Water extract of Rheum officinale Baill. induces apoptosis in human lung adenocarcinoma A549 and human breast cancer MCF-7 cell lines

Author name used in this publication: De-Jian GuoAuthor name used in this publication: Peter Hoi-Fu Yu2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Self-repairing symmetry in jellyfish through mechanically driven reorganization

What happens when an animal is injured and loses important structures? Some animals simply heal the wound, whereas others are able to regenerate lost parts. In this study, we report a previously unidentified strategy of self-repair, where moon jellyfish respond to injuries by reorganizing existing parts, and rebuilding essential body symmetry, without regenerating what is lost. Specifically, in response to arm amputation, the young jellyfish of Aurelia aurita rearrange their remaining arms, recenter their manubria, and rebuild their muscular networks, all completed within 12 hours to 4 days. We call this process symmetrization. We find that symmetrization is not driven by external cues, cell proliferation, cell death, and proceeded even when foreign arms were grafted on. Instead, we find that forces generated by the muscular network are essential. Inhibiting pulsation using muscle relaxants completely, and reversibly, blocked symmetrization. Furthermore, we observed that decreasing pulse frequency using muscle relaxants slowed symmetrization, whereas increasing pulse frequency by lowering the magnesium concentration in seawater accelerated symmetrization. A mathematical model that describes the compressive forces from the muscle contraction, within the context of the elastic response from the mesoglea and the ephyra geometry, can recapitulate the recovery of global symmetry. Thus, self-repair in Aurelia proceeds through the reorganization of existing parts, and is driven by forces generated by its own propulsion machinery. We find evidence for symmetrization across species of jellyfish (Chrysaora pacifica, Mastigias sp., and Cotylorhiza tuberculata)

Architecting centralized coordination of soccer robots based on principle solution

This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 2015, available online:http://www.tandfonline.com/10.1080/01691864.2015.1017534Coordination strategy is a relevant topic in multi-robot systems, and robot soccer offers a suitable domain to conduct research in multi-robot coordination. Team strategy collects and uses environmental information to derive optimal team reactions, through cooperation among individual soccer robots. This paper presents a diagrammatic approach to architecting the coordination strategy of robot soccer teams by means of a principle solution. The proposed model focuses on robot soccer leagues that possess a central decision-making system, involving the dynamic selection of the roles and behaviors of the robot soccer players. The work sets out from the conceptual design phase, facilitating cross-domain development efforts, where different layers must be interconnected and coordinated to perform multiple tasks. The principle solution allows for intuitive design and the modeling of team strategies in a highly complex robot soccer environment with changing game conditions. Furthermore, such an approach enables systematic realization of collaborative behaviors among the teammates.This work was partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-01/02. Jose G. Guarnizo was supported by a scholarship from the Administrative Department of Science, Technology and Innovation COLCIENCIAS, Colombia.Guarnizo Marín, JG.; Mellado Arteche, M.; Low, CY.; Blanes Noguera, F. (2015). Architecting centralized coordination of soccer robots based on principle solution. Advanced Robotics. 29(15):989-1004. https://doi.org/10.1080/01691864.2015.1017534S98910042915Farinelli, A., Iocchi, L., & Nardi, D. (2004). Multirobot Systems: A Classification Focused on Coordination. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34(5), 2015-2028. doi:10.1109/tsmcb.2004.832155Tews, A., & Wyeth, G. (2000). MAPS: a system for multi-agent coordination. Advanced Robotics, 14(1), 37-50. doi:10.1163/156855300741429Stulp, F., Utz, H., Isik, M., & Mayer, G. (2010). Implicit Coordination with Shared Belief: A Heterogeneous Robot Soccer Team Case Study. Advanced Robotics, 24(7), 1017-1036. doi:10.1163/016918610x496964Guarnizo, J. G., Mellado, M., Low, C. Y., & Aziz, N. (2013). Strategy Model for Multi-Robot Coordination in Robotic Soccer. Applied Mechanics and Materials, 393, 592-597. doi:10.4028/www.scientific.net/amm.393.592Riley, P., & Veloso, M. (2002). Recognizing Probabilistic Opponent Movement Models. Lecture Notes in Computer Science, 453-458. doi:10.1007/3-540-45603-1_59Ros, R., Arcos, J. L., Lopez de Mantaras, R., & Veloso, M. (2009). A case-based approach for coordinated action selection in robot soccer. Artificial Intelligence, 173(9-10), 1014-1039. doi:10.1016/j.artint.2009.02.004Atkinson, J., & Rojas, D. (2009). On-the-fly generation of multi-robot team formation strategies based on game conditions. Expert Systems with Applications, 36(3), 6082-6090. doi:10.1016/j.eswa.2008.07.039Costelha, H., & Lima, P. (2012). Robot task plan representation by Petri nets: modelling, identification, analysis and execution. Autonomous Robots, 33(4), 337-360. doi:10.1007/s10514-012-9288-xAbreu, P. H., Silva, D. C., Almeida, F., & Mendes-Moreira, J. (2014). Improving a simulated soccer team’s performance through a Memory-Based Collaborative Filtering approach. Applied Soft Computing, 23, 180-193. doi:10.1016/j.asoc.2014.06.021Duan, Y., Liu, Q., & Xu, X. (2007). Application of reinforcement learning in robot soccer. Engineering Applications of Artificial Intelligence, 20(7), 936-950. doi:10.1016/j.engappai.2007.01.003Hwang, K.-S., Jiang, W.-C., Yu, H.-H., & Li, S.-Y. (2011). Cooperative Reinforcement Learning Based on Zero-Sum Games. Mobile Robots - Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training. doi:10.5772/26620Gausemeier, J., Dumitrescu, R., Kahl, S., & Nordsiek, D. (2011). Integrative development of product and production system for mechatronic products. Robotics and Computer-Integrated Manufacturing, 27(4), 772-778. doi:10.1016/j.rcim.2011.02.005Klančar, G., Zupančič, B., & Karba, R. (2007). Modelling and simulation of a group of mobile robots. Simulation Modelling Practice and Theory, 15(6), 647-658. doi:10.1016/j.simpat.2007.02.002Gausemeier, J., Frank, U., Donoth, J., & Kahl, S. (2009). Specification technique for the description of self-optimizing mechatronic systems. Research in Engineering Design, 20(4), 201-223. doi:10.1007/s00163-008-0058-

