A practical procedure for 11CN-labeling of native peptides has been developed. The process involves two sequential Pd-mediated cross-coupling reactions at the cysteine residue of a peptide and operates under mild conditions. The method was shown to be highly chemoselective for cysteine over other potentially nucleophilic residues and the radiolabelled products were synthesized and purified in less than 15 minutes. Appropriate for biomedical applications, the method could be used on extremely small scale (20 nmol) with high radiochemical yield. The success of the protocol stems from the use of a Pd-reagent based on a dihaloarene, which enables direct “nucleophile-nucleophile” coupling of the peptide and [11C]-cyanide by temporal separation of nucleophile addition.
The first enantioselective total syntheses of prenylflavonoid Diels-Alder natural products (-)-kuwanon I, (+)-kuwanon J, (-)-brosimone A, and (-)-brosimone B have been accomplished from a common intermediate based on a concise synthetic strategy. Key elements of the synthesis include a biosynthesis-inspired asymmetric Diels-Alder cycloaddition mediated by a chiral ligand/boron Lewis acid, as well as a process involving regioselective Schenck ene reaction, reduction, and dehydration to realize a biomimetic dehydrogenation for generation of the required diene precursor. Furthermore, a remarkable tandem inter-/intramolecular asymmetric Diels-Alder cycloaddition process was applied for the synthesis of (-)-brosimone A.
This paper is concerned with data-driven distributed optimal consensus control for unknown multiagent systems (MASs) with input delays. The input-delayed MAS model is first converted into a delay-free form using a model reduction method. By establishing an equivalent relationship on the predesigned performance indices of the two MASs, optimal consensus control of input-delayed MAS can be fully transformed to that of delay-free MAS. Based on the coupled Hamilton-Jacobi equations and Bellman's optimality principle, optimal consensus control policies are derived for the transformed delay-free MAS. Then a policy iteration algorithm based on distributed asynchronous update mechanism is proposed to learn the coupled Hamilton-Jacobi-Bellman equations online. To perform the proposed data-driven adaptive dynamic programming algorithm, we adopt the measured data-based critic-actor neural networks to approximate the value functions and the control policies, respectively. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.
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