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IntroductionTwitter represents a mainstream news source for the American public, offering a valuable vehicle for learning how citizens make sense of pandemic health threats like Covid-19. Masking as a risk mitigation measure became controversial in the US. The social amplification risk framework offers insight into how a risk event interacts with psychological, social, institutional, and cultural communication processes to shape Covid-19 risk perception.MethodsQualitative content analysis was conducted on 7,024 mask tweets reflecting 6,286 users between January 24 and July 7, 2020, to identify how citizens expressed Covid-19 risk perception over time. Descriptive statistics were computed for (a) proportion of tweets using hyperlinks, (b) mentions, (c) hashtags, (d) questions, and (e) location.ResultsSix themes emerged regarding how mask tweets amplified and attenuated Covid-19 risk: (a) severity perceptions (18.0%) steadily increased across 5 months; (b) mask effectiveness debates (10.7%) persisted; (c) who is at risk (26.4%) peaked in April and May 2020; (d) mask guidelines (15.6%) peaked April 3, 2020, with federal guidelines; (e) political legitimizing of Covid-19 risk (18.3%) steadily increased; and (f) mask behavior of others (31.6%) composed the largest discussion category and increased over time. Of tweets, 45% contained a hyperlink, 40% contained mentions, 33% contained hashtags, and 16.5% were expressed as a question.ConclusionsUsers ascribed many meanings to mask wearing in the social media information environment revealing that COVID-19 risk was expressed in a more expanded range than objective risk. The simultaneous amplification and attenuation of COVID-19 risk perception on social media complicates public health messaging about mask wearing.

Tomato brown rugose fruit virus (ToBRFV) is an emerging, rapidly spreading virus belonging to the genus Tobamovirus which seriously decreases tomato yields. RNA silencing is an evolutionarily conserved antiviral mechanism. In virus-infected plants, virus-derived small interfering RNAs (vsiRNAs) are one of the key components involved in the RNA silencing-based antiviral activity in plants. The main function of vsiRNAs is to target and degrade viral RNA. Studies have found that some vsiRNAs can also target host transcripts, further regulating host responses and symptoms and promoting viral survival and spread. In this study, the vsiRNA profiles of ToBRFV-infected tomato plants were obtained by deep sequencing. VsiRNAs were mainly 21 and 22 nucleotides in length and had a U-bias at the 5′ end. The single-nucleotide resolution profile shows that vsiRNAs exhibit a heterogeneous continuous distribution in the ToBRFV genomic RNA, with hotspot regions on the antisense strand located at the 5′ end of the RdRP and in the coding regions of MP and CP. The presence of vsiRNAs was confirmed in tomato plants infected with ToBRFV through RT-PCR, and GO and KEGG enrichment analyses were performed on the predicted vsiRNA target genes (with an expectation value less than or equal to 2.5). Seven potential target genes were selected for qRT-PCR analysis, confirming that their transcript accumulation significantly decreased in the leaves of tomato plants infected with ToBRFV. These genes may play an important role in the process of viral infection in tomatoes. Our results suggest a role for vsiRNAs in the ToBRFV–tomato interaction. [ABSTRACT FROM AUTHOR]

Abstract Chronic diseases are one of the most severe health issues in the world, due to their terrible clinical presentations such as long onset cycle, insidious symptoms, and various complications. Recently, machine learning has become a promising technique to assist the early diagnosis of chronic diseases. However, existing works ignore the problems of feature hiding and imbalanced class distribution in chronic disease datasets. In this paper, we present a universal and efficient diagnostic framework to alleviate the above two problems for diagnosing chronic diseases timely and accurately. Specifically, we first propose a network-limited polynomial neural network (NLPNN) algorithm to efficiently capture high-level features hidden in chronic disease datasets, which is data augmentation in terms of its feature space and can also avoid over-fitting. Then, to alleviate the class imbalance problem, we further propose an attention-empowered NLPNN algorithm to improve the diagnostic accuracy for sick cases, which is also data augmentation in terms of its sample space. We evaluate the proposed framework on nine public and two real chronic disease datasets (partly with class imbalance). Extensive experiment results demonstrate that the proposed diagnostic algorithms outperform state-of-the-art machine learning algorithms, and can achieve superior performances in terms of accuracy, recall, F1, and G_mean. The proposed framework can help to diagnose chronic diseases timely and accurately at an early stage.

Recurrent neural networks are efficient ways of training language models, and various RNN networks have been proposed to improve performance. However, with the increase of network scales, the overfitting problem becomes more urgent. In this paper, we propose a framework-G2Basy-to speed up the training process and ease the overfitting problem. Instead of using predefined hyperparameters, we devise a gradient increasing and decreasing technique that changes the parameters training batch size and input dropout simultaneously by a user-defined step size. Together with a pretrained word embedding initialization procedure and the introduction of different optimizers at different learning rates, our framework speeds up the training process dramatically and improves performance compared with a benchmark model of the same scale. For the word embedding initialization, we propose the concept of "artificial features" to describe the characteristics of the obtained word embeddings. We experiment on two of the most often used corpora-the Penn Treebank and WikiText-2 datasets-and both outperform the benchmark results and show potential towards further improvement. Furthermore, our framework shows better results with the larger and more complicated WikiText-2 corpus than with the Penn Treebank. Compared with other state-of-the-art results, we achieve comparable results with network scales hundreds of times smaller and within fewer training epochs.

