wanyu/IteraTeR_full_sent huggingface dataset:
Original: In this paper, we propose a new real-time differential virus transmission model, which can give more accurate and robust short-term predictions of COVID-19 transmitted infectious disease with benefits of near-term  projection.
Revised: In this paper, we propose a new real-time differential virus transmission model, which can give more accurate and robust short-term predictions of COVID-19 transmitted infectious disease with benefits of near-term trend projection.
Edit intent: meaning-changed
Original:  Numerical results on the recent COVID-19 data from China, Italy and US, California and New York states have been analyzed . Based on the projection as of April 13, 2020 from the proposed model, the time for peak medical resource usage for US as a whole will be around the beginning of May. Also US will reach the peak in terms of daily new infected cases and death cases around middle of April. The total cumulative infected cases will reach to peak of 1.09 million people around June 12, 2020 and the estimated total death cases will reach to 90K in the end .
Revised:  Numerical results on the recent COVID-19 data from China, Italy and US, California and New York states have been analyzed .
Edit intent: meaning-changed
Original:  The approach in these studies is called topological data analysis , and it has been used to study the branching structures of veins in leaves, how people vote in elections, flight patterns in models of bird flocking, and more.
Revised:  The approach in these studies is called 'topological data analysis' , and it has been used to study the branching structures of veins in leaves, how people vote in elections, flight patterns in models of bird flocking, and more.
Edit intent: fluency
Original: The COVID-19 pandemic highlights the importance of effective non-pharmaceutical public health interventions. While social distancing and isolation has been introduced widely  , more moderate contact reduction policies could become desirable owing to adverse social, psychological, and economic consequences of a complete or near-complete lockdown.
Revised: Social distancing and isolation has been introduced widely , more moderate contact reduction policies could become desirable owing to adverse social, psychological, and economic consequences of a complete or near-complete lockdown.
Edit intent: coherence
Original:  While social distancing and isolation has been introduced widely  , more moderate contact reduction policies could become desirable owing to adverse social, psychological, and economic consequences of a complete or near-complete lockdown.
Revised:  While social distancing and isolation has been introduced widely to counter the COVID-19 pandemic. However , more moderate contact reduction policies could become desirable owing to adverse social, psychological, and economic consequences of a complete or near-complete lockdown.
Edit intent: meaning-changed
Original:  It turns out that the SMT approach is faster than the polyhedral approach for models that would otherwise take more than one minute to compute, in many cases by a factor of 25 or more .
Revised:  It turns out that the SMT approach is faster than the polyhedral approach for models that would otherwise take more than one minute to compute, in many cases by a factor of 60 or more, and in the worst case is only slower by a factor of two .
Edit intent: meaning-changed
Original:  We simulate the "escape from Milan" that occurred on March 7th-8th  , when many travelers moved from a high-risk zone (Milan) to southern regions of Italy (Campania and Lazio) immediately after an imminent lockdown was announced.
Revised:  We simulate the "escape from Milan" that occurred on March 7th-8th 2020 , when many travelers moved from a high-risk zone (Milan) to southern regions of Italy (Campania and Lazio) immediately after an imminent lockdown was announced.
Edit intent: meaning-changed
Original:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after  restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Revised:  Taking the example of data from New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Edit intent: clarity
Original:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after  restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Revised:  Taking the example of COVID-19 data for New York City, we show that currently used models significantly underestimate the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Edit intent: clarity
Original:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after  restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Revised:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction number (R_0). The correct description, based on integral equations, can be implemented in most of the reported models and it much more accurately accounts for the dynamics of the epidemic after restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Edit intent: meaning-changed
Original:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after  restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Revised:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after sharp changes in R_0 due to restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Edit intent: meaning-changed
Original:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after  restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period .
Revised:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after restrictive public congregation measures . It also provides a novel way to determine the incubation period .
Edit intent: coherence
Original:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after  restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period . We suggest that no decisions about public health or economic measuresshould be based on any unimproved model .
