Development and Use of a Kinetical and Real-Time Monitoring System to Analyze the Replication of Hepatitis C Virus

In microbiological research, it is important to understand the time course of each step in a pathogen’s lifecycle and changes in the host cell environment induced by infection. This study is the first to develop a real-time monitoring system that kinetically detects luminescence reporter activity over time without sampling cells or culture supernatants for analyzing the virus replication. Subgenomic replicon experiments with hepatitis C virus (HCV) showed that transient translation and genome replication can be detected separately, with the first peak of translation observed at 3–4 h and replication beginning around 20 h after viral RNA introduction into cells. From the bioluminescence data set measured every 30 min (48 measurements per day), the initial rates of translation and replication were calculated, and their capacity levels were expressed as the sums of the measured signals in each process, which correspond to the areas on the kinetics graphs. The comparison of various HuH-7-derived cell lines showed that the bioluminescence profile differs among cell lines, suggesting that both translation and replication capacities potentially influence differences in HCV susceptibility. The effects of RNA mutations within the 5′ UTR of the replicon on viral translation and replication were further analyzed in the system developed, confirming that mutations to the miR-122 binding sites primarily reduce replication activity rather than translation. The newly developed real-time monitoring system should be applied to the studies of various viruses and contribute to the analysis of transitions and progression of each process of their life cycle.     

miR-199a: A Tumor Suppressor with Noncoding RNA Network and Therapeutic Candidate in Lung Cancer

Lung cancer is the leading cause of cancer death worldwide. miR-199a, which has two mature molecules: miR-199a-3p and miR-199a-5p, plays an important biological role in the genesis and development of tumors. We collected recent research results on lung cancer and miR-199a from Google Scholar and PubMed databases. The biological functions of miR-199a in lung cancer are reviewed in detail, and its potential roles in lung cancer diagnosis and treatment are discussed. With miR-199a as the core point and a divergence outward, the interplay between miR-199a and other ncRNAs is reviewed, and a regulatory network covering various cancers is depicted, which can help us to better understand the mechanism of cancer occurrence and provide a means for developing novel therapeutic strategies. In addition, the current methods of diagnosis and treatment of lung cancer are reviewed. Finally, a conclusion was drawn: miR-199a inhibits the development of lung cancer, especially by inhibiting the proliferation, infiltration, and migration of lung cancer cells, inhibiting tumor angiogenesis, increasing the apoptosis of lung cancer cells, and affecting the drug resistance of lung cancer cells. This review aims to provide new insights into lung cancer therapy and prevention.     

Biophotonic probes for bio-detection and imaging

The rapid development of biophotonics and biomedical sciences makes a high demand on photonic structures to be interfaced with biological systems that are capab...     

火力发电厂锅炉外部定期检验探讨

分析火力发电厂锅炉外部定期检验的重要性,介绍锅炉外部定期检验的重点及易出现的问题,提出加强锅炉外部定期检验工作的建议.     

焊接与切割设备的使用和维修(七十八)——烘干箱、变位机、操作机的使用与维修

针对维修难度较大的自控远红外焊条烘干箱,简单介绍了其结构和工作原理.并以ZYH-100型、DH-60型、YHX-80型3种烘干箱为例,整理出电路原理图,并介绍了工作程序以及常出现的故障和排除方法.     

高速轧机润滑油品含水率实时检测系统的设计与应用

本文简要介绍了高速轧机润滑油品含水率实时检测系统的设计内容与应用情况,本系统使用效果明显,在国内本专业领域处于领先地位。     

PROCONTROL-P控制系统人机接口分析与改造

对南阳鸭河口电厂MMI系统存在问题进行了分析,提出相应改造方案并对2台机组PROCONTROL-P系统MMI进行了升级改造。改造采用通用PC及主流操作系统,并采用先进可靠的快速网络技术,有效地提高了现有PROCONTROL-P控制系统的安全可靠性和设备的技术水平,大大降低了系统维护成本。     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.