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Succinate dehydrogenase inhibitors (SDHIs) have emerged in fungicide markets as one of the fastest-growing categories that are widely applied in agricultural production for crop protection. Currently, the structural modification focusing on the flexible amide link of SDHI molecules is being gradually identified as one of the innovative strategies for developing novel highly efficient and broad-spectrum fungicides. Based on the above structural features, a series of pyrazole-4-acetohydrazide derivatives potentially targeting fungal SDH were constructed and evaluated for their antifungal effects against Rhizoctonia solani, Fusarium graminearum, and Botrytis cinerea. Strikingly, the in vitro EC50 values of constructed pyrazole-4-acetohydrazides 6w against R. solani, 6c against F. graminearum, and 6f against B. cinerea were, respectively, determined as 0.27, 1.94, and 1.93 μg/mL, which were obviously superior to that of boscalid against R. solani (0.94 μg/mL), fluopyram against F. graminearum (9.37 μg/mL), and B. cinerea (1.94 μg/mL). Concurrently, the effects of the substituent steric, electrostatic, hydrophobic, and hydrogen-bond fields on structure–activity relationships were elaborated by the reliable comparative molecular field analysis and comparative molecular similarity index analysis models. Subsequently, the practical value of pyrazole-4-acetohydrazide derivative 6w as a potential SDHI was ascertained by the relative surveys on the in vivo anti-R. solani preventative efficacy, inhibitory effects against fungal SDH, and molecular docking studies. The present results provide an indispensable complement for the structural optimization of antifungal leads potentially targeting SDH.

Magnesium is an essential ion for numerous physiological processes. MgtE is a Mg2+ selective channel involved in the maintenance of intracellular Mg2+ homeostasis, whose gating is regulated by intracellular Mg2+ levels. Here, we report that ATP binds to MgtE, regulating its Mg2+-dependent gating. Crystal structures of MgtE–ATP complex show that ATP binds to the intracellular CBS domain of MgtE. Functional studies support that ATP binding to MgtE enhances the intracellular domain affinity for Mg2+ within physiological concentrations of this divalent cation, enabling MgtE to function as an in vivo Mg2+ sensor. ATP dissociation from MgtE upregulates Mg2+ influx at both high and low intracellular Mg2+ concentrations. Using site-directed mutagenesis and structure based-electrophysiological and biochemical analyses, we identify key residues and main structural changes involved in the process. This work provides the molecular basis of ATP-dependent modulation of MgtE in Mg2+ homeostasis.

The CNNM/CorC family proteins are Mg2+ transporters that are widely distributed in all domains of life. In bacteria, CorC has been implicated in the survival of pathogenic microorganisms. In humans, CNNM proteins are involved in various biological events, such as body absorption/reabsorption of Mg2+ and genetic disorders. Here, we determined the crystal structure of the Mg2+-bound CorC TM domain dimer. Each protomer has a single Mg2+ binding site with a fully dehydrated Mg2+ ion. The residues at the Mg2+ binding site are strictly conserved in both human CNNM2 and CNNM4, and many of these residues are associated with genetic diseases. Furthermore, we determined the structures of the CorC cytoplasmic region containing its regulatory ATP-binding domain. A combination of structural and functional analyses not only revealed the potential interface between the TM and cytoplasmic domains but also showed that ATP binding is important for the Mg2+ export activity of CorC.

<p class="Abstract">Artificial Neural Networks (ANNs) are the nonlinear and adaptive information processing systems which are combined by numerous processing units, with the characteristics of self-adapting, self-organizing and real-time learning, and play an important in pattern recognition, machine learning and data mining. But we&rsquo;ve encountered many problems, such as the selection of the structure and the parameters of the networks, the selection of the learning samples, the selection of the initial values, the convergence of the learning algorithms and so on. Genetic algorithms (GA) is a kind of random search algorithm, on one hand, it simulates the nature selection and evolution, on the other, it has the advantages of good global search abilities and learning the approximate optimal solution without the gradient information of the error functions. In this paper, some optimization algorithms for ANNs with GA are studied. Firstly, an optimizing BP neural network is set up. It is using GA to optimize the connection weights of the neural network, and using GA to optimize both the connection weights and the architecture. Secondly, an optimizing RBF neural network is proposed. It used hybrid encoding method, that is, to encode the network by binary encoding and the weights by real encoding, the network architecture is self-adapted adjusted, the weights are learned, and the network is further adjusted by pseudo-inverse method or LMS method. Then they are used in real world classification tasks, and compared with the modified BP algorithm with adaptive learning rate. Experiments prove that the network got by this method has a better architecture and stronger classification ability, and the time of constructing the network artificially is saved. The algorithm is a self-adapted and intelligent learning algorithm.</p>

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Landslide is a kind of serious geologic disaster. In the viewpoint of system theory, the landslides may be regarded as a nonlinear open system, and they are ceaselessly exchanging information and energy with their surrounding environment and inside of themselves. The occurrence of landslide is due to the energy the landslides obtain from the environment, and then the states of landslide triggering factors will be changed from disorder to order. Based on information theory, this paper presents a novel landslide stability analysis approach, that is, generalized information entropy approach. First of all, the surveying data time series of landslide triggering factors should be transformed into serial data on probability, and then the generalized information entropy of these landslide triggering factors can be evaluated by these probability serial data. From the change of generalized information entropy, it can be seen that there is a sudden increase of generalized information entropy before landslide occurs, and then generalized information entropy trends to stationary change after landslide occurs. Keywords-landslide；stability analysis； generalized information entropy；landslide triggering factorI.

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