Revised:  Taking the example of COVID-19 data for New York City, we show that our model yields a significantly larger estimate for the initial basic reproduction rate than other models, much more accurately accounts for the dynamics of the epidemic after restrictive public congregation measures were introduced, and provides a novel way to determine the incubation period , and most importantly, as we demonstrate for several countries, this method allows an accurate monitoring of R_0 and thus a fine-tuning of any restrictive measures. Integral equation based models do not only provide the conceptually correct description, they also have more predictive power than differential equation based models, therefore we do not see any reason for using the latter .
Edit intent: meaning-changed
Original:  Additionally, ] \choose } \to _{\geq 0}, our approximate exchange property implies that a simple local search algorithm gives a k^{O(k)}-approximation of S \mu(S) when \mu is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when \mu is strongly Rayleigh.
Revised:  In particular, given function \mu: ] \choose } \to _{\geq 0}, our approximate exchange property implies that a simple local search algorithm gives a k^{O(k)}-approximation of S \mu(S) when \mu is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when \mu is strongly Rayleigh.
Edit intent: meaning-changed
Original:  Additionally, ] \choose } \to _{\geq 0}, our approximate exchange property implies that a simple local search algorithm gives a k^{O(k)}-approximation of S \mu(S) when \mu is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when \mu is strongly Rayleigh.
Revised:  Additionally, n] \choose } \to _{\geq 0}, our approximate exchange property implies that a simple local search algorithm gives a k^{O(k)}-approximation of S \mu(S) when \mu is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when \mu is strongly Rayleigh.
Edit intent: meaning-changed
Original:  Additionally, ] \choose } \to _{\geq 0}, our approximate exchange property implies that a simple local search algorithm gives a k^{O(k)}-approximation of S \mu(S) when \mu is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when \mu is strongly Rayleigh.
Revised:  Additionally, ] \choose k} \to _{\geq 0}, our approximate exchange property implies that a simple local search algorithm gives a k^{O(k)}-approximation of S \mu(S) when \mu is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when \mu is strongly Rayleigh.
Edit intent: fluency
Original:  Additionally, ] \choose } \to _{\geq 0}, our approximate exchange property implies that a simple local search algorithm gives a k^{O(k)}-approximation of S \mu(S) when \mu is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when \mu is strongly Rayleigh.
Revised:  Additionally, ] \choose } \to \mathbb{R_{\geq 0}, our approximate exchange property implies that a simple local search algorithm gives a k^{O(k)}-approximation of S \mu(S) when \mu is generated by a log-concave polynomial, and that greedy gives the same approximation ratio when \mu is strongly Rayleigh.
Edit intent: meaning-changed
Original:  As an application, } we show how to leverage down-up random walks to approximately sample  spanning trees in a graph with n edges in time O(n\log^2 n) , improving on the almost-linear time algorithm by Schild [Sch18].
Revised:  As an application, } we show how to leverage down-up random walks to approximately sample random forests or random spanning trees in a graph with n edges in time O(n\log^2 n) , improving on the almost-linear time algorithm by Schild [Sch18].
Edit intent: meaning-changed
Original:  As an application, } we show how to leverage down-up random walks to approximately sample  spanning trees in a graph with n edges in time O(n\log^2 n) , improving on the almost-linear time algorithm by Schild [Sch18].
Revised:  As an application, } we show how to leverage down-up random walks to approximately sample spanning trees in a graph with n edges in time O(n\log^2 n) . The best known result for sampling random forest was a FPAUS with high polynomial runtime recently found by \mbox{%DIFAUXCMD ALOV19, CGM19 on the almost-linear time algorithm by Schild [Sch18].
Edit intent: meaning-changed
Original:  As an application, } we show how to leverage down-up random walks to approximately sample  spanning trees in a graph with n edges in time O(n\log^2 n) , improving on the almost-linear time algorithm by Schild [Sch18].
Revised:  As an application, } we show how to leverage down-up random walks to approximately sample spanning trees in a graph with n edges in time O(n\log^2 n) , improving on the almost-linear time algorithm by [Sch18].
