wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Data obtained from MD were in good agreement with those obtained from quantum mechanical (QM) methods, but more interactions within the dynamics of the system were revealed than from QM. A strong correlation between the tumbling of the protein and the transitions of the X4 dihedral angle of the MTSL was observed with a consequent effect on the distribution of the nitroxide (NO) group in space and time .
Revised:  It was found that the activation loop oscillated between two conformational states separated by 7 Angstrom and the average structures obtained from the MD trajectories showed the MTSL exposed to the solvent and probing the C-lobe of the protein .
Edit intent: meaning-changed
Original:  The theoretical  EPR spectra were calculated using selected configurations of MTSL probing different micro-environments of the protein characterized by different polarity. The comparison between the theoretical and experimental 9 GHz and 94 GHz EPR spectra revealed that some fits were in good agreement with the experimental EPR spectra, indicating a predominance of some conformational states of the full spin-labelled system.
Revised:  The theoretical EPR spectra were calculated using configurations representing the interactions between MTSL and water and the tyrosine residue 208 in the C-lobe; and the comparison with experimental EPR spectra revealed that some fits were in good agreement with the experimental EPR spectra, indicating a predominance of some conformational states of the full spin-labelled system.
Edit intent: meaning-changed
Original:  The comparison between the theoretical and experimental 9 GHz and 94 GHz EPR spectra revealed that some fits were in good agreement with the experimental EPR spectra, indicating a predominance of some conformational states of the full spin-labelled system.
Revised:  The comparison between the theoretical and experimental 9 GHz and 94 GHz EPR spectra revealed that fits successfully reproduced the experimental spectra in agreement with the experimental EPR spectra, indicating a predominance of some conformational states of the full spin-labelled system.
Edit intent: meaning-changed
Original:  The comparison between the theoretical and experimental 9 GHz and 94 GHz EPR spectra revealed that some fits were in good agreement with the experimental EPR spectra, indicating a predominance of some conformational states of the full spin-labelled system. This work is a starting point for deeper experimental and theoretical studies of the diffusion properties of the Aurora-A kinase protein related to its overall tumbling and biological activity .
Revised:  The comparison between the theoretical and experimental 9 GHz and 94 GHz EPR spectra revealed that some fits were in good agreement with the MD results .
Edit intent: clarity
Original:  We extend the results obtained by Kifer  Kifer in the case of a perfect market model to the case of imperfections on the market taken into account via the nonlinearity of the wealth dynamics.
Revised:  We extend the results obtained by Kifer in Kifer in the case of a perfect market model to the case of imperfections on the market taken into account via the nonlinearity of the wealth dynamics.
Edit intent: meaning-changed
Original:  We extend the results obtained by Kifer  Kifer in the case of a perfect market model to the case of imperfections on the market taken into account via the nonlinearity of the wealth dynamics.
Revised:  We extend the results obtained by Kifer Kifer in the case of a perfect market model to the case of an imperfect market with default, when the imperfections are taken into account via the nonlinearity of the wealth dynamics.
Edit intent: meaning-changed
Original:  In this framework, the pricing system is expressed as a nonlinear g-expectation/evaluation induced by a nonlinear BSDE with jump. We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We introduce the{\em { coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: coherence
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em seller's price { coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: meaning-changed
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em of the game option as the infimum of the initial wealths which allow the seller to be superhedged. We{ coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: meaning-changed
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em {prove coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: clarity
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em { that this price coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: meaning-changed
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em { coincides with the value function of an associated {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: clarity
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em { coincides with the value function of a corresponding {\em generalized} Dynkin game , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: clarity
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em { coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 , expressed with a nonlinear expectation induced by a nonlinear BSDE with default jump. We moreover study the existence of superhedging strategies. We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: meaning-changed
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em { coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: fluency
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em { coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: fluency
Original:  We prove that the superhedging price of a game option{\em  {  coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the superhedging price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Revised:  We prove that the superhedging price of a game option{\em { coincides with the value function of a corresponding {\em generalized} Dynkin game expressed in terms of the g-evaluation , recently introduced in DQS2 . We then address the case of ambiguity on the model, - for example an ambiguity on the default probability - , and characterize the robust seller's price of a game option as the value function of a {\em mixed generalized} Dynkin game.
