Dataset Information

An Efficient Tetraplex Surveillance Tool for Salmonid Pathogens.

ABSTRACT: Fish disease surveillance methods can be complicated and time consuming, which limits their value for timely intervention strategies on aquaculture farms. Novel molecular-based assays using droplet digital Polymerase Chain Reaction (ddPCR) can produce immediate results and enable high sample throughput with the ability to multiplex several targets using different fluorescent dyes. A ddPCR tetraplex assay was developed for priority salmon diseases for farmers in New Zealand including New Zealand Rickettsia-like organism 1 (NZ-RLO1), NZ-RLO2, Tenacibaculum maritimum, and Yersinia ruckeri. The limit of detection in singleplex and tetraplex assays was reached for most targets at 10^-9 ng/μl with, respectively, NZ-RLO1 = 0.931 and 0.14 copies/μl, NZ-RLO2 = 0.162 and 0.21 copies/μl, T. maritimum = 0.345 and 0.93 copies/μl, while the limit of detection for Y. ruckeri was 10^-8 with 1.0 copies/μl and 0.7 copies/μl. While specificity of primers was demonstrated in previous studies, we detected cross-reactivity of T. maritimum with some strains of Tenacibaculum dicentrarchi and Y. ruckeri with Serratia liquefaciens, respectively. The tetraplex assay was applied as part of a commercial fish disease surveillance program in New Zealand for 1 year to demonstrate the applicability of tetraplex tools for the salmonid aquaculture industry.

SUBMITTER: von Ammon U

PROVIDER: S-EPMC9069008 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An Efficient Tetraplex Surveillance Tool for Salmonid Pathogens.

von Ammon Ulla U Averink Tessa T Kumanan Karthiga K Brosnahan Cara L CL Pochon Xavier X Hutson Kate S KS Symonds Jane E JE

Frontiers in microbiology 20220421

Fish disease surveillance methods can be complicated and time consuming, which limits their value for timely intervention strategies on aquaculture farms. Novel molecular-based assays using droplet digital Polymerase Chain Reaction (ddPCR) can produce immediate results and enable high sample throughput with the ability to multiplex several targets using different fluorescent dyes. A ddPCR tetraplex assay was developed for priority salmon diseases for farmers in New Zealand including New Zealand ...[more]

PMID: 35531301

Similar Datasets

Project description:Environmental DNA (eDNA) metabarcoding is a relatively new monitoring tool featuring in an increasing number of applications such as the facilitation of the accurate and cost effective detection of species in environmental samples. eDNA monitoring is likely to have a major impact on the ability of salmonid aquaculture industry producers and their regulators to detect the presence and abundance of pathogens and other biological threats in the surrounding environment. However, for eDNA metabarcoding to develop into a useful bio-monitoring tool it is necessary to (a) validate that sequence datasets derived from amplification of metabarcoding markers reflect the true species' identity, (b) test the sensitivity under different abundance levels and environmental noise and (c) establish a low-cost sequencing method to enable the bulk processing of field samples. In this study, we employed an elaborate experimental design whereby different combinations of five biological agents were crossed at three abundance levels and exposed to sterile pre-filtered and unfiltered seawater, prior to coarse filtering and then eDNA ultrafiltration of the resultant material. We then benchmarked the low-cost, scalable, Ion Torrent sequencing method against the current gold-standard Illumina platform for eDNA surveys in aquaculture. Based on amplicon-seq of the 18S SSU rDNA v9 region, we were able to identify two parasites (Lepeophtheirus salmonis and Paramoeba perurans) to species level, whereas the microalgae species Prymnesium parvum, Pseudo-nitzschia seriata, and P. delicatissima could be assigned correctly only to the genus level. Illumina and Ion Torrent provided near identical results in terms of community composition in our samples, whereas Ion Torrent was more sensitive in detecting species richness when the medium was unfiltered seawater. Both methods were able to reflect the difference in relative abundance between treatments in 4 out of 5 species when samples were exposed to the unfiltered seawater, despite the significant amount of background noise from both bacteria and eukaryotes. Our findings indicate that eDNA metabarcoding offers significant potential in the monitoring of species harmful to aquaculture and for this purpose, the low-cost Ion Torrent sequencing is as accurate as Illumina in determining differences in their relative abundance between samples.

Project description:Smart surveillance is a difficult task that is gaining popularity due to its direct link to human safety. Today, many indoor and outdoor surveillance systems are in use at public places and smart cities. Because these systems are expensive to deploy, these are out of reach for the vast majority of the public and private sectors. Due to the lack of a precise definition of an anomaly, automated surveillance is a challenging task, especially when large amounts of data, such as 24/7 CCTV footage, must be processed. When implementing such systems in real-time environments, the high computational resource requirements for automated surveillance becomes a major bottleneck. Another challenge is to recognize anomalies accurately as achieving high accuracy while reducing computational cost is more challenging. To address these challenge, this research is based on the developing a system that is both efficient and cost effective. Although 3D convolutional neural networks have proven to be accurate, they are prohibitively expensive for practical use, particularly in real-time surveillance. In this article, we present two contributions: a resource-efficient framework for anomaly recognition problems and two-class and multi-class anomaly recognition on spatially augmented surveillance videos. This research aims to address the problem of computation overhead while maintaining recognition accuracy. The proposed Temporal based Anomaly Recognizer (TAR) framework combines a partial shift strategy with a 2D convolutional architecture-based model, namely MobileNetV2. Extensive experiments were carried out to evaluate the model’s performance on the UCF Crime dataset, with MobileNetV2 as the baseline architecture; it achieved an accuracy of 88% which is 2.47% increased performance than available state-of-the-art. The proposed framework achieves 52.7% accuracy for multiclass anomaly recognition on the UCF Crime2Local dataset. The proposed model has been tested in real-time camera stream settings and can handle six streams simultaneously without the need for additional resources.

Dataset Information

An Efficient Tetraplex Surveillance Tool for Salmonid Pathogens.

Publications

An Efficient Tetraplex Surveillance Tool for Salmonid Pathogens.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets