Molecular mechanisms for T-cell immune responses modulated by T cell-inhibitory molecules during tuberculosis (TB) infection remain unclear. Here, we show that active human TB infection up-regulates CD244 and CD244 signaling-associated molecules in CD8 + T cells and that blockade of CD244 signaling enhances production of IFN-γ and TNF-α. CD244 expression/signaling in TB correlates with high levels of a long noncoding RNA (lncRNA)-BC050410 [named as lncRNA-AS-GSTT1(1-72) or lncRNA-CD244] in the CD244 + CD8 + T-cell subpopulation. CD244 signaling drives lncRNA-CD244 expression via sustaining a permissive chromatin state in the lncRNA-CD244 locus. By recruiting polycomb protein enhancer of zeste homolog 2 (EZH2) to infg/tnfa promoters, lncRNA-CD244 mediates H3K27 trimethylation at infg/tnfa loci toward repressive chromatin states and inhibits IFN-γ/TNF-α expression in CD8 + T cells. Such inhibition can be reversed by knock down of lncRNA-CD244. Interestingly, adoptive transfer of lncRNA-CD244-depressed CD8 + T cells to Mycobacterium tuberculosis (MTB)-infected mice reduced MTB infection and TB pathology compared with lncRNA-CD244-expressed controls. Thus, this work uncovers previously unidentified mechanisms in which T cell-inhibitory signaling and lncRNAs regulate T-cell responses and host defense against TB infection.tuberculosis | lncRNA | CD8 + T cells
The quality of images captured by wireless capsule endoscopy (WCE) is key for doctors to diagnose diseases of gastrointestinal (GI) tract. However, there exist many lowquality endoscopic images due to the limited illumination and complex environment in GI tract. After an enhancement process, the severe noise become an unacceptable problem. The noise varies with different cameras, GI tract environments and image enhancement. And the noise model is hard to be obtained. This paper proposes a convolutional blind denoising network for endoscopic images. We apply Deep Image Prior (DIP) method to reconstruct a clean image iteratively using a noisy image without a specific noise model and ground truth. Then we design a blind image quality assessment network based on MobileNet to estimate the quality of the reconstructed images. The estimated quality is used to stop the iterative operation in DIP method. The number of iterations is reduced about 36% by using transfer learning in our DIP process. Experimental results on endoscopic images and real-world noisy images demonstrate the superiority of our proposed method over the state-of-the-art methods in terms of visual quality and quantitative metrics.
Keywords-Wireless capsule endoscopy, blind denoising, image quality assessment, convolutional neural network
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.