Chromophores that absorb in the tissue-penetrant far-red/near-infrared window have long served as photocatalysts to generate singlet oxygen for photodynamic therapy. However, the cytotoxicity and side reactions associated with singlet oxygen sensitization have posed a problem for using long-wavelength photocatalysis to initiate other types of chemical reactions in biological environments. Herein, silicon-Rhodamine compounds (SiRs) are described as photocatalysts for inducing rapid bioorthogonal chemistry using 660 nm light through the oxidation of a dihydrotetrazine to a tetrazine in the presence of trans-cyclooctene dienophiles. SiRs have been commonly used as fluorophores for bioimaging but have not been applied to catalyze chemical reactions. A series of SiR derivatives were evaluated, and the Janelia Fluor-SiR dyes were found to be especially effective in catalyzing photooxidation (typically 3%). A dihydrotetrazine/tetrazine pair is described that displays high stability in both oxidation states. A protein that was site-selectively modified by trans-cyclooctene was quantitatively conjugated upon exposure to 660 nm light and a dihydrotetrazine. By contrast, a previously described methylene blue catalyst was found to rapidly degrade the protein. SiR-red light photocatalysis was used to cross-link hyaluronic acid derivatives functionalized by dihydrotetrazine and trans-cyclooctenes, enabling 3D culture of human prostate cancer cells. Photoinducible hydrogel formation could also be carried out in live mice through subcutaneous injection of a Cy7-labeled hydrogel precursor solution, followed by brief irradiation to produce a stable hydrogel. This cytocompatible method for using red light photocatalysis to activate bioorthogonal chemistry is anticipated to find broad applications where spatiotemporal control is needed in biological environments.

No abstract

In the field of robot path planning, aiming at the problems of the standard genetic algorithm, such as premature maturity, low convergence path quality, poor population diversity, and difficulty in breaking the local optimal solution, this paper proposes a multi-population migration genetic algorithm. The multi-population migration genetic algorithm randomly divides a large population into several small with an identical population number. The migration mechanism among the populations is used to replace the screening mechanism of the selection operator. Operations such as the crossover operator and the mutation operator also are improved. Simulation results show that the multi-population migration genetic algorithm (MPMGA) is not only suitable for simulation maps of various scales and various obstacle distributions, but also has superior performance and effectively solves the problems of the standard genetic algorithm.

No abstract

No abstract