Effect of Different Drying Procedures on the Nutritional Value of the Mantle and Tentacles of <Emphasis Type="Italic">Sepia officinalis</Emphasis>

The aim of this work was to investigate the influence of two different drying methods, traditionally by sun and controlled temperatures of 45, 50, 60 and 70 °C and relative humidity of 20 and 30% on the chemical composition of Sepia officinalis. When compared to fresh samples, lipid content increased under both drying methods whereas protein levels significantly increased only under controlled conditions. Drying procedures increased the percentage of saturated fatty acids mainly in sun‐dried samples and at high temperature (70 °C). A significant decrease in the amount of polyunsaturated fatty acids was detected in sun‐dried samples. No significant differences were found between the levels of polyunsaturated fatty acids of fresh cuttlefish and those under controlled conditions. The highest value of malondialdehyde in mantles was observed in sun‐dried samples, while in tentacles the highest level was seen at 70 °C. The highest level of advanced oxidation protein products was noted in traditionally sun‐dried samples. The results obtained showed that 45–50 °C and 20–30% humidity conserved the value of proteins and lipids more than traditional drying. Results of this study may be suitable for developing a reliable industrial index.     

The influence of blood and/or hemostatic agent contamination on Micro-TBS to dentin

Aim: The purpose of this study was to evaluate the effects of blood contamination and hemostatic agent Ankaferd Blood Stopper (ABS: Ankaferd Drug Inc, Istanbul, Turkey) on the microtensile bond strength of a self-etching adhesive. Material and methods: Flat dentin surfaces were created from 40-M teeth and randomly divided to four groups according to contamination and adhesive procedure. The specimens of Group 1 are contaminated with blood; ABS was applied to the specimens of Group 2 after blood contamination and applied to the specimens of Group 3 without blood contamination. Group 4 is control group and self-etching adhesive was applied to all specimens. Teeth were restored with a nanohybrid composite. The specimens were sectioned to the beams and microtensile testing was carried out and the data were statistically analyzed with analysis of variance test. Tukey’s honestly significant difference post hoc test was also performed for multiple comparisons to compare subgroups. Results: Group 4 had the highest strength value, followed by Group 3, while Group 1, which contacts only with blood, had the lowest strength value. Conclusion: ABS has a negative effect on the bond strength of one-step self-etching adhesive system.     

Aroma,Sugar and Anthocyanin Profile of Fruit and Seed of Mahlab (Prunus mahaleb L.): Optimization of Bioactive Compounds Extraction by Simplex Lattice Mixture Design

In the present study, bioactive properties of black mahlab fruit and seed were investigated. Simplex lattice mixture design was used to determine the best solvent composition for the extraction of bioactive compounds of the samples. The content of total phenolics, total flavonoids, condensed tannins, total anthocyanins, and antiradical efficiency of mahlab fruit and seed extracts were investigated by spectrophotometric methods. Moreover, major sugar composition, individual anthocyanins, fatty acids, and aroma profile of the samples were determined by chromatographic methods. Effect of temperature on degradation of anthocyanins was also investigated, and degradation kinetic model parameters were calculated. Finally, the constructed simplex lattice mixture design model was optimized to estimate the optimum solvent mixtures that would yield maximum total phenolic content. The best solvent mixture was found to be 36 % of acetone and 64 % of water. It was concluded that the experimental and predicted values were in good agreement. Total phenolics and flavonoids of the fruit were 2,266 mg gallic acid equivalent/100 g of dry sample and 946.57 mg catechin equivalent/100 g of dry sample, respectively. Total anthocyanin content of the fruit was 505.7 mg/100 g of dry sample. Temperature showed a significant degradation effect on the anthocyanins.     

[4.3.1]Bicyclic FKBP Ligands Inhibit Legionella Pneumophila Infection by LpMip-Dependent and LpMip-Independent Mechanisms**

Legionella pneumophila is the causative agent of Legionnaires’ disease, a serious form of pneumonia. Its macrophage infectivity potentiator (Mip), a member of a highly conserved family of FK506-binding proteins (FKBPs), plays a major role in the proliferation of the gram-negative bacterium in host organisms. In this work, we test our library of >1000 FKBP-focused ligands for inhibition of LpMip. The [4.3.1]-bicyclic sulfonamide turned out as a highly preferred scaffold and provided the most potent LpMip inhibitors known so far. Selected compounds were non-toxic to human cells, displayed antibacterial activity and block bacterial proliferation in cellular infection-assays as well as infectivity in human lung tissue explants. The results confirm [4.3.1]-bicyclic sulfonamides as anti-legionellal agents, although their anti-infective properties cannot be explained by inhibition of LpMip alone.     

Plasma surface modification of graphene oxide nanosheets for the synthesis of GO/PES nanocomposite ultrafiltration membrane for enhanced oily separation

In this study, two different monomers, namely hexafluorobutyl acrylate (HFBA) and diethylaminoethyl methacrylate (DEAEMA) were individually used to modify graphene oxide (GO) nanosheets via environmentally friendly plasma enhanced chemical vapor deposition (PECVD) method. The results from instrumental analyses confirmed the successful deposition of respective functional material onto the nanomaterials. Modified GOs were used as the nano-fillers to develop composite polyethersulfone (PES) ultrafiltration (UF) membrane with improved surface properties for oily solution treatment. All the developed membranes were characterized with a series of analytical instruments to support the findings of membrane filtration performance. The results indicated that the membrane incorporated with DEAEMA-GOs (coated with hydrophilic polymer) could achieve better results in terms of oil rejection, antifouling resistance and water recovery rate than the membrane incorporated with HFBA-GOs (coated with hydrophobic polymer). This is due to the reduced agglomeration between modified GOs as well as better interaction of hydrophilic-coated GOs with polymer membrane. Compared to the pure water flux of the membrane incorporated with unmodified GO, the membrane incorporated with DEAEMA-GO achieve approximately 85% higher value with oil removal rate remained almost unchanged (98.94% rejection).     

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.     

Deep SARSA-based reinforcement learning approach for anomaly network intrusion detection system

International Journal of Information Security - The growing evolution of cyber-attacks imposes a risk in network services. The search of new techniques is essential to detect and classify dangerous...