To look for superior and safe high energy density compounds (HEDCs), 2,2',4,4',6,6'-hexanitroazobenzene (HNAB) and its -NO(2), -NH(2), -CN, -NC, -ONO(2), -N(3), or -NF(2) derivatives were studied at the B3LYP/6-31G* level of density functional theory (DFT). The isodesmic reactions were applied to calculate the heats of formation (HOFs) for these compounds. The theoretical molecular density (ρ), detonation energy (E(d)), detonation pressure (P), and detonation velocity (D), estimated using the Kamlet-Jacobs equations, showed that the detonation properties of these compounds were excellent. The effects of substituent groups on HOF, ρ, E(d), P, and D were studied. The order of contribution of the substituent groups to P and D was -NF(2) > -ONO(2) > -NO(2) > -N(3) > -NH(2). Sensitivity was evaluated using the nitro group charges, frontier orbital energies, and bond dissociation enthalpies (BDEs). The trigger bonds in the pyrolysis process for all these HNAB derivatives may be Ring-NO(2), Ring-N═N, Ring-NF(2), or O-NO(2) varying with the attachment of different substituents. BDEs of trigger bonds except those of -ONO(2) derivatives are relatively large, which means these compounds suffice the stability request of explosives. Taking both detonation properties and sensitivities into consideration, some -NF(2) and -NO(2) derivatives may be potential candidates for HEDCs.
DNA N6-methyladenine is an important type of DNA modification that plays important roles in multiple biological processes. Despite the recent progress in developing DNA 6mA site prediction methods, several challenges remain to be addressed. For example, although the hand-crafted features are interpretable, they contain redundant information that may bias the model training and have a negative impact on the trained model. Furthermore, although deep learning (DL)-based models can perform feature extraction and classification automatically, they lack the interpretability of the crucial features learned by those models. As such, considerable research efforts have been focused on achieving the trade-off between the interpretability and straightforwardness of DL neural networks. In this study, we develop two new DL-based models for improving the prediction of N6-methyladenine sites, termed LA6mA and AL6mA, which use bidirectional long short-term memory to respectively capture the long-range information and self-attention mechanism to extract the key position information from DNA sequences. The performance of the two proposed methods is benchmarked and evaluated on the two model organisms Arabidopsis thaliana and Drosophila melanogaster. On the two benchmark datasets, LA6mA achieves an area under the receiver operating characteristic curve (AUROC) value of 0.962 and 0.966, whereas AL6mA achieves an AUROC value of 0.945 and 0.941, respectively. Moreover, an in-depth analysis of the attention matrix is conducted to interpret the important information, which is hidden in the sequence and relevant for 6mA site prediction. The two novel pipelines developed for DNA 6mA site prediction in this work will facilitate a better understanding of the underlying principle of DL-based DNA methylation site prediction and its future applications.
The effect of hydrostatic pressure on the geometrical, electronic, and thermodynamic properties of the energetic material 2-diazo-4,6-dinitrophenol (DDNP) has been investigated by density functional theory periodic calculations. The crystal structure optimized by the local density approximation with the CeperleyÀAlder exchange-correlation potential parametrized by Perdew and Zunger compares well with the experimental results at the ambient pressure. When the hydrostatic compression is exerted upon the DDNP crystal, the interatomic distances, bond angles, and dihedral angles of DDNP molecule change regularly with the increase in pressure except at 10, 59, and 66 GPa where the structural transformations occur. The same is true for the unit cell lattice parameters, density, total energy, band gap, density of states, and thermodynamic functions. When the pressure is below 10 GPa, DDNP molecule exists in the quinoid form (I). As the pressure is between 10 and 58 GPa, it has the cyclic azoxy form (II). In the range of 0À58 GPa, DDNP crystal is anisotropic.
Characteristic gene selection and tumor classification of gene expression data play major roles in genomic research. Due to the characteristics of a small sample size and high dimensionality of gene expression data, it is a common practice to perform dimensionality reduction prior to the use of machine learning-based methods to analyze the expression data. In this context, classical principal component analysis (PCA) and its improved versions have been widely used. Recently, methods based on supervised discriminative sparse PCA have been developed to improve the performance of data dimensionality reduction. However, such methods still have limitations: most of them have not taken into consideration the improvement of robustness to outliers and noise, label information, sparsity, as well as capturing intrinsic geometrical structures in one objective function. To address this drawback, in this study, we propose a novel PCA-based method, known as the robust Laplacian supervised discriminative sparse PCA, termed RLSDSPCA, which enforces the L2,1 norm on the error function and incorporates the graph Laplacian into supervised discriminative sparse PCA. To evaluate the efficacy of the proposed RLSDSPCA, we applied it to the problems of characteristic gene selection and tumor classification problems using gene expression data. The results demonstrate that the proposed RLSDSPCA method, when used in combination with other related methods, can effectively identify new pathogenic genes associated with diseases. In addition, RLSDSPCA has also achieved the best performance compared with the state-of-the-art methods on tumor classification in terms of major performance metrics. The codes and data sets used in the study are freely available at .
A strategy for the synthesis of quinazolinones via Ru-catalyzed redox isomerization/acceptorless dehydrogenation was proposed and accomplished. In the presence of a commercially available [(pcymene)Cl 2 ] 2 , a range of desirable products were obtained with o-aminobenzamides and allylic alcohols as starting materials in moderate to high yields. This strategy is attractive due to high atom efficiency, and minimal consumption of chemicals and energy.
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