wanyu/IteraTeR_full_sent huggingface dataset:
Original:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies in the form i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing then, increasing in an S-shaped curve.
Revised:  For example, with an increase in the binding rate of target proteins , the signal flow varies in the form i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing then, increasing in an S-shaped curve.
Edit intent: clarity
Original:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies in the form i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing then, increasing in an S-shaped curve.
Revised:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies by i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing then, increasing in an S-shaped curve.
Edit intent: clarity
Original:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies in the form i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing then, increasing in an S-shaped curve.
Revised:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies in the form i) monotonically increasing ; ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing then, increasing in an S-shaped curve.
Edit intent: fluency
Original:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies in the form i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing then, increasing in an S-shaped curve.
Revised:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies in the form i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve ; or iii) increasing, decreasing then, increasing in an S-shaped curve.
Edit intent: fluency
Original:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies in the form i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing then, increasing in an S-shaped curve.
Revised:  For example, with an increase in the binding rate of target proteins to the membrane , the signal flow varies in the form i) monotonically increasing , ii) increasing then decreasing in a bell-shaped curve , or iii) increasing, decreasing , then increasing in an S-shaped curve.
Edit intent: fluency
Original:  In particular, when the system exhibits  large signal flow, the signaling proteins tend to surround the receptors  , and the target proteins tend to become distributed around the receptor--signaling protein clusters.
Revised:  In particular, when the system exhibits large signal flow, the signaling proteins tend to surround the receptors to form receptor-signaling protein clusters , and the target proteins tend to become distributed around the receptor--signaling protein clusters.
Edit intent: meaning-changed
Original:  We show that, according to a widely implemented model for DNA denaturation, one can engineer DNA "heattronic" devices that have a rapidly increasing thermal conductance over a narrow temperature range across the denaturation transition (~ 350K ).
Revised:  We show that, according to a widely implemented model for DNA denaturation, one can engineer DNA "heattronic" devices that have a rapidly increasing thermal conductance over a narrow temperature range across the denaturation transition (~ 350 K ).
Edit intent: fluency
Original:  The origin of the switching behavior is the release of the base pairs from their confining potential as DNA denatures, which both softens the lattice and suppresses nonlinear effects , increasing the conductance.
Revised:  The origin of the switching behavior is the release of the base pairs from their confining potential as DNA denatures, which both softens the lattice and suppresses nonlinear effects and thus increases the thermal conductance.
Edit intent: coherence
Original:  The origin of the switchingbehavior is the release of the base pairs from their confining potential as DNA denatures, which both softens the lattice and suppresses nonlinear effects and thus increases the thermal conductance .
Revised:  The origin of this rapid increase of conductance, or "switching", is the softening of the lattice and suppression of nonlinear effects as the temperature crosses the transition temperature and DNA denatures .
Edit intent: clarity
Original: We consider  a group of Bayesian agents who each possess an independent private signal about an unknown state of the world . We study the question of efficient learning: in which games is private information efficiently disseminated among the agents? In particular, we explore the notion of asymptotic learning, which is said to occur when agents learn the state of the world with probability that approaches one as the number of agents tends to infinity. We show that under general conditions asymptotic learning follows from agreement on posterior actions or posterior beliefs, regardless of the information exchange dynamics .
Revised: We consider a group of Bayesian agents who each possess an independent private signal about an unknown state of the world , observe private signals, share the same utility function, and act in a general dynamic setting. We introduce Social Learning Equilibria, a static equilibrium concept that abstracts away from the details of the given dynamics, but nevertheless captures the corresponding asymptotic equilibrium behavior. We establish strong equilibrium properties on agreement, herding, and information aggregation .
