BAPRP: a machine learning approach to blackhole attacks prevention routing protocol in vehicular Ad Hoc networks

International Journal of Information Security - One of the network performance challenges of Vehicular Ad Hoc Networks (VANET) is the Black Hole attack. This destructive attack severely damages the...     

In situ H2O2 Generation by Choline Oxidase and Its Application in Amino Polysaccharide Degradation by Coupling to Lytic Polysaccharide Monooxygenase

Chitin, the most abundant amino polysaccharide in Nature, has many applications in different fields. However, processing of this recalcitrant biopolymer in an environmentally friendly manner remains a major challenge. In this context, lytic polysaccharide monooxygenases (LPMOs) are of interest, as they can act on the most recalcitrant parts of chitin and related insoluble biopolymers such as cellulose. Efficient LPMO catalysis can be achieved by feeding reactions with H₂O₂, but careful control of H₂O₂ is required to avoid autocatalytic enzyme inactivation. Herein, we present a coupled enzyme system in which a choline oxidase from Arthrobacter globiformis is employed for controlled in situ generation of H₂O₂ that fuels LPMO-catalyzed oxidative degradation of chitin. We show that the rate, stability and extent of the LPMO reaction can be manipulated by varying the amount of choline oxidase and/or its substrate, choline chloride, and that efficient peroxygenase reactions may be achieved using sub-μM concentrations of the H₂O₂-generating enzyme. This coupled system requires only sub-stoichiometric amounts of the reductant that is needed to keep the LPMO in its active, reduced state. It is conceivable that this enzyme system may be used for bioprocessing of chitin in choline-based natural deep eutectic solvents.     

《H'Mong花》——丝巾纹样创新设计北大核心

作品说明:创作灵感源于越南H’Mong族织锦纹样。纹样选用花纹、螺纹、太阳纹、星星纹等,表达丰收富裕、家庭幸福之意;色彩选用极具H’Mong族特色的红橙色、天蓝色、黄色等;构图炫彩斑斓,或灵动活泼,或安静端庄。     

1,2,4-Triazole-3-Thione Analogues with a 2-Ethylbenzoic Acid at Position 4 as VIM-type Metallo-β-Lactamase Inhibitors

Metallo-β-lactamases (MBLs) are increasingly involved as a major mechanism of resistance to carbapenems in relevant opportunistic Gram-negative pathogens. Unfortunately, clinically efficient MBL inhibitors still represent an unmet medical need. We previously reported several series of compounds based on the 1,2,4-triazole-3-thione scaffold. In particular, Schiff bases formed between diversely 5-substituted-4-amino compounds and 2-carboxybenzaldehyde were broad-spectrum inhibitors of VIM-type, NDM-1 and IMP-1 MBLs. Unfortunately, these compounds were unable to restore antibiotic susceptibility of MBL-producing bacteria, probably because of poor penetration and/or susceptibility to hydrolysis. To improve their microbiological activity, we synthesized and characterized compounds where the hydrazone-like bond of the Schiff base analogues was replaced by a stable ethyl link. This small change resulted in a narrower inhibition spectrum, as all compounds were poorly or not inhibiting NDM-1 and IMP-1, but showed a significantly better activity on VIM-type enzymes, with K_i values in the μM to sub-μM range. The resolution of the crystallographic structure of VIM-2 in complex with one of the best inhibitors yielded valuable information about their binding mode. Interestingly, several compounds were shown to restore the β-lactam susceptibility of VIM-type-producing E. coli laboratory strains and also of K. pneumoniae clinical isolates. In addition, selected compounds were found to be devoid of toxicity toward human cancer cells at high concentration, thus showing promising safety.     

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS.