173 research outputs found

    Cosmogenic activation of Germanium and its reduction for low background experiments

    Full text link
    Production of 60^{60}Co and 68^{68}Ge from stable isotopes of Germanium by nuclear active component of cosmic rays is a principal background source for a new generation of 76^{76}Ge double beta decay experiments like GERDA and Majorana. The biggest amount of cosmogenic activity is expected to be produced during transportation of either enriched material or already grown crystal. In this letter properties and feasibility of a movable iron shield are discussed. Activation reduction factor of about 10 is predicted by simulations with SHIELD code for a simple cylindrical configuration. It is sufficient for GERDA Phase II background requirements. Possibility of further increase of reduction factor and physical limitations are considered. Importance of activation reduction during Germanium purification and detector manufacturing is emphasized.Comment: 10 pages, 3 tables, 6 figure

    Block of NMDA receptor channels by endogenous neurosteroids: implications for the agonist induced conformational states of the channel vestibule

    Get PDF
    N-methyl-D-aspartate receptors (NMDARs) mediate synaptic plasticity, and their dysfunction is implicated in multiple brain disorders. NMDARs can be allosterically modulated by numerous compounds, including endogenous neurosteroid pregnanolone sulfate. Here, we identify the molecular basis of the use-dependent and voltage-independent inhibitory effect of neurosteroids on NMDAR responses. The site of action is located at the extracellular vestibule of the receptor's ion channel pore and is accessible after receptor activation. Mutations in the extracellular vestibule in the SYTANLAAF motif disrupt the inhibitory effect of negatively charged steroids. In contrast, positively charged steroids inhibit mutated NMDAR responses in a voltage-dependent manner. These results, in combination with molecular modeling, characterize structure details of the open configuration of the NMDAR channel. Our results provide a unique opportunity for the development of new therapeutic neurosteroid-based ligands to treat diseases associated with dysfunction of the glutamate system

    Analytical expressions for stopping-power ratios relevant for accurate dosimetry in particle therapy

    Full text link
    In particle therapy, knowledge of the stopping-power ratios (STPRs) of the ion beam for air and water is necessary for accurate ionization chamber dosimetry. Earlier work has investigated the STPRs for pristine carbon ion beams, but here we expand the calculations to a range of ions (1 <= z <= 18) as well as spread out Bragg peaks (SOBPs) and provide a theoretical in-depth study with a special focus on the parameter regime relevant for particle therapy. The Monte Carlo transport code SHIELD-HIT is used to calculate complete particle-fluence spectra which are required for determining STPRs according to the recommendations of the International Atomic Energy Agency (IAEA). We confirm that the STPR depends primarily on the current energy of the ions rather than on their charge z or absolute position in the medium. However, STPRs for different sets of stopping-power data for water and air recommended by the International Commission on Radiation Units & Measurements (ICRU) are compared, including also the recently revised data for water, yielding deviations up to 2% in the plateau region. In comparison, the influence of the secondary particle spectra on the STPR is about two orders of magnitude smaller in the whole region up till the practical range. The gained insights enable us to propose an analytic approximation for the STPR for both pristine and SOBPs as a function of penetration depth, which parametrically depend only on the initial energy and the residual range of the ion, respectively.Comment: 21 pages, 5 figures, fixed bug with figures in v

    Parkinson’s disease, epilepsy, and amyotrophic lateral sclerosis—emerging role of AMPA and kainate subtypes of ionotropic glutamate receptors

    Get PDF
    Ionotropic glutamate receptors (iGluRs) mediate the majority of excitatory neurotransmission and are implicated in various neurological disorders. In this review, we discuss the role of the two fastest iGluRs subtypes, namely, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and kainate receptors, in the pathogenesis and treatment of Parkinson’s disease, epilepsy, and amyotrophic lateral sclerosis. Although both AMPA and kainate receptors represent promising therapeutic targets for the treatment of these diseases, many of their antagonists show adverse side effects. Further studies of factors affecting the selective subunit expression and trafficking of AMPA and kainate receptors, and a reasonable approach to their regulation by the recently identified novel compounds remain promising directions for pharmacological research

