wanyu/IteraTeR_full_sent huggingface dataset:
Original:   Homology modeling of PfHop , and of human Hop (HsHop ) in complex with its own cytosolic Hsp90 and Hsp70 C-terminal peptide partners , indicated high conservation of the Hop concave TPR sites bound to the C-terminal motifs of partner proteins.
Revised:  Homology modeling of PfHop and HsHop in complex with its own cytosolic Hsp90 and Hsp70 C-terminal peptide partners , indicated high conservation of the Hop concave TPR sites bound to the C-terminal motifs of partner proteins.
Edit intent: clarity
Original:   Homology modeling of PfHop , and of human Hop (HsHop ) in complex with its own cytosolic Hsp90 and Hsp70 C-terminal peptide partners , indicated high conservation of the Hop concave TPR sites bound to the C-terminal motifs of partner proteins.
Revised:  Homology modeling of PfHop , and of human Hop (HsHop ) in complex with their own cytosolic Hsp90 and Hsp70 C-terminal peptide partners , indicated high conservation of the Hop concave TPR sites bound to the C-terminal motifs of partner proteins.
Edit intent: fluency
Original:   Homology modeling of PfHop , and of human Hop (HsHop ) in complex with its own cytosolic Hsp90 and Hsp70 C-terminal peptide partners , indicated high conservation of the Hop concave TPR sites bound to the C-terminal motifs of partner proteins.
Revised:  Homology modeling of PfHop , and of human Hop (HsHop ) in complex with its own cytosolic Hsp90 and Hsp70 C-terminal peptide partners indicated excellent conservation of the Hop concave TPR sites bound to the C-terminal motifs of partner proteins.
Edit intent: clarity
Original:  These two sites are located on the convex surface of Hop TPR2  and involved in interactions with  Hsp90 middle and C-terminal domains. Motif analysis was combined with phylogenetic trees and structural mapping to investigate the evolutionary conservation of important structural and functional sites of Hop. Statistical coupling analysis identified several sectors of coevolving residues in Hop. The convex sites of interaction between Hop TPR2 and Hsp90 middle and C-terminal domains were found to be part of separately coevolving sectors. This provides further evidence that these convex interaction sites are important to complex formation with Hsp90, and are thus potential sites for inhibitor design (in addition to the concave sites which have been the focus of previous efforts) .
Revised:  These two sites are located on the convex surface of Hop TPR2 and involved in interactions with Hsp90 middle domain. Since the convex sites are less conserved than the concave sites, it makes their potential for malarial inhibitor design extremely attractive (as opposed to the concave sites which have been the focus of previous efforts) .
Edit intent: clarity
Original:  This provides further evidence that these convex interaction sites are important to complex formation with Hsp90, and are thus potential sites for inhibitor design (in addition to the concave sites which have been the focus of previous efforts) . Further the convex sites are less conserved than the concave sites, which make their potential for malarial inhibitor design extremely attractive .
Revised:  This provides further evidence that these convex interaction sites are important to complex formation with Hsp90, and are thus potential sites for inhibitor design (in addition to the concave sites which have been the focus of previous efforts) .
Edit intent: coherence
Original:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the  outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Revised:  We decompose our design into two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Edit intent: clarity
Original:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the  outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Revised:  To this extent, we decompose the problem in two nested optimization steps: the outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Edit intent: coherence
Original:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the  outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Revised:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the inner step characterizes the optimal network operating point subject to an average energy consumption constraint, while the outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Edit intent: meaning-changed
Original:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the  outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Revised:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the outer step provides online energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Edit intent: meaning-changed
Original:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the  outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Revised:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the outer step , we provide optimal energy management policies making the system energetically self-sufficient , given the the statistical description of the energy source .
Edit intent: clarity
Original:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the  outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source .
Revised:  To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the outer step , we provide optimal energy management policies to make the system self-sufficient in the presence of unpredictable and intermittent energy sources .
Edit intent: clarity
Original:  Our framework   permits to gauge the impact of key sensor network parameters, such as the battery size , the harvester size (e.g., solar panel), the information transmission rate and the nodes' duty cycle.
Revised:  Our framework sheds new light into the design of pragmatic schemes for the control of energy harvesting sensor networks permits to gauge the impact of key sensor network parameters, such as the battery size , the harvester size (e.g., solar panel), the information transmission rate and the nodes' duty cycle.
