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Abstract Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. In this study, we introduce a novel microfluidic approach that simplifies the trapping and identification process of microplastics in surface seawater, eliminating the need for labeling. We examine various models, including support vector machine, random forest, convolutional neural network (CNN), and residual neural network (ResNet34), to assess their performance in identifying 11 common plastics. Our findings reveal that the CNN method outperforms the other models, achieving an impressive accuracy of 93% and a mean area under the curve of 98 ± 0.02%. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts. 

ACYL CARRIER PROTEIN4 (ACP4) is the most abundant ACP isoform in Arabidopsis (Arabidopsis thaliana) leaves and acts as a scaffold for de novo fatty acid biosynthesis and as a substrate for acyl-ACP-utilizing enzymes. Recently, ACP4 was found to interact with a protein-designated plastid RHOMBOID LIKE10 (RBL10) that affects chloroplast monogalactosyldiacylglycerol (MGDG) biosynthesis, but the cellular function of this interaction remains to be explored. Here, we generated and characterized acp4 rbl10 double mutants to explore whether ACP4 and RBL10 directly interact in influencing chloroplast lipid metabolism. Alterations in the content and molecular species of chloroplast lipids such as MGDG and phosphatidylglycerol were observed in the acp4 and rbl10 mutants, which are likely associated with the changes in the size and profiles of diacylglycerol (DAG), phosphatidic acid (PA), and acyl-ACP precursor pools. ACP4 contributed to the size and profile of the acyl-ACP pool and interacted with acyl-ACP-utilizing enzymes, as expected for its role in fatty acid biosynthesis and chloroplast lipid assembly. RBL10 appeared to be involved in the conversion of PA to DAG precursors for MGDG biosynthesis as evidenced by the increased 34:x PA and decreased 34:x DAG in the rbl10 mutant and the slow turnover of radiolabeled PA in isolated chloroplasts fed with [14C] acetate. Interestingly, the impaired PA turnover in rbl10 was partially reversed in the acp4 rbl10 double mutant. Collectively, this study shows that ACP4 and RBL10 affect chloroplast lipid biosynthesis by modulating substrate precursor pools and appear to act independently.