wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The model recapitulates observed  cell density distribution and the epidermal turnover time . We apply the model to study the onset, recurrence and phototherapy-induced remission of psoriasis.
Revised:  The model recapitulates observed cell density distribution and the epidermal turnover time and the multilayered tissue structure. We extend the model to study the onset, recurrence and phototherapy-induced remission of psoriasis.
Edit intent: meaning-changed
Original:  The model considers the psoriasis as a parallel homeostasis of normal and psoriatic keratinocytes originated from a shared stem-cell niche environment and predicts two steady-state modes of the psoriasis: a disease mode and a quiescent mode.
Revised:  The model considers the psoriasis as a parallel homeostasis of normal and psoriatic keratinocytes originated from a shared stem-cell niche environment and predicts two homeostatic modes of the psoriasis: a disease mode and a quiescent mode.
Edit intent: clarity
Original:  The prediction of a quiescent state potentially explains the efficacy of the multi-episode UVB irradiation therapy and recurrence of psoriasis plaques, which can further guide designs of therapeutics that  target the immune system and/or the keratinocytes.
Revised:  The prediction of a quiescent state potentially explains the efficacy of the multi-episode UVB irradiation therapy and recurrence of psoriasis plaques, which can further guide designs of therapeutics that specifically target the immune system and/or the keratinocytes.
Edit intent: meaning-changed
Original:  In particular , there is no sufficient evidence to justify an all-inclusive replacement of ES by expectiles in applications, especially as we provide an alternative way for backtesting of ES .
Revised:  In particular , there is no sufficient evidence to justify an all-inclusive replacement of ES by Expectiles in applications. For backtesting ES, we propose an empirical approach that consists in replacing ES by a set of four quantiles, which should allow to make use of backtesting methods for VaR .
Edit intent: meaning-changed
Original:  These macromolecules, nowadays investigated with advanced experimental techniques to unveil their molecular mechanisms and the properties of their kinetic cycles, are implicated in many biological processes, ranging from biopolymerisation ( RNA polymerases, ribosomes,... ) to intracellular transport (motor proteins such as kinesins or dyneins).
Revised:  These macromolecules, nowadays investigated with advanced experimental techniques to unveil their molecular mechanisms and the properties of their kinetic cycles, are implicated in many biological processes, ranging from biopolymerisation ( e.g. RNA polymerases and ribosomes ) to intracellular transport (motor proteins such as kinesins or dyneins).
Edit intent: clarity
Original:  We also consider the case when the correlation \rho%DIFDELCMD < \le %%%  0 ; in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Revised:  We also consider the case when the correlation \rho%DIFDELCMD < \le %%% 0 , our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Edit intent: coherence
Original:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Revised:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le 0. We later augment the model with a single jump-to-default with intensity\lm\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Edit intent: meaning-changed
Original:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Revised:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm, which produces qualitatively different behaviour for the short-maturity smile; in particular, for \rho=0, log-moneyness x\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Edit intent: meaning-changed
Original:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Revised:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne 0, the implied volatility increases by\lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Edit intent: meaning-changed
Original:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Revised:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm f(x) t +o(t) for some symmetric function f(x) which blows up at x=0, and we see that the jump affects the smile convexity but not the skew at leading order as t\to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Edit intent: meaning-changed
Original:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm \to 0pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Revised:  in this case our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le \lm\ne \lm \to 0. Finally, we compare our result with the general asymptotic expansion in Lorig,Pagliarani\&Pascucci\mbox{%DIFAUXCMD LPP130pt%DIFAUXCMD , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov\&Spector\mbox{%DIFAUXCMD AS12 }\hspace{0pt}%DIFAUXCMD for the case \rho=} 0.
Edit intent: meaning-changed
Original:  For \rho < 0, our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le 0.
Revised:  If the correlation \rho < 0, our approach still works if the drift of the volatility takes a specific functional form and there is no local volatility component, and our results include the SABR model for \beta=1, \rho \le 0.
Edit intent: meaning-changed
Original:  in particular, for \rho=0, log-moneyness x %DIFDELCMD < \ne %%%  0, the implied volatility increases by \lm f(x)
Revised:  in particular, for \rho=0, log-moneyness x %DIFDELCMD < \ne %%% > 0, the implied volatility increases by \lm f(x)
Edit intent: meaning-changed
Original:  t +o(t) for some symmetric function f(x) which blows up at x=0, and we see that the jump affects the smile convexity but not the skew at leading order as t%DIFDELCMD < \to %%%  0. Finally, we compare our result with the general asymptotic expansion in Lorig, Pagliarani \& Pascucci \mbox{%DIFAUXCMD LPP13 , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov \& Spector AS12 for the case \rho=0.
