Decision-Making Algorithm with Geographic Mobility for Cognitive Radio

The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability

Efficacy, Pharmacokinetics, and Toxicity Profiles of a Broad Anti-SARS-CoV-2 Neutralizing Antibody

We recently reported the isolation and characterization of an anti-SARS-CoV-2 antibody, called IgG-A7, that protects transgenic mice expressing the human angiotensin-converting enzyme 2 (hACE-2) from an infection with SARS-CoV-2 Wuhan. We show here that IgG-A7 protected 100% of the transgenic mice infected with Delta (B.1.617.2) and Omicron (B.1.1.529) at doses of 0.5 and 5 mg/kg, respectively. In addition, we studied the pharmacokinetic (PK) profile and toxicology (Tox) of IgG-A7 in CD-1 mice at single doses of 100 and 200 mg/kg. The PK parameters at these high doses were proportional to the doses, with serum half-life of ~10.5 days. IgG-A7 was well tolerated with no signs of toxicity in urine and blood samples, nor in histopathology analyses. Tissue cross-reactivity (TCR) with a panel of mouse and human tissues showed no evidence of IgG-A7 interaction with the tissues of these species, supporting the PK/Tox results and suggesting that, while IgG-A7 has a broad efficacy profile, it is not toxic in humans. Thus, the information generated in the CD-1 mice as a PK/Tox model complemented with the mouse and human TCR, could be of relevance as an alternative to Non-Human Primates (NHPs) in rapidly emerging viral diseases and/or quickly evolving viruses such as SARS-CoV-2