Prevalence of epitheliocystis in freshwater Atlantic salmon reared in flow-through and recirculation aquaculture systems.

Epitheliocystis, an intracellular bacterial infection in the gills and skin epithelium, has been frequently reported in Atlantic salmon (Salmo salar) during freshwater production in a number of countries. This study describes the prevalence and intensity of a natural epitheliocystis infection present in the gills of two strains of Atlantic salmon reared in either a flow-through (FT) or a recirculation aquaculture system (RAS) in Ireland. Repeated sampling of gills prior to and throughout seawater transfer, histology and quantitative real-time PCR were used to determine infection prevalence and intensity. Despite no clinical gill disease, and minor histopathological changes, epitheliocystis lesions were identified in histology at all time points. Specific PCR confirmed the presence of Candidatus Clavichlamydia salmonicola in both strains and its number of copies was correlated with intensity of epitheliocystis lesions. A significant interaction between hatchery system and fish strain on the prevalence and intensity of gill epitheliocystis was found both using histological and molecular methods. Specifically, fish from FT had higher prevalence and intensity than RAS reared fish and within FT, the Irish cohort were more affected than Icelandic.

Microplastic detection and identification by Nile red staining: Towards a semi-automated, cost- and time-effective technique.

Microplastic pollution is an issue of concern due to the accumulation rates in the marine environment combined with the limited knowledge about their abundance, distribution and associated environmental impacts. However, surveying and monitoring microplastics in the environment can be time consuming and costly. The development of cost- and time-effective methods is imperative to overcome some of the current critical bottlenecks in microplastic detection and identification, and to advance microplastics research. Here, an innovative approach for microplastic analysis is presented that combines the advantages of high-throughput screening with those of automation. The proposed approach used Red Green Blue (RGB) data extracted from photos of Nile red-fluorescently stained microplastics (50-1200 µm) to train and validate a 'Plastic Detection Model' (PDM) and a 'Polymer Identification Model' (PIM). These two supervised machine learning models predicted with high accuracy the plastic or natural origin of particles (95.8%), and the polymer types of the microplastics (88.1%). The applicability of the PDM and the PIM was demonstrated by successfully using the models to detect (92.7%) and identify (80%) plastic particles in spiked environmental samples that underwent laboratorial processing. The classification models represent a semi-automated, high-throughput and reproducible method to characterize microplastics in a straightforward, cost- and time-effective yet reliable way.