ORCID Profile
0000-0001-6403-3512
Current Organisation
Australian Institute of Marine Science
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: MDPI AG
Date: 30-06-2011
DOI: 10.3390/S110706842
Publisher: Springer Science and Business Media LLC
Date: 06-1990
DOI: 10.1038/345767A0
Publisher: MDPI AG
Date: 24-02-2016
DOI: 10.3390/JMSE4010017
Publisher: Frontiers Media SA
Date: 12-07-2019
Publisher: Royal Society of Chemistry (RSC)
Date: 2014
DOI: 10.1039/C3EM00675A
Abstract: In response to coral bleaching in the Torres Strait in 2009–10 an ocean monitoring program was established. This included a bleaching early warning system that uses real time data, climatologies and Bayesian models to deliver risk indicators linked to management outcomes.
Publisher: Future Medicine Ltd
Date: 05-2015
DOI: 10.2217/RME.15.2
Abstract: Aim: Peripheral blood-derived endothelial cells (pBD-ECs) are an attractive tool for cell therapies and tissue engineering, but have been limited by their low isolation yield. We increase pBD-EC yield via administration of the chemokine receptor type 4 antagonist AMD3100, as well as via a diluted whole blood incubation (DWBI). Materials & Methods: Porcine pBD-ECs were isolated using AMD3100 and DWBI and tested for EC markers, acetylated LDL uptake, growth kinetics, metabolic activity, flow-mediated nitric oxide production and seeded onto titanium tubes implanted into vessels of pigs. Results: DWBI increased the yield of porcine pBD-ECs 6.6-fold, and AMD3100 increased the yield 4.5-fold. AMD3100-mobilized ECs were phenotypically indistinguishable from nonmobilized ECs. In porcine implants, the cells expressed endothelial nitric oxide synthase, reduced thrombin-antithrombin complex systemically and prevented thrombosis. Conclusion: Administration of AMD3100 and the DWBI method both increase pBD-EC yield.
Publisher: Wiley
Date: 25-06-2016
DOI: 10.1002/LOM3.10119
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 05-2010
Publisher: IEEE
Date: 09-2011
Publisher: Informa UK Limited
Date: 02-01-2017
Publisher: American Meteorological Society
Date: 04-2022
DOI: 10.1175/JTECH-D-21-0095.1
Abstract: The study addresses a network of remote weather stations on the Great Barrier Reef (GBR) that house Licor192 quantum sensors measuring photosynthetically active radiation (PAR) above water. There is evidence of significant degradation in the signal from the sensors after a 2-yr deployment. Main sources of uncertainty in the calibration are outlined, which include degradation of the photodiode, soiling of the sensors by dust and salt spray, cosine responses, and sensitivity to air temperature. Raw PAR data are improved using correction factors based on a cloudless PAR model. Uncertainties in cosine responses of the instrument are low but significant errors may occur if the supporting platform is misaligned and not horizontal. A set of recommendations are provided to improve the quality of the PAR data. A method is described to correct historical PAR data collected on the Great Barrier Reef, such that these valuable observations may be improved and used effectively.
Publisher: IEEE
Date: 05-10-2020
Publisher: IEEE
Date: 05-2010
Publisher: Springer Science and Business Media LLC
Date: 30-12-2012
Publisher: IEEE
Date: 05-2010
Publisher: IEEE
Date: 04-2014
Publisher: IEEE
Date: 04-2014
Publisher: American Geophysical Union (AGU)
Date: 12-10-2010
DOI: 10.1029/2010EO410001
Publisher: Frontiers Media SA
Date: 28-02-2019
Publisher: Frontiers Media SA
Date: 02-08-2022
DOI: 10.3389/FMARS.2022.944582
Abstract: Machine-assisted object detection and classification of fish species from Baited Remote Underwater Video Station (BRUVS) surveys using deep learning algorithms presents an opportunity for optimising analysis time and rapid reporting of marine ecosystem statuses. Training object detection algorithms for BRUVS analysis presents significant challenges: the model requires training datasets with bounding boxes already applied identifying the location of all fish in iduals in a scene, and it requires training datasets identifying species with labels. In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. Here, we present a “machine-assisted” approach for i) a generalised model to automate the application of bounding boxes to any underwater environment containing fish and ii) fish detection and classification to species identification level, up to 12 target species. A catch-all “ fish ” classification is applied to fish in iduals that remain unidentified due to a lack of available training and validation data. Machine-assisted bounding box annotation was shown to detect and label fish on out-of-s le datasets with a recall between 0.70 and 0.89 and automated labelling of 12 targeted species with an F 1 score of 0.79. On average, 12% of fish were given a bounding box with species labels and 88% of fish were located and given a fish label and identified for manual labelling. Taking a combined, machine-assisted approach presents a significant advancement towards the applied use of deep learning for fish species detection in fish analysis and workflows and has potential for future fish ecologist uptake if integrated into video analysis software. Manual labelling and classification effort is still required, and a community effort to address the limitation presented by a severe paucity of training data would improve automation accuracy and encourage increased uptake.
No related grants have been discovered for Scott Bainbridge.