Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Climate
Title: Linking sea level rise and socioeconomic indicators underthe Shared Socioeconomic Pathways
Abstract: In order to assess future sea level rise and its societal impacts, we need to study climate change pathways combined with different scenarios of socioeconomic development. Here, we present Sea Level Rise (SLR) projections for the Shared Socioeconomic Pathway (SSP) storylines and different year-2100 radiative Forcing Targets (FTs). Future SLR is estimated with a comprehensive SLR emulator that accounts for Antarctic rapid discharge from hydrofracturing and ice cliff instability. Across all baseline scenario realizations (no dedicated climate mitigation), we find 2100 median SLR relative to 1986-2005 of 89 cm (likely range: 57 to 130 cm) for SSP1, 105 cm (73 to 150 cm) for SSP2, 105 cm (75 to 147 cm) for SSP3, 93 cm (63 to 133 cm) for SSP4, and 132 cm (95 to 189 cm) for SSP5. The 2100 sea level responses for combined SSP-FT scenarios are dominated by the mitigation targets and yield median estimates of 52 cm (34 to 75 cm) for FT 2.6 Wm-2, 62 cm (40 to 96 cm) for FT 3.4 Wm-2, 75 cm (47 to 113 cm) for FT 4.5 Wm-2, and 91 cm (61 to 132 cm) for FT 6.0 Wm-2. Average 2081-2100 annual SLR rates are 5 mm yr-1 and 19 mm yr-1 for FT 2.6 Wm-2 and the baseline scenarios, respectively. Our model setup allows linking scenario-specific emission and socioeconomic indicators to projected SLR. We find that 2100 median SSP SLR projections could be limited to around 50 cm if 2050 cumulative CO2 emissions since pre-industrial stay below 850 GtC ,with a global coal phase-out nearly completed by that time. For SSP mitigation scenarios, a 2050 carbon price of 100 US$2005 tCO2 -1 would correspond to a median 2100 SLR of around 65 cm. Our results confirm that rapid and early emission reductions are essential for limiting 2100 SLR.
Extracted statement — has  start of period: 1986-2005
Research field: Climate
Title: Linking sea level rise and socioeconomic indicators underthe Shared Socioeconomic Pathways
Abstract: In order to assess future sea level rise and its societal impacts, we need to study climate change pathways combined with different scenarios of socioeconomic development. Here, we present Sea Level Rise (SLR) projections for the Shared Socioeconomic Pathway (SSP) storylines and different year-2100 radiative Forcing Targets (FTs). Future SLR is estimated with a comprehensive SLR emulator that accounts for Antarctic rapid discharge from hydrofracturing and ice cliff instability. Across all baseline scenario realizations (no dedicated climate mitigation), we find 2100 median SLR relative to 1986-2005 of 89 cm (likely range: 57 to 130 cm) for SSP1, 105 cm (73 to 150 cm) for SSP2, 105 cm (75 to 147 cm) for SSP3, 93 cm (63 to 133 cm) for SSP4, and 132 cm (95 to 189 cm) for SSP5. The 2100 sea level responses for combined SSP-FT scenarios are dominated by the mitigation targets and yield median estimates of 52 cm (34 to 75 cm) for FT 2.6 Wm-2, 62 cm (40 to 96 cm) for FT 3.4 Wm-2, 75 cm (47 to 113 cm) for FT 4.5 Wm-2, and 91 cm (61 to 132 cm) for FT 6.0 Wm-2. Average 2081-2100 annual SLR rates are 5 mm yr-1 and 19 mm yr-1 for FT 2.6 Wm-2 and the baseline scenarios, respectively. Our model setup allows linking scenario-specific emission and socioeconomic indicators to projected SLR. We find that 2100 median SSP SLR projections could be limited to around 50 cm if 2050 cumulative CO2 emissions since pre-industrial stay below 850 GtC ,with a global coal phase-out nearly completed by that time. For SSP mitigation scenarios, a 2050 carbon price of 100 US$2005 tCO2 -1 would correspond to a median 2100 SLR of around 65 cm. Our results confirm that rapid and early emission reductions are essential for limiting 2100 SLR.
Extracted statement — has end of period: 2081-2100
Research field: Communication Neuroscience
Title: Increased attention but more efficient disengagement: Neuroscientific evidence for defensive processing of threatening health information.
