wanyu/IteraTeR_full_sent huggingface dataset:
Original: We demonstrate that the oft-reported enhanced diffusion of certain enzymes at substrate concentrations above biological concentrations, meaning above kM(the Michaelis-Menten constant ), can be accounted for by dissociation of these oligomericenzymes into their smaller subunits.
Revised: We demonstrate that the Michaelis-Menten constant ), can be accounted for by dissociation of these oligomericenzymes into their smaller subunits.
Edit intent: coherence
Original: We demonstrate that the oft-reported enhanced diffusion of certain enzymes at substrate concentrations above biological concentrations, meaning above kM(the Michaelis-Menten constant ), can be accounted for by dissociation of these oligomericenzymes into their smaller subunits.
Revised: We demonstrate that the oft-reported enhanced diffusion of certain enzymes at substrate concentrations above biological concentrations, meaning above kM(the Michaelis-Menten constant . Moreover, many of the enzymes studied experimentally to date are oligomeric. Urease, a hexamer of subunits, has been considered to be the gold standard demonstrating enhanced diffusion. Here we show that urease and certain other oligomeric enzymes of high catalytic activity above kM dissociate into their smaller subunits.
Edit intent: meaning-changed
Original: We demonstrate that the oft-reported enhanced diffusion of certain enzymes at substrate concentrations above biological concentrations, meaning above kM(the Michaelis-Menten constant ), can be accounted for by dissociation of these oligomericenzymes into their smaller subunits. Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text ), and hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions.
Revised: We demonstrate that the oft-reported enhanced diffusion of certain enzymes at substrate concentrations above biological concentrations, meaning above kM(the Michaelis-Menten constant ), can be accounted for by dissociation of these oligomericenzymes into their smaller subunit fragments that diffuse more rapidly, thus providing a simple physical mechanism of enhanced diffusion in this regime of concentrations. Mindful that this conclusion may be controversial, our findings are sup-ported by four independent analytical techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text ), and hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions.
Edit intent: meaning-changed
Original:  Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text ), and hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions.
Revised:  Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chroma-tography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text ), and hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions.
Edit intent: fluency
Original:  Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text ), and hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions.
Revised:  Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) . Data for urease are presented in the main text ), and hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions.
Edit intent: clarity
Original:  Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text ), and hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions.
Revised:  Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text and the con-clusion is validated for hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions.
Edit intent: clarity
Original:  Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text ), and hexokinase and acetylcholinesterase (data shown in the supplementary material), each of which catalyzes different reactions. The conclusion is robust for urease obtained from different sources. For substrate concentrations in the range of biological relevance, meaning below kM , these enzymes do not dissociate and enhanced diffusion is observed nonetheless.
Revised:  Our measurements based on using four independent techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chromatography (SEC), and fluorescence correlation spectroscopy (FCS) , lead to this conclusion for the enzymes urease (emphasized in the main text ), and hexokinase and acetylcholinesterase with data presented in supplementary information. For substrate concentration regimes below kM at which these enzymes do not dissociate and enhanced diffusion is observed nonetheless.
Edit intent: clarity
Original:  For substrate concentrations in the range of biological relevance, meaning below kM , these enzymes do not dissociate and enhanced diffusion is observed nonetheless.
Revised:  For substrate concentrations in the range of biological relevance, meaning below kM , these enzymes do not dissociate , our findings from both FCS and DLS validate that enzymatic catalysis does lead to the enhanced diffusion phenomenon. INTRODUCT
Edit intent: meaning-changed
Original: Extrinsic noise-induced transitions  have been largely investigated in a variety of chemical and physicalsystems. The key properties that make these systems mathematically tractable is that the noise appears linearly in the dynamical equations  and it is assumed Gaussian and white.
Revised: Extrinsic noise-induced transitions have been largely investigated in a variety of chemical , physical, and biological systems. In the standard approach in physical and chemical systems, the key properties that make these systems mathematically tractable is that the noise appears linearly in the dynamical equations and it is assumed Gaussian and white.
Edit intent: meaning-changed
Original:  Here we consider the simplest unit of gene regulation ,  the repressed gene, which is instead characterized by nonlinear dependencies on external parameters , leading to corresponding nonlinear extrinsic noise.
Revised:  In biology, the Gaussian approximation has been successful in specific systems, but the relevant noise being usually non-Gaussian , the repressed gene, which is instead characterized by nonlinear dependencies on external parameters , leading to corresponding nonlinear extrinsic noise.
