Search Results Heading

MBRLSearchResults

mbrl.module.common.modules.added.book.to.shelf
Title added to your shelf!
View what I already have on My Shelf.
Oops! Something went wrong.
Oops! Something went wrong.
While trying to add the title to your shelf something went wrong :( Kindly try again later!
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
    Done
    Filters
    Reset
  • Discipline
      Discipline
      Clear All
      Discipline
  • Is Peer Reviewed
      Is Peer Reviewed
      Clear All
      Is Peer Reviewed
  • Series Title
      Series Title
      Clear All
      Series Title
  • Reading Level
      Reading Level
      Clear All
      Reading Level
  • Year
      Year
      Clear All
      From:
      -
      To:
  • More Filters
      More Filters
      Clear All
      More Filters
      Content Type
    • Item Type
    • Is Full-Text Available
    • Subject
    • Publisher
    • Source
    • Donor
    • Language
    • Place of Publication
    • Contributors
    • Location
104,271 result(s) for "Pattern recognition"
Sort by:
Recognition and inhibition of SARS-CoV-2 by humoral innate immunity pattern recognition molecules
The humoral arm of innate immunity includes diverse molecules with antibody-like functions, some of which serve as disease severity biomarkers in coronavirus disease 2019 (COVID-19). The present study was designed to conduct a systematic investigation of the interaction of human humoral fluid-phase pattern recognition molecules (PRMs) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Of 12 PRMs tested, the long pentraxin 3 (PTX3) and mannose-binding lectin (MBL) bound the viral nucleocapsid and spike proteins, respectively. MBL bound trimeric spike protein, including that of variants of concern (VoC), in a glycan-dependent manner and inhibited SARS-CoV-2 in three in vitro models. Moreover, after binding to spike protein, MBL activated the lectin pathway of complement activation. Based on retention of glycosylation sites and modeling, MBL was predicted to recognize the Omicron VoC. Genetic polymorphisms at the MBL2 locus were associated with disease severity. These results suggest that selected humoral fluid-phase PRMs can play an important role in resistance to, and pathogenesis of, COVID-19, a finding with translational implications.Stravalaci et al. examined recognition of SARS-CoV-2 by human soluble innate pattern recognition receptor. They report that pentraxin 3 and mannose-binding protein recognize viral nucleoprotein and spike, respectively. Mannose-binding lectin has antiviral activity, and human genetic polymorphisms of MBL2 are associated with more severe COVID-19.
Innate immunity: the first line of defense against SARS-CoV-2
The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2, continues to cause substantial morbidity and mortality. While most infections are mild, some patients experience severe and potentially fatal systemic inflammation, tissue damage, cytokine storm and acute respiratory distress syndrome. The innate immune system acts as the first line of defense, sensing the virus through pattern recognition receptors and activating inflammatory pathways that promote viral clearance. Here, we discuss innate immune processes involved in SARS-CoV-2 recognition and the resultant inflammation. Improved understanding of how the innate immune system detects and responds to SARS-CoV-2 will help identify targeted therapeutic modalities that mitigate severe disease and improve patient outcomes.Kanneganti and Diamond review the key role played by innate immunity in the control and immunopathology of COVID-19.
Plant receptor-like protein activation by a microbial glycoside hydrolase
Plants rely on cell-surface-localized pattern recognition receptors to detect pathogen- or host-derived danger signals and trigger an immune response 1 – 6 . Receptor-like proteins (RLPs) with a leucine-rich repeat (LRR) ectodomain constitute a subgroup of pattern recognition receptors and play a critical role in plant immunity 1 – 3 . Mechanisms underlying ligand recognition and activation of LRR-RLPs remain elusive. Here we report a crystal structure of the LRR-RLP RXEG1 from Nicotiana benthamiana that recognizes XEG1 xyloglucanase from the pathogen Phytophthora sojae . The structure reveals that specific XEG1 recognition is predominantly mediated by an amino-terminal and a carboxy-terminal loop-out region (RXEG1(ID)) of RXEG1. The two loops bind to the active-site groove of XEG1, inhibiting its enzymatic activity and suppressing Phytophthora infection of N. benthamiana . Binding of XEG1 promotes association of RXEG1(LRR) with the LRR-type co-receptor BAK1 through RXEG1(ID) and the last four conserved LRRs to trigger RXEG1-mediated immune responses. Comparison of the structures of apo-RXEG1(LRR), XEG1–RXEG1(LRR) and XEG1–BAK1–RXEG1(LRR) shows that binding of XEG1 induces conformational changes in the N-terminal region of RXEG1(ID) and enhances structural flexibility of the BAK1-associating regions of RXEG1(LRR). These changes allow fold switching of RXEG1(ID) for recruitment of BAK1(LRR). Our data reveal a conserved mechanism of ligand-induced heterodimerization of an LRR-RLP with BAK1 and suggest a dual function for the LRR-RLP in plant immunity. A structural analysis focusing on plant immunity reveals how LRR-containing receptor-like proteins recognize pathogenic ligands and consequently become activated, with the data suggesting that these proteins target pathogens through two different mechanisms.
Background modeling and foreground detection for video surveillance
Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.Incorporating both established and new ideas, Background Modeling and Foreground Detection for Video Surveillance provides a complete overview of the concepts, algorithms, and applications related to background modeling and foreground detection.
Two cGAS-like receptors induce antiviral immunity in Drosophila
In mammals, cyclic GMP–AMP (cGAMP) synthase (cGAS) produces the cyclic dinucleotide 2′3′-cGAMP in response to cytosolic DNA and this triggers an antiviral immune response. cGAS belongs to a large family of cGAS/DncV-like nucleotidyltransferases that is present in both prokaryotes 1 and eukaryotes 2 – 5 . In bacteria, these enzymes synthesize a range of cyclic oligonucleotides and have recently emerged as important regulators of phage infections 6 – 8 . Here we identify two cGAS-like receptors (cGLRs) in the insect Drosophila melanogaster . We show that cGLR1 and cGLR2 activate Sting- and NF-κB-dependent antiviral immunity in response to infection with RNA or DNA viruses. cGLR1 is activated by double-stranded RNA to produce the cyclic dinucleotide 3′2′-cGAMP, whereas cGLR2 produces a combination of 2′3′-cGAMP and 3′2′-cGAMP in response to an as-yet-unidentified stimulus. Our data establish cGAS as the founding member of a family of receptors that sense different types of nucleic acids and trigger immunity through the production of cyclic dinucleotides beyond 2′3′-cGAMP. Two cGAS-like receptors, cGLR1 and cGLR2, identified in Drosophila melanogaster are shown to induce antiviral immunity in response to RNA or DNA virus infections through the production of 2′3′-cGAMP and 3′2′-cGAMP.
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
This paper introduces a video representation based on dense trajectories and motion boundary descriptors. Trajectories capture the local motion information of the video. A dense representation guarantees a good coverage of foreground motion as well as of the surrounding context. A state-of-the-art optical flow algorithm enables a robust and efficient extraction of dense trajectories. As descriptors we extract features aligned with the trajectories to characterize shape (point coordinates), appearance (histograms of oriented gradients) and motion (histograms of optical flow). Additionally, we introduce a descriptor based on motion boundary histograms (MBH) which rely on differential optical flow. The MBH descriptor shows to consistently outperform other state-of-the-art descriptors, in particular on real-world videos that contain a significant amount of camera motion. We evaluate our video representation in the context of action classification on nine datasets, namely KTH, YouTube, Hollywood2, UCF sports, IXMAS, UIUC, Olympic Sports, UCF50 and HMDB51. On all datasets our approach outperforms current state-of-the-art results.