Bionic Duplication of Fresh Navodon septentrionalis

Biomimetic superhydrophobic surface was fabricated by replicating topography of the fresh fish skin surface of Navodon septentrionalis with polydimethylsiloxane (PDMS) elastomer. A two-step replicating method was developed to make the surface structure of the fresh fish skin be replicated with high fidelity. After duplication, it was found that the static contact angle of the replica was as large as 173°. Theoretic analysis based on Young's and Cassie-Baxter (C-B) model was performed to explain the relationship between structure and hydrophobicity

Four-channel coarse WDM for inter-and intra-satellite optical communications

Abstract A polymer volume grating-based four-channel coarse wavelength division multiplexer (WDM) for inter-and intra-satellite optical communication application is reported for the first time. This compact four-channel WDM device working at 0.83, 1.06, 1.34 and 1.55 mm is designed to build a complete optical link between two satellites, where wavelengths of 0.83 and 1.55 mm are used for data stream channels, 1.06 and 1.34 mm are used for inter-and intra-satellite connection. It is for the first time reported that a WDM device can cover such a large wavelength range in a single substrate. For transverse electric (TE) wave, the channel efficiencies at 0.83, 1.06, 1.34 and 1.55 mm are 55%, 40%, 35% and 45%, respectively. Channel efficiencies for transverse magnetic (TM) waves are 20% lower than those of TE waves on average. Wavelength shifts due to Doppler effect, temperature variations and radiation effects in space can be adequately accommodated. Published by Elsevier Ltd. Keywords: Wavelength division multiplexing; Satellite communication; Holographic gratings The concept of space-based, free space optical communications among satellites was developed in the early 1960s [1]. However, there was no system demonstration coming into reality until 2001 by ASTRIUM Coarse wavelength division multiplexing (WDM) technology, which is developed for storage access networks (SANs), finds its great potential for applications in the space-based optical communication system. The data bit-rate independence of the WDM technology ARTICLE IN PRESS www.elsevier.com/locate/optlastec 0030-3992/$ -see front matter Published by Elsevier Ltd

B and T Lymphocyte Attenuator Down-regulation by HIV-1 Depends on Type I Interferon and Contributes to T-Cell Hyperactivation

Background. Nonspecific T-cell hyperactivation is the main driving force for human immunodeficiency virus (HIV)–1 disease progression, but the reasons why the excess immune response is not properly shut off are poorly defined

Transient mTOR Inhibition Facilitates Continuous Growth of Liver Tumors by Modulating the Maintenance of CD133+ Cell Populations

The mammalian target of the rapamycin (mTOR) pathway, which drives cell proliferation, is frequently hyperactivated in a variety of malignancies. Therefore, the inhibition of the mTOR pathway has been considered as an appropriate approach for cancer therapy. In this study, we examined the roles of mTOR in the maintenance and differentiation of cancer stem-like cells (CSCs), the conversion of conventional cancer cells to CSCs and continuous tumor growth in vivo. In H-Ras-transformed mouse liver tumor cells, we found that pharmacological inhibition of mTOR with rapamycin greatly increased not only the CD133+ populations both in vitro and in vivo but also the expression of stem cell-like genes. Enhancing mTOR activity by over-expressing Rheb significantly decreased CD133 expression, whereas knockdown of the mTOR yielded an opposite effect. In addition, mTOR inhibition severely blocked the differentiation of CD133+ to CD133- liver tumor cells. Strikingly, single-cell culture experiments revealed that CD133- liver tumor cells were capable of converting to CD133+ cells and the inhibition of mTOR signaling substantially promoted this conversion. In serial implantation of tumor xenografts in nude BALB/c mice, the residual tumor cells that were exposed to rapamycin in vivo displayed higher CD133 expression and had increased secondary tumorigenicity compared with the control group. Moreover, rapamycin treatment also enhanced the level of stem cell-associated genes and CD133 expression in certain human liver tumor cell lines, such as Huh7, PLC/PRC/7 and Hep3B. The mTOR pathway is significantly involved in the generation and the differentiation of tumorigenic liver CSCs. These results may be valuable for the design of more rational strategies to control clinical malignant HCC using mTOR inhibitors

