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Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.

Currently, the knowledge of long noncoding RNA (lncRNA)‐encoded peptides is quite lacking in esophageal squamous cell carcinoma (ESCC). In this study, we simultaneously identified six lncRNA open reading frames (ORFs) with peptide‐coding abilities including lysine‐specific demethylase 4A antisense RNA 1 (KDM4A‐AS1) ORF by combining weighted gene co‐expression network analysis (WGCNA) for ESCC clinical samples, ribosome footprints, ORF prediction, mass spectrometry (MS) identification, and western blotting. KDM4A‐AS1 ORF‐encoded peptide reduced ESCC cell viability and migratory ability. Co‐immunoprecipitation and MS analysis revealed that KDM4A‐AS1‐encoded peptide specifically bound with 103 proteins in ESCC cells, and enrichment analysis suggested that peptide‐bound proteins were related to fatty acid metabolism and redox process. Cell and molecular experiments demonstrated that KDM4A‐AS1‐encoded peptide inhibited stearoyl‐CoA desaturase and fatty acid synthase expression, increased reactive oxygen species level, and reduced mitochondrial membrane potential in ESCC cells. In summary, multiple lncRNAs with translation potential were simultaneously identified by combining multiple approaches in ESCC, providing novel identification strategies for lncRNA‐encoded peptides. Moreover, lncRNA KDM4A‐AS1‐encoded peptide weakened ESCC cell viability and migratory capacity and functioned in fatty acid metabolism and redox process. [ABSTRACT FROM AUTHOR]