Targeting the Achilles’ Heel of Multidrug-Resistant Staphylococcus aureus by the Endocannabinoid Anandamide

Antibiotic-resistant Staphylococcus aureus is a major health issue that requires new therapeutic approaches. Accumulating data suggest that it is possible to sensitize these bacteria to antibiotics by combining them with inhibitors targeting efflux pumps, the low-affinity penicillin-binding protein PBP2a, cell wall teichoic acid, or the cell division protein FtsZ. We have previously shown that the endocannabinoid Anandamide (N-arachidonoylethanolamine; AEA) could sensitize drug-resistant S. aureus to a variety of antibiotics, among others, through growth arrest and inhibition of drug efflux. Here, we looked at biochemical alterations caused by AEA. We observed that AEA increased the intracellular drug concentration of a fluorescent penicillin and augmented its binding to membrane proteins with concomitant altered membrane distribution of these proteins. AEA also prevented the secretion of exopolysaccharides (EPS) and reduced the cell wall teichoic acid content, both processes known to require transporter proteins. Notably, AEA was found to inhibit membrane ATPase activity that is necessary for transmembrane transport. AEA did not affect the membrane GTPase activity, and the GTPase cell division protein FtsZ formed the Z-ring of the divisome normally in the presence of AEA. Rather, AEA caused a reduction in murein hydrolase activities involved in daughter cell separation. Altogether, this study shows that AEA affects several biochemical processes that culminate in the sensitization of the drug-resistant bacteria to antibiotics.     

A re-organizing biosurveillance framework based on fog and mobile edge computing

Multimedia Tools and Applications - Biological threats are becoming a serious security issue for many countries across the world. Effective biosurveillance systems can primarily support appropriate...     

Electron transport in dye-sanitized solar cell with tin-doped titanium dioxide as photoanode materials

Journal of Materials Science: Materials in Electronics - Pure and tin-incorporated TiO2 (Sn-TiO2) nanoparticles were prepared utilizing photolysis method. Field emission-scanning electron...     

Metaheuristic Optimization of Time Series Models for Predicting NetworksTraffic

Traffic prediction of wireless networks attracted many researchers and practitioners during the past decades. However, wireless traffic frequently exhibits strong nonlinearities and complicated patterns, which makes it challenging to be predicted accurately. Many of the existing approaches for predicting wireless network traffic are unable to produce accurate predictions because they lack the ability to describe the dynamic spatial-temporal correlations of wireless network traffic data. In this paper, we proposed a novel meta-heuristic optimization approach based on fitness grey wolf and dipper throated optimization algorithms for boosting the prediction accuracy of traffic volume. The proposed algorithm is employed to optimize the hyper-parameters of long short-term memory (LSTM) network as an efficient time series modeling approach which is widely used in sequence prediction tasks. To prove the superiority of the proposed algorithm, four other optimization algorithms were employed to optimize LSTM, and the results were compared. The evaluation results confirmed the effectiveness of the proposed approach in predicting the traffic of wireless networks accurately. On the other hand, a statistical analysis is performed to emphasize the stability of the proposed approach.     

Nano-Scale Cordierite Glass-Ceramic from Natural Raw Materials with Different Fluoride Additions

Silicon - Stable cordierite glass-ceramics were prepared from natural kaolin, silica sand and magnesite as raw materials. Furthermore the effect of fluorine additions was tested by addingAlF3 or...     

Synthesis and characterization of unsaturated polyesters based on the aminolysis of poly(ethylene terephthalate)

The depolymerization of poly(ethylene terephthalate) via an aminolysis process was studied. An excess of ethanol amine in the presence of sodium acetate as a catalyst was used to produce bis(2‐hydroxyl ethylene) terephthalamide (BHETA). Unsaturated polyester (UP) resins were obtained by the reaction of BHETA with different long‐chain dibasic acids such as decanedioic acid, tetradecanoic acid, and octadecanoic acid in conjunction with maleic anhydride as a source of unsaturation. The chemical structure of the UP resins was confirmed by ¹H‐NMR. The vinyl ester resins were used as crosslinking agents for UP. The curing behavior and mechanical properties of the UP resins with vinyl ester were evaluated at different temperatures ranging from 25 to 55°C. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection (FS) techniques to increase classification accuracy by minimizing the number of features selected. On the other hand, metaheuristic optimization algorithms have been widely used in feature selection in recent decades. In this paper, we proposed a hybrid optimization algorithm for feature selection in IDS. The proposed algorithm is based on grey wolf (GW), and dipper throated optimization (DTO) algorithms and is referred to as GWDTO. The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance. On the employed IoT-IDS dataset, the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in the literature to validate its superiority. In addition, a statistical analysis is performed to assess the stability and effectiveness of the proposed approach. Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.     

Doppler Volume Rendering: A Dynamic,Piecewise Linear Spectral Representation for Visualizing Astrophysics Simulations

We present a novel approach for rendering volumetric data including the Doppler effect of light. Similar to the acoustic Doppler effect, which is caused by relative motion between a sound emitter and an observer, light waves also experience compression or expansion when emitter and observer exhibit relative motion. We account for this by employing spectral volume rendering in an emission–absorption model, with the volumetric matter moving according to an accompanying vector field, and emitting and attenuating light at wavelengths subject to the Doppler effect. By introducing a novel piecewise linearear representation of the involved light spectra, we achieve accurate volume rendering at interactive frame rates. We compare our technique to rendering with traditional point-based spectral representation, and demonstrate its utility using a simulation of galaxy formation.