296 research outputs found

    Identification and Characterization of AES-135, a Hydroxamic Acid-Based HDAC Inhibitor That Prolongs Survival in an Orthotopic Mouse Model of Pancreatic Cancer

    Get PDF
    Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, incurable cancer with a 20% 1 year survival rate. While standard-of-care therapy can prolong life in a small fraction of cases, PDAC is inherently resistant to current treatments, and novel therapies are urgently required. Histone deacetylase (HDAC) inhibitors are effective in killing pancreatic cancer cells in in vitro PDAC studies, and although there are a few clinical studies investigating combination therapy including HDAC inhibitors, no HDAC drug or combination therapy with an HDAC drug has been approved for the treatment of PDAC. We developed an inhibitor of HDACs, AES-135, that exhibits nanomolar inhibitory activity against HDAC3, HDAC6, and HDAC11 in biochemical assays. In a three-dimensional coculture model, AES-135 kills low-passage patient-derived tumor spheroids selectively over surrounding cancer-associated fibroblasts and has excellent pharmacokinetic properties in vivo. In an orthotopic murine model of pancreatic cancer, AES-135 prolongs survival significantly, therefore representing a candidate for further preclinical testing

    Level densities and γ\gamma-strength functions in 148,149^{148,149}Sm

    Full text link
    The level densities and γ\gamma-strength functions of the weakly deformed 148^{148}Sm and 149^{149}Sm nuclei have been extracted. The temperature versus excitation energy curve, derived within the framework of the micro canonical ensemble, shows structures, which we associate with the break up of Cooper pairs. The nuclear heat capacity is deduced within the framework of both the micro canonical and the canonical ensemble. We observe negative heat capacity in the micro canonical ensemble whereas the canonical heat capacity exhibits an S-shape as function of temperature, both signals of a phase transition. The structures in the γ\gamma-strength functions are discussed in terms of the pygmy resonance and the scissors mode built on exited states. The samarium results are compared with data for the well deformed 161,162^{161,162}Dy, 166,167^{166,167}Er and 171,172^{171,172}Yb isotopes and with data from (n,γ\gamma)-experiments and giant dipole resonance studies.Comment: 12 figure

    Present Status and Future Programs of the n_TOF Experiment

    Get PDF
    This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial License 3.0, which permits unrestricted use, distribution, and reproduction in any noncommercial medium, provided the original work is properly citedThe neutron time-of-flight facility n_TOF at CERN, Switzerland, operational since 2001, delivers neutrons using the Proton Synchrotron (PS) 20 GeV/c proton beam impinging on a lead spallation target. The facility combines a very high instantaneous neutron flux, an excellent time of flight resolution due to the distance between the experimental area and the production target (185 meters), a low intrinsic background and a wide range of neutron energies, from thermal to GeV neutrons. These characteristics provide a unique possibility to perform neutron-induced capture and fission cross-section measurements for applications in nuclear astrophysics and in nuclear reactor technology.The most relevant measurements performed up to now and foreseen for the future will be presented in this contribution. The overall efficiency of the experimental program and the range of possible measurements achievable with the construction of a second experimental area (EAR-2), vertically located 20 m on top of the n_TOF spallation target, might offer a substantial improvement in measurement sensitivities. A feasibility study of the possible realisation of the installation extension will be also presented

    Measurement of the Ge 70 (n,γ) cross section up to 300 keV at the CERN n-TOF facility

    Get PDF
    ©2019 American Physical Society.Neutron capture data on intermediate mass nuclei are of key importance to nucleosynthesis in the weak component of the slow neutron capture processes, which occurs in massive stars. The (n,γ) cross section on Ge70, which is mainly produced in the s process, was measured at the neutron time-of-flight facility n-TOF at CERN. Resonance capture kernels were determined up to 40 keV neutron energy and average cross sections up to 300 keV. Stellar cross sections were calculated from kT=5 keV to kT=100 keV and are in very good agreement with a previous measurement by Walter and Beer (1985) and recent evaluations. Average cross sections are in agreement with Walter and Beer (1985) over most of the neutron energy range covered, while they are systematically smaller for neutron energies above 150 keV. We have calculated isotopic abundances produced in s-process environments in a 25 solar mass star for two initial metallicities (below solar and close to solar). While the low metallicity model reproduces best the solar system germanium isotopic abundances, the close to solar model shows a good global match to solar system abundances in the range of mass numbers A=60-80.Peer reviewedFinal Published versio

    Measurement of the (90,91,92,93,94,96)Zr(n,gamma) and (139)La(n,gamma) cross sections at n_TOF