Abstract: OBJECTIVE Previous studies indicate that people respond defensively to threatening health information, especially when the information challenges self-relevant goals. The authors investigated whether reduced acceptance of self-relevant health risk information is already visible in early attention processes, that is, attention disengagement processes. DESIGN In a randomized, controlled trial with 29 smoking and nonsmoking students, a variant of Posner's cueing task was used in combination with the high-temporal resolution method of event-related brain potentials (ERPs). MAIN OUTCOME MEASURES Reaction times and P300 ERP. RESULTS Smokers showed lower P300 amplitudes in response to high- as opposed to low-threat invalid trials when moving their attention to a target in the opposite visual field, indicating more efficient attention disengagement processes. Furthermore, both smokers and nonsmokers showed increased P300 amplitudes in response to the presentation of high- as opposed to low-threat valid trials, indicating threat-induced attention-capturing processes. Reaction time measures did not support the ERP data, indicating that the ERP measure can be extremely informative to measure low-level attention biases in health communication. CONCLUSION The findings provide the first neuroscientific support for the hypothesis that threatening health information causes more efficient disengagement among those for whom the health threat is self-relevant.
Extracted statement — Has method: ERP
Research field: Communication Neuroscience
Title: Neural Correlates of Risk Perception during Real-Life Risk Communication
Abstract: During global health crises, such as the recent H1N1 pandemic, the mass media provide the public with timely information regarding risk. To obtain new insights into how these messages are received, we measured neural data while participants, who differed in their preexisting H1N1 risk perceptions, viewed a TV report about H1N1. Intersubject correlation (ISC) of neural time courses was used to assess how similarly the brains of viewers responded to the TV report. We found enhanced intersubject correlations among viewers with high-risk perception in the anterior cingulate, a region which classical fMRI studies associated with the appraisal of threatening information. By contrast, neural coupling in sensory-perceptual regions was similar for the high and low H1N1-risk perception groups. These results demonstrate a novel methodology for understanding how real-life health messages are processed in the human brain, with particular emphasis on the role of emotion and differences in risk perceptions.
Extracted statement — Has approach: fMRI
Research field: Communication Neuroscience
Title: Neural Correlates of Risk Perception during Real-Life Risk Communication
Abstract: During global health crises, such as the recent H1N1 pandemic, the mass media provide the public with timely information regarding risk. To obtain new insights into how these messages are received, we measured neural data while participants, who differed in their preexisting H1N1 risk perceptions, viewed a TV report about H1N1. Intersubject correlation (ISC) of neural time courses was used to assess how similarly the brains of viewers responded to the TV report. We found enhanced intersubject correlations among viewers with high-risk perception in the anterior cingulate, a region which classical fMRI studies associated with the appraisal of threatening information. By contrast, neural coupling in sensory-perceptual regions was similar for the high and low H1N1-risk perception groups. These results demonstrate a novel methodology for understanding how real-life health messages are processed in the human brain, with particular emphasis on the role of emotion and differences in risk perceptions.
Extracted statement — Has method: fMRI
Research field: Communication Neuroscience
Title: Content Matters: Neuroimaging Investigation of Brain and Behavioral Impact of Televised Anti-Tobacco Public Service Announcements
Abstract: Televised public service announcements are video ads that are a key component of public health campaigns against smoking. Understanding the neurophysiological correlates of anti-tobacco ads is an important step toward novel objective methods of their evaluation and design. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate the brain and behavioral effects of the interaction between content (“argument strength,” AS) and format (“message sensation value,” MSV) of anti-smoking ads in humans. Seventy-one nontreatment-seeking smokers viewed a sequence of 16 high or 16 low AS ads during an fMRI scan. Dependent variables were brain fMRI signal, the immediate recall of the ads, the immediate change in intentions to quit smoking, and the urine levels of a major nicotine metabolite cotinine at a 1 month follow-up. Whole-brain ANOVA revealed that AS and MSV interacted in the inferior frontal, inferior parietal, and fusiform gyri; the precuneus; and the dorsomedial prefrontal cortex (dMPFC). Regression analysis showed that the activation in the dMPFC predicted the urine cotinine levels 1 month later. These results characterize the key brain regions engaged in the processing of persuasive communications and suggest that brain fMRI response to anti-smoking ads could predict subsequent smoking severity in nontreatment-seeking smokers. Our findings demonstrate the importance of the quality of content for objective ad outcomes and suggest that fMRI investigation may aid the prerelease evaluation of televised public health ads.