Edit intent: meaning-changed
Original:  Specifically we identify the `smallest CFSTR atoms of multistationarity', namely those containing one non-flow reaction, which may be irreversible or reversible. We will refer to such atoms as one-reaction atoms of multistationarity. CFSTR atoms of multistationarity were introduced in the recent work of Joshi and Shiu. We recall that a fully open network (alternatively a fully open CFSTR) is a chemical reaction network where all chemical species participate in the inflow and the outflow;
Revised:  We consider the set of fully open networks, those for which all chemical species participate in the inflow and the outflow;
Edit intent: clarity
Original:  We recall that a fully open network (alternatively a fully open CFSTR) is a chemical reaction network where all chemical species participate in the inflow and the outflow; and a CFSTR atom of multistationarity N is a multistationary fully open network such that the operation of `removing reactions' or `removing species' destroys the multistationarity of N. The class of CFSTR atoms of multistationarity determines the entire class of multistationary fully open networks since possessing a CFSTR atom of multistationarity as an `embedded network' (N is embedded in G if N can be obtained from G by removing a finite subset of reactions and species) was proven recently to be a sufficient condition for a fully open network to admit multistationarity.
Revised:  We recall that a fully open network (alternatively a fully open CFSTR) is a chemical reaction network where all chemical species participate in inflow and outflow, containing one non-flow (reversible or irreversible) reaction. We show that such a network admits multiple positive mass-action steady states if and only if the stoichiometric coefficients in the non-flow reaction satisfy a certain simple arithmetic relation. The multistationary fully open networks since possessing a CFSTR atom of multistationarity as an `embedded network' (N is embedded in G if N can be obtained from G by removing a finite subset of reactions and species) was proven recently to be a sufficient condition for a fully open network to admit multistationarity.
Edit intent: meaning-changed
Original:  The class of CFSTR atoms of multistationarity determines the entire class of multistationary fully open networks since possessing a CFSTR atom of multistationarity as an `embedded network' (N is embedded in G if N can be obtained from G by removing a finite subset of reactions and species) was proven recently to be a sufficient condition for a fully open network to admit multistationarity. We find that there are infinitely many one-reaction atoms, however the set of such atoms can be characterized using two types, each type containing two parameters. The first type contains one chemical species while the second type contains two chemical species; while both types contain one irreversible reaction. We identify both types with the chemical process of autocatalysis.
Revised:  The class of CFSTR atoms of multistationarity determines the entire class of multistationary fully open one-reaction networks are identified with the chemical process of autocatalysis.
Edit intent: clarity
Original:  Furthermore, we give a complete classification by multistationarity of all fully open networks which contain one non-flow (possibly reversible) reaction. Moreover, we obtain new sufficient conditions for establishing multistationarity of certain fully open networks  beyond the one-reaction setting.
Revised:  Using the notion of `embedded network' defined recently by Joshi and Shiu, we provide new sufficient conditions for establishing multistationarity of certain fully open networks beyond the one-reaction setting.
Edit intent: meaning-changed
Original:  It is found that these criteria are satisfied for the experimental data analyzed, confirming that data taken with different constraint origins are sampling the same effective energy surface .
Revised:  It is found that these criteria can detect a situation where the energy is not a single-valued function of the measured reaction coordinate. The criteria were found to be satisfied for the experimental data analyzed, confirming that data taken with different constraint origins are sampling the same effective energy surface .
Edit intent: meaning-changed
Original:  In passing, we have rederived and made more precise the result of [   P. L. Krapivsky and S. Redner, Am.
Revised:  In passing, we have rederived and made more precise the result of [ L. Turban, J. Phys. A 25, 127 (1992) P. L. Krapivsky and S. Redner, Am.
Edit intent: meaning-changed
Original:  These equations are the natural candidates for the probabilistic interpretation of  fully non-linear partial integro-differential equations, which is the point of our paper 18%DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty.
Revised:  These equations are a natural candidate for the probabilistic interpretation of fully non-linear partial integro-differential equations, which is the point of our paper 18%DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty.
