This paper characterized the observed low frequency acoustic signals generated by rock falls, thunderstorm, and wind turbulence in large rocky landslide. A digital infrasonic recording system was deployed on site to capture real-time low frequency acoustic signals associated with rock falls. An advanced non-stationary signal analysis method, i.e. Empirical Mode Decomposition (EMD), was applied to get insight to the characteristics of the low frequency acoustic signals induced by the hazards. Joint time–frequency distribution spectra technique was used to detect distinctive features of the events. The study shows that the low frequency acoustic signals can be excited by rock falls, thunderstorm and wind turbulence in the field environment, but the signal varies in both time domain and frequency domain with different patterns depending on the physical processes. The results demonstrated that the EMD-based signal processing technique is capable of extracting distinctive features to differentiate acoustic signals in real environment. 相似文献
The histone lysine methyltransferase EZH2 has been reported to play important roles in cancer aggressiveness, metastasis and poor prognosis. In this study, a series of benzomorpholine derivatives were synthesized and biologically evaluated as EZH2 inhibitors. The target compounds were obtained in good yields from 3-amino-5-bromo-2-hydroxybenzoic acid via cyclization, Suzuki coupling and amidation as the key steps. A preliminary optimization study led to the discovery of several potent novel EZH2 inhibitors (6b, 6c, 6x and 6y). Moreover, 6y inhibited the A549 and NCI-H1975 cell lines (IC50?=?1.1 µM and 1.1 µM, respectively). Further studies indicated that 6y can reduce EZH2 expression in intact cells and cause cell arrest in the G2/M phase.
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES neglects the characteristics of chemical reactions, where the molecular graph topology is largely unaltered from reactants to products, resulting in the suboptimal performance of SMILES if straightforwardly applied. In this article, we propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient synthesis prediction. Due to the strict one-to-one mapping and reduced edit distance, the computational model is largely relieved from learning the complex syntax and dedicated to learning the chemical knowledge for reactions. We compare the proposed R-SMILES with various state-of-the-art baselines and show that it significantly outperforms them all, demonstrating the superiority of the proposed method.We propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient sequence-based synthesis prediction.相似文献
The semi-diurnal mean aerosol mass concentration, chemical composition, and optical properties of PM2.5were investigated in Shanghai during the spring of 2012. Slight pollution was observed during the study period. The average PM2.5 concentration was 64.11 土 22.83(xg/m3. The mean coefficients of extinction,scattering, and absorption at 532 nm were 125.9 士 78.5,91.1 士 56.3,and 34.9 士 23.6 Mm-1, respectively. A relatively low mean single scattering albedo at 532 nm(0.73 士 0.04) and low level of elemental carbon(EC,2.67 士 1.96 |xg/m3) suggested that the light absorption was enhanced due to the internal mixing of the EC.Sulfate contributed the most to aerosol light scattering in Shanghai. The chemical composition of PM2.5was dominated by particulate organic matter, sulfate, nitrate, ammonium, and EC. Anthropogenic sources made a significant contribution to the emission and loading of the particulate pollutants. A relatively good correlation between the aerosol chemical composition and the cloud condensation nuclei(CCN) activation indicated that aerosol chemistry is an important factor that influences the saturated hygroscopicity and growth of the aerosol. 相似文献