    МЕТОД ПОЛУЧЕНИЯ ГРАНИЦ ОБЛАСТЕЙ ОПРЕДЕЛЕНИЯ ФУНКЦИЙ ГЛОБАЛЬНЫХ ЗАВИСИМОСТЕЙ В ЗАДАЧАХ РАСПАРАЛЛЕЛИВАНИЯ ВЫЧИСЛЕНИЙ

    Get PDF
    A method to obtain the domains of the functions of global dependences based on the refined approximations of the set of tiles is developed. The exact presentation of the boundaries of the domains of the functions of global dependences avoids redundant computations in the performance of communication operations and provides more opportunities for granular paralleling algorithms.Разработан способ получения областей определения функций глобальных зависимостей, основанный на уточненных аппроксимациях множества тайлов. Точное представление границ областей определения функций глобальных зависимостей позволяет избегать избыточных вычислений при выполнении коммуникационных операций и дает больше возможностей для распараллеливания зернистых алгоритмов

    Q/R site interactions with the M3 helix in GluK2 kainate receptor channels revealed by thermodynamic mutant cycles

    Get PDF
    RNA editing at the Q/R site near the apex of the pore loop of AMPA and kainate receptors controls a diverse array of channel properties, including ion selectivity and unitary conductance and susceptibility to inhibition by polyamines and cis-unsaturated fatty acids, as well as subunit assembly into tetramers and regulation by auxiliary subunits. How these different aspects of channel function are all determined by a single amino acid substitution remains poorly understood; however, several lines of evidence suggest that interaction between the pore helix (M2) and adjacent segments of the transmembrane inner (M3) and outer (M1) helices may be involved. In the present study, we have used double mutant cycle analysis to test for energetic coupling between the Q/R site residue and amino acid side chains along the M3 helix. Our results demonstrate interaction with several M3 locations and particularly strong coupling to substitution for L614 at the level of the central cavity. In this location, replacement with smaller side chains completely and selectively reverses the effect of fatty acids on gating of edited channels, converting strong inhibition of wild-type GluK2(R) to nearly 10-fold potentiation of GluK2(R) L614A

    Exploring the potential of phone call data to characterize the relationship between social network and travel behavior

