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This work highlights the use of push-pull hydroxylphenylpolyenylpyridinium fluorophores coupled with trimethyl lock quinone to engineer the ratiometric two-photon probes for cellular and intravital imaging of mitochondrial NAD(P)H:quinone oxidoreductase 1 (NQO1), a critical antioxidant enzyme responsible for detoxifying quinones. As a typical representative, QBMP showed favorable binding with NQO1 with a Michaelis constant of 12.74 μM and exhibited a suite of superior properties, including rapid response (4 min), large Stokes shift (162 nm), ultralow detection limit (0.9 nM), favorable two-photon cross section for the released fluorophore (70.5 GM), and deep tissue penetration (225 μm) in fixed brain tissues. More importantly, this probe was successfully applied for distinguishing different NQO1-expressing cancer and normal cells, revealing decreased NQO1 activity in a cellular Parkinson's disease model, screening NQO1 inducers as neuroprotective agents, and imaging of NQO1 in live mouse brain.

Thioredoxin reductase (TrxR) is a pivotal antioxidant enzyme, but there remains a challenge for its fast imaging. This work describes the combination of a hydroxyl styrylpyridinium scaffold as the push-pull fluorophore with a carbonate-bridged 1,2-dithiolane unit as the reaction site to develop a fast mitochondrial TrxR2 probe

The distribution characteristics of the content of As, Cr, Cu, Hg, Pb, Zn, and Cd were researched based on the geochemical analysis of 159 surface sediment samples in Haikou Bay, Puqian Bay, Dongzhai Harbor, and Mulan Bay Mathematical statistics methods were used to interpret the sources of heavy metals, and the main controlling factors for heavy metal distribution were confirmed, having been analyzed qualitatively and semi-quantitatively. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Pb, and Zn were 8.40, 0.06, 32.50, 8.32, 0.02, 18.77, and 35.87 μg·g

Support vector machine (SVM) is an effective method for resolving regression problem. However, tradition SVM is very sensitive to noises in the training sample. In order to overcome this problem, fuzzy support vector regression (FSVR) based on combining cluster center with affinity is proposed in this paper. The fuzzy membership is defined not only by the distance between a point and its cluster center, but also by the two different points of the sample, which is depicted as the affinity between them. And the method of soft sensor modeling based on FSVR with the new membership function is proposed. Simulation results for artificial data show the proposed method gives good performance on reducing the effects of noise and improves the regression accuracy and generalization.