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6 results for “host monitoring”
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Seasonal monitoring of Drosophila suzukii and its non-crop hosts in Wuhu, Eastern China
<p><i>Drosophila</i><i> suzukii</i> (Matsumura) (Diptera: Drosophilidae) is a serious pest that prefers fresh fruits native to Southeast Asia. In our study apple cider vinegar bait traps were used to capture and monitor the population dynamics of this native pest in Wuhu City, China, from May/June, 2017 to May, 2018. The research was conducted at 15 locations in two fruit orchards in Wuhu. Traps caught more adults in general in Meiling blueberry orchard than in Xicun mixed orchard, and the highest trap counts occurred near harvest time (October). Females had more mature eggs from September to November and the number of mature eggs declined thereafter. We found several non-crop hosts of this pest, which can provide food and reproductive resources for <i>D. suzukii</i>, are common in forests and field margins. By comparing the number of captured adults in the Meiling and Xicun orchards, we found that blueberry was preferred by <i>D. suzukii </i>among the fruits in our search. Fruit ripening time differed among crops and therefore fly populations moved between crop and non-crop habitats during the year or had varying population dynamics on different crops in different seasons. The <i>D. suzukii</i> population and the number of mature eggs decreased in summer and winter but increased in spring and autumn. <i>Drosophila suzukii</i> had higher survival rates on blueberry than other fruits and <i>D. suzukii </i>could use four non-crop species growing around the orchards as host plants.</p>
Data from: Mitochondrial DNA variants help monitor the dynamics of Wolbachia invasion into host populations
Open the record for dataset details and reuse information.
Seasonal monitoring of Drosophila suzukii and its non-crop hosts in Wuhu, Eastern China
Open the record for dataset details and reuse information.
Monitoring systemic immune responses to checkpoint inhibition in breast cancer reveals host responses and mechanisms of resistance
GEO Series GSE293819. Mus musculus. 142 samples. Type: Expression profiling by high throughput sequencing.
Monitoring ORF10's Impact on Host Transcriptome Using Doxycycline-Inducible A549 and 293T Cell Lines.
GEO Series GSE281252. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
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