wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Recently experimental methods have been developed for counting the number of nascent RNA molecules in a single cell for a gene of interest.
Revised:  One approach has been to measure the transcriptional output (fluorescently labeled messenger RNAs or proteins) from single cells in a genetically identical population, which could then be compared to predictions from models that incorporate different molecular mechanisms of transcription initiation. However, this approach suffers from the problem, that processes downstream from transcription can affect the measured output and therefore mask the signature of stochastic transcription initiation on the cell-to-cell variability of the transcriptional outputs.
Edit intent: coherence
Original:  In particular, we derive exact expressions for the first two moments of the distribution of nascent RNA molecules for an arbitrary mechanism of transcription initiation. These theoretical results are applied to published data on the nascent RNA distributions obtained for a collection of constitutively expressed yeast genes.
Revised:  In particular, we derive exact expressions for the first two moments of the distribution of nascent RNA molecules and apply our theory to published data on the nascent RNA distributions obtained for a collection of constitutively expressed yeast genes.
Edit intent: clarity
Original:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from these single-family EC methods that focus on only a single protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in itself and also (distantly)
Revised:  analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, respectively. This paper presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning . Different from these single-family EC methods that focus on only a single protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in itself and also (distantly)
Edit intent: meaning-changed
Original:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from these single-family EC methods that focus on only a single protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in itself and also (distantly)
Revised:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from existing single-family EC methods that focus on only a single protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in itself and also (distantly)
Edit intent: clarity
Original:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from these single-family EC methods that focus on only a single protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in itself and also (distantly)
Revised:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from these single-family EC analysis that uses residue co-evolution information in only the target protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in itself and also (distantly)
Edit intent: clarity
Original:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from these single-family EC methods that focus on only a single protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in itself and also (distantly)
Revised:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from these single-family EC methods that focus on only a single protein family, our joint EC analysis uses residue co-evolution in itself and also (distantly)
Edit intent: clarity
Original:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from these single-family EC methods that focus on only a single protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in itself and also (distantly) related familieswith divergent sequences but similar folds , and enforcing their co-evolution pattern consistency based upon their evolutionary distance .
Revised:  analysis , which predicts contactsby analyzing residue co-evolution in a single target family, has made good progress due to better statistical and optimization techniques . Different from these single-family EC methods that focus on only a single protein family, this paper presents a joint multi-family EC analysis method that predicts contacts of one target family by jointly modeling residue co-evolution in both the target family and its related families, which may have divergent sequences but similar folds , and enforcing their co-evolution pattern consistency based upon their evolutionary distance .
Edit intent: clarity
Original:  To implement this multi-family EC analysisstrategy, this paper presents a novel joint graphical lasso method to model a set of related protein families . In particular, we model a set of related families using a set of correlated multivariate Gaussian distributions, the inverse covariance matrix (or precision matrix) of each distribution encoding the contact pattern of one family. Then we co-estimate the precision matrices by maximizing the occurring probability of all the involved sequences , subject to the constraint that the  matrices shall share similar patterns .
Revised:  To implement this multi-family EC analysisstrategy, this paper presents a novel joint graphical lasso method to model a set of related protein families using Gaussian graphical models (GGM) and then co-estimate the precision matrices by maximizing the occurring probability of all the involved sequences , subject to the constraint that the matrices shall share similar patterns .
Edit intent: coherence
Original:  Then we co-estimate the precision matrices by maximizing the occurring probability of all the involved sequences , subject to the constraint that the  matrices shall share similar patterns .
Revised:  Then we co-estimate the precision matrices by maximizing the occurring probability of all the involved sequences , subject to the constraint that the matrices shall share similar residue co-evolution patterns. To further improve the accuracy of the estimated precision matrices, we employ a supervised learning method to predict contact probability from a variety of evolutionary and non-evolutionary information and then incorporate the predicted probability as prior into our GGL framework .
Edit intent: meaning-changed
Original:  Finally we solve this optimization problem using Alternating Direction Method of Multipliers (ADMM). Experiments show that joint multi-family EC analysis can reveal many more native contacts than single-family analysis even for a target family with 4000-5000 non-redundant sequence homologs, which makes many more protein families amenable to co-evolution-based structure and function prediction .
Revised:  Experiments show that joint multi-family EC analysis can reveal many more native contacts than single-family analysis even for a target family with 4000-5000 non-redundant sequence homologs, which makes many more protein families amenable to co-evolution-based structure and function prediction .
