wanyu/IteraTeR_full_sent huggingface dataset:
Original:  RTNs with binary elements and random topology have been found to display a phase transition to an ordered phase at low connectivities Physica A 310, 245 (2002)%DIFDELCMD < ]%%% . Here we show that this phase transition is the result of a seemingly minor detail in the update rule, which acts like a biasing external field for a paramagnetic system. For unbiased update rules, there is no phase transition for RTNs of nonzero connectivity. We also show that RTNs with constant number of inputs, K, which have been claimed to be ordered for K=1 Phys.
Revised:  RTNs with binary elements and random topology have been found to display a phase transition to an ordered phase at low connectivities Physica A 310, 245 (2002)%DIFDELCMD < ]%%% Phys.
Edit intent: meaning-changed
Original:  We also show that RTNs with constant number of inputs, K, which have been claimed to be ordered for K=1 Phys. Lett. A 129, 157 (1988)%DIFDELCMD < ]%%% , are actually critical .
Revised:  We also show that RTNs with constant number of inputs, K, which have been claimed to be ordered for K=1 %DIFDELCMD < ]%%% , are actually critical .
Edit intent: coherence
Original:  Here we show that cooperation remains rather stable by applying long-term learning + innovative strategy adoption rules on a variety of random, regular, small-word, scale-free and modular networks in repeated, multi-agent  games.
Revised:  Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular networks in repeated, multi-agent games.
Edit intent: meaning-changed
Original:  Here we show that cooperation remains rather stable by applying long-term learning + innovative strategy adoption rules on a variety of random, regular, small-word, scale-free and modular networks in repeated, multi-agent  games.
Revised:  Here we show that cooperation remains rather stable by applying long-term learning + innovative strategy adoption rules on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent games.
Edit intent: clarity
Original:  Here we show that cooperation remains rather stable by applying long-term learning + innovative strategy adoption rules on a variety of random, regular, small-word, scale-free and modular networks in repeated, multi-agent  games.
Revised:  Here we show that cooperation remains rather stable by applying long-term learning + innovative strategy adoption rules on a variety of random, regular, small-word, scale-free and modular networks in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games.
Edit intent: meaning-changed
Original: Using our recent contribution, the "  partitioned leaping algorithm"  [L. A. Harris and P. Clancy, J. Chem.
Revised: Using our recent contribution, the " Leaping" methods show great promise for significantly accelerating stochastic simulations of complex biochemical reaction networks. However, few practical applications of leaping have appeared in the literature to date. Here, we address this issue using the " partitioned leaping algorithm" [L. A. Harris and P. Clancy, J. Chem.
Edit intent: meaning-changed
Original:  By considering situations both where "leaping" proves beneficial and where it does not, we gain valuable insight that can aid in and advance the use of leaping methods in computational biology. In particular, we identify reaction subnetworks with small populations and large rate constants as a major bottleneck for leaping. We also demonstrate the use of "model reduction" in conjunction with leaping to circumvent this problem and expose a current need in this area: a model-reduction hierarchy analogous to that on which the leaping methods are based D. T. Gillespie, J. Chem.
Revised:  D. T. Gillespie, J. Chem.
Edit intent: meaning-changed
Original:  We also demonstrate the use of "model reduction" in conjunction with leaping to circumvent this problem and expose a current need in this area: a model-reduction hierarchy analogous to that on which the leaping methods are based D. T. Gillespie, J. Chem. Phys. 113, 297 (2000); 115, 1716 (2001)%DIFDELCMD < ]%%% . In situations where leaping is seen to perform well, we emphasize the significant advantages to using these methods over more traditional simulation approaches.
Revised:  We also demonstrate the use of "model reduction" in conjunction with leaping to circumvent this problem and expose a current need in this area: a model-reduction hierarchy analogous to that on which the leaping methods are based %DIFDELCMD < ]%%% . In situations where leaping is seen to perform well, we emphasize the significant advantages to using these methods over more traditional simulation approaches.
Edit intent: coherence
Original:  115, 1716 (2001)%DIFDELCMD < ]%%% . In situations where leaping is seen to perform well, we emphasize the significant advantages to using these methods over more traditional simulation approaches. We show how leaping allows for statistical investigations beyond the reach of "exact-stochastic" methods and demonstrate how one can uncover, via such investigations, subtle stochastic effects that would be ignored in deterministic treatments.
Revised:  115, 1716 (2001)%DIFDELCMD < ]%%% The networks that we consider are simple enough so as to be accessible to our intuition but sufficiently complex so as to test the limits of the leaping approach. We demonstrate how the PLA allows us to quantify subtle effects of stochasticity that would be ignored in deterministic treatments.
