Subtype diversity and reassortment potential for co‐circulating avian influenza viruses at a diversity hot spot

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106946/1/jane12167.pd

Титульные страницы и содержание

Avian influenza viruses (AIVs) have been pivotal to the origination of human pandemic strains. Despite their scientific and public health significance, however, there remains much to be understood about the ecology and evolution of AIVs in wild birds, where major pools of genetic diversity are generated and maintained. Here, we present comparative phylodynamic analyses of human and AIVs in North America, demonstrating (i) significantly higher standing genetic diversity and (ii) phylogenetic trees with a weaker signature of immune escape in AIVs than in human viruses. To explain these differences, we performed statistical analyses to quantify the relative contribution of several potential explanations. We found that HA genetic diversity in avian viruses is determined by a combination of factors, predominantly subtype-specific differences in host immune selective pressure and the ecology of transmission (in particular, the durability of subtypes in aquatic environments). Extending this analysis using a computational model demonstrated that virus durability may lead to long-term, indirect chains of transmission that, when coupled with a short host lifespan, can generate and maintain the observed high levels of genetic diversity. Further evidence in support of this novel finding was found by demonstrating an association between subtype-specific environmental durability and predicted phylogenetic signatures: genetic diversity, variation in phylogenetic tree branch lengths, and tree height. The conclusion that environmental transmission plays an important role in the evolutionary biology of avian influenza viruses—a manifestation of the “storage effect”—highlights the potentially unpredictable impact of wildlife reservoirs for future human pandemics and the need for improved understanding of the natural ecology of these viruses

Dissecting a wildlife disease hotspot: the impact of multiple host species, environmental transmission and seasonality in migration, breeding and mortality

Avian influenza viruses (AIVs) have been implicated in all human influenza pandemics in recent history. Despite this, surprisingly little is known about the mechanisms underlying the maintenance and spread of these viruses in their natural bird reservoirs. Surveillance has identified an AIV ‘hotspot’ in shorebirds at Delaware Bay, in which prevalence is estimated to exceed other monitored sites by an order of magnitude. To better understand the factors that create an AIV hotspot, we developed and parametrized a mechanistic transmission model to study the simultaneous epizootiological impacts of multi-species transmission, seasonal breeding, host migration and mixed transmission routes. We scrutinized our model to examine the potential for an AIV hotspot to serve as a ‘gateway’ for the spread of novel viruses into North America. Our findings identify the conditions under which a novel influenza virus, if introduced into the system, could successfully invade and proliferate

Efficient polynomial commitment schemes for multiple points and polynomials

We present an enhanced version of the Kate, Zaverucha and Goldberg polynomial commitment scheme [KZG, ASIACRYPT 2010] where a single group element can be an opening proof for multiple polynomials each evaluated at a different arbitrary subset of points. As a sample application we ``plug in\u27\u27 this scheme into the PLONK proving system[GWC, 2019] to obtain improved proof size and prover run time at the expense of additional verifier ${\mathbb{G}}_2$ operations and pairings, and additional ${\mathbb{G}}_2$ SRS elements. We also present a second scheme where the proof consists of two group elements and the verifier complexity is better than previously known batched verification methods for [KZG]

Dissecting a wildlife disease hotspot: the impact of multiple host species, environmental transmission and seasonality in migration, breeding and mortality

Avian influenza viruses (AIVs) have been implicated in all human influenza pandemics in recent history. Despite this, surprisingly little is known about the mechanisms underlying the maintenance and spread of these viruses in their natural bird reservoirs. Surveillance has identified an AIV ‘hotspot’ in shorebirds at Delaware Bay, in which prevalence is estimated to exceed other monitored sites by an order of magnitude. To better understand the factors that create an AIV hotspot, we developed and parametrized a mechanistic transmission model to study the simultaneous epizootiological impacts of multi-species transmission, seasonal breeding, host migration and mixed transmission routes. We scrutinized our model to examine the potential for an AIV hotspot to serve as a ‘gateway’ for the spread of novel viruses into North America. Our findings identify the conditions under which a novel influenza virus, if introduced into the system, could successfully invade and proliferate

Domains within RbpA serve specific functional roles that regulate the expression of distinct mycobacterial gene subsets

The RNA polymerase (RNAP) binding protein A (RbpA) contributes to the formation of stable RNAP-promoter open complexes (R

A Search for Extra-Tidal RR Lyrae in the Globular Cluster NGC 5024 and NGC 5053

Recently, Kundu et al (2019) reported that the globular cluster NGC 5024 (M53) possesses five extra-tidal RR Lyrae. In fact, four of them were instead known members of a nearby globular cluster NGC 5053. The status of the remaining extra-tidal RR Lyrae is controversial depending on the adopted tidal radius of NGC 5024. We have also searched for additional extra-tidal RR Lyrae within an area of ∼8~deg² covering both globular clusters. This includes other known RR Lyrae within the search area, as well as stars that fall within the expected range of magnitudes and colors for RR Lyrae (and yet outside the cutoff of 2/3 of the tidal radii of each globular clusters for something to be called "extra-tidal") if they were extra-tidal RR Lyrae candidates for NGC 5024 or NGC 5053. Based on the the proper motion information and their locations on the color-magnitude diagram, none of the known RR Lyrae belong to the extra-tidal RR Lyrae of either globular clusters. In the cases where the stars satisfied the magnitude and color ranges of RR Lyrae, analysis of time series data taken from the Zwicky Transient Facility do not reveal periodicities, suggesting that none of these stars are RR Lyrae. We conclude that there are no extra-tidal RR Lyrae associated with either NGC 5024 or NGC 5053 located within our search area

Computational medicine, present and the future: obstetrics and gynecology perspective.

Medicine is, in its essence, decision making under uncertainty; the decisions are made about tests to be performed and treatments to be administered. Traditionally, the uncertainty in decision making was handled using expertise collected by individual providers and, more recently, systematic appraisal of research in the form of evidence-based medicine. The traditional approach has been used successfully in medicine for a very long time. However, it has substantial limitations because of the complexity of the system of the human body and healthcare. The complex systems are a network of highly coupled components intensely interacting with each other. These interactions give those systems redundancy and thus robustness to failure and, at the same time, equifinality, that is, many different causative pathways leading to the same outcome. The equifinality of the complex systems of the human body and healthcare system demand the individualization of medical care, medicine, and medical decision making. Computational models excel in modeling complex systems and, consequently, enabling individualization of medical decision making and medicine. Computational models are theory- or knowledge-based models, data-driven models, or models that combine both approaches. Data are essential, although to a different degree, for computational models to successfully represent complex systems. The individualized decision making, made possible by the computational modeling of complex systems, has the potential to revolutionize the entire spectrum of medicine from individual patient care to policymaking. This approach allows applying tests and treatments to individuals who receive a net benefit from them, for whom benefits outweigh the risk, rather than treating all individuals in a population because, on average, the population benefits. Thus, the computational modeling-enabled individualization of medical decision making has the potential to both improve health outcomes and decrease the costs of healthcare