Long noncoding RNA urothelial carcinoma-associated 1 (lncRNA UCA1) has gained more attention in recent years due to its oncogenic roles in various cancers. MicroRNA-144 (miR-144) participates in the regulation of the growth of many cancer cells. This study investigated the interaction between lncRNA UCA1 and miR-144 in lung cancer cells. The potential downstream protein of miR-144 was also assessed. Our results found that lncRNA UCA1 was highly expressed in human lung cancer A549, H517, H4006, H1299, and H1650 cells compared to normal embryonic lung WI-38 and HEL-1 cells. Knockdown of lncRNA UCA1 significantly inhibited lung cancer A549 cell viability, migration, invasion, and cell cycle progression, but promoted cell apoptosis. Besides, we found that lncRNA UCA1 was bound to miR-144. miR-144 participated in the regulation effects of lncRNA UCA1 on A549 cell viability, migration, invasion, cell cycle transition, and cell apoptosis. In addition, Pre-B-cell leukemia homeobox 3 (PBX3) was found to be a direct target gene of miR-144. Overexpression of PBX3 promoted A549 cell proliferation and metastasis. Suppression of PBX3 had an opposite effect.

In order to improve the performance and change the current situation of the cost minimization model widely used in the cold chain logistics distribution process, a multi-objective optimization model based on cost, carbon emissions and customer satisfaction is proposed. Considering the characteristic of this proposed optimization model, we design an improved ant colony algorithm with a multi-objective heuristic function to solve it, termed as ACOMO. Experimental results show that the proposed ACOMO can effectively solve the vehicle routing problem of the multi-objective optimization model, and outperforms the classic ant colony algorithms, resulting in more Pareto optimal solutions. It offers an environmentally friendly distribution solution for the problem. Specifically, the distribution path obtained by the improved ant colony algorithm manages to achieve the above multiple goals, including reduction of distribution costs and carbon emissions, and improvement of customer satisfaction. In addition, compared with a single-target model that only provides one single distribution route to cost minimization, multi-objective optimization can provide a variety of distribution route options for logistics companies in practice. Finally, through the sensitivity analysis of temperature changes and cargo damage coefficients, the proposed system successfully provides reference for the optimization of the path of cold chain logistics enterprises, and promotes logistics enterprises to effectively arrange their work and to be more socially responsible. INDEX TERMS Cold chain logistics, path optimization, multi-objective optimization, carbon constraints, customer satisfaction.

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