    Full text link
    [EN] Social network contacts have significant influence on individual travel behavior. However, transport models rarely consider social interaction. One of the reasons is the difficulty to properly model social influence based on the limited data available. Non-conventional, passively collected data sources, such as Twitter, Facebook or mobile phones, provide large amounts of data containing both social interaction and spatiotemporal information. The analysis of such data opens an opportunity to better understand the influence of social networks on travel behavior. The main objective of this paper is to examine the relationship between travel behavior and social networks using mobile phone data. A huge dataset containing billions of registers has been used for this study. The paper analyzes the nature of co-location events and frequent locations shared by social network contacts, aiming not only to provide understanding on why users share certain locations, but also to quantify the degree in which the different types of locations are shared. Locations have been classified as frequent (home, work and other) and non-frequent. A novel approach to identify co-location events based on the intersection of users' mobility models has been proposed. Results show that other locations different from home and work are frequently associated to social interaction. Additionally, the importance of non-frequent locations in co-location events is shown. Finally, the potential application of the data analysis results to improve activity-based transport models and assess transport policies is discussed.The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no 318367 (EUNOIA project) and no 611307 (INSIGHT project). The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educacion, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017.Picornell Tronch, M.; Ruiz Sánchez, T.; Lenormand, M.; Ramasco, JJ.; Dubernet, T.; Frías-Martínez, E. (2015). Exploring the potential of phone call data to characterize the relationship between social network and travel behavior. Transportation. 42(4):647-668. https://doi.org/10.1007/s11116-015-9594-1S647668424Ahas, R., Aasa, A., Silm, S., Tiru, M.: Daily rhythms of suburban commuters’ movements in the tallinn metropolitan area: case study with mobile positioning data. Transp. Res. Part C 18, 45–54 (2010)Arentze, T.,Timmermans, H. J.: social networks, social interactions and activity-travel behavior: a framework for micro-simulation. Paper presented at the 85th annual meeting of the Transportation Research Board, Washington, D. C., Jan 2006 (2006)Arentze, T., Timmermans, H.: Social networks, social interactions, and activity-travel behavior: a framework for microsimulation. Environ. Plan. 35, 1012–1027 (2008)Axhausen, K.W.: Social networks and travel: some hypotheses. In: Donaghy, K.P., Poppelreuter, S., Rudinger, G. (eds.) Social Aspects of Sustainable Transport: Transatlantic Perspectives, pp. 90–108. Ashgate, Aldershot (2005)Bagrow, J.P., Lin, Y.-R.: Mesoscopic structure and social aspects of human mobility. PLoS One 7(5), 1–11 (2012)Bar-Gera, H.: Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: a case study from israel. Transp. Res. Part C 15(2007), 380–391 (2007)Becker, R.A., Cáceres, R., Hanson, K., Loh, J.M., Urbanek, S., Varshavsky, A., Volinsky, C.: A tale of one city: using cellular network data for urban planning. Pervasive Comput. IEEE 10(4), 18–26 (2011)Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439, 462 (2006)Caceres, N., Wideberg, J.P., Benitez, F.G.: Deriving origin–destination data from a mobile phone network. IET Intell. Transp. Syst. 1(1), 5–26 (2007)Caceres, N., Wideberg, J.P., Benitez, F.G.: Review of traffic data estimations extracted from cellular networks. IET Intell. Transp. Syst. 2(3), 179–192 (2008)Caceres, N., Romero, L.M., Benitez, F.G., Castillo, J.M.D.: Traffic flow estimation models using cellular phone data. IEEE Trans. Intell. Transp. Syst. 13(3), 1430–1441 (2012)Calabrese, F., Pereira, F. C., Lorenzo, G. D., Liu, L., Ratti, C.: The geography of taste: analyzing cell-phone mobility and social events. In: Proceedings of IEEE International Conference on Pervasive Computing (2010)Calabrese, F., Smoreda, Z., Blondel, V.D., Ratti, C.: Interplay between telecommunications and face-to-face interactions: a study using mobile phone data. PLoS One 6(7), e20814 (2011a). doi: 10.1371/journal.pone.0020814Calabrese, F., Lorenzo, G.D., Liu, L., Ratti, C.: Estimating