Extracted statement — Has method: fMRI
Research field: Communication Neuroscience
Title: Communicating with Sensation Seekers: An fMRI Study of Neural Responses to Antidrug Public Service Announcements
Abstract: ABSTRACT This study examined the neural basis of processing high- and low-message sensation value (MSV) antidrug public service announcements (PSAs) in high (HSS) and low sensation seekers (LSS) using fMRI. HSS more strongly engaged the salience network when processing PSAs (versus LSS), suggesting that high-MSV PSAs attracted their attention. HSS and LSS participants who engaged higher level cognitive processing regions reported that the PSAs were more convincing and believable and recalled the PSAs better immediately after testing. In contrast, HSS and LSS participants who strongly engaged visual attention regions for viewing PSAs reported lower personal relevance. These findings provide neurobiological evidence that high-MSV content is salient to HSS, a primary target group for antidrug messages, and additional cognitive processing is associated with higher perceived message effectiveness.
Extracted statement — Has method: fMRI
Research field: Computational Engineering
Title: Predicting Infections Using Computational Intelligence – A Systematic Review
Abstract: Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.
Extracted statement — Has result: SSI
Research field: Computational Linguistics
Title: GENETAG: a tagged corpus for gene/protein named entity recognition
Abstract: Abstract Background Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE ® sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. Results To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. Conclusion The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz" ext-link-type="uri">ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz</jats:ext-link>. A newer version of GENETAG-05, will be released later this year.
Extracted statement — Dataset name: GENETAG
Research field: Computational Linguistics
Title: MedTag: A Collection of Biomedical Annotations
Abstract: We present a database of annotated biomedical text corpora merged into a portable data structure with uniform conventions. MedTag combines three corpora, MedPost, ABGene and GENETAG, within a common relational database data model. The GENETAG corpus has been modified to reflect new definitions of genes and proteins. The MedPost corpus has been updated to include 1,000 additional sentences from the clinical medicine domain. All data have been updated with original MEDLINE text excerpts, PubMed identifiers, and tokenization independence to facilitate data accuracy, consistency and usability. The data are available in flat files along with software to facilitate loading the data into a relational SQL database from ftp://ftp.ncbi.nlm.nih.gov/pub/lsmith/MedTag/medtag.tar.gz.
Extracted statement — Other resources: GENETAG
Research field: Computational Linguistics
Title: The ITI TXM corpora: Tissue expressions and protein-protein interactions
Abstract: We report on two large corpora of semantically annotated full-text biomedical research papers created in order to devel op information extraction ( IE) tools for the TXM project. Both corpora have been annotated with a range of entities (CellLine, Complex, DevelopmentalStage, Disease, DrugCompound, ExperimentalMethod, Fragment, Fusion, GOMOP, Gene, Modification, mRNAcDNA, Mutant, Protein, Tissue), normalisations of selected entities to the NCBI Taxonomy, RefSeq, EntrezGene, ChEBI and MeSH and enriched relations (protein-protein interactions, tissue expressions and fr agment- or mutant-protein relations). While one corpus targets protein-protein interactions ( PPIs), the focus of other is on tissue expressions ( TEs). This paper describes the selected markables and the annotation process of the ITI TXM corpora, and provides a detailed breakdown of the inter-annotator agreement (IAA).
Extracted statement — Concept types: GOMOP
Research field: Computational Linguistics
Title: Chemical names: terminological resources and corpora annotation
Abstract: Chemical compounds like small signal molecules or other biological active chemical substances are an important entity class in life science publications and patents. The recognition of these named entities relies on appropriate dictionary resources as well as on training and evaluation corpora. In this work we give an overview of publicly available chemical information resources with respect to chemical terminology. The coverage, amount of synonyms, and especially the inclusion of SMILES or InChI are considered. Normalization of different chemical names to a unique structure is only possible with these structure representations. In addition, the generation and annotation of training and testing corpora is presented. We describe a small corpus for the evaluation of dictionaries containing chemical enities as well as a training and test corpus for the recognition of IUPAC and IUPAC-like names, which cannot be fully enumerated in dictionaries. Corpora can be found on http://www.scai.fraunhofer.de/chem-corpora.html
Extracted statement — classes: IUPAC
Research field: Computational Linguistics
Title: Syntax Annotation for the GENIA Corpus
Abstract: Linguistically annotated corpus based on texts in biomedical domain has been constructed to tune natural language processing (NLP) tools for biotextmining. As the focus of information extraction is shifting from "nominal" information such as named entity to "verbal" information such as function and interaction of substances, application of parsers has become one of the key technologies and thus the corpus annotated for syntactic structure of sentences is in demand. A subset of the GENIA corpus consisting of 500 MEDLINE abstracts has been annotated for syntactic structure in an XMLbased format based on Penn Treebank II (PTB) scheme. Inter-annotator agreement test indicated that the writing style rather than the contents of the research abstracts is the source of the difficulty in tree annotation, and that annotation can be stably done by linguists without much knowledge of biology with appropriate guidelines regarding to linguistic phenomena particular to scientific texts.
