wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show how a strictly local process at the scale of individual node pairs directly affects the behavior of the system at the whole network scale. Individual nodes responding to information from the upstream nodes it is connected to produce the observable emergent dynamics of the entire networkat a global scale, independent of and without any knowledge of what all the other nodes in the network may be doing. We then provide empirical evidence that at least some important examples of both naturally occurring and engineered networks are capable of approaching a state of optimal dynamical efficiency, one of the key results of the theory.
Revised:  We show how a strictly local process at the scale of individual node pairs directly affects the behavior of the network as a whole in a deterministic way. Here, we show that in any physically constructible geometric network, the geometry of the network constrains and bounds signaling and the flow of information through the network, and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics. We provide empirical evidence that at least some important examples of both naturally occurring and engineered networks are capable of approaching a state of optimal dynamical efficiency, one of the key results of the theory.
Edit intent: meaning-changed
Original:  We then provide empirical evidence that at least some important examples of both naturally occurring and engineered networks are capable of approaching a state of optimal dynamical efficiency, one of the key results of the theory. While we progressively build up to what this formally means, informally it means that these networks have evolved or have been designed to optimize how they are able to handle the processing of information by matching the dynamical requirements of individual nodes to the flow of information (i.e. signals) between nodes in the network . In different ways, we use the theory to investigate properties of pyramidal neurons in biological neural networks in the visual cortex, the prevalence of the small world network topology, and the internet router network  .
Revised:  We then provide empirical evidence that at least some important examples of both naturally occurring and engineered networks , specifically, the prevalence of the small world network topology, axonal branching of pyramidal neurons in biological neural networks in the visual cortex, the prevalence of the small world network topology, and the internet router network .
Edit intent: meaning-changed
Original:  In different ways, we use the theory to investigate properties of pyramidal neurons in biological neural networks in the visual cortex, the prevalence of the small world network topology, and the internet router network  .
Revised:  In different ways, we use the theory to investigate properties of pyramidal neurons in biological neural networks in the visual cortex, the prevalence of the small world network topology, and the internet router network , are capable of approaching a state of optimal dynamical efficiency, a concept that is derived from the theory. The framework we develop has a wide range of (non-biological) engineering and neurophysiological applications .
Edit intent: meaning-changed
Original:  Here, we show that in any physically constructible geometric network, the geometry of the network  constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics.
Revised:  Here, we address this question by developing a generalized theoretical framework that formally describes how in any physically constructible geometric network, the geometry of the network constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics.
Edit intent: meaning-changed
Original:  Here, we show that in any physically constructible geometric network, the geometry of the network  constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics.
Revised:  Here, we show that in any physically constructible geometric network, the geome- try of the network constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics.
Edit intent: fluency
Original:  Here, we show that in any physically constructible geometric network, the geometry of the network  constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics.
Revised:  Here, we show that in any physically constructible geometric network, the geometry of the network i.e. its physical structure, constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics.
Edit intent: meaning-changed
Original:  Here, we show that in any physically constructible geometric network, the geometry of the network  constrains and bounds signaling and the flow of information through the network , and develop a theory derived from foundational principles of neural signaling that can both describe and predict the dynamics of geometric networks from considerations of node dynamics. We provide empirical evidence that at least some important examples of widely different naturally occurring and engineered networks , specifically, the prevalence of the small world network topology, axonal branching of pyramidal neurons in the visual cortex, and the internet router network, are capable of approaching a state of optimal dynamical efficiency, a concept that is derived from the theory.
Revised:  Here, we show that in any physically constructible geometric network, the geometry of the network constrains and bounds signaling and the flow of information through the network . This then in turn predicts the emergent dynamics of the network. Our results illustrate how the in- terplay between strictly local geometric and temporal process at the scale of individual node pairs directly affects the behavior of the whole network. We also provide empirical evidence that at least some important examples of widely different naturally occurring and engineered networks , specifically, the prevalence of the small world network topology, axonal branching of pyramidal neurons in the visual cortex, and the internet router network, are capable of approaching a state of optimal dynamical efficiency, a concept that is derived from the theory.
Edit intent: meaning-changed
Original:  We provide empirical evidence that at least some important examples of widely different naturally occurring and engineered networks , specifically, the prevalence of the small world network topology, axonal branching of pyramidal neurons in the visual cortex, and the internet router network, are capable of approaching a state of optimal dynamical efficiency, a concept that is derived from the theory. The framework we develop has a wide range of (non-biological) engineering and neurophysiological applications .
Revised:  We provide empirical evidence that at least some important examples of widely different naturally occurring and engineered networks follow these theoretical principles .
Edit intent: clarity
Original: The functional and computational power of a network emerges as a prop- erty of the system operating as a coherent whole at a global scale, not at the scale of individual nodes. A natural question to ask is what are the physical principles that allow a collection of nodes to interact in such away that they produce emergent global network dynamics? Here, we address this question by developing a generalized theoretical framework that formally describes how in any physically constructible geometric network, the geome- try of the network i.e. its physical structure, constrains and bounds signaling and the flow of information through the network.
