Academic literature on the topic 'Backbone dynamic'
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Journal articles on the topic "Backbone dynamic"
Kharchenko, Vladlena, Michal Nowakowski, Mariusz Jaremko, Andrzej Ejchart, and Łukasz Jaremko. "Dynamic 15N{1H} NOE measurements: a tool for studying protein dynamics." Journal of Biomolecular NMR 74, no. 12 (September 12, 2020): 707–16. http://dx.doi.org/10.1007/s10858-020-00346-6.
Full textYE, JIEPING, RAVI JANARDAN, and SONGTAO LIU. "PAIRWISE PROTEIN STRUCTURE ALIGNMENT BASED ON AN ORIENTATION-INDEPENDENT BACKBONE REPRESENTATION." Journal of Bioinformatics and Computational Biology 02, no. 04 (December 2004): 699–717. http://dx.doi.org/10.1142/s021972000400082x.
Full textWalker, Ian D. "Continuous Backbone “Continuum” Robot Manipulators." ISRN Robotics 2013 (July 16, 2013): 1–19. http://dx.doi.org/10.5402/2013/726506.
Full textDent, Erik W., Elliott B. Merriam, and Xindao Hu. "The dynamic cytoskeleton: backbone of dendritic spine plasticity." Current Opinion in Neurobiology 21, no. 1 (February 2011): 175–81. http://dx.doi.org/10.1016/j.conb.2010.08.013.
Full textBertini, Ivano, Donald A. Bryant, Stefano Ciurli, Alexander Dikiy, Claudio O. Fernández, Claudio Luchinat, Niyaz Safarov, Alejandro J. Vila, and Jindong Zhao. "Backbone Dynamics of Plastocyanin in Both Oxidation States." Journal of Biological Chemistry 276, no. 50 (August 16, 2001): 47217–26. http://dx.doi.org/10.1074/jbc.m100304200.
Full textKim, Yeon Su, Kyeong Ho Moon, Se Ky Chang, and Jai Kyun Mok. "Strength Analysis of Chassis Structure for Medium-Sized Low-Floor Vehicle under Dynamic Load Cases." Key Engineering Materials 452-453 (November 2010): 709–12. http://dx.doi.org/10.4028/www.scientific.net/kem.452-453.709.
Full textSaeedvand, Saeed, Hadi S. Aghdasi, and Leili Mohammad Khanli. "Novel Distributed Dynamic Backbone-based Flooding in Unstructured Networks." Peer-to-Peer Networking and Applications 13, no. 3 (November 18, 2019): 872–89. http://dx.doi.org/10.1007/s12083-019-00817-0.
Full textBONONI, LUCIANO, MARCO DI FELICE, and SARA PIZZI. "DBA-MAC: DYNAMIC BACKBONE-ASSISTED MEDIUM ACCESS CONTROL PROTOCOL FOR EFFICIENT BROADCAST IN VANETS." Journal of Interconnection Networks 10, no. 04 (December 2009): 321–44. http://dx.doi.org/10.1142/s0219265909002601.
Full textRong, Xiao Yang, Tian Hong Yang, Pei Tao Wang, Hong Wang, and Yang Li. "Dynamic Triaxial Test Research of Stage Change of Cohesive Soil." Applied Mechanics and Materials 353-356 (August 2013): 937–40. http://dx.doi.org/10.4028/www.scientific.net/amm.353-356.937.
Full textWu, Celimuge, Xianfu Chen, Yusheng Ji, Satoshi Ohzahata, and Toshihiko Kato. "Efficient Broadcasting in VANETs Using Dynamic Backbone and Network Coding." IEEE Transactions on Wireless Communications 14, no. 11 (November 2015): 6057–71. http://dx.doi.org/10.1109/twc.2015.2447812.
Full textDissertations / Theses on the topic "Backbone dynamic"
Olwal, Thomas. "Dynamic power control in backbone wireless mesh networks : a decentralized approach." Phd thesis, Université Paris-Est, 2010. http://tel.archives-ouvertes.fr/tel-00598277.
Full textHuang, He. "Large-Amplitude Vibration of Imperfect Rectangular, Circular and Laminated Plate with Viscous Damping." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1924.
Full textCOGLIATI, CLELIA. "NMR study of chicken Liver Bile Acid Binding Protein: interaction and dynamics." Doctoral thesis, Università degli Studi di Verona, 2010. http://hdl.handle.net/11562/343942.