origin-destination flows using mobile phone location data. Pervasive Comput. IEEE 10(4), 36–44 (2011b)Carrasco, J.A., Miller, E.J.: Exploring the propensity to perform social activities: social networks approach. Transportation 33, 463–480 (2006)Carrasco, J.A., Hogan, B., Wellman, B., Miller, E.J.: Collecting social network data to study social activity-travel behaviour: an egocentric approach. Environ. Plan. B 35(6), 961–980 (2008a)Carrasco, J.A., Hogan B., Wellman B., Miller E. J.: Agency in social activity and ICT interactions: The role of social networks in time and space, Tijdschrift voor Economische en Sociale Geografie (J. Eco. Soc. Geogr.), 99(5), 562–583 (2008b)Carrasco, J.A., Miller, E.J., Wellman, B.: How far and with whom do people socialize? Empirical evidence about the distance between social network members. Transp. Res. Rec. 2076, 114–122 (2008b)Carrasco, J.A., Miller, E.J.: The social dimension in action: a multilevel, personal networks model of social activity frequency. Transp. Res. Part A 43(1), 90–104 (2009)Chen, C., Mei, Y.: Does distance still matter in facilitating social ties? The roles of mobility patterns and the built environment. Presented at 93rd TRB annual meeting (2014)Cho E., Myers S.A., Leskovek J.: Friendship and mobility: user movement in location-based social networks. In: KDD ‘11 Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1082–1090 (2011)Clifton, K.J.: The social context of travel behavior. In: Zmud, J., et al. (eds.) Transport Survey Methods: Best Practice for Decision Making, pp. 441–448. Emerald Press, London (2013)Do T., Gatica-Perez D.: Contextual conditional models for smartphone-based human mobility prediction. In: Proceedings ACM International Conference on Ubiquitous Computing, Pittsburgh, Sept (2012)Doyle, J., Hung, P., Kelly, D., Mcloone, S., Farrell, R.: Utilising mobile phone billing records for travel mode discovery. ISSC 2011, Trinity College Dublin, June (2011)Dubernet, T., Axhausen K. W.: Solution concepts for the simulation of household-level joint descision making in multi-agent travel simulation tools, paper presented at the 14th Swiss Transport Research Conference (STRC), Ascona (2014)Dugundji, E., Walker, J.: Discrete choice with social and spatial network interdependencies: an empirical example using mixed GEV models with field and “panel” effects. Transp. Res. Rec. 1921, 70–78 (2005)Eagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. Proc. Natl. Acad. Sci. (PNAS) 106(36), 15274–15278 (2009)González, M.C., Hidalgo, C.A., Barabási, A.-L.: Understanding individual human mobility patterns. Nature 453(2008), 779–782 (2008)Gould, J.: Cell phone enabled travel surveys: the medium moves the message. In: Zmud, J., et al. (eds.) Transport Survey Methods: Best Practice for Decision Making, pp. 51–70. Emerald Press, Bingley (2013)Habib, K.N., Carrasco, J.A.: Investigating the role of social networks in start time and duration of activities: a trivariate simultaneous econometric model. Transportation Research Record: Journal of the Transportation Research Board 2230, 1–8 (2011)Hackney, Jeremy K., Kay W. Axhausen: An agent model of social network and travel behavior interdependence. Paper presented at the 11th international conference on Travel Behaviour Research, Kyoto, Aug (2006)Hackney, J., Marchal, F.: A model for coupling multi-agent social interactions and traffic simulation, in: TRB 2009 annual meeting (2009)Hackney, J., Marchal, F.: A coupled multi-agent microsimulation of social interactions and transportation behavior. Transp. Res. Part A 45, 296–309 (2011)Horni, A.: Destination choice modeling of discretionary activities in transport microsimulations, Ph.D. Thesis, ETH Zurich, Zurich (2013)Isaacman, S.,Becker, R., Caceres, R., Kobourov, S., Martonosi, M., Rowland, J., Varshavsky, A.: Identifying important places in people’s lives from cellular network data. In: Procedings International Conference on Pervasive Computing, San Francisco, June (2011)Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. Commun. Mag. IEEE 48(9), 140–150 (2010)Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., Van Alstyne, M.: Computational Social Science. Science 323, 721 (2009)Ma, H., Ronald, N., Arentze, T.A., Timmermans, H.J.P.: New credit mechanism for semicooperative agent-mediated joint activity-travel scheduling. Transp. Res. Rec. 2230, 104–110 (2011)Ma, H., Arentze, T. A., Timmermans, H. J. P.: Incorporating selfishness and altruism into dynamic joint activity-travel