Edit intent: meaning-changed
Original:  Here we consider the simplest unit of gene regulation ,  the repressed gene, which is instead characterized by nonlinear dependencies on external parameters , leading to corresponding nonlinear extrinsic noise.
Revised:  Here we consider the simplest unit of gene regulation , non-white, and nonlinear poses serious limitations to its general applicability. Here we revisit the fundamental features of linear Gaussian noise, pinpoint its limitations, and review recent new approaches based on nonlinear bounded noises, which highlight novel mechanisms to account for transitions to bimodal behaviour. We do this by considering a simple but fundamental gene expression model, the repressed gene, which is instead characterized by nonlinear dependencies on external parameters , leading to corresponding nonlinear extrinsic noise.
Edit intent: meaning-changed
Original:  Here we consider the simplest unit of gene regulation ,  the repressed gene, which is instead characterized by nonlinear dependencies on external parameters , leading to corresponding nonlinear extrinsic noise. We adopt a general methodology to address this nonlinearity based on assuming that the fluctuations exhibit a finite and large correlation time .
Revised:  Here we consider the simplest unit of gene regulation , the repressed gene, which is instead characterized by nonlinear dependencies on external parameters . We then review a general methodology to address this nonlinearity based on assuming that the fluctuations exhibit a finite and large correlation time .
Edit intent: clarity
Original:  We also relax the Gaussian assumption, and consider the more biologically relevant log-normal noise. We find that in contrast to Gaussian noise, log-normal noise leads to noise-induced transitions, with the system becoming bimodal. We emphasize that no feedback loops are present in the system, and therefore our findings identify a novel class of noise-induced transitions , based on both features of noise nonlinearity and boundedness .
Revised:  We also present a derivation of it, which highlights its dynamical origin. Testing the methodology on the repressed gene confirms that the emergence of noise-induced transitions , based on both features of noise nonlinearity and boundedness .
Edit intent: meaning-changed
Original:  We emphasize that no feedback loops are present in the system, and therefore our findings identify a novel class of noise-induced transitions , based on both features of noise nonlinearity and boundedness .
Revised:  We emphasize that no feedback loops are present in the system, and therefore our findings identify a novel class of noise-induced transitions appears to be strongly dependent on the type of noise adopted, and on the degree of nonlinearity present in the system .
Edit intent: meaning-changed
Original:   A two-step non-parametric bootstrap was used to assess Z and to investigate the log-linear relationship (slope = 1) between Z and input growth parameters, with implications for previous meta-analyses.
Revised:  Estimates for K and Linf were imprecise, but they formed well-structured pairs along a narrow Phi' isopleth, leading to a high precision for Phi'. A two-step non-parametric bootstrap was used to assess Z and to investigate the log-linear relationship (slope = 1) between Z and input growth parameters, with implications for previous meta-analyses.
Edit intent: meaning-changed
Original:  Total mortality was Z = 2.18 y-1 (95\% CI = 1.7 to 4.5 y-1) . Age of captured individuals was 1.5 years (20.9 mm) to 7.0 years (70.0 mm). Recruitment was continuous, with an increase in the dry season .
Revised:  Total mortality was Z = 2.18 y-1 (95\% CI = 1.7 to 4.5 y-1) .
Edit intent: coherence
Original:  This study applies a new bootstrap-based approach, within the fishboot R package, to a case study of blue land crabs (Cardisoma guanhumi) , which are widely harvested and consumed in Brazil . Crabs were sampled at upper mangrove fringes of Itamaraca Island over 12 months, from April 2015.
Revised:  This study applies a new bootstrap-based approach, within the fishboot R package, to a case study of semi-terrestrial crabs (Cardisoma guanhumi) , which are widely harvested and consumed in Brazil . Crabs were sampled at upper mangrove fringes of Itamaraca Island over 12 months, from April 2015.
Edit intent: meaning-changed
Original:  This study applies a new bootstrap-based approach, within the fishboot R package, to a case study of blue land crabs (Cardisoma guanhumi) , which are widely harvested and consumed in Brazil . Crabs were sampled at upper mangrove fringes of Itamaraca Island over 12 months, from April 2015.
Revised:  This study applies a new bootstrap-based approach, within the fishboot R package, to a case study of blue land crabs (Cardisoma guanhumi) . Crabs were sampled at upper mangrove fringes of Itamaraca Island over 12 months, from April 2015.
