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Anemone flaccida Fr. Schmidt, a family of ancient hopanoids, have been used as traditional Asian herbs for the treatments of inflammation and convulsant diseases. Previous study on HeLa cells suggested that triterpenoid saponins from Anemone flaccida Fr. Schmidt may have potential antitumor effect due to their apoptotic activities. Here, we confirmed the apoptotic activities of the following five triterpenoid saponins: glycoside St-I4a (1), glycoside St-J (2), anhuienoside E (3), hedera saponin B (4), and flaccidoside II (5) on human BEL-7402 and HepG2 hepatoma cell lines, as well as the model of HeLa cells treated with lipopolysaccharide (LPS). We found that COX-2/PGE2 signaling pathway, which plays key roles in the development of cancer, is involved in the antitumor activities of these saponins. These data provide the evidence that triterpenoid saponins can induce apoptosis via COX-2/PGE2 pathway, implying a preventive role of saponins from Anemone flaccida in tumor.

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Nanocrystalline materials with superior properties are of great interest. Much is discussed about obtaining nanograins, but little is known about maintaining grain‐size uniformity that is critical for reliability. An especially intriguing question is whether it is possible to achieve a size distribution narrower than what Hillert theoretically predicted for normal grain growth, a possibility suggested—for growth with a higher growth exponent—by the generalized mean‐field theory of Lifshitz, Slyozov, Wagner (LSW), and Hillert but never realized in practice. Following a rationally designed two‐step sintering route, it has been made possible in bulk materials by taking advantage of the large growth exponent in the intermediate sintering stage to form a uniform microstructure despite residual porosity, and freezing the grain growth thereafter while continuing densification to reach full density. The bulk dense Al2O3 ceramic thus obtained has an average grain size of 34 nm and a size distribution much narrower than Hillert's prediction. Bulk Al2O3 with a grain‐size distribution narrower than the particle‐size distribution of starting powders is also demonstrated, as are highly uniform bulk engineering metals (refractory Mo and W‐Re alloy) and complex functional ceramics (BaTiO3‐based alloys with superior dielectric strength and energy capacity).

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Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNNs that can meet a stringent energy budget is becoming increasingly important. This paper proposes ECC, a framework that compresses DNNs to meet a given energy constraint while minimizing accuracy loss.The key idea of ECC is to model the DNN energy consumption via a novel bilinear regression function. The energy estimate model allows us to formulate DNN compression as a constrained optimization that minimizes the DNN loss function over the energy constraint. The optimization problem, however, has nontrivial constraints. Therefore, existing deep learning solvers do not apply directly. We propose an optimization algorithm that combines the essence of the Alternating Direction Method of Multipliers (ADMM) framework with gradient-based learning algorithms. The algorithm decomposes the original constrained optimization into several subproblems that are solved iteratively and efficiently. ECC is also portable across different hardware platforms without requiring hardware knowledge. Experiments show that ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art resource-constrained DNN compression techniques.