This review presents an up-to-date survey of natural polyacetylenes isolated from the family Apiaceae, and their biosynthesis and biological activities up to May 2013, with 107 references. A total of 103 polyacetylenes from 72 species of 41 genera of Apiaceae have been isolated so far, among which falcarinol-type polyacetylenes are most widely distributed.

One pair of new C-8-C-3'/C-7-O-C-4' linked neolignan enantiomers (1a/1b) and one new guaiane sesquiterpene (2) first featuring the 1(2),9(10)-conjugated double bond were isolated from the stems of Solanum erianthum (Solanceae). Their structures were characterized on the basis of extensive spectroscopic analyses, especially from their 2D nuclear magnetic resonance (NMR) spectra. The absolute configurations of 1a/2b were rigorously elucidated by electronic circular dichroism (ECD) experiments combined with the reversed helicity rule for the 2,3-dihydrobenzo[b]furan chromophore, and compound 2 is the first report on the sterochemical assignment of a guaiane sesquiterpene by using the allylic axial chirality rule for the conjugated diene chromophore in combination with the calculated ECD spectrum.

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Four fractions were prepared from the crude extract of Caesalpinia minax Hance and the inhibitory activity of nitric oxide (NO) production release of RAW 264.7 cells stimulated by lipopolysaccharide (LPS) was evaluated. The ethyl acetate (EtOAc) fraction showed obvious inhibitory effect. Bioassay-guided fractionation led to the isolation of three new cassane diterpenes, caesalmin X (1), caesalmin Y (2) and caesalmin Z (3), together with 19 known cassane diterpenoids (4-22). Their structures were mainly characterised on the basis of extensive spectroscopic analyses and comparison with reported data. Moreover, three compounds (20-22) which possessed furanditerpenoid 7,17-lactone structures, displayed moderate activities, with IC value of 29.85, 27.38 and 25.40 μM, respectively.

Focusing on the shortage of mechanical defect detection and diagnosis technology for disconnectors, a wireless monitoring method for the mechanical state of disconnectors is proposed. The splitcore current sensors and improved voltage sensors are used to measure the motor currents and voltages of the disconnector under typical mechanical states at different working voltages. The wireless communication network is used to upload the acquisition data to the cloud server quickly, and the received data are processed by the software system. By comparing and analyzing the curves of current, input power, and output power under different states, it is concluded that the motor output power can adequately reflect the mechanical state of the disconnector. Twenty-three time-domain features of the output power time curve are extracted to form the original feature vector. Kernel principal component analysis (KPCA) method is used to reduce the dimension of the nonlinear features, and the Fisher's criterion function is constructed to determine the width parameter of the kernel function in the feature optimization. Grid search algorithm is used to optimize the kernel parameters of the support vector machine (SVM), and the trained SVM model is used to classify the mechanical state data whose working voltage part is known, and part is unknown, with a classification accuracy of 100%. The results show that the proposed wireless monitoring method can effectively diagnose the mechanical state of the disconnector and has a good generalization ability.