Edit intent: coherence
Original:  Finally we solve this optimization problem using Alternating Direction Method of Multipliers (ADMM). Experiments show that joint multi-family EC analysis can reveal many more native contacts than single-family analysis even for a target family with 4000-5000 non-redundant sequence homologs, which makes many more protein families amenable to co-evolution-based structure and function prediction .
Revised:  Finally we solve this optimization problem using Alternating Direction Method of Multipliers (ADMM). Experiments show that our method can predict contacts much more accurately than existing methods, and that our method performs better on both conserved and family-specific contacts .
Edit intent: clarity
Original:  To help determine relationships between macromolecular properties and their crystallization propensity, we train statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics Consortium. Gaussian Processes , which capture trends beyond the reach of linear statistical models, help us distinguish between two main physico-chemical mechanisms driving crystallization.
Revised:  To help determine relationships between macromolecular properties and their crystallization propensity, we train statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes , which capture trends beyond the reach of linear statistical models, help us distinguish between two main physico-chemical mechanisms driving crystallization.
Edit intent: fluency
Original:  Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from precursor macromolecular solution conditions , the model offers  avenues for optimizing crystallization screens based on partial structural information or even from primary sequence analyses .
Revised:  Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization , the model offers avenues for optimizing crystallization screens based on partial structural information or even from primary sequence analyses .
Edit intent: clarity
Original:  Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from precursor macromolecular solution conditions , the model offers  avenues for optimizing crystallization screens based on partial structural information or even from primary sequence analyses .
Revised:  Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from precursor macromolecular solution conditions , the model offers future avenues for optimizing crystallization screens based on partial structural information or even from primary sequence analyses .
Edit intent: meaning-changed
Original:  Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from precursor macromolecular solution conditions , the model offers  avenues for optimizing crystallization screens based on partial structural information or even from primary sequence analyses .
Revised:  Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from precursor macromolecular solution conditions , the model offers avenues for optimizing crystallization screens based on partial structural information .
Edit intent: clarity
Original: This research is a review and assay of literature data on the properties of DNA open states . The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA  . A comparative analysis of kinetic and thermodynamic experimental data on  DNA open states has been performed for a wide temperature range .
Revised: This research is a review and assay of literature data on the properties of DNA open states are reviewed and analyzed. These states are formed as a result of strong DNA fluctuations and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA . A comparative analysis of kinetic and thermodynamic experimental data on DNA open states has been performed for a wide temperature range .
Edit intent: meaning-changed
Original:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA  . A comparative analysis of kinetic and thermodynamic experimental data on  DNA open states has been performed for a wide temperature range .
Revised:  The states result from large fluctuations of a duplex and have a great impact on a number of biochemical processes , including electric charge transfer in DNA . A comparative analysis of kinetic and thermodynamic experimental data on DNA open states has been performed for a wide temperature range .
Edit intent: clarity
Original:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA  . A comparative analysis of kinetic and thermodynamic experimental data on  DNA open states has been performed for a wide temperature range .
Revised:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes ; among them is charge transfer in DNA . A comparative analysis of kinetic and thermodynamic experimental data on DNA open states has been performed for a wide temperature range .
Edit intent: clarity
Original:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA  . A comparative analysis of kinetic and thermodynamic experimental data on  DNA open states has been performed for a wide temperature range .
Revised:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA , for example . A comparative analysis of kinetic and thermodynamic experimental data on DNA open states has been performed for a wide temperature range .
Edit intent: coherence
Original:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA  . A comparative analysis of kinetic and thermodynamic experimental data on  DNA open states has been performed for a wide temperature range .
Revised:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA . A comparative analysis of experimental data on DNA open states has been performed for a wide temperature range .
Edit intent: clarity
Original:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA  . A comparative analysis of kinetic and thermodynamic experimental data on  DNA open states has been performed for a wide temperature range .
Revised:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA . A comparative analysis of kinetic and thermodynamic experimental data on the kinetics and thermodynamics of DNA open states has been performed for a wide temperature range .
Edit intent: clarity
Original:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA  . A comparative analysis of kinetic and thermodynamic experimental data on  DNA open states has been performed for a wide temperature range .
Revised:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA . A comparative analysis of kinetic and thermodynamic experimental data on DNA open states for a wide temperature range .
Edit intent: clarity
Original:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA  . A comparative analysis of kinetic and thermodynamic experimental data on  DNA open states has been performed for a wide temperature range . Apparent contradictions between the data of different experiments have been explained.
