Hepatitis C virus 3'UTR regulates viral translation through direct interactions with the host translation machinery.

The 3' untranslated region (3'UTR) of hepatitis C virus (HCV) messenger RNA stimulates viral translation by an undetermined mechanism. We identified a high affinity interaction, conserved among different HCV genotypes, between the HCV 3'UTR and the host ribosome. The 3'UTR interacts with 40S ribosomal subunit proteins residing primarily in a localized region on the 40S solvent-accessible surface near the messenger RNA entry and exit sites. This region partially overlaps with the site where the HCV internal ribosome entry site was found to bind, with the internal ribosome entry site-40S subunit interaction being dominant. Despite its ability to bind to 40S subunits independently, the HCV 3'UTR only stimulates translation in cis, without affecting the first round translation rate. These observations support a model in which the HCV 3'UTR retains ribosome complexes during translation termination to facilitate efficient initiation of subsequent rounds of translation

Modelling and characterization of the quantum dot floatiing gate flash memory

Master'sMASTER OF ENGINEERIN

Detection and Removal of Noise in Images Based on Amount of Knowledge Associated with Intuitionistic Fuzzy Sets

In response to the shortcomings of existing image noise detection algorithms that rely on the flawed intuitionistic fuzzy entropy (IFE) theory, a method of image noise detection and removal based on intuitionistic fuzzy amount of knowledge (IFAK) is proposed by introducing the latest knowledge measure (KM) theory and model. In the noise detection stage, the optimal average intensity of the noisy image foreground and background is determined based on the maximum IFAK, and the parametric model of noise detection is constructed accordingly to mark the probability of noise pixels and suspected noise pixels, showing excellent performance of noise detection. In the noise removal stage, a denoising model based on IFAK and probability of noise pixels is proposed by using the noise probability matrix, which can not only effectively denoise, but also better protect the characteristics of image edges and non-noise extreme pixels. Comparative experiments are carried out on standard datasets and classical test images, respectively. Experimental results show that the proposed method can accurately identify the image impulse noise and effectively realize image denoising. The overall performance outperforms other similar algorithms. The key metrics PSNR and SSIM are increased by 14.81% and 11.35%, respectively. In this paper, the latest KM theory is applied to image denoising, and excellent evaluation metrics and visual effects are obtained, while innovative applications of this theory in other related fields are also achieved

RNA-guided complex from a bacterial immune system enhances target recognition through seed sequence interactions

Prokaryotes have evolved multiple versions of an RNA-guided adaptive immune system that targets foreign nucleic acids. In each case, transcripts derived from clustered regularly interspaced short palindromic repeats (CRISPRs) are thought to selectively target invading phage and plasmids in a sequence-specific process involving a variable cassette of CRISPR-associated (cas) genes. The CRISPR locus in Pseudomonas aeruginosa (PA14) includes four cas genes that are unique to and conserved in microorganisms harboring the Csy-type (CRISPR system yersinia) immune system. Here we show that the Csy proteins (Csy1-4) assemble into a 350 kDa ribonucleoprotein complex that facilitates target recognition by enhancing sequence-specific hybridization between the CRISPR RNA and complementary target sequences. Target recognition is enthalpically driven and localized to a "seed sequence" at the 5' end of the CRISPR RNA spacer. Structural analysis of the complex by small-angle X-ray scattering and single particle electron microscopy reveals a crescent-shaped particle that bears striking resemblance to the architecture of a large CRISPR-associated complex from Escherichia coli, termed Cascade. Although similarity between these two complexes is not evident at the sequence level, their unequal subunit stoichiometry and quaternary architecture reveal conserved structural features that may be common among diverse CRISPR-mediated defense systems

Structural basis for CRISPR RNA-guided DNA recognition by Cascade

The CRISPR (clustered regularly interspaced short palindromic repeats) immune system in prokaryotes uses small guide RNAs to neutralize invading viruses and plasmids. In Escherichia coli, immunity depends on a ribonucleoprotein complex called Cascade. Here we present the composition and low-resolution structure of Cascade and show how it recognizes double-stranded DNA (dsDNA) targets in a sequence-specific manner. Cascade is a 405-kDa complex comprising five functionally essential CRISPR-associated (Cas) proteins (CasA1B2C6D1E1) and a 61-nucleotide CRISPR RNA (crRNA) with 5′-hydroxyl and 2′,3′-cyclic phosphate termini. The crRNA guides Cascade to dsDNA target sequences by forming base pairs with the complementary DNA strand while displacing the noncomplementary strand to form an R-loop. Cascade recognizes target DNA without consuming ATP, which suggests that continuous invader DNA surveillance takes place without energy investment. The structure of Cascade shows an unusual seahorse shape that undergoes conformational changes when it binds target DNA.

Learning from experience in Hangzhou: WLCE leisure experience research opportunity

This text was collaboratively written by the 12 students – from Brazil, Canada, China, Hong Kong and Hungary – who participated in the WLCE Leisure Experience Research Opportunity, a fieldwork project focusing on resident, national and international visitors to the Chinese city of Hangzhou. The project, designed and implemented by the WLO, was supported by the Hangzhou Municipal Bureau of Commerce and Hangzhou Commerce and Tourism Group, and supervised by Dr. Marcel Bastiaansen (Breda University of Applied Sciences, the Netherlands), Dr. Marie Young (University of the Western Cape, South Africa) and Dr. Isabel Verdet (WLO Secretariat)

Hepatitis C virus 3'UTR regulates viral translation through direct interactions with the host translation machinery.

The 3' untranslated region (3'UTR) of hepatitis C virus (HCV) messenger RNA stimulates viral translation by an undetermined mechanism. We identified a high affinity interaction, conserved among different HCV genotypes, between the HCV 3'UTR and the host ribosome. The 3'UTR interacts with 40S ribosomal subunit proteins residing primarily in a localized region on the 40S solvent-accessible surface near the messenger RNA entry and exit sites. This region partially overlaps with the site where the HCV internal ribosome entry site was found to bind, with the internal ribosome entry site-40S subunit interaction being dominant. Despite its ability to bind to 40S subunits independently, the HCV 3'UTR only stimulates translation in cis, without affecting the first round translation rate. These observations support a model in which the HCV 3'UTR retains ribosome complexes during translation termination to facilitate efficient initiation of subsequent rounds of translation

Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines

At present, many large-scale engineering equipment can obtain massive in-situ data at runtime. In-depth data mining is conducive to the real-time understanding of equipment operation status or recognition of service environment. This paper proposes a geological type recognition system by the analysis of in-situ data recorded during TBM tunneling to address geological information acquisition during TBM construction. Owing to high dimensionality and nonlinear coupling between parameters of TBM in-situ data, the dimensionality reduction feature engineering and machine learning methods are introduced into TBM in-situ data analysis. The chi-square test is used to screen for sensitive features due to the disobedience to common distributions of TBM parameters. Considering complex relationships, ANN, SVM, KNN, and CART algorithms are used to construct a geology recognition classifier. A case study of a subway tunnel project constructed using an earth pressure balance tunnel boring machine (EPB-TBM) in China is used to verify the effectiveness of the proposed geological recognition method. The result shows that the recognition accuracy gradually increases to a stable level with the increase of input features, and the accuracy of all algorithms is higher than 97%. Seven features are considered as the best selection strategy among SVM, KNN, and ANN, while feature selection is an inherent part of the CART method which shows a good recognition performance. This work provides an intelligent path for obtaining geological information for underground excavation TBM projects and a possibility for solving the problem of engineering recognition of more complex geological conditions