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In an attempt to search for potent fungicide, a series of novel N-aryl-1H-pyrazole-5-carboxylate derivatives was designed and synthesized. Their chemical structures were characterized by 1H NMR spectra and high resolution mass spectrometry(HRMS). The preliminary bioassay results indicated that some target compounds displayed better fungicidal activities against certain fungi at 50 µg/mL or favorable antitumor activities at 5 µg/mL compared with chlorothalonil and 5-fluorouracil, respectively. The structure-activity relationship demonstrated that the introduction of ester group and amide bond was favorable to the improvement of activities against Physalospora piricolaand Phytophthora capsici.

In order to systematically study the structure-activity relationship of anthranilic diamides and develop insecticides with simple structure and high efficiency, a series of novel anthranilic diamides containing N-H/CH3-1H-pyrazole was designed and synthesized. Their chemical structures were characterized by 1H NMR spectra, high resolution mass spectrometry(HRMS) or 13C NMR spectra. The preliminary bioassay results indicated that all title compounds displayed moderate insecticidal activity against oriental armyworm(Mythimna separata) at 200 mg/L and fungicidal activities against six kinds of fungi at 50 mg/L, especially compound 5i showed 50% insecticidal activity at 25 mg/L. In addition, some compounds exhibited certain antitumor activities. It was demonstrated that the introduction of CH3group into pyrazole ring was superior to H for the insecticidal activity.

Supervised deep learning techniques have been widely explored in real photograph denoising and achieved noticeable performances. However, being subject to specific training data, most current image denoising algorithms can easily be restricted to certain noisy types and exhibit poor generalizability across testing sets. To address this issue, we propose a novel flexible and well-generalized approach, coined as dual meta attention network (DMANet). The DMANet is mainly composed of a cascade of the self-meta attention blocks (SMABs) and collaborative-meta attention blocks (CMABs). These two blocks have two forms of advantages. First, they simultaneously take both spatial and channel attention into account, allowing our model to better exploit more informative feature interdependencies. Second, the attention blocks are embedded with the meta-subnetwork, which is based on metalearning and supports dynamic weight generation. Such a scheme can provide a beneficial means for self and collaborative updating of the attention maps on-the-fly. Instead of directly stacking the SMABs and CMABs to form a deep network architecture, we further devise a three-stage learning framework, where different blocks are utilized for each feature extraction stage according to the individual characteristics of SMAB and CMAB. On five real datasets, we demonstrate the superiority of our approach against the state of the art. Unlike most existing image denoising algorithms, our DMANet not only possesses a good generalization capability but can also be flexibly used to cope with the unknown and complex real noises, making it highly competitive for practical applications.