Edit intent: coherence
Original:  Seasonality in growth was significant (p = 0.006, 95\% CI for C: 0.15 to 0.93  ). Estimates for K and Linf were imprecise, but they formed well-structured pairs along a narrow Phi' isopleth, leading to a high precision for Phi'.
Revised:  Seasonality in growth was significant (p = 0.006, 95\% CI for C: 0.15 to 0.93 , median: C = 0.56 ). Estimates for K and Linf were imprecise, but they formed well-structured pairs along a narrow Phi' isopleth, leading to a high precision for Phi'.
Edit intent: meaning-changed
Original: Tagging and length-frequency analysis (LFA) are common methods to study body growth and mortality in populations of fish and invertebrates, but little has been done to assess and compare the inherent uncertainty of these methods.
Revised: The land crab Cardisoma guanhumi Latreille, 1828 is harvested in several countries in Latin America, and a critically endangered species. This is the first study to conduct bootstrapped tagging analysis (BTA) together with bootstrapped length-frequency analysis (LFA) are common methods to study body growth and mortality in populations of fish and invertebrates, but little has been done to assess and compare the inherent uncertainty of these methods.
Edit intent: meaning-changed
Original: Tagging and length-frequency analysis (LFA) are common methods to study body growth and mortality in populations of fish and invertebrates, but little has been done to assess and compare the inherent uncertainty of these methods. This study applies a new bootstrap-based approach, within the fishboot R package, to a case study of semi-terrestrial crabs (Cardisoma guanhumi) .
Revised: Tagging and length-frequency analyses (BLFA) .
Edit intent: clarity
Original:  Crabs were sampled at upper mangrove fringes of Itamaraca Island over 12 months, from April 2015. A total of 1,078 individuals were captured and measured. Among these, 291 individuals were marked with PIT tags.
Revised:  Crabs were sampled at upper mangrove fringes of Itamaraca Island over 12 months, and marked with PIT tags.
Edit intent: clarity
Original:  130 size increments were obtained. Carapace widths varied from 20.9 to 70.0 mm (mean: 43.4 mm). Tagging and LFA produced similar median parameter estimates, but tagging produced significantly (p < 0.0001) narrower 95\% CIs. Estimates for K from tagging were 3 times more precise than from LFA, estimates for Linf were 2.2 times more precise. Both methods indicate very slow growth and Linf far above Lmax.
Revised:  Both methods (BTA and BLFA) indicate very slow growth and Linf far above Lmax.
Edit intent: clarity
Original:  Tagging-based estimates were K = 0.12 y-1 (95\% CI: 0.024 to 0.26 y-1), Linf = 118 mm (95\% CI: 81 to 363 mm), Phi' = 1.23 log10(cm y-1) (95\% CI: 0.86 to 1.36 log10(cm y-1)).
Revised:  BTA estimates were K = 0.12 y-1 (95\% CI: 0.024 to 0.26 y-1), Linf = 118 mm (95\% CI: 81 to 363 mm), Phi' = 1.23 log10(cm y-1) (95\% CI: 0.86 to 1.36 log10(cm y-1)).
Edit intent: clarity
Original:  Estimates for K and Linf were imprecise, but they formed well-structured pairs along a narrow Phi' isopleth, leading to a high precision for Phi'. A two-step non-parametric bootstrap was used to assess Z and to investigate the log-linear relationship (slope = 1) between Z and input growth parameters, with implications for previous meta-analyses.
Revised:  Estimates for K and Linf were imprecise, but they formed well-structured pairs along a narrow Phi' isopleths.
Edit intent: coherence
Original:  We further make an investigation of several different c-TextGen techniques, and illustrate the advantages and disadvantages of commonly used neural network models . Finally, we discuss the open issues and promising research directions of c-TextGen .
Revised:  We further make an investigation of several different c-TextGen techniques, and illustrate the advantages and disadvantages of commonly used neural network models . Finally, we discuss the open issues and promising research directions of CTG .
Edit intent: clarity
Original: Within the well-known framework of financial portfolio optimization, we analyze the existing relationships between the condition of arbitrage and the utility maximization in presence of insider information. That is, we assume that, since the initial time, the information flow is altered by adding the knowledge of an additional random variable including future information.
Revised: Within the well-known framework of financial portfolio optimization, we analyze the existing relationships between the condition of arbitrage and the utility maximization in presence of insider information assume that, since the initial time, the information flow is altered by adding the knowledge of an additional random variable including future information.
Edit intent: coherence
Original:  By "learning" URLanizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning can help delineate a high-dimensional structure . We hypothesize that the shape of this structure conveys inner dynamics information via the novel three-dimensional (3D) image.
