The Methanolic Extract of Perilla frutescens Robustly Restricts Ebola Virus Glycoprotein-Mediated Entry

Ebola virus (EBOV), one of the most infectious human viruses and a leading cause of viral hemorrhagic fever, imposes a potential public health threat with several recent outbreaks. Despite the difficulties associated with working with this pathogen in biosafety level-4 containment, a protective vaccine and antiviral therapeutic were recently approved. However, the high mortality rate of EBOV infection underscores the necessity to continuously identify novel antiviral strategies to help expand the scope of prophylaxis/therapeutic management against future outbreaks. This includes identifying antiviral agents that target EBOV entry, which could improve the management of EBOV infection. Herein, using EBOV glycoprotein (GP)-pseudotyped particles, we screened a panel of natural medicinal extracts, and identified the methanolic extract of Perilla frutescens (PFME) as a robust inhibitor of EBOV entry. We show that PFME dose-dependently impeded EBOV GP-mediated infection at non-cytotoxic concentrations, and exerted the most significant antiviral activity when both the extract and the pseudoparticles are concurrently present on the host cells. Specifically, we demonstrate that PFME could block viral attachment and neutralize the cell-free viral particles. Our results, therefore, identified PFME as a potent inhibitor of EBOV entry, which merits further evaluation for development as a therapeutic strategy against EBO

Transgenic Expression of Decoy Receptor 3 Protects Islets from Spontaneous and Chemical-induced Autoimmune Destruction in Nonobese Diabetic Mice

Decoy receptor 3 (DCR3) halts both Fas ligand– and LIGHT-induced cell deaths, which are required for pancreatic β cell damage in autoimmune diabetes. To directly investigate the therapeutic potential of DCR3 in preventing this disease, we generated transgenic nonobese diabetic mice, which overexpressed DCR3 in β cells. Transgenic DCR3 protected mice from autoimmune and cyclophosphamide-induced diabetes in a dose-dependent manner and significantly reduced the severity of insulitis. Local expression of the transgene did not alter the diabetogenic properties of systemic lymphocytes or the development of T helper 1 or T regulatory cells. The transgenic islets had a higher transplantation success rate and survived for longer than wild-type islets. We have demonstrated for the first time that the immune-evasion function of DCR3 inhibits autoimmunity and that genetic manipulation of grafts may improve the success and survival of islet transplants

Phenanthrene-Based Tylophorine-1 (PBT-1) Inhibits Lung Cancer Cell Growth through the Akt and NF-κB Pathways

Tylophorine and related natural compounds exhibit potent antitumor activities. We previously showed that PBT-1, a synthetic C9-substituted phenanthrene-based tylophorine (PBT) derivative, significantly inhibits growth of various cancer cells. In this study, we further explored the mechanisms and potential of PBT-1 as an anticancer agent. PBT-1 dose-dependently suppressed colony formation, induced cell cycle G2/M arrest and apoptosis. DNA microarray and pathway analysis showed that PBT-1 activated the apoptosis pathway and mitogen-activated protein kinase signaling. In contrast, PBT-1 suppressed the nuclear factor kappaB (NF-κB) pathway and focal adhesion. We further confirmed that PBT-1 suppressed Akt activation accelerated RelA degradation via IκB kinase-α, and downregulated NF-κB target gene expression. The reciprocal recruitment of RelA and RelB on COX-2 promoter region led to downregulation of transcriptional activity. We conclude that PBT-1 induces cell cycle G2/M arrest and apoptosis by inactivating Akt and by inhibiting the NF-κB signaling pathway. PBT-1 may be a good drug candidate for anticancer chemotherapy

Antitumor agents 283. Further elaboration of Desmosdumotin C analogs as potent antitumor agents: Activation of spindle assembly checkpoint as possible mode of action

In our ongoing study of the desmosdumotin C (1) series, twelve new analogues, 21–32, mainly with structural modifications in ring-A, were prepared and evaluated for in vitro antiproliferative activity against several human tumor cell lines. Among them, the 4′-iodo-3,3,5-tripropyl-4-methoxy analogue (31) showed significant antiproliferative activity against multiple human tumor cell lines with ED50 values of 1.1–2.8 μM. Elongation of the C-3 and C-5 carbon chains reduced activity relative to propyl substituted analogues; however, activity was still better than that of natural compound 1. Among analogues with various ether groups on C-4, compounds with methyl (2) and propyl (26) ethers inhibited cell growth of multiple tumor cells lines, while 28 with an isobutyl ether showed selective antiproliferative activity against lung cancer A549 cells (ED50 1.7 μM). The gene expression profiles showed that 3 may modulate the spindle assembly checkpoint (SAC) and chromosome separation, and thus, arrest cells at the G2/M-phase

