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One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. 

Abstract microRNAs (miRNAs) are post‐transcriptional regulators of gene expression and can play an important role in modulating organismal development and physiology in response to environmental stress. However, the role of miRNAs in mediating adaptation to diverse environments in natural study systems remains largely unexplored. Here, we characterized miRNAs and their expression inPoecilia mexicana, a species of small fish that inhabits both normal streams and extreme environments in the form of springs rich in toxic hydrogen sulphide (H₂S). We found thatP. mexicanahas a similar number of miRNA genes as other teleosts. In addition, we identified a large population of mature miRNAs that were differentially expressed between locally adapted populations in contrasting habitats, indicating that miRNAs may contribute toP. mexicanaadaptation to sulphidic environments. In silico identification of differentially expressed miRNA‐mRNA pairs revealed, in the sulphidic environment, the downregulation of miRNAs predicted to target mRNAs involved in sulphide detoxification and cellular homeostasis, which are pathways essential for life in H₂S‐rich springs. In addition, we found that predicted targets of upregulated miRNAs act in the mitochondria (16.6% of predicted annotated targets), which is the main site of H₂S toxicity and detoxification, possibly modulating mitochondrial function. Together, the differential regulation of miRNAs between these natural populations suggests that miRNAs may be involved in H₂S adaptation by promoting functions needed for survival and reducing functions affected by H₂S. This study lays the groundwork for further research to directly demonstrate the role of miRNAs in adaptation to H₂S. Overall, this study provides a critical stepping‐stone towards a comprehensive understanding of the regulatory mechanisms underlying the adaptive variation in gene expression in a natural system.