Edit intent: clarity
Original:  We show how leaping allows for statistical investigations beyond the reach of "exact-stochastic" methods and demonstrate how one can uncover, via such investigations, subtle stochastic effects that would be ignored in deterministic treatments. We then show, using examples taken from the literature, that conditions identified here as lending well to leaping do, in fact, arise in natural biological systems. This leads us to discuss and demonstrate how the statistical information garnered from a leaping study can be used to infer mechanistic details of a biological process and motivate experiments .
Revised:  We show how leaping allows for statistical investigations beyond the reach of "exact-stochastic" methods and demonstrate how one can uncover, via such investigations, subtle stochastic effects that would be difficult to ascertain otherwise as well as not-so-subtle behaviors that would strain commonly-used "exact" stochastic methods. We also illustrate prime bottlenecks that can hinder the approach and discuss possible strategies for overcoming them .
Edit intent: clarity
Original:  We demonstrate how the PLA allows us to quantify subtle effects of stochasticity  that would be difficult to ascertain otherwise as well as not-so-subtle behaviors that would strain commonly-used "exact" stochastic methods.
Revised:  We demonstrate how the PLA allows us to quantify subtle effects of stochasticity in these systems that would be difficult to ascertain otherwise as well as not-so-subtle behaviors that would strain commonly-used "exact" stochastic methods.
Edit intent: meaning-changed
Original: The major objective of this paper is to introduce a novel method for the functional annotation of genes. Our method is based on a graph theoretical measure we call joint betweenness, which is an extension of the well known betweenness centrality measure, involving pairs of genes. We apply our method to the transcriptional regulatory network of yeast to, first, provide a large scale proof of concept of our method and, second, make predictions about the biological function of previously unknown genes .
Revised: This paper has been withdrawn .
Edit intent: coherence
Original:  We calculate analytically the probability distribution of the metastable state of the regulated component and show that deterministic reaction rate equations may fail in predicting the average number of regulated molecules even when this number is large, and the time is short compared to the mean extinction time.
Revised:  We calculate the probability distribution of the metastable state of the regulated component and show that deterministic reaction rate equations may fail in predicting the average number of regulated molecules even when this number is large, and the time is short compared to the mean extinction time.
Edit intent: clarity
Original:  We calculate analytically the probability distribution of the metastable state of the regulated component and show that deterministic reaction rate equations may fail in predicting the average number of regulated molecules even when this number is large, and the time is short compared to the mean extinction time.
Revised:  We calculate analytically the probability distribution of the metastable state of the plasmids and show on this example that the reaction rate equations may fail in predicting the average number of regulated molecules even when this number is large, and the time is short compared to the mean extinction time.
Edit intent: meaning-changed
Original:  We calculate analytically the probability distribution of the metastable state of the regulated component and show that deterministic reaction rate equations may fail in predicting the average number of regulated molecules even when this number is large, and the time is short compared to the mean extinction time.
Revised:  We calculate analytically the probability distribution of the metastable state of the regulated component and show that deterministic reaction rate equations may fail in predicting the average number of regulated molecules even when this number is large, and the time is much shorter than the mean extinction time.
Edit intent: clarity
Original: Microtubules (MTs), nano-tubes which act as struts in the scaffolding of eucaryotic cells, also serve as tracks for intracellular molecular motor transport in eucaryotic cells. In many biological processes, the length distribution of the MTs is controlled by a class of motor proteins called depolymerases (DPs).
Revised: In many intracellular processes, the length distribution of the MTs is controlled by a class of motor proteins called depolymerases (DPs).
Edit intent: clarity
Original:  In many biological processes, the length distribution of the MTs is controlled by a class of motor proteins called depolymerases (DPs). A DP diffuses or walks along a MT to reach one of the tips of the MT andthen begins depolymerizing the MT itself .
Revised:  In many biological processes, the length distribution of the MTs is controlled by a class of motor proteins , called depolymerases. Experiments have shown that, following binding to the MT andthen begins depolymerizing the MT itself .
Edit intent: meaning-changed
Original:  A DP diffuses or walks along a MT to reach one of the tips of the MT andthen begins depolymerizing the MT itself .
Revised:  A DP diffuses or walks along a MT to reach one of the tips of the surface of a microtubule, depolymerases are transported to the microtubule tip(s) by diffusion or directed walk and, then, depolymerize the microtubule from the tip(s) after accumulating there .
