Synthesis, Characterization and DNA Cleavage of Copper(II) Complex with D,L-Dithiothreitol

Purpose: To study deoxyribonucleic acid (DNA) shearing capability of copper(II) complex of dithiothreitol (DTT) and to fevaluate its potential application in cancer therapy.Methods: A parrot green complex was synthesized by grinding copper acetate monohydrate and DTT in 1:2 molar ratio in a mortar until no fumes of acetic acid were observed. The complex was characterized using attenuated total reflectance-Fourier transform infra-red (ATR-FTIR), and x-ray diffraction (XRD) techniques. Further information was also collected through Karl Fischer titration, thermogravimetric analysis (TGA) and (magnetic moment. Cleavage of DNA was determined by agarose gel electrophoresis. The gel was then stained, analyzed and photographed under ultraviolet (UV) light.Results: ATR-FTIR confirmed the formation of copper(II) complex with DTT by binding through thiol group based on the disappearance of the thiol (-SH) stretching peak at 2545 cm-1. The crystalline structure was elucidated by a sharp intense peak at 38.520 in XRD spectrum while the octahedral geometry of complex was inferred from a magnetic moment of 1.72 B.M. The results for water content obtained by Karl Fischer titration and TGA revealed that water molecules are not part of the coordination sphere of the complex. Cleavage study of DNA showed that the complex completely sheared the circular DNA compared to pure DTT.Conclusion: Solvent free synthesis of Copper(II)-DTT complex has been successfully achieved, and an anhydrous complex with octahedral geometry obtained. The complex has a greater potential to shear DNA molecule than pure DTT.Keywords: DNA shearing, Copper(II) complex, Dithiothreitol, Attenuated total reflectance-Fourier transform infra-red, Karl Fischer titration, Magnetic momen

Development of ANFIS Control System for Seismic Response Reduction using Multi-Objective Genetic Algorithm

Adaptive neuro fuzzy inference system (ANFIS) and Genetic algorithm (GA) was proposed in this study to reduce dynamic responses of a seismically excited building. A multi-objective genetic algorithm (MOGA) was used to optimize the ANFIS+GA controller. Two MR dampers were used as multiple control devices and a scaled five-story building model was selected as an example structure. A fuzzy control algorithm was compared with the proposed ANFIS and ANFIS+GA controller. Adaptive neuro-fuzzy inference system (ANFIS) and Ganetic algorithm with several outputs was proposed. In case study, after numerical simulation, it has been verified that the ANFIS control algorithm can present better control performance compared to the fuzzy control algorithm in reducing both displacement and acceleration responses

Efficient Non-Linear Covert Channel Detection in TCP Data Streams

Cyber-attacks are causing losses amounted to billions of dollars every year due to data breaches and vulnerabilities. The existing tools for data leakage prevention and detection are often bypassed by using various different types of sophisticated techniques such as network steganography for stealing the data. This is due to several weaknesses which can be exploited by a threat actor in existing detection systems. The weaknesses are high time and memory training complexities as well as large training datasets. These challenges become worse when the amount of generated data increases in every second in many realms. In addition, the number of false positives is high which makes them inaccurate. Finally, there is a lack of a framework catering for the needs such as raising alerts as well as data monitoring and updating/adapting of a threshold value used for checking the data packets for covert data. In order to overcome these weaknesses, this paper proposes a novel framework that includes elements such as continuous data monitoring, threshold maintenance, and alert notification. This paper also proposes a model based on statistical measures to detect covert data leakages, especially for non-linear chaotic data. The main advantage of the proposed model is its capability to provide results with tolerance/threshold values much more efficiently. Our experiments indicate that the proposed framework has low false positives and outperforms various existing techniques in terms of accuracy and efficiency

Factors associated with long-term species composition in dry tropical forests of Central India

The long-term future of species composition in forests depends on regeneration. Many factors can affect regeneration, including human use, environmental conditions, and species' traits. This study examines the influence of these factors in a tropical deciduous forest of Central India, which is heavily used by local, forest-dependent residents for livestock grazing, fuel-wood extraction, construction and other livelihood needs. We measure size-class proportions (the ratio of abundance of a species at a site in a higher size class to total abundance in both lower and higher size classes) for 39 tree species across 20 transects at different intensities of human use. The size-class proportions for medium to large trees and for small to medium-sized trees were negatively associated with species that are used for local construction, while size class proportions for saplings to small trees were positively associated with those species that are fire resistant and negatively associated with livestock density. Results indicate that grazing and fire prevent non-fire resistant species from reaching reproductive age, which can alter the long term composition and future availability of species that are important for local use and ecosystem services. Management efforts to reduce fire and forest grazing could reverse these impacts on long-term forest composition

The Green Synthesis of Silver Nanoparticles from Avena fatua Extract: Antifungal Activity against Fusarium oxysporum f.sp. lycopersici

Using plant extracts as eco-friendly reducing and stabilizing agents for the synthesis of nanoparticles has gained significant attention in recent years. The current study explores the green synthesis of silver nanoparticles (AgNPs) using the Avena fatua extract and evaluates their antifungal activity against Fusarium oxysporum f.sp. lycopersici (Fol), a fungal plant pathogen. A green and sustainable approach was adopted to synthesize silver nanoparticles before these nanoparticles were employed for anti-fungal activity. The primary indication that AgNPs had formed was performed using UV-vis spectroscopy, where a strong peak at 425 nm indicated the effective formation of these nanoparticles. The indication of important functional groups acting as reducing and stabilizing agents was conducted using the FTIR study. Additionally, morphological studies were executed via SEM and AFM, which assisted with more effectively analyzing AgNPs. Crystalline behavior and size were estimated using powder XRD, and it was found that AgNPs were highly crystalline, and their size ranged from 5 to 25 nm. Synthesized AgNPs exhibited significant antifungal activity against Fol at a concentration of 40 ppm. Furthermore, the inhibitory index confirmed a positive correlation between increasing AgNPs concentration and exposure duration. This study suggests that the combined phytochemical mycotoxic effect of the plant extract and the smaller size of synthesized AgNPs were responsible for the highest penetrating power to inhibit Fol growth. Moreover, this study highlights the potential of using plant extracts as reducing and capping agents for the green synthesis of AgNPs with antifungal properties. The study concludes that A. fatua extract can synthesize antifungal AgNPs as a sustainable approach with robust antifungal efficacy against Fol, underscoring their promising potential for integration into plant protection strategies