wanyu/IteraTeR_full_sent huggingface dataset:
Original: Each mammalian olfactory sensory neuron stochastically expresses only one out of  thousands of olfactory receptor alleles and the molecular mechanism for this selection remains as one of the biggest puzzles in neurobiology. Through constructing and analyzing a mathematical model based on extensive experimental observations , we identified an evolutionarily optimized three-layer regulation mechanism that robustly generates single-allele expression.
Revised: Each mammalian olfactory sensory neuron stochastically expresses only one out of thousands of olfactory receptor (OR) gene alleles; URLanism level the types of expressed ORs need to be maximized. Existing models focus only on monoallele activation, and cannot explain recent observations in mutants, especially the reduced global diversity of expressed ORs in G9a/GLP knockouts. In this work we integrated existing information on OR expression, and proposed an evolutionarily optimized three-layer regulation mechanism that robustly generates single-allele expression.
Edit intent: meaning-changed
Original:  Through constructing and analyzing a mathematical model based on extensive experimental observations , we identified an evolutionarily optimized three-layer regulation mechanism that robustly generates single-allele expression. Zonal separation reduces the number of competing alleles. Bifunctional LSD1 and cooperative histone modification dynamics minimize multiple allele epigenetic activation and alleles trapped in incomplete epigenetic activation states. Subsequent allele competition for a limited number of enhancers through cooperative binding serves as final safeguard for single allele expression.
Revised:  Through constructing and analyzing a mathematical model based on extensive experimental observations , we identified an evolutionarily optimized three-layer regulation mechanism , which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a novel enhancer competition step. This model not only recapitulates monoallelic OR expression, but also elucidates how the olfactory system maximizes and maintains the diversity of OR expression.
Edit intent: meaning-changed
Original:  In this work we integrated existing information on OR expression, and proposed an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a novel enhancer competition step.
Revised:  In this work we integrated existing information on OR expression, and constructed a comprehensive model that has all its components based on physical interactions. Analyzing the model reveals an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a novel enhancer competition step.
Edit intent: meaning-changed
Original:  In this work we integrated existing information on OR expression, and proposed an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a novel enhancer competition step.
Revised:  In this work we integrated existing information on OR expression, and proposed an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic barrier crossing coupled to a negative feedback loop that mechanistically differs from previous theoretical proposals, and a previously unidentified enhancer competition step.
Edit intent: clarity
Original:  This model not only recapitulates monoallelic OR expression, but also elucidates how the olfactory system maximizes and maintains the diversity of OR expression . The model has multiple predictions validated by existing experimental results , and particularly underscores cooperativity and synergy as a general design principle for multi-objective optimization in biology 
Revised:  This model not only recapitulates monoallelic OR expression, but also elucidates how the olfactory system maximizes and maintains the diversity of OR expression , and has multiple predictions validated by existing experimental results , and particularly underscores cooperativity and synergy as a general design principle for multi-objective optimization in biology 
Edit intent: coherence
Original:  The model has multiple predictions validated by existing experimental results , and particularly underscores cooperativity and synergy as a general design principle for multi-objective optimization in biology 
Revised:  The model has multiple predictions validated by existing experimental results . Through making analogy to a physical system with thermally activated barrier crossing and comparative reverse engineering analyses, the study reveals that the olfactory receptor selection system is optimally designed, and particularly underscores cooperativity and synergy as a general design principle for multi-objective optimization in biology 
Edit intent: meaning-changed
Original:  Away from the money, we express the asymptotics explicitly using the volatility process' self-similarity parameter H, its first Karhunen-Lo\`{e eigenvalue at time 1, and the latter's multiplicity.
Revised:  Away from the money, we express the asymptotics explicitly using the volatility process' self-similarity parameter H, its first Karhunen-Loeve eigenvalue at time 1, and the latter's multiplicity.
Edit intent: clarity
Original:  Up until now , it has usually been cost prohibitive to determine the convergence of these calculations with respect to the size of the QM region. Recent advances in reformulating electronic structure algorithms for stream processors such as graphical processing units have made QM/MM calculations of optimized reaction paths with QM regions comprising up to O(10^3) atoms feasible. Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase  . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy of the catalyzed reaction can be quite slow.
