Herein
a simple, catalyst- and solvent-free system for highly atom-economic
synthesis of phthalazinones has been developed using phthalaldehydic
acid, 2-acyl-benzoic acid, and substituted hydrazine as simple substrates.
The reaction time was shortened to 20–60 min. Structurally
diverse phthalaldehydic acids, 2-acyl-benzoic acids, and hydrazines
were transformed into phthalazinones with a nearly 100% yield regardless
of the aggregate state and electronic nature of the substituents.
The transformation was demonstrated to be amenable for scale-up with
multiple liquid and solid materials. In addition, isolation and purification
of the crude products can be simply done with only crystallization.
The heavy metal pollution was also eliminated from the source.
FabI, enoyl-ACP reductase (ENR), is the rate-limiting enzyme in the last step for fatty acids biosynthesis in many bacteria. Triclosan (TCL) is a commercial bactericide, and as a FabI inhibitor, it can depress the substrate (trans-2-enoyl-ACP) binding with FabI to hinder the fatty acid synthesis. The structure-activity relationship between TCL derivatives and FabI protein has already been acknowledged, however, their combination at the molecular level has never been investigated. This paper uses the computer-aided approaches, such as molecular docking, molecular dynamics simulation, and binding free energy calculation based on the molecular mechanics/Poisson-Bolzmann surface area (MM/PBSA) method to illustrate the interaction rules of TCL derivatives with FabI and guide the development of new derivatives. The consistent data of the experiment and corresponding activity demonstrates that electron-withdrawing groups on side chain are better than electron-donating groups. 2-Hydroxyl group on A ring, promoting the formation of hydrogen bond, is vital for bactericidal effect; and the substituents at 4-position of A ring, 2'-position and 4'-position of B ring benefit antibacterial activity due to forming a hydrogen bond or stabilizing the conformation of active pocket residues of receptor. While the substituents at 3'-position and 5'-position of B ring destroy the π-π stacking interaction of A ring and NAD which depresses the antibacterial activity. This study provides a new sight for designing novel TCL derivatives with superior antibacterial activity.
Because of the irregular geometries, earthquake-induced adjacent curved bridge pounding may lead to more complex local damage or even collapse. The relevant research is mainly concentrated on the numerical analysis which lack experimental verification and discussion by changing of structural parameters. In this paper, a scaled three-dimensional numerical model of a curved bridge is established based on 3D contact friction theory for investigating the uneven distribution of pounding forces at the expansion joint of the bridge. Shaking table tests were carried out at first on a curved bridge to validate the numerical model. A series of parametric studies were then conducted to examine the impacts of the radius of curvature and longitudinal slope of the superstructure of the curved bridge on its seismic pounding response. The results show that the maximum pounding force first increases and then decreases as the radius of curvature increases, but that it decreases monotonically with the growth of the longitudinal slope. These results suggest that controlling the radius of curvature and the longitudinal slope of the superstructure of the bridge can reduce the localized high stress that is induced by seismic pounding. Also, the unevenly distributed pounding forces can significantly increase the relative radial displacement of the bridge’s deck corners, although the relative tangential displacement may decrease. It is thus necessary to adopt effective anti-pounding measures to prevent the superstructure of the bridge from being unseated.
Network-based intrusion detection system (NIDS) monitors network traffic for malicious activities, forming the frontline defense against increasing attacks over information infrastructures. Although promising, our quantitative analysis shows that existing methods perform inconsistently in declaring various unknown attacks (e.g., 9% and 35% F1 respectively for two distinct unknown threats for an SVM-based method) or detecting diverse known attacks (e.g., 31% F1 for the Backdoor and 93% F1 for DDoS by a GCN-based state-of-the-art method), and reveals that the underlying cause is entangled distributions of flow features. This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme. Specifically, we first disentangle traffic features by a non-parameterized optimization based on mutual information, automatically differentiating tens and hundreds of complex features of various attacks. Such differentiated features will be fed into a memory model to generate representations, which are further disentangled to highlight the attack-specific features. Finally, we use a novel graph diffusion method that dynamically fuses the network topology for spatial-temporal aggregation in evolving data streams. By doing so, we can effectively identify various attacks in encrypted traffics, including unknown threats and
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