13 research outputs found
A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species
We thank the countless individuals who collected and/or processed the nearly 85,000 images used in this study and those who assisted, particularly those who sorted these images from the millions that did not end up in the catalogues. Additionally, we thank the other Kaggle competitors who helped develop the ideas, models and data used here, particularly those who released their datasets to the public. The graduate assistantship for Philip T. Patton was funded by the NOAA Fisheries QUEST Fellowship. This paper represents HIMB and SOEST contribution numbers 1932 and 11679, respectively. The technical support and advanced computing resources from University of Hawaii Information Technology Services—Cyberinfrastructure, funded in part by the National Science Foundation CC* awards # 2201428 and # 2232862 are gratefully acknowledged. Every photo–identification image was collected under permits according to relevant national guidelines, regulation and legislation.Peer reviewedPublisher PD
Ultrasensitive detection of norovirus using a magnetofluoroimmunoassay based on synergic properties of gold/magnetic nanoparticle hybrid nanocomposites and quantum dots
Enhanced colorimetric detection of norovirus using in-situ growth of Ag shell on Au NPs
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Dual modality sensor using liposome-based signal amplification technique for ultrasensitive norovirus detection
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Single-step detection of norovirus tuning localized surface plasmon resonance-induced optical signal between gold nanoparticles and quantum dots
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Dual modality sensor using liposome-based signal amplification technique for ultrasensitive norovirus detection
Controlling distance, size and concentration of nanoconjugates for optimized LSPR based biosensors
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