Free Access
Issue
Math. Model. Nat. Phenom.
Volume 7, Number 5, 2012
Immunology
Page(s) 65 - 77
DOI https://doi.org/10.1051/mmnp/20127506
Published online 17 October 2012
  1. V. Agoshkov, A. Quarteroni, G. Rozza. Shape design in aorto-coronaric bypass anastomoses using perturbation theory. SIAM Journal on Numerical Analysis, 44 (2006), 367–384. [Google Scholar]
  2. J. Alastruey, K. H. Parker, J. Peiró, S. Sherwin. Lumped parameter outflow models for 1-D blood flow simulations : Effect on pulsewaves and parameter estimation. Communications in Computational Physics, 4 (2008), 2–19. [Google Scholar]
  3. J. Alfon, T. Royo, X. Garcia-Moll, L. Badimon. Platelet deposition on eroded vessel walls at a stenotic shear rate is inhibited by lipid-lowering treatment with atorvastatin. Arterioscler. Thromb. Vasc. Biol., 19 (1999), 1812–1817. [CrossRef] [PubMed] [Google Scholar]
  4. A. Attarian, J. Batzel, B. Matzuka, H. T. Tran. Application of the unscented Kalman filtering to parameter estimation. Mathematical Model Development and Validation in Physiology : Application to the Cardiovascular and Respiratory Systems, J. J. Batzel, M. Bachar, and F. Kappel, eds., vol. 2064 of Lecture Notes in Mathematics, Berlin, 2012, Springer-Verlag.to appear. [Google Scholar]
  5. E. O. Attinger. The physics of pulsatile blood flow with particular reference to small vessels. Investigative Ophthalmology, 4 (1965), 973–987. [PubMed] [Google Scholar]
  6. H. T. Banks, A. Cintrón-Arias, F. Kappel. Parameter selection methods in inverse problem formulation. Mathematical Modeling and Validation in Physiology : Application to the Cardiovascular and Respiratory Systems, J. J. Batzel, M. Bachar, F. Kappel, eds., vol. 2064 of Lecture Notes in Mathematics, Berlin, 2012, Springer-Verlag.to appear. [Google Scholar]
  7. H. T. Banks, S. Dediu, S. Ernstberger, F. Kappel. Generalized sensitivities and optimal experimental design. J. Inverse and Ill-Posed Problems, 18 (2010), 25–83. [Google Scholar]
  8. H.T. Banks, K. Holm, F. Kappel. Comparison of optimal design methods in inverse problems. Inverse Problems, 27 (2011). [Google Scholar]
  9. H. T. Banks, K. Holm, F. Kappel. A Monte Carlo based analysis of optimal design criteria. J. Inverse and Ill-Posed Problems, 20 (2012), 1–38. [Google Scholar]
  10. J. Batzel, M. Fink, F. Kappel. Modeling the human cardiovascular-respiratory control response to blood volume loss due to hemorrhage. Positive Systems. C. Commault, N. Marchand, eds., vol. 341 of Lecture Notes in Control and Information Sciences, Berlin, 2006, Springer-Verlag, 145–152. [Google Scholar]
  11. J. J. Batzel, M. Bachar, V. Bhalani, F. Kappel, P. Kotanko, J. Raiman. Haemodynamics, Chapter 10, “Mathematical Physiology” (A. De Gaetano and P. Palumbo, Eds.), Encyclopedia of Life Support Systems (EOLSS), Eolss Publishers, Oxford, UK, 2008. [Google Scholar]
  12. J. J. Batzel, M. Bachar, F. Kappel.The Circulatory System. Chapter 9, “Mathematical Physiology” (A. De Gaetano and P. Palumbo, Eds.), Encyclopedia of Life Support Systems (EOLSS), Eolss Publishers, Oxford, UK, 2008. [Google Scholar]
  13. J. J. Batzel, M. Bachar, F. Kappel. Respiration and Gas Exchange, Chapter 12, “Mathematical Physiology” (A. De Gaetano and P. Palumbo, Eds), Encyclopedia of Life Support Systems (EOLSS), Eolss Publishers, Oxford, UK, 2008. [Google Scholar]
  14. J. J. Batzel, N. Goswami, H. K. Lackner, A. Roessler, M. Bachar, F. Kappel, H. Hinghofer-Szalkay. Patterns of cardiovascular control during repeated tests of orthostatic loading. Cardiovascular Engineering : An international Journal, 9 (2009), 134–143. [CrossRef] [Google Scholar]
