wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To achieve a long-term benefit for the host, we show that memory generation should be actively regulated and dependent on immune receptors ' affinity, with a preference for cross-reactive receptors with a moderate affinity against pathogens as opposed to high affinity receptors -
Revised:  To achieve a long-term benefit for the host, we show that memory production should be actively regulated and dependent on immune receptors ' affinity, with a preference for cross-reactive receptors with a moderate affinity against pathogens as opposed to high affinity receptors -
Edit intent: clarity
Original:  To achieve a long-term benefit for the host, we show that memory generation should be actively regulated and dependent on immune receptors ' affinity, with a preference for cross-reactive receptors with a moderate affinity against pathogens as opposed to high affinity receptors -- a recipe that is consistent with recent experimental findings 1,2 . Moreover, we show that the specificity of memory should depend on URLanism's lifespan, and URLanisms with fewer pathogenic encounters throughout their lifetime should store more cross-reactive memory.
Revised:  To achieve a long-term benefit for the host, we show that memory generation should be actively regulated 1,2 . Moreover, we show that the specificity of memory should depend on URLanism's lifespan, and URLanisms with fewer pathogenic encounters throughout their lifetime should store more cross-reactive memory.
Edit intent: coherence
Original: - a recipe that is consistent with recent experimental findings 1,2 . Moreover, we show that the specificity of memory should depend on URLanism's lifespan, and URLanisms with fewer pathogenic encounters throughout their lifetime should store more cross-reactive memory.
Revised: - a recipe that is consistent with recent experimental findings to balance between affinity and cross-reactivity of immune receptors for an effective protection against evolving pathogens . Moreover, we show that the specificity of memory should depend on URLanism's lifespan, and URLanisms with fewer pathogenic encounters throughout their lifetime should store more cross-reactive memory.
Edit intent: meaning-changed
Original: - a recipe that is consistent with recent experimental findings 1,2 . Moreover, we show that the specificity of memory should depend on URLanism's lifespan, and URLanisms with fewer pathogenic encounters throughout their lifetime should store more cross-reactive memory.
Revised: - a recipe that is consistent with recent experimental findings 1,2 . Moreover, we predict that specificity of memory should depend on URLanism's lifespan, and URLanisms with fewer pathogenic encounters throughout their lifetime should store more cross-reactive memory.
Edit intent: clarity
Original: - a recipe that is consistent with recent experimental findings 1,2 . Moreover, we show that the specificity of memory should depend on URLanism's lifespan, and URLanisms with fewer pathogenic encounters throughout their lifetime should store more cross-reactive memory.
Revised: - a recipe that is consistent with recent experimental findings 1,2 . Moreover, we show that the specificity of memory should depend on URLanism's lifespan, and URLanisms with fewer pathogenic encounters should store more cross-reactive memory.
Edit intent: clarity
Original: Homeostasis occurs when an observable of a system (such as inner body temperature) remains approximately constant over a range of an external parameter (such as ambient temperature). More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Revised: Homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Edit intent: clarity
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostasis occurs frequently in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Edit intent: clarity
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks and in other networks of interacting elements where mathematical models are based on differential equations associated to the network. These networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Edit intent: clarity
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a distinguished input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Edit intent: fluency
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota , a different distinguished output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Edit intent: fluency
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis :
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o , and a number of regulatory nodes \rho_1,\ldots,\rho_n. In these models the input-output map x_o(I) . We then study infinitesimal homeostasis :
Edit intent: meaning-changed
Original:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) . We then study infinitesimal homeostasis : points \mathcal{I_0 where dx_o{d\mathcal{I}}(\mathcal{I}} _0. Stability implies that there is a stable equilibrium X(\mathcal{I})
Revised:  More precisely, homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostatic phenomena are ubiquitous in biochemical networks of differential equations and these networks can be abstracted as digraphs G with a fixed input node \iota and a different fixed output node o . We assume that only the input node depends explicitly on \mathcal{I x_o(I) _0 where dx_o{d\mathcal{I}}(\mathcal{I}} _0. Stability implies that there is a stable equilibrium X(\mathcal{I})
Edit intent: meaning-changed
Original:  points \mathcal{I_0 where dx_o{d\mathcal{I}}(\mathcal{I}} _0. Stability implies that there is a stable equilibrium X(\mathcal{I})
Revised:  points \mathcal{I_0 where dx_o{d\mathcal{I}}(\mathcal{I}} is defined by a stable equilibrium X_0 at \mathcal{I_0. Stability implies that there is a stable equilibrium X(\mathcal{I})
Edit intent: meaning-changed
Original:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Revised:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0. We show that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Edit intent: coherence
Original:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Revised:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is an (n+1)\times(n+1) homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Edit intent: meaning-changed
Original:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Revised:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H (\mathcal{I) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Edit intent: clarity
Original:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Revised:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. We use combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis.