Full textThe aim of this thesis is to understand the role played by a naturally occurring disulphide bridge on the bile acid (BA) binding and functional properties of cytosolic Liver Bile Acid Binding Protein (L-BABP). Bile acids circulate between liver and intestine through a mechanism defined as “enterohepatic circulation”, which is a tightly regulated process, particularly by BAs themselves. Indeed BAs are able to influence the expression of numerous genes involved in their synthesis and transport by binding to the primary intracellular nuclear bile acid receptor, farnesoid X receptor (FXR). Understanding the mechanism regulating the interactions of intracellular carriers with bile acid is a key step to provide a model for the transfer of BAs from cytoplasm to the nucleus and can be used to inspire design of therapeutic agents in the treatment of metabolic disorders, such as obesity, type 2 diabetes, hyperlipidaemia and atherosclerosis. To achieve a detailed molecular and dynamical description of the binding mechanism driving to the formation of the ternary complex of L-BABPs with two BA molecules, spectroscopic methods together with kinetic and thermodynamic analysis have been applied and implemented. In particular structural, dynamical and interaction properties of two forms of chicken L-BABP (cL-BABP), differing by the presence/absence of a naturally occurring disulphide bridge, have been investigated through nuclear magnetic resonance (NMR) approaches. The study of protein-ligand interactions by NMR was performed analysing complexes where, alternatively, either the protein or the ligand were isotopically labelled. 15N enriched glycocholic (GCA) and glycochenodeoxycholic acid (GCDA), two of the most important members of bile salts pool, were employed for protein titrations and their resonances followed through the acquisition and analysis of several NMR experiments (HSQC, DOSY). The obtained results shed light on binding stoichiometry and ligand exchange phenomena but were not sufficient to derive detailed information on affinity, cooperativity and binding mechanism. Thus NMR lineshape analysis as a function of ligand concentration was chosen as an appropriate tool to investigate the complex interaction mechanism within the cL-BABP/BA system. In this line, new NMR approaches have been recently described which allow a reliable and sensitive investigation of ligand binding events occurring on microsecond to millisecond (μs-ms) time scales using lineshape and relaxation dispersion experiments[1]. Particularly, the combination of these NMR methods can be useful in the study of complex multi-step mechanisms, allowing the correlation between protein dynamics and function[2]. 15N relaxation studies, performed on the apo-protein, revealed the presence of slow motions occurring on the microseconds-milliseconds timescale. The central question to be addressed is here whether these motions are essential for ligand uptake, how they can eventually lead to conformations competent for binding and how they are influenced by the presence of the disulfide bridge. The analysis of titration experiments of 15N labelled protein with unlabelled GCDA through lineshape analysis and relaxation dispersion allowed to define a multi-step binding mechanism for bile salt binding to liver BABPs and to provide an estimate of the kinetics involved.
Vivona, Sandro. "VAMP7: a model system to study the Longin Domain-SNARE motif." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3421900.
Full textLe cellule eucariote sono caratterizzate da un complesso sistema di membrane, che offre svariate compartimentazioni con diverse condizioni chimico-fisiche. Se da una parte tale sistema permette la realizzazione di un’ampia gamma di processi biochimici, dall’altra richiede un altrettanto complesso sistema di interscambio atto al suo mantenimento. Tale interscambio è assicurato dal trafficking di vescicole che originano da un compartimento donatore e riversano il loro contenuto in un compartimento accettore attraverso un processo che richiede la fusione delle membrane lipidiche. Tale processo si fonda sull’organizzazione di complessi macromolecolari a cui contribuiscono varie famiglie proteiche ben conservate attraverso l’evoluzione eucariotica. La famiglia delle SNARE è una di queste. Le SNAREs sono considerate i motori della fusione di membrane. La loro capacità di formare complessi specifici in trans tra le due memrane su cui risiedono fornisce il contributo energetico necessario a indurre la fusione degli strati lipidici. Tali complessi consistono in un intreccio di quattro eliche chiamate SNARE motifs, domini di circa 60-70 amino acidi che definiscono tutte le SNAREs. Oltre allo SNARE motif, le SNAREs contengono spesso domini accessori a funzione regolativa. Uno di questi è il Longin Domain (LD). Il LD non è limitato alle sole SNAREs e anzi si ritrova in altre famiglie proteiche tutte coinvolte in processi molecolari riguardanti il ciclo vitale di una vescicola. Nelle SNAREs, il LD definisce una famiglia chiamata Longins, suddivisa a sua volta nelle proteine Ykt6, Sec22b e VAMP7. Il LD consiste di circa 120 aminoacidi organizzati in una struttura spaziale globulare che comprende un piano di cinque foglietti ? (?1- ?5), complessati da un’alfa elica (?1) su un lato e da altre due eliche (?2-?3) sull’altro. In Ykt6 e Sec22b si è dimostrata la possibilità che il LD si ripieghi sullo SNARE motif e lo coordini su una sua superficie idrofobica compresa tra ?1 e ?3. Questo meccanismo si è dimostrato in grado di prevenire la formazione di complessi SNARE non specifici. Tuttavia ben poco si conosce ad oggi sulla natura di questa interazione in termini dinamici, a differenza di quanto invece si sa per un analogo meccanismo osservato nella famiglia SNARE delle Sintaxine. In altri temrini non è dato sapere se nelle Longine questo meccanismo implica una conformazione stabilmente “chiusa” di LD e SNARE, o se piuttosto esso si realizza come un equilibrio dinamico tra conformazioni aperte e chiuse. Una serie di motivi, tra cui l’assenza di dati diretti per questo fenomeno in VAMP7 e la possibilità di usufruire di sue varianti naturali, ci hanno spinto a scegliere VAMP7 come sistema modello per fornire le risposte ai suddetti interrogativi. I nostri dati suggeriscono per le Longine una conformazione stabilmente chiusa, ma non omogenea e capace di cambi conformazionali molto rapidi. Questo lavoro complementa bene quanto già noto per le sintaxine e fornisce dunque la possibilità di comprendere meglio i meccanismi regolativi gneralmente adottati nella fusione vescicolare.