  15. J. J. Batzel, F. Kappel, D. Schneditz, H. T. Tran.Cardiovascular and Respiratory Systems : Modeling, Analysis and Control. vol. 34 of Frontiers in Applied Mathematics, SIAM, Philadelphia, 2007. [Google Scholar]
  16. M. P. F. Berger, W. K. Wong, eds., Applied Optimal Designs, John Wiley & Sons, Chichester, UK, 2005. [Google Scholar]
  17. M. J. Bishop, G. Plank, E. Vigmond. Investigating the role of the coronary vasculature in the mechanisms of defibrillation. Circ Arrhythm Electrophysiol, 5 (2012), 210–219. [CrossRef] [PubMed] [Google Scholar]
  18. A. Brunberg, S. Heinke, J. Spillner, R. Autschbach, D. Abel, S. Leonhardt. Modeling and simulation of the cardiovascular system : a review of applications, methods, and potentials. Biomed. Tech., 54 (2009), 233–244. [CrossRef] [Google Scholar]
  19. S. Cavalcanti, S. Cavani, A. Ciandrini, G. Avanzolini. Mathematical modeling of arterial pressure response to hemodialysis-induced hypovolemia. Computers in Biology and Medicine, 36 (2006), 128–144. [CrossRef] [PubMed] [Google Scholar]
  20. S. Cavalcanti, S. Cavani, A. Santoro. Role of short-term regulatory mechanisms on pressure response to hemodialysis-induced hypovolemia. Kidney International, 61 (2002), 228–238. [CrossRef] [PubMed] [Google Scholar]
  21. S. Cavalcanti, A. Ciandrini, S. Severi, F. Badiali, S. Bini, A. Gattiani, L. Cagnoli, A. Santoro. Model-based study of the effects of the hemodialysis technique on the compensatory response to hypovolemia, Kidney International, 65 (2004), 1499–1510. [CrossRef] [PubMed] [Google Scholar]
  22. S. Cavalcanti, L. Y. Di Marco. Numerical simulation of the hemodynamic response to hemodialysis-induced hypovolemia. Artif. Organs, 23 (1999), 1063–1073. [CrossRef] [PubMed] [Google Scholar]
  23. S. Cavani, S. Cavalcanti, G. Avanzolini. Model based sensitivity analysis of arterial pressure response to hemodialysis-induced hypovolemia. ASAIO Journal, 2001 (2001), 377–388. [CrossRef] [Google Scholar]
  24. P. Crosetto, S. Deparis, G. Fourestey, A. Quarteroni. Parallel algorithms for fluid-structure interaction problems in haemodynamics. SIAM Journal on Scientific Computing, 33 (2011), 1598–1622. [Google Scholar]
  25. C. D’Angelo, A. Quarteroni. On the coupling of 1d and 3d diffusion-reaction equations : Application to tissue perfusion problems. Mathematical Models and Methods in Applied Sciences, 18 (2008), 1481–1504. [CrossRef] [MathSciNet] [Google Scholar]
  26. M. Danielsen, J. T. Ottesen. A dynamical approach to the baroreceptor regulation of the cardiovascular system. Proceeding to the 5th International Symposium, Symbiosis ’97, 1997, 25 – 29. [Google Scholar]
  27. M. Danielsen, J. T. Ottesen. Describing the pumping heart as a pressure source. J. Theor. Biol., 212 (2001), 71–81. [CrossRef] [PubMed] [Google Scholar]
  28. A. de los Reyes V, F. Kappel. Modeling pulsatility in the human cardiovascular system. Mathematica Balcanica, New Series, 24 (2010), 229–242. [Google Scholar]
  29. A. A. de los Reyes V. A mathematical model for the cardiovascular system with a measurable pulsatile pressure output. PhD thesis, University of Graz, Graz (Austria), March 2010. [Google Scholar]
  30. R. Fåhræus, T. Lindqvist. The viscosity of blood in narrow capillary tubes. Am. J. Physiol., 96 (1931), 562–568. [Google Scholar]
  31. V. V. Fedorov, P. Hackel. Model-Oriented Design of Experiments. Springer-Verlag, New York, NY, 1997. [Google Scholar]
