Discovery of Diverse Rodent and Bat Pestiviruses With Distinct Genomic and Phylogenetic Characteristics in Several Chinese Provinces

Bats and rodents are widely distributed worldwide and can be native or intermediate reservoirs of many important zoonotic viruses. Pestiviruses are a group of virus species of the genus Pestivirus under the family Flaviviridae that can infect a wide variety of artiodactylous hosts, including swine and ruminants. Two classic types of pestiviruses, bovine viral diarrhea virus and classical swine fever virus, are important causative agents of mild-to-severe disease in bovine and swine hosts, respectively, and cause tremendous economic losses in these industries. Recent reports revealed that bats and rodents could also act as natural hosts of pestiviruses and an atypical porcine pestivirus, which cause disease in piglets, showed a close genetic relationship with a specific bat pestivirus, RaPestV-1. This study aimed to describe the detection and characterization of novel pestiviruses from bats and rodents in different locations by analyzing the available bat and rodent virome data from throughout China. Two bat pestivirus species and four rodent pestivirus species that are distinct from other known viruses were identified and sequenced. These viruses were identified from two bat species and four rodent species in different Chinese provinces. There were two distinct lineages present in these viruses, that differ from artiodactylous pestivirus. These findings expand our understanding of the genetic diversity of pestiviruses in bats and rodents and suggest the presence of a diverse set of pestiviruses in non-artiodactylous hosts. This study may provide new insight for the prevention of future viral disease outbreaks originating from bats and rodents

Decrease in the production of beta-amyloid by berberine inhibition of the expression of beta-secretase in HEK293 cells

<p>Abstract</p> <p>Background</p> <p>Berberine (BER), the major alkaloidal component of <it>Rhizoma coptidis</it>, has multiple pharmacological effects including inhibition of acetylcholinesterase, reduction of cholesterol and glucose levels, anti-inflammatory, neuroprotective and neurotrophic effects. It has also been demonstrated that BER can reduce the production of beta-amyloid_40/42, which plays a critical and primary role in the pathogenesis of Alzheimer's disease. However, the mechanism by which it accomplishes this remains unclear.</p> <p>Results</p> <p>Here, we report that BER could not only significantly decrease the production of beta-amyloid_40/42and the expression of beta-secretase (BACE), but was also able to activate the extracellular signal-regulated kinase1/2 (ERK1/2) pathway in a dose- and time-dependent manner in HEK293 cells stably transfected with APP695 containing the Swedish mutation. We also find that U0126, an antagonist of the ERK1/2 pathway, could abolish (1) the activation activity of BER on the ERK1/2 pathway and (2) the inhibition activity of BER on the production of beta-amyloid_40/42and the expression of BACE.</p> <p>Conclusion</p> <p>Our data indicate that BER decreases the production of beta-amyloid_40/42by inhibiting the expression of BACE via activation of the ERK1/2 pathway.</p

Human-animal interactions and bat coronavirus spillover potential among rural residents in Southern China

Human interaction with animals has been implicated as a primary risk factor for several high impact zoonoses, including many bat-origin viral diseases; however, the animal-to-human spillover events that lead to emerging diseases are rarely observed or clinically examined, and the link between specific interactions and spillover risk is poorly understood. To investigate this phenomenon, we conducted biological-behavioral surveillance among rural residents in the Yunnan, Guangxi, and Guangdong provinces of Southern China, where we have identified a number of SARS-related coronaviruses in bats. Serum samples were tested for four bat-borne coronaviruses using newly developed enzyme-linked immunosorbent assays (ELISA). Survey data were used to characterize associations between human-animal contact and bat coronavirus spillover risk. A total of 1,596 residents were enrolled in the study from 2015 to 2017. Nine participants (0.6%) tested positive for bat coronaviruses. 265 (17%) participants reported severe acute respiratory infection (SARI) and/or influenza-like illness (ILI) symptoms in the past year, which were associated with poultry, carnivore, rodent/shrew, and bat contact, with variability by family income and province of residence. This study provides serological evidence of bat coronavirus spillover in rural communities in Southern China. The low seroprevalence observed in this study suggests that bat coronavirus spillover is a rare event. Nonetheless, this study highlights associations between human-animal interaction and zoonotic spillover risk. These findings can be used to support targeted biological behavioral surveillance in high-risk geographic areas in order to reduce the risk of zoonotic disease emergence

Whole-genome sequencing of the snub-nosed monkey provides insights into folivory and evolutionary history

Colobines are a unique group of Old World monkeys that principally eat leaves and seeds rather than fruits and insects. We report the sequencing at 146× coverage, de novo assembly and analyses of the genome of a male golden snub-nosed monkey (Rhinopithecus roxellana) and resequencing at 30× coverage of three related species (Rhinopithecus bieti, Rhinopithecus brelichi and Rhinopithecus strykeri). Comparative analyses showed that Asian colobines have an enhanced ability to derive energy from fatty acids and to degrade xenobiotics. We found evidence for functional evolution in the colobine RNASE1 gene, encoding a key secretory RNase that digests the high concentrations of bacterial RNA derived from symbiotic microflora. Demographic reconstructions indicated that the profile of ancient effective population sizes for R. roxellana more closely resembles that of giant panda rather than its congeners. These findings offer new insights into the dietary adaptations and evolutionary history of colobine primates

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

A measure of identifying influential waypoints in air route networks.

As the basic carrier of air flight operation, air route network (ARN) is of great significance to the smooth operation of flights. However, the waypoint is a core part of the route, so it is an important topic to identify influential waypoints in ARN. In this paper, a method to identify the influence of the node in ARN based on an improved entropy weight (IEW) method is proposed. Then, centrality measures including degree, closeness, betweenness and eigenvector as the multi-attribute of ARN in IEW application. IEW method is used to aggregate the multi-attribute to obtain the evaluation of the influence of each waypoint. To demonstrate the effectiveness of the IEW method, three real ARNs are selected to conduct several experiments with susceptible infected recovered (SIR) model. The results show the efficiency and practicability of the proposed method

Improved YOLO v5 with balanced feature pyramid and attention module for traffic sign detection

With the development of automatic driving technology, traffic sign detection has become a very important task. However, it is a challenging task because of the complex traffic sign scene and the small size of the target. In recent years, a number of convolutional neural network (CNN) based object detection methods have brought great progress to traffic sign detection. Considering the still high false detection rate, as well as the high time overhead and computational overhead, the effect is not satisfactory. Therefore, we employ lightweight network model YOLO v5 (You Only Look Once) as our work foundation. In this paper, we propose an improved YOLO v5 method by using balances feature pyramid structure and global context block to enhance the ability of feature fusion and feature extraction. To verify our proposed method, we have conducted a lot of comparative experiments on the challenging dataset Tsinghua-Tencent-100K (TT100K). The experimental results demonstrate that the [email protected] and [email protected]:0.95 are improved by 1.9% and 2.1%, respectively