Biology subjects

Pollock, E.

Publications and source records attributed to Pollock, E..

4 recordsLinked to original sources

bioRxiv2023

Characterization of cotton virus A, a novel and distinct member of the genus Caulimovirus with endogenous viral elements in Gossypium spp.

The complete genome sequence of cotton virus A, a new virus infecting cotton (Gossypium spp.), was determined using high-throughput sequencing, PCR and rolling circle amplification. The 7,482-nt genome is a circular dsDNA molecule that codes for six open reading frames similar to other members of the Caulimovirus genus (family Caulimoviridae) but presenting key genome organization differences. P3, P5 and P6 are presumed movement, coat, and reverse transcriptase proteins, respectively. P1, P2 and P4 showed no homology to virus proteins. Endogenous viral elements were found in G. hirsutum and G. tomentosum, but not in G. barbadense, G. arboreum, or G. herbaceum.

plant biology↗

bioRxiv2021

The viral vectored vaccine targeting Thymosin β10 controls tumour growth in a murine model of prostate cancer

Direct analysis of MHC-presented antigens by mass spectrometry has been successfully applied for the evaluation of the antigenic landscape of different human tissues and malignancies in the past. In the current study, for the first time this approach has been applied to investigate a murine prostate cancer peptidome for the development of next generation prostate cancer vaccines using the TRansgenic Adenocarcinoma of Mouse Prostate, TRAMP, model. We have performed immunopeptidomic analysis of the normal mouse prostate and mouse prostate adenocarcinoma samples and identified a number of peptides overexpressed on tumour tissue compared to the normal prostate. We selected two of these proteins previously reported to play a role in carcinogenesis, thymosin beta 4 (Thy{beta}4) and thymosin beta 10 (Thy{beta}10), expressed them in the chimpanzee adenovirus (ChAdOx1) and Modified Vaccinia Ankara (MVA) recombinant viral vectors and evaluated these constructs as a candidate prostate cancer vaccine in a murine prostate cancer model. We have found that the vaccine encoding the Thy{beta}10 polypeptide tethered to the MHC class II associated invariant chain, CD74, significantly delayed outgrowth of established tumours in an ectopic TRAMP-C1 mouse model of prostate cancer. The observed tumour-protective efficacy was not associated with Thy{beta}10-specific cellular or antibody responses, suggesting an existence of other mechanisms underlying anti-tumour effect in this model that are yet to be elucidated.

immunology↗

bioRxiv2019

Engineering recurrent neural networks from task-relevant manifolds and dynamics

Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons. Author SummaryNeurons in the brain form intricate networks that can produce a vast array of activity patterns. To support goal-directed behavior, the brain must adjust the connections between neurons so that network dynamics can perform desirable computations on behaviorally relevant variables. A fundamental goal in computational neuroscience is to provide an understanding of how network connectivity aligns the dynamics in the brain to the dynamics needed to track those variables. Here, we develop a mathematical framework for creating recurrent neural network models that can address this problem. Specifically, we derive a set of linear equations that constrain the connectivity to afford a direct mapping of task-relevant dynamics onto network activity. We demonstrate the utility of this technique by creating and analyzing a set of network models that can perform a simple working memory task. We then extend the approach to show how additional constraints can furnish networks whose dynamics are controlled flexibly by external inputs. Finally, we exploit the flexibility of this technique to explore the robustness and capacity limitations of recurrent networks. This network synthesis method provides a powerful means for generating and validating hypotheses about how task-relevant computations can emerge from network dynamics.

neuroscience↗

bioRxiv2018

Dynamic self-organized error-correction of grid cells by border cells

Grid cells in the entorhinal cortex are believed to establish their regular, spatially correlated firing patterns by path integration of the animals motion. Mechanisms for path integration, e.g. in attractor network models, predict stochastic drift of grid responses, which is not observed experimentally. We demonstrate a biologically plausible mechanism of dynamic self-organization by which border cells, which fire at environmental boundaries, can correct such drift in grid cells. In our model, experience-dependent Hebbian plasticity during exploration allows border cells to learn connectivity to grid cells. Border cells in this learned network reset the phase of drifting grids. This error-correction mechanism is robust to environmental shape and complexity, including enclosures with interior barriers, and makes distinctive predictions for environmental deformation experiments. Our work demonstrates how diverse cell types in the entorhinal cortex could interact dynamically and adaptively to achieve robust path integration.

neuroscience↗