Revised:  t +o(t) for some function f(x) which blows up at x=0, and we see that the jump affects the smile convexity but not the skew at leading order as t%DIFDELCMD < \to %%% 0. Finally, we compare our result with the general asymptotic expansion in Lorig, Pagliarani \& Pascucci \mbox{%DIFAUXCMD LPP13 , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov \& Spector AS12 for the case \rho=0.
Edit intent: clarity
Original:  t +o(t) for some symmetric function f(x) which blows up at x=0, and we see that the jump affects the smile convexity but not the skew at leading order as t%DIFDELCMD < \to %%%  0. Finally, we compare our result with the general asymptotic expansion in Lorig, Pagliarani \& Pascucci \mbox{%DIFAUXCMD LPP13 , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov \& Spector AS12 for the case \rho=0.
Revised:  t +o(t) for some symmetric function f(x) which blows up %DIFDELCMD < \to %%% 0. Finally, we compare our result with the general asymptotic expansion in Lorig, Pagliarani \& Pascucci \mbox{%DIFAUXCMD LPP13 , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov \& Spector AS12 for the case \rho=0.
Edit intent: coherence
Original:  t +o(t) for some symmetric function f(x) which blows up at x=0, and we see that the jump affects the smile convexity but not the skew at leading order as t%DIFDELCMD < \to %%%  0. Finally, we compare our result with the general asymptotic expansion in Lorig, Pagliarani \& Pascucci \mbox{%DIFAUXCMD LPP13 , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov \& Spector AS12 for the case \rho=0.
Revised:  t +o(t) for some symmetric function f(x) which blows up at x=0, and we see that the jump affects the smile convexity but not the skew at leading order as t%DIFDELCMD < \to %%% as x \searrow 0. Finally, we compare our result with the general asymptotic expansion in Lorig, Pagliarani \& Pascucci \mbox{%DIFAUXCMD LPP13 , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov \& Spector AS12 for the case \rho=0.
Edit intent: meaning-changed
Original:  t +o(t) for some symmetric function f(x) which blows up at x=0, and we see that the jump affects the smile convexity but not the skew at leading order as t%DIFDELCMD < \to %%%  0. Finally, we compare our result with the general asymptotic expansion in Lorig, Pagliarani \& Pascucci \mbox{%DIFAUXCMD LPP13 , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov \& Spector AS12 for the case \rho=0.
Revised:  t +o(t) for some symmetric function f(x) which blows up at x=0, and we see that the jump affects the smile convexity but not the skew at leading order as t%DIFDELCMD < \to %%% 0. Finally, we compare our result with the general asymptotic expansion in Lorig, Pagliarani \& Pascucci \mbox{%DIFAUXCMD LPP15 , and we verify our results numerically for the SABR model using Monte Carlo simulation and the exact closed-form solution given in Antonov \& Spector AS12 for the case \rho=0.
Edit intent: meaning-changed
Original:  We implement this multi-family strategy by co-estimating their inverse covariance matrices  subject to the constraint that these matrices shall have similar patterns to some degree.
Revised:  We implement this multi-family EC analysis strategy, we use a set of correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices subject to the constraint that these matrices shall have similar patterns to some degree.
Edit intent: meaning-changed
Original:  Different from these single-family EC methods  , 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 families with divergent sequences but similar folds, and enforcing their co-evolution pattern consistency based upon their evolutionary distance.
Revised:  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 families with divergent sequences but similar folds, and enforcing their co-evolution pattern consistency based upon their evolutionary distance.
Edit intent: meaning-changed
Original:  To implement this multi-family EC analysis strategy, we use a set of  correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices of the Gaussian distributions subject to the constraint that these matrices shall have similar patternsto some degree.
Revised:  To implement this multi-family EC analysis strategy, this paper presents a novel joint graphical lasso method to model a set of related protein families. In particular, we model a set of correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices of the Gaussian distributions subject to the constraint that these matrices shall have similar patternsto some degree.