Revised:  The states result from large fluctuations of a duplex and have a great influence on a wide range of biochemical processes , including electric charge transfer in DNA . A comparative analysis of kinetic and thermodynamic experimental data on DNA open states has been performed for a wide temperature range was carried out. Discrepancies between the results of various experiments have been explained.
Edit intent: clarity
Original: This paper addresses the question of non-arbitrage (precisely No-Unbounded-Profit-with-Bounded-Risk , NUPBR hereafter) after a specific random time.
Revised: This paper quantifies the interplay between the non-arbitrage (precisely No-Unbounded-Profit-with-Bounded-Risk , NUPBR hereafter) after a specific random time.
Edit intent: meaning-changed
Original:  \mbox{%DIFAUXCMD aksamit/choulli/deng/jeanblanc ]  the part after the random time under consideration.
Revised:  \mbox{%DIFAUXCMD aksamit/choulli/deng/jeanblanc ] in which the authors studied similar topics for the case of stopping at the random time instead, while herein we are concerned with the part after the random time under consideration.
Edit intent: meaning-changed
Original:  \mbox{%DIFAUXCMD aksamit/choulli/deng/jeanblanc ]  the part after the random time under consideration. We restrict our attention to honest times, and we characterize the pairs of market and honest time for which the resulting model preserves the NUPBR property .
Revised:  \mbox{%DIFAUXCMD aksamit/choulli/deng/jeanblanc ] the part after the occurrence of the random time. Given that all the literature -up to our knowledge- proves that the NUPBR notion is always violated after honest times that avoid stopping times in a continuous filtration, herein we propose a new class of honest times for which the NUPBR notion can be preserved for some models. For this family of honest times, we elaborate two principal results. The first main result characterizes the pairs of initial market and honest time for which the resulting model preserves the NUPBR property .
Edit intent: meaning-changed
Original:  We restrict our attention to honest times, and we characterize the pairs of market and honest time for which the resulting model preserves the NUPBR property . Furthermore, we characterize the honest times that preserve the NUPBR property .
Revised:  We restrict our attention to honest times, and we characterize the pairs of market and honest time for which the resulting model preserves the NUPBR property , while the second main result characterizes the honest times that preserve the NUPBR property .
Edit intent: meaning-changed
Original:  We assume that all real entities are embedded in the same space, which means they have the same dimension. We model the lack of information for a given object as  affine subspace in R^d with dimension k . Our goal in this paper is to find clusters of objects .
Revised:  We model the lack of information for a given object as affine subspace in R^d with dimension k . Our goal in this paper is to find clusters of objects .
Edit intent: coherence
Original:  We model the lack of information for a given object as  affine subspace in R^d with dimension k . Our goal in this paper is to find clusters of objects . The main problem is to cope with the partial information .
Revised:  We model the lack of information for a given object as affine subspace in R^d with dimension k . Our goal in this paper is to find clusters of objects where the main problem is to cope with the partial information .
Edit intent: clarity
Original:  We studied a simple algorithm we callData clustering using flats minimum distances , using the following assumptions: 1) There are m clusters. 2) Each cluster is modeled as a  ball in \mathbb{R}^d  . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
Revised:  We studied a simple algorithm we callData clustering using flats minimum distances partial information and high dimension. Assuming the data set is separable, namely, its emergence from clusters that can be modeled as a ball in \mathbb{R}^d . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
Edit intent: meaning-changed
Original:  2) Each cluster is modeled as a  ball in \mathbb{R}^d  . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
Revised:  2) Each cluster is modeled as a set of disjoint ball in \mathbb{R}^d . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
Edit intent: meaning-changed
Original:  2) Each cluster is modeled as a  ball in \mathbb{R}^d  . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
Revised:  2) Each cluster is modeled as a ball in \mathbb{R}^d , we suggest a simple data clustering algorithm . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
Edit intent: meaning-changed
Original:  2) Each cluster is modeled as a  ball in \mathbb{R}^d  . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
Revised:  2) Each cluster is modeled as a ball in \mathbb{R}^d . {m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
Edit intent: clarity
Original:  An alternative  is qualitative modeling since it requires few to no parameters. This article presents a qualitative modeling derived from boolean networks where fuzzy logic is used and where edges can be tuned.
Revised:  An alternative is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic when modeling biological processes. Consequently, this work proposes a modeling approach derived from Boolean networks where fuzzy logic is used and where edges can be tuned.
Edit intent: meaning-changed