86 research outputs found

    Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds

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    Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs’ feeding trails, which are unvegetated winding tracks left after feeding, have been used as an indicator of their feeding ground utilization. However, current ground-based measurements of these trails require a large amount of time and effort. Here, we developed effective methods to observe the dugongs’ feeding trails using unmanned aerial vehicle (UAV) images (1) by extracting the dugong feeding trails using deep neural networks. Furthermore, we demonstrated two applications as follows; (2) extraction of the daily new feeding trails with deep neural networks and (3) estimation the direction of the feeding trails. We obtained aerial photographs from the intertidal seagrass bed at Talibong Island, Trang Province, Thailand. The F1 scores, which are a measure of binary classification model’s accuracy taking false positives and false negatives into account, for the method (1) were 89.5% and 87.7% for the images with ground sampling resolutions of 1 cm/pixel and 0.5 cm/pixel, respectively, while the F1 score for the method (2) was 61.9%. The F1 score for the method (1) was high enough to perform scientific studies on the dugong. However, the method (2) should be improved, and there remains a need for manual correction. The mean area of the extracted daily new feeding trails from September 12–27, 2019, was 187.8 m2 per day (n = 9). Total 63.9% of the feeding trails was estimated to have direction within a range of 112.5° and 157.5°. These proposed new methods will reduce the time and efforts required for future feeding trail observations and contribute to future assessments of the dugongs’ seagrass habitat use

    Novel variant fibrinogen γp.C352R produced hypodysfibrinogenemia leading to a bleeding episode and failure of infertility treatment

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    ArticleInternational journal of hematology. 114(3): 325-333. (2021)journal articl

    Efficient increase of ɣ-aminobutyric acid (GABA) content in tomato fruits by targeted mutagenesis

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    γ-Aminobutyric acid (GABA) is a non-proteinogenic amino acid that has hypotensive effects. Tomato (Solanum lycopersicum L.) is among the most widely cultivated and consumed vegetables in the world and contains higher levels of GABA than other major crops. Increasing these levels can further enhance the blood pressure-lowering function of tomato fruit. Glutamate decarboxylase (GAD) is a key enzyme in GABA biosynthesis; it has a C-terminal autoinhibitory domain that regulates enzymatic function, and deleting this domain increases GAD activity. The tomato genome has five GAD genes (SlGAD1–5), of which two (SlGAD2 and SlGAD3) are expressed during tomato fruit development. To increase GABA content in tomato, we deleted the autoinhibitory domain of SlGAD2 and SlGAD3 using clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas)9 technology. Introducing a stop codon immediately before the autoinhibitory domain increased GABA accumulation by 7 to 15 fold while having variable effects on plant and fruit size and yield. This is the first study describing the application of the CRISPR/Cas9 system to increase GABA content in tomato fruits. Our findings provide a basis for the improvement of other types of crop by CRISPR/Cas9-based genetic modification
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