Biology subjects

Turnbull, C. J.

Publications and source records attributed to Turnbull, C. J..

2 recordsLinked to original sources

bioRxiv2025

Polli-markers: spectral and chemical biomarkers for detecting cryptic early plant pollination responses

O_LIPollination is essential for plant reproduction, ecosystem resilience and human health. Yet, our capability to map pollination service delivery in real-time across large areas remains poor. Determining where and when flowers are pollinated is vital to mitigate widespread pollination deficits, increase plant health and yield, and support pollinator management. Hence, innovative approaches are urgently needed for establishing scalable predictive bioindicators of plant pollination status with the goal of achieving real-time landscape-scale monitoring. C_LIO_LIHere we present two parallel controlled pollination assays in which we characterise the post-pollination petal physiology of a world leading flowering crop, Brassica napus, using in-situ close-range hyperspectral reflectance and semi-untargeted metabolomics. C_LIO_LIThis multiomics approach coupled with supervised machine learning and biomarker detection reveals cryptic changes in the UV petal reflectance spectrum which are predictive of pollination status, representing a novel set of candidate pollination bioindicators ( polli-markers), and our high-resolution time series enables prediction of when this pollination event occurred. It also reveals an associated set of candidate metabolites, including flavonoids and senescence markers, shedding light on the functional pathways related to our polli-markers. C_LIO_LIThis study provides key insights into floral development, enabling a transformative step towards predicting, mapping and quantifying pollination service delivery at the landscape scale. C_LI

ecology↗

bioRxiv2025

Improved Sleep Spindle Detection Using the BOSC Method: A Comparison with Traditional Approaches

Sleep spindles are brief bursts of 6-20 Hz local field potential (LFP/EEG) activity that occur during non-rapid eye movement sleep. Traditional spindle detection methods rely on manually set amplitude and duration thresholds, but this approach can be vulnerable to false detections and could miss low-amplitude spindles due to the inflexible nature of the thresholding technique. The Better OSCillation (BOSC) detection method offers a more robust alternative by applying frequency-specific power thresholds calibrated to the signal itself and requiring a minimum number of oscillation cycles. In this study, we compared traditional and BOSC methods for spindle detection in a variety of ways. First, we created two synthetic datasets: one with synthetic spindles modelled on previous data and one with single wave pulses that had a period consistent with the spindle frequency band. These datasets were used to demonstrate each methods performance when given events that should be detected (synthetic spindles) and those that should not (single wave pulses). Second, we analyzed cortical local field potentials from recordings of rats during natural sleep using both methods to determine their relative effectiveness given biological LFP data. BOSC consistently outperformed the traditional approach in all situations, identifying more valid spindles while minimizing false (or likely false) detections. These findings validate BOSC as a superior method of spindle detection due to its better calibration to the actual signal.

neuroscience↗