Shape-appearance-correlated active appearance model

© 2016 Elsevier Ltd Among the challenges faced by current active shape or appearance models, facial-feature localization in the wild, with occlusion in a novel face image, i.e. in a generic environment, is regarded as one of the most difficult computer-vision tasks. In this paper, we propose an Active Appearance Model (AAM) to tackle the problem of generic environment. Firstly, a fast face-model initialization scheme is proposed, based on the idea that the local appearance of feature points can be accurately approximated with locality constraints. Nearest neighbors, which have similar poses and textures to a test face, are retrieved from a training set for constructing the initial face model. To further improve the fitting of the initial model to the test face, an orthogonal CCA (oCCA) is employed to increase the correlation between shape features and appearance features represented by Principal Component Analysis (PCA). With these two contributions, we propose a novel AAM, namely the shape-appearance-correlated AAM (SAC-AAM), and the optimization is solved by using the recently proposed fast simultaneous inverse compositional (Fast-SIC) algorithm. Experiment results demonstrate a 5–10% improvement on controlled and semi-controlled datasets, and with around 10% improvement on wild face datasets in terms of fitting accuracy compared to other state-of-the-art AAM models

Communication-constrained distributed quantile regression with optimal statistical guarantees

We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions. This is challenging due to the non-smooth nature of the quantile regression (QR) loss function, which invalidates the use of existing methodology. The difficulties are resolved through a double-smoothing approach that is applied to the local (at each data source) and global objective functions. Despite the reliance on a delicate combination of local and global smoothing parameters, the quantile regression model is fully parametric, thereby facilitating interpretation. In the low-dimensional regime, we establish a finite-sample theoretical framework for the sequentially defined distributed QR estimators. This reveals a trade-off between the communication cost and statistical error. We further discuss and compare several alternative confidence set constructions, based on inversion of Wald and score-type tests and resampling techniques, detailing an improvement that is effective for more extreme quantile coefficients. In high dimensions, a sparse framework is adopted, where the proposed doubly-smoothed objective function is complemented with an ℓ1-penalty. We show that the corresponding distributed penalized QR estimator achieves the global convergence rate after a near-constant number of communication rounds. A thorough simulation study further elucidates our findings

Integral Human Pose Regression

State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time

Development of human single-chain antibodies against SARS-associated coronavirus.

The outbreak of severe acute respiratory syndrome (SARS), caused by a distinct coronavirus, in 2003 greatly threatened public health in China, Southeast Asia as well as North America. Over 1,000 patients died of the SARS virus, representing 10% of infected people. Like other coronaviruses, the SARS virus also utilizes a surface glycoprotein, namely the spike protein, to infect host cells. The spike protein of SARS virus consists of 1,255 amino acid residues and can be divided into two sub-domains, S1 and S2. The S1 domain mediates the binding of the virus to its receptor angiotensin-converting enzyme 2, which is abundantly distributed on the surface of human lung cells. The S2 domain mediates membrane fusion between the virus and the host cell. Hence two strategies can be used to block the infection of the SARS virus, either by interfering with the binding of the S1 domain to the receptor or by blocking the fusion of the virus with the cell membrane mediated by the S2 domain. Several antibodies against the S1 domain have been generated and all of them are able to neutralize the virus in vitro and in vivo using animal models. Unfortunately, point mutations have been identified in the S1 domain, so that the virus isolated in the future may not be recognized by these antibodies. As no mutation has been found in the S2 domain indicating that this region is more conserved than the S1 domain, it may be a better target for antibody binding. After predicting the immunogenicity of the epitopes of the S2 domain, we chemically synthesized two peptides and also expressed one of them using a recombinant DNA method. We screened a phage displaying library of human single-chain antibodies (ScFv) against the predicted epitopes and obtained a human ScFv which can recognize the SARS virus in vitro

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods

Pandemic A/H1N1 2009 Influenza Virus-like Particles Elicited Higher and Broader Immune Responses than the Commercial Panenza Vaccine

Objectives: The aim was to construct 2009 pandemic A/H1N1 influenza VLPs (virus-like particles) and compare the immunogenicity and protection efficacy with the commercial Panenza vaccine in BALB/c mouse model. Methods: VLPs derived from influenza A/Hong Kong/01/2009 (H1N1) virus were constructed by Bac-to-Bac baculovirus expression system. VLPs were purified by sucrose density gradient ultracentrifugation and then characterized by Western blotting analysis and transmission electron microscopy. After single dose vaccination with 3 µg of VLPs and equal amount of Panenza vaccine, the immune responses and efficacy of protection induced by VLPs were compared with those elicited by the Panenza vaccine in 6-8 week female BALB/c mice. Key findings: VLPs could induce higher antibody titer as determined by hemagglutinin inhibition and microneutralization assay. Furthermore, we demonstrated that VLPs induced better antibody response to neuraminidase. In addition, VLP vaccinated mice had stronger cell-mediated immune response. As a result, our VLPs conferred 100% protection while the Panenza vaccine only conferred 67% protection. Conclusion: From the results, we concluded that influenza VLPs are highly immunogenic and they are promising to be developed as an alternative strategy to vaccine production in order to control the spread of influenza viruses.published_or_final_versio

MT1-MMP cleaves Dll1 to negatively regulate Notch signalling to maintain normal B-cell development