Edit intent: fluency
Original:  These equations are the natural candidates for the probabilistic interpretation of  fully non-linear partial integro-differential equations, which is the point of our paper 18%DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty.
Revised:  These equations are the natural candidates for the probabilistic interpretation of some fully non-linear partial integro-differential equations, which is the point of our paper 18%DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty.
Edit intent: clarity
Original:  These equations are the natural candidates for the probabilistic interpretation of  fully non-linear partial integro-differential equations, which is the point of our paper 18%DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty.
Revised:  These equations are the natural candidates for the probabilistic interpretation of fully non-linear partial integro-differential equations, which is the point of 18%DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty.
Edit intent: coherence
Original:  These equations are the natural candidates for the probabilistic interpretation of  fully non-linear partial integro-differential equations, which is the point of our paper 18%DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty.
Revised:  These equations are the natural candidates for the probabilistic interpretation of fully non-linear partial integro-differential equations, which is the point of our paper %DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty.
Edit intent: fluency
Original:  These equations are the natural candidates for the probabilistic interpretation of  fully non-linear partial integro-differential equations, which is the point of our paper 18%DIFDELCMD < ]%%% . Finally, we give an application of second order BSDEs to the study of a robust exponential utility maximization problem under model uncertainty. The uncertainty affects both the volatility process and the jump measure compensator. We prove existence of an optimal strategy, and that the value function of the problem is the unique solution of a particular second order BSDE with jumps .
Revised:  These equations are the natural candidates for the probabilistic interpretation of fully non-linear partial integro-differential equations, which is the point of our paper 18%DIFDELCMD < ]%%% the second part of this work. We prove a non-linear Feynman-Kac formula and show that solutions to 2BSDEJs provide viscosity solutions of the associated PIDEs .
Edit intent: meaning-changed
Original:  The motivating examples are certain families of reaction networks --- including reversible, weakly reversible, endotactic, and "strongly endotactic " reaction networks --- that give rise to vertexical families of mass-action differential inclusions.
Revised:  The motivating examples are certain families of reaction networks -- including reversible, weakly reversible, endotactic, and "strongly endotactic " reaction networks --- that give rise to vertexical families of mass-action differential inclusions.
Edit intent: fluency
Original:  The motivating examples are certain families of reaction networks --- including reversible, weakly reversible, endotactic, and "strongly endotactic " reaction networks --- that give rise to vertexical families of mass-action differential inclusions.
Revised:  The motivating examples are certain families of reaction networks --- including reversible, weakly reversible, endotactic, and strongly endotactic reaction networks -- that give rise to vertexical families of mass-action differential inclusions.
Edit intent: fluency
Original: Progress in signal transduction research has been restricted by the use of differential equation modeling which produces high complexity for the analysis and comparison even of small-size systems. Other approaches are usually not directly compatible with mass-action kinetic reaction models.
Revised: We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic reaction models.
Edit intent: meaning-changed
Original:  Other approaches are usually not directly compatible with mass-action kinetic reaction models. We suggest to analyze concentration changes as input-dependent response functions and separately calculate delays until steady states are reached.
Revised:  Other approaches are usually not directly compatible with mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent response functions and separately calculate delays until steady states are reached.
Edit intent: meaning-changed
Original:  We suggest to analyze concentration changes as input-dependent response functions and separately calculate delays until steady states are reached. This allows to generate pre-computed matrices as transfer functions for any range of inputs ('protein signaling functions' (psfs)). They can be used to perform discrete dynamical simulations, but most importantly, these matrices offer an unprecedented range of possibilities to analyze seemingly complex systems in a simple manner. We show for instance how we can predict active vs. inactive signal transmission links, so that we know which units in the network will respond to input. The results correspond closely with biological knowledge .
Revised:  We suggest to analyze concentration changes as input-dependent signal transmission inactivation .
Edit intent: clarity
Original:  Our main result is that the appropriate maps of graphs called graph fibrations give rise to maps of the appropriate dynamical systems and thereby to conserved quantities .
