Global prevalence of Plasmodium infection in wild birds: A systematic review and meta-analysis.

Avian malaria is a vector-borne parasitic disease caused by Plasmodium infection transmitted to birds by mosquitoes. The aim of this systematic review was to analyze the global prevalence of malaria and risk factors associated with infection in wild birds. A systematic search of the databases CNKI, WanFang, VIP, PubMed, and ScienceDirect was performed from database inception to 24 February 2023. The search identified 3181 retrieved articles, of which 52 articles met predetermined inclusion criteria. Meta-analysis was performed using the random-effects model. The estimated pooled global prevalence of Plasmodium infection in wild birds was 16%. Sub-group analysis showed that the highest prevalence was associated with adult birds, migrant birds, North America, tropical rainforest climate, birds captured by mist nets, detection of infection by microscopy, medium quality studies, and studies published after 2016. Our study highlights the need for more understanding of Plasmodium prevalence in wild birds and identifying risk factors associated with infection to inform future infection control measures.

Construction, implementation and testing of an image identification system using computer vision methods for fruit flies with economic importance (Diptera: Tephritidae).

BACKGROUND: Many species of Tephritidae are damaging to fruit, which might negatively impact international fruit trade. Automatic or semi-automatic identification of fruit flies are greatly needed for diagnosing causes of damage and quarantine protocols for economically relevant insects. RESULTS: A fruit fly image identification system named AFIS1.0 has been developed using 74 species belonging to six genera, which include the majority of pests in the Tephritidae. The system combines automated image identification and manual verification, balancing operability and accuracy. AFIS1.0 integrates image analysis and expert system into a content-based image retrieval framework. In the the automatic identification module, AFIS1.0 gives candidate identification results. Afterwards users can do manual selection based on comparing unidentified images with a subset of images corresponding to the automatic identification result. The system uses Gabor surface features in automated identification and yielded an overall classification success rate of 87% to the species level by Independent Multi-part Image Automatic Identification Test. CONCLUSION: The system is useful for users with or without specific expertise on Tephritidae in the task of rapid and effective identification of fruit flies. It makes the application of computer vision technology to fruit fly recognition much closer to production level. © 2016 Society of Chemical Industry.