    Get PDF
    Open AccessNeutron capture cross sections of Zr and La isotopes have important implications in the field of nuclear astrophysics as well as in the nuclear technology. In particular the Zr isotopes play a key role for the determination of the neutron density in the He burning zone of the Red Giant star, while the (139)La is important to monitor the s-process abundances from Ba up to Ph. Zr is also largely used as structural materials of traditional and advanced nuclear reactors. The nuclear resonance parameters and the cross section of (90,91,92,93,94,96)Zr and (139)La have been measured at the n_TOF facility at CERN. Based on these data the capture resonance strength and the Maxwellian-averaged cross section were calculated

    Cross section measurements of 155,157Gd(n, γ) induced by thermal and epithermal neutrons

    Get PDF
    © SIF, Springer-Verlag GmbH Germany, part of Springer Nature 2019Neutron capture cross section measurements on 155Gd and 157Gd were performed using the time-of-flight technique at the n_TOF facility at CERN on isotopically enriched samples. The measurements were carried out in the n_TOF experimental area EAR1, at 185 m from the neutron source, with an array of 4 C6D6 liquid scintillation detectors. At a neutron kinetic energy of 0.0253 eV, capture cross sections of 62.2(2.2) and 239.8(8.4) kilobarn have been derived for 155Gd and 157Gd, respectively, with up to 6% deviation relative to values presently reported in nuclear data libraries, but consistent with those values within 1.6 standard deviations. A resonance shape analysis has been performed in the resolved resonance region up to 181 eV and 307 eV, respectively for 155Gd and 157Gd, where on average, resonance parameters have been found in good agreement with evaluations. Above these energies and up to 1 keV, the observed resonance-like structure of the cross section has been analysed and characterised. From a statistical analysis of the observed neutron resonances we deduced: neutron strength function of 2. 01 (28) × 10 - 4 and 2. 17 (41) × 10 - 4; average total radiative width of 106.8(14) meV and 101.1(20) meV and s-wave resonance spacing 1.6(2) eV and 4.8(5) eV for n + 155Gd and n + 157Gd systems, respectively.Peer reviewedFinal Accepted Versio

    Time-of-flight and activation experiments on 147Pm and 171Tm for astrophysics

    Get PDF
    The neutron capture cross section of several key unstable isotopes acting as branching points in the s-process are crucial for stellar nucleosynthesis studies, but they are very challenging to measure due to the difficult production of sufficient sample material, the high activity of the resulting samples, and the actual (n,γ) measurement, for which high neutron fluxes and effective background rejection capabilities are required. As part of a new program to measure some of these important branching points, radioactive targets of 147Pm and 171Tm have been produced by irradiation of stable isotopes at the ILL high flux reactor. Neutron capture on 146Nd and 170Er at the reactor was followed by beta decay and the resulting matrix was purified via radiochemical separation at PSI. The radioactive targets have been used for time-of-flight measurements at the CERN n-TOF facility using the 19 and 185 m beam lines during 2014 and 2015. The capture cascades were detected using a set of four C6D6 scintillators, allowing to observe the associated neutron capture resonances. The results presented in this work are the first ever determination of the resonance capture cross section of 147Pm and 171Tm. Activation experiments on the same 147Pm and 171Tm targets with a high-intensity 30 keV quasi-Maxwellian flux of neutrons will be performed using the SARAF accelerator and the Liquid-Lithium Target (LiLiT) in order to extract the corresponding Maxwellian Average Cross Section (MACS). The status of these experiments and preliminary results will be presented and discussed as well

    Developments in Capture- γ Libraries for Nonproliferation Applications

    Get PDF
    The neutron-capture reaction is fundamental for identifying and analyzing the γ-ray spectrum from an unknown assembly because it provides unambiguous information on the neutron-absorbing isotopes. Nondestructive-assay applications may exploit this phenomenon passively, for example, in the presence of spontaneous-fission neutrons, or actively where an external neutron source is used as a probe. There are known gaps in the Evaluated Nuclear Data File libraries corresponding to neutron-capture γ-ray data that otherwise limit transport-modeling applications. In this work, we describe how new thermal neutron-capture data are being used to improve information in the neutron-data libraries for isotopes relevant to nonproliferation applications. We address this problem by providing new experimentally-deduced partial and total neutron-capture reaction cross sections and then evaluate these data by comparison with statistical-model calculations

    Towards the high-accuracy determination of the 238U fission cross section at the threshold region at CERN - N-TOF

    Get PDF
    The 238U fission cross section is an international standard beyond 2 MeV where the fission plateau starts. However, due to its importance in fission reactors, this cross-section should be very accurately known also in the threshold region below 2 MeV. The 238U fission cross section has been measured relative to the 235U fission cross section at CERN - n-TOF with different detection systems. These datasets have been collected and suitably combined to increase the counting statistics in the threshold region from about 300 keV up to 3 MeV. The results are compared with other experimental data, evaluated libraries, and the IAEA standards

