wanyu/IteraTeR_full_sent huggingface dataset:
Original:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: clarity
Original:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: clarity
Original:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: clarity
Original:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative fashion . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: clarity
Original:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: clarity
Original:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and works just as well for transmembrane proteins.
Edit intent: clarity
Original:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Revised:  Applied to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Edit intent: coherence
Original:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Revised:  We apply it to all Pfam domains without known structures, confident models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Edit intent: clarity
Original:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Revised:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called dark families were produced in under a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Edit intent: clarity
Original:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Revised:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Edit intent: clarity
Original:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Revised:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 core cluster . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Edit intent: clarity
Original:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Revised:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, generates accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Edit intent: clarity
Original:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
Revised:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with fewer than 100 sequences in some cases, and is freely available.
Edit intent: clarity
Original:  Using the data from the National Health Interview Survey (NHIS) , we apply this  identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on  health care access and utilization of old Americans.
Revised:  The point estimates of the treatment effect using a simple fuzzy difference-in-discontinuities design are biased if the change in the probability of a treatment applying at the cutoff differs across treatments. Modifications of the fuzzy difference-in-discontinuities approach that rely on milder assumptions are also proposed. Our results suggest caution is needed when applying before-and-after methods in the presence of fuzzy discontinuities. Using data from the National Health Interview Survey (NHIS) , we apply this identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on health care access and utilization of old Americans.
Edit intent: meaning-changed
Original:  Using the data from the National Health Interview Survey (NHIS) , we apply this  identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on  health care access and utilization of old Americans.
Revised:  Using the data from the National Health Interview Survey , we apply this identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on health care access and utilization of old Americans.
Edit intent: clarity
Original:  Using the data from the National Health Interview Survey (NHIS) , we apply this  identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on  health care access and utilization of old Americans.
Revised:  Using the data from the National Health Interview Survey (NHIS) , we apply this new identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on health care access and utilization of old Americans.
Edit intent: meaning-changed
Original:  Using the data from the National Health Interview Survey (NHIS) , we apply this  identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on  health care access and utilization of old Americans.
Revised:  Using the data from the National Health Interview Survey (NHIS) , we apply this identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on older Americans' health care access and utilization of old Americans.
Edit intent: clarity
Original:  Using the data from the National Health Interview Survey (NHIS) , we apply this  identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on  health care access and utilization of old Americans. We find results suggesting that the implementation of the Affordable Care Act has led to an increase in the hospitalization rate of elderly American--5\% more hospitalization.
Revised:  Using the data from the National Health Interview Survey (NHIS) , we apply this identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on health care access and utilization . Our results suggest that the ACA has (1) led to a 5\% increase in the hospitalization rate of elderly American--5\% more hospitalization.
Edit intent: clarity
Original:  We find results suggesting that the implementation of the Affordable Care Act has led to an increase in the hospitalization rate of elderly American--5\% more hospitalization. It has caused a minor increase of cost-related direct barrier to access to care--3.6\% increase in the probability of delaying care for cost reasons .
Revised:  We find results suggesting that the implementation of the Affordable Care Act has led to an increase in the hospitalization rate of elderly Americans, (2) increased the probability of delaying care for cost reasons .
Edit intent: meaning-changed
Original:  It has caused a minor increase of cost-related direct barrier to access to care--3.6\% increase in the probability of delaying care for cost reasons . The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Revised:  It has caused a minor increase of cost-related direct barrier to access to care--3.6\% increase in the probability of delaying care for cost reasons by 3.6\%, and (3) exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Edit intent: meaning-changed
Original:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Revised:  The ACA has also exacerbated cost-related barriers to follow-up care and continuity of care: 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Edit intent: clarity
Original:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Revised:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly individuals could not afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Edit intent: clarity
Original:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Revised:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more could not see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Edit intent: fluency
Original:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Revised:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist , and 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Edit intent: fluency
Original:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Revised:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more could not afford a follow-up visit -- as result of ACA
Edit intent: fluency
Original:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit -- as result of ACA
Revised:  The ACA has also exacerbated cost-related barriers to follow-up and continuity care -- 7\% more elderly couldn't afford prescriptions, 7\% more couldn't see a specialist and, 5.5\% more couldn't afford a follow-up visit . Our results can be explained by an increase in the demand for health services without a corresponding adjustment in supply following the implementation of the ACA.
Edit intent: meaning-changed
Original:  In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) model to exploit both spatially and temporally discriminating features and account for seizure variabilities.
Revised:  In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) to exploit both spatially and temporally discriminating features and account for seizure variabilities.
Edit intent: clarity
Original:  In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) model to exploit both spatially and temporally discriminating features and account for seizure variabilities.
Revised:  In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) model to exploit both spatial and temporal discriminating features and account for seizure variabilities.
Edit intent: fluency
Original:  In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) model to exploit both spatially and temporally discriminating features and account for seizure variabilities.
Revised:  In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) model to exploit both spatially and temporally discriminating features and overcome seizure variabilities.
Edit intent: clarity
Original:  By accounting for both spatial and temporal variations of seizures, the proposed method is more robust across subjects. The testing results over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods.
Revised:  Cross-validation experiments and cross-patient experiments over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods.
Edit intent: meaning-changed
Original:  The testing results over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods.
Revised:  The testing results over the noisy real data of CHB-MIT are performed to evaluate our proposed approach. The obtained average sensitivity of 87.00\%, specificity of 88.60\% and precision of 88.63\% in cross-validation experiments are higher than using the current state-of-the-art methods.
Edit intent: meaning-changed
Original:  The testing results over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods. In both mixing-patients and cross-patient experiments, the average sensitivity and specificity are both higher while their corresponding standard deviations are lower than the methods in comparison .
Revised:  The testing results over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods, and the standard deviations of our approach are lower. The evaluation results of cross-patient experiments, the average sensitivity and specificity are both higher while their corresponding standard deviations are lower than the methods in comparison .
Edit intent: meaning-changed
Original:  However, the performances obtained with different approaches are difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature selection (FS) and cross-validation (CV) procedure.
Revised:  However, classification performance obtained with different approaches are difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature selection (FS) and cross-validation (CV) procedure.
Edit intent: clarity