wanyu/IteraTeR_full_sent huggingface dataset:
Original: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction. Here we present an efficient method for determining protein structures from experimental NMR NOESY distances , by combining the ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper) bound refinement, force field-based energy minimization (EM) and water refinement.
Revised: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply the methods to reconstruct protein structures. In this paper, we present an efficient method for determining protein structures from experimental NMR NOESY distances , by combining the ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper) bound refinement, force field-based energy minimization (EM) and water refinement.
Edit intent: clarity
Original:  Here we present an efficient method for determining protein structures from experimental NMR NOESY distances , by combining the ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper) bound refinement, force field-based energy minimization (EM) and water refinement.
Revised:  Here we present an efficient method to determine protein structures based on experimental NMR NOESY distances , by combining the ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper) bound refinement, force field-based energy minimization (EM) and water refinement.
Edit intent: fluency
Original:  Here we present an efficient method for determining protein structures from experimental NMR NOESY distances , by combining the ScaledASD algorithm with several afterwards procedures including chirality refinement, distance lower (upper) bound refinement, force field-based energy minimization (EM) and water refinement.
Revised:  Here we present an efficient method for determining protein structures from experimental NMR NOESY distances . ScaledASD algorithm is used in the method with several post-procedures including chirality refinement, distance lower (upper) bound refinement, force field-based energy minimization (EM) and water refinement.
Edit intent: coherence
Original:  In particular, our results show higher validities in MPscore, Molprobity clash-score, Procheck dihedral angles G-factor than PDB models. In the end, we compared our calculation results with PDB models by checking the structural similarity to X-ray crystallographic structure for a special dataset.
Revised:  In particular, our results show higher validities in MPscore, Molprobity clash-score, Procheck dihedral angles G-factor . Furthermore, we compare our calculation results with PDB models by checking the structural similarity to X-ray crystallographic structure for a special dataset.
Edit intent: coherence
Original: Direct cDNA preamplification protocols developed for single-cell RNA-seq (scRNA-seq) have enabled transcriptome profiling of  rare cells without having to pool multiple samples or to perform RNA extraction.
Revised: Direct cDNA preamplification protocols developed for single-cell RNA-seq (scRNA-seq) have enabled transcriptome profiling of precious clinical samples and rare cells without having to pool multiple samples or to perform RNA extraction.
Edit intent: meaning-changed
Original:  We term this approach limiting-cell RNA-seq (lcRNA-seq) . Unlike scRNA-seq, which focuses on 'cell-atlasing', lcRNA-seq focuses on identifying differentially expressed genes (DEGs) between experimental groups.
Revised:  Currently, there is no algorithm optimized to reveal and remove noisy transcripts in limiting-cell RNA-seq (lcRNA-seq) . Unlike scRNA-seq, which focuses on 'cell-atlasing', lcRNA-seq focuses on identifying differentially expressed genes (DEGs) between experimental groups.
Edit intent: meaning-changed
Original:  We term this approach limiting-cell RNA-seq (lcRNA-seq) . Unlike scRNA-seq, which focuses on 'cell-atlasing', lcRNA-seq focuses on identifying differentially expressed genes (DEGs) between experimental groups. This requires accounting for systems noise which can obscure biological differences. We present CLEAR, a workflow that identifies robust transcripts in lcRNA-seq data for between-group comparisons.
Revised:  We term this approach limiting-cell RNA-seq (lcRNA-seq) data for downstream analyses. Herein, we present CLEAR, a workflow that identifies robust transcripts in lcRNA-seq data for between-group comparisons.
Edit intent: clarity
Original:  We present CLEAR, a workflow that identifies robust transcripts in lcRNA-seq data for between-group comparisons. To develop CLEAR, we compared DEGs from RNA extracted from FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia patient diluted to input RNA levels of 10-, 100- and 1,000pg.
Revised:  We present CLEAR, a workflow that identifies robust transcripts in lcRNA-seq data for differentially expressed gene (DEG) analysis. Libraries at three input amounts of FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia patient diluted to input RNA levels of 10-, 100- and 1,000pg.
Edit intent: clarity
Original:  To develop CLEAR, we compared DEGs from RNA extracted from FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia patient diluted to input RNA levels of 10-, 100- and 1,000pg.
Revised:  To develop CLEAR, we compared DEGs from RNA extracted from FACS-derived CD5+ and CD5- cells from a chronic lymphocytic leukemia patient diluted to input RNA levels of 10-, 100- and 1,000pg.