  32. G. D. Fink. Hypothesis : the systemic circulation as a regulated free-market economy. A new approach for understanding the long-term control of blood pressure. Clin. Exp. Pharmacol. Physiol., 32 (2005), 377–383. [CrossRef] [PubMed] [Google Scholar]
  33. M. Fink, A. Attarian, H. T. Tran. Subset selection for parameter estimation in an hiv model. Proc. Applied Math. and Mechanics, 7 (2008), 11212,501–11221,502. [Google Scholar]
  34. A. Fishman, N. Cherniack, J. Widdicombe, A. P. Society, Handbook of Physiology : A Critical, Comprehensive Presentation of Physiological Knowledge and Concepts. The respiratory system. Control of breathing, / volume editors, Neil S. Cherniack, John G. Widdicombe / executive editor, Stephen R. Geiger, American Physiological Society, 1986. [Google Scholar]
  35. L. Formaggia, J. F. Gerbeau, F. Nobile, A. Quarteroni. On the coupling of 3D and 1D Navier-Stokes equations for flow problems in compliant vessels. Computer Methods in Applied Mechanics and Engineering, 191 (2001), 561–582. [Google Scholar]
  36. G. C. Goodwin, R. L. Payne. Dynamic System Identification, Experimental Design and Data Analysis. vol. 136 of Mathematics in Science and Engineering, Academic Press, New York, 1977. [Google Scholar]
  37. N. Goswami, H. Lackner, I. Papousek, J. P. Montani, D. D. Jezova, H. Hinghofer-Szalkay. Does mental arithmetic before head up tilt have an effect on the orthostatic cardiovascular and hormonal responses. Acta Astronautica, 68 (2011), 1589–1594. [CrossRef] [Google Scholar]
  38. D. M. Gu, S. C. Eisenstat. Efficient algorithms for computing a strong rank-revealing QR factorization. SIAM J. Sci. Comput., 17 (1996), 848–869. [CrossRef] [Google Scholar]
  39. A. C. Guyton, Textbook of Medical Physiology, W. B. Saunders Company, Philadelphia, Pa, 8 ed., 1991. [Google Scholar]
  40. A. C. Guyton, J. E. Hall, Guyton Hall Textbook of Medical Physiology, Saunders/Elsevier, Philadelphia, Pa, 11 ed., 2005. [Google Scholar]
  41. M. Habib. Control of the Human Cardiovascular-Respiratory System under a Time-Varying Ergonometric Workload. PhD thesis, University of Graz, Graz (Austria), May 2011. [Google Scholar]
  42. T. Heldt, E. B. Shim, R. D. Kamm, R. G. Mark. Computational modeling of cardiovascular response to orthostatic stress. J. Appl. Physiol., 92 (2002), 1239–1254. [PubMed] [Google Scholar]
  43. F. C. Hoppensteadt, C. S. Peskin. Mathematics in Medicine and the Life Sciences. vol. 10 of Texts in Applied Mathematics, Springer Verlag, New York, NY, 1992. [Google Scholar]
  44. F. Kappel, J. J. Batzel. Survey of research in modeling the human respiratory and cardiovascular systems. Research Directions in Distributed Parameter Systems, R. C. Smith and M. A. Demetriou, eds., vol. 27 of Frontiers in Applied Mathematics, SIAM, Philadelphia, Pa, 2003, ch. 8, 187–218. [Google Scholar]
  45. F. Kappel, M. Fink, J. Batzel. Aspects of control of the cardiovascular-respiratory system during orthostatic stress induced by lower body negative pressure. Math. Biosciences, 206 (2007), 273–308. [CrossRef] [Google Scholar]
  46. F. Kappel, S. Lafer, R. O. Peer. A model for the cardiovascular system under an ergometric workload. Surv. Math. Ind., 7 (1997), 239–250. [Google Scholar]
  47. F. Kappel, R. O. Peer. A mathematical model for fundamental regulation processes in the cardiovascular system. J. Math. Biol., 31 (1993), 611–631. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  48. J. Keener, J. Sneyd, Mathematical Physiology, Vol II : Systems Physiology, vol. 8 of Interdisciplinary Applied Mathematics, Springer Verlag, New York, 2nd ed., 2008. [Google Scholar]
