wanyu/IteraTeR_full_sent huggingface dataset:
Original:  we reveal a quantitative relation between stochastic fluctuations and feedback regulation at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations ] . Based on such relation, we also develop an effective method to extract the topological information of gene regulatory networks from single-cell gene expression data  .
Revised:  we reveal a quantitative relation between stochastic fluctuations and feedback regulation at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations ] . Based on such relation, we also develop an effective method to extract the topological information of gene regulatory networks from single-cell gene expression data via only three statistical quantities: the mean, variance, and decaying rate of the steady-state protein distribution .
Edit intent: meaning-changed
Original:  Here by solving the analytical steady-state protein distribution in a general  model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology , perfecting the results in Phys.
Revised:  Here by solving the analytical steady-state distribution of the protein copy number in a general model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology , perfecting the results in Phys.
Edit intent: meaning-changed
Original:  Here by solving the analytical steady-state protein distribution in a general  model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology , perfecting the results in Phys.
Revised:  Here by solving the analytical steady-state protein distribution in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology , perfecting the results in Phys.
Edit intent: meaning-changed
Original:  Here by solving the analytical steady-state protein distribution in a general  model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology , perfecting the results in Phys.
Revised:  Here by solving the analytical steady-state protein distribution in a general model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology Phys.
Edit intent: clarity
Original:  Here by solving the analytical steady-state protein distribution in a general  model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology , perfecting the results in Phys. Rev. Lett. 113:268105, 2014%DIFDELCMD < ]%%% . In negative-feedback networks, we find a fundamental inequality similar to the Carnot theorem in classical thermodynamics . Based on the fluctuation-feedback relation, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data via only three statistical quantities: the mean, variance, and decaying rate of the steady-state protein distribution .
Revised:  Here by solving the analytical steady-state protein distribution in a general model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology , perfecting the results in %DIFDELCMD < ]%%% . In negative-feedback networks, we find a fundamental inequality similar to the Carnot theorem in classical thermodynamics . Based on the fluctuation-feedback relation, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data via only three statistical quantities: the mean, variance, and decaying rate of the steady-state protein distribution .
Edit intent: coherence
Original:  113:268105, 2014%DIFDELCMD < ]%%% . In negative-feedback networks, we find a fundamental inequality similar to the Carnot theorem in classical thermodynamics . Based on the fluctuation-feedback relation, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data via only three statistical quantities: the mean, variance, and decaying rate of the steady-state protein distribution .
Revised:  113:268105, 2014%DIFDELCMD < ]%%% at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations . Based on the fluctuation-feedback relation, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data via only three statistical quantities: the mean, variance, and decaying rate of the steady-state protein distribution .
Edit intent: meaning-changed
Original:  113:268105, 2014%DIFDELCMD < ]%%% . In negative-feedback networks, we find a fundamental inequality similar to the Carnot theorem in classical thermodynamics . Based on the fluctuation-feedback relation, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data via only three statistical quantities: the mean, variance, and decaying rate of the steady-state protein distribution .
Revised:  113:268105, 2014%DIFDELCMD < ]%%% . In negative-feedback networks, we find a fundamental inequality similar to the Carnot theorem in classical thermodynamics . Based on such relation, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data via only three statistical quantities: the mean, variance, and decaying rate of the steady-state protein distribution .
Edit intent: clarity
Original:  113:268105, 2014%DIFDELCMD < ]%%% . In negative-feedback networks, we find a fundamental inequality similar to the Carnot theorem in classical thermodynamics . Based on the fluctuation-feedback relation, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data via only three statistical quantities: the mean, variance, and decaying rate of the steady-state protein distribution .
Revised:  113:268105, 2014%DIFDELCMD < ]%%% . In negative-feedback networks, we find a fundamental inequality similar to the Carnot theorem in classical thermodynamics . Based on the fluctuation-feedback relation, we also develop an effective method to extract the topological information of a gene regulatory network from single-cell gene expression data .
Edit intent: coherence
Original:  Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal a quantitative relation between stochastic fluctuations and feedback topology at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations.
Revised:  Here by solving the analytical steady-state distribution of the protein copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regulation, we reveal the relationship between stochastic fluctuations and feedback topology at the single-molecule level, which provides novel insights into how and to what extent a feedback loop can enhance or suppress molecular fluctuations.