Edit intent: clarity
Original:  We develop a quantitative model that captures both these processes within a single theoretical framework. We show that the action of both diffusing and walking DPs leads to a length-dependent depolymerization of the MT; however, their effects on the length distribution are different.
Revised:  We develop a quantitative model to study the depolymerizing action of such a generic motor protein, and its possible effects on the length distribution are different.
Edit intent: clarity
Original:  however, their effects on the length distribution are different. Under experimental conditions, the diffusing DP produces a non-monotonic distribution of lengths as opposed to a nearly exponential distribution in the case of the walking DP.
Revised:  however, their effects on the length distribution of microtubules. We show that, when the motor protein concentration in solution exceeds a critical value, a steady state is reached where the distribution is non-monotonic distribution of lengths as opposed to a nearly exponential distribution in the case of the walking DP.
Edit intent: meaning-changed
Original:  Under experimental conditions, the diffusing DP produces a non-monotonic distribution of lengths as opposed to a nearly exponential distribution in the case of the walking DP. The former also shows a minimum in the r.m.s fluctuation to mean ratio of the length against the DP concentration, while the latter does not. Our findings imply that a diffusing DP, like MCAK, might be used by the cell for precise length control during cell division, while a walking DP, like Kip3p, would be more useful for fast depolymerization .
Revised:  Under experimental conditions, the diffusing DP produces a non-monotonic provided the motor processivity is sufficiently small. Our findings suggest that such motor proteins have a central role in ensuring precise control of MT lengths .
Edit intent: clarity
Original: In many intracellular processes, the length distribution of microtubules is controlled by a class of motor proteins, called depolymerases . Experiments have shown that, following  binding to the surface of a microtubule, depolymerases are transported to the microtubule tip(s) by diffusion or directed walk and, then, depolymerize the microtubule from the tip(s) after accumulating there.
Revised: In many intracellular processes, the length distribution of microtubules is controlled by depolymerizing motor proteins . Experiments have shown that, following binding to the surface of a microtubule, depolymerases are transported to the microtubule tip(s) by diffusion or directed walk and, then, depolymerize the microtubule from the tip(s) after accumulating there.
Edit intent: clarity
Original: In many intracellular processes, the length distribution of microtubules is controlled by a class of motor proteins, called depolymerases . Experiments have shown that, following  binding to the surface of a microtubule, depolymerases are transported to the microtubule tip(s) by diffusion or directed walk and, then, depolymerize the microtubule from the tip(s) after accumulating there.
Revised: In many intracellular processes, the length distribution of microtubules is controlled by a class of motor proteins, called depolymerases . Experiments have shown that, following non-specific binding to the surface of a microtubule, depolymerases are transported to the microtubule tip(s) by diffusion or directed walk and, then, depolymerize the microtubule from the tip(s) after accumulating there.
Edit intent: meaning-changed
Original: In many intracellular processes, the length distribution of microtubules is controlled by a class of motor proteins, called depolymerases . Experiments have shown that, following  binding to the surface of a microtubule, depolymerases are transported to the microtubule tip(s) by diffusion or directed walk and, then, depolymerize the microtubule from the tip(s) after accumulating there.
Revised: In many intracellular processes, the length distribution of microtubules is controlled by a class of motor proteins, called depolymerases . Experiments have shown that, following binding to the surface of a microtubule, depolymerizers are transported to the microtubule tip(s) by diffusion or directed walk and, then, depolymerize the microtubule from the tip(s) after accumulating there.
Edit intent: clarity
Original:  We show that, when the motor protein concentration in solution exceeds a critical value, a steady state is reached where the distribution is non-monotonic provided the motor processivity is sufficiently small .
Revised:  We show that, when the motor protein concentration in solution exceeds a critical value, a steady state is reached where the distribution is non-monotonic with a single peak. However, for highly processive motors and large motor densities, this distribution effectively becomes an exponential decay .
Edit intent: meaning-changed
Original: We develop a network-based algorithm to predict and verify indirect regulatory interactions in a large-scale genetic regulatory network. Indirect regulations are important as they constitute the majority of experimental data. Our approach is based on the network topology and can be easily incorporated using a matrix formalism.
Revised: We develop a network-based algorithm to predict and verify indirect interactions in gene and protein regulatory networks. It is based on the network topology and can be easily incorporated using a matrix formalism.
Edit intent: clarity
Original:  A regulates B and B regulates C implies A regulates C) to longer cascades and effectively take care of the signs of the regulations. This algorithm is tailored for large and heavily interconnected networks, which are of growing importance due to the accruement of data from high-throughput experiments. We apply the algorithm to the regulatory networks of Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster , resulting in novel predictions with calibrated reliability .
