Biology subjects

Webb, L.

Publications and source records attributed to Webb, L..

6 recordsLinked to original sources

bioRxiv2026

Inferring population-level physiologically based model parameters for sleep in infancy and young childhood

The sleep patterns of infants and young children differ from adult sleep patterns, with longer duration and multiple bouts per 24 hours. There is also considerable heterogeneity in infant sleep, both between individuals and within individuals across development. While mathematical models have been used to understand the mechanisms that regulate adult sleep, the development of sleep from infancy through early childhood has remained largely unexplored. Here we used an established mathematical model for adult sleep regulation to investigate how the underlying mechanisms of sleep mature from ages 1 month to 5 years, and identify a basis for inter-individual differences at each age. Using a Bayesian approach to estimate joint distributions of model parameters at different ages, we found that: (i) decreases in the rate of accumulation of sleep homeostatic pressure captured the reduction in sleep duration across development; while (ii) increases in the time scale of the sleep homeostatic drive captured the consolidation of sleep into fewer sleep bouts per day across development. In terms of inter-individual differences, we found larger spread in the parameters within the earlier stages of infancy (<1 year). The center and boundaries of the joint distributions evolved with age through the parameter space, converging towards previously described adult parameter values. A bifurcation analysis of the homeostatic time scale parameter revealed that progressive consolidation of sleep occurs through abrupt loss of bouts one at a time, punctuated by narrow intervals of non-entrained (>24-h) cycles. These results establish plausible, population-level trajectories in physiological parameters underpinning maturation of sleep regulation through infancy and early childhood.

neuroscience↗

bioRxiv2023

Mapping the physiological changes in sleep regulation across infancy and young childhood

Sleep patterns in infancy and early childhood vary greatly and change rapidly during development. In adults, sleep patterns are regulated by interactions between neuronal populations in the brainstem and hypothalamus, driven by the circadian and sleep homeostatic processes. However, the neurophysiological mechanisms underlying the sleep patterns and their variations across infancy and early childhood are poorly understood. We investigated whether a well-established mathematical model for sleep regulation in adults can model infant sleep characteristics and explain the physiological basis for developmental changes. By fitting longitudinal sleep data spanning 2 to 540 days after birth, we inferred parameter trajectories across age. We found that the developmental changes in sleep patterns are consistent with a faster accumulation and faster clearance of sleep homeostatic pressure in infancy and a weaker circadian rhythm in early infancy. We also find greater sensitivity to phase-delaying effects of light in infancy and early childhood. These findings reveal fundamental mechanisms that regulate sleep in infancy and early childhood. Given the critical role of sleep in healthy neurodevelopment, this framework could be used to pinpoint pathophysiological mechanisms and identify ways to improve sleep quality in early life.

neuroscience↗

bioRxiv2023

Bacterial mechanosensing of surface stiffness promotes signaling and growth leading to biofilm formation by Pseudomonas aeruginosa

The attachment of bacteria onto a surface, consequent signaling, and the accumulation and growth of the surface-bound bacterial population are key initial steps in the formation of pathogenic biofilms. While recent reports have hinted that the stiffness of a surface may affect the accumulation of bacteria on that surface, the processes that underlie bacterial perception of and response to surface stiffness are unknown. Furthermore, whether, and how, the surface stiffness impacts biofilm development, after initial accumulation, is not known. We use thin and thick hydrogels to create stiff and soft composite materials, respectively, with the same surface chemistry. Using quantitative microscopy, we find that the accumulation, motility, and growth of the opportunistic human pathogen Pseudomonas aeruginosa respond to surface stiffness, and that these are linked through cyclic-di-GMP signaling that depends on surface stiffness. The mechanical cue stemming from surface stiffness is elucidated using finite-element modeling combined with experiments - adhesion to stiffer surfaces results in greater changes in mechanical stress and strain in the bacterial envelope than does adhesion to softer surfaces with identical surface chemistry. The cell-surface-exposed protein PilY1 acts as a mechanosensor, that upon surface engagement, results in higher cyclic-di-GMP levels, lower motility, and greater accumulation on stiffer surfaces. PilY1 impacts the biofilm lag phase, which is extended for bacteria attaching to stiffer surfaces. This study shows clear evidence that bacteria actively respond to different stiffness of surfaces where they adhere via perceiving varied mechanical stress and strain upon surface engagement. ImportanceBacteria colonize many types of biological and medical surfaces with a large range of stiffnesses. Colonization leads to the formation of biofilms, which cause costly and life-impairing chronic infections. However, whether and how bacteria can sense and respond to the mechanical cue provided by surface stiffness has remained unknown. We find that bacteria do indeed respond to surface stiffness in a way that is both consistent with expectations based on equilibrium continuum mechanics and that quantitatively impacts multiple aspects of early biofilm formation. This is a new understanding for the nascent field of bacterial mechanobiology. Furthermore, this finding suggests the possibility of a new category of approaches to hindering biofilm development by tuning the mechanical properties of biomedical surfaces.