Edit intent: meaning-changed
Original: The origin of multicellularity is a fundamental open question in biology. For URLanisms to evolve from an aggregate of URLanisms, cells with an identical genotype must first differentiate into several types.
Revised: As cells grow and divide under a given environment, they become crowded and resources are limited, as seen in bacterial biofilms and multicellular aggregates. These cells often show strong interactions through exchanging chemicals, as in quorum sensing, to achieve mutualism. Here, to achieve stable division of labor, three properties are required. First, isogenous cells differentiate into several types.
Edit intent: meaning-changed
Original:  To reveal how an ensemble of primitive cells achieves these conditions, we developed a dynamical-systems model of cells consisting of chemical components with intracellular catalytic reaction dynamics.
Revised:  We here address how cells acquire the ability of cell differentiation and division of labor simultaneously, which is also connected with the robustness of a cell society. For this purpose, we developed a dynamical-systems model of cells consisting of chemical components with intracellular catalytic reaction dynamics.
Edit intent: meaning-changed
Original:  We found that cells sharing an identical catalytic network spontaneously differentiate induced by cell-cell interactions, and then achieve cooperative division of labor, the mutual use of products among differentiated cell types, enabling a higher growth rate than that in the unicellular case.
Revised:  We found that cells sharing an identical catalytic network spontaneously differentiate via induction from cell-cell interactions, and then achieve cooperative division of labor, the mutual use of products among differentiated cell types, enabling a higher growth rate than that in the unicellular case.
Edit intent: clarity
Original:  We found that cells sharing an identical catalytic network spontaneously differentiate induced by cell-cell interactions, and then achieve cooperative division of labor, the mutual use of products among differentiated cell types, enabling a higher growth rate than that in the unicellular case.
Revised:  We found that cells sharing an identical catalytic network spontaneously differentiate induced by cell-cell interactions, and then achieve division of labor, the mutual use of products among differentiated cell types, enabling a higher growth rate than that in the unicellular case.
Edit intent: clarity
Original:  We found that cells sharing an identical catalytic network spontaneously differentiate induced by cell-cell interactions, and then achieve cooperative division of labor, the mutual use of products among differentiated cell types, enabling a higher growth rate than that in the unicellular case.
Revised:  We found that cells sharing an identical catalytic network spontaneously differentiate induced by cell-cell interactions, and then achieve cooperative division of labor, enabling a higher growth rate than that in the unicellular case.
Edit intent: clarity
Original:  The simplicity and generality of the present mechanism suggests that evolution to multicellularity is a natural consequence of interacting cells with limited resources, being consistent with the  behaviors and forms of several extant primitive forms of multicellularity, such as certain bacteria .
Revised:  The present mechanism is simple and general as a natural result of interacting cells with limited resources, being consistent with the behaviors and forms of several extant primitive forms of multicellularity, such as certain bacteria .
Edit intent: clarity
Original:  The simplicity and generality of the present mechanism suggests that evolution to multicellularity is a natural consequence of interacting cells with limited resources, being consistent with the  behaviors and forms of several extant primitive forms of multicellularity, such as certain bacteria .
Revised:  The simplicity and generality of the present mechanism suggests that evolution to multicellularity is a natural consequence of interacting cells with resource limitation, and is consistent with the behaviors and forms of several extant primitive forms of multicellularity, such as certain bacteria .
Edit intent: clarity
Original:  The simplicity and generality of the present mechanism suggests that evolution to multicellularity is a natural consequence of interacting cells with limited resources, being consistent with the  behaviors and forms of several extant primitive forms of multicellularity, such as certain bacteria .