Revised:  By "learning" URLanizing the subtle change of waveform morphology from large amounts of raw waveform data, unsupervised manifold learning can help delineate a high-dimensional structure and display it as a novel three-dimensional (3D) image.
Edit intent: clarity
Original:  For non-ST-elevation ECG obtained from public database, a classification tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented . Support vector machine (SVM) shows classification accuracyof 99.8\%.
Revised:  We model each beat or pulse to be a point lying on a manifold, like a surface, and use the diffusion map (DMap) to establish the relationship among those pulses. For ECG datasets, first we analyzed the non-ST-elevation ECG obtained from public database, a classification tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented . Support vector machine (SVM) shows classification accuracyof 99.8\%.
Edit intent: meaning-changed
Original:  For non-ST-elevation ECG obtained from public database, a classification tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented . Support vector machine (SVM) shows classification accuracyof 99.8\%.
Revised:  For non-ST-elevation ECG waveform distribution from unstable angina to healthy control, and we investigated intraoperative ST-elevation ECG waveforms to show the dynamic ECG waveform changes. For ABP datasets, we analyzed waveforms collected under endotracheal intubation and administration of vasodilator. To quantify the dynamic separation, we applied the support vector machine (SVM) analysis and the trajectory analysis. For the non-ST-elevation ECG, a hierarchical tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented . Support vector machine (SVM) shows classification accuracyof 99.8\%.
Edit intent: meaning-changed
Original:  For non-ST-elevation ECG obtained from public database, a classification tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented . Support vector machine (SVM) shows classification accuracyof 99.8\%. Manifold learning in ST-elevation ECG waveform analysis helps quantify the evolving direction of  ST-elevation myocardial episode in a 1-hour period  .
Revised:  For non-ST-elevation ECG obtained from public database, a classification tree structure comprising consecutive ECG waveforms spanning from unstable angina to healthy control is presented in the 3D image (accuracy=97.6\%, macro-F1=96.1\%). The DMap helps quantify and visualize the evolving direction of ST-elevation myocardial episode in a 1-hour period .
Edit intent: meaning-changed
Original:  Manifold learning in ST-elevation ECG waveform analysis helps quantify the evolving direction of  ST-elevation myocardial episode in a 1-hour period  .
Revised:  Manifold learning in ST-elevation ECG waveform analysis helps quantify the evolving direction of ST-elevation myocardial episode in a 1-hour period (accuracy=97.58\%, macro-F1=96.06\%) .
Edit intent: meaning-changed
Original:  The ABP waveform analysis of Nicardipine administration shows inter-individual difference ( SVM classification accuracy= 99.3\% ) and their common directions from intra-individual moving trajectories.
Revised:  The ABP waveform analysis of Nicardipine administration shows inter-individual difference ( SVM classification accuracy= 95.01\%, macro-F1=96.9\% ) and their common directions from intra-individual moving trajectories.
Edit intent: meaning-changed
Original:  The dynamic change of  ABP waveform during endo-tracheal intubation shows a loop-like trajectory structure, which can be further classified using the knowledge of antihypertensive medication.
Revised:  The dynamic change of the ABP waveform during endo-tracheal intubation shows a loop-like trajectory structure, which can be further classified using the knowledge of antihypertensive medication.
Edit intent: fluency
Original:  The dynamic change of  ABP waveform during endo-tracheal intubation shows a loop-like trajectory structure, which can be further classified using the knowledge of antihypertensive medication. The application of manifold learning in ECG and ABP waveform analysis provides clues of acute coronary syndrome diagnosis, predicts clinical course in myocardial ischemic episode and reveals underneath physiological mechanism via 3D visualization .
Revised:  The dynamic change of ABP waveform during endo-tracheal intubation shows a loop-like trajectory structure, which can be further classified using the knowledge obtained from Nicardipine. The 3D visualization .
Edit intent: meaning-changed
Original:  The application of manifold learning in ECG and ABP waveform analysis provides clues of acute coronary syndrome diagnosis, predicts clinical course in myocardial ischemic episode and reveals underneath physiological mechanism via 3D visualization .
Revised:  The application of manifold learning in ECG and ABP waveform analysis provides clues of acute coronary syndrome diagnosis, predicts clinical course in myocardial ischemic episode and reveals underneath physiological mechanism via 3D images provide clues of underneath physiological mechanisms .
Edit intent: clarity