Variation in the Levels of Anisakid Infection in the European Anchovy Engraulis Encrasicolus (linnaeus) from the Bay of Biscay During the Period 2000–2023 (ICES Subarea 8)
Parasitology Research(2024)
AZTI
Abstract
The European anchovy Engraulis encrasicolus is one of the most important commercial species in the Bay of Biscay (ICES Subarea 8), and our analysis focused on the analysis of the temporal mean abundance, prevalence, and intensity of Anisakis spp. larvae species in anchovies from ICES Subarea 8 in the years 2000, 2001, 2014–2016, and 2019–2023. Prevalence in adult individuals of anchovy was only 1% in 2000 but increased to 90% in 2014. Since 2015, the prevalence has decreased, and the number of individuals affected in 2023 accounted for 17.6%. The mean abundance showed a similar trend, with a peak of 3.79 nematodes/anchovy in 2014, falling to 0.21 in 2023. The species A. simplex sensu stricto and A. pegreffii were identified by PCR/SANGER sequencing and PCR/RLFP techniques in 2019 and 2020. Anisakis simplex (s.s.) was the most abundant species and, according to the results returned by these two techniques, it accounted for an average of 62.4% and 52.1% of total nematodes in 2019 and 2020, respectively. The results of studies monitoring infection levels in anchovies showed that the mean abundance and prevalence changed over the course of the study period and that the proportion of different species of Anisakis is also subject to variation from year to year.
MoreTranslated text
Key words
Anisakis,Anchovy,Bay of Biscay,Epidemiology,Genetics
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
PARASITOLOGISTS UNITED JOURNAL 2024
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话