Edit intent: meaning-changed
Original:  Our framework   permits to gauge the impact of key sensor network parameters, such as the battery size , the harvester size (e.g., solar panel), the information transmission rate and the nodes' duty cycle.
Revised:  Our framework and permits to gauge the impact of key sensor network parameters, such as the battery size , the harvester size (e.g., solar panel), the information transmission rate and the nodes' duty cycle.
Edit intent: meaning-changed
Original:  In addition, the closed form solution of the inner optimization problem lends itself to the implementation, as an online algorithm, on computationally constrained embedded devices. Finally, we provide results describing the consequences of most of the relevant design choices in terms of network behavior, maximum achievable throughput and dynamics associated with the optimal energy management policies .
Revised:  We analyze the robustness of the obtained energy management policies in the cases where the nodes have differing energy inflow statistics and where topology changes may occur, devising effective heuristics. Our energy management policies are finally evaluated considering real solar radiation traces, validating them against state of the art solutions and describing the impact of relevant design choices in terms of network behavior, maximum achievable throughput and dynamics associated with the optimal energy management policies .
Edit intent: meaning-changed
Original: The inositol trisphosphate receptor (IPR) is a crucial Ca^{2+ channel that regulates the Ca^{2+} influx from the endoplasmic reticulum (ER) to the cytoplasm.
Revised: The inositol trisphosphate receptor (IPR) is a crucial ion channel that regulates the Ca^{2+} influx from the endoplasmic reticulum (ER) to the cytoplasm.
Edit intent: clarity
Original:  Based on the patch-clamp experimental data obtained from the outer membranes of isolated nuclei of theXenopus oocyte, we construct an allosteric competition model of single IPR channels on their native ER membrane environment.
Revised:  Based on the patch-clamp experimental data obtained from the outer membranes of isolated nuclei of the oocyte, we construct an allosteric competition model of single IPR channels on their native ER membrane environment.
Edit intent: clarity
Original:  Based on the patch-clamp experimental data obtained from the outer membranes of isolated nuclei of theXenopus oocyte, we construct an allosteric competition model of single IPR channels on their native ER membrane environment.
Revised:  Based on the patch-clamp experimental data obtained from the outer membranes of isolated nuclei of theXenopus It has long been observed that the IPR channel is a typical biological system which performs adaptation. However, recent advances on the physical essence of adaptation show that adaptation systems with a negative feedback mechanism, such as the IPR channel, must break detailed balance and always operate out of equilibrium with energy dissipation. Almost all previous IPR models are equilibrium models assuming detailed balance and thus violate the physical essence of adaptation. In this article, we constructed a nonequilibrium allosteric model of single IPR channels on their native ER membrane environment.
Edit intent: meaning-changed
Original:  Based on the patch-clamp experimental data obtained from the outer membranes of isolated nuclei of theXenopus oocyte, we construct an allosteric competition model of single IPR channels on their native ER membrane environment. In our model, each IPR channel consists of four subunits, each of which can exist in two configurations. Each subunit in both configurations has one IP_3 binding site, together with one activating and one inhibitory Ca^{2+ the well-known Monod-Wyman-Changeux allosteric model, we construct our model from the subunit level to the channel level  .
Revised:  Based on the patch-clamp experimental data obtained from the outer membranes of isolated nuclei of theXenopus oocyte, we construct an allosteric competition model of single IPR channels based on the well-known Monod-Wyman-Changeux allosteric model, we construct our model from the subunit level to the channel level .
Edit intent: coherence
Original:  Each subunit in both configurations has one IP_3 binding site, together with one activating and one inhibitory Ca^{2+ the well-known Monod-Wyman-Changeux allosteric model, we construct our model from the subunit level to the channel level  .
Revised:  Each subunit in both configurations has one IP_3 binding site, together with one activating and one inhibitory Ca^{2+ the patch-clamp experimental data obtained from the IPR in the outer membranes of isolated nuclei of the .
Edit intent: meaning-changed
Original:  Each subunit in both configurations has one IP_3 binding site, together with one activating and one inhibitory Ca^{2+ the well-known Monod-Wyman-Changeux allosteric model, we construct our model from the subunit level to the channel level  .
Revised:  Each subunit in both configurations has one IP_3 binding site, together with one activating and one inhibitory Ca^{2+ the well-known Monod-Wyman-Changeux allosteric model, we construct our model from the subunit level to the channel levelXenopus .
