wanyu/IteraTeR_full_sent huggingface dataset:
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). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences.
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 ( Q2= 91\%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that protein LMs learned some of the grammar of the language of life realized in protein sequences.
Edit intent: meaning-changed
Original: Among the many aspects that characterize the COVID-19 pandemic, two seem particularly challenging to understand: i), the great geographical differences in the degree of virus contagiousness and lethality which were found in the different phases of the epidemic progression, and , ii) , the potential role of the infected people's blood type in both the virus infectivity and the progression of the disease.
Revised: Among the many aspects that characterize the COVID-19 pandemic, two seem particularly challenging to understand: (i) the great geographical differences in the degree of virus contagiousness and lethality which were found in the different phases of the epidemic progression, and , ii) , the potential role of the infected people's blood type in both the virus infectivity and the progression of the disease.
Edit intent: fluency
Original: Among the many aspects that characterize the COVID-19 pandemic, two seem particularly challenging to understand: i), the great geographical differences in the degree of virus contagiousness and lethality which were found in the different phases of the epidemic progression, and , ii) , the potential role of the infected people's blood type in both the virus infectivity and the progression of the disease.
Revised: Among the many aspects that characterize the COVID-19 pandemic, two seem particularly challenging to understand: i), the great geographical differences in the degree of virus contagiousness and lethality which were found in the different phases of the epidemic progression, and (ii) the potential role of the infected people's blood type in both the virus infectivity and the progression of the disease.
Edit intent: fluency
Original:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Revised:  A recent hypothesis could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Edit intent: clarity
Original:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Revised:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on both aspects. Specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Edit intent: meaning-changed
Original:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Revised:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that in the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Edit intent: clarity
Original:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Revised:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject . Thus these conserved antigens can potentially cause an immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Edit intent: clarity
Original:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Revised:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject that has previously acquired specific antibodies to the source subject antigens.
Edit intent: fluency
Original:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies to the source subject antigens.
Revised:  Recently, Breiman et al. (2020) presented a hypothesis that could shed some light on i) and ii), specifically, it has been proposed that during the subject-to-subject transfer SARS-CoV-2 conserves on its capsid the erythrocytes' antigens of the source subject , causing a potential immune reaction in a receiving subject which has previously acquired specific antibodies for the source subject antigens.
Edit intent: fluency
Original:  The strong geographical dependence of the blood type distribution could be, therefore, one of the factors at the origin of i). Here we present an epidemiological deterministic model where the infection rules hypothesized in Breiman et al. (2020) are taken into account .
Revised:  The strong geographical dependence of the blood type distribution could be, therefore, one of the factors at the origin of the observed heterogeneity in the epidemics spread. Here, we present an epidemiological deterministic model where the infection rules hypothesized in Breiman et al. (2020) are taken into account .
Edit intent: meaning-changed
Original:  Here we present an epidemiological deterministic model where the infection rules hypothesized in Breiman et al. (2020) are taken into account . The comparison of the model outcome with exiting worldwide infection progression data seems to support the Breiman et al. (2020) hypothesis .
Revised:  Here we present an epidemiological deterministic model where the infection rules hypothesized in Breiman et al. (2020) are taken into account and compare our model outcomes with the exiting worldwide infection progression data seems to support the Breiman et al. (2020) hypothesis .
Edit intent: coherence
Original: This paper describes a large dataset covering over 63 million coronavirus-related Twitter posts from more than 13 million unique users since 28 January to 1 July 2020.
Revised: This paper presents a large annotated dataset on public expressions related to the COVID-19 pandemic. Through Twitter's standard search application programming interface, we retrieved over 63 million coronavirus-related Twitter posts from more than 13 million unique users since 28 January to 1 July 2020.
Edit intent: meaning-changed
Original:  As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1)
Revised:  As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using natural language processing techniques and machine learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1)
Edit intent: fluency
Original:  As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1)
Revised:  As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using natural language processing techniques and machine-learning based algorithms, we annotated each public tweet with seventeen latent semantic attributes associated with each tweet, including 1)
Edit intent: clarity
Original:  As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1)
Revised:  As strong concerns and emotions are expressed in the tweets, we analysed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes , including: 1)
Edit intent: clarity
Original:  ten  attributes indicating the tweet's relevance  to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Revised:  ten binary attributes indicating the tweet's relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Edit intent: meaning-changed
Original:  ten  attributes indicating the tweet's relevance  to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Revised:  ten attributes indicating the tweet's relevance or irrelevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Edit intent: meaning-changed
Original:  ten  attributes indicating the tweet's relevance  to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Revised:  ten attributes indicating the tweet's relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity) , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Edit intent: meaning-changed
Original:  ten  attributes indicating the tweet's relevance  to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Revised:  ten attributes indicating the tweet's relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Edit intent: coherence
Original:  ten  attributes indicating the tweet's relevance  to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Revised:  ten attributes indicating the tweet's relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Edit intent: coherence
Original:  ten  attributes indicating the tweet's relevance  to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
Revised:  ten attributes indicating the tweet's relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness)and emotional intensities across four primary emotions of fear , anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the dominant emotion category, respectively.