Micro- and nano-periodic-structure-based devices for laser beam control

With the progress of microfabrication and nanofabrication technologies, there has been a reawakened interest in the possibility of controlling the propagation of light in various materials periodically structured at a scale comparable to, or slightly smaller than the wavelength. We can now engineer materials with periodic structures to implement a great variety of optical phenomena. These include well known effects, such as dispersing a variety of wavelength to form a spectrum and diffracting light and controlling its propagation directions, to new ones such as prohibiting the propagation of light in certain directions at certain wavelengths and localizing light with defects in some artificially synthesized dielectric materials. Advances in this field have had tremendous impact on modern optical and photonic technologies. This doctoral research was aimed at investigating some of the physics and applications of periodic structures for building blocks of the optical communication and interconnection system. Particular research emphasis was placed on the exploitation of innovative periodic structure-based optical and photonic devices featuring better functionality, higher performance, more compact size, and easier fabrication. Research topics extended from one-dimensional periodic-structure-based wavelength-division-multiplexing (WDM) optical interconnects (beam wavelength selection devices), and liquid crystal beam steerers (beam steering devices), to two-dimensional periodic-structure-based silicon photonic-crystal thermo-optic and electro-optic modulators (beam switching devices). This research was specifically targeted to seek novel and effective solutions to some long-standing technical problems, such as the limited wavelength coverage of coarse WDM devices, small bandwidth of highly dispersed dense WDM devices, low deflection efficiency of high-resolution liquid crystal beam steerers, slow switching speed, large device size, and high power consumption of silicon optical modulators, among others. For each subtopic, research challenges were presented and followed by the proposed solutions with extensive theoretical analysis. The proposals were then verified by experimental implementations. Experimental results were carefully interpreted and the future improvements were also discussed.Electrical and Computer Engineerin

Valid Probabilistic Anomaly Detection Models for System Logs

System logs can record the system status and important events during system operation in detail. Detecting anomalies in the system logs is a common method for modern large-scale distributed systems. Yet threshold-based classification models used for anomaly detection output only two values: normal or abnormal, which lacks probability of estimating whether the prediction results are correct. In this paper, a statistical learning algorithm Venn-Abers predictor is adopted to evaluate the confidence of prediction results in the field of system log anomaly detection. It is able to calculate the probability distribution of labels for a set of samples and provide a quality assessment of predictive labels to some extent. Two Venn-Abers predictors LR-VA and SVM-VA have been implemented based on Logistic Regression and Support Vector Machine, respectively. Then, the differences among different algorithms are considered so as to build a multimodel fusion algorithm by Stacking. And then a Venn-Abers predictor based on the Stacking algorithm called Stacking-VA is implemented. The performances of four types of algorithms (unimodel, Venn-Abers predictor based on unimodel, multimodel, and Venn-Abers predictor based on multimodel) are compared in terms of validity and accuracy. Experiments are carried out on a log dataset of the Hadoop Distributed File System (HDFS). For the comparative experiments on unimodels, the results show that the validities of LR-VA and SVM-VA are better than those of the two corresponding underlying models. Compared with the underlying model, the accuracy of the SVM-VA predictor is better than that of LR-VA predictor, and more significantly, the recall rate increases from 81% to 94%. In the case of experiments on multiple models, the algorithm based on Stacking multimodel fusion is significantly superior to the underlying classifier. The average accuracy of Stacking-VA is larger than 0.95, which is more stable than the prediction results of LR-VA and SVM-VA. Experimental results show that the Venn-Abers predictor is a flexible tool that can make accurate and valid probability predictions in the field of system log anomaly detection

Detecting Overlapping Data in System Logs Based on Ensemble Learning Method

Machine learning techniques are essential for system log anomaly detection. It is prone to the phenomenon of class overlap because of too many similar system log data. The occurrence of this phenomenon will have a serious impact on the anomaly detection of the system logs. To solve the problem of class overlap in system logs, this paper proposes an anomaly detection model for class overlap problem on system logs. We first calculate the relationship between the sample data and the membership of different classes, normal or anomaly, and use the fuzziness to separate the sample data of the overlapping parts of the classes from the data of the other parts. AdaBoost, an ensemble learning approach, is used to detect overlapping data. Compared with machine learning algorithms, ensemble learning can better classify the data of the overlapping parts, so as to achieve the purpose of detecting the anomalies of the system logs. We also discussed the possible impact of different voting methods on ensemble learning results. Experimental results show that our model can be effectively applied in a variety of basic algorithms, and the results of each measure have been improved