Revised: Understanding how local interactions among connected nodes in a network result in global network dynamics and behaviors remains a critical open problem in network theory. This is important both for understanding complex networks, including the brain, and for the controlled design of networks intended to achieve a specific function. Here, we describe the construction and theoretical analysis of a framework derived from canonical neurophysiological principles that models the competing dynamics of incident signals into nodes along directed edges in a network. The framework describes the dynamics between the offset in the latencies of propagating signals, which reflect the geometry of the edges and conduction velocities, and the geome- try of the network i.e. its physical structure, constrains and bounds signaling and the flow of information through the network.
Edit intent: meaning-changed
Original:  Here, we address this question by developing a generalized theoretical framework that formally describes how in any physically constructible geometric network, the geome- try of the network i.e. its physical structure, constrains and bounds signaling and the flow of information through the network. This then in turn predicts the emergent dynamics of the network. Our results illustrate how the in- terplay between strictly local geometric and temporal process at the scale of individual node pairs directly affects the behavior of the whole network. We also provide empirical evidence that at least some important examples of widely different naturally occurring and engineered networks follow these theoretical principles .
Revised:  Here, we address this question by developing a generalized theoretical framework that formally describes how in any physically constructible geometric network, the internal refractory dynamics and processing times of the downstream node. One of the main theoretical results is the definition of a ratio between the speed of signaling or information flow, which is bounded by the spatial geometry, and the internal time it takes for individual nodes to process incoming signals. We show that an optimal ratio is one where the speed of information propagation between connected nodes does not exceed the internal dynamic time scale of the nodes. A mismatch of this ratio leads to sub-optimal signaling and information flows in a network, and even a breakdown in signaling all together .
Edit intent: clarity
Original: Understanding how local interactions among connected nodes in a network result in global network dynamics and behaviors remains a critical open problem in network theory. This is important both for understanding complex networks, including the brain, and for the controlled design of networks intended to achieve a specific function. Here, we describe the construction and theoretical analysis of a framework derived from canonical neurophysiological principles that models the competing dynamics of incident signals into nodesalong directed edges in a network.
Revised: Networks are ubiquitous throughout science and engineering. A number of methods, including some from our own group, have explored how one goes about computing or predicting the dynamics of networks given information about internal models of individual nodes and network connectivity, possibly with additional information provided by statistical or descriptive metrics that characterize the network. But what can be inferred about network dynamics when there is no knowledge or information about the internal model or dynamics of participating nodes? Here, we explore how connected subsets of nodes competitively interact in order to activate a common downstream node they connect into. We achieve this by assuming a simple set of rules borrowed from neurophysiology. The model we develop reflects a local process from which global network dynamics emerges. We call this model a competitive refractory dynanics model. It is derived from a consideration of spatial and temporal summation in biological neurons, whereby summating post synaptic potentials (PSPs) along the dendritic tree contribute towards the membrane potential at the initial segment reaching a threshold potential. We first show how the 'winning node' or set of 'winning' nodes that achieve activation of a downstream node is computable by the model. We then derive a formal definition of optimized network signaling within our framework. We define a network.
Edit intent: clarity
Original:  Here, we describe the construction and theoretical analysis of a framework derived from canonical neurophysiological principles that models the competing dynamics of incident signals into nodesalong directed edges in a network. The framework describes the dynamics between the offset in the latencies of propagating signals, which reflect the geometry of the edges and conduction velocities, and the internal refractory dynamics and processing times of the downstream node. One of the main theoretical results is the definition of a ratio between the speed of signaling or information flow, which is bounded by the spatial geometry, and the internal time it takes for individual nodes to process incoming signals.
Revised:  Here, we describe the construction and theoretical analysis of a framework derived from canonical neurophysiological principles that models the competing dynamics of incident signals into nodesalong directed edges in a ratio between the speed of signaling or information flow, which is bounded by the spatial geometry, and the internal time it takes for individual nodes to process incoming signals.
Edit intent: clarity
Original:  One of the main theoretical results is the definition of a ratio between the speed of signaling or information flow, which is bounded by the spatial geometry, and the internal time it takes for individual nodes to process incoming signals.
Revised:  One of the main theoretical results is the definition of a ratio between the speed of signaling or information flow, which is bounded by the spatial geometry, and the internal time it takes individual nodes to process incoming signals.
Edit intent: fluency
Original:  The appearance of the unfolded intermediate occurs at a higher temperature in nCaM than it does in cCaM . That is, we find that cCaM unfolds more readily along the transition route than nCaM. Furthermore, unfolding and refolding of the domain significantly slows the domain opening and closing rates of cCaM , a distinct scenario which can potentially influence the mechanism of calcium binding to each domain.
