Biology subjects

Hewitt, T.

Publications and source records attributed to Hewitt, T..

6 recordsLinked to original sources

bioRxiv2026

A Large-Scale Computer-Vision Mapping of the Geometric Structures of Stroboscopically-Induced Visual Hallucinations

Visual hallucinations (VHs) occur across psychedelic states and diverse psychiatric and neurological conditions, yet their phenomenology remains difficult to characterise. Empirical research on VHs is hindered by the lack of large-scale phenomenological datasets, which limits both mechanistic accounts and the systematic characterisation of when and how they arise. Stroboscopic light stimulation (SLS) viewed with closed eyes provides a reliable, non-pharmacological method of inducing VHs in healthy populations. These hallucinations typically consist of vivid colours and dynamic geometric patterns that resemble simple VHs described in both psychedelic and clinical contexts, suggesting partially overlapping neural mechanisms. We developed and applied an unsupervised computer-vision pipeline to analyse a large dataset of 10,598 drawings made following exposure to hallucination-inducing SLS. These drawings were produced by attendees of Dreamachine, a large-scale public installation designed to elicit stroboscopically induced visual hallucinations (SIVHs). We extracted feature embeddings with a self-supervised deep vision transformer, then applied dimensionality reduction and density-based clustering to identify recurrent visual motifs in a data-driven manner. The majority of drawings contained geometric forms, consistent with prior observations of simple VHs under SLS. However, we also identified novel and underreported geometric formations, such as concentric squares, crosses, hyperbolic patterns, and other geometries. Our results show how an unsupervised computer-vision pipeline can organise large, openly shared phenomenological datasets into interpretable classes. By mapping the diversity of simple geometric VHs at scale, this work places new constraints on existing theoretical accounts and motivates targeted experimental work linking SLS parameters, neural dynamics, and geometric visual hallucinations.

neuroscience↗

bioRxiv2025

Superior batch alignment and hyper-dimensional cytometry representations allow ultra-sensitive classification of disease phenotypes

Analysis of cytometry data predominantly relies on clustering and dimensionality reduction approaches for computational tractability. This is particularly relevant for modern spectral flow cytometers, which can simultaneously measure an increasingly large number of antibody marker channels. While dimensionality reduction provides for more efficient data processing, this comes at the expense of data loss that may miss subtle patterns among rare cell types that may be critical for disease detection. Maintaining analysis at full dimensions presents opportunities to preserve resolution and provides complete downstream data interpretability. However, a significant obstacle to analysis of cytometry data without dimensionality reduction is the significant batch effects observed in this data between experiment days, operators and equipment types. Here we show a new strategy to denoise batch variation from both flow- and mass-cytometry datasets using an autoencoder neural network architecture. We generated a benchmark flow cytometry dataset in mice to compare this approach to current toolsets and find our approach shows superior preservation of biological signals, whilst also performing batch correction equal to current best methodology. Our batch alignment approach works to such an extent that it becomes practically possible to project batch aligned data into multidimensional space to generate a novel representation of cellular phenotype for downstream model building. This hyperdimensional approach maintains original data resolution without requiring dimensionality reduction, and thus any resultant cell populations that differentiate phenotypes remain fully interpretable. We show with two large clinical datasets that our batch-alignment approach coupled with the multi-dimensional representation successfully detects meaningful patterns in cases where the original analysis methods struggled. This new framework removes some of the inherent technical limitations encountered in the integration of large, multi-batch cytometry datasets and provides a framework for machine learning model building from this modality. An implementation of this framework and an associated web application accompanies this manuscript at http://voxelcoder.cloud. Significance StatementThis work addresses a critical bottleneck in cytometry analysis by introducing a neural network-based approach that effectively removes technical batch variation while preserving biological signals. The novel hyperdimensional representation maintains full data resolution without dimensionality reduction, enabling more sensitive detection of disease-associated cellular signatures than current methods. Importantly, this framework enables reliable batch normalization of fresh samples processed at different times and locations, overcoming the practical constraints of clinical sample collection where simultaneous processing is often impossible. The enhanced sensitivity for identifying pathogenic cellular patterns has immediate implications for biomarker discovery and personalized medicine applications.

systems biology↗

bioRxiv2025

Benchmark of Wide Range of Pairwise Distance Metrics for Automated Classification of Mouse Mutant Phenotypes from Flow Cytometry Data

