A duplex real-time reverse transcriptase polymerase chain reaction assay for detecting western equine and eastern equine encephalitis viruses

In order to establish an accurate, ready-to-use assay for simultaneous detection of Eastern equine encephalitis virus (EEEV) and Western equine encephalitis virus (WEEV), we developed one duplex TaqMan real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay, which can be used in human and vector surveillance. First, we selected the primers and FAM-labeled TaqMan-probe specific for WEEV from the consensus sequence of NSP3 and the primers and HEX-labeled TaqMan-probe specific for EEEV from the consensus sequence of E3, respectively. Then we constructed and optimized the duplex real-time RT-PCR assay by adjusting the concentrations of primers and probes. Using a series of dilutions of transcripts containing target genes as template, we showed that the sensitivity of the assay reached 1 copy/reaction for EEEV and WEEV, and the performance was linear within the range of at least 10(6 )transcript copies. Moreover, we evaluated the specificity of the duplex system using other encephalitis virus RNA as template, and found no cross-reactivity. Compared with virus isolation, the gold standard, the duplex real time RT-PCR assay we developed was 10-fold more sensitive for both WEEV and EEEV detection

Virulence of H5N1 virus in mice attenuates after in vitro serial passages

The virulence of A/Vietnam/1194/2004 (VN1194) in mice attenuated after serial passages in MDCK cells and chicken embryos, because the enriched large-plaque variants of the virus had significantly reduced virulence. In contrast, the small-plaque variants of the virus and the variants isolated from the brain of mice that were infected with the parental virus VN1194 had much higher virulence in mice. The virulence attenuation of serially propagated virus may be caused by the reduced neurotropism in mice. Our whole genome sequence analysis revealed substitutions of a total of two amino acids in PB1, three in PB2, two in PA common for virulence attenuated variants, all or part of which may be correlated with the virulence attenuation and reduced neurotropism of the serially propagated VN1194 in mice. Our study indicates that serial passages of VN1194 in vitro lead to adaptation and selection of variants that have markedly decreased virulence and neurotropism, which emphasizes the importance of direct analysis of original or less propagated virus samples

Development of an ELISA-array for simultaneous detection of five encephalitis viruses

Japanese encephalitis virus(JEV), tick-borne encephalitis virus(TBEV), and eastern equine encephalitis virus (EEEV) can cause symptoms of encephalitis. Establishment of accurate and easy methods by which to detect these viruses is essential for the prevention and treatment of associated infectious diseases. Currently, there are still no multiple antigen detection methods available clinically. An ELISA-array, which detects multiple antigens, is easy to handle, and inexpensive, has enormous potential in pathogen detection. An ELISA-array method for the simultaneous detection of five encephalitis viruses was developed in this study. Seven monoclonal antibodies against five encephalitis-associated viruses were prepared and used for development of the ELISA-array. The ELISA-array assay is based on a "sandwich" ELISA format and consists of viral antibodies printed directly on 96-well microtiter plates, allowing for direct detection of 5 viruses. The developed ELISA-array proved to have similar specificity and higher sensitivity compared with the conventional ELISAs. This method was validated by different viral cultures and three chicken eggs inoculated with infected patient serum. The results demonstrated that the developed ELISA-array is sensitive and easy to use, which would have potential for clinical use

Discovery of re-purposed drugs that slow SARS-CoV-2 replication in human cells

From PLOS via Jisc Publications RouterHistory: received 2021-05-27, accepted 2021-07-26, collection 2021-09, epub 2021-09-09Publication status: PublishedFunder: wellcome (london); Grant(s): 110126/Z/15/ZFunder: Wellcome (London); Grant(s): 203128/Z/16/ZFunder: NIHR Manchester Research CentreFunder: Fungal Infection TrustCOVID-19 vaccines based on the Spike protein of SARS-CoV-2 have been developed that appear to be largely successful in stopping infection. However, therapeutics that can help manage the disease are still required until immunity has been achieved globally. The identification of repurposed drugs that stop SARS-CoV-2 replication could have enormous utility in stemming the disease. Here, using a nano-luciferase tagged version of the virus (SARS-CoV-2-ΔOrf7a-NLuc) to quantitate viral load, we evaluated a range of human cell types for their ability to be infected and support replication of the virus, and performed a screen of 1971 FDA-approved drugs. Hepatocytes, kidney glomerulus, and proximal tubule cells were particularly effective in supporting SARS-CoV-2 replication, which is in-line with reported proteinuria and liver damage in patients with COVID-19. Using the nano-luciferase as a measure of virus replication we identified 35 drugs that reduced replication in Vero cells and human hepatocytes when treated prior to SARS-CoV-2 infection and found amodiaquine, atovaquone, bedaquiline, ebastine, LY2835219, manidipine, panobinostat, and vitamin D3 to be effective in slowing SARS-CoV-2 replication in human cells when used to treat infected cells. In conclusion, our study has identified strong candidates for drug repurposing, which could prove powerful additions to the treatment of COVID

Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing

The proliferation of IoT applications has pushed the horizon of edge computing, which provides processing ability at the edge of networks. Task offloading is one of the most important issues in edge computing and has attracted continuous research attention in recent years. With task offloading, end devices can offload the entire task or only subtasks to the edge servers to meet the delay and energy requirements. Most existing offloading schemes are limited by the increasing complexity of task topologies, as considerable time is wasted for local/edge subtasks to wait for their precedent subtasks being executed at the edge/local device. This problem becomes even worse when the dependencies among subtasks become complex and the number of end-users increases. To address this problem, our key methodology is to exploit subtask duplications to reduce the inter-subtask delay and shorten the task completion time. Based on this, we propose a Duplication-based and Energy-aware Task Offloading scheme (DETO), which duplicates critical subtasks that have a large impact on the completion time and thus enhances the parallelism between local and edge computing. In addition, among numerous choices of subtask duplications, DETO evaluates the gain/cost ratio for each possible duplication and chooses the most efficient ones. As a result, the extra resource for duplications is greatly reduced. We also design a distributed DETO algorithm to support multi-user, multi-server edge computing. Extensive evaluation results show that DETO can effectively reduce the task completion time (by 12.22%) and improve the resource utilization (by 15.17%), in particular for multi-user edge computing networks

Detail-Aware Deep Homography Estimation for Infrared and Visible Image

Homography estimation of infrared and visible images is a highly challenging task in computer vision. Recently, the deep learning homography estimation methods have focused on the plane, while ignoring the details in the image, resulting in the degradation of the homography estimation performance in infrared and visible image scenes. In this work, we propose a detail-aware deep homography estimation network to preserve more detailed information in images. First, we design a shallow feature extraction network to obtain meaningful features for homography estimation from multi-level multi-dimensional features. Second, we propose a Detail Feature Loss (DFL), which utilizes refined features for computation and retains more detailed information while reducing the influence of unimportant features, enabling effective unsupervised learning. Finally, considering that the evaluation indicators of the previous homography estimation tasks are difficult to reflect severe distortion or the workload of manually labelling feature points is too large, we propose an Adaptive Feature Registration Rate (AFRR) to adaptive extraction of image pair feature points to calculate the registration rate. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic benchmark dataset and real dataset

Detail-Aware Deep Homography Estimation for Infrared and Visible Image

Homography estimation of infrared and visible images is a highly challenging task in computer vision. Recently, the deep learning homography estimation methods have focused on the plane, while ignoring the details in the image, resulting in the degradation of the homography estimation performance in infrared and visible image scenes. In this work, we propose a detail-aware deep homography estimation network to preserve more detailed information in images. First, we design a shallow feature extraction network to obtain meaningful features for homography estimation from multi-level multi-dimensional features. Second, we propose a Detail Feature Loss (DFL), which utilizes refined features for computation and retains more detailed information while reducing the influence of unimportant features, enabling effective unsupervised learning. Finally, considering that the evaluation indicators of the previous homography estimation tasks are difficult to reflect severe distortion or the workload of manually labelling feature points is too large, we propose an Adaptive Feature Registration Rate (AFRR) to adaptive extraction of image pair feature points to calculate the registration rate. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic benchmark dataset and real dataset

Infrared and Visible Image Homography Estimation Using Multiscale Generative Adversarial Network

In computer vision, the homography estimation of infrared and visible multi-source images based on deep learning is a current research hotspot. Existing homography estimation methods ignore the feature differences of multi-source images, which leads to poor homography performance in infrared and visible image scenes. To address this issue, we designed an infrared and visible image homography estimation method using a Multi-scale Generative Adversarial Network, called HomoMGAN. First, we designed two shallow feature extraction networks to extract fine features of infrared and visible images, respectively, which extract important features in source images from two dimensions: color channel and imaging space. Second, we proposed an unsupervised generative adversarial network to predict the homography matrix directly. In our adversarial network, the generator captures meaningful features for homography estimation at different scales by using an encoder–decoder structure and further predicts the homography matrix. The discriminator recognizes the feature difference between the warped and target image. Through the adversarial game between the generator and the discriminator, the fine features of the warped image in the homography estimation process are closer to the fine features of the target image. Finally, we conduct extensive experiments in the synthetic benchmark dataset to verify the effectiveness of HomoMGAN and its components. We conduct extensive experiments and the results show that HomoMGAN outperforms existing state-of-the-art methods in the synthetic benchmark datasets both qualitatively and quantitatively

Nuclear protein 1 promotes unfolded protein response during endoplasmic reticulum stress, and alleviates apoptosis induced by cisplatin in non-small cell lung cancer cells

Purpose: To investigate the role of nuclear protein 1 (NUPR1) in the drug resistance of non-small cell lung cancer (NSCLC) and its regulatory mechanisms. Methods: Quantitative polymerase chain reaction (qPCR) and immunoblot assays were conducted to determine NUPR1 expression in A549 cells. Cisplatin sensitivity and cisplatin-induced apoptosis were investigated in NUPR1 knockdown or overexpressed cells via 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) assay and flow cytometry. The potential association between unfolded protein response (UPR) and NUPR1 levels in response to cisplatin were explored. The effect of endoplasmic reticulum (ER) stress on apoptosis was examined using flow cytometry. Results: Cisplatin treatment promoted the expression of NUPR1 in NSCLC cells. NUPR1 regulated cisplatin resistance in NSCLC and also regulated UPR in ER stress induced by cisplatin. The results show NUPR1 regulated apoptosis induced by ER stress following tunicamycin treatment. Conclusion: NSCLC cells may promote the UPR in ER stress by promoting the expression of NUPR1, thereby reducing the ER stress induced by cisplatin