microbiology↗

bioRxiv2021

Tonic IKK signalling regulates naive T cell survival in vivo by both repressing RIPK1 dependent extrinsic cell death pathways and independent activation of NF-κB

The Inhibitor of Kappa B Kinase (IKK) complex is a critical regulator of NF-{kappa}B activation. In addition, IKK has recently been shown to repress RIPK1 dependent extrinsic cell death pathways by directly phosphorylating RIPK1. Our previous work shows that normal thymopoiesis relies on IKK exclusively for repression of TNF triggered cell death pathways, and that NF-{kappa}B activation by IKK is redundant for development. The role of these pathways in mature naive T cells has not previously been reported. Here, we show that, like thymocytes, naive peripheral T cells require continued IKK1/2 expression for survival. In contrast, however, cell loss is only partially prevented by blocking extrinsic cell death pathways by either deleting Casp8 or inhibiting RIPK1 kinase activity. Inducible deletion of Rela in mature CD4+ T cells also results in a significant loss of naive CD4+ T cells and loss of IL7R expression, revealing an additional reliance upon NF-{kappa}B for long term survival of mature T cells. Together, these data show that IKK dependent survival of naive T cells depends upon both repression of extrinsic cell death pathways and activation of NF-{kappa}B survival programme. One sentence summaryIKK regulates naive T cell survival

immunology↗

bioRxiv2021

Automated detection of artefacts in neonatal EEG with residual neural networks

Background and ObjectiveTo develop a computational algorithm that detects and identifies different artefact types in neonatal electroencephalography (EEG) signals. MethodsAs part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Performance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm. ResultsThe Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact. ConclusionArtefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms. HighlightsO_LIWe applied a Residual Deep Neural Network as part of an artefact detection algorithm in neonatal electroencephalograms. C_LIO_LIThe algorithm shows high accuracy in identifying artefactual data in general and for specific artefact types. C_LIO_LIEEG channels near the top of the head are less prone to artefact. C_LI

neuroscience↗

bioRxiv2020

Th2-like T-follicular helper cells promote functional antibody production during Plasmodium falciparum infection

The most advanced malaria vaccine only has approximately 30% efficacy in target populations, and avenues to improve next generation vaccines need to be identified. Functional antibodies are key effectors of both vaccine induced and naturally acquired immunity, with induction driven by T-follicular helper cells (TfH) CD4+ T cells. We assessed circulating TfH (cTfH) responses and functional antibody production in human volunteers experimentally infected with Plasmodium falciparum. Longitudinal single-cell RNA-sequencing of cTfH revealed peak transcriptional activation and clonal expansion of major cTfH subsets occurred at day 8 following infection and a population structure of cTfH capturing phenotypical subsets of Th1- and Th2-like cells. Among 40 volunteers, infection resulted in the emergence of activated ICOS+ cTfH cells. During peak infection, activation was restricted to Th2-like cTfH cells, while Th1-like cTfH cell activation occurred one week after treatment. To link cTfH activation to antibody induction, we assessed the magnitude and function of anti-malarial IgM and IgG after infection. The functional breadth and magnitude of parasite-specific antibodies was positively associated with Th2-cTfH activation. In contrast, Th1-cTfH activation was associated with the induction of plasma cells, which we have previously shown have a detrimental role in germinal cell formation and antibody development. Thus, we identified that during P. falciparum malaria infection in humans, the activation of Th2-cTfH but not other subsets correlates with the development of functional antibodies required for protective immunity. Data for the first time identify a specific cellular response that can be targeted by future malaria vaccines to improve antibody induction.

immunology↗