Frequency and patterns of early recanalization after vasectomy

BACKGROUND: Our understanding of early post-vasectomy recanalization is limited to histopathological studies. The objective of this study was to estimate the frequency and to describe semen analysis patterns of early recanalization after vasectomy. METHODS: Charts displaying serial post-vasectomy semen analyses were created using the semen analysis results from 826 and 389 men participating in a randomized trial of fascial interposition (FI) and an observational study of cautery, respectively. In the FI trial, participants were randomly allocated to vas occlusion by ligation and excision with or without FI. In the cautery study, sites used their usual cautery occlusion technique, two with and two without FI. Presumed early recanalization was based on the assessment of individual semen analysis charts by three independent reviewers. Discrepancies were resolved by consensus. RESULTS: Presumed early recanalization was characterized by a very low sperm concentration within two weeks after vasectomy followed by return to large numbers of sperm over the next few weeks. The overall proportion of men with presumed early recanalization was 13% (95% CI 12%–15%). The risk was highest with ligation and excision without FI (25%) and lowest for thermal cautery with FI (0%). The highest proportion of presumed early recanalization was observed among men classified as vasectomy failures. CONCLUSION: Early recanalization, occurring within the first weeks after vasectomy, is more common than generally recognized. Its frequency depends on the occlusion technique performed

A hidden HIV epidemic among women in Vietnam

<p>Abstract</p> <p>Background</p> <p>The HIV epidemic in Vietnam is still concentrated among high risk populations, including IDU and FSW. The response of the government has focused on the recognized high risk populations, mainly young male drug users. This concentration on one high risk population may leave other populations under-protected or unprepared for the risk and the consequences of HIV infection. In particular, attention to women's risks of exposure and needs for care may not receive sufficient attention as long as the perception persists that the epidemic is predominantly among young males. Without more knowledge of the epidemic among women, policy makers and planners cannot ensure that programs will also serve women's needs.</p> <p>Methods</p> <p>More than 300 documents appearing in the period 1990 to 2005 were gathered and reviewed to build an understanding of HIV infection and related risk behaviors among women and of the changes over time that may suggest needed policy changes.</p> <p>Results</p> <p>It appears that the risk of HIV transmission among women in Vietnam has been underestimated; the reported data may represent as little as 16% of the real number. Although modeling predicted that there would be 98,500 cases of HIV-infected women in 2005, only 15,633 were accounted for in reports from the health system. That could mean that in 2005, up to 83,000 women infected with HIV have not been detected by the health care system, for a number of possible reasons. For both detection and prevention, these women can be divided into sub-groups with different risk characteristics. They can be infected by sharing needles and syringes with IDU partners, or by having unsafe sex with clients, husbands or lovers. However, most new infections among women can be traced to sexual relations with young male injecting drug users engaged in extramarital sex. Each of these groups may need different interventions to increase the detection rate and thus ensure that the women receive the care they need.</p> <p>Conclusion</p> <p>Women in Vietnam are increasingly at risk of HIV transmission but that risk is under-reported and under-recognized. The reasons are that women are not getting tested, are not aware of risks, do not protect themselves and are not being protected by men. Based on this information, policy-makers and planners can develop better prevention and care programs that not only address women's needs but also reduce further spread of the infection among the general population.</p

IRDRC: An Intelligent Real-Time Dual-Functional Radar-Communication System for Automotive Vehicles

© 2012 IEEE. This letter introduces an intelligent Real-time Dual-functional Radar-Communication (iRDRC) system for autonomous vehicles (AVs). This system enables an AV to perform both radar and data communications functions to maximize bandwidth utilization as well as significantly enhance safety. In particular, the data communications function allows the AV to transmit data, e.g., of current traffic, to edge computing systems and the radar function is used to enhance the reliability and reduce the collision risks of the AV, e.g., under bad weather conditions. The problem of the iRDRC is to decide when to use the communication mode or the radar mode to maximize the data throughput while minimizing the miss detection probability of unexpected events given the uncertainty of surrounding environment. To solve the problem, we develop a deep reinforcement learning algorithm that allows the AV to quickly obtain the optimal policy without requiring any prior information about the environment. Simulation results show that the proposed scheme outperforms baseline schemes in terms of data throughput, miss detection probability, and convergence rate

Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning

This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts. This mechanism enables RSMA to flexibly manage interference and thus enhance energy and spectral efficiency. Although possessing outstanding advantages, optimizing power allocation in RSMA is very challenging under the uncertainty of the communication channel and the transmitter has limited knowledge of the channel information. To solve the problem, we first develop a Markov Decision Process framework to model the dynamic of the communication channel. The deep reinforcement algorithm is then proposed to find the optimal power allocation policy for the transmitter without requiring any prior information of the channel. The simulation results show that the proposed scheme can outperform baseline schemes in terms of average sum-rate under different power and QoS requirements

Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications

Autonomous Vehicles (AVs) are required to operate safely and efficiently in dynamic environments. For this, the AVs equipped with Joint Radar-Communications (JRC) functions can enhance the driving safety by utilizing both radar detection and data communication functions. However, optimizing the performance of the AV system with two different functions under uncertainty and dynamic of surrounding environments is very challenging. In this work, we first propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions in selecting JRC operation functions under the dynamic and uncertainty of the surrounding environment. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV without requiring any prior information about surrounding environment. Furthermore, to make our proposed framework more scalable, we develop a Transfer Learning (TL) mechanism that enables the AV to leverage valuable experiences for accelerating the training process when it moves to a new environment. Extensive simulations show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches. With the deep reinforcement learning and transfer learning approaches, our proposed solution can find its applications in a wide range of autonomous driving scenarios from driver assistance to full automation transportation