Edit intent: clarity
Original:  To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss  possible usage in communication, psychology, public health, economics and epidemiology research.
Revised:  We report basic descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss possible usage in communication, psychology, public health, economics and epidemiology research.
Edit intent: clarity
Original:  To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss  possible usage in communication, psychology, public health, economics and epidemiology research.
Revised:  To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss its possible usage in communication, psychology, public health, economics and epidemiology research.
Edit intent: fluency
Original: This paper presents a large annotated dataset on public expressions related to the COVID-19 pandemic . Through Twitter 's standard search application programming interface , we retrieved over 63 million coronavirus-related public posts from more than 13 million unique users since 28 January to 1 July 2020.
Revised: This paper presents a large annotated dataset on public expressions related to the COVID-19 pandemic over the Twitter platform. From 28 January 2020 to 1 Jan 2021 , we retrieved over 63 million coronavirus-related public posts from more than 13 million unique users since 28 January to 1 July 2020.
Edit intent: meaning-changed
Original:  Through Twitter 's standard search application programming interface , we retrieved over 63 million coronavirus-related public posts from more than 13 million unique users since 28 January to 1 July 2020.
Revised:  Through Twitter 's standard search application programming interface , we retrieved over 132 million public Twitter posts (i.e., tweets) from more than 13 million unique users since 28 January to 1 July 2020.
Edit intent: meaning-changed
Original:  Through Twitter 's standard search application programming interface , we retrieved over 63 million coronavirus-related public posts from more than 13 million unique users since 28 January to 1 July 2020.
Revised:  Through Twitter 's standard search application programming interface , we retrieved over 63 million coronavirus-related public posts from more than 20 million unique users since 28 January to 1 July 2020.
Edit intent: meaning-changed
Original:  Through Twitter 's standard search application programming interface , we retrieved over 63 million coronavirus-related public posts from more than 13 million unique users since 28 January to 1 July 2020. Using natural language processing techniques and machine learning based algorithms, we annotated each public tweet with seventeen latent semantic attributes, including :
Revised:  Through Twitter 's standard search application programming interface , we retrieved over 63 million coronavirus-related public posts from more than 13 million unique users using four keywords: "corona", "wuhan", "nCov" and "covid". Leveraging natural language processing techniques and machine learning based algorithms, we annotated each public tweet with seventeen latent semantic attributes, including :
Edit intent: meaning-changed
Original:  Using natural language processing techniques and machine learning based algorithms, we annotated each public tweet with seventeen latent semantic attributes, including : 1 ) ten binary attributes indicating the tweet's relevance or irrelevance to  ten detected topics,
Revised:  Using natural language processing techniques and machine learning based algorithms, we annotated each public tweet with seventeen latent semantic attributes, including a ) ten binary attributes indicating the tweet's relevance or irrelevance to ten detected topics,
Edit intent: coherence
Original:  2 ) five quantitative  attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity), and 3 ) two qualitative attributes indicating the sentiment category and the dominant emotion category , respectively .
Revised:  b ) five quantitative attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity), and 3 ) two qualitative attributes indicating the sentiment category and the dominant emotion category , respectively .
Edit intent: fluency
Original:  2 ) five quantitative  attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity), and 3 ) two qualitative attributes indicating the sentiment category and the dominant emotion category , respectively .
Revised:  2 ) five quantitative emotion intensity attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity), and 3 ) two qualitative attributes indicating the sentiment category and the dominant emotion category , respectively .
Edit intent: clarity
Original:  2 ) five quantitative  attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity), and 3 ) two qualitative attributes indicating the sentiment category and the dominant emotion category , respectively .
Revised:  2 ) five quantitative attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from barely noticeable to extremely high intensity), and 3 ) two qualitative attributes indicating the sentiment category and the dominant emotion category , respectively .
Edit intent: clarity
Original:  2 ) five quantitative  attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity), and 3 ) two qualitative attributes indicating the sentiment category and the dominant emotion category , respectively .
Revised:  2 ) five quantitative attributes indicating the degree of intensity of the valence or sentiment (from extremely negative to extremely positive), and the degree of intensity of fear, of anger, of sadness and of joy emotions (from extremely low intensity to extremely high intensity), and c ) two qualitative attributes indicating the sentiment category and the dominant emotion category , respectively .
Edit intent: meaning-changed