Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Immunology and Infectious Disease
Title: Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection
Abstract: Deep Learning has achieved state of the art performance in medical imaging. However, these methods for disease detection focus exclusively on improving the accuracy of classification or predictions without quantifying uncertainty in a decision. Knowing how much confidence there is in a computer-based medical diagnosis is essential for gaining clinicians trust in the technology and therefore improve treatment. Today, the 2019 Coronavirus (SARS-CoV-2) infections are a major healthcare challenge around the world. Detecting COVID-19 in X-ray images is crucial for diagnosis, assessment and treatment. However, diagnostic uncertainty in the report is a challenging and yet inevitable task for radiologist. In this paper, we investigate how drop-weights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solution to improve the diagnostic performance of the human-machine team using publicly available COVID-19 chest X-ray dataset and show that the uncertainty in prediction is highly correlates with accuracy of prediction. We believe that the availability of uncertainty-aware deep learning solution will enable a wider adoption of Artificial Intelligence (AI) in a clinical setting.
Extracted statement — Has implementation: Bayesian Convolutional Neural Network
Research field: Immunology and Infectious Disease
Title: Phase 1 Assessment of the Safety and Immunogenicity of an mRNA- Lipid Nanoparticle Vaccine Candidate Against SARS-CoV-2 in Human Volunteers
Abstract: There is an urgent need for vaccines to counter the COVID-19 pandemic due to infections with severe acute respiratory syndrome coronavirus (SARS-CoV-2). Evidence from convalescent sera and preclinical studies has identified the viral Spike (S) protein as a key antigenic target for protective immune responses. We have applied an mRNA-based technology platform, RNActive, to develop CVnCoV which contains sequence optimized mRNA coding for a stabilized form of S protein encapsulated in lipid nanoparticles (LNP). Following demonstration of protective immune responses against SARS-CoV-2 in animal models we performed a dose-escalation phase 1 study in healthy 18-60 year-old volunteers. This interim analysis shows that two doses of CVnCoV ranging from 2 g to 12 g per dose, administered 28 days apart were safe. No vaccine-related serious adverse events were reported. There were dose-dependent increases in frequency and severity of solicited systemic adverse events, and to a lesser extent of local reactions, but the majority were mild or moderate and transient in duration. Immune responses when measured as IgG antibodies against S protein or its receptor-binding domain (RBD) by ELISA, and SARS-CoV-2-virus neutralizing antibodies measured by micro-neutralization, displayed dose-dependent increases. Median titers measured in these assays two weeks after the second 12 g dose were comparable to the median titers observed in convalescent sera from COVID-19 patients. Seroconversion (defined as a 4-fold increase over baseline titer) of virus neutralizing antibodies two weeks after the second vaccination occurred in all participants who received 12 g doses. Preliminary results in the subset of subjects who were enrolled with known SARS-CoV-2 seropositivity at baseline show that CVnCoV is also safe and well tolerated in this population, and is able to boost the pre-existing immune response even at low dose levels. Based on these results, the 12 g dose is selected for further clinical investigation, including a phase 2b/3 study that will investigate the efficacy, safety, and immunogenicity of the candidate vaccine CVnCoV.
Extracted statement — Delivery Vehicle: Lipid nanoparticles
Research field: Immunology of Infectious Disease
Title: Longitudinal assessment of IFN-I activity and immune profile in critically ill COVID-19 patients with acute respiratory distress syndrome
Abstract: Abstract Background Since the onset of the pandemic, only few studies focused on longitudinal immune monitoring in critically ill COVID-19 patients with acute respiratory distress syndrome (ARDS) whereas their hospital stay may last for several weeks. Consequently, the question of whether immune parameters may drive or associate with delayed unfavorable outcome in these critically ill patients remains unsolved. Methods We present a dynamic description of immuno-inflammatory derangements in 64 critically ill COVID-19 patients including plasma IFNα2 levels and IFN-stimulated genes (ISG) score measurements. Results ARDS patients presented with persistently decreased lymphocyte count and mHLA-DR expression and increased cytokine levels. Type-I IFN response was initially induced with elevation of IFNα2 levels and ISG score followed by a rapid decrease over time. Survivors and non-survivors presented with apparent common immune responses over the first 3 weeks after ICU admission mixing gradual return to normal values of cellular markers and progressive decrease of cytokines levels including IFNα2. Only plasma TNF-α presented with a slow increase over time and higher values in non-survivors compared with survivors. This paralleled with an extremely high occurrence of secondary infections in COVID-19 patients with ARDS. Conclusions Occurrence of ARDS in response to SARS-CoV2 infection appears to be strongly associated with the intensity of immune alterations upon ICU admission of COVID-19 patients. In these critically ill patients, immune profile presents with similarities with the delayed step of immunosuppression described in bacterial sepsis.