  49. T. Kenner. Physiology of circulation. Cardiology, 1st ed., S. D. Volta, E. Braunwald, A. B. D. Luna, V. Jezek, M. L. Brochier, S. A. Mortensen, F. Dienstl, P. A. Poole-Wilson, eds., Clinical Medicine, New York, 1999, McGraw-Hill, 15–25. [Google Scholar]
  50. R. C. P. Kerckhoffs, ed.. Patient-Specific Modeling of the Cardiovascular System, Technology-Driven Personalized Medicine, Springer-Verlag, New York, 2010. [Google Scholar]
  51. A. S. Kholodov, S. S. Simakov, A. V. Evdokimov, Y. A. Kholodov.Matter transport simulations using 2D model of peripheral circulation coupled with the model of large vessels. Proc. II Int. Conf. On Comput. Bioeng., September 14-16, Lisbon, H. Rodrigues, M. Cerrolaza, M. Doblaré, J. Ambrósio, and M. Viceconti, eds., vol. 1, Lisbon, 2005, IST Press, 479–490. [Google Scholar]
  52. R. E. Klabunde. Cardiovascular Physiology Concepts. Lippincott Williams & Wilkins, Baltimore, Md, 2005. [Google Scholar]
  53. P. Kuijper, H. G. Torres, J.-W. Lammers, J. Sixma, L. Koenderman, J. Zwaginga. Platelet and fibrin deposition at the damaged vessel wall : Cooperative substrates for neutrophil adhesion under flow conditions. Blood, 89 (1997), 166–175. [PubMed] [Google Scholar]
  54. J. R. Levick. An Introduction to Cardiovascular Physiology. Oxford Univ. Press, New York, 4th ed., 2003. [Google Scholar]
  55. S. L. Mabry, L. F. Bic, K. M. Baldwin. CVSys : a coordination framework for dynamic and fully distributed cardiovascular modeling and simulation. Biomedical Sensing and Imaging Technologies, R. A. Lieberman and T. Vo-Dinh, eds., vol. 3253 of Proc. SPIE, 1998, 208–218. [Google Scholar]
  56. R. Mittal, G. Iaccarino. Immersed boundary methods. Annual Rev. Fluid Mech., 37 (2005), 239–261. [Google Scholar]
  57. M. E. C. Mutsaers, M. Bachar, J. J. Batzel, F. Kappel, S. Volkwein. Receding horizon controller for the baroreceptor loop in a model for the cardiovascular system. Cardiovascular Engineering : An international Journal, 8 (2008), 14–22. [CrossRef] [Google Scholar]
  58. S. Muzdeka, E. Barbieri. Control theory inspired considerations of the mathematical models of defibrillation. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005, IEEE Conderence Publications, 2005, 7416–7421. [Google Scholar]
  59. S. Neumann. Modeling Acute Hemorrhage in the Human Cardiovascular System. PhD thesis, University of Pennsylvania, Pensylvania, 1996. [Google Scholar]
  60. P. Novak, V. Novak, J. Spies, V. Gordon, T. Lagerlund, G. Petty. Evaluation of cerebral autoregulation in orthostatic hypotension and POTS. Clin. Auton. Res., 7 (1997), p. 238. [Google Scholar]
  61. V. Novak, P. Novak, J. M. Spies, P. A. Low. Autoregulation of cerebral blood flow in orthostatic hypotension. Stroke, 29 (1998), 104–111. [CrossRef] [PubMed] [Google Scholar]
  62. M. S. Olufsen. Modeling flow and pressure in systemic arteries. Applied Mathematical Models in Human Physiology, J. T. Ottesen, M. Olufsen, and J. K. Larsen, eds., SIAM Monographs on Mathematical Modeling and Computation, SIAM, Philadelphia, Pa, 2004, ch. 5, 91–136. [Google Scholar]
  63. M. S. Olufsen, A. Nadim, L. A. Lipsitz. Dynamics of cerebral blood flow regulation explained using a lumped parameter model. Am. J. Physiol., 282 (2002), R611–R622. [Google Scholar]
  64. M. S. Olufsen, J. T. Ottesen, H. T. Tran. Modeling cerebral blood flow control during posture change from sitting to standing. J. Cardiov. Eng., 4 (2004), 47–58. [CrossRef] [Google Scholar]
