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Metformin is the first-line antidiabetic drug and one of the most prescribed medications worldwide. Because of its ubiquitous occurrence in global waters and demonstrated ecotoxicity, metformin, as with other pharmaceuticals, has become a concerning emerging contaminant. Metformin is subject to transformation, producing numerous problematic transformation byproducts (TPs). The occurrence, removal, and toxicity of metformin have been continually reviewed; yet, a comprehensive analysis of its transformation pathways, byproduct generation, and the associated change in adverse effects is lacking. In this review, we provide a critical overview of the transformation fate of metformin during water treatments and natural processes and compile the 32 organic TPs generated from biotic and abiotic pathways. These TPs occur in aquatic systems worldwide along with metformin. Enhanced toxicity of several TPs compared to metformin has been demonstrated through organism tests and necessitates the development of complete mineralization techniques for metformin and more attention on TP monitoring. We also assess the potential of metformin to indicate overall contamination of pharmaceuticals in aquatic environments, and compared to the previously acknowledged ones, metformin is found to be a more robust or comparable indicator of such overall pharmaceutical contamination. In addition, we provide insightful avenues for future research on metformin.

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Metformin (MET), a worldwide used drug for type 2 diabetes, has been found with the largest amount by weight among all drugs in aquatic environment, including the drinking water sources where chlorination inevitably transforms MET into chlorination byproducts. Although MET has health-promoting properties, whether or how its chlorination byproducts affect health remains largely unknown. Here we reveal that MET chlorination byproducts Y (C4H6ClN5) and C (C4H6ClN3) exhibit marked toxicity, even higher than that of the well-known poisonous arsenic, to live worms and human cells. Moreover, both byproducts are harmful to mice and Y at 250 ng/L destroys the mouse small intestine integrity. Strikingly, we detected MET and byproduct C in worldwide drinking water. Both byproducts are increasingly produced with more MET present during chlorination process. Unprecedentedly, we unveil boiling and activated carbon adsorption as effective solutions that are in urgent demand globally for removing these byproducts from water.

In an early paper, He and Tang [Biometrika 100 (2013) 254-260] introduced and studied a new class of designs, strong orthogonal arrays, for computer experiments, and characterized such arrays through generalized orthogonal arrays. The current paper presents a simple characterization for strong orthogonal arrays of strength three. Besides being simple, this new characterization through a notion of semi-embeddability is more direct and penetrating in terms of revealing the structure of strong orthogonal arrays. Some other results on strong orthogonal arrays of strength three are also obtained along the way, and in particular, two $\operatorname {SOA}(54,5,27,3)$'s are constructed.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1225 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Intended for computer experiments with both qualitative and quantitative factors, marginally coupled designs were introduced by Deng, Hung and Lin (2015) as a more economical strategy than the original, sliced space-filling designs. Among the designs constructed in Deng, Hung and Lin (2015), the corresponding designs for quantitative factors possess only the one-dimensional space-filling property with respect to each level of any factor in designs for qualitative factors. In addition, their designs for quantitative factors have clustered points. To avoid clustered points and enhance two-and higher-dimensional space-filling property in designs for quantitative factors, we propose three approaches to construct marginally coupled designs. Theoretical results of marginally coupled designs are also derived.

Recent researches on designs for computer experiments with both qualitative and quantitative factors have advocated the use of marginally coupled designs. This paper proposes a general method of constructing such designs for which the designs for qualitative factors are multi-level orthogonal arrays and the designs for quantitative factors are Latin hypercubes with desirable space-filling properties. Two cases are introduced for which we can obtain the guaranteed lowdimensional space-filling property for quantitative factors. Theoretical results on the proposed constructions are derived. For practical use, some constructed designs for three-level qualitative factors are tabulated.