57 research outputs found
Dp-brane Dynamics and Thermalization in Type IIB Ben Ami-Kuperstein-Sonnenschein Models
No full textWe extend and complete our analysis arXiv:1608.02380 and study the induced world volume metrics and Hawking temperatures of all type IIB rotating probe Dp-branes, dual to the temperature of different flavors at finite R-charge, in the Ben Ami–Kuperstein–Sonnenschein holographic models including the effects of spontaneous conformal and chiral flavor symmetry breakdown. The model embeds type IIB probe flavor Dp-branes into the Klebanov-Witten gravity dual of conformal gauge theory, with the embedding parameter, given by the minimal radial extension of the probes, dual to the IR scale of conformal and chiral flavor symmetry breakdown. We show that when the minimal extension is positive definite, the induced world volume metrics of type IIB rotating probe branes admit thermal horizons and Hawking temperatures despite the absence of black holes in the bulk subject to the world volume and topology of the nontrivial internal cycle wrapped by the probe. We also derive the energy–stress tensor of the thermal probes and study their backreaction and energy dissipation. We show that at the IR scale the backreaction is nonnegligible and find the energy can flow from the probes to the bulk, dual to the energy dissipation from the flavor sectors into the gauge theory
Tobacco\u27s Weakest Link: Why Tobacco Farmers are Essential Players in the Fight Against Big Tobacco
No full textOn the pp-wave limit and the BMN structure of new Sasaki-Einstein spaces
No full textWe construct the pp-wave string associated with the Penrose limit of Y p,q and Lp,q,r families of Sasaki-Einstein geometries. We identify in the dual quiver gauge theories the chiral and the non-chiral operators that correspond to the ground state and the first excited states. We present an explicit identification in a prototype model of L1,7,3. © SISSA 2006.SCOPUS: re.jinfo:eu-repo/semantics/publishe
Echocardiographic Delineation of Type a Aortic Dissection
No full textIntroduction In addition to proving the visualisation data, studies on the intimal flaps of aortic dissection based on echocardiography have been very limited. Methods Twenty-seven patients undergoing an operation of type A aortic dissection with preoperative transthoracic and/or intraoperative transesophageal echocardiography archived in the Horizon Cardiology Web of the hospital available for review and measurement were selected into this retrospective study. By way of quantitative and qualitative approaches, flap movement, dissection extent and aortic obstruction were sufficiently evaluated. Results An intimal flap was visualised in 22 (81.5%) patients and linear artifact was viewed in 1 (3.7%), and in the remaining 4 (14.8%), neither an intimal flap nor a linear artifact was visible. Dissection extents were 2.78±1.53 cm and 2.03±1.19 cm in the horizontal and vertical direction, respectively. Sub- or semi-circumferential dissection was noted in 14 (51.8%) patients. No total circumferential dissection was found in this patient population with type A aortic dissection. The obstruction of the aortic valve orifice or aortic cavity developed in 11 (40.7%) patients. Flow disturbance and thrombus in the false lumen were visualised in 24 (88.9%) and 4 (14.8%) patients, respectively. The breadth of flap movement in short-axis plane was much larger in the Aortic Obstruction Group than that of the Non-Obstruction Group (1.55±1.14 cm vs. 0.75±0.526 cm, p <0.05). Conclusions Preoperative transthoracic or transesophageal echocardiographic evaluations offer a convenient and precise diagnostic tool for aortic dissection. The dissection extent may directly correlate and substantially reflect the clinical symptoms of the patients with type A aortic dissection in response to the haemodynamic impairment. Preoperative echocardiographic delineation would assure adequate means and extent of the impending operation. </jats:sec
Non-SUSY fractional branes
No full text© 2015, The Author(s). Abstract: We consider a simplified Ansatz for supergravity solutions describing fractional p-brane solutions (throat geometries supported by fluxes) for various p. For p = 3 the Ansatz captures the Klebanov-Tseytlin (KT) solution. The equations of motion can be derived from an effective action by performing a dimensional reduction to a flat domain wall geometry in p + 2 dimensions. We find an interesting deformation of the known superpotential defining the SUSY domain wall flow. The deformation parameter breaks supersymmetry but still preserves the property that a test Dp brane feels no force inside the throat. The new solutions come in two classes. Both classes have the same UV asymptotics as the KT solution and one class has also similar IR behavior, which makes them potentially interesting holographic backgrounds for studying cascading gauge theories with broken SUSY. We explain furthermore how the curved domain wall solutions of the same (p + 2)-dimensional theories are expected to lift to new AdS compactifications of type IIA/B supergravity.sponsorship: We like to thank Riccardo Argurio and Ruben Monten for useful discussions. BT is Aspirant FWO. TVR is supported by a Pegasus fellowship and by the Odysseus programme of the FWO. SK is supported in part by the ERC Starting Grant 240210 - String-QCD-BH and by the John Templeton Foundation Grant 48222. We also acknowledge support from the European Science Foundation Holograv Network. (Pegasus fellowship, Odysseus programme of the FWO, ERC|240210 - String-QCD-BH, John Templeton Foundation|48222, European Science Foundation Holograv Network)status: Publishe
Visualization of the steady states for the toy model in dependence of various parameters.
