long antecedents, remains a challenging problem, particularly in the context of tissue samples. UNSEG, an easy-to-use algorithm, provides an unsupervised approach to overcome this bottleneck, and as we discuss, can help improve deep learning based segmentation methods by providing a bridge between unsupervised and supervised learning paradigms.

bioinformatics↗

bioRxiv2022

Loss of transgene expression limits liver gene therapy in primates

Introductory ParagraphEfforts to improve liver gene therapy have focused on next-generation adeno-associated virus (AAV) vector capsids, transgene delivery, and immunomodulating drugs, such as corticosteroids, to avoid destructive T-cell responses. We conducted a detailed characterization of AAV transduction in nonhuman primate liver across multiple capsids and transgenes to better define interactions that may limit stable and efficient transgene expression. We show that the initial transduction of hepatocytes is high, but the transduction rapidly diminishes to a lower stable baseline of <1% of cells, even though ~10% of the cells retain vector DNA that is localized within the nucleus. Further characterization showed genomic vector integration at frequencies of 1/100 to 1/1,000 genomes, suggesting that one mechanism for stable expression may occur via genome integration. These studies suggest that strategies to enhance durable transgene expression by activating retained nuclear episomes or by increasing the frequency of vector integrations may improve liver directed AAV gene therapy.

molecular biology↗

bioRxiv2018

The low dimensional dynamic and integrative core of cognition in the human brain

The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here, we used multi-task fMRI data from the Human Connectome Project to examine the spatiotemporal architecture of cognition in the human brain. By investigating the spatial, dynamic and molecular signatures of system-wide neural activity across a range of cognitive tasks, we show that large-scale neuronal activity converges onto a low dimensional manifold that facilitates the dynamic execution of diverse task states. Flow within this attractor space is associated with dissociable cognitive functions, and with unique patterns of network-level topology and information processing complexity. The axes of the low-dimensional neurocognitive architecture align with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between low dimensional neural activity, network topology, neuromodulatory systems and cognitive function.\n\nOne Sentence SummaryA diverse set of neuromodulators facilitates the formation of a dynamic, low-dimensional integrative core in the brain that is recruited by diverse cognitive demands

neuroscience↗