Molecular aspects of MERS-CoV

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Middle East respiratory syndrome coronavirus (MERS-CoV) is a betacoronavirus which can cause acute respiratory distress in humans and is associated with a relatively high mortality rate. Since it was first identified in a patient who died in a Jeddah hospital in 2012, the World Health Organization has been notified of 1735 laboratory-confirmed cases from 27 countries, including 628 deaths. Most cases have occurred in Saudi Arabia. MERS-CoVancestors may be found in OldWorld bats of the Vespertilionidae family. After a proposed bat to camel switching event, transmission of MERS-CoV to humans is likely to have been the result of multiple zoonotic transfers from dromedary camels. Human-to-human transmission appears to require close contact with infected persons, with outbreaks mainly occurring in hospital environments. Outbreaks have been associated with inadequate infection prevention and control implementation, resulting in recommendations on basic and more advanced infection prevention and control measures by the World Health Organization, and issuing of government guidelines based on these recommendations in affected countries including Saudi Arabia. Evolutionary changes in the virus, particularly in the viral spike protein which mediates virus-host cell contact may potentially increase transmission of this virus. Efforts are on-going to identify specific evidence-based therapies or vaccines. The broad-spectrum antiviral nitazoxanide has been shown to have in vitro activity against MERS-CoV. Synthetic peptides and candidate vaccines based on regions of the spike protein have shown promise in rodent and non-human primate models. GLS-5300, a prophylactic DNA-plasmid vaccine encoding S protein, is the first MERS-CoV vaccine to be tested in humans, while monoclonal antibody, m336 has given promising results in animal models and has potential for use in outbreak situations

Implementing a Pre-Exposure Prophylaxis Intervention for Safer Conception among HIV Serodiscordant Couples: Recommendations for Health Care Providers

Couples in HIV serodiscordant relationships frequently desire children. Although partners who are virally suppressed pose almost no risk of transmitting HIV to their partners, partners who are inconsistently on therapy may transmit HIV to their partners when attempting to conceive. Pre-exposure prophylaxis (PrEP) is an available safer conception strategy for these couples but is not consistently offered. We sought to better understand barriers to PrEP implementation for couples seeking conception and patient perceptions on what providers could do to encourage use. We conducted in-depth, qualitative interviews with 11 participants representing six couples taking PrEP for safer conception in a safety-net hospital in New England. Semi-structured qualitative interviews assessed the following: Relationship nature and contextual factors; attitudes and perceptions regarding PrEP for safer conception; experience within health care systems related to HIV and PrEP; and facilitators, barriers, and other experiences using PrEP for safer conception. Four key themes have important implications for implementation of PrEP for safer conception: Knowledge and understanding gaps regarding HIV and PrEP among both members of the couple, role of insurance and financing in decision-making, learning to manage and adhere to a treatment plan, and the need for providers to enhance knowledge and offer further support. Addressing barriers to safer conception strategies at multiple levels is needed to prevent HIV transmission within serodiscordant couples who desire children. Providers can play an important role in lowering these barriers through the use of multiple strategies

A framework for the construction of generative models for mesoscale structure in multilayer networks

Multilayer networks allow one to represent diverse and coupled connectivity patterns—such as time-dependence, multiple subsystems, or both—that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers. We discuss the parameters and properties of our framework, and we illustrate examples of its use with benchmark models for community-detection methods and algorithms in multilayer networks

Community detection in temporal multilayer networks, with an application to correlation networks

Networks are a convenient way to represent complex systems of interacting entities. Many networks contain "communities" of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the detection of communities in temporal networks represented as multilayer networks. As a focal example, we study time-dependent financial-asset correlation networks. We first argue that the use of the "modularity" quality function---which is defined by comparing edge weights in an observed network to expected edge weights in a "null network"---is application-dependent. We differentiate between "null networks" and "null models" in our discussion of modularity maximization, and we highlight that the same null network can correspond to different null models. We then investigate a multilayer modularity-maximization problem to identify communities in temporal networks. Our multilayer analysis only depends on the form of the maximization problem and not on the specific quality function that one chooses. We introduce a diagnostic to measure \emph{persistence} of community structure in a multilayer network partition. We prove several results that describe how the multilayer maximization problem measures a trade-off between static community structure within layers and larger values of persistence across layers. We also discuss some computational issues that the popular "Louvain" heuristic faces with temporal multilayer networks and suggest ways to mitigate them.Comment: 42 pages, many figures, final accepted version before typesettin

Analysis of incremental augmented affine projection algorithm for distributed estimation of complex-valued signals

This paper considers the problem of distributed estimation in an incremental network when the measurements taken by the node follow a widely linear model. The proposed algorithm which we refer to it as incremental augmented affine projection algorithm (incAAPA) utilizes the full second order statistical information in the complex domain. Moreover, it exploits spatio-temporal diversity to improve the estimation performance. We derive steady-state performance metric of the incAAPA in terms of the mean-square deviation (MSD). We further derive sufficient conditions to ensure mean-square convergence. Our analysis illustrate that the proposed algorithm is able to process both second order circular (proper) and noncircular (improper) signals. The validity of the theoretical results and the good performance of the proposed algorithm are demonstrated by several computer simulations

Tracking analysis of minimum kernel risk-sensitive loss algorithm under general non-Gaussian noise

In this paper the steady-state tracking performance of minimum kernel risk-sensitive loss (MKRSL) in a non-stationary environment is analyzed. In order to model a non-stationary environment, a first-order random-walk model is used to describe the variations of optimum weight vector over time. Moreover, the measurement noise is considered to have non-Gaussian distribution. The energy conservation relation is utilized to extract an approximate closed-form expression for the steady-state excess mean square error (EMSE). Our analysis shows that unlike for the stationary case, the EMSE curve is not an increasing function of step-size parameter. Hence, the optimum step-size which minimizes the EMSE is derived. We also discuss that our approach can be used to extract steady-state EMSE for a general class of adaptive filters. The simulation results with different noise distributions support the theoretical derivations