Revised:  Up until now , it has usually been cost prohibitive to determine the convergence of these calculations with respect to increasingly large QM regions. Leveraging recent advances in electronic structure efficiency and accuracy, we investigate catalytic properties in catechol O-methyltransferase . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy of the catalyzed reaction can be quite slow.
Edit intent: clarity
Original:  Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase  . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy of the catalyzed reaction can be quite slow.
Revised:  Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase , a representative example of a methyltransferase critical to human health . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy of the catalyzed reaction can be quite slow.
Edit intent: meaning-changed
Original:  Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase  . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy of the catalyzed reaction can be quite slow.
Revised:  Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase . Using QM regions ranging in size from reactants-only (64 atoms) to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy of the catalyzed reaction can be quite slow.
Edit intent: meaning-changed
Original:  Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase  . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy of the catalyzed reaction can be quite slow.
Revised:  Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that properties such as the activation energy of the catalyzed reaction can be quite slow.
Edit intent: clarity
Original:  Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase  . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy of the catalyzed reaction can be quite slow. Convergence to within chemical accuracy for this case requires a quantum mechanical region with approximately 500 atoms . These results call for a more careful determination of QM region sizes in future QM/MM studies of enzymes.
Revised:  Here, we leverage these GPU-accelerated quantum chemistry methods to investigate catalytic properties in catechol O-methyltransferase . Using QM regions ranging in size from the reactant only (63 atoms) up to nearly one-third of the entire protein (940 atoms), we show that convergence of properties such as the activation energy approach within chemical accuracy of the large-QM asymptotic limits rather slowly, requiring approximately 500-600 atoms if the QM residues are chosen simply by distance from the substrate. This slow approach to asymptotic limit is due to charge transfer from protein residues to the reacting substrates. Our large QM/MM calculations enable identification of charge separation for fragments in the transition state as a key component of enzymatic methyl transfer rate enhancement. We introduce charge shift analysis that reveals the minimum number of protein residues (ca. 11-16 residues or 200-300 atoms for COMT) needed for quantitative agreement with large-QM simulations. The identified residues are not those that would be typically selected using criteria such as chemical intuition or proximity. These results provide a recipe for a more careful determination of QM region sizes in future QM/MM studies of enzymes.
Edit intent: meaning-changed
Original:  An assumption of "smallness" type - imposed through the new notion of "closeness to Pareto optimality" - is shown to be sufficient for existence and uniqueness  . Central role in our analysis is played by a fully-coupled nonlinear system of quadratic BSDEs.
Revised:  An assumption of "smallness" type is shown to be sufficient for existence and uniqueness . Central role in our analysis is played by a fully-coupled nonlinear system of quadratic BSDEs.
Edit intent: clarity
Original:  An assumption of "smallness" type - imposed through the new notion of "closeness to Pareto optimality" - is shown to be sufficient for existence and uniqueness  . Central role in our analysis is played by a fully-coupled nonlinear system of quadratic BSDEs.
Revised:  An assumption of "smallness" type - imposed through the new notion of "closeness to Pareto optimality" - is shown to be sufficient for existence and uniqueness . In particular, this assumption encapsulates settings with small endowments, small time-horizon, or a large population of weakly heterogeneous agents . Central role in our analysis is played by a fully-coupled nonlinear system of quadratic BSDEs.
Edit intent: meaning-changed
Original:  In order to demonstrate its performance we test it on multiple large in-silico datasets with several levels and types of noise. Our results support the notion that network analysis is applicable to large datasets, and that the production of such datasets is desirable for the study of gene regulatory networks .
Revised:  Our algorithm can handle time series data for any network type and steady state data for acyclic networks. Using in-silico time series data we demonstrate good performance on large datasets with a significant level of noise .
