wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In particular, we define the stability of a Boolean state sequence in consistency with the stability  of the original continuous trajectory that has been discretized. For a class of randomly connected systems with randomly drawn Boolean functions, so-called Kauffman networks, we find that the dynamics is stable for almost all choices of parameter values. The so-called chaotic regime in Kauffman networks appears only as a damage spreading effect after flip perturbations . We conclude that regulatory systems amenable to state discretization do not exhibit chaotic behaviour. Both resilience to noise and sensitivity to input signals come "for free" as typical properties of sufficiently dense regulatory networks.
Revised:  In particular, we define the stability of a Boolean state sequence in consistency with the stability of the original continuous dynamics probed by small perturbations. In particular, random networks of nodes with large sensitivity yield stable dynamics, in contrast to the prediction of chaos obtained by flip perturbations in Boolean networks.
Edit intent: clarity
Original:  These proteins are so tightly packed that their excluded area interactions can alter the free energy landscape associated with their conformational degrees of freedom .
Revised:  These proteins are so tightly packed that their excluded area interactions can alter the free energy landscape controlling the conformational transitions suffered by such proteins. For membrane channels, this effect can alter the critical membrane tension at which they undergo a transition from a closed to an open state, and therefore influence protein function .
Edit intent: meaning-changed
Original:  These proteins are so tightly packed that their excluded area interactions can alter the free energy landscape associated with their conformational degrees of freedom .
Revised:  These proteins are so tightly packed that their excluded area interactions can alter the free energy landscape associated with their conformational degrees of freedom. Despite their obvious importance, crowding phenomena in membranes are much less well studied than in the cytoplasm. Using statistical mechanics results for hard disk liquids, we show that crowding induces an entropic tension in the membrane, which influences transitions that alter the projected area and circumference of a membrane protein .
Edit intent: meaning-changed
Original:  As a specific case study in these effects , we consider the impact of crowding on the gating tension for mechanosensitive channels. We show that crowding can alter the gating energies by more than \approx 2  k_BT  , a substantial fraction of the gating energies themselves in some cases  .
Revised:  As a specific case study in these effects , we consider the impact of crowding on the gating properties of bacterial mechanosensitive membrane channels, which are thought to confer osmoprotection when these cells are subjected to osmotic shock. We find that crowding can alter the gating energies by more than \approx 2 k_BT , a substantial fraction of the gating energies themselves in some cases .
Edit intent: meaning-changed
Original:  We show that crowding can alter the gating energies by more than \approx 2  k_BT  , a substantial fraction of the gating energies themselves in some cases  .
Revised:  We show that crowding can alter the gating energies by more than \approx 2 k_BT in physiological conditions , a substantial fraction of the gating energies themselves in some cases .
Edit intent: meaning-changed
Original:  We show that crowding can alter the gating energies by more than \approx 2  k_BT  , a substantial fraction of the gating energies themselves in some cases  .
Revised:  We show that crowding can alter the gating energies by more than \approx 2 k_BT , a substantial fraction of the gating energies themselves in some cases . Given the ubiquity of membrane crowding, the nonspecific nature of excluded volume interactions, and the fact that the function of many membrane proteins involve significant conformational changes, this specific case study highlights a general aspect in the function of membrane proteins .
Edit intent: meaning-changed
Original: In a previous paper it was shown that a Markov-functional model with log-normally distributed rates in the terminal measure displays nonanalytic behaviour as a function of the volatility, which is similar to a phase transition in condensed matter physics.
Revised: We consider an interest rate model with log-normally distributed rates in the terminal measure displays nonanalytic behaviour as a function of the volatility, which is similar to a phase transition in condensed matter physics.
Edit intent: meaning-changed
Original: In a previous paper it was shown that a Markov-functional model with log-normally distributed rates in the terminal measure displays nonanalytic behaviour as a function of the volatility, which is similar to a phase transition in condensed matter physics.
Revised: In a previous paper it was shown that a Markov-functional model with log-normally distributed rates in the terminal measure in discrete time. Such models are used in financial practice as parametric versions of the Markov functional model, or as approximations to the log-normal Libor market model. We show that the model has two distinct regimes, at high and low volatilities, with different qualitative behavior. The two regimes are separated by a sharp transition, which is similar to a phase transition in condensed matter physics.
Edit intent: meaning-changed
Original:  Here we discuss the implications of these results for the pricing of interest rates derivatives. We point out the presence of nonanalyticity effects in other quantities of the model, focusing on the properties of the Libor probability distribution function in a measure in which it is simply related to caplet prices. We show that the moments of this distribution function have nonanalytic dependence on the volatility , which are also similar to a phase transition. We study in some detail the pricing of caplets on Libor rates and Libor payments in arrears, and show that the convexity adjustment for the latter is also nonanalytic in the model volatility .