scheduling. Paper presented at the 13th international conference on Travel Behaviour Research (IATBR), Toronto, July (2012)Marchal, F., Nagel, K.: Allowed cooperative agents in a microsimulation to share information with each other about activity locations and about other agents, in order to optimize trip chains (2006)Molin, E.J.E., Arentze, T.A., Timmermans, H.J.P.: Social activities and travel demands : a model-based analysis of social-network data. Transp. Res. Rec. 2082, 168–175 (2007)Moore, J., Carrasco, J.A., Tudela, A.: Exploring the links between personal networks, time use, and the spatial distribution of social contacts. Transportation 40(4), 773–788 (2013)Onnela, J.-P., Saramaki, J., Hyvonen, J., Szabo, G., Lazer, D., et al.: Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. U.S.A. 104, 7332–7336 (2007)Páez, A., Scott, D.M.: Social influence on travel behavior: a simulation example of the decision to telecommute. Environ. Plan. A 39(3), 647–665 (2007)Phithakkitnukoon, S., Calabrese, F., Smoreda, Z., Ratti, C.: Out of sight out of mind: how our mobile social network changes during migration. Proceedings of the IEEE International Conference on Social Computing, pp. 515–520. Cambridge University Press, Cambridge (2011)Phithakkitnukoon, S., Smoreda, Z., Olivier, P.: Socio-geography of human mobility: a study using longitudinal mobile phone data. PLoS One 7(6), e39253 (2012). doi: 10.1371/journal.pone.0039253Ronald, N.A., Arentze, T.A., Timmermans, H.J.P.: Modeling social interactions between individuals for joint activity scheduling. Transp. Res. Part B 46, 276–290 (2012a)Ronald, N.A., Dignum, V., Jonker, C., Arentze, T.A., Timmermans, H.J.P.: On the engineering of agent-based simulations of social activities with social networks. Inf. Softw. Technol. 54(6), 625–638 (2012b)Rose, G.: Mobile phones as traffic probes: practices, prospects and issues. Transp. Rev. 26(3), 275–291 (2006)Sharmeen, F., Arentze, T., Timmermans, H.: A multilevel path analysis of social network dynamics and the mutual interdependencies between face-to-face and ICT modes of social interaction in the context of life-cycle events. In: Roorda, M.J., Miller, E.J. (eds.) Travel Behaviour Research: Current Foundations, Future Prospects, pp. 411–432. Lulu Press, Toronto (2013)Sharmeen, F., Arentze, T.A., Timmermans, H.J.P.: Dynamics of face-to-face social interaction frequency: role of accessibility, urbanization, changes in geographical distance and path dependence. J. Transp. Geogr. 34, 211–220 (2014)Silm, S., Ahas, R.: The seasonal variability of population in estonian municipalities. Environ. Plan. A 42, 2527–2546 (2010)Silvis, J., Niemeier, D., D’Souza, R.: Social networks and travel behavior: report from an integrated travel diary. Paper presented at the 11th international conference on Travel Behaviour Research, Kyoto, Aug (2006)Sobolevsky, S., Szell, M., Campari, R., Couronné, T., Smoreda, Z., et al.: Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS One 8(12), e81707 (2013)Sohn, K., Kim, D.: Dynamic origin–destination flow estimation using cellular communication system. IEEE Trans. Veh. Technol. 57(5), 2703–2713 (2008)Song, C., Koren, T., Wang, P., Barabási, A.-L.: Modelling the scaling properties of human mobility. Nat. Phys. 6(2010), 818–823 (2010a)Song, C., Qu, Z., Blumm, N., Barabási, L.-L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010b)Steenbruggen, J., Borzacchiello, M.T., Nijkamp, P., Scholten, H.: Mobile phone data from gsm networks for traffic parameter and urban spatial pattern assessment: A review of applications and opportunities. GeoJournal 78, 223–243 (2011). doi: 10.1007/s10708-011-9413-yVan den Berg, P., Arentze, T., Timmermans, H.J.P.: A path analysis of social networks, telecommunication and social activity–travel patterns. Transp. Res. Part C 26(2013), 256–268 (2013)Wang, H., Calabrese, F., Lorenzo, G. D., Ratti, C.: Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: 13th international IEEE annual conference on intelligent transportation systems, 318–323 (2010)White, J. and Wells, I.: Extracting origin destination information from mobile phone data. Road transport information and Control, 19–21 Mar (2002)Yim, Y.: The state of cellular probes. California PATH Working Paper, UCB-ITS-PRR-2003-25 (2003)Ythier, J., Walker, J.L., Bierlaire, M.: The influence of social contacts and communication use on travel behavior: a smartphone-based study. In: Transportation Research Board annual meeting (2013
    corecore