nanoparticle-mediated-sirna-gene-silencing-in-adult-zebrafish-heart

Nanoparticle-mediated siRNA Gene-silencing in Adult Zebrafish Heart

Mammals have a very limited capacity to regenerate the heart after myocardial infarction. On the other hand, the adult zebrafish regenerates its heart ...

mas4sysml-multi-agent-framework-for-sysml-v2-model-generation-from

MAS4SysML: A Multi-Agent Framework for SysML v2 Model Generation from Natural Language

Automatically generating accurate SysML models from natural-language requirements can substantially accelerate the adoption of Model-Based Systems ...

Peking University

China Aerospace Academy of Systems Science and Engineering

Nedd1 is a new member of the gamma-tubulin ring complex (gammaTuRC) and targets the gammaTuRC to the centrosomes for microtubule nucleation and spindle assembly in mitosis. Although its role is known, its functional regulation mechanism remains unclear. Here we report that the function of Nedd1 is regulated by Cdk1 and Plk1. During mitosis, Nedd1 is firstly phosphorylated at T550 by Cdk1, which creates a binding site for the polo-box domain of Plk1. Then, Nedd1 is further phosphorylated by Plk1 at four sites: T382, S397, S637 and S426. The sequential phosphorylation of Nedd1 by Cdk1 and Plk1 promotes its interaction with gamma-tubulin for targeting the gammaTuRC to the centrosome and is important for spindle formation. Knockdown of Plk1 by RNAi decreases Nedd1 phosphorylation and attenuates Nedd1 accumulation at the spindle pole and subsequent gamma-tubulin recruitment at the spindle pole for microtubule nucleation. Taken together, we propose that the sequential phosphorylation of Nedd1 by Cdk1 and Plk1 plays a pivotal role in targeting gammaTuRC to the centrosome by promoting the interaction of Nedd1 with the gammaTuRC component gamma-tubulin, during mitosis.

Sequential phosphorylation of Nedd1 by Cdk1 and Plk1 is required for targeting of the gammaTuRC to the centrosome.

The spatial correlation of carbon emissions poses new challenges to constructing ecological civilisation and sustainable development in the Yangtze River Economic Belt. This study explains the formation mechanism of the carbon emission efficiency network and attempts to explore its structural complexity and spatial directivity. It focuses on the carbon emission efficiency network's structural entropy, node efficiency, hierarchy, connection symmetry, and transmission characteristics. The main conclusions are as follows: (1) The carbon emission efficiency of the Yangtze River Economic Belt has significantly improved, and the gap between cities is narrowing, but there is gradient differentiation on the provincial spatial scale that exhibits a strong Matthew effect; (2) There is an imbalance in carbon emission efficiency, which is primarily reflected in spatial distribution and hierarchical structure. Low-efficiency cities have played an important role in promoting the spatial evolution of carbon emission efficiency in the Yangtze River Economic Belt, but they have not completely changed the imbalance in carbon emission efficiency; (3) Supporting sub-networks and basic sub-networks have emerged as critical groups in promoting the complexity of carbon emission efficiency network structures, and have formed distinctive network structures in different basins of the Yangtze River; (4) The overall convergence of the carbon emission efficiency network has improved, and it exhibits preference attachment characteristics. The connection symmetry has been reduced from 5 to 7 times to 1 to 3 times, and the situation of unilateral unequal connection has been alleviated. Finally, this study puts forward some policy suggestions to improve carbon emission efficiency from the aspects of low-carbon technology research and development and carbon emission rights market construction.

Carbon emission efficiency network formation mechanism and spatial correlation complexity analysis: Taking the Yangtze River Economic Belt as an example.

We proposed a novel decision-making method, the large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets, to address the challenge of large-scale group multi-attribute decision-making in fuzzy environments. This method concurrently accounted for the membership and non-membership degrees of decision-making experts in fuzzy environments and the corresponding probabilistic value to quantify expert decision information. Furthermore, it applied to complex scenarios involving groups of 20 or more decision-making experts. We delineated five major steps of the method, elaborating on the specific models and algorithms used in each phase. We began by constructing a probabilistic dual hesitant fuzzy information evaluation matrix and determining attribute weights. The following steps involved classifying large-scale decision-making expert groups and selecting the optimal classification scheme based on effectiveness assessment criteria. A global consensus degree threshold was established, followed by implementing a consensus-reaching model to synchronize opinions within the same class of expert groups. Decision information was integrated within and between classes using an information integration model, leading to a comprehensive decision matrix. Decision outcomes for the objects were then determined through a ranking method. The method's effectiveness and superiority were validated through a case study on urban emergency capability assessment, and its advantages were further emphasized in comparative analyses with other methods.

ArticleTotal : 2	Year
Publication title Cited by 11	2018
Publication title	2026

Article	Year
Sequential phosphorylation of Nedd1 by Cdk1 and Plk1 is required for targeting of the gammaTuRC to the centrosome. Journal of cell science\| PubMed ID: 19509060	2009
Carbon emission efficiency network formation mechanism and spatial correlation complexity analysis: Taking the Yangtze River Economic Belt as an example. The Science of the total environment\| PubMed ID: 35724790	2022
The large-scale group consensus multi-attribute decision-making method based on probabilistic dual hesitant fuzzy sets. Mathematical biosciences and engineering : MBE\| PubMed ID: 38549314	2024

Junjie Hou

Biography

JoVE Journal Publications

Other Publications