Extracted statement — Study population: Critically ill COVID-19 patients
Research field: Industrial and Organizational Psychology
Title: Socioeconomic status and well-being during COVID-19: A resource-based examination.
Abstract: The authors assess levels and within-person changes in psychological well-being (i.e., depressive symptoms and life satisfaction) from before to during the COVID-19 pandemic for individuals in the United States, in general and by socioeconomic status (SES). The data is from 2 surveys of 1,143 adults from RAND Corporation's nationally representative American Life Panel, the first administered between April-June, 2019 and the second during the initial peak of the pandemic in the United States in April, 2020. Depressive symptoms during the pandemic were higher than population norms before the pandemic. Depressive symptoms increased from before to during COVID-19 and life satisfaction decreased. Individuals with higher education experienced a greater increase in depressive symptoms and a greater decrease in life satisfaction from before to during COVID-19 in comparison to those with lower education. Supplemental analysis illustrates that income had a curvilinear relationship with changes in well-being, such that individuals at the highest levels of income experienced a greater decrease in life satisfaction from before to during COVID-19 than individuals with lower levels of income. We draw on conservation of resources theory and the theory of fundamental social causes to examine four key mechanisms (perceived financial resources, perceived control, interpersonal resources, and COVID-19-related knowledge/news consumption) underlying the relationship between SES and well-being during COVID-19. These resources explained changes in well-being for the sample as a whole but did not provide insight into why individuals of higher education experienced a greater decline in well-being from before to during COVID-19. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Extracted statement — Indicator for well-being: Life satisfaction
Research field: Industrial and Organizational Psychology
Title: Individual differences and changes in subjective wellbeing during the early stages of the COVID-19 pandemic.
Abstract: The COVID-19 pandemic has considerably impacted many people's lives. This study examined changes in subjective wellbeing between December 2019 and May 2020 and how stress appraisals and coping strategies relate to individual differences and changes in subjective wellbeing during the early stages of the pandemic. Data were collected at 4 time points from 979 individuals in Germany. Results showed that, on average, life satisfaction, positive affect, and negative affect did not change significantly between December 2019 and March 2020 but decreased between March and May 2020. Across the latter timespan, individual differences in life satisfaction were positively related to controllability appraisals, active coping, and positive reframing, and negatively related to threat and centrality appraisals and planning. Positive affect was positively related to challenge and controllable-by-self appraisals, active coping, using emotional support, and religion, and negatively related to threat appraisal and humor. Negative affect was positively related to threat and centrality appraisals, denial, substance use, and self-blame, and negatively related to controllability appraisals and emotional support. Contrary to expectations, the effects of stress appraisals and coping strategies on changes in subjective wellbeing were small and mostly nonsignificant. These findings imply that the COVID-19 pandemic represents not only a major medical and economic crisis, but also has a psychological dimension, as it can be associated with declines in key facets of people's subjective wellbeing. Psychological practitioners should address potential declines in subjective wellbeing with their clients and attempt to enhance clients' general capability to use functional stress appraisals and effective coping strategies. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Extracted statement — Indicator for well-being: Life satisfaction
Research field: Inequality and Stratification
Title: Who is most affected by the Corona crisis? An analysis of changes in stress and well-being in Switzerland
Abstract: ABSTRACT This study analyses the consequences of the Covid-19 crisis on stress and well-being in Switzerland. In particular, we assess whether vulnerable groups in terms of social isolation, increased workload and limited socioeconomic resources are affected more than others. Using longitudinal data from the Swiss Household Panel, including a specific Covid-19 study, we estimate change score models to predict changes in perceived stress and life satisfaction at the end of the semi-lockdown in comparison to before the crisis. We find no general change in life satisfaction and a small decrease in stress. Yet, in line with our expectations, more vulnerable groups in terms of social isolation (young adults, Covid-19 risk group members, individuals without a partner), workload (women) and socioeconomic resources (unemployed and those who experienced a deteriorating financial situation) reported a decrease in life satisfaction. Stress levels decreased most strongly among high earners, workers on short-time work and the highly educated.
Extracted statement — Indicator for well-being: Life satisfaction
Research field: Inequality and Stratification
Title: Who is most affected by the Corona crisis? An analysis of changes in stress and well-being in Switzerland
Abstract: ABSTRACT This study analyses the consequences of the Covid-19 crisis on stress and well-being in Switzerland. In particular, we assess whether vulnerable groups in terms of social isolation, increased workload and limited socioeconomic resources are affected more than others. Using longitudinal data from the Swiss Household Panel, including a specific Covid-19 study, we estimate change score models to predict changes in perceived stress and life satisfaction at the end of the semi-lockdown in comparison to before the crisis. We find no general change in life satisfaction and a small decrease in stress. Yet, in line with our expectations, more vulnerable groups in terms of social isolation (young adults, Covid-19 risk group members, individuals without a partner), workload (women) and socioeconomic resources (unemployed and those who experienced a deteriorating financial situation) reported a decrease in life satisfaction. Stress levels decreased most strongly among high earners, workers on short-time work and the highly educated.
