Infection of human cytomegalovirus in cultured human gingival tissue.

BackgroundHuman cytomegalovirus (HCMV) infection in the oral cavity plays an important role in its horizontal transmission and in causing viral-associated oral diseases such as gingivitis. However, little is currently known about HCMV pathogenesis in oral mucosa, partially because HCMV infection is primarily limited to human cells and few cultured tissue or animal models are available for studying HCMV infection.ResultsIn this report, we studied the infection of HCMV in a cultured gingival tissue model (EpiGingival, MatTek Co.) and investigated whether the cultured tissue can be used to study HCMV infection in the oral mucosa. HCMV replicated in tissues that were infected through the apical surface, achieving a titer of at least 300-fold at 10 days postinfection. Moreover, the virus spread from the apical surface to the basal region and reduced the thickness of the stratum coreum at the apical region. Viral proteins IE1, UL44, and UL99 were expressed in infected tissues, a characteristic of HCMV lytic replication in vivo. Studies of a collection of eight viral mutants provide the first direct evidence that a mutant with a deletion of open reading frame US18 is deficient in growth in the tissues, suggesting that HCMV encodes specific determinants for its infection in oral mucosa. Treatment by ganciclovir abolished viral growth in the infected tissues.ConclusionThese results suggest that the cultured gingival mucosa can be used as a tissue model for studying HCMV infection and for screening antivirals to block viral replication and transmission in the oral cavity

Impact of hyper-elasticity on cyclic sand modelling: A numerical study based on SANISAND-MS

publishedVersio

Characterization of Neurospora crassa albino mutants that were previously unassigned to locus

Neurospora crassa mutant hunts by various groups have identified albino (al) mutants that map to the al-1 - al-2 region on the right arm of linkage group I. The cloning of the al-1 and al-2 genes (Schmidhauser et al. 1990 Mol. Cell. Biol. 10:5064-5070, Schmidhauser et al. 1994 J. Biol. Chem. 269:12060-12066) allows assignment of locus to the above mutants by DNA mediated transformation. Analysis of phytoene desaturases from different organisms indicates at least three types of enzymes as defined by the number of desaturation steps carried out (Sandmann 1994 J. Plant Physiol. 143:444-447). The N. crassa phytoene desaturase, the al-1 gene product, introduces four double bonds converting phytoene to lycopene. Of the three intermediates in this reaction sequence two are colored. The occurrence of visibly distinguishable albino alleles in N. crassa has been noted (Perkins 1989 Fungal Genetics Newsl. 36:63). Assignment of locus to N. crassaalbino alleles represents a first step in the functional characterization of N. crassa carotenogenic loci

A Model of Customer Lifetime Value Consider with Word-of-mouth Marketing Value

With the rapid development of IT technology and fierce competition of market, the customer relationship management(CRM) has gained its importance in the market. Companies have attached importance to acquiring and retaining the most profitable customers. So calculating customer’s value is a significant segment for every effective CRM. Many researches have been performed to calculate customer’s value based on customer lifetime value (LTV). But, these calculations can’t effectively include the whole customer value, especially for the word-of-mouth marketing value. This paper proposes a new LTV model which considers the customer’s past profit contribution, potential value and word-of-mouth marketing value, and gives a more reasonable LTV value in CRM for the company to make a decision

OTS: A One-shot Learning Approach for Text Spotting in Historical Manuscripts

Historical manuscript processing poses challenges like limited annotated training data and novel class emergence. To address this, we propose a novel One-shot learning-based Text Spotting (OTS) approach that accurately and reliably spots novel characters with just one annotated support sample. Drawing inspiration from cognitive research, we introduce a spatial alignment module that finds, focuses on, and learns the most discriminative spatial regions in the query image based on one support image. Especially, since the low-resource spotting task often faces the problem of example imbalance, we propose a novel loss function called torus loss which can make the embedding space of distance metric more discriminative. Our approach is highly efficient and requires only a few training samples while exhibiting the remarkable ability to handle novel characters, and symbols. To enhance dataset diversity, a new manuscript dataset that contains the ancient Dongba hieroglyphics (DBH) is created. We conduct experiments on publicly available VML-HD, TKH, NC datasets, and the new proposed DBH dataset. The experimental results demonstrate that OTS outperforms the state-of-the-art methods in one-shot text spotting. Overall, our proposed method offers promising applications in the field of text spotting in historical manuscripts

Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the SpatialTemporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https: //vlis2022.github.io/cvpr23/egvsr.Comment: Accepted by CVPR202

Engineered external guide sequences are highly effective in inducing RNase P for inhibition of gene expression and replication of human cytomegalovirus

External guide sequences (EGSs), which are RNA molecules derived from natural tRNAs, bind to a target mRNA and render the mRNA susceptible to hydrolysis by RNase P, a tRNA processing enzyme. Using an in vitro selection procedure, we have previously generated EGS variants that efficiently direct human RNase P to cleave a target mRNA in vitro. In this study, a variant was used to target the overlapping region of the mRNAs encoding human cytomegalovirus (HCMV) essential transcription regulatory factors IE1 and IE2. The EGS variant was ∼25-fold more active in inducing human RNase P to cleave the mRNA in vitro than the EGS derived from a natural tRNA. Moreover, a reduction of 93% in IE1/IE2 gene expression and a reduction of 3000-fold in viral growth were observed in HCMV-infected cells that expressed the variant, while cells expressing the tRNA-derived EGS exhibited a reduction of 80% in IE1/IE2 expression and an inhibition of 150-fold in viral growth. Our results provide the first direct evidence that EGS variant is highly effective in blocking HCMV gene expression and growth and furthermore, demonstrate the feasibility of developing effective EGS RNA variants for anti-HCMV applications by using in vitro selection procedures

Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning

How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psychology, and artificial intelligence. Although the traditional artificial neural network using fixed-length vectors to represent symbols has gained good performance in some specific tasks, it is still a black box that lacks interpretability, far from how humans perceive the world. Inspired by the grandmother-cell hypothesis in neuroscience, this work investigates how population encoding and spiking timing-dependent plasticity (STDP) mechanisms can be integrated into the learning of spiking neural networks, and how a population of neurons can represent a symbol via guiding the completion of sequential firing between different neuron populations. The neuron populations of different communities together constitute the entire commonsense knowledge graph, forming a giant graph spiking neural network. Moreover, we introduced the Reward-modulated spiking timing-dependent plasticity (R-STDP) mechanism to simulate the biological reinforcement learning process and completed the related reasoning tasks accordingly, achieving comparable accuracy and faster convergence speed than the graph convolutional artificial neural networks. For the fields of neuroscience and cognitive science, the work in this paper provided the foundation of computational modeling for further exploration of the way the human brain represents commonsense knowledge. For the field of artificial intelligence, this paper indicated the exploration direction for realizing a more robust and interpretable neural network by constructing a commonsense knowledge representation and reasoning spiking neural networks with solid biological plausibility