Biology subjects

Gill, A. B.

Publications and source records attributed to Gill, A. B..

3 recordsLinked to original sources

bioRxiv2023

Do electromagnetic fields from subsea power cables effect elasmobranch behaviour? A risk-based approach for the Dutch Continental Shelf

Subsea power cables cause electromagnetic fields (EMFs) into the marine environment. Elasmobranchs (rays, skates, sharks) are particularly sensitive to EMFs as they use electromagnetic-receptive sensory systems for orientation, navigation and locating conspecifics or buried prey. Cables may intersect with egg laying sites, foraging habitat and migration routes of elasmobranchs and the effects of encountering EMFs on species of elasmobranchs are largely unknown. Demonstrated behavioural effects are attraction, disturbance and indifference, depending on EMF characteristics, exposed life stage, exposure level and duration. We estimated exposure levels of elasmobranchs to subsea cable EMFs, based on modelled magnetic fields in the Dutch Continental Shelf and compared these to reported elasmobranch sensory sensitivity ranges and experimental effect levels. We conclude that the risk from subsea power cables has a large uncertainty and varies per life stage and species ecology. Based on estimated no-observed effect levels (from 10-3 to 10-1 {micro}T) we discuss what will probably be the most affected species and life stage for six common benthic elasmobranchs in the Southern North Sea. We identify critical knowledge gaps for reducing the uncertainty in the risk assessments for EMFs effects on elasmobranchs.

animal behavior and cognition↗

bioRxiv2019

High-sensitivity monitoring of ctDNA by patient-specific sequencing panels and integration of variant reads

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Patients with small tumors have few copies of ctDNA in plasma, resulting in limited sensitivity to detect low-volume or residual disease. We show that sampling limitations can be overcome and sensitivity for ctDNA detection can be improved by massively parallel sequencing when hundreds to thousands of mutations are identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) analysis pipeline, which combines patient-specific mutation lists with both custom error-suppression methods and signal enrichment based on biological features of ctDNA. In this framework, the sensitivity can be estimated independently for each sample based on the number of informative reads, which is the product of the number of mutations analyzed and the average depth of unique sequencing reads. We applied INVAR to deep sequencing data generated by custom hybrid-capture panels, and showed that when ~106 informative reads were obtained INVAR allowed detection of tumor-derived DNA fractions to parts per million (ppm). In serial samples from patients with advanced melanoma on treatment, we detected ctDNA when imaging confirmed tumor volume of ~1cm3. In patients with resected early-stage melanoma, ctDNA was detected in 40% of patients who later relapsed, with higher rates of detection when more informative reads were obtained. We further demonstrated that INVAR can be generalized and allows improved detection of ctDNA from whole-exome and low-depth whole-genome sequencing data.

cancer biology↗

bioRxiv2019

3D-printed moulds of renal tumours for image-guided tissue sampling in the clinical setting

PURPOSESpatial heterogeneity of tumours is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood, as it relies on the accurate co-registration of medical images and tissue biopsies. tumour moulds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumour heterogeneity data. METHODSWe have developed an open source computational framework to automatically produce patient-specific 3D-printed moulds that can be used in the clinical setting. Our approach achieves accurate co-registration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging and pathology workflows. RESULTSWe applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalised moulds for five patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.93 for tumour regions, and between 0.70 and 0.76 for healthy kidney. The framework required minimal manual intervention, producing the final mould design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes. CONCLUSIONOur work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumour heterogeneity on multiple spatial scales.

bioengineering↗