J

Jiangtao Shen

University of Exeter

ORCID: 0000-0002-2070-940X

Publishes on Advanced Multi-Objective Optimization Algorithms, Metaheuristic Optimization Algorithms Research, Heat Transfer and Optimization. 30 papers and 729 citations.

30Publications
729Total Citations

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Top publicationsby citations

Strategies in the delivery of Cas9 ribonucleoprotein for CRISPR/Cas9 genome editing
Song Zhang, Jiangtao Shen, Dali Li et al.|Theranostics|2020
Cited by 397Open Access

CRISPR/Cas9 genome editing has gained rapidly increasing attentions in recent years, however, the translation of this biotechnology into therapy has been hindered by efficient delivery of CRISPR/Cas9 materials into target cells. Direct delivery of CRISPR/Cas9 system as a ribonucleoprotein (RNP) complex consisting of Cas9 protein and single guide RNA (sgRNA) has emerged as a powerful and widespread method for genome editing due to its advantages of transient genome editing and reduced off-target effects. In this review, we summarized the current Cas9 RNP delivery systems including physical approaches and synthetic carriers. The mechanisms and beneficial roles of these strategies in intracellular Cas9 RNP delivery were reviewed. Examples in the development of stimuli-responsive and targeted carriers for RNP delivery are highlighted. Finally, the challenges of current Cas9 RNP delivery systems and perspectives in rational design of next generation materials for this promising field will be discussed.

A Controlled Strengthened Dominance Relation for Evolutionary Many-Objective Optimization
Jiangtao Shen, Peng Wang, Xinjing Wang|IEEE Transactions on Cybernetics|2020
Cited by 62

Maintaining a balance between convergence and diversity is particularly crucial in evolutionary multiobjective optimization. Recently, a novel dominance relation called “strengthened dominance relation” (SDR) is proposed, which outperforms the existing dominance relations in balancing convergence and diversity. In this article, two points that influence the performance of SDR are studied and a new dominance relation, which is mainly based on SDR, is proposed (CSDR). An adaptation strategy is presented to dynamically adjust the dominance relation according to the current generation number. The CSDR is embedded into NSGA-II to substitute the Pareto dominance, labeled as NSGA-II/CSDR. The performance of our proposed method is validated by comparing it with five state-of-the-art algorithms on commonly used benchmark problems. NSGA-II/CSDR outperforms other algorithms in the most test instances considering both convergence and diversity.