Revised:  The simplicity and generality of the present mechanism suggests that evolution to multicellularity is a natural consequence of interacting cells with limited resources, being consistent with the observed behaviors and forms of several extant primitive forms of multicellularity, such as certain bacteria .
Edit intent: clarity
Original:  The simplicity and generality of the present mechanism suggests that evolution to multicellularity is a natural consequence of interacting cells with limited resources, being consistent with the  behaviors and forms of several extant primitive forms of multicellularity, such as certain bacteria .
Revised:  The simplicity and generality of the present mechanism suggests that evolution to multicellularity is a natural consequence of interacting cells with limited resources, being consistent with the behaviors and forms of several aggregates of URLanisms .
Edit intent: clarity
Original: On the basis of experimental data and mathematical equations in the literature, we remodel the ionic dynamics of smooth muscle cells (SMCs) as an eigensystem formulation, that is valid for investigating finite variations from equilibrium like in common experimental operations.
Revised: On the basis of experimental data and mathematical equations in the literature, we remodel the ionic dynamics of smooth muscle cells (SMCs) as an eigensystem formulation, which is valid for investigating finite variations from equilibrium like in common experimental operations.
Edit intent: fluency
Original: On the basis of experimental data and mathematical equations in the literature, we remodel the ionic dynamics of smooth muscle cells (SMCs) as an eigensystem formulation, that is valid for investigating finite variations from equilibrium like in common experimental operations.
Revised: On the basis of experimental data and mathematical equations in the literature, we remodel the ionic dynamics of smooth muscle cells (SMCs) as an eigensystem formulation, that is valid for investigating finite variations of variables from the equilibrium like in common experimental operations.
Edit intent: clarity
Original:  This algorithm provides an alternate viewpoint on frequency-domain analysis and enables one to quantify the synchronizing timings among signaling pathways for rhythmical calcium oscillations . Numerical results show three types of calcium oscillations of SMCs in mesenteric arterioles: the spontaneous calcium oscillation, the agonist-dependent calcium oscillation, and the agonist-dependent calcium spike.
Revised:  This algorithm provides an alternate viewpoint from frequency-domain analysis and enables one to quantify the synchronizing timings among signaling pathways for rhythmical calcium oscillations . Numerical results show three types of calcium oscillations of SMCs in mesenteric arterioles: the spontaneous calcium oscillation, the agonist-dependent calcium oscillation, and the agonist-dependent calcium spike.
Edit intent: fluency
Original:  This algorithm provides an alternate viewpoint on frequency-domain analysis and enables one to quantify the synchronizing timings among signaling pathways for rhythmical calcium oscillations . Numerical results show three types of calcium oscillations of SMCs in mesenteric arterioles: the spontaneous calcium oscillation, the agonist-dependent calcium oscillation, and the agonist-dependent calcium spike.
Revised:  This algorithm provides an alternate viewpoint on frequency-domain analysis and enables one to probe functionalities of SMC's rhythm by means of a resonance-related mechanism . Numerical results show three types of calcium oscillations of SMCs in mesenteric arterioles: the spontaneous calcium oscillation, the agonist-dependent calcium oscillation, and the agonist-dependent calcium spike.
Edit intent: meaning-changed
Original:  This algorithm provides an alternate viewpoint on frequency-domain analysis and enables one to quantify the synchronizing timings among signaling pathways for rhythmical calcium oscillations . Numerical results show three types of calcium oscillations of SMCs in mesenteric arterioles: the spontaneous calcium oscillation, the agonist-dependent calcium oscillation, and the agonist-dependent calcium spike.
Revised:  This algorithm provides an alternate viewpoint on frequency-domain analysis and enables one to quantify the synchronizing timings among signaling pathways for rhythmical calcium oscillations . Numerical results show three types of calcium oscillations of SMCs in mesenteric arterioles: spontaneous calcium oscillation, the agonist-dependent calcium oscillation, and the agonist-dependent calcium spike.
Edit intent: fluency