Edit intent: clarity
Original: Brownian motion has been a pillar of statistical physics for more than a century, and recent high-frequency trading data have shed new light on microstructure of Brownian motion in financial markets. Though evidences of trend-following behaviour of traders were indirectly shown in such trading data, the microscopic model has not been established so far by direct observation of trajectories for individual traders .
Revised: A microscopic model is established for financial Brownian motion from the direct observation of trajectories for individual traders .
Edit intent: clarity
Original:  Though evidences of trend-following behaviour of traders were indirectly shown in such trading data, the microscopic model has not been established so far by direct observation of trajectories for individual traders . In this paper, we present a minimal microscopic model for financial Brownian motion through an intensive analysis of trajectory data for all individuals in a foreign exchange market . This model includes a novel empirical law quantifying traders' trend-following behaviour that can create the inertial motion in market prices over short durations.
Revised:  Though evidences of trend-following behaviour of traders were indirectly shown in such trading data, the microscopic model has not been established so far by direct observation of the dynamics of high-frequency traders (HFTs) in a foreign exchange market. Furthermore, a theoretical framework parallel to molecular kinetic theory is developed for the systematic description of the financial market from microscopic dynamics of HFTs. We report first on a microscopic empirical law of traders' trend-following behaviour that can create the inertial motion in market prices over short durations.
Edit intent: meaning-changed
Original:  This model includes a novel empirical law quantifying traders' trend-following behaviour that can create the inertial motion in market prices over short durations. We present a systematic solution paralleling molecular kinetic theory to reveal mesoscopic and macroscopic dynamics of our model .
Revised:  This model includes a novel empirical law quantifying traders' trend-following behavior by tracking the trajectories of all individuals, which quantifies the collective motion of HFTs but has not been captured in conventional order-book models. We next introduce the corresponding microscopic model of HFTs and present its theoretical solution paralleling molecular kinetic theory to reveal mesoscopic and macroscopic dynamics of our model .
Edit intent: meaning-changed
Original:  We next introduce the corresponding microscopic model of HFTs and present its theoretical solution paralleling molecular kinetic theory: Boltzmann-like and Langevin-like equations are derived from the microscopic dynamics via the BBGKY hierarchy.
Revised:  We next introduce the corresponding microscopic model of HFTs and present its theoretical solution paralleling molecular kinetic theory: Boltzmann-like and Langevin-like equations are derived from the microscopic dynamics via the Bogoliubov-Born-Green-Kirkwood-Yvon hierarchy.
Edit intent: meaning-changed
Original:  More specifically, we first propose an adaptive formula for the Levenberg-Marquardt parameter and analyse the local convergence of the method under H\"{o}lder metric subregularity . We then introduce a bounded version of the Levenberg-Marquardt parameter and analyse the local convergence of the modified method under the \L ojasiewicz gradient inequality  .
Revised:  More specifically, we first propose an adaptive formula for the Levenberg-Marquardt parameter and analyse the local convergence of the method under H\"{o}lder metric subregularity of the function defining the equation and H\"older continuity of its gradient. Further, we analyse the local convergence of the modified method under the \L ojasiewicz gradient inequality .
Edit intent: meaning-changed
Original:  The BR signalling pathway acts by altering the phosphorylation state of its transcription factors BZR1/2, whereas the GA signalling pathway acts by reducing the stability of its transcription factor DELLA. Both signalling pathways include a negative feedback, with high levels of BR causing increased repression of key BR-biosynthetic genes mediated by BZR1/2, and high levels of GA causing decreased stability of DELLA, where DELLA is responsible for activating key genes involved in GA biosynthesis. We derive and analyse new mathematical models for the BR signalling pathway and  the crosstalk between the BR and GA signalling pathways.
Revised:  In this paper we derive and analyse new mathematical models for the BR signalling pathway and the crosstalk between the BR and GA signalling pathways.
Edit intent: coherence
Original:  To analyse the effects of spatial heterogeneity of the signalling processes, along with  ODE models we derive  PDE-ODE systems modelling the temporal and spatial dynamics of molecules involved in the signalling pathways.
Revised:  To analyse the effects of spatial heterogeneity of the signalling processes, along with spatially-homogeneous ODE models we derive PDE-ODE systems modelling the temporal and spatial dynamics of molecules involved in the signalling pathways.
Edit intent: meaning-changed
Original:  To analyse the effects of spatial heterogeneity of the signalling processes, along with  ODE models we derive  PDE-ODE systems modelling the temporal and spatial dynamics of molecules involved in the signalling pathways.