Edit intent: clarity
Original:  To develop CLEAR, we compared DEGs from RNA extracted from FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia patient diluted to input RNA levels of 10-, 100- and 1,000pg. Data quality at ultralow input levels are known to be noisy.
Revised:  To develop CLEAR, we compared DEGs from RNA extracted from FACS-derived CD5+ and CD5- cells from a single chronic lymphocytic leukemia patient were used to develop CLEAR.
Edit intent: clarity
Original:  When using CLEAR transcripts vs. using all available transcripts, downstream analyses reveal more shared DEGs , improved Principal Component Analysis separation of cell type, and increased similarity between results across different input RNA amounts.
Revised:  When using CLEAR transcripts vs. using all available transcripts, downstream analyses revealed more shared transcripts across different input RNA amounts , improved Principal Component Analysis separation of cell type, and increased similarity between results across different input RNA amounts.
Edit intent: clarity
Original:  When using CLEAR transcripts vs. using all available transcripts, downstream analyses reveal more shared DEGs , improved Principal Component Analysis separation of cell type, and increased similarity between results across different input RNA amounts.
Revised:  When using CLEAR transcripts vs. using all available transcripts, downstream analyses reveal more shared DEGs , improved Principal Component Analysis (PCA) separation, and yielded more DEGs between cell types. As proof-of-principle,
Edit intent: meaning-changed
Original:  CLEAR was applied to two publicly available ultralow input RNA-seq data and an in-house murine neural cell lcRNA-seq dataset . CLEAR provides a novel way to visualize the public datasetswhile validates cell phenotype markers for astrocytes, neural stem and progenitor cells .
Revised:  CLEAR was applied to an in-house murine neural cell lcRNA-seq dataset . CLEAR provides a novel way to visualize the public datasetswhile validates cell phenotype markers for astrocytes, neural stem and progenitor cells .
Edit intent: coherence
Original:  CLEAR was applied to two publicly available ultralow input RNA-seq data and an in-house murine neural cell lcRNA-seq dataset . CLEAR provides a novel way to visualize the public datasetswhile validates cell phenotype markers for astrocytes, neural stem and progenitor cells .
Revised:  CLEAR was applied to two publicly available ultralow input RNA-seq data and an in-house lcRNA-seq dataset . CLEAR provides a novel way to visualize the public datasetswhile validates cell phenotype markers for astrocytes, neural stem and progenitor cells .
Edit intent: clarity
Original:  CLEAR was applied to two publicly available ultralow input RNA-seq data and an in-house murine neural cell lcRNA-seq dataset . CLEAR provides a novel way to visualize the public datasetswhile validates cell phenotype markers for astrocytes, neural stem and progenitor cells .
Revised:  CLEAR was applied to two publicly available ultralow input RNA-seq data and an in-house murine neural cell lcRNA-seq dataset and two public datasets. When imputation is used, CLEAR is also adaptable to large clinical studies and for single cell analyses .
Edit intent: meaning-changed
Original:  While calcium influx through NMDA-R plays a critical role in synaptic plasticity, indirect experimental evidence also exists demonstrating actions of NMDAR-mediated calcium influx on neuronal excitability through the activation of calcium-activated potassium channels.
Revised:  While calcium influx through NMDA-R plays a critical role in synaptic plasticity, experimental evidence indicates that NMDAR-mediated calcium influx on neuronal excitability through the activation of calcium-activated potassium channels.
Edit intent: clarity
Original:  While calcium influx through NMDA-R plays a critical role in synaptic plasticity, indirect experimental evidence also exists demonstrating actions of NMDAR-mediated calcium influx on neuronal excitability through the activation of calcium-activated potassium channels.
Revised:  While calcium influx through NMDA-R plays a critical role in synaptic plasticity, indirect experimental evidence also exists demonstrating actions of NMDAR-mediated calcium influx also modifies neuronal excitability through the activation of calcium-activated potassium channels.
Edit intent: clarity
Original:  Furthermore, the presence of tonic NMDA-R activity promotes bistability in electrical activity by dramatically increasing the stimulus interval where both a stable steady state and repetitive firing can exist. This results could provide an intrinsic mechanism for the constitution of memory traces in neuronal circuits.