The coat protein (CP) is an important structural protein that plays many functional roles during the viral cycle. In this study, the CP of pepper mild mottle virus (PMMoV) was genetically fused to GFP using the foot-and-mouth disease virus peptide 2A linker peptide and the construct (PMMoV-GFP2A) was shown to be infectious. The systemic spread of the virus was monitored by its fluorescence in infected plants. Electron microscopy and immunocolloidal gold labelling confirmed that PMMoV-GFP2A forms rod-shaped particles on which GFP is displayed. Studies of tissue ultrastructure and virion self-assembly confirmed that PMMoV-GFP2A could be used to monitor the real-time dynamic changes of CP location during virus infection. Aggregations of GFP-tagged virions appeared as fluorescent plaques in confocal laser microscopy. Altogether, PMMoV-GFP2A is a useful tool for studying the spatial and temporal changes of PMMoV CP during viral infection. [ABSTRACT FROM AUTHOR]

Tomato brown rugose fruit virus (ToBRFV) is a newly-emerging tobamovirus which was first reported on tomatoes in Israel and Jordan, and which has now spread rapidly in Asia, Europe, North America, and Africa. ToBRFV can overcome the resistance to other tobamoviruses conferred by tomato Tm-1, Tm-2, and Tm-22 genes, and it has seriously affected global crop production. The rapid and comprehensive transcription reprogramming of host plant cells is the key to resisting virus attack, but there have been no studies of the transcriptome changes induced by ToBRFV in tomatoes. Here, we made a comparative transcriptome analysis between tomato leaves infected with ToBRFV for 21 days and those mock-inoculated as controls. A total of 522 differentially expressed genes were identified after ToBRFV infection, of which 270 were up-regulated and 252 were down-regulated. Functional analysis showed that DEGs were involved in biological processes such as response to wounding, response to stress, protein folding, and defense response. Ten DEGs were selected and verified by qRT-PCR, confirming the reliability of the high-throughput sequencing data. These results provide candidate genes or signal pathways for the response of tomato leaves to ToBRFV infection. [ABSTRACT FROM AUTHOR]

Animal studies have shown that virus infection causes changes in host chromatin accessibility, but little is known about changes in chromatin accessibility of plants infected by viruses and its potential impact. Here, rice infected by rice stripe virus (RSV) was used to investigate virus‐induced changes in chromatin accessibility. Our analysis identified a total of 6462 open‐ and 3587 closed‐differentially accessible chromatin regions (DACRs) in rice under RSV infection by ATAC‐seq. Additionally, by integrating ATAC‐seq and RNA‐seq, 349 up‐regulated genes in open‐DACRs and 126 down‐regulated genes in closed‐DACRs were identified, of which 34 transcription factors (TFs) were further identified by search of upstream motifs. Transcription levels of eight of these TFs were validated by reverse transcription‐PCR. Importantly, four of these TFs (OsWRKY77, OsWRKY28, OsZFP12 and OsERF91) interacted with RSV proteins and are therefore predicted to play important roles in RSV infection. This is the first application of ATAC‐seq and RNA‐seq techniques to analyse changes in rice chromatin accessibility caused by RSV infection. Integrating ATAC‐seq and RNA‐seq provides a new approach to select candidate TFs in response to virus infection. [ABSTRACT FROM AUTHOR]

Nanoviruses have multipartite, circular, single‐stranded DNA genomes and cause huge production losses in legumes and other crops. No viral suppressor of RNA silencing (VSR) has yet been reported from a member of the genus Nanovirus. Here, we demonstrate that the nanovirus U2 protein is a VSR. The U2 protein of milk vetch dwarf virus (MDV) suppressed the silencing of the green fluorescent protein (GFP) gene induced by single‐stranded and double‐stranded RNA, and the systemic spread of the GFP silencing signal. An electrophoretic mobility shift assay showed that the U2 protein was able to bind double‐stranded 21‐nucleotide small interfering RNA (siRNA). The cysteine residues at positions 43, 79 and 82 in the MDV U2 protein are critical to its nuclear localization, self‐interaction and siRNA‐binding ability, and were essential for its VSR activity. In addition, expression of the U2 protein via a potato virus X vector induced more severe necrosis symptoms in Nicotiana benthamiana leaves. The U2 proteins of other nanoviruses also acted as VSRs, and the three conserved cysteine residues were indispensable for their VSR activity. [ABSTRACT FROM AUTHOR]