scholarly journals Proliferation activity in the polyps of Cassiopea xamachana: where the planuloid buds grow

2021 ◽  
Vol 66 (4) ◽  
Author(s):  
Valeriia Khabibulina ◽  
Viktor Starunov

Polyps of the Cassiopeidae family possess a unique type of asexual reproduction by producing free-swimming buds — planuloids. The process of planuloid development and transformation to polyp has been described earlier, however, the source of tissue formation is still poorly studied. Using the method of EdU incorporation we have analyzed DNA synthesis activity during planuloid formation and growth in Cassiopea xamachana. We revealed the active proliferation zone at the early stage of bud formation. This zone continued to function during planuloid growth, providing the formation of polyp structures, and preserved in polyp calyx after metamorphosis. Its proliferation activity varied at different growth stages, whereas the localization remained relatively the same.

Author(s):  
Z.H. Guo ◽  
X. Li ◽  
Y.F. Huang ◽  
X. Lan

Background: The avian leukosis virus (ALV-J) is a retrovirus causing irreversible damage and loss of function in tissues and organs in a chicken body, especially in those related to the immune system, thus resulting in considerable economic loss. Methods: We measured the changes in the weights of immune organs, such as the spleen, bursa of Fabricius, thymus, heart and liver and body weight at days 3, 5, 7, 14, 21, 28 and 42 after ALV-J infection and analysed the differences between the corresponding tissues and normal groups at each time. Result: The unique weight change in pspleen tissues indicates that the organ plays an important role in fighting ALV-J infection in the early stage. Moreover, the phenotypic inhibition of ALV-J in the tissues and organs started to appear 28 days after infection.


Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1890
Author(s):  
André Silva Aguiar ◽  
Sandro Augusto Magalhães ◽  
Filipe Neves dos Santos ◽  
Luis Castro ◽  
Tatiana Pinho ◽  
...  

The agricultural sector plays a fundamental role in our society, where it is increasingly important to automate processes, which can generate beneficial impacts in the productivity and quality of products. Perception and computer vision approaches can be fundamental in the implementation of robotics in agriculture. In particular, deep learning can be used for image classification or object detection, endowing machines with the capability to perform operations in the agriculture context. In this work, deep learning was used for the detection of grape bunches in vineyards considering different growth stages: the early stage just after the bloom and the medium stage where the grape bunches present an intermediate development. Two state-of-the-art single-shot multibox models were trained, quantized, and deployed in a low-cost and low-power hardware device, a Tensor Processing Unit. The training input was a novel and publicly available dataset proposed in this work. This dataset contains 1929 images and respective annotations of grape bunches at two different growth stages, captured by different cameras in several illumination conditions. The models were benchmarked and characterized considering the variation of two different parameters: the confidence score and the intersection over union threshold. The results showed that the deployed models could detect grape bunches in images with a medium average precision up to 66.96%. Since this approach uses low resources, a low-cost and low-power hardware device that requires simplified models with 8 bit quantization, the obtained performance was satisfactory. Experiments also demonstrated that the models performed better in identifying grape bunches at the medium growth stage, in comparison with grape bunches present in the vineyard after the bloom, since the second class represents smaller grape bunches, with a color and texture more similar to the surrounding foliage, which complicates their detection.


1997 ◽  
Vol 99 (1) ◽  
pp. 185-189
Author(s):  
Wen-Shaw Chen ◽  
Kuang-Liang Huang ◽  
Hsiao-Ching Yu

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.


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