No full text<p>The phase diagrams (<i>r</i><sub>2</sub>×<i>r</i><sub>1</sub> plane) show the qualitative behavior for small, intermediate and large values of the model parameters. Red color corresponds to the state for which <i>P<sup>s</sup></i>>0.5 (DNA is repaired with probability >50%). If <i>P<sup>s</sup></i><0.5 then the color is chosen as green if the probability of trapping in the intermediate state (<i>I</i>) is bigger than the propability of the initial unrepaired state (<i>S</i>), and as blue in the opposite case. The case <i>k</i><sub>1</sub> = 0 (F1↓) is represented separately on the right. In this case, another parameter is used instead of <i>r</i><sub>1</sub> for the phase plane. The case <i>k−</i><sub>1</sub> = 0 (R1↓) is treated separately on the top and only the <i>r</i><sub>2</sub> value is varied. The 14 model simulations listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003016#pcbi-1003016-g006" target="_blank">Figure 6</a> are shown by the circled numbers in the position of the chosen parameters.</p
Drug-Target identification in COVID-19 disease mechanisms using computational systems biology approaches
No full textThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms.Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue-or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. AN
acknowledges support from SANOFI-AVENTIS R&D via the CIFRE contract, n° 2020/0766. MK, FH, NP, FE, and CE acknowledge the
support of the ZonMw COVID-19 programme (Grant No.10430012010015). JD Spanish Ministry of Science and Innovation
(Grant no. PID2020-117979RB-I00) and Instituto de Salud Carlos III (Grant no. IMP/00019). MAi, KK, FS: Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) -Project-ID 251654672 - TRR 161 and under Germany’s Excellence
Strategy - EXC 2117 - 422037984. FM: “5 per 1000–2021” grant of the Italian Ministry of Health (Grant No. 5M-2021-23683787) and
European Commission with HORIZON programme, BY-COVID project (Grant No. 101046203—BY-COVID). National Institute for
Infectious Diseases Lazzaro Spallanzani–IRCCS received financial support from the Italian Ministry of Health grant “Ricerca
Corrente”. JP, LF: IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020
Research and Innovation Programme and EFPIA [GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA:964537
RISKHUNT3R]; Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development
Fund (ERDF) and Generalitat de Catalunya; Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the
European Union, European Regional Development Fund (ERDF, “Away to make Europe”). AMo, MP and AV acknowledge the support of
the European Commission under the INFORE project (H2020-ICT825070) and the PerMedCoE (H2020-ICT-951773). Contributions by
TH and BLP were supported by NIH grant #R35GM119770 to TH. MaGo acknowledges funding from Deutsche Forschungsgemeinschaft
(DFG) through grants no. 442326535 (NFDI4Health) and 451265285 (NFDI4Health Task Force COVID-19), from the European
Commission through the Horizon 2020 framework program under grant no. 825843 (EU-STANDS4PM) and through the Digital Europe
program under grant no. 101083771 (EDITH), as well as from the Klaus Tschira Foundation. AL acknowledges support from the
Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health (NIH).Peer Reviewed"Article signat per més de 50 autors/es: Anna Niarakis, Marek Ostaszewski, Alexander Mazein, Inna Kuperstein, Martina Kutmon, Marc E. Gillespie, Akira Funahashi, Marcio L. Acencio, Ahmed Hemedan, Michael Aichem, Karsten Klein, Tobias Czauderna, Felicia Burtscher, Takahiro G. Yamada, Yusuke Hiki, Noriko F. Hiroi, Finterly Hu, Nhung Pham, Friederike Ehrhart, Egon Willighagen, Alberto Valdeolivas, Aurelien Dugourd, Francesco Messina, Marina Esteban Medina, Maria Peña-Chilet, Kinza Rian, Sylvain Soliman, Sara S. Aghamiri, Bhanwar Lal Puniya, Aurélien Naldi, Tomáš Helikar, Vidisha Singh, Marco Fariñas Fernández, Viviam Bermudez, Eirini Tsirvouli, Arnau Montagud, Vincent Noël, Miguel Ponce De Leon, Dieter Maier, Angela Bauch, Benjamin M. Gyori, John A. Bachman, Augustin Luna, Janet Piñero, Laura I. Furlong, Irina Balaur, Adrien Rougny, Yohan Jarosz, Rupert W. Overall, Robert Phair, Livia Perfetto, Lisa Matthews, Devasahayam Arokia Balaya Rex, Marija Orlic-Milacic, Cristobal Luis, Bertrand De Meulder, Jean M. Ravel, Bijay Jassal, Venkata P. Satagopam, Guanming Wu, Martin Golebiewski, Piotr Gawron, Laurence Calzone, Jacques S. Beckmann, Chris Evelo, Peter D'eustachio, Falk Schreiber, Julio Saez-Rodriguez, Joaquin Dopazo, Martin Kuiper, Alfonso Valencia, Olaf Wolkenhauer, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider "Postprint (published version