Edit intent: meaning-changed
Original: In this paper we discuss sufficient conditions for weak convergence of the Euler approximation to general multi-dimensional stochastic differential equations (SDEs) driven by correlated Brownian motions and with discontinuous and path-dependent coefficients. We prove tightness of the approximating processes and the weak solution of the limiting process. Furthermore, motivated by a basket credit default swap (CDS ) pricing problem with counterparty risk and contagion risk, we show weak convergence for some functions of correlated first-passage times of the approximating processes.
Revised: We investigate the computational aspects of the basket CDS pricing with counterparty risk and contagion risk, we show weak convergence for some functions of correlated first-passage times of the approximating processes.
Edit intent: clarity
Original:  Furthermore, motivated by a basket credit default swap (CDS ) pricing problem with counterparty risk and contagion risk, we show weak convergence for some functions of correlated first-passage times of the approximating processes. We also discuss an open question on the probability of hitting a fixed point by correlated Brownian motions in a fixed time-interval .
Revised:  Furthermore, motivated by a basket credit default swap (CDS ) pricing problem with counterparty risk under a credit contagion model of multinames. This model enables us to capture the systematic volatility increases in the market triggered by a particular bankruptcy. The drawback of this problem is its analytical complication due to its path-dependent functional, which bears a potential failure in its convergence of numerical approximation under standing assumptions. In this paper we find sufficient conditions for the desired convergence by means of the weak convergence method to a class of path-dependent stochastic differential equations .
Edit intent: meaning-changed
Original:  In this paper we find sufficient conditions for the desired convergence by means of the weak convergence method to a class of path-dependent stochastic differential equations  .
Revised:  In this paper we find sufficient conditions for the desired convergence by means of the weak convergence method to a class of path-dependent stochastic differential equations . The main ingredient is to identify the weak convergence of the approximated solution to the underlying path-dependent stochastic differential equation .
Edit intent: meaning-changed
Original:  In this study, the number of cross-links and their discriminative power was systematically analyzed in silico on a set of 2,055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive cross-linkers between 1 \r{A and 60 \r{A . Depending on the protein size a heuristic was developed that determines the optimal cross-linker length.
Revised:  In this study, the number of cross-links and their discriminative power was systematically analyzed in silico on a set of 2,055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive cross-linkers between 1 \AA and 60 \r{A . Depending on the protein size a heuristic was developed that determines the optimal cross-linker length.
Edit intent: fluency
Original:  In this study, the number of cross-links and their discriminative power was systematically analyzed in silico on a set of 2,055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive cross-linkers between 1 \r{A and 60 \r{A . Depending on the protein size a heuristic was developed that determines the optimal cross-linker length.
Revised:  In this study, the number of cross-links and their discriminative power was systematically analyzed in silico on a set of 2,055 non-redundant protein folds considering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactive cross-linkers between 1 \r{A and 60 \AA . Depending on the protein size a heuristic was developed that determines the optimal cross-linker length.
Edit intent: fluency
Original: The interfirm buyer-seller network is important from both macroeconomic and microeconomic perspectives. From a macroeconomic perspective, this network represents a form of interconnectedness that allows firm-level idiosyncratic shocksto be propagated to other firms. This propagation mechanism interferes with the averaging out process of shocks, having a possible impact on aggregate fluctuation. From a microeconomic perspective, the interfirm buyer-seller network is a result of a firm's strategic link renewal processes.
Revised: A firm's buyer--seller relationships can be characterized as a longitudinal network in which the connectivity patterns evolve as each firm faces productivity shocks. Despite its importance in characterizing the production network of an economy, little is known about how the network revises its linkage structure in the face of global and individual exogenous shocks. Using a unique data set covering 10 years of interfirm buyer--seller networks and structural equation modeling, we show that the evolution of the interfirm buyer--seller network is a result of a firm's strategic link renewal processes.
Edit intent: meaning-changed
Original:  From a microeconomic perspective, the interfirm buyer-seller network is a result of a firm's strategic link renewal processes. There has been substantial research that models strategic link formation processes, but the economy-wide consequences of such strategic behaviors are not clear. We address these two questions using a unique dataset for the Japanese interfirm buyer-seller network. We take a structural equation modeling, and show that a large proportion of fluctuation in the average log growth rate of firms can be explained by the network and that link renewal by firms decreases the standard deviation of the log growth rate.