Revised:  Furthermore, the presence of tonic NMDA-R activity promotes bistability in electrical activity by dramatically increasing the stimulus interval where both a stable steady state and repetitive firing can coexist. These results could provide an intrinsic mechanism for the constitution of memory traces in neuronal circuits.
Edit intent: clarity
Original: Rationale: Heart muscle contraction is  activated by a synchronized systolic Ca release from sarcoplasmic reticulum (SR) via Ca sparks. In disease, Ca sparks fail to terminate, causing a diastolic Ca leak that decreases contraction amplitude and increases the risk of arrhythmia.
Revised: Rationale: Heart muscle contraction is activated by a synchronized systolic Ca release from sarcoplasmic reticulum (SR) , a major intracellular Ca store. However, under abnormal conditions Ca leaks from the SR, decreasing heart contraction amplitude and increases the risk of arrhythmia.
Edit intent: clarity
Original:  We have recently shown that spark termination emerges as collective behavior (synchronized closings) of Ca release channels (RyRs) that is identical to synchronization of spin orientation in ferromagnets described by a phase transition in Ising model . Objective: We employed the Ising approach to investigate and classify mechanisms of spark termination failure and Ca leak.
Revised:  Here we employed both numerical and analytical modeling to get mechanistic insights into the emergent Ca leak phenomenon. Our numerical simulations using a detailed realistic model of Ca release channels (RyRs) that is identical to synchronization of spin orientation in ferromagnets described by a phase transition in Ising model . Objective: We employed the Ising approach to investigate and classify mechanisms of spark termination failure and Ca leak.
Edit intent: meaning-changed
Original:  We have recently shown that spark termination emerges as collective behavior (synchronized closings) of Ca release channels (RyRs) that is identical to synchronization of spin orientation in ferromagnets described by a phase transition in Ising model . Objective: We employed the Ising approach to investigate and classify mechanisms of spark termination failure and Ca leak. Methods and Results: The key parametersdetermining whether spark termination succeeds or fails are SR Ca and RyR opening/closing rates. They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta*  . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Revised:  We have recently shown that spark termination emerges as collective behavior (synchronized closings) of Ca release unit (CRU) reveal sharp transitions resulting in Ca leak. The emergence of leak is closely mapped mathematically to the Ising model from statistical mechanics. The system steady-state behavior is determined by two aggregate parameters: the analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta* . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Edit intent: clarity
Original:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta*  . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Revised:  They define analogues of magnetic field (h) and the inverse temperature (\beta) in Ising model\beta*\beta* \beta* . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Edit intent: fluency
Original:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta*  . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Revised:  They define analogues of magnetic field h and the inverse temperature (\beta) in the Ising model, for which we have explicit formulas in terms of SR Ca and release channel opening/closing rates. The classification of leak regimes takes the shape of a phase \beta-h diagram, with the regime boundaries occurring at h=0 and a critical value of \beta (\beta*\beta* \beta* . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Edit intent: meaning-changed
Original:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta*  . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Revised:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*) which we estimate using a classical Ising model and mean field theory. Our theory predicts that a synchronized Ca leak will occur when h>0 and \beta>\beta* \beta* . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Edit intent: meaning-changed
Original:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta*  . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Revised:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* and a disordered leak occurs when \beta<\beta* . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Edit intent: meaning-changed
Original:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta*  . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Revised:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta* and h is not too negative . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign.
Edit intent: meaning-changed
Original:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta*  . Sparks terminate via a phase transition known as "hpolarity reversal" and the leak emerges when hfails to change its sign. This happens when the SR depletes insufficiently and RyR openings remained partially synchronized via Ca-induced-Ca-release, generating long-lasting sparks. Leak can also occur via \beta, known as Onsager's "order-disorder" transition. This happens at low SR Ca, reducing RyR current and RyRs interactions, resulting in independent RyR openings.
Revised:  They define analogues of magnetic field h and the inverse temperature (\beta) in Ising model\beta*\beta* \beta* . 
Edit intent: meaning-changed
Original:  Conclusions: Abnormal leak results from either a probability imbalance during synchronized firing of RyRs or from disordered RyR firing. Each leak type requires different and balanced treatment to shift RyR operation towards normal spark termination .
Revised:  Thus, in addition to our detailed numerical model approach we also offer an instantaneous computational tool using analytical formulas of the Ising model for respective RyR parameters and SR Ca load that describe and classify phase transitions and leak emergence .