Wong, Kam-Bo. "Structure and backbone dynamics of native proteins and their denatured states." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627135.
Full textWood, Matthew James. "Solution structure and backbone dynamics of the thrombomodulin fragments TMEGF45 and TMEGF45ox /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2000. http://wwwlib.umi.com/cr/ucsd/fullcit?p9988316.
Full textBabur, Tamoor [Verfasser]. "Structure and relaxation dynamics of comb-like polymers with rigid backbone / Tamoor Babur." Halle, 2017. http://d-nb.info/1139253743/34.
Full textIbrahim, Moustafa Ismaiel Omar. "Biophysical studies of the structure and backbone dynamics of gsPGK using NMR relaxation methods." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543234.
Full textGuan, Xiao, and 关晓. "NMR approaches to protein conformation and backbone dynamics: studies on hyperthermophilicacylphosphatase and neuropeptide secretoneurin." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44079230.
Full textGuan, Xiao. "NMR approaches to protein conformation and backbone dynamics studies on hyperthermophilic acylphosphatase and neuropeptide secretoneurin /." Click to view the E-thesis via HKUTO, 2010. http://sunzi.lib.hku.hk/hkuto/record/B44079230.
Full textBooks on the topic "Backbone dynamic"
Wendling, Fabrice, and Fernando H. Lopes da Silva. Dynamics of EEGs as Signals of Neuronal Populations. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0003.
Full textWatts, Michael J. Thinking the African Food Crisis. Edited by Ronald J. Herring. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195397772.013.016.
Full textRadivojević, Ana, and Linda Hildebrand. SUSTAINABLE AND RESILIENT BUILDING DESIGN: approaches, methods and tools. Edited by Saja Kosanović, Tillmann Klein, and Thaleia Konstantinou. TU Delft Bouwkunde, 2018. http://dx.doi.org/10.47982/bookrxiv.26.
Full textBook chapters on the topic "Backbone dynamic"
Rhee, Seung H., Jaewoo Yoon, Heonjun Choi, and Insoo Choi. "Dynamic Capacity Resizing of Virtual Backbone Networks." In Networking — ICN 2001, 698–707. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47728-4_68.
Full textMenéndez, C., S. Accordino, J. Rodriguez, D. Gerbino, and G. Appignanesi. "Dynamic Analysis of Backbone-Hydrogen-Bond Propensity for Protein Binding and Drug Design." In Biopolymers for Medical Applications, 317–38. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315368863-14.
Full textWielemborek, Radosław, Dariusz Laskowski, and Piotr Łubkowski. "Effectiveness of Providing Data Confidentiality in Backbone Networks Based on Scalable and Dynamic Environment Technologies." In Advances in Intelligent Systems and Computing, 523–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19216-1_50.
Full textSingh, Harsimran, and Laura S. Busenlehner. "Probing Backbone Dynamics with Hydrogen/Deuterium Exchange Mass Spectrometry." In Protein Dynamics, 81–99. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-658-0_5.
Full textGronenborn, Angela M., and G. Marius Clore. "Analysis of Backbone Dynamics of Interleukin-1β." In Computational Aspects of the Study of Biological Macromolecules by Nuclear Magnetic Resonance Spectroscopy, 227–31. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-9794-7_17.
Full textClore, G. Marius, and Angela M. Gronenborn. "Analysis of backbone dynamics of interleukin-1β." In Proteins, 53–56. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-010-9063-6_6.