Revised:  To analyse the effects of spatial heterogeneity of the signalling processes, along with ODE models we derive coupled PDE-ODE systems modelling the temporal and spatial dynamics of molecules involved in the signalling pathways.
Edit intent: meaning-changed
Original:  We show that solutions of our models can exhibit oscillatory behaviour depending on the parameters associated with BZR1/2 phosphorylation. Comparison between ODE and PDE-ODE models demonstrates that the spatial heterogeneity in the signalling processes has significant effect only in the case when we have periodic solutions for at least one of the models .
Revised:  We show that solutions of our mathematical model, shown for a wide range of parameters, can be related to the BR homeostasis .
Edit intent: clarity
Original:  Our results for the crosstalk model suggest that the interaction between transcription factors  exerts more influence on the dynamics of the signalling pathways , and that perturbations in the GA signalling pathway have larger effects on the dynamics of components of the BR signalling pathway , apart from in the case when the model for the BR signalling pathway has periodic solutions .
Revised:  Our results for the crosstalk model suggest that the interaction between transcription factors BZR and DELLA exerts more influence on the dynamics of the signalling pathways , and that perturbations in the GA signalling pathway have larger effects on the dynamics of components of the BR signalling pathway , apart from in the case when the model for the BR signalling pathway has periodic solutions .
Edit intent: meaning-changed
Original:  Our results for the crosstalk model suggest that the interaction between transcription factors  exerts more influence on the dynamics of the signalling pathways , and that perturbations in the GA signalling pathway have larger effects on the dynamics of components of the BR signalling pathway , apart from in the case when the model for the BR signalling pathway has periodic solutions .
Revised:  Our results for the crosstalk model suggest that the interaction between transcription factors exerts more influence on the dynamics of the signalling pathways than BZR-mediated biosynthesis of GA, suggesting that the interaction between transcription factors may constitute the principal mechanism of the crosstalk between the BR and GA signalling pathways. In general, perturbations in the GA signalling pathway have larger effects on the dynamics of components of the BR signalling pathway , apart from in the case when the model for the BR signalling pathway has periodic solutions .
Edit intent: meaning-changed
Original:  Our results for the crosstalk model suggest that the interaction between transcription factors  exerts more influence on the dynamics of the signalling pathways , and that perturbations in the GA signalling pathway have larger effects on the dynamics of components of the BR signalling pathway , apart from in the case when the model for the BR signalling pathway has periodic solutions .
Revised:  Our results for the crosstalk model suggest that the interaction between transcription factors exerts more influence on the dynamics of the signalling pathways , and that perturbations in the GA signalling pathway have larger effects on the dynamics of components of the BR signalling pathway than perturbations in the BR signalling pathway on the dynamics of the GA pathway. The perturbation in the crosstalk mechanism also has a larger effect on the dynamics of the BR pathway than of the GA pathway. Large changes in the dynamics of the GA signalling processes can be observed only in the case where there are disturbances in both the BR signalling pathway and the crosstalk mechanism .
Edit intent: clarity
Original: During asymmetric cell divisions, cortical dyneins generate forcesessential to position the spindle after polarity cues, prescribing daughter cells fate. In nematode zygote, cortical dynein pulls on microtubules transiently, raising the question of its targeting and dynamics.
Revised: During asymmetric cell divisions, cortical dyneins generate forcesessential to position the spindle to reflect polarity and ensure correct daughter cell fates. The transient cortical localization of dynein raises the question of its targeting and dynamics.
Edit intent: clarity
Original:  In nematode zygote, cortical dynein pulls on microtubules transiently, raising the question of its targeting and dynamics. Tracking and fluorescence correlation spectroscopy revealed that in the cytoplasm, dynein spots displayed directed motions toward the cortex, localized at microtubule plus-ends through EBP-2 / EB but are not actively transported.
Revised:  In nematode zygote, cortical dynein pulls on microtubules transiently, raising the question of its targeting . We found that it accumulates at the microtubule plus-ends through EBP-2 / EB but are not actively transported.
Edit intent: clarity
Original:  Tracking and fluorescence correlation spectroscopy revealed that in the cytoplasm, dynein spots displayed directed motions toward the cortex, localized at microtubule plus-ends through EBP-2 / EB but are not actively transported.
Revised:  Tracking and fluorescence correlation spectroscopy revealed that in the cytoplasm, dynein spots displayed directed motions toward the cortex, localized at microtubule plus-ends like in budding yeast, indirectly hitch-hiking on \text{EBP-2 / EB but are not actively transported.