Revised:  Our main result is that the appropriate maps of graphs called graph fibrations give rise to maps of the appropriate dynamical systems . Consequently surjective graph fibrations give rise to invariant subsystems and injective graph fibrations give rise to projections of dynamical systems .
Edit intent: meaning-changed
Original: Consistently predicting protein structure at atomic resolution from sequence alone remains an unsolved challenge in computational biophysics. Even small puzzles involving protein loops excised out of crystallographic structures can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased.
Revised: Consistently predicting protein structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design, can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased.
Edit intent: meaning-changed
Original:  This article presents a "stepwise ansatz" for recursively enumerating a physically realistic subspace of protein conformations, implemented as a stepwise assembly (SWA) protocol in the Rosetta framework.
Revised:  This article introduces a modeling strategy based on a 'stepwise ansatz', recently developed for RNA modeling, which posits that any realistic all-atom molecular conformation can be built up by residue-by-residue stepwise enumeration. When harnessed to a dynamic-programming-like recursion in the Rosetta framework, the resulting stepwise assembly (SWA) protocol in the Rosetta framework.
Edit intent: meaning-changed
Original:  This article presents a "stepwise ansatz" for recursively enumerating a physically realistic subspace of protein conformations, implemented as a stepwise assembly (SWA) protocol in the Rosetta framework. For 32 of 40 loops that challenged prior approaches, at least one of five lowest energy SWA models agrees with the crystallographic conformation with C \alpha%DIFDELCMD < } %%% RMSD accuracy better than 1.0 \AA%DIFDELCMD < }%%% . SWA successes include hairpin-like loops , cis-Pro touch turns, loops with lengths of up to 24 residues (well outside the range of previous methods), and five blind tests:
Revised:  This article presents a "stepwise ansatz" for recursively enumerating a physically realistic subspace of protein conformations, implemented as a stepwise assembly (SWA) protocol \alpha%DIFDELCMD < } %%% RMSD accuracy better than 1.0 \AA%DIFDELCMD < }%%% . SWA successes include hairpin-like loops , cis-Pro touch turns, loops with lengths of up to 24 residues (well outside the range of previous methods), and five blind tests:
Edit intent: coherence
Original:  For 32 of 40 loops that challenged prior approaches, at least one of five lowest energy SWA models agrees with the crystallographic conformation with C \alpha%DIFDELCMD < } %%% RMSD accuracy better than 1.0 \AA%DIFDELCMD < }%%% . SWA successes include hairpin-like loops , cis-Pro touch turns, loops with lengths of up to 24 residues (well outside the range of previous methods), and five blind tests:
Revised:  For 32 of 40 loops that challenged prior approaches, at least one of five lowest energy SWA models agrees with the crystallographic conformation with C %DIFDELCMD < } %%% RMSD accuracy better than 1.0 \AA%DIFDELCMD < }%%% . SWA successes include hairpin-like loops , cis-Pro touch turns, loops with lengths of up to 24 residues (well outside the range of previous methods), and five blind tests:
Edit intent: fluency
Original:  For 32 of 40 loops that challenged prior approaches, at least one of five lowest energy SWA models agrees with the crystallographic conformation with C \alpha%DIFDELCMD < } %%% RMSD accuracy better than 1.0 \AA%DIFDELCMD < }%%% . SWA successes include hairpin-like loops , cis-Pro touch turns, loops with lengths of up to 24 residues (well outside the range of previous methods), and five blind tests:
Revised:  For 32 of 40 loops that challenged prior approaches, at least one of five lowest energy SWA models agrees with the crystallographic conformation with C \alpha%DIFDELCMD < } %%% \AA%DIFDELCMD < }%%% . SWA successes include hairpin-like loops , cis-Pro touch turns, loops with lengths of up to 24 residues (well outside the range of previous methods), and five blind tests:
Edit intent: clarity