  65. M. S. Olufsen, J. T. Ottesen, H. T. Tran, L. M. Ellwein, L. A. Lipsitz, V. Novak. Blood pressure and blood flow variation during postural change from sitting to standing : Model development and validation. J. Appl. Physiol., 99 (2005), 1523–1537. [CrossRef] [PubMed] [Google Scholar]
  66. J. T. Ottesen. Modelling the baroreflex-feedback mechanism with time-delay. J. Math. Biol., 36 (1997), 41–63. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  67. J. T. Ottesen, M. Danielsen, eds., Mathematical Modelling in Medicine, vol. 71 of Studies in Health Technology and Informatics, IOS Press, Amsterdam, 2000. [Google Scholar]
  68. J. T. Ottesen, M. S. Olufsen, J. K. Larsen, eds., Applied Mathematical Models in Human Physiology, Monographs on Mathematical Modeling and Computation, SIAM, Philadelphia, 2004. [Google Scholar]
  69. T. Passerini, M. de Luca, L. Formaggia, A. Quarteroni, A. Veneziani. A 3D/1D geometrical multiscale model of cerebral vasculature. Journal of Engineering Mathematics, 64 (2009), 319 – 330. [Google Scholar]
  70. A. Pázman. Foundations of Optimum Experimental Design, Mathematics and Its Applications. D. Reidel Publ. Comp., Dordrecht, 1986. [Google Scholar]
  71. K. Perktold, M. Hofer, G. Rappitsch, M. Loew, B. D. Kuban, M. H. Friedman. Validated computation of physiologic flow in a realistic coronary artery branch. J. Biomech., 31 (1998), 217–228. [CrossRef] [PubMed] [Google Scholar]
  72. K. Perktold, G. Rappitsch. Mathematical modeling of arterial blood flow and correlation to atherosclerosis. Technol. Health Care, 3 (1995), 139 – 151. [PubMed] [Google Scholar]
  73. C. S. Peskin. Flow Patterns around Heart Valves. PhD thesis, Albert Einstein College of Medicine, New York, 1972. [Google Scholar]
  74. C. S. Peskin, D. M. McQueen. Modeling prosthetic heart valves for numerical analysis of blood flow in the heart. J. Comput. Phys., 37 (1980), 113–132. [CrossRef] [Google Scholar]
  75. C. S. Peskin, D. M. McQueen. Cardiac fluid dynamics. High-performance Computing in Biomedical Research, T. C. Pilkington et al., ed., CRC Press, Boca Raton, 1993. [Google Scholar]
  76. C. S. Peskin, D. M. McQueen. Mechanical equilibrium determines the fractal fiber architecture of aortic heart valve leaflets. Am. J. Physiol., 266 (1994), H319–H328. [PubMed] [Google Scholar]
  77. C. S. Peskin, D. M. McQueen.Fluid dynamics of the heart and its valves, in Case Studies in Mathematical Modeling – Ecology, Physiology, and Cell Biology, H. G. Othmer, F. R. Adler, M. A. Lewis, J. C. Dallon, eds., Prentice Hall, Englewood Cliffs, New Jersey, 1996, ch. 14, 309–337. [Google Scholar]
  78. C. S. Peskin, B. F. Printz. Improved volume conservation in the computation of flows with immersed boundaries. J. Comput. Phys., 105 (1993), 33–46. [CrossRef] [Google Scholar]
  79. S. R. Pope, L. M. Ellwein, C. L. Zapata, V. Novak, C. T. Kelley, M. S. Olufsen. Estimation and identification of parameters in a lumped cerebrovascular model. Mathematical Biosciences and Engineering, 6 (2009), 93–115. [CrossRef] [MathSciNet] [Google Scholar]
  80. M. Prosi, P. Zunino, K. Perktold, A. Quarteroni. Mathematical and numerical models for transfer of low-density lipoproteins through the arterial walls : A new methodology for the model set up with applications to the study of disturbed lumenal flow. Journal of Biomechanics, 38 (2005), 903–917. [Google Scholar]
