To study the response of 20S proteasome in wheat (Triticum aestivum L.) roots to salt stress, the root tips from wheat seedlings treated with 200 mM NaCl for different times were used for studying its carbonyl level, caseinolytic activity, protein abundance and other biochemical characteristics. The contents of carbonylated and ubiquitinated proteins (Ub-P) were also investigated. During this stressed process, both the productive rate of O 2 -and the content of H 2 O 2 gradually increased, with the concomitant increase in carbonyl level of total soluble proteins and 20S proteasome, together with the gradual increase in the activities of the total and 20S proteasome in salt-treated root tips. However, the amounts of 20S proteasome decreased particularly during this process. Moreover, metal-catalyzed oxidation of proteins from control plants in vitro validated that the oxidative modification also could increase the activity of 20S proteasome, but decrease its abundance. In addition, the amounts of Ub-P with molecular weights above 35 kDa remained similar to the control plants, but that below 35 kDa decreased significantly in treated root tips. The changes in the proteasome activity and amount argue in favor of the active involvement of this proteolytic system in salt-stressed plants.

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Stable high-oxidation-state Mn complexes were employed for efficient cancer therapy through an in situ Mn(v)–Mn(iii) transition to disrupt the redox balance.

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Mutual information (MI) has been widely used for association mining in complex chemical processes, but how to precisely estimate MI between variables of different numerical types, discriminate their association relationships with targets and finally achieve compact and interpretable prediction has not been discussed in detail, which may limit MI in more complicated industrial applications. Therefore, this paper first reviews the existing information-based association measures and proposes a general framework, GIEF, to consistently detect associations and independence between different types of variables. Then, the study defines four mutually exclusive association relations of variables from an information-theoretic perspective to guide feature selection and compact prediction in high-dimensional processes. Based on GIEF and conditional mutual information maximization (CMIM), a new algorithm, CMIM-GIEF, is proposed and tested on a fluidized catalytic cracking (FCC) process with 217 variables, one which achieves significantly improved accuracies with fewer variables in predicting the yields of four crucial products. The compact variables identified are also consistent with the results of Shapley Additive exPlanations (SHAP) and industrial experience, proving good adaptivity of the method for chemical process data.