Revised:  Here we discuss the implications of these results for the pricing of interest rate derivatives. In the large volatility phase, certain expectation values and convexity adjustments have an explosive behavior. For sufficiently low volatilities the caplet smile is log-normal to a very good approximation, while in the large volatility phase the latter is also nonanalytic in the model volatility .
Edit intent: meaning-changed
Original:  We study in some detail the pricing of caplets on Libor rates and Libor payments in arrears, and show that the convexity adjustment for the latter is also nonanalytic in the model volatility .
Revised:  We study in some detail the pricing of caplets on Libor rates and Libor payments in arrears, and show that the convexity adjustment for the model develops a non-trivial caplet skew. The phenomenon discussed here imposes thus an upper limit on the volatilities for which the model behaves as intended .
Edit intent: meaning-changed
Original: We study an asymptotic behaviour of the difference between value-at-risks VaR(L) and VaR(L+S) for heavy-tailed random variables L and S as an application to  sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II.
Revised: We study the asymptotic behaviour of the difference between value-at-risks VaR(L) and VaR(L+S) for heavy-tailed random variables L and S as an application to sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II.
Edit intent: fluency
Original: We study an asymptotic behaviour of the difference between value-at-risks VaR(L) and VaR(L+S) for heavy-tailed random variables L and S as an application to  sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II.
Revised: We study an asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy-tailed random variables L and S as an application to sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II.
Edit intent: fluency
Original: We study an asymptotic behaviour of the difference between value-at-risks VaR(L) and VaR(L+S) for heavy-tailed random variables L and S as an application to  sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II.
Revised: We study an asymptotic behaviour of the difference between value-at-risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II.
Edit intent: fluency
Original: We study an asymptotic behaviour of the difference between value-at-risks VaR(L) and VaR(L+S) for heavy-tailed random variables L and S as an application to  sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II.
Revised: We study an asymptotic behaviour of the difference between value-at-risks VaR(L) and VaR(L+S) for heavy-tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II.
Edit intent: fluency
Original:   We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then  VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Revised:  Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor. We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Edit intent: meaning-changed
Original:   We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then  VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Revised:  We have different types of results according to the magnitude of the relationship of the thicknesses of the tails of L and S. Especially if the tail of S is enough thinner than the one of L, then VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Edit intent: clarity
Original:   We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then  VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Revised:  We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is sufficiently thinner than that of L, then VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Edit intent: clarity
Original:   We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then  VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Revised:  We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then the difference between prior and posterior risk amounts VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Edit intent: meaning-changed
Original:   We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then  VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent .
Revised:  We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then VaR(L+S) - VaR(L) is asymptotically equivalent to the component VaR of S (which is equal to the expected loss of S when L and S are independent .
Edit intent: meaning-changed
Original:   We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then  VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent . We also give some generalized results without the assumption of independence .
Revised:  We have different types of results according to the magnitude relationship of thickness of tails of L and S. Especially if the tail of S is enough thinner than the one of L, then VaR(L+S) - VaR(L) is asymptotically equivalent to an expected loss of S when L and S are independent ) .
Edit intent: coherence
Original:  In regenerative medicine, one approach to generate 3D tissues has been to seed a porous scaffold with cells and growth factors under pulsatile flow conditions.
Revised:  In particular, the homeostasis of blood vessels is dependent on shear stress. To mimic this process ex vivo, efforts have been made to seed scaffolds with vascular and other cell types in the presence of growth factors and under pulsatile flow conditions.
Edit intent: meaning-changed
Original:  However, the resulting bioreactors frequently lack information on intrinsic shear stress and flow distributions . Recent studies have focused on optimizing the microstructural parameters of the scaffold to gain more control over the shear stress.
Revised:  However, the resulting bioreactors lack information on intrinsic shear stress and flow distributions . Recent studies have focused on optimizing the microstructural parameters of the scaffold to gain more control over the shear stress.
Edit intent: clarity
Original:  However, the resulting bioreactors frequently lack information on intrinsic shear stress and flow distributions . Recent studies have focused on optimizing the microstructural parameters of the scaffold to gain more control over the shear stress.
Revised:  However, the resulting bioreactors frequently lack information on shear stress and flow distributions . Recent studies have focused on optimizing the microstructural parameters of the scaffold to gain more control over the shear stress.
Edit intent: clarity
Original:  However, the resulting bioreactors frequently lack information on intrinsic shear stress and flow distributions . Recent studies have focused on optimizing the microstructural parameters of the scaffold to gain more control over the shear stress.
Revised:  However, the resulting bioreactors frequently lack information on intrinsic shear stress and flow distributions within the scaffold. Consequently, it is difficult to interpret the effects of shear stress on cell function. Such knowledge would enable researchers to improve upon cell culture protocols. Recent work has focused on optimizing the microstructural parameters of the scaffold to gain more control over the shear stress.