    Robustness of optimal channel reservation using handover prediction in multiservice wireless networks

    Full text link
    The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. Although the optimum anticipation time depends on system parameters, we find that its value changes very little when the system parameters vary within a reasonable range. We also find that, in terms of system performance, deploying prediction is always advantageous when compared to a system without prediction, even when the system parameters are estimated with poor precision. © Springer Science+Business Media, LLC 2012.The authors would like to thank the reviewers for their valuable comments that helped to improve the quality of the paper. This work has been supported by the Spanish Ministry of Education and Science and European Comission (30% PGE, 70% FEDER) under projects TIN2008-06739-C04-02 and TIN2010-21378-C02-02 and by Comunidad de Madrid through project S-2009/TIC-1468.Martínez Bauset, J.; Giménez Guzmán, JM.; Pla, V. (2012). Robustness of optimal channel reservation using handover prediction in multiservice wireless networks. Wireless Networks. 18(6):621-633. https://doi.org/10.1007/s11276-012-0423-6S621633186Ji, S., Chen, W., Ding, X., Chen, Y., Zhao, C., & Hu, C. (2010). Potential benefits of GPS/GLONASS/GALILEO integration in an urban canyon–Hong Kong. Journal of Navigation, 63(4), 681–693.Soh, W., & Kim, H. (2006). A predictive bandwidth reservation scheme using mobile positioning and road topology information. IEEE/ACM Transactions on Networking, 14(5), 1078–1091.Kwon, H., Yang, M., Park, A., & Venkatesan, S. (2008). Handover prediction strategy for 3G-WLAN overlay networks. In Proceedings: IEEE network operations and management symposium (NOMS) (pp. 819–822).Huang, C., Shen, H., & Chuang, Y. (2010). An adaptive bandwidth reservation scheme for 4G cellular networks using flexible 2-tier cell structure. Expert Systems with Applications, 37(9), 6414–6420.Wanalertlak, W., Lee, B., Yu, C., Kim, M., Park, S., & Kim, W. (2011). Behavior-based mobility prediction for seamless handoffs in mobile wireless networks. Wireless Networks, 17(3), 645–658.Becvar, Z., Mach, P., & Simak, B. (2011). Improvement of handover prediction in mobile WiMAX by using two thresholds. Computer Networks, 55, 3759–3773.Sgora, A., & Vergados, D. (2009). Handoff prioritization and decision schemes in wireless cellular networks: a survey. IEEE Communications Surveys and Tutorials, 11(4), 57–77.Choi, S., & Shin, K. G. (2002). Adaptive bandwidth reservation and admission control in QoS-sensitive cellular networks. IEEE Transactions on Parallel and Distributed Systems, 13(9), 882–897.Ye, Z., Law, L., Krishnamurthy, S., Xu, Z., Dhirakaosal, S., Tripathi, S., & Molle, M. (2007). Predictive channel reservation for handoff prioritization in wireless cellular networks. Computer Networks, 51(3), 798–822.Abdulova, V., & Aybay, I. (2011). Predictive mobile-oriented channel reservation schemes in wireless cellular networks. Wireless Networks, 17(1), 149–166.Ramjee, R., Nagarajan, R., & Towsley, D. (1997). On optimal call admission control in cellular networks. Wireless Networks, 3(1), 29–41.Bartolini, N. (2001). Handoff and optimal channel assignment in wireless networks. Mobile Networks and Applications, 6(6), 511–524.Bartolini, N., & Chlamtac, I. (2002). Call admission control in wireless multimedia networks. In Proceedings: Personal, indoor and mobile radio communications (PIMRC) (pp. 285–289).Pla, V., & Casares-Giner, V. (2003). Optimal admission control policies in multiservice cellular networks. In Proceedings of the international network optimization conference (INOC) (pp. 466–471).Chu, K., Hung, L., & Lin, F. (2009). Adaptive channel reservation for call admission control to support prioritized soft handoff calls in a cellular CDMA system. Annals of Telecommunications, 64(11), 777–791.El-Alfy, E., & Yao, Y. (2011). Comparing a class of dynamic model-based reinforcement learning schemes for handoff prioritization in mobile communication networks. Expert Systems With Applications, 38(7), 8730–8737.Gimenez-Guzman, J. M., Martinez-Bauset, J., & Pla, V. (2007). A reinforcement learning approach for admission control in mobile multimedia networks with predictive information. IEICE Transactions on Communications , E-90B(7), 1663–1673.Sutton R., Barto A. G. (1998) Reinforcement learning: An introduction. The MIT press, Cambridge, MassachusettsBusoniu, L., Babuska, R., De