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The development of simplified synthetic strategy to create structurally and functionally diverse pseudo‐natural macrocyclic molecules is highly appealing but poses a marked challenge. Inspired by natural scaffolds, herein, we describe a practical and concise ligand‐enabled Pd(II)‐catalyzed sp3 C−H alkylation, olefination and arylation macrocyclization, which could offer a novel set of pseudo‐natural macrocyclic sulfonamides. Interestingly, the potential of ligand acceleration in C−H activation is also demonstrated by an unprecedented enantioselective sp3 C−H alkylation macrocyclization. Moreover, a combination of in silico screening and biological evaluation led to the identification of a novel spiro‐grafted macrocyclic sulfonamide 2 a, which showed a promising efficacy for the treatment of Parkinson's disease (PD) in a mouse model through the activation of silent information regulator sirtuin 3 (SIRT3). [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is a practical approach for identifying landslide-prone areas. In this letter, we apply a semisupervised learning method, positive unlabeled-bagging (PU-bagging) to generate a landslide susceptibility map over a study area from the Loess Plateau in North-Central China. PU-bagging deals with the lack of negative samples in a training set and uses only positive and unlabeled samples. We prove the effectiveness of our approach by comparing the PU-bagging decision tree (DT) generated landslide susceptibility map with the ground truth (known landslide locations), as well as by comparing its performance with three widely used models (logistic regression, support vector machine, and artificial neural network). The promising results and the fact that the method is general urge us to believe that the PU-bagging should be able to perform in other landslide-prone areas where only positive samples are provided. [ABSTRACT FROM AUTHOR]