Revised:  A regulates B and B regulates C implies A regulates C) and optimally takes into account the length of a cascade and signs of intermediate interactions. Our method is at its most powerful when applied to large and densely interconnected networks. It successfully predicts both the yet unknown indirect regulations, as well as the sign (activation or repression) of already known ones. The reliability of sign predictions was calibrated using the gold-standard sets of positive and negative interactions. We fine-tuned the parameters of our algorithm by maximizing the area under the Receiver Operating Characteristic (ROC) curve. We then applied the optimized algorithm to large literature-derived networks of all direct and indirect regulatory interactions in several URLanisms ( Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster , resulting in novel predictions with calibrated reliability .
Edit intent: meaning-changed
Original:  We apply the algorithm to the regulatory networks of Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster , resulting in novel predictions with calibrated reliability .
Revised:  We apply the algorithm to the regulatory networks of Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster ) .
Edit intent: coherence
Original:  However  the use of a standard t copula is often criticized due to its restriction of having a single parameter for the degrees of freedom (dof) that may limit its capability to model the tail dependence structure in a multivariate case.
Revised:  However , the use of a standard t copula is often criticized due to its restriction of having a single parameter for the degrees of freedom (dof) that may limit its capability to model the tail dependence structure in a multivariate case.
Edit intent: fluency
Original:  We explain simulation and calibration procedures and provide examples .
Revised:  We explain simulation and calibration procedures , including a simulation study on finite sample properties of the maximum likelihood estimators and Kendall's tau approximation. This new copula can be significantly different from the standard t copula in terms of risk measures such as tail dependence, value at risk and expected shortfall. Keywords: grouped t copula, tail dependence, risk management .
Edit intent: meaning-changed
Original: Stimulated by the long term goal of identifying gene targets for corn crop improvement, we perturbed a simple plant cell system by the addition of abscisic acid, a well-characterized plant hormone, and measured the gene expression with transcriptional microarrays for 150 minutes, every 10 minutes after treatment. Parameters of biochemistry-based models were inferred for 25 downstream (regulated) genes of interest using a thoroughly studied approximation of the posterior distribution, accounting for model uncertainty through Bayesian model averaging. The four causal gene interactions thereby identified as probable under a first-order difference model present testable hypotheses for future experimentation that could conceivably build knowledge of the complex biological system. The small scale of this result compared to many of the reconstructed networks reported in recent literature reflects a cautious approach to systems biology, but without a priori skepticism. The number of putative genes represented on the microarrays used is on the order of the size of the genome; a modification of the methodology for cases in which the number of measured genes is a small fraction of the genome size is also provided. For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the representation of a large enough portion of the genome enables computation of an informative upper bound of how much confidence one may reasonably place in causal relationships between genes on the basis of observed data.
Revised: Motivation: Measurements of gene expression over time enable the reconstruction of transcriptional networks. However, Bayesian networks and many other current reconstruction methods rely on assumptions that conflict with the differential equations that describe transcriptional kinetics. Practical approximations of kinetic models would enable inferring causal relationships between genes from expression data of microarray, tag-based and conventional platforms, but conclusions are sensitive to the assumptions made. Results: The representation of a large enough portion of the genome enables computation of an informative upper bound of how much confidence one may reasonably place in causal relationships between genes on the basis of observed data.
Edit intent: coherence
Original:  For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the representation of a large enough portion of the genome enables computation of an informative upper bound of how much confidence one may reasonably place in causal relationships between genes on the basis of observed data.
Revised:  For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the representation of a sufficiently large portion of genome enables computation of an informative upper bound of how much confidence one may reasonably place in causal relationships between genes on the basis of observed data.
Edit intent: clarity
Original:  For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the representation of a large enough portion of the genome enables computation of an informative upper bound of how much confidence one may reasonably place in causal relationships between genes on the basis of observed data.
Revised:  For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the representation of a large enough portion of the genome enables computation of an upper bound on how much confidence one may reasonably place in causal relationships between genes on the basis of observed data.
Edit intent: clarity
Original:  For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the representation of a large enough portion of the genome enables computation of an informative upper bound of how much confidence one may reasonably place in causal relationships between genes on the basis of observed data.
Revised:  For purposes of inferring biological networks, the main advantage of microarray technology over low-throughput methods may be that the representation of a large enough portion of the genome enables computation of an informative upper bound of how much confidence one may place in influences between genes on the basis of observed data.
Edit intent: clarity