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.
TRIM5 is a RING domain-E3 ubiquitin ligase that restricts infection by human immunodeficiency virus (HIV)-1 and other retroviruses immediately following virus invasion of the target cell cytoplasm. ...Antiviral potency correlates with TRIM5 avidity for the retrovirion capsid lattice and several reports indicate that TRIM5 has a role in signal transduction, but the precise mechanism of restriction is unknown. Here we demonstrate that TRIM5 promotes innate immune signalling and that this activity is amplified by retroviral infection and interaction with the capsid lattice. Acting with the heterodimeric, ubiquitin-conjugating enzyme UBC13-UEV1A (also known as UBE2N-UBE2V1), TRIM5 catalyses the synthesis of unattached K63-linked ubiquitin chains that activate the TAK1 (also known as MAP3K7) kinase complex and stimulate AP-1 and NFκB signalling. Interaction with the HIV-1 capsid lattice greatly enhances the UBC13-UEV1A-dependent E3 activity of TRIM5 and challenge with retroviruses induces the transcription of AP-1 and NF-κB-dependent factors with a magnitude that tracks with TRIM5 avidity for the invading capsid. Finally, TAK1 and UBC13-UEV1A contribute to capsid-specific restriction by TRIM5. Thus, the retroviral restriction factor TRIM5 has two additional activities that are linked to restriction: it constitutively promotes innate immune signalling and it acts as a pattern recognition receptor specific for the retrovirus capsid lattice.
Plants rely on cell-surface-localized pattern recognition receptors to detect pathogen- or host-derived danger signals and trigger an immune response
. 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
. 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.
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
and eukaryotes
. In bacteria, these enzymes synthesize a range of cyclic oligonucleotides and have recently emerged as important regulators of phage infections
. 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.
Receptor-like kinases (RLKs) and Receptor-like proteins (RLPs) play crucial roles in plant immunity, growth, and development. Plants deploy a large number of RLKs and RLPs as pattern recognition ...receptors (PRRs) that detect microbe- and host-derived molecular patterns as the first layer of inducible defense. Recent advances have uncovered novel PRRs, their corresponding ligands, and mechanisms underlying PRR activation and signaling. In general, PRRs associate with other RLKs and function as part of multiprotein immune complexes at the cell surface. Innovative strategies have emerged for the rapid identification of microbial patterns and their cognate PRRs. Successful pathogens can evade or block host recognition by secreting effector proteins to “hide” microbial patterns or inhibit PRR-mediated signaling. Furthermore, newly identified pathogen effectors have been shown to manipulate RLKs controlling growth and development by mimicking peptide hormones of host plants. The ongoing studies illustrate the importance of diverse plant RLKs in plant disease
EPnP: An Accurate O(n) Solution to the PnP Problem Lepetit, Vincent; Moreno-Noguer, Francesc; Fua, Pascal
International journal of computer vision,
02/2009, Letnik:
81, Številka:
2
Journal Article, Publication
Recenzirano
Odprti dostop
We propose a non-iterative solution to the P
n
P problem—the estimation of the pose of a calibrated camera from
n
3D-to-2D point correspondences—whose computational complexity grows linearly with
n
. ...This is in contrast to state-of-the-art methods that are
O
(
n
5
) or even
O
(
n
8
), without being more accurate. Our method is applicable for all
n
≥4 and handles properly both planar and non-planar configurations. Our central idea is to express the
n
3D points as a weighted sum of four virtual control points. The problem then reduces to estimating the coordinates of these control points in the camera referential, which can be done in
O
(
n
) time by expressing these coordinates as weighted sum of the eigenvectors of a 12×12 matrix and solving a small
constant
number of quadratic equations to pick the right weights. Furthermore, if maximal precision is required, the output of the closed-form solution can be used to initialize a Gauss-Newton scheme, which improves accuracy with negligible amount of additional time. The advantages of our method are demonstrated by thorough testing on both synthetic and real-data.
We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable. Our ...approach—Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say ‘dog’ in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (
e.g.
VGG), (2) CNNs used for structured outputs (
e.g.
captioning), (3) CNNs used in tasks with multi-modal inputs (
e.g.
visual question answering) or reinforcement learning, all
without architectural changes or re-training
. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are robust to adversarial perturbations, (d) are more faithful to the underlying model, and (e) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show that even non-attention based models learn to localize discriminative regions of input image. We devise a way to identify important neurons through Grad-CAM and combine it with neuron names (Bau et al. in Computer vision and pattern recognition, 2017) to provide textual explanations for model decisions. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a ‘stronger’ deep network from a ‘weaker’ one even when both make identical predictions. Our code is available at
https://github.com/ramprs/grad-cam/
, along with a demo on CloudCV (Agrawal et al., in: Mobile cloud visual media computing, pp 265–290. Springer, 2015) (
http://gradcam.cloudcv.org
) and a video at
http://youtu.be/COjUB9Izk6E
.
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.
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep ...learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.
Multi-object tracking (MOT) is an important problem in computer vision which has a wide range of applications. Formulating MOT as multi-task learning of object detection and re-ID in a single network ...is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency. However, we find that the two tasks tend to compete with each other which need to be carefully addressed. In particular, previous works usually treat re-ID as a secondary task whose accuracy is heavily affected by the primary detection task. As a result, the network is biased to the primary detection task which is not
fair
to the re-ID task. To solve the problem, we present a simple yet effective approach termed as
FairMOT
based on the anchor-free object detection architecture CenterNet. Note that it is not a naive combination of CenterNet and re-ID. Instead, we present a bunch of detailed designs which are critical to achieve good tracking results by thorough empirical studies. The resulting approach achieves high accuracy for both detection and tracking. The approach outperforms the state-of-the-art methods by a large margin on several public datasets. The source code and pre-trained models are released at
https://github.com/ifzhang/FairMOT
.
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