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Radulescu Ovidiu - One of the best experts on this subject based on the ideXlab platform.
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Push-Forward method for piecewise deterministic biochemical simulations
2021Co-Authors: Innocentini, Guilherme C. P., Hodgkinson Arran, Antoneli Fernando, Debussche Arnaud, Radulescu OvidiuAbstract:A biochemical network can be simulated by a set of ordinary differential equations (ODE) under well stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the Push-Forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing.This work is an extended version of the work presented at the conference CMSB2019.Comment: arXiv admin note: text overlap with arXiv:1905.0023
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Push-Forward method for piecewise deterministic biochemical simulations
'Elsevier BV', 2021Co-Authors: Innocentini, Guilherme C. P., Hodgkinson Arran, Antoneli Fernando, Debussche Arnaud, Radulescu OvidiuAbstract:arXiv admin note: text overlap with arXiv:1905.00235International audienceA biochemical network can be simulated by a set of ordinary differential equations (ODE) under well stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the Push-Forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing.This work is an extended version of the work presented at the conference CMSB2019
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Push-Forward method for piecewise deterministic biochemical simulations
HAL CCSD, 2021Co-Authors: Innocentini, Guilherme C. P., Hodgkinson Arran, Antoneli Fernando, Debussche Arnaud, Radulescu OvidiuAbstract:arXiv admin note: text overlap with arXiv:1905.00235A biochemical network can be simulated by a set of ordinary differential equations (ODE) under well stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the Push-Forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing.This work is an extended version of the work presented at the conference CMSB2019
Navid Nabijou - One of the best experts on this subject based on the ideXlab platform.
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the fundamental solution matrix and relative stable maps
European Journal of Mathematics, 2019Co-Authors: Navid NabijouAbstract:Givental’s Lagrangian cone $${\mathscr {L}}_X$$ is a Lagrangian submanifold of a symplectic vector space which encodes the genus-zero Gromov–Witten invariants of X. Building on work of Braverman, Coates has obtained the Lagrangian cone as the Push-Forward of a certain class on the moduli space of stable maps to . This provides a conceptual description for an otherwise mysterious change of variables called the dilaton shift. We recast this construction in its natural context, namely the moduli space of stable maps to relative the divisor . We find that the resulting Push-Forward is another familiar object, namely the transform of the Lagrangian cone under the action of the fundamental solution matrix. This hints at a generalisation of Givental’s quantisation formalism to the setting of relative invariants. Finally, we use a hidden polynomiality property implied by our construction to obtain a sequence of universal relations for the Gromov–Witten invariants, as well as new proofs of several foundational results concerning both the Lagrangian cone and the fundamental solution matrix.
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the fundamental solution matrix and relative stable maps
European Journal of Mathematics, 2019Co-Authors: Navid NabijouAbstract:Givental’s Lagrangian cone \({\mathscr {L}}_X\) is a Lagrangian submanifold of a symplectic vector space which encodes the genus-zero Gromov–Witten invariants of X. Building on work of Braverman, Coates has obtained the Lagrangian cone as the Push-Forward of a certain class on the moduli space of stable maps to Open image in new window . This provides a conceptual description for an otherwise mysterious change of variables called the dilaton shift. We recast this construction in its natural context, namely the moduli space of stable maps to Open image in new window relative the divisor Open image in new window . We find that the resulting Push-Forward is another familiar object, namely the transform of the Lagrangian cone under the action of the fundamental solution matrix. This hints at a generalisation of Givental’s quantisation formalism to the setting of relative invariants. Finally, we use a hidden polynomiality property implied by our construction to obtain a sequence of universal relations for the Gromov–Witten invariants, as well as new proofs of several foundational results concerning both the Lagrangian cone and the fundamental solution matrix.
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the fundamental solution matrix and relative stable maps
arXiv: Algebraic Geometry, 2018Co-Authors: Navid NabijouAbstract:Givental's Lagrangian cone $\mathcal{L}_X$ is a Lagrangian submanifold of a symplectic vector space which encodes the genus-zero Gromov-Witten invariants of $X$. Building on work of Braverman, Coates has obtained the Lagrangian cone as the Push-Forward of a certain class on the moduli space of stable maps to $X \times \mathbb{P}^1$. This provides a conceptual description for an otherwise mysterious change of variables called the dilaton shift. In this note we recast this construction in its natural context, namely the moduli space of stable maps to $X \times \mathbb{P}^1$ relative the divisor $X \times \infty$. We find that the resulting Push-Forward is another familiar object, namely the transform of the Lagrangian cone under the action of the fundamental solution matrix. This hints at a generalisation of Givental's quantisation formalism to the setting of relative invariants. Finally, we use a hidden polynomiality property implied by our construction to obtain a sequence of universal relations for the Gromov-Witten invariants, as well as new proofs of several foundational results concerning both the Lagrangian cone and the fundamental solution matrix.
Richard J. Hawkins - One of the best experts on this subject based on the ideXlab platform.