A Novel and Effective Cooperative RANSAC Image Matching Method Using Geometry Histogram-Based Constructed Reduced Correspondence Set

The success of many computer vision and pattern recognition applications depends on matching local features on two or more images. Because the initial correspondence set—i.e., the set of the initial feature pairs—is often contaminated by mismatches, removing mismatches is a necessary task prior to image matching. In this paper, we first propose a fast geometry histogram-based (GH-based) mismatch removal strategy to construct a reduced correspondence set Creduced,GH from the initial correspondence set Cini. Next, we propose an effective cooperative random sample consensus (COOSAC) method for remote sensing image matching. COOSAC consists of a RANSAC, called RANSACini working on Cini, and a tiny RANSAC, called RANSACtiny,GH working on a randomly selected subset of Creduced,GH. In RANSACtiny,GH, an iterative area constraint-based sampling strategy is proposed to estimate the model solution of Ctiny,GH until the specified confidence level is reached, and then RANSACini utilizes the estimated model solution of Ctiny,GH to calculate the inlier rate of Cini. COOSAC repeats the above cooperation between RANSACtiny,GH and RANSACini until the specified confidence level is reached, reporting the resultant model solution of Cini. For convenience, our image matching method is called the GH-COOSAC method. Based on several testing datasets, thorough experimental results demonstrate that the proposed GH-COOSAC method achieves lower computational cost and higher matching accuracy benefits when compared with the state-of-the-art image matching methods

A Novel and Effective Cooperative RANSAC Image Matching Method Using Geometry Histogram-Based Constructed Reduced Correspondence Set

The success of many computer vision and pattern recognition applications depends on matching local features on two or more images. Because the initial correspondence set—i.e., the set of the initial feature pairs—is often contaminated by mismatches, removing mismatches is a necessary task prior to image matching. In this paper, we first propose a fast geometry histogram-based (GH-based) mismatch removal strategy to construct a reduced correspondence set Creduced,GH from the initial correspondence set Cini. Next, we propose an effective cooperative random sample consensus (COOSAC) method for remote sensing image matching. COOSAC consists of a RANSAC, called RANSACini working on Cini, and a tiny RANSAC, called RANSACtiny,GH working on a randomly selected subset of Creduced,GH. In RANSACtiny,GH, an iterative area constraint-based sampling strategy is proposed to estimate the model solution of Ctiny,GH until the specified confidence level is reached, and then RANSACini utilizes the estimated model solution of Ctiny,GH to calculate the inlier rate of Cini. COOSAC repeats the above cooperation between RANSACtiny,GH and RANSACini until the specified confidence level is reached, reporting the resultant model solution of Cini. For convenience, our image matching method is called the GH-COOSAC method. Based on several testing datasets, thorough experimental results demonstrate that the proposed GH-COOSAC method achieves lower computational cost and higher matching accuracy benefits when compared with the state-of-the-art image matching methods

Compression for Bayer CFA Images: Review and Performance Comparison

Bayer color filter array (CFA) images are captured by a single-chip image sensor covered with a Bayer CFA pattern which has been widely used in modern digital cameras. In the past two decades, many compression methods have been proposed to compress Bayer CFA images. These compression methods can be roughly divided into the compression-first-based (CF-based) scheme and the demosaicing-first-based (DF-based) scheme. However, in the literature, no review article for the two compression schemes and their compression performance is reported. In this article, the related CF-based and DF-based compression works are reviewed first. Then, the testing Bayer CFA images created from the Kodak, IMAX, screen content images, videos, and classical image datasets are compressed on the Joint Photographic Experts Group-2000 (JPEG-2000) and the newly released Versatile Video Coding (VVC) platform VTM-16.2. In terms of the commonly used objective quality, perceptual quality metrics, the perceptual effect, and the quality–bitrate tradeoff metric, the compression performance comparison of the CF-based compression methods, in particular the reversible color transform-based compression methods and the DF-based compression methods, is reported and discussed