Edit intent: meaning-changed
Original:  However, the resulting bioreactors frequently lack information on intrinsic shear stress and flow distributions . Recent studies have focused on optimizing the microstructural parameters of the scaffold to gain more control over the shear stress.
Revised:  However, the resulting bioreactors frequently lack information on intrinsic shear stress and flow distributions . Recent studies have focused on optimizing the microstructural parameters of the scaffold to fine tune the shear stress.
Edit intent: clarity
Original:  In this study, we adopt a different approach whereby macroscopic flows are redirected throughout the bioreactor along patterned channels in the scaffold that result in optimized shear stress distributions  .
Revised:  In this study, we adopt a different approach whereby macroscopic flows are redirected throughout the bioreactor along patterned channels in the scaffold that result in optimized shear stress distributions that are optimized for uniformity centered on a target value .
Edit intent: meaning-changed
Original:  Unlike methods based on varying micro-architectures, the described approach has the ability to achieve a target shear stress and to distribute the shear stress uniformly throughout the scaffold. The ability of topology optimization to alter the shear stress distribution is implemented in experiments where magnetic resonance imaging (MRI) is used to image the flow field.
Revised:  The method is validated by experiments using magnetic resonance imaging (MRI) is used to image the flow field.
Edit intent: clarity
Original: The mechanical properties of gram-negative bacteria are governed by a rigid peptidoglycan (PG) cell wall and the turgor pressure generated by the large concentration of solutes in the cytoplasm. The elasticity of the PG has been measured in bulk and in isolated sacculi and shown to be compliant compared to the overall stiffness of the cell itself. However, the stiffness of the cell wall in live cells has not been measured. In particular, the effects that pressure-induced stress might have on the stiffness of the mesh-like PG network have not been addressed even though polymeric materials often exhibit large amounts of stress-stiffening. We study bulging Escherichia coli cells using atomic force microscopy to separate the contributions of the cell wall and turgor pressure to the overall cell stiffness.
Revised: We study intact and bulging Escherichia coli cells using atomic force microscopy to separate the contributions of the cell wall and turgor pressure to the overall cell stiffness.
Edit intent: clarity
Original:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm  ) than in unpressurized saculli .
Revised:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.22 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm ) than in unpressurized saculli .
Edit intent: meaning-changed
Original:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm  ) than in unpressurized saculli .
Revised:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.12 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm ) than in unpressurized saculli .
Edit intent: meaning-changed
Original:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm  ) than in unpressurized saculli .
Revised:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in intact cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm ) than in unpressurized saculli .
Edit intent: clarity
Original:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm  ) than in unpressurized saculli .
Revised:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% = 23\pm \pm ) than in unpressurized saculli .
Edit intent: meaning-changed
Original:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm  ) than in unpressurized saculli .
Revised:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm 8 MPa and 49\pm ) than in unpressurized saculli .
Edit intent: meaning-changed
Original:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm  ) than in unpressurized saculli .
Revised:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm 20 MPa in the axial and circumferential directions ) than in unpressurized saculli .
Edit intent: meaning-changed
Original:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm  ) than in unpressurized saculli .
Revised:  We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.07 \pm 0.25 , such that the wall is significantly stiffer in live cells (E %DIFDELCMD < \sim32\pm10 %%% MPa\pm \pm ) than in unpressurized sacculi .
Edit intent: fluency
Original:  We prove a law of large numbers for the default rate in the pool, which describes the "typical" behavior of defaults . Large deviation arguments are then used to identify the way that atypically large (i.e., "rare") default clusters are most likely to occur. Our results give insights into how different sources of default correlation interact to generate excessive portfolio losses .
Revised:  We prove a law of large numbers for the default rate in the pool, which describes the "typical" behavior of defaults .
Edit intent: clarity
Original: We present the first systematic measurement of critical dynamics of a 2D liquid with conserved order parameter embedded in bulk 3D fluid, here a lipid membrane in water .
Revised: Near a critical point, the time scale of thermally-induced fluctuations diverges in a manner determined by the dynamic universality class. Experiments have verified predicted 3D dynamic critical exponents in many systems, but similar experiments in 2D liquid with conserved order parameter embedded in bulk 3D fluid, here a lipid membrane in water .
Edit intent: meaning-changed
Original: We present the first systematic measurement of critical dynamics of a 2D liquid with conserved order parameter embedded in bulk 3D fluid, here a lipid membrane in water . We use scaling to collapse time dependent structure factors at multiple wavenumbers and find the effective dynamic exponent z_{ eff } \approx 3 near the critical point.
Revised: We present the first systematic measurement of critical dynamics of a 2D liquid with conserved order parameter . Here we analyze time-dependent correlation functions of a quasi-2D lipid bilayer in water to show that its critical dynamics agree with a recently predicted universality class. In particular, the effective dynamic exponent z_{ eff } \approx 3 near the critical point.
Edit intent: meaning-changed