A novel method for rapid in vivo induction of homogeneous polyploids via calluses in a woody fruit tree (Ziziphus jujuba Mill.)

2015 ◽  
Vol 121 (2) ◽  
pp. 423-433 ◽  
Author(s):  
Qing-Hua Shi ◽  
Ping Liu ◽  
Meng-Jun Liu ◽  
Jiu-Rui Wang ◽  
Juan Xu
2015 ◽  
Vol 188 ◽  
pp. 30-35 ◽  
Author(s):  
Qinghua Shi ◽  
Ping Liu ◽  
Jiurui Wang ◽  
Juan Xu ◽  
Qiang Ning ◽  
...  

2001 ◽  
Vol 2 (3) ◽  
pp. 188-195 ◽  
Author(s):  
Tara C Brutzki ◽  
Myron J Kulczycky ◽  
Leslie Bardossy ◽  
Bryan J Clarke ◽  
Morris A Blajchman

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.


2009 ◽  
Vol 6 (9) ◽  
pp. 961-968 ◽  
Author(s):  
L. Steven Beck ◽  
Arthur J. Ammann ◽  
Thomas B. Aufdemorte ◽  
Leo Deguzman ◽  
Yvette Xu ◽  
...  

2010 ◽  
Vol 22 (8) ◽  
pp. 1262 ◽  
Author(s):  
Xing Yang ◽  
Kylie R. Dunning ◽  
Linda L.-Y. Wu ◽  
Theresa E. Hickey ◽  
Robert J. Norman ◽  
...  

Lipid droplet proteins regulate the storage and utilisation of intracellular lipids. Evidence is emerging that oocyte lipid utilisation impacts embryo development, but lipid droplet proteins have not been studied in oocytes. The aim of the present study was to characterise the size and localisation of lipid droplets in mouse oocytes during the periovulatory period and to identify lipid droplet proteins as potential biomarkers of oocyte lipid content. Oocyte lipid droplets, visualised using a novel method of staining cumulus–oocyte complexes (COCs) with BODIPY 493/503, were small and diffuse in oocytes of preovulatory COCs, but larger and more centrally located after maturation in response to ovulatory human chorionic gonadotrophin (hCG) in vivo, or FSH + epidermal growth factor in vitro. Lipid droplet proteins Perilipin, Perilipin-2, cell death-inducing DNA fragmentation factor 45-like effector (CIDE)-A and CIDE-B were detected in the mouse ovary by immunohistochemistry, but only Perilipin-2 was associated with lipid droplets in the oocyte. In COCs, Perilipin-2 mRNA and protein increased in response to ovulatory hCG. IVM failed to induce Perilipin-2 mRNA, yet oocyte lipid content was increased in this context, indicating that Perilipin-2 is not necessarily reflective of relative oocyte lipid content. Thus, Perilipin-2 is a lipid droplet protein in oocytes and its induction in the COC concurrent with dynamic reorganisation of lipid droplets suggests marked changes in lipid utilisation during oocyte maturation.


1997 ◽  
Vol 117 (5) ◽  
pp. 719-723 ◽  
Author(s):  
Iwao Yoshioka ◽  
Tetsuo Himi ◽  
Akikatsu Kataura

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