Revised:  The appearance of the unfolded intermediate occurs at a higher temperature in nCaM than it does in cCaM consistent with nCaM's higher thermal stability. Under approximate physiological conditions, the simulated unfolded state population of cCaM accounts for 10\% of the population with nearly all of the sampled transitions (approximately 95\%) unfolding and refolding of the domain significantly slows the domain opening and closing rates of cCaM , a distinct scenario which can potentially influence the mechanism of calcium binding to each domain.
Edit intent: meaning-changed
Original:  Furthermore, unfolding and refolding of the domain significantly slows the domain opening and closing rates of cCaM , a distinct scenario which can potentially influence the mechanism of calcium binding to each domain.
Revised:  Furthermore, unfolding and refolding during the conformational change. Transient unfolding significantly slows the domain opening and closing rates of cCaM , a distinct scenario which can potentially influence the mechanism of calcium binding to each domain.
Edit intent: meaning-changed
Original: In this paper we provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork.
Revised: Our work is motivated by an interest in constructing a protein-protein interaction network that captures key features associated with Parkinson's disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation. We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork.
Edit intent: meaning-changed
Original:  We use this null model to select subnetworks which significantly deviate from random on an appropriate set of statistics and hence make suggestions as to which of the many network sampling methods might capture useful information for a real world protein-protein interaction network.
Revised:  We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and hence make suggestions as to which of the many network sampling methods might capture useful information for a real world protein-protein interaction network.
Edit intent: fluency
Original: Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes . In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i. e. the slow democracy exercising the 'wisdom of crowds' .
Revised: I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This "core-periphery learning" theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery-driven innovation and a number of recent reports on the adaptation of protein, neuronal and social networks. The coreperiphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision-making processes. Moreover, the power of network periphery-related 'wisdom of crowds' .
Edit intent: meaning-changed
Original:  Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i. e. the slow democracy exercising the 'wisdom of crowds' . This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution.
Revised:  Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i. e. the slow democracy exercising the 'wisdom of crowds' inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed by complex systems during evolution.
Edit intent: clarity
Original:  The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution.The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies. (Illustrative videos can be downloaded from here: URL
Revised:  The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed as the success of a billion-year evolution.
Edit intent: clarity
Original: Molecular dynamics (MD) simulations using a graphics processing unit (GPU) has been employed in order to determine the conformational space of the methane-thiosulfonate spin label (MTSL) attached to the activation loop of the Aurora-A kinase protein and compared with quantum mechanical (QM) methods rooted on density functional theory (DFT).
Revised: Classical molecular dynamics (MD) simulations using a graphics processing unit (GPU) has been employed in order to determine the conformational space of the methane-thiosulfonate spin label (MTSL) attached to the activation loop of the Aurora-A kinase protein and compared with quantum mechanical (QM) methods rooted on density functional theory (DFT).
Edit intent: meaning-changed
Original: Molecular dynamics (MD) simulations using a graphics processing unit (GPU) has been employed in order to determine the conformational space of the methane-thiosulfonate spin label (MTSL) attached to the activation loop of the Aurora-A kinase protein and compared with quantum mechanical (QM) methods rooted on density functional theory (DFT).
Revised: Molecular dynamics (MD) simulations , within the AMBER program package that runs entirely on a CUDA-enabled NVIDIA graphic processing unit (GPU) has been employed in order to determine the conformational space of the methane-thiosulfonate spin label (MTSL) attached to the activation loop of the Aurora-A kinase protein and compared with quantum mechanical (QM) methods rooted on density functional theory (DFT).
Edit intent: meaning-changed
Original: Molecular dynamics (MD) simulations using a graphics processing unit (GPU) has been employed in order to determine the conformational space of the methane-thiosulfonate spin label (MTSL) attached to the activation loop of the Aurora-A kinase protein and compared with quantum mechanical (QM) methods rooted on density functional theory (DFT).
Revised: Molecular dynamics (MD) simulations using a graphics processing unit (GPU) , were employed to study the dynamics of the methane-thiosulfonate spin label (MTSL) attached to the activation loop of the Aurora-A kinase protein and compared with quantum mechanical (QM) methods rooted on density functional theory (DFT).
Edit intent: clarity
Original: Molecular dynamics (MD) simulations using a graphics processing unit (GPU) has been employed in order to determine the conformational space of the methane-thiosulfonate spin label (MTSL) attached to the activation loop of the Aurora-A kinase protein and compared with quantum mechanical (QM) methods rooted on density functional theory (DFT).
Revised: Molecular dynamics (MD) simulations using a graphics processing unit (GPU) has been employed in order to determine the conformational space of the methane-thiosulfonate spin labelled (MTSL) attached to the activation loop of the Aurora-A kinase protein and compared with quantum mechanical (QM) methods rooted on density functional theory (DFT).
Edit intent: fluency