Edit intent: meaning-changed
Original:  Tracking and fluorescence correlation spectroscopy revealed that in the cytoplasm, dynein spots displayed directed motions toward the cortex, localized at microtubule plus-ends through EBP-2 / EB but are not actively transported. Surprisingly CLIP-1/CLIP170 is not involved. ebp-2(0) slightly reduced spindle rocking, thus most cortical forcesremain suggesting a redundant mechanism .
Revised:  Tracking and fluorescence correlation spectroscopy revealed that in the cytoplasm, dynein spots displayed directed motions toward the cortex, localized at microtubule plus-ends through EBP-2 / unbinding dynamics as \text{EBP-2 .
Edit intent: clarity
Original:  At the cortex, to relate dynein residency and forces generating, we tracked dynein and found two dynamically distinct populations. One of them would correspond to force generating events. Our experiments also indicated that an asymmetric (polarized) dynein-microbutule on-rate, causes the force imbalance positioning the spindle.
Revised:  At the cortex, dynein tracks can be classified as having either directed or diffusive-like motion. Diffusive-like tracks reveal force-generating dyneins. Their densities are higher on the posterior tip of the embryos, where \text{GPR-1/2 the force imbalance positioning the spindle.
Edit intent: meaning-changed
Original:  Our experiments also indicated that an asymmetric (polarized) dynein-microbutule on-rate, causes the force imbalance positioning the spindle. GPR-1/2 increases the overall dynein density but also residency time (pulling processivity), although this latter was not polarized .
Revised:  Our experiments also indicated that an asymmetric (polarized) dynein-microbutule on-rate, causes the force imbalance reflecting polarity, and supplements the regulation of mitotic progression via the non-polarized detachment rate .
Edit intent: meaning-changed
Original:  Motivated by the rapid recent expansion of so-called Internet of Things (IoT) devices, we are particularly interested in improving the execution time of code running on embedded system architectures. We demonstrate the effectiveness of our approach on code compiled by the GNU C Compiler (GCC) for the ARM Cortex-M3 (CM3) processor;
Revised:  We begin by developing our approach on code compiled by the GNU C Compiler (GCC) for the ARM Cortex-M3 (CM3) processor;
Edit intent: clarity
Original:  and we show how our method outperforms the current industry standard -O3 optimization level across a diverse embedded system benchmark suitecalled BEEBS. We begin by conducting an investigatory study to quantify the potential gains by using existing iterative compilation approaches that time-intensively search for optimal configurations for each given benchmark.
Revised:  and we show how our method outperforms the current industry standard -O3 optimization level across a diverse embedded benchmark suite. First we quantify the potential gains by using existing iterative compilation approaches that time-intensively search for optimal configurations for each given benchmark.
Edit intent: clarity
Original:  Although this is a time consuming process, our approach eventually constructs a simple variation of -O3, which we call -Ocm3, that realizes nearly two thirds of the known available gains on the CM3 architecture (beyond -O3) and significantly outperforms a far more complex state-of-the-art predictive method .
Revised:  Although this is a time-consuming process, our approach eventually constructs a simple variation of -O3, which we call -Ocm3, that realizes nearly two thirds of the known available gains on the CM3 architecture (beyond -O3) and significantly outperforms a far more complex state-of-the-art predictive method .
Edit intent: fluency
Original:  Although this is a time consuming process, our approach eventually constructs a simple variation of -O3, which we call -Ocm3, that realizes nearly two thirds of the known available gains on the CM3 architecture (beyond -O3) and significantly outperforms a far more complex state-of-the-art predictive method .
Revised:  Although this is a time consuming process, our approach constructs an optimized variation of -O3, which we call -Ocm3, that realizes nearly two thirds of the known available gains on the CM3 architecture (beyond -O3) and significantly outperforms a far more complex state-of-the-art predictive method .
Edit intent: clarity
Original:  Although this is a time consuming process, our approach eventually constructs a simple variation of -O3, which we call -Ocm3, that realizes nearly two thirds of the known available gains on the CM3 architecture (beyond -O3) and significantly outperforms a far more complex state-of-the-art predictive method .
Revised:  Although this is a time consuming process, our approach eventually constructs a simple variation of -O3, which we call -Ocm3, that realizes nearly two thirds of known available gains on the CM3 architecture (beyond -O3) and significantly outperforms a far more complex state-of-the-art predictive method .
Edit intent: fluency