Short Communication High Prevalence of Transmitted Antiretroviral Drug Resistance Among Newly HIV Type 1 Diagnosed Adults in Mombasa, Kenya

In view of the recent antiretroviral therapy (ART) scale-up in Kenya, surveillance of transmitted HIV drug resistance (TDR) is important. A cross-sectional survey was conducted among newly HIV-1 diagnosed, anti-retroviral-naive adults in Mombasa, Kenya. Surveillance drug resistance mutations (SDRMs) were identified according to the 2009 WHO list. HIV-1 subtypes were determined using REGA and SCUEAL subtyping tools. Genotypic test results were obtained for 68 of 81 participants, and SDRMs were identified in 9 samples. Resistance to nonnucleoside reverse transcriptase inhibitors (K103N) occurred in five participants, yielding a TDR prevalence of 7.4% (95% confidence interval 2.4-16.3%). Frequencies of HIV-1 subtypes were A (70.6%), C (5.9%), D (2.9%), and unique recombinant forms (20.6%). The TDR prevalence found in this survey is higher than previously reported in different regions in Kenya. These findings justify increased vigilance with respect to TDR surveillance in African regions where ART programs are scaled-up in order to inform treatment guideline

Reinforcement learning-based spectrum management for cognitive radio networks: A literature review and case study

In cognitive radio (CR) networks, the cognition cycle, i.e., the ability of wireless transceivers to learn the optimal configuration meeting environmen- tal and application requirements, is considered as important as the hardware components which enable the dynamic spectrum access (DSA) capabilities. To this purpose, several machine learning (ML) techniques have been applied on CR spectrum and network management issues, including spectrum sensing, spectrum selection, and routing. In this paper, we focus on reinforcement learning (RL), an online ML paradigm where an agent discovers the optimal sequence of actions required to perform a task via trial-end-error interactions with the environment. Our study provides both a survey and a proof of concept of RL applications in CR networking. As a survey, we discuss pros and cons of the RL framework compared to other ML techniques, and we provide an exhaustive review of the RL-CR literature, by considering a twofold perspective, i.e., an applicationdriven taxonomy and a learning methodology-driven taxonomy. As a proof of concept, we investigate the application of RL techniques on joint spectrum sensing and decision problems, by comparing different algorithms and learning strategies and by further analyzing the impact of information sharing techniques in purely cooperative or mixed cooperative/competitive tasks