Edit intent: clarity
Original:  I show here these dramatic variations in fatality rates across age and gender are impressively strongly correlated (r^2=0.91 with only one free parameter) with levels of the ACE2 protein in rat lungs, which are in turn qualitatively similar to expression in humans .
Revised:  I show that these variations across age and gender are impressively strongly correlated (r^2=0.91 with only one free parameter) with levels of the ACE2 protein in rat lungs, which are in turn qualitatively similar to expression in humans .
Edit intent: clarity
Original:  I show here these dramatic variations in fatality rates across age and gender are impressively strongly correlated (r^2=0.91 with only one free parameter) with levels of the ACE2 protein in rat lungs, which are in turn qualitatively similar to expression in humans .
Revised:  I show here these dramatic variations in fatality rates across age and gender are quantitatively explained by the levels of the ACE2 protein in rat lungs, which are in turn qualitatively similar to expression in humans .
Edit intent: clarity
Original:  I show here these dramatic variations in fatality rates across age and gender are impressively strongly correlated (r^2=0.91 with only one free parameter) with levels of the ACE2 protein in rat lungs, which are in turn qualitatively similar to expression in humans .
Revised:  I show here these dramatic variations in fatality rates across age and gender are impressively strongly correlated (r^2=0.91 with only one free parameter) with levels of the ACE2 protein in the lungs. This correlation also rationalizes the association between Covid-19 lethality and hypertension. Surprisingly, despite ACE2 is the viral receptor, higher values of ACE2 lead to lower fatality .
Edit intent: meaning-changed
Original:  This behaviour is predicted by a model that assumes that deaths are caused by the degradation of the ACE2 receptor by the virus , which uses it as entry point in the cells.
Revised:  This behaviour is consistent with a previous mathematical model that predicts that the speed of the virus in URLanism has a maximum and then declines with receptor level. SARS-Cov-2 degrades ACE2 receptor by the virus , which uses it as entry point in the cells.
Edit intent: meaning-changed
Original:  This behaviour is predicted by a model that assumes that deaths are caused by the degradation of the ACE2 receptor by the virus , which uses it as entry point in the cells. The insufficient levelof ACE2 exacerbates the inflammatory response eventually leading to death, but if the initial levels of ACE2 are high the immune system has enough time to resolve the infection before it gets to extreme consequences.
Revised:  This behaviour is predicted by a model that assumes that deaths are caused by the degradation of the ACE2 and thus worsens lung injury, causes vasoconstriction, possible thrombotic problems and exacerbates the inflammatory response eventually leading to death, but if the initial levels of ACE2 are high the immune system has enough time to resolve the infection before it gets to extreme consequences.
Edit intent: meaning-changed
Original:  The insufficient levelof ACE2 exacerbates the inflammatory response eventually leading to death, but if the initial levels of ACE2 are high the immune system has enough time to resolve the infection before it gets to extreme consequences. These results are consistent with previous hypothesis on the protective role of ACE2, and suggest that , counterintuitively, drugs that act synergistically with ACE2 and enhance its expression, such as ACE inhibitors and angiotensin receptor blockers used to treat high blood pressure, may offer a promising therapy against the most adverse effects of the Covid-19 disease.
Revised:  The insufficient levelof ACE2 exacerbates the inflammatory response eventually leading to death, but if the initial levels of ACE2 are high the cross-talk between ACE2, and suggest that , counterintuitively, drugs that act synergistically with ACE2 and enhance its expression, such as ACE inhibitors and angiotensin receptor blockers used to treat high blood pressure, may offer a promising therapy against the most adverse effects of the Covid-19 disease.
Edit intent: coherence
Original:  These results are consistent with previous hypothesis on the protective role of ACE2, and suggest that , counterintuitively, drugs that act synergistically with ACE2 and enhance its expression, such as ACE inhibitors and angiotensin receptor blockers used to treat high blood pressure, may offer a promising therapy against the most adverse effects of the Covid-19 disease.