A novel membrane distillation-thermophilic bioreactor system: Biological stability and trace organic compound removal

The removal of trace organic compounds (TrOCs) by a novel membrane distillation-thermophilic bioreactor (MDBR) system was examined. Salinity build-up and the thermophilic conditions to some extent adversely impacted the performance of the bioreactor, particularly the removal of total nitrogen and recalcitrant TrOCs. While most TrOCs were well removed by the thermophilic bioreactor, compounds containing electron withdrawing functional groups in their molecular structure were recalcitrant to biological treatment and their removal efficiency by the thermophilic bioreactor was low (0-53%). However, the overall performance of the novel MDBR system with respect to the removal of total organic carbon, total nitrogen, and TrOCs was high and was not significantly affected by the conditions of the bioreactor. All TrOCs investigated here were highly removed (>95%) by the MDBR system. Biodegradation, sludge adsorption, and rejection by MD contribute to the removal of TrOCs by MDBR treatment. © 2014

Second-order Democratic Aggregation

Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this paper, we study a class of orderless aggregation functions designed to minimize interference or equalize contributions in the context of second-order features and we show that they can be computed just as efficiently as their first-order counterparts and they have favorable properties over aggregation by summation. Another line of work has shown that matrix power normalization after aggregation can significantly improve the generalization of second-order representations. We show that matrix power normalization implicitly equalizes contributions during aggregation thus establishing a connection between matrix normalization techniques and prior work on minimizing interference. Based on the analysis we present {\gamma}-democratic aggregators that interpolate between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both on several classification tasks. Moreover, unlike power normalization, the {\gamma}-democratic aggregations can be computed in a low dimensional space by sketching that allows the use of very high-dimensional second-order features. This results in a state-of-the-art performance on several datasets

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behaviour of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48 machines test system and a realistic power system of China

Field Emission Properties and Fabrication of CdS Nanotube Arrays

A large area arrays (ca. 40 cm2) of CdS nanotube on silicon wafer are successfully fabricated by the method of layer-by-layer deposition cycle. The wall thicknesses of CdS nanotubes are tuned by controlling the times of layer-by-layer deposition cycle. The field emission (FE) properties of CdS nanotube arrays are investigated for the first time. The arrays of CdS nanotube with thin wall exhibit better FE properties, a lower turn-on field, and a higher field enhancement factor than that of the arrays of CdS nanotube with thick wall, for which the ratio of length to the wall thickness of the CdS nanotubes have played an important role. With increasing the wall thickness of CdS nanotube, the enhancement factorβdecreases and the values of turn-on field and threshold field increase

The Epstein-Barr Virus G-Protein-Coupled Receptor Contributes to Immune Evasion by Targeting MHC Class I Molecules for Degradation

Epstein-Barr virus (EBV) is a human herpesvirus that persists as a largely subclinical infection in the vast majority of adults worldwide. Recent evidence indicates that an important component of the persistence strategy involves active interference with the MHC class I antigen processing pathway during the lytic replication cycle. We have now identified a novel role for the lytic cycle gene, BILF1, which encodes a glycoprotein with the properties of a constitutive signaling G-protein-coupled receptor (GPCR). BILF1 reduced the levels of MHC class I at the cell surface and inhibited CD8+ T cell recognition of endogenous target antigens. The underlying mechanism involves physical association of BILF1 with MHC class I molecules, an increased turnover from the cell surface, and enhanced degradation via lysosomal proteases. The BILF1 protein of the closely related CeHV15 c1-herpesvirus of the Rhesus Old World primate (80% amino acid sequence identity) downregulated surface MHC class I similarly to EBV BILF1. Amongst the human herpesviruses, the GPCR encoded by the ORF74 of the KSHV c2-herpesvirus is most closely related to EBV BILF1 (15% amino acid sequence identity) but did not affect levels of surface MHC class I. An engineered mutant of BILF1 that was unable to activate G protein signaling pathways retained the ability to downregulate MHC class I, indicating that the immune-modulating and GPCR-signaling properties are two distinct functions of BILF1. These findings extend our understanding of the normal biology of an important human pathogen. The discovery of a third EBV lytic cycle gene that cooperates to interfere with MHC class I antigen processing underscores the importance of the need for EBV to be able to evade CD8+ T cell responses during the lytic replication cycle, at a time when such a large number of potential viral targets are expressed

Corrosive-Abrasive Wear Induced by Soot in Boundary Lubrication Regime

Soot is known to induce high wear in engine components. The mechanism by which soot induces wear is not well understood. Although several mechanisms have been suggested, there is still no consensus. This study aims to investigate the most likely mechanism responsible for soot-induced wear in the boundary lubrication regime. Results from this study have shown that previously suggested mechanisms such as abrasion and additive adsorption do not fully explain the high wear observed when soot is present. Based on the results obtained from tests conducted at varying temperature and soot levels, it has been proven that the corrosive–abrasive mechanism was responsible for high wear that occurred in boundary lubrication conditions