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Heck reaction is one of the most important carbon‐carbon bond forming reactions with wide applications in organic synthesis. Considerable advances of enantioselective Heck reaction have been achieved in the past decades. This review focuses on recent development of catalytic asymmetric Heck reaction and reductive Heck reaction, which covers intermolecular and intramolecular versions since 2011. The article is organized in terms of the catalysts and olefin substrates.

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Rotating machinery plays a vital role in modern mechanical systems. Effective state monitoring of a rotary machine is important to guarantee its safe operation and prevent accidents. Traditional bearing fault diagnosis techniques rely on manual feature extraction, which in turn relies on complex signal processing and rich professional experience. The collected bearing signals are invariably complicated and unstable. Deep learning can voluntarily learn representative features without a large amount of prior knowledge, thus becoming a significant breakthrough in mechanical fault diagnosis. A new method for bearing fault diagnosis, called improved hierarchical adaptive deep belief network (DBN), which is optimized by Nesterov momentum (NM), is presented in this research. The frequency spectrum is used as inputs for feature learning. Then, a learning rate adjustment strategy is applied to adaptively select the descending step length during gradient updating, combined with NM. The developed method is validated by bearing vibration signals. In comparison to support vector machine and the conventional DBN, the raised approach exhibits a more satisfactory performance in bearing fault type and degree diagnosis. It can steadily and effectively improve convergence during model training and enhance the generalizability of DBN.

Summary Aims The study reports the feasibility and efficiency of vascular endothelial growth factor (VEGF) delivery using nanoparticles synthesized from glycidyl methacrylated dextran (Dex‐GMA) and gelatin for therapeutic angiogenesis. Methods The nanoparticles were prepared using phase separation method, and the drug release profile was determined by ELISA study. The bioactivity of VEGF‐incorporated nanoparticles (VEGF‐NPs) were determined using tube formation assay. A rabbit hind limb ischemia model was employed to evaluate the in vivo therapeutic effect. Blood perfusion was measured by single‐photon emission computed tomography (SPECT). Vessel formation was evaluated by contrast angiography and immunohistochemistry. Results The nanoparticles synthesized were spherical in shape with evenly distributed size of about 130 ± 3.5 nm. The VEGF encapsulated was released in a biphase manner, with the majority of 69% released over 1–12 days. Tube formation assays showed increased tubular structures by VEGF‐NP compared with empty nanoparticles and no treatment. Both free VEGF and VEGF‐NP significantly increased blood perfusion compared with empty nanoparticles (both P < 0.001), but it was much higher in VEGF‐NP‐treated limbs (P < 0.001). Contrast angiography and immunohistological analysis also revealed more significant collateral artery formation and higher capillary density in VEGF‐NP‐treated limbs. Conclusions Dex‐GMA and gelatin‐based nanoparticles could provide sustained release of VEGF and may serve as a new way for angiogenesis.

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