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• The expression of MSTRG3207 is upregulated during cold acclimation in zebrafish ZF4 cells and negatively associated with dre-miR-736. • MSTRG3207 promotes apoptosis by targeting dre-miR-736. • Dre-miR-736 inhibits apoptosis during cold acclimation by downregulating apoptosis-related genes bbc3 and LOC101885512. Long non-coding RNAs (lncRNAs) play essential roles in a variety of biological processes. It has been recently reported that lncRNAs can regulate mRNA expression by binding to microRNAs (miRNAs) as competing endogenous RNAs (ceRNAs). However, the involvement of this regulatory mechanism during cold acclimation in fish remains unclear. In this study, we constructed a ceRNA network mediated by lncRNAs in cold-acclimated zebrafish ZF4 cells through bioinformatic analysis of the mRNA, miRNA, and lncRNA profiles obtained from ZF4 cells cultured at 18 °C for 30 days. A previously uncharacterized lncRNA, MSTRG3207, was selected for further analysis. MSTRG3207 was upregulated and dre-miR-736 was downregulated during cold acclimation. MSTRG3207 was cloned by rapid amplification of cDNA ends (RACE) and functionally characterized. The binding of MSTRG3207 to dre-miR-736 was validated by dual-luciferase reporter assay. Under cold acclimation, MSTRG3207 promoted apoptosis by sponging dre-miR-736 and upregulating bbc3 and LOC101885512, two apoptotic genes targeted by dre-miR-736. Taken together, our findings indicate that MSTRG3207 upregulation promotes apoptosis by sponging dre-miR-736 during cold acclimation in fish. [ABSTRACT FROM AUTHOR]

Wind pressure serves as the fundamental base for architectural design, particularly the reference wind pressure value, which affects the reliability and economy of high-rise, towering, and large-span structures. To facilitate the calculation of reference wind pressure values for design reference periods, load codes from various countries offer probabilistic distribution models for wind speed or reference wind pressure. However, the application of a uniform load probabilistic distribution model for calculating the reference wind pressure for the reference period often results in overestimation or underestimation of the reference wind pressure. To this end, this study collected annual maximum wind speed data from 34 meteorological stations in Liaoning Province, a region in Northeast China, and established extreme value type I, extreme value type III, lognormal, and generalized extreme value distribution models for the annual maximum wind speeds using the maximum likelihood method and method of moment, respectively. The goodness-of-fit test was conducted utilizing the Kolmogorov–Smirnov method. The results showed that the extreme value type III model and the method of moment achieved the best goodness-of-fit for the probabilistic distribution of annual maximum wind speeds. Based on the measured wind direction data corresponding to the annual maximum wind speed, joint probability density functions and cumulative probability density functions of wind speed and wind direction were established. Finally, the influence of different load statistical periods on the reference wind pressure was analyzed. [ABSTRACT FROM AUTHOR]