Biology subjects

Nicholls, A.

Publications and source records attributed to Nicholls, A..

5 recordsLinked to original sources

bioRxiv2026

Active eye modeling: a tool for estimating retinal image quality across the visual field in everyday life

Purpose: The "visual diet," the totality of visual experience, shapes retinal image formation and thereby refractive development, but measuring retinal image statistics during everyday behavior is challenging. Limits in mobile eye tracking, egocentric video, and full-field optical measurement leave gaps in our understanding of how environmental and behavioral factors shape image quality across the retina. Methods: We introduce active eye modeling, a computational platform combining optically validated, wide-field, accommodating eye models (Hastings et al. 2024) with realistic three-dimensional virtual environments and gaze behavior derived from human data. This approach enables systematic estimation of retinal blur across the visual field under parametrically controlled conditions, including varying viewing distances, ambient light levels, and environmental depth structure. Inputs may be simulated and/or drawn from real-world measurements: scene depth, video, and gaze/pupil recordings. Results: During simulated far work, peripheral image quality is depth-tolerant, owing to wide depth-of-field from off-axis aberrations. Near work under dim indoor lighting substantially increases peripheral blur, reducing contrast at mid and high spatial frequencies. This contrast reduction is comparable in magnitude to a moderate diffusing filter (Bangerter 0.4 strength) that affects eye growth when imposed experimentally in animal models. A consistency check with eye-tracked virtual driving confirms peripheral depth-tolerance during far work. Conclusions: Active eye modeling provides a flexible framework for investigating how visual experience shapes the retinal image, relevant to refractive development, display and lens design, and peripheral vision. Applied to common visual behaviors, the platform identifies optical conditions during indoor near work that warrant empirical investigation, complementing measurement-based approaches.

neuroscience↗

bioRxiv2025

Analysis of human visual experience data

Exposure to the optical environment -- often referred to as visual experience -- profoundly influences human physiology and behavior across multiple time scales. In controlled laboratory settings, stimuli can be held constant or manipulated parametrically. However, such exposures rarely replicate real-world conditions, which are inherently complex and dynamic, generating high-dimensional datasets that demand rigorous and flexible analysis strategies. This tutorial presents an analysis pipeline for visual experience datasets, with a focus on reproducible workflows for human chronobiology and myopia research. Light exposure and its retinal encoding affect human physiology and behavior across multiple time scales. Here we provide step-by-step instructions for importing, visualizing, and processing viewing distance and light exposure data. This includes time-series analyses for working distance, biologically relevant light metrics, and spectral characteristics. The tasks are standardized through the open-source R package LightLogR. By leveraging a modular approach, the tutorial supports researchers in building flexible and robust pipelines that accommodate diverse experimental paradigms and measurement systems.

neuroscience↗

bioRxiv2025

Predicting Personalised Therapeutic Combinations in Non-Small Cell Lung Cancer Using In Silico Modelling

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with around a million new cases diagnosed globally each year, and a 5-year survival rate of less than 20%. A lack of therapeutic options personalized to individual patient genetics, and the targeted therapies that exist quickly succumbing to resistance, leads to high variation in survival. Patient stratification combined with greater personalisation of therapies have the potential to improve outcomes, however, the wide variation in mutations found in NSCLC adenocarcinoma patients mean that experimentally determining suitable treatment combinations is time-consuming and expensive. Here we present an in silico model encompassing tumour intrinsic key oncogenic signalling pathways, including EGFR, AKT, JAK/STAT and WNT for efficiently predicting rational drug-drug and drug-radiotherapy combination therapies in NSCLC. Using this model, we simulate diverse genetic profiles and test over 10,000 therapeutic combinations to identify optimal strategies to overcome resistance mechanisms specific to genetic profiles and p53 status. Our in silico model reproduces drug additivity experiments, predicts radio-sensitising genes validated in a CRISPR screen and identifies 53BP1 as a potential drug target that improves the therapeutic window during radiotherapy, as well as potential to use ATM inhibitors to overcome p53 loss-of-function driven radiotherapy resistance. We further use the in silico model to identify a 19-gene signature to stratify patients most likely to benefit from radiotherapy and validated this using TCGA data. These results further demonstrate the utility of in silico mechanistic modelling and present a bespoke computational resource for large-scale screening of personalised therapies applied to NSCLC.

cancer biology↗

bioRxiv2024

The Visual Experience Evaluation Tool: A Myopia Research Instrument for Quantifying Visual Experience

Current myopia research has demonstrated the role of extended visual experience in healthy ocular development. Optical cues and the spectrum, intensity, and temporal characteristics of light landing on the retina are all known factors affecting the development of the eye. However, there is still limited understanding as to which of these extrinsic factors are most important or how they interplay with intrinsic physical and neural differences between individuals. Part of the problem is inadequate tooling. Our team at Reality Labs Research created the Visual Environment Evaluation Tool (VEET), a non-commercial research instrument, to accelerate myopia research. In this paper, we describe the VEETs physical design, sensor suite and capabilities, and the associated software which makes it well-suited for research of quantified visual experience.

neuroscience↗

bioRxiv2022

Creating a 'Timeline' of ductal carcinoma in situ to identify processes and biomarkers for progression towards invasive ductal carcinoma

Ductal carcinoma in situ (DCIS) is considered a non-invasive precursor to breast cancer, and although associated with an increased risk of developing invasive disease, many women with DCIS will never progress beyond their in situ diagnosis. The path from normal duct to invasive disease is not well understood, and efforts to do so are hampered by the substantial heterogeneity that exists between patients and even within patients. Using gene expression analysis, we have generated a Timeline of disease progression, utilising the variability within patients and combining >2,000 individually micro-dissected ductal lesions from 145 patients into one continuous trajectory. Using this Timeline we show there is a progressive loss in basal layer integrity, coupled with two epithelial to mesenchymal transitions (EMT), one early in the timeline and a second just prior to cells leaving the duct. We identify early processes and potential biomarkers, including CAMK2N1, MNX1, ADCY5, HOXC11 and ANKRD22, whose reduced expression is associated with the progression of DCIS to invasive breast cancer.

cancer biology↗