Revised:  These results are consistent with previous hypothesis on the protective role of ACE2, the prothrombotic state and the immune system. I develop here a mathematical model based on the influence of ACE2 on the speed of viral propagation and on the negative effects of its degradation. The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2~0.9). Strikingly, rescaling the fitted models with the binding rates of the spike proteins of SARS and SARS-CoV-2 allow predicting the fatality rate of SARS across age and gender with high accuracy, in particular its higher severity for young patients. The epidemiology of the common-cold coronavirus NL63 is also consistent with the model and existing data on the affinity of its spike protein, thus linking the molecular and epidemiological levels. These results support the suggestion that drugs that act synergistically with ACE2 and enhance its expression, such as ACE inhibitors and angiotensin receptor blockers used to treat high blood pressure, may offer a promising therapy against the most adverse effects of the Covid-19 disease.
Edit intent: meaning-changed
Original:  These results are consistent with previous hypothesis on the protective role of ACE2, and suggest that , counterintuitively, drugs that act synergistically with ACE2 and enhance its expression, such as ACE inhibitors and angiotensin receptor blockers used to treat high blood pressure, may offer a promising therapy against the most adverse effects of the Covid-19 disease.
Revised:  These results are consistent with previous hypothesis on the protective role of ACE2, and suggest that , counterintuitively, drugs that act synergistically with ACE2 and enhance its expression, such as ACE inhibitors and angiotensin receptor blockers , may constitute a promising therapy against the most adverse effects of the Covid-19 disease.
Edit intent: clarity
Original:  These results are consistent with previous hypothesis on the protective role of ACE2, and suggest that , counterintuitively, drugs that act synergistically with ACE2 and enhance its expression, such as ACE inhibitors and angiotensin receptor blockers used to treat high blood pressure, may offer a promising therapy against the most adverse effects of the Covid-19 disease.
Revised:  These results are consistent with previous hypothesis on the protective role of ACE2, and suggest that , counterintuitively, drugs that act synergistically with ACE2 and enhance its expression, such as ACE inhibitors and angiotensin receptor blockers used to treat high blood pressure, may offer a promising therapy against the most adverse effects of CoViD-19, including hyperinflammation and thrombotic complications. Furthermore,
Edit intent: meaning-changed
Original:  In the past decade, network medicine had developed a framework consisting of a series of quantitative approaches and predictive tools to study host-pathogen interactions, unveil the molecular mechanisms of the infection, identify comorbidities as well as rapidly detect drug repurpusing candidates. Here, we adapt the network-based toolset to COVID-19, recovering the primary pulmonary manifestations of the virus in the lung as well as observed comorbidities associated with cardiovascular diseases.
Revised:  In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based toolset to COVID-19, recovering the primary pulmonary manifestations of the virus in the lung as well as observed comorbidities associated with cardiovascular diseases.
Edit intent: clarity
Original:  Here, we adapt the network-based toolset to COVID-19, recovering the primary pulmonary manifestations of the virus in the lung as well as observed comorbidities associated with cardiovascular diseases. We predict that the virus can manifest itself in other tissues, such as the reproductive system, and brain regions, moreover we predict neurological comorbidities. We build on these findings to deploy three network-based drug repurposing strategies, relying on network proximity, diffusion, and AI-based metrics, allowing to rank all approved drugs based on their likely efficacy for COVID-19 patients, aggregate all predictions, and, thereby to arrive at 81 promising repurposing candidates.
Revised:  Here, we adapt the network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and AI-based metrics, allowing to rank all approved drugs based on their likely efficacy for COVID-19 patients, aggregate all predictions, and, thereby to arrive at 81 promising repurposing candidates.
Edit intent: clarity
Original:  We build on these findings to deploy three network-based drug repurposing strategies, relying on network proximity, diffusion, and AI-based metrics, allowing to rank all approved drugs based on their likely efficacy for COVID-19 patients, aggregate all predictions, and, thereby to arrive at 81 promising repurposing candidates.
Revised:  We build on these findings to deploy three network-based drug repurposing strategies, relying on network proximity, diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 patients, aggregate all predictions, and, thereby to arrive at 81 promising repurposing candidates.
Edit intent: meaning-changed