Edit intent: meaning-changed
Original:  To implement this multi-family EC analysis strategy, we use a set of  correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices of the Gaussian distributions subject to the constraint that these matrices shall have similar patternsto some degree.
Revised:  To implement this multi-family EC analysis strategy, we use a set of related families using a set of correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices of the Gaussian distributions subject to the constraint that these matrices shall have similar patternsto some degree.
Edit intent: fluency
Original:  To implement this multi-family EC analysis strategy, we use a set of  correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices of the Gaussian distributions subject to the constraint that these matrices shall have similar patternsto some degree.
Revised:  To implement this multi-family EC analysis strategy, we use 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 inverse covariance matrices of the Gaussian distributions subject to the constraint that these matrices shall have similar patternsto some degree.
Edit intent: meaning-changed
Original:  To implement this multi-family EC analysis strategy, we use a set of  correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices of the Gaussian distributions subject to the constraint that these matrices shall have similar patternsto some degree.
Revised:  To implement this multi-family EC analysis strategy, we use a set of correlated multivariate Gaussian distributions to model the related families and then co-estimate the precision matrices by maximizing the occurring probability of all the involved sequences, subject to the constraint that these matrices shall have similar patternsto some degree.
Edit intent: meaning-changed
Original:  To implement this multi-family EC analysis strategy, we use a set of  correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices of the Gaussian distributions subject to the constraint that these matrices shall have similar patternsto some degree.
Revised:  To implement this multi-family EC analysis strategy, we use a set of correlated multivariate Gaussian distributions to model the related families and then co-estimate the inverse covariance matrices of the Gaussian distributions subject to the constraint that the matrices shall share similar patterns. Finally we solve this optimization problem using Alternating Direction Method of Multipliers (ADMM).
Edit intent: meaning-changed
Original:  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 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: meaning-changed
Original:  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 sequence homologs, which makes many more protein families amenable to co-evolution-based structure and function prediction . We also find out that contact prediction may be worsened by merging multiple related families into a single one followed by single-family EC analysis, or by consensus of single-family EC analysis results .
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 sequence homologs, which makes many more protein families amenable to co-evolution-based structure and function prediction .
Edit intent: coherence
Original: To provide insight into the early process of degradation often occurring in severely debilitating diseases with myelin pathology an increased level of spatial structural resolution is needed to bear in the biological realm. Although many observations have connected changes in the periodicity of myelin with illness, few information exist about the microscopic process in the early period of damage of the nerve and how these changes time percolate in space .
Revised: Degradation of the myelin sheath is a common pathology underlying demyelinating neurological diseases from Multiple Sclerosis to Leukodistrophies. Although large malformations of myelin ultrastructure in the advanced stages of Wallerian degradation is known, its subtle structural variations at early stages of demyelination remains poorly characterized. This is partly due to the lack of suitable and non-invasive experimental probes possessing sufficient resolution to detect the degradation .
Edit intent: meaning-changed
Original:  Here we fill this gap by using first, a short time scale for data collection of scanning micro X-ray diffraction microscopy and second, methods of statistical physics for the analysis of time evolution of this non-invasive local structure experimental approach . We have mapped the time evolution of the fluctuations in myelin period in the degradation nerve process in a freshly extracted sciatic nerve of Xenopus laevis with a spatial resolution of 1 micron. We identify the first stage of myelin degradation with the period evolving through a bimodal distribution with a spatial phase separation, and evidence that the orientation of axons in the fresh sample show fractal fluctuations that are reduced with aging .
Revised:  Here we fill this gap by using first, a short time scale for data collection of scanning micro X-ray diffraction microscopy and second, methods of statistical physics for the analysis of time evolution of this non-invasive local structure experimental approach for imaging the changes of statistical structural fluctuations in the first stage of myelin degeneration. Scanning micro X-ray diffraction, using advances in synchrotron x-ray beam focusing, fast data collection, paired with spatial statistical analysis, has been used to unveil temporal changes in the myelin structure of dissected nerves following extraction of the Xenopus laevis sciatic nerve. The early myelin degeneration is a specific ordered compacted phase preceding the swollen myelin phase of Wallerian degradation. Our demonstration of the feasibility of the statistical analysis of SmXRD measurements using biological tissue paves the way for further structural investigations of degradation and death of neurons and other cells and tissues in diverse pathological states where nanoscale structural changes may be uncovered .
Edit intent: coherence