Edit intent: meaning-changed
Original:  Each subunit in both configurations has one IP_3 binding site, together with one activating and one inhibitory Ca^{2+ the well-known Monod-Wyman-Changeux allosteric model, we construct our model from the subunit level to the channel level  .
Revised:  Each subunit in both configurations has one IP_3 binding site, together with one activating and one inhibitory Ca^{2+ the well-known Monod-Wyman-Changeux allosteric model, we construct our model from the subunit level to the channel level oocyte .
Edit intent: meaning-changed
Original:  It turns out that our model successfully reproduces the patch-clamp experimental data of the steady-state open probability, the mean close duration, and the bi-exponential distribution of the open duration . Particularly, our model successfully describes the  bimodal [Ca^{2+}]
Revised:  It turns out that our model reproduces the patch-clamp experimental data of the steady-state open probability, the mean close duration, and the bi-exponential distribution of the open duration . Particularly, our model successfully describes the bimodal [Ca^{2+}]
Edit intent: fluency
Original:  It turns out that our model successfully reproduces the patch-clamp experimental data of the steady-state open probability, the mean close duration, and the bi-exponential distribution of the open duration . Particularly, our model successfully describes the  bimodal [Ca^{2+}]
Revised:  It turns out that our model successfully reproduces the patch-clamp experimental data reasonably well and produces both the correct steady-state open probability, the mean close duration, and the bi-exponential distribution of the open duration . Particularly, our model successfully describes the bimodal [Ca^{2+}]
Edit intent: meaning-changed
Original:  It turns out that our model successfully reproduces the patch-clamp experimental data of the steady-state open probability, the mean close duration, and the bi-exponential distribution of the open duration . Particularly, our model successfully describes the  bimodal [Ca^{2+}]
Revised:  It turns out that our model successfully reproduces the patch-clamp experimental data of the steady-state and dynamic properties of the channel . Particularly, our model successfully describes the bimodal [Ca^{2+}]
Edit intent: meaning-changed
Original:  It turns out that our model successfully reproduces the patch-clamp experimental data of the steady-state open probability, the mean close duration, and the bi-exponential distribution of the open duration . Particularly, our model successfully describes the  bimodal [Ca^{2+}]
Revised:  It turns out that our model successfully reproduces the patch-clamp experimental data of the steady-state open probability, the mean close duration, and the bi-exponential distribution of the open duration . Particularly, our model successfully describes the complicated bimodal [Ca^{2+}]
Edit intent: clarity
Original:  dependence of the mean open duration at high [IP_3], a steady-state behavior which fails to be correctly described in previous IPR models , and the adaptation of the IPR channel, an important dynamic behavior which is seldom discussed in previous IPR models. In addition, we find that the gating of the IPR channel is most likely to be a biochemical process that consumes energy.
Revised:  dependence of the mean open duration at high [IP_3], a steady-state behavior which fails to be correctly described in previous IPR models . Finally, we used the patch-clamp experimental data to validate that the IPR channel indeed breaks detailed balance and thus is a nonequilibrium system which consumes energy.
Edit intent: coherence
Original: We fill a void in merging empirical and phenomenological characterisation of the dynamical phase transitions in complex systems by identifying three , essentially different, dynamical phase transitions on real-life financial markets driven by 'macrodynamics' of a superstar-like superhub.
Revised: We fill a void in merging empirical and phenomenological characterisation of the dynamical phase transitions in complex systems by identifying three of them on real-life financial markets driven by 'macrodynamics' of a superstar-like superhub.
Edit intent: clarity
Original: We fill a void in merging empirical and phenomenological characterisation of the dynamical phase transitions in complex systems by identifying three , essentially different, dynamical phase transitions on real-life financial markets driven by 'macrodynamics' of a superstar-like superhub. We collect and interpret the empirical, numerical, and semi-analytical evidences for the existence of these phase transtions , by considering the Frankfurt Stock Exchange (FSE), as a typical example of a financial market of a medium size.
Revised: We fill a void in merging empirical and phenomenological characterisation of the dynamical phase transitions in complex systems by identifying three , essentially different, dynamical phase transitions on real-life financial markets . We extract and interpret the empirical, numerical, and semi-analytical evidences for the existence of these phase transtions , by considering the Frankfurt Stock Exchange (FSE), as a typical example of a financial market of a medium size.
Edit intent: clarity
Original:  We collect and interpret the empirical, numerical, and semi-analytical evidences for the existence of these phase transtions , by considering the Frankfurt Stock Exchange (FSE), as a typical example of a financial market of a medium size.
