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The advent of 5G heralds unprecedented connectivity with high throughput and low latency for network users. Software-defined networking (SDN) plays a significant role in fulfilling these requirements. However, it poses substantial security challenges due to its inherent centralized management strategy. Moreover, SDN confronts limitations in handling malicious traffic under 5G's extensive data flow. To deal with these issues, this paper presents a novel intrusion detection system (IDS) designed for 5G SDN networks, leveraging the advanced capabilities of binarized deep spiking capsule fire hawk neural networks (BSHNN) and blockchain technology, which operates across multiple layers. Initially, the lightweight encryption algorithm (LEA) is used at the data acquisition layer to authenticate mobile users via trusted third parties. Followed by optimal switch selection using the mud-ring algorithm in the switch layer, and the data flow rules are secured by employing blockchain technology incorporating searchable encryption algorithms within the blockchain plane. The domain controller layer utilizes binarized deep spiking capsule fire hawk neural network (BSHNN) for real-time data packet classification, while the smart controller layer uses enhanced adapting hidden attribute-weighted naive bayes (EAWNB) to identify suspicious packets during data transmission. The experimental results show that the proposed technique outperforms the state-of-the-art approaches in terms of accuracy (98.02%), precision (96.40%), detection rate (96.41%), authentication time (16.2 s), throughput, delay, and packet loss ratio. [ABSTRACT FROM AUTHOR]

AbstractMicropollutants (MPs) are the burgeoning contaminants which are released in wastewater (WW) due to the anthropogenic activities. Though MPs are detected in low amount in WW but are highly toxic and non-degradable; are accountable for global water quality and health hazards. The conventional WW treatment is energy inefficient and also results in hazardous sludge; hence alternative sustainable approaches are the ‘need of hour’. Recent micro-biotechnological methods based on bio-adsorption, membrane filtration, biocatalysts, bioreactors, and nanotechnology are effective in removal of MPs. Bacteria, fungi, and microalgae can degrade these MPs through the mechanisms of biosorption, biotransformation, bioaccumulation, and bioconversion, even at high concentrations. The nanotechnological tools using microorganisms are synthesized as microbial based nanoparticles (MNPs) employing potential strains of bacteria, fungi, and microalgae, can be possibly integrated into existing membrane or adsorption technological methods. MNPs have revealed as a potential for elimination of MPs from WW, the systematic studies are still in infancy. However, the aspects related to their recovery, regeneration and efficacy must be justified to confirm their application as future workhorses in the real WW treatment plants. This comprehensive review aims to provide information on MNPs as sustainable and cost-effective tools for bioremediation of MPs in WW. [ABSTRACT FROM AUTHOR]

The advent of 5G heralds unprecedented connectivity with high throughput and low latency for network users. Software-defined networking (SDN) plays a significant role in fulfilling these requirements. However, it poses substantial security challenges due to its inherent centralized management strategy. Moreover, SDN confronts limitations in handling malicious traffic under 5G’s extensive data flow. To deal with these issues, this paper presents a novel intrusion detection system (IDS) designed for 5G SDN networks, leveraging the advanced capabilities of binarized deep spiking capsule fire hawk neural networks (BSHNN) and blockchain technology, which operates across multiple layers. Initially, the lightweight encryption algorithm (LEA) is used at the data acquisition layer to authenticate mobile users via trusted third parties. Followed by optimal switch selection using the mud-ring algorithm in the switch layer, and the data flow rules are secured by employing blockchain technology incorporating searchable encryption algorithms within the blockchain plane. The domain controller layer utilizes binarized deep spiking capsule fire hawk neural network (BSHNN) for real-time data packet classification, while the smart controller layer uses enhanced adapting hidden attribute-weighted naive bayes (EAWNB) to identify suspicious packets during data transmission. The experimental results show that the proposed technique outperforms the state-of-the-art approaches in terms of accuracy (98.02%), precision (96.40%), detection rate (96.41%), authentication time (16.2 s), throughput, delay, and packet loss ratio.

Host-pathogen interactions play a critical role in infectious diseases, and understanding the underlying mechanisms is vital for developing effective therapeutic strategies. The visualization and characterization of bacterial proteins within host cells is key to unraveling the dynamics of these interactions. Various protein labeling strategies have emerged as powerful tools for studying host-pathogen interactions, enabling the tracking, localization, and functional analysis of bacterial proteins in real-time. However, the labeling and localization of Salmonella secreted type III secretion system (T3SS) effectors in host cells poses technical challenges. Conventional methods disrupt effector stoichiometry and often result in non-specific staining. Bulky fluorescent protein fusions interfere with effector secretion, while other tagging systems such as 4Cys-FLaSH/Split-GFP suffer from low labeling specificity and a poor signal-to-noise ratio. Recent advances in state-of-the-art techniques have augmented the existing toolkit for monitoring the translocation and dynamics of bacterial effectors. This comprehensive review delves into the bacterial protein labeling strategies and their application in imaging host-pathogen interactions. Lastly, we explore the obstacles faced and potential pathways forward in the realm of protein labeling strategies for visualizing interactions between hosts and pathogens.