Notch signalling controls the differentiation of haematopoietic progenitor cells (HPCs). Here, we show that loss of membrane-type 1 matrix metalloproteinase (MT1-MMP, MMP14), a cell surface protease expressed in bone marrow stromal cells (BMSCs), increases Notch signalling in HPCs and specifically impairs B-lymphocyte development. When co-cultured with BMSCs in vitro, HPCs differentiation towards B lymphocytes is significantly compromised on MT1-MMP-deficient BMSCs and this defect could be completely rescued by DAPT, a specific Notch signalling inhibitor. The defective B-lymphocyte development could also be largely rescued by DAPT in vivo. MT1-MMP interacts with Notch ligand Delta-like 1 (Dll1) and promotes its cleavage on cell surface in BMSCs. Ectopic MT1-MMP cleaves Dll1 and results in diminished Notch signalling in co-cultured cells. In addition, recombinant MT1-MMP cleaves a synthetic Dll1 peptide at the same site where MT1-MMP cleaves Dll1 on the cell surface. Our data suggest that MT1-MMP directly cleaves Dll1 on BMSCs to negatively regulate Notch signalling to specifically maintain normal B-cell development in bone marrow. © 2011 European Molecular Biology Organization.postprin

A safe and convenient pseudovirus-based inhibition assay to detect neutralizing antibodies and screen for viral entry inhibitors against the novel human coronavirus MERS-CoV.

BACKGROUND: Evidence points to the emergence of a novel human coronavirus, Middle East respiratory syndrome coronavirus (MERS-CoV), which causes a severe acute respiratory syndrome (SARS)-like disease. In response, the development of effective vaccines and therapeutics remains a clinical priority. To accomplish this, it is necessary to evaluate neutralizing antibodies and screen for MERS-CoV entry inhibitors. METHODS: In this study, we produced a pseudovirus bearing the full-length spike (S) protein of MERS-CoV in the Env-defective, luciferase-expressing HIV-1 backbone. We then established a pseudovirus-based inhibition assay to detect neutralizing antibodies and anti-MERS-CoV entry inhibitors. RESULTS: Our results demonstrated that the generated MERS-CoV pseudovirus allows for single-cycle infection of a variety of cells expressing dipeptidyl peptidase-4 (DPP4), the confirmed receptor for MERS-CoV. Consistent with the results from a live MERS-CoV-based inhibition assay, the antisera of mice vaccinated with a recombinant protein containing receptor-binding domain (RBD, residues 377-662) of MERS-CoV S fused with Fc of human IgG exhibited neutralizing antibody response against infection of MERS-CoV pseudovirus. Furthermore, one small molecule HIV entry inhibitor targeting gp41 (ADS-J1) and the 3-hydroxyphthalic anhydride-modified human serum albumin (HP-HSA) could significantly inhibit MERS-CoV pseudovirus infection. CONCLUSION: Taken together, the established MERS-CoV inhibition assay is a safe and convenient pseudovirus-based alternative to BSL-3 live-virus restrictions and can be used to rapidly screen MERS-CoV entry inhibitors, as well as evaluate vaccine-induced neutralizing antibodies against the highly pathogenic MERS-CoV.published_or_final_versio

Type I interferon signaling deficiency results in dysregulated innate immune responses to SARS-CoV-2 in mice

SARS-CoV-2 is a newly emerged coronavirus, causing the global pandemic of respiratory coronavirus disease (COVID-19). The type I interferon (IFN) pathway is of particular importance for anti-viral defence and recent studies identified that type I IFNs drive early inflammatory responses to SARS-CoV-2. Here, we use a mouse model of SARS-CoV-2 infection, facilitating viral entry by intranasal recombinant Adeno-Associated Virus (rAAV) transduction of hACE2 in wildtype (WT) and type I IFN-signalling-deficient (Ifnar1-/- ) mice, to study type I IFN signalling deficiency and innate immune responses during SARS-CoV-2 infection. Our data show that type I IFN signaling is essential for inducing anti-viral effector responses to SARS-CoV-2, control of virus replication and to prevent enhanced disease. Furthermore, hACE2-Ifnar1-/- mice had increased gene expression of the chemokine Cxcl1 and airway infiltration of neutrophils as well as a reduced and delayed production of monocyte-recruiting chemokine CCL2. hACE2-Ifnar1-/- mice showed altered recruitment of inflammatory myeloid cells to the lung upon SARS-CoV-2 infection, with a shift from Ly6C+ to Ly6C- expressing cells. Together, our findings suggest that type I IFN deficiency results in a dysregulated innate immune response to SARS-CoV-2 infection. This article is protected by copyright. All rights reserved

Globally Continuous and Non-Markovian Crowd Activity Analysis from Videos

Automatically recognizing activities in video is a classic problem in vision and helps to understand behaviors, describe scenes and detect anomalies. We propose an unsupervised method for such purposes. Given video data, we discover recurring activity patterns that appear, peak, wane and disappear over time. By using non-parametric Bayesian methods, we learn coupled spatial and temporal patterns with minimum prior knowledge. To model the temporal changes of patterns, previous works compute Markovian progressions or locally continuous motifs whereas we model time in a globally continuous and non-Markovian way. Visually, the patterns depict flows of major activities. Temporally, each pattern has its own unique appearance-disappearance cycles. To compute compact pattern representations, we also propose a hybrid sampling method. By combining these patterns with detailed environment information, we interpret the semantics of activities and report anomalies. Also, our method fits data better and detects anomalies that were difficult to detect previously