  81. F. Pukelsheim. Optimal Design of Experiments. JohnWiley & Sons, New York, NY, 1993. [Google Scholar]
  82. A. Quarteroni, A. Veneziani, P. Zunino. Mathematical and numerical modeling of solute dynamics in blood flow and arterial walls. SIAM Journal on Numerical Analysis, 39 (2001), 1488 – 511. [CrossRef] [MathSciNet] [Google Scholar]
  83. L. B. Rowell. Human Cardiovascular Control. Oxford University Press, New York, 1993. [Google Scholar]
  84. G. A. F. Seber, C. J. Wild, Nonlinear Regression.Wiley Series in Probability and Mathematical Statistics. J. Wiley, New York, 1989. [Google Scholar]
  85. B. W. Smith, J. G. Chase, G. M. Shaw, R. I. Nokes. Experimentally verified minimal cardiovascular system model for rapid diagnostic assistance, Control Engineering Practice, 13 (2005), 1183–1193. [CrossRef] [Google Scholar]
  86. J. Smith, J. Kampine.Circulatory Physiology. Williams, Wilkins, Baltimore, 1990. [Google Scholar]
  87. W.-B. Tay, Y.-H. Tseng, L.-Y. Lin, W.-Y. Tseng. Towards patient-specific cardiovascular modeling system using the immersed boundary technique. BioMedical Engineering OnLine, 10 (2011). [Google Scholar]
  88. W. D. Timmons.Cardiovascular models and control. “The Biomedical Engineering Handbook” (Chapter 160), J. D. Branzino, ed., Boca Raton, 2000, CRC Press LLC. [Google Scholar]
  89. N. Trayanova, G. Plank, B. Rodríguez. What have we learned from mathematical models of defibrillation and postshock arrhythmogenesis ? Application of bidomain simulations. Heart Rhythm, 3 (2006), 1232–1235. [CrossRef] [PubMed] [Google Scholar]
  90. R. F. Tuma, W. N. Duràn, K. Ley, eds.. Microcirculation. Elsevier, Amsterdam, 2 ed., 2008. [Google Scholar]
  91. D. Ucinski, A. Atkinson. Experimental design for time-dependent models with correlated observations. Studies in Nonlinear Dynamics and Econometrics, 8 (2004). [Google Scholar]
  92. M. Ursino. Interaction between carotid baroregulation and the pulsating heart : A mathematical model. Am. J. Physiol., 275 (1998), H1733–H1747. [PubMed] [Google Scholar]
  93. M. Ursino. A mathematical model of the carotid baroregulation in pulsating conditions. IEEE Trans. Biomed. Eng., 46 (1999), 382–392. [CrossRef] [PubMed] [Google Scholar]
  94. M. Ursino, A. Fiorenzi, E. Belardinelli. The role of pressure pulsatility in the carotid baroreflex control : A computer simulation study. Comput. Biol. Med., 26 (1996), 297–314. [CrossRef] [PubMed] [Google Scholar]
  95. M. Ursino, M. Innocenti. Mathematical investigation of some physiological factors involved in hemodialysis hypotension. Artif. Organs, 21 (1997), 891–902. [CrossRef] [PubMed] [Google Scholar]
  96. M. Ursino, M. Innocenti. Modeling arterial hypotension during hemodialysis. Artif. Organs, 21 (1997), 873–890. [CrossRef] [PubMed] [Google Scholar]
  97. F. Vadakkumpadan, L. J. Rantner, B. Tice, P. Boyle, A. J. Prassl, E. Vigmond, G. Plank, N. Trayanova. Image-based models of cardiac structure with applications in arrhythmia and defibrillation studies. J Electrocardiol, 42 (2009), 157.e1–157.10. [CrossRef] [PubMed] [Google Scholar]
  98. N. Westerhof, N. Stergiopulos. Models of the arterial tree. Mathematical Modelling in Medicine, J. T. Ottesen, M. Danielsen, eds., vol. 71 of Studies in Health Technology and Informatics, Amsterdam, The Netherlands, 2000, IOS Press, 65–78. [Google Scholar]
  99. N. Westerhof, N. Stergiopulos, M. I. M. Noble. Snapshots of Hemodynamics. vol. 18 of Basic Science for the Cardiologist, Kluwer Academic Publishers, Dordrecht, 2005. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.