Precision medicine requires a comprehensive mapping of genotype to phenotype to provide patients with individually tailored treatment. However, when using flow cytometry to identify phenotypes, such as the quantity of various immune cell populations in tissue and blood used to identify autoimmune disorders, it is often unclear which cellular phenotypes are from healthy and disease individuals, especially when including the effects of population diversity, due to the high-dimensional nature of the data. To identify and segregate healthy phenotype from various disease phenotypes, we use pairwise distance metrics between each samples cell populations. By comparing distance metrics between C57BL/6 clone mice with mutations of known phenotype, we find that cosine similarity is best suited for segregating wildtype from mutant samples while respecting minute differences in already small cell populations, and that standardised Euclidean distance is best suited for machine-learning input due to its sensitivity. Both metrics outperform other tested metrics (including Aitchison, Euclidean, Manhattan, Earth-Movers Distance, and squared Euclidean). We demonstrate the utility of these different pairwise metrics through their application to a classification task of known mutant phenotypes: using an existing FACS phenotype dataset derived from X000 inbred C57BL/6 mice that harbour potentially phenotypic genetic variation introduced through ENU mutagenesis of individual pedigree-founding G0 male mice.

bioinformatics↗

bioRxiv2022

Extensive somatic nuclear exchanges shape global populations of the wheat leaf rust pathogen Puccinia triticina

Non-sexual processes such as somatic nuclear exchange are postulated to play a role in the diversity of clonally reproducing dikaryotic rust fungi but have been difficult to detect due to the lack of genome resolution between the two haploid nuclei. We examined three nuclear-phased genome assemblies of Puccinia triticina, which causes wheat leaf rust disease. We found that the most recently emerged Australian lineage is derived by nuclear exchange between two pre-existing lineages, which originated in Europe and North America. Haplotype-specific phylogenetic analysis reveal that repeated somatic exchange events have shuffled haploid nuclei between long-term clonal lineages, leading to a global population representing different combinations of a limited number of haploid genomes. Thus, nuclear exchange seems to be the predominant mechanism generating diversity and the emergence of new strains in this otherwise clonal pathogen. Such genomics-accelerated surveillance of pathogen evolution paves the way for more accurate global disease monitoring.

genomics↗

bioRxiv2022

A chromosome-level, fully phased genome assembly of the oat crown rust fungus Puccinia coronata f. sp. avenae: a resource to enable comparative genomics in the cereal rusts

Advances in sequencing technologies as well as development of algorithms and workflows have made it possible to generate fully phased genome references for organisms with non-haploid genomes such as dikaryotic rust fungi. To enable discovery of pathogen effectors and further our understanding of virulence evolution, we generated a chromosome-scale assembly for each of the two nuclear genomes of the oat crown rust pathogen, Puccinia coronata f. sp. avenae (Pca). This resource complements two previous released partially phased genome references of Pca, which display virulence traits absent in the isolate of historic race 203 (isolate Pca203) which was selected for this genome project. A fully phased, chromosome-level reference for Pca203 was generated using PacBio reads and Hi-C data and a recently developed pipeline named NuclearPhaser for phase assignment of contigs and phase switch correction. With 18 chromosomes in each haplotype and a total size of 208.10 Mbp, Pca203 has the same number of chromosomes as other cereal rust fungi such as Puccinia graminis f. sp. tritici and Puccinia triticina, the causal agents of wheat stem rust and wheat leaf rust, respectively. The Pca203 reference marks the third fully-phased chromosome-level assembly of a cereal rust to date. Here, we demonstrate that the chromosomes of these three Puccinia species are syntenous and that chromosomal size variations are primarily due to differences in repeat element content.

genomics↗

bioRxiv2021

Identification and correction of phase switches with Hi-C data in the Nanopore and HiFi chromosome-scale assemblies of the dikaryotic leaf rust fungus Puccinia triticina

BackgroundMost animals and plants have more than one set of chromosomes and package these haplotypes into a single nucleus within each cell. In contrast, many fungal species carry multiple haploid nuclei per cell. Rust fungi are such species with two nuclei (karyons) that contain a full set of haploid chromosomes each. The physical separation of haplotypes in dikaryons means that, unlike in diploids, Hi-C chromatin contacts between haplotypes are false positive signals. ResultsWe generate the first chromosome-scale, fully-phased assembly for the dikaryotic leaf rust fungus Puccinia triticina and compare Nanopore MinION and PacBio HiFi sequence-based assemblies. We show that false positive Hi-C contacts between haplotypes are predominantly caused by phase switches rather than by collapsed regions or Hi-C read mis-mappings. We introduce a method for phasing of dikaryotic genomes into the two haplotypes using Hi-C contact graphs, including a phase switch correction step. In the HiFi assembly, relatively few phase switches occur, and these are predominantly located at haplotig boundaries and can be readily corrected. In contrast, phase switches are widespread throughout the Nanopore assembly. We show that haploid genome read coverage of 30-40 times using HiFi sequencing is required for phasing of the leaf rust genome (~0.7% heterozygosity) and that HiFi sequencing resolves genomic regions with low heterozygosity that are otherwise collapsed in the Nanopore assembly. ConclusionsThis first Hi-C based phasing pipeline for dikaryons and comparison of long-read sequencing technologies will inform future genome assembly and haplotype phasing projects in other non-haploid organisms.

genomics↗