Extracted statement — data source: MEDLINE
Research field: Computational Linguistics
Title: Syntax Annotation for the GENIA Corpus
Abstract: Linguistically annotated corpus based on texts in biomedical domain has been constructed to tune natural language processing (NLP) tools for biotextmining. As the focus of information extraction is shifting from "nominal" information such as named entity to "verbal" information such as function and interaction of substances, application of parsers has become one of the key technologies and thus the corpus annotated for syntactic structure of sentences is in demand. A subset of the GENIA corpus consisting of 500 MEDLINE abstracts has been annotated for syntactic structure in an XMLbased format based on Penn Treebank II (PTB) scheme. Inter-annotator agreement test indicated that the writing style rather than the contents of the research abstracts is the source of the difficulty in tree annotation, and that annotation can be stably done by linguists without much knowledge of biology with appropriate guidelines regarding to linguistic phenomena particular to scientific texts.
Extracted statement — Data formats: PTB
Research field: Computational Linguistics
Title: Syntax Annotation for the GENIA Corpus
Abstract: Linguistically annotated corpus based on texts in biomedical domain has been constructed to tune natural language processing (NLP) tools for biotextmining. As the focus of information extraction is shifting from "nominal" information such as named entity to "verbal" information such as function and interaction of substances, application of parsers has become one of the key technologies and thus the corpus annotated for syntactic structure of sentences is in demand. A subset of the GENIA corpus consisting of 500 MEDLINE abstracts has been annotated for syntactic structure in an XMLbased format based on Penn Treebank II (PTB) scheme. Inter-annotator agreement test indicated that the writing style rather than the contents of the research abstracts is the source of the difficulty in tree annotation, and that annotation can be stably done by linguists without much knowledge of biology with appropriate guidelines regarding to linguistic phenomena particular to scientific texts.
Extracted statement — Data formats: XML
Research field: Computational Linguistics
Title: The ITI TXM corpora: Tissue expressions and protein-protein interactions
Abstract: We report on two large corpora of semantically annotated full-text biomedical research papers created in order to devel op information extraction ( IE) tools for the TXM project. Both corpora have been annotated with a range of entities (CellLine, Complex, DevelopmentalStage, Disease, DrugCompound, ExperimentalMethod, Fragment, Fusion, GOMOP, Gene, Modification, mRNAcDNA, Mutant, Protein, Tissue), normalisations of selected entities to the NCBI Taxonomy, RefSeq, EntrezGene, ChEBI and MeSH and enriched relations (protein-protein interactions, tissue expressions and fr agment- or mutant-protein relations). While one corpus targets protein-protein interactions ( PPIs), the focus of other is on tissue expressions ( TEs). This paper describes the selected markables and the annotation process of the ITI TXM corpora, and provides a detailed breakdown of the inter-annotator agreement (IAA).
Extracted statement — Other resources: MeSH
Research field: Computational Linguistics
Title: MedTag: A Collection of Biomedical Annotations
Abstract: We present a database of annotated biomedical text corpora merged into a portable data structure with uniform conventions. MedTag combines three corpora, MedPost, ABGene and GENETAG, within a common relational database data model. The GENETAG corpus has been modified to reflect new definitions of genes and proteins. The MedPost corpus has been updated to include 1,000 additional sentences from the clinical medicine domain. All data have been updated with original MEDLINE text excerpts, PubMed identifiers, and tokenization independence to facilitate data accuracy, consistency and usability. The data are available in flat files along with software to facilitate loading the data into a relational SQL database from ftp://ftp.ncbi.nlm.nih.gov/pub/lsmith/MedTag/medtag.tar.gz.
Extracted statement — Other resources: ABGene