Insights into bacterial community metatranscriptome and metabolome in river water influenced by palm oil mill effluent final discharge.

AIMS: This study aimed to investigate the effect of palm oil mill effluent (POME) final discharge on the active bacterial composition, gene expression, and metabolite profiles in the receiving rivers to establish a foundation for identifying potential biomarkers for monitoring POME pollution in rivers. METHODS AND RESULTS: The POME final discharge, upstream (unpolluted by POME), and downstream (effluent receiving point) parts of the rivers from two sites were physicochemically characterized. The taxonomic and gene profiles were then evaluated using de novo metatranscriptomics, while the metabolites were detected using qualitative metabolomics. A similar bacterial community structure in the POME final discharge samples from both sites was recorded, but their composition varied. Redundancy analysis showed that several families, particularly Comamonadaceae and Burkholderiaceae [Pr(>F) = 0.028], were positively correlated with biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). The results also showed significant enrichment of genes regulating various metabolisms in the POME-receiving rivers, with methane, carbon fixation pathway, and amino acids among the predominant metabolisms identified (FDR < 0.05, PostFC > 4, and PPDE > 0.95). This was further validated through qualitative metabolomics, whereby amino acids were detected as the predominant metabolites. CONCLUSIONS: The results suggest that genes regulating amino acid metabolism have significant potential for developing effective biomonitoring and bioremediation strategies in river water influenced by POME final discharge, fostering a sustainable palm oil industry.

Performance Evaluation Of Convolutions And Atrous Convolutions In Deep Networks For Retinal Disease Segmentation On Optical Coherence Tomography Volumes.

The deterioration of the retina center could be the main reason for vision loss. Older people usually ranging from 50 years and above are exposed to age-related macular degeneration (AMD) disease that strikes the retina. The lack of human expertise to interpret the complexity in diagnosing diseases leads to the importance of developing an accurate method to detect and localize the targeted infection. Approaching the performance of ophthalmologists is the consistent main challenge in retinal disease segmentation. Artificial intelligence techniques have shown enormous achievement in various tasks in computer vision. This paper depicts an automated end-to-end deep neural network for retinal disease segmentation on optical coherence tomography (OCT) scans. The work proposed in this study shows the performance difference between convolution operations and atrous convolution operations. Three deep semantic segmentation architectures, namely U-net, Segnet, and Deeplabv3+, have been considered to evaluate the performance of varying convolution operations. Empirical outcomes show a competitive performance to the human level, with an average dice score of 0.73 for retinal diseases.