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Fluorotelomer carboxylic acids (FTCAs) represent an important group of per- and polyfluoroalkyl substances (PFAS) given their high toxicity, bioaccumulation potential, and frequent detection in landfill leachates and PFAS-impacted sites. In this study, we assessed the biodegradability of 6:2 FTCA and 5:3 FTCA by activated sludges from four municipal wastewater treatment plants (WWTPs) in the New York Metropolitan area. Coupling with 6:2 FTCA removal, significant fluoride release (0.56∼1.83 F-/molecule) was evident in sludge treatments during 7 days of incubation. Less-fluorinated transformation products (TPs) were formed, including 6:2 fluorotelomer unsaturated carboxylic acid (6:2 FTUCA), perfluorohexanoic acid (PFHxA), perfluoropentanoic acid (PFPeA), and perfluorobutanoic acid (PFBA). In contrast, little fluoride (0.01∼0.09 F-/molecule) was detected in 5:3 FTCA-dosed microcosms, though 25∼68% of initially dosed 5:3 FTCA was biologically removed. This implies the dominance of “non-fluoride-releasing pathways” that may contribute to the formation of CoA adducts or other conjugates over 5:3 FTCA biotransformation. The discovery of defluorinated 5:3 FTUCA revealed the possibility of microbial attacks of the C-F bond at the γ carbon to initiate the transformation. Microbial community analysis revealed the possible involvement of 9 genera, such as Hyphomicrobium and Dechloromonas, in aerobic FTCA biotransformation. This study unraveled that biotransformation pathways of 6:2 and 5:3 FTCAs can be divergent, resulting in biodefluorination at distinctive degrees. Further research is underscored to uncover the nontarget TPs and investigate the involved biotransformation and biodefluorination mechanisms and molecular basis. 

Summary Kernel two-sample tests have been widely used for multivariate data to test equality of distributions. However, existing tests based on mapping distributions into a reproducing kernel Hilbert space mainly target specific alternatives and do not work well for some scenarios when the dimension of the data is moderate to high due to the curse of dimensionality. We propose a new test statistic that makes use of a common pattern under moderate and high dimensions and achieves substantial power improvements over existing kernel two-sample tests for a wide range of alternatives. We also propose alternative testing procedures that maintain high power with low computational cost, offering easy off-the-shelf tools for large datasets. The new approaches are compared to other state-of-the-art tests under various settings and show good performance. We showcase the new approaches through two applications: the comparison of musks and nonmusks using the shape of molecules, and the comparison of taxi trips starting from John F. Kennedy airport in consecutive months. All proposed methods are implemented in an R package kerTests.