Edit intent: meaning-changed
Original: In economics, it is well accepted that adoption of items is governed by the utility that a user derives from their adoption.
Revised: Motivated by applications such as viral marketing, the problem of influence maximization (IM) has been extensively studied in the literature. The goal is to select a small number of users to adopt an item such that it results in a large cascade of adoptions by others. Existing works have three key limitations. (1) They do not account for economic considerations of a user in buying/adopting items. (2) Most studies on multiple items focus on competition, with complementary items receiving limited attention. (3) For the network owner, maximizing social welfare is important to ensure customer loyalty, which is not addressed in prior work in the IM literature.
Edit intent: meaning-changed
Original:  In this paper, we propose a model called EPIC that combines utility-driven item adoption with the viral network effect helping to propagate adoption of and desire for items from users to their peers. We focus on the case of mutually complementary items and model their adoption behavior via supermodular value functions. We assume price is additive and use zero mean random noise to capture the uncertainty in our knowledge of user valuations. In this setting, we study a novel problem ofsocial welfare maximization : given item budgets, find an optimal allocation of items to seed nodes that maximizes the sum of expected utilities derived by users when the diffusion terminates .
Revised:  In this paper, we propose a model called EPIC that combines utility-driven item adoption with influence propagation over networks. Focusing on the mutually complementary setting, we study a novel problem ofsocial welfare maximization : given item budgets, find an optimal allocation of items to seed nodes that maximizes the sum of expected utilities derived by users when the diffusion terminates .
Edit intent: clarity
Original:  In this setting, we study a novel problem ofsocial welfare maximization : given item budgets, find an optimal allocation of items to seed nodes that maximizes the sum of expected utilities derived by users when the diffusion terminates .
Revised:  In this setting, we social welfare maximization : given item budgets, find an optimal allocation of items to seed nodes that maximizes the sum of expected utilities derived by users when the diffusion terminates .
Edit intent: clarity
Original:  In this setting, we study a novel problem ofsocial welfare maximization : given item budgets, find an optimal allocation of items to seed nodes that maximizes the sum of expected utilities derived by users when the diffusion terminates .
Revised:  In this setting, we study a novel problem of : given item budgets, find an optimal allocation of items to seed nodes that maximizes the sum of expected utilities derived by users when the diffusion terminates .
Edit intent: clarity
Original:  We show the expected social welfare is monotone but neither submodular nor supermodular . Nevertheless, we show that a simple greedy allocation can ensure a (1-1/e-\epsilon) -approximation to the optimum .
Revised:  We show the expected social welfare is monotone but neither submodular nor supermodular , surprisingly a simple greedy allocation can ensure a (1-1/e-\epsilon) -approximation to the optimum .
Edit intent: coherence
Original:  Nevertheless, we show that a simple greedy allocation can ensure a (1-1/e-\epsilon) -approximation to the optimum . To the best of our knowledge, this is the first instance where for a non-submodular objective in the context of viral marketing, such a high approximation ratio is achieved. We provide the analysis of this result, which is highly nontrivial and along the way we give a solution to the prefix-preserving influence maximization problem, which could be of independent interest. With extensive\textsf{  experiments on real and synthetic datasets, we show that our algorithm significantly outperforms all the baselines.
Revised:  Nevertheless, we show that a simple greedy allocation can ensure a (1-1/e-\epsilon) of the optimum expected social welfare. We develop\textsf{ experiments on real and synthetic datasets, we show that our algorithm significantly outperforms all the baselines.
Edit intent: clarity
Original:  We present a complete wireless neural stimulation system consisting of a 2.2 mm3 wireless, batteryless, leadless implantable stimulator implant (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Revised: Neural stimulation is a powerful technique for modulating physiological functions and for writing information into the nervous system as part of brain-machine interfaces. Current clinically approved neural stimulators require batteries and are many cubic centimeters in size- typically much larger than their intended targets. We present a complete wireless neural stimulation system consisting of a 2.2 mm3 wireless, batteryless, leadless implantable stimulator implant (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Edit intent: meaning-changed
Original:  We present a complete wireless neural stimulation system consisting of a 2.2 mm3 wireless, batteryless, leadless implantable stimulator implant (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Revised:  We present a complete wireless neural stimulation system consisting of a 2.2 mm^3 wireless, batteryless, leadless implantable stimulator implant (the "mote"), an ultrasonic wireless link for power and bi-directional communication, and a hand-held external transceiver.
Edit intent: clarity