Software Verification for Weak Memory via Program Transformation
No full textThis version previously deposited at arXiv:1207.7264v1 [cs.LO
COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms
No full textWe need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.We would like to thank Andjela Tatarovic, architect, and Gina Crovetto, a researcher in the field of cancer, for their help with the design of the top-level view diagrams. We would like to acknowledge the Responsible and Reproducible Research (R3) team of the Luxembourg Centre for Systems Biomedicine for supporting the project and providing necessary communication and data sharing resources. The work presented in this paper was carried out using the ELIXIR Luxembourg tools and services. This study was supported by the Luxembourg National Research Fund (FNR) COVID-19 Fast-Track grant programme, grant COVID-19/2020-1/14715687/CovScreen (E. Glaab); European Commission, INFORE grant H2020-ICT-825070 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia); European Commission, PerMedCoE grant H2020-ICT-951773 (A. Montagud, M. Ponce de Leon, M. Vazques and A. Valencia) the Federal Ministry of Education and Research (BMBF, Germany) and the Baden-Württemberg Ministry of Science, the Excellence Strategy of the German Federal and State Governments (A. Renz); German Center for Infection Research (DZIF), grant no 8020708703 (A. Dräger); The Netherlands Organisation for Health Research and Development (ZonMw), grant no 10430012010015, (M. Kutmon, S. Coort, F. Ehrhart, N. Pham, E.L. Willighagen, C.T. Evelo); H2020 Marie Skłodowska-Curie Actions, grant number 765274 (J. Scheel); National Institutes of Health, USA (NIH), grant number U41 HG003751 (L.D. Stein). The development of Reactome is supported by grants from the US National Institutes of Health (U41 HG003751) and the European Molecular Biology Laboratory.Peer Reviewed"Article signat per més de 50 autors/es: Marek Ostaszewski, Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta-Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, Francesco Messina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang,Emily E Ackerman, Jason E Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, Devasahayam Arokia Balaya,Denise Slenter, Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic-Milacic, Andrea Senff Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean-Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen51,Carole Goble, Xiaoming Hu, Rupert W Overall, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco57, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Stephane Ballereau, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Tamas Korcsmaros, Tom C Freeman, Franck Augé, Jacques S Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L Wilighagen, Alexander R Pico, Chris T Evelo, Marc E Gillespie, Lincoln D Stein, Henning Hermjakob, Peter D'Eustachio, Julio Saez-Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider "Postprint (published version
Partial Orders for Efficient BMC of Concurrent Software
No full textThis version previously deposited at arXiv:1301.1629v1 [cs.LO]The vast number of interleavings that a concurrent program can have is typically identified as the root cause of the difficulty of automatic analysis of concurrent software. Weak memory is generally believed to make this problem even harder. We address both issues by modelling programs' executions with partial orders rather than the interleaving semantics (SC). We implemented a software analysis tool based on these ideas. It scales to programs of sufficient size to achieve first-time formal verification of non-trivial concurrent systems code over a wide range of models, including SC, Intel x86 and IBM Power
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