Edit intent: coherence
Original:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory and graph theory to H allows us to classify types of homeostasis. We prove that the homeostasis types correspond to a set of irreducible blocks in H each associated with a subnetwork and these subnetworks.
Revised:  for each \mathcal{I} near \mathcal{I}_0 and infinitesimal homeostasis occurs at \mathcal{I}_0 when (dx_o/d\mathcal{I})(\mathcal{I}} _0) = 0 by showing that there is a square homeostasis matrix H associated to \mathcal{G) for which dx_o/d\mathcal{I} = 0 if and only if } \det(H) = 0. Applying combinatorial matrix theory to factor the polynomial \det(H) and thereby determine a menu of different types of possible homeostasis associated with each digraph \mathcal{G.
Edit intent: clarity
Original:  For example, a feedforward loop motif is a structural type whereas a negative feedback loop motif is an appendage type. We give two algorithms for determining a menu of homeostasis types that are possible in \mathcal{G: one algorithm enumerates the structural types and one enumerates the appendage types.
Revised:  : one algorithm enumerates the structural types and one enumerates the appendage types.
Edit intent: coherence
Original:  These subnetworks can be read directly from \mathcal{G} without performing calculations }  on model equations  .
Revised:  These subnetworks can be read directly from \mathcal{G} without performing calculations } Structural factors correspond to feedforward motifs and appendage factors correspond to feedback motifs. Finally, we discover an algorithm for determining the homeostatic subnetwork motif corresponding to each factor of \det(H) without performing numerical simulations on model equations .
Edit intent: meaning-changed
Original:  These subnetworks can be read directly from \mathcal{G} without performing calculations }  on model equations  .
Revised:  These subnetworks can be read directly from \mathcal{G} without performing calculations } on model equations . The algorithm allows us to classify low degree factors. There are two types of degree 1 homeostasis (negative feedback loops and kinetic or Haldane motifs) and there are two types of degree 2 homeostasis (feedforward loops and a degree two appendage motif) .
Edit intent: meaning-changed
Original:  Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models ( Bert, Albert ) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia) .
Revised:  Here, we trained two auto-regressive models (Transformer-XL, XLNet) and two auto-encoder models ( Bert, Albert ) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia) .
Edit intent: clarity
Original:  Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models ( Bert, Albert ) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia) .
Revised:  Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and four auto-encoder models ( Bert, Albert ) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia) .
Edit intent: meaning-changed
Original:  Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models ( Bert, Albert ) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia) .
Revised:  Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models ( BERT, Albert, Electra, T5 ) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia) .
Edit intent: meaning-changed
Original:  Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models ( Bert, Albert ) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia) .
Revised:  Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models ( Bert, Albert ) on data from UniRef and BFD containing up to 393 billion amino acids .
Edit intent: coherence
Original:  The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs ) and one TPU Pod (V3-512 or V3-1024).
Revised:  The LMs were trained on the Summit supercomputer using 5616 GPUs ) and one TPU Pod (V3-512 or V3-1024).
Edit intent: clarity
Original:  The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs ) and one TPU Pod (V3-512 or V3-1024).
Revised:  The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences.
Edit intent: meaning-changed
Original:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble (  Q2= 89).
Revised:  We validated the advantage of using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble ( Q2= 89).
Edit intent: meaning-changed
Original:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble (  Q2= 89).
Revised:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-state accuracy Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble ( Q2= 89).
Edit intent: clarity
Original:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble (  Q2= 89).
Revised:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 81\%-87\%); the second were per-protein predictions of protein sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble ( Q2= 89).
Edit intent: meaning-changed
Original:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble (  Q2= 89).
Revised:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization (ten-state accuracy: Q10= 74) and whether a protein is membrane-bound or water-soluble ( Q2= 89).
Edit intent: clarity
Original:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble (  Q2= 89).
Revised:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 81\%) and membrane vs. water-soluble ( Q2= 89).
Edit intent: meaning-changed
Original:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble (  Q2= 89).
Revised:  We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure ( 3-states: Q3= 76-84, 8-states: Q8=65-73), sub-cellular localization for 10 cellular compartments ( Q10= 74) and whether a protein is membrane-bound or water-soluble ( 2-state accuracy Q2= 89).
Edit intent: clarity