Schutter, B., & Ernst, D. (2010). Reinforcement learning and dynamic programming using function approximators. Boca Raton, FL: CRC Press.Watkins, C., & Dayan, P. (1992). Q-learning. Machine learning, 8(3–4), 279–292.Brown, T. (2001). Switch packet arbitration via queue-learning. Advances in Neural Information Processing Systems, 14, 1337–1344.Proper, S., & Tadepalli, P. (2006). Scaling model-based average-reward reinforcement learning for product delivery. In Proceedings 17th European conference on machine learning (pp. 735–742).Driessens, K., Ramon, J., & Gärtner, T. (2006). Graph kernels and Gaussian processes for relational reinforcement learning. Machine Learning, 64(1), 91–119.Banerjee, B., & Stone, P. (2007). General game learning using knowledge transfer. In Proceedings 20th international joint conference on artificial intelligence (pp. 672–677).Martinez-Bauset, J., Pla, V., Garcia-Roger, D., Domenech-Benlloch, M. J., & Gimenez-Guzman, J. M. (2008). Designing admission control policies to minimize blocking/forced-termination. In G. Ming, Y. Pan & P. Fan (Eds.), Advances in wireless networks: Performance modelling, analysis and enhancement (pp. 359–390). New York: Nova Science Pub Inc.Biswas, S., & Sengupta, B. (1997). Call admissibility for multirate traffic in wireless ATM networks. In Proceedings IEEE INFOCOM (2, pp. 649–657).Evans, J. S., & Everitt, D. (1999). Effective bandwidth-based admission control for multiservice CDMA cellular networks. IEEE Transactions on Vehicular Technology, 48(1), 36–46.Gilhousen, K., Jacobs, I., Padovani, R., Viterbi, A., Weaver, L. A. J., & Wheatley, C. E., III. (1991). On the capacity of a cellular CDMA system. IEEE Transactions on Vehicular Technology, 40(2), 303–312.Hegde, N., & Altman, E. (2006). Capacity of multiservice WCDMA networks with variable GoS. Wireless Networks, 12, 241–253.Ben-Shimol, Y., Kitroser, I., & Dinitz, Y. (2006). Two-dimensional mapping for wireless OFDMA systems. IEEE Transactions on Broadcasting, 52(3), 388–396.Gao, D., Cai, J., & Ngan, K. N. (2005). Admission control in IEEE 802.11e wireless LANs. IEEE Network, 19(4), 6–13.Liu, T., Bahl, P., & Chlamtac, I. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications, 16(6), 922–936.Hu, F., & Sharma, N. (2004). Priority-determined multiclass handoff scheme with guaranteed mobile qos in wireless multimedia networks. IEEE Transactions on Vehicular Technology, 53(1), 118–135.Chan, J., & Seneviratne, A. (1999). A practical user mobility prediction algorithm for supporting adaptive QoS in wireless networks. In Proceedings IEEE international conference on networks (ICON) (pp. 104–111).Jayasuriya, A., & Asenstorfer, J. (2002). Mobility prediction model for cellular networks based on the observed traffic patterns. In Proceedings of IASTED international conference on wireless and optical communication (WOC) (pp. 386–391).Diederich, J., & Zitterbart, M. (2005). A simple and scalable handoff prioritization scheme. Computer Communications, 28(7), 773–789.Rashad, S., Kantardzic, M., & Kumar, A. (2006). User mobility oriented predictive call admission control and resource reservation for next-generation mobile networks. Journal of Parallel and Distributed Computing, 66(7), 971–988.Soh, W. -S., & Kim, H. (2003). QoS provisioning in cellular networks based on mobility prediction techniques. IEEE Communications Magazine, 41(1), 86 – 92.Lott, M., Siebert, M., Bonjour, S., vonHugo, D., & Weckerle, M. (2004). Interworking of WLAN and 3G systems. Proceedings IEE Communications, 151(5), 507 – 513.Sanabani, M., Shamala, S., Othman, M., & Zukarnain, Z. (2007). An enhanced bandwidth reservation scheme based on road topology information for QoS sensitive multimedia wireless cellular networks. In Proceedings of the 2007 international conference on computational science and its applications—Part II (ICCSA) (pp. 261–274).Mahadevan, S. (1996). Average reward reinforcement learning: Foundations, algorithms, and empirical results. Machine Learning, 22(1–3), 159–196.Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.Das, T. K., Gosavi, A., Mahadevan, S., & Marchalleck, N. (1999). Solving semi-markov decision problems using average reward reinforcement learning. Management Science, 45(4), 560–574.Darken, C., Chang, J., & Moody, J. (1992). Learning rate schedules for faster stochastic gradient search. In Proceedings of the IEEE-SP workshop on neural networks for signal processing II. (pp. 3–12)
    corecore