Extracted statement — has data: Swiss Household Panel
Research field: Information Science
Title: Curating Scientific Information in Knowledge Infrastructures
Abstract: Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information – meaningful secondary or derived data – about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation – as machine readable data and their machine-readable meaning – is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Extracted statement — Material: (elaborated) data products
Research field: Information Science
Title: OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science
Abstract: Hundreds of years of biodiversity research have resulted in the accumulation of a substantial pool of communal knowledge; however, most of it is stored in silos isolated from each other, such as published articles or monographs. The need for a system to store and manage collective biodiversity knowledge in a community-agreed and interoperable open format has evolved into the concept of the Open Biodiversity Knowledge Management System (OBKMS). This paper presents OpenBiodiv: An OBKMS that utilizes semantic publishing workflows, text and data mining, common standards, ontology modelling and graph database technologies to establish a robust infrastructure for managing biodiversity knowledge. It is presented as a Linked Open Dataset generated from scientific literature. OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Through the application of semantic technologies, the project showcases the value of open publishing of Findable, Accessible, Interoperable, Reusable (FAIR) data towards the establishment of open science practices in the biodiversity domain.
Extracted statement — method: a graph database
Research field: Information Science
Title: Curating Scientific Information in Knowledge Infrastructures
Abstract: Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information – meaningful secondary or derived data – about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation – as machine readable data and their machine-readable meaning – is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Extracted statement — Material: a Jupyter based prototype infrastructure
Research field: Information Science
Title: OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science
Abstract: Hundreds of years of biodiversity research have resulted in the accumulation of a substantial pool of communal knowledge; however, most of it is stored in silos isolated from each other, such as published articles or monographs. The need for a system to store and manage collective biodiversity knowledge in a community-agreed and interoperable open format has evolved into the concept of the Open Biodiversity Knowledge Management System (OBKMS). This paper presents OpenBiodiv: An OBKMS that utilizes semantic publishing workflows, text and data mining, common standards, ontology modelling and graph database technologies to establish a robust infrastructure for managing biodiversity knowledge. It is presented as a Linked Open Dataset generated from scientific literature. OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Through the application of semantic technologies, the project showcases the value of open publishing of Findable, Accessible, Interoperable, Reusable (FAIR) data towards the establishment of open science practices in the biodiversity domain.
Extracted statement — Material: a Linked Open Dataset
Research field: Information Science
Title: Situational Knowledge Representation for Traffic Observed by a Pavement Vibration Sensor Network
Abstract: Information systems that build on sensor networks often process data produced by measuring physical properties. These data can serve in the acquisition of knowledge for real-world situations that are of interest to information services and, ultimately, to people. Such systems face a common challenge, namely the considerable gap between the data produced by measurement and the abstract terminology used to describe real-world situations. We present and discuss the architecture of a software system that utilizes sensor data, digital signal processing, machine learning, and knowledge representation and reasoning to acquire, represent, and infer knowledge about real-world situations observable by a sensor network. We demonstrate the application of the system to vehicle detection and classification by measurement of road pavement vibration. Thus, real-world situations involve vehicles and information for their type, speed, and driving direction.
Extracted statement — utilizes: A sensor network
Research field: Information Science
Title: Curating Scientific Information in Knowledge Infrastructures
Abstract: Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information – meaningful secondary or derived data – about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation – as machine readable data and their machine-readable meaning – is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Extracted statement — Process: a studied natural phenomenon
Research field: Information Science
Title: OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science
Abstract: Hundreds of years of biodiversity research have resulted in the accumulation of a substantial pool of communal knowledge; however, most of it is stored in silos isolated from each other, such as published articles or monographs. The need for a system to store and manage collective biodiversity knowledge in a community-agreed and interoperable open format has evolved into the concept of the Open Biodiversity Knowledge Management System (OBKMS). This paper presents OpenBiodiv: An OBKMS that utilizes semantic publishing workflows, text and data mining, common standards, ontology modelling and graph database technologies to establish a robust infrastructure for managing biodiversity knowledge. It is presented as a Linked Open Dataset generated from scientific literature. OpenBiodiv encompasses data extracted from more than 5000 scholarly articles published by Pensoft and many more taxonomic treatments extracted by Plazi from journals of other publishers. The data from both sources are converted to Resource Description Framework (RDF) and integrated in a graph database using the OpenBiodiv-O ontology and an RDF version of the Global Biodiversity Information Facility (GBIF) taxonomic backbone. Through the application of semantic technologies, the project showcases the value of open publishing of Findable, Accessible, Interoperable, Reusable (FAIR) data towards the establishment of open science practices in the biodiversity domain.
Extracted statement — Material: a substantial pool