Full textCammarano, A., P. L. Green, T. L. Hill, and S. A. Neild. "Nonlinear System Identification Through Backbone Curves and Bayesian Inference." In Nonlinear Dynamics, Volume 1, 255–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-15221-9_23.
Full textLondono, Julian M., Simon A. Neild, and Jonathan E. Cooper. "Systems with Bilinear Stiffness: Extraction of Backbone Curves and Identification." In Nonlinear Dynamics, Volume 1, 307–13. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-15221-9_27.
Full textHill, T. L., A. Cammarano, S. A. Neild, and D. J. Wagg. "Relating Backbone Curves to the Forced Responses of Nonlinear Systems." In Nonlinear Dynamics, Volume 1, 113–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-15221-9_9.
Full textPeter, Simon, Robin Riethmüller, and Remco I. Leine. "Tracking of Backbone Curves of Nonlinear Systems Using Phase-Locked-Loops." In Nonlinear Dynamics, Volume 1, 107–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29739-2_11.
Full textConference papers on the topic "Backbone dynamic"
Bettig, B., C. Sandu, A. Joshi, and K. Birru. "Dynamic solver selection for an Internet simulation backbone." In the 2003 ACM symposium. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/952532.952566.
Full textLiu, Kai, Tianyi Wu, Cong Liu, and Guodong Guo. "Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/166.
Full textMelidis, P., P. Nicopolitidis, G. Papadimitriou, and E. Varvarigos. "Energy efficient optical backbone networks: A dynamic threshold approach." In 2014 IEEE 21st Symposium on Communications and Vehicular Technology in the Benelux (SCVT). IEEE, 2014. http://dx.doi.org/10.1109/scvt.2014.7046708.
Full textDevi, Monisha, Nityananda Sarma, and Sanjib Kumar Deka. "Dynamic virtual backbone based routing in cognitive radio networks." In 2015 IEEE International Conference on Advanced Networks and Telecommuncations Systems (ANTS). IEEE, 2015. http://dx.doi.org/10.1109/ants.2015.7413663.
Full textTanaka, Hirokazu. "A Case Study of the Backbone System Based on the Dynamic Equilibrium View : A dynamic equilibrium approach to backbone system design and implementation." In 2020 6th International Conference on Information Management (ICIM). IEEE, 2020. http://dx.doi.org/10.1109/icim49319.2020.244684.
Full textDi Felice, Marco, Luca Bedogni, and Luciano Bononi. "Dynamic backbone for fast information delivery invehicular ad hoc networks." In the 8th ACM Symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2069063.2069065.
Full textFajjari, Ilhem, Nadjib Aitsaadi, Guy Pujolle, and Hubert Zimmermann. "An optimised dynamic resource allocation algorithm for Cloud's backbone network." In 2012 IEEE 37th Conference on Local Computer Networks (LCN 2012). IEEE, 2012. http://dx.doi.org/10.1109/lcn.2012.6423621.
Full textKrebs, Martin. "Dynamic Virtual Backbone Management for Service Discovery in Wireless Mesh Networks." In 2009 IEEE Wireless Communications and Networking Conference. IEEE, 2009. http://dx.doi.org/10.1109/wcnc.2009.4917691.
Full textClad, Francois, Antoine Gallais, and Pascal Merindol. "Energy-efficient data collection in WSN: A sink-oriented dynamic backbone." In ICC 2012 - 2012 IEEE International Conference on Communications. IEEE, 2012. http://dx.doi.org/10.1109/icc.2012.6363937.
Full textYamamoto, Hiroshi, Shohei Kamamura, Rie Hayashi, Takafumi Hamano, and Koichi Genda. "Effectiveness of dynamic reconfiguration of path protection for Carrier's backbone network." In 2015 10th Asia-Pacific Symposium on Information and Telecommunication Technologies (APSITT). IEEE, 2015. http://dx.doi.org/10.1109/apsitt.2015.7217081.
Full textReports on the topic "Backbone dynamic"
Perdigão, Rui A. P. Beyond Quantum Security with Emerging Pathways in Information Physics and Complexity. Synergistic Manifolds, June 2022. http://dx.doi.org/10.46337/220602.
Full textTeye, Joseph Kofi, and Ebenezer Nikoi. The Political Economy of the Cocoa Value Chain in Ghana. Institute of Development Studies (IDS), March 2021. http://dx.doi.org/10.19088/apra.2021.007.
Full textYu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.
Full textCarpita, Nicholas C., Ruth Ben-Arie, and Amnon Lers. Pectin Cross-Linking Dynamics and Wall Softening during Fruit Ripening. United States Department of Agriculture, July 2002. http://dx.doi.org/10.32747/2002.7585197.bard.
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