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Serratus Anterior Muscle Activity During Selected Rehabilitation Exercises
The American journal of sports medicine, 1999Co-Authors: Michael J. Decker, R. A. Hintermeister, Kenneth J. Faber, Richard J. HawkinsAbstract:The purpose of this study was to document the electromyographic activity and applied resistance associated with eight scapulohumeral exercises performed below shoulder height. We used this information to design a continuum of serratus anterior muscle exercises for progressive rehabilitation or training. Five muscles in 20 healthy subjects were studied with surface electrodes for the following exercises: shoulder extension, forward punch, serratus anterior punch, dynamic hug, scaption (with external rotation), press-up, push-up plus, and knee push-up plus. Electromyographic data were collected from the middle serratus anterior, upper and middle trapezius, and anterior and posterior deltoid muscles. Each exercise was partitioned into phases of increasing and decreasing force and analyzed for average and peak electromyographic amplitude. Resistance was provided by body weight, an elastic cord, or dumbbells. The serratus anterior punch, scaption, dynamic hug, knee push-up plus, and push-up plus exercises consistently elicited serratus anterior muscle activity greater than 20% maximal voluntary contraction. The exercises that maintained an upwardly rotated scapula while accentuating scapular protraction, such as the push-up plus and the newly designed dynamic hug, elicited the greatest electromyographic activity from the serratus anterior muscle. Normal shoulder motion results from a complex interplay of the scapulohumeral, acromioclavicular, sternoclavicular, and scapulothoracic articulations. The coordination of these articulations provides the shoulder with an ample range of motion necessary for overhead sporting activities. Proper positioning of the humerus in the glenoid cavity, known as scapulohumeral rhythm, 6 is critical to the proper function of the glenohumeral joint during overhead motion. A disturbance in normal scapulohumeral rhythm may cause inappropriate positioning of the glenoid relative to the humeral head, resulting in injury. 16, 18, 22 One of the primary muscles responsible for maintaining normal rhythm and shoulder motion is the serratus anterior. 8, 32 Lack of strength or endurance in this muscle
Kalyan Banerjee - One of the best experts on this subject based on the ideXlab platform.
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on the closed embedding of the product of theta divisors into product of jacobians and chow groups
International Journal of Mathematics, 2018Co-Authors: Kalyan BanerjeeAbstract:In this paper, we generalize the injectivity of the Push-Forward homomorphism at the level of Chow groups, induced by the closed embedding of SymmC into SymnC for m ≤ n, where C is a smooth project...
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algebraic cycles on the fano variety of lines of a cubic fourfold
arXiv: Algebraic Geometry, 2016Co-Authors: Kalyan BanerjeeAbstract:In this text we prove that if a smooth cubic in $\PR^5$ has its Fano variety of lines birational to the Hilbert scheme of two points on a K3 surface, then there exists a smooth projective curve or a smooth projective surface embedded in the Fano variety, such that the kernel of the Push-Forward (at the level of zero cycles ) induced by the closed embedding is torsion.
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on the closed embedding of the product of theta divisors into product of jacobians and chow groups
arXiv: Algebraic Geometry, 2016Co-Authors: Kalyan BanerjeeAbstract:In this article we generalize the injectivity of the Push-Forward homomorphism at the level of Chow groups, induced by the closed embedding of $\Sym^m C$ into $\Sym^n C$ for $m\leq n$, where $C$ is a smooth projective curve, to symmetric powers of a smooth projective variety of higher dimension. We also prove the analog of this theorem for product of symmetric powers of smooth projective varieties. As an application we prove the injectivity of the Push-Forward homomorphism at the level of Chow groups, induced by the closed embedding of self product of theta divisor into the self product of the Jacobian of a smooth projective curve.
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on the kernel of the push forward homomorphism between chow groups
arXiv: Algebraic Geometry, 2015Co-Authors: Kalyan Banerjee, Jaya Nn IyerAbstract:In this note we prove that the kernel of the Push-Forward homomorphism on $d$-cycles modulo rational equivalence, induced by the closed embedding of an ample divisor linearly equivalent to some multiple of the theta divisor inside the Jacobian variety $J(C)$ is trivial. Here $C$ is a smooth projective curve of genus $g$.
Innocentini, Guilherme C. P. - One of the best experts on this subject based on the ideXlab platform.
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Push-Forward method for piecewise deterministic biochemical simulations
2021Co-Authors: Innocentini, Guilherme C. P., Hodgkinson Arran, Antoneli Fernando, Debussche Arnaud, Radulescu OvidiuAbstract:A biochemical network can be simulated by a set of ordinary differential equations (ODE) under well stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the Push-Forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing.This work is an extended version of the work presented at the conference CMSB2019.Comment: arXiv admin note: text overlap with arXiv:1905.0023
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Push-Forward method for piecewise deterministic biochemical simulations
'Elsevier BV', 2021Co-Authors: Innocentini, Guilherme C. P., Hodgkinson Arran, Antoneli Fernando, Debussche Arnaud, Radulescu OvidiuAbstract:arXiv admin note: text overlap with arXiv:1905.00235International audienceA biochemical network can be simulated by a set of ordinary differential equations (ODE) under well stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the Push-Forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing.This work is an extended version of the work presented at the conference CMSB2019
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Push-Forward method for piecewise deterministic biochemical simulations
HAL CCSD, 2021Co-Authors: Innocentini, Guilherme C. P., Hodgkinson Arran, Antoneli Fernando, Debussche Arnaud, Radulescu OvidiuAbstract:arXiv admin note: text overlap with arXiv:1905.00235A biochemical network can be simulated by a set of ordinary differential equations (ODE) under well stirred reactor conditions, for large numbers of molecules, and frequent reactions. This is no longer a robust representation when some molecular species are in small numbers and reactions changing them are infrequent. In this case, discrete stochastic events trigger changes of the smooth deterministic dynamics of the biochemical network. Piecewise-deterministic Markov processes (PDMP) are well adapted for describing such situations. Although PDMP models are now well established in biology, these models remain computationally challenging. Previously we have introduced the Push-Forward method to compute how the probability measure is spread by the deterministic ODE flow of PDMPs, through the use of analytic expressions of the corresponding semigroup. In this paper we provide a more general simulation algorithm that works also for non-integrable systems. The method can be used for biochemical simulations with applications in fundamental biology, biotechnology and biocomputing.This work is an extended version of the work presented at the conference CMSB2019