Revised:  We collect and interpret the empirical, numerical, and semi-analytical evidences for the existence of these phase transitions , by considering the Frankfurt Stock Exchange (FSE), as a typical example of a financial market of a medium size.
Edit intent: fluency
Original:  Using the canonical object for the graph theory, i.e. the Minimal Spanning Tree (MST) network, we observe: (i) The initial phase transition from the equilibrium to non-equilibrium MST network in its nucleation phase, occuring at some critical time 2005-08-11 . Coalescence of edges on the FSE's transient leader SALZGITTER (SZG) AG-Stahl und Technologie company, is observed within the nucleation and is approximately characterised by the Lifsthiz-Slyozov growth exponent;
Revised:  Using the canonical object for the graph theory, i.e. the Minimal Spanning Tree (MST) network, we observe: (i) The initial phase transition from the equilibrium to non-equilibrium MST network in its nucleation phase, occurring at some critical time 2005-08-11 . Coalescence of edges on the FSE's transient leader SALZGITTER (SZG) AG-Stahl und Technologie company, is observed within the nucleation and is approximately characterised by the Lifsthiz-Slyozov growth exponent;
Edit intent: fluency
Original:  Using the canonical object for the graph theory, i.e. the Minimal Spanning Tree (MST) network, we observe: (i) The initial phase transition from the equilibrium to non-equilibrium MST network in its nucleation phase, occuring at some critical time 2005-08-11 . Coalescence of edges on the FSE's transient leader SALZGITTER (SZG) AG-Stahl und Technologie company, is observed within the nucleation and is approximately characterised by the Lifsthiz-Slyozov growth exponent;
Revised:  Using the canonical object for the graph theory, i.e. the Minimal Spanning Tree (MST) network, we observe: (i) The initial phase transition from the equilibrium to non-equilibrium MST network in its nucleation phase, occuring at some critical time . Coalescence of edges on the FSE's transient leader SALZGITTER (SZG) AG-Stahl und Technologie company, is observed within the nucleation and is approximately characterised by the Lifsthiz-Slyozov growth exponent;
Edit intent: clarity
Original:  Using the canonical object for the graph theory, i.e. the Minimal Spanning Tree (MST) network, we observe: (i) The initial phase transition from the equilibrium to non-equilibrium MST network in its nucleation phase, occuring at some critical time 2005-08-11 . Coalescence of edges on the FSE's transient leader SALZGITTER (SZG) AG-Stahl und Technologie company, is observed within the nucleation and is approximately characterised by the Lifsthiz-Slyozov growth exponent;
Revised:  Using the canonical object for the graph theory, i.e. the Minimal Spanning Tree (MST) network, we observe: (i) The initial phase transition from the equilibrium to non-equilibrium MST network in its nucleation phase, occuring at some critical time 2005-08-11 . Coalescence of edges on the FSE's transient leader is observed within the nucleation and is approximately characterised by the Lifsthiz-Slyozov growth exponent;
Edit intent: coherence
Original:  Using the canonical object for the graph theory, i.e. the Minimal Spanning Tree (MST) network, we observe: (i) The initial phase transition from the equilibrium to non-equilibrium MST network in its nucleation phase, occuring at some critical time 2005-08-11 . Coalescence of edges on the FSE's transient leader SALZGITTER (SZG) AG-Stahl und Technologie company, is observed within the nucleation and is approximately characterised by the Lifsthiz-Slyozov growth exponent;
Revised:  Using the canonical object for the graph theory, i.e. the Minimal Spanning Tree (MST) network, we observe: (i) The initial phase transition from the equilibrium to non-equilibrium MST network in its nucleation phase, occuring at some critical time 2005-08-11 . Coalescence of edges on the FSE's transient leader SALZGITTER (SZG) AG-Stahl und Technologie company, is observed within the nucleation and is approximately characterized by the Lifsthiz-Slyozov growth exponent;
Edit intent: fluency
Original:  (ii) The nucleation accelerates and transforms to the condensation process, in the second phase transition, forming a logarithmically diverging lambda-peak of short-range order parameters at the subsequent critical time 2007-01-25 ;
Revised:  (ii) The nucleation accelerates and transforms to the condensation process, in the second phase transition, forming a logarithmically diverging lambda-peak of short-range order parameters at the subsequent critical time - an analogon of such a transition in superfluidity ;
Edit intent: meaning-changed