Biology subjects

Jinks, E.

Publications and source records attributed to Jinks, E..

4 recordsLinked to original sources

bioRxiv2022

A cell-based, spike protein binding assay highlights differences in antibody neutralising capacity for SARS-CoV-2 variants

The engagement of the SARS-CoV-2 spike protein with ACE2 is a critical step for viral entry to human cells and accordingly blocking this interaction is a major determinant of the efficacy of monoclonal antibody therapeutics and vaccine-elicited serum antibodies. The emergence of SARS-CoV-2 variants necessitates the development of adaptable assays that can be applied to assess the effectiveness of therapeutics. Through testing of a range of recombinant spike proteins, we have developed a cell based, ACE2/spike protein binding assay that characterises monoclonal anti-spike protein antibodies and neutralising antibodies in donor serum. The assay uses high-content imaging to quantify cell bound spike protein fluorescence. Using spike proteins from the original Wuhan SARS-CoV-2 virus, as well as the delta and omicron variants, we identify differential blocking activity of three monoclonal antibodies directed against the spike receptor binding domain. Importantly, biological activity in the spike binding assay translated to efficacy in a SARS-CoV-2 infection assay. Hence, the spike binding assay has utility to monitor anti-spike antibodies against the major known SARS-CoV-2 variants and is readily adaptable to quantify impact of antibodies against new and emerging SARS-CoV-2 variants.

cell biology↗

bioRxiv2021

Improved accuracy for automated counting of a fish in baited underwater videos for stock assessment

The ongoing need to sustainably manage fishery resources necessitates fishery-independent monitoring of the status of fish stocks. Camera systems, particularly baited remote underwater video stations (BRUVS), are a widely-used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g. 1 hr). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often occlude other fish. We build upon existing deep learning methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: 1) varying confidence thresholds (CTs), 2) on/off use of sequential non-maximum suppression (Seq-NMS), and 3) statistical correction equations. Output from the deep learning model was accurate at very low abundances of snapper; at higher abundances, however, the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1 to 2 of manual counts (R2 = 93.4%), used Seq-NMS, a 55% confidence threshold, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in BRUV footage. Further testing is required to ensure that automated counts of snapper remain accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here would help to increase the usefulness of BRUVS, while decreasing costs.

ecology↗

bioRxiv2020

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

Environmental monitoring guides conservation, and is thus particularly important for coastal aquatic habitats, which are heavily impacted by human activities. Underwater cameras and unmanned devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments. Here, we produce three deep learning models using an object detection framework to detect an ecologically important fish, luderick (Girella tricuspidata). Two were trained on footage from single habitats (seagrass or reef), and one on footage from both habitats. All models were subjected to tests from both habitat types. Models performed well on test data from the same habitat type (object detection measure: mAP50: 91.7 and 86.9% performance for seagrass and reef, respectively), but poorly on test sets from a different habitat type (73.3 and 58.4%, respectively). The model trained on a combination of both habitats produced the highest object detection results for both tests (92.4 and 87.8%, respectively). Performance in terms of the ability for models to correctly estimate the ecological metric, MaxN, showed similar patterns. The findings demonstrate that deep learning models extract ecologically useful information from video footage accurately and consistently, and can perform across habitat types when trained on footage from the variety of habitat types.

ecology↗

bioRxiv2019

Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

Aquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as cameras and unmanned underwater devices have allowed footage to be captured efficiently and safely. It has, however, led to immense volumes of data being collected that require manual processing, and thus significant time, labour and money. The use of deep learning to automate image processing has substantial benefits, but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomised 80:20 ratios of training:validation data-sets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%), and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computers performance in determining MaxN was 7.1% better than human marine experts, and 13.4% better than citizen scientists in single image test data-sets, and 1.5% and 7.8% higher in video data-sets, respectively. We show that deep learning is a more accurate tool than humans at determining abundance, and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance. Deep learning techniques have much to offer the field of aquatic ecology.

ecology↗