Revised:  From a microeconomic perspective, the interfirm buyer-seller network is a result of a firm's myopic decisions to avoid other firms' negative shocks and share their positive shocks. We show that the current network is often the best network configuration, which improves both the propagation of positive shocks and the avoidance of negative shocks compared with previous networks. Furthermore, we show that for positive shocks, the future network is often better than the current network in the sense that it propagates positive shocks better than the current network. This is explained by the network and that link renewal by firms decreases the standard deviation of the log growth rate.
Edit intent: meaning-changed
Original:  We take a structural equation modeling, and show that a large proportion of fluctuation in the average log growth rate of firms can be explained by the network and that link renewal by firms decreases the standard deviation of the log growth rate.
Revised:  We take a structural equation modeling, and show that a large proportion of fluctuation in the average log growth rate of firms can be explained by the asymmetry in cost between severing a link and link formation. We also investigate the role of the network in aggregate fluctuations and show that at least 37
Edit intent: meaning-changed
Original:  Despite its importance in characterizing the production network of an economy, little is known about how the network revises its linkage structure in the face of global and individual exogenous shocks. Using a unique data set covering 10 years of interfirm buyer--seller networks and structural equation modeling, we show that the evolution of the interfirm buyer--seller network is a result of a firm's myopic decisions to avoid other firms ' negative shocks and share their positive shocks.
Revised:  Based on a data set describing the evolution of buyer--seller networks and structural equation modeling, we show that the evolution of the interfirm buyer--seller network is a result of a firm's myopic decisions to avoid other firms ' negative shocks and share their positive shocks.
Edit intent: clarity
Original:  Using a unique data set covering 10 years of interfirm buyer--seller networks and structural equation modeling, we show that the evolution of the interfirm buyer--seller network is a result of a firm's myopic decisions to avoid other firms ' negative shocks and share their positive shocks.
Revised:  Using a unique data set covering 10 years of interfirm buyer--seller links among 55,608 firms over a decade and structural equation modeling, we show that the evolution of the interfirm buyer--seller network is a result of a firm's myopic decisions to avoid other firms ' negative shocks and share their positive shocks.
Edit intent: meaning-changed
Original:  Using a unique data set covering 10 years of interfirm buyer--seller networks and structural equation modeling, we show that the evolution of the interfirm buyer--seller network is a result of a firm's myopic decisions to avoid other firms ' negative shocks and share their positive shocks.
Revised:  Using a unique data set covering 10 years of interfirm buyer--seller networks and structural equation modeling, we find some evidence that interfirm networks evolve reflecting a firm's myopic decisions to avoid other firms ' negative shocks and share their positive shocks.
Edit intent: clarity
Original:  Using a unique data set covering 10 years of interfirm buyer--seller networks and structural equation modeling, we show that the evolution of the interfirm buyer--seller network is a result of a firm's myopic decisions to avoid other firms ' negative shocks and share their positive shocks. We show that the current network is often the best network configuration, which improves both the propagation of positive shocks and the avoidance of negative shocks compared with previous networks. Furthermore, we show that for positive shocks, the future network is often better than the current network in the sense that it propagates positive shocks better than the current network. This is explained by the asymmetry in cost between severing a link and link formation.
Revised:  Using a unique data set covering 10 years of interfirm buyer--seller networks and structural equation modeling, we show that the evolution of the interfirm buyer--seller network is a result of a firm's local decisions to mitigate adverse effects from neighbor firms through interfirm linkage, while enjoying positive effects from them. As a result, link renewal tends to have a positive impact on the growth rates of firms.
Edit intent: meaning-changed
Original:  We also investigate the role of the network in aggregate fluctuations and show that at least 37%DIFDELCMD < \\%%% %DIF < of the aggregate fluctuations can be explained by the network effect.
Revised:  We also investigate the role of networks in aggregate fluctuations and show that at least 37%DIFDELCMD < \\%%% %DIF < of the aggregate fluctuations can be explained by the network effect.
Edit intent: fluency