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Access to greenspace in the form of urban parks is frequently used to study the mental health benefits of nature and may alleviate depression. However, there is a lack of research that considers the different types of vegetated and non-vegetated spaces that parks can provide. Our aim was to investigate whether different types of accessible park area, grassy; tree covered; and non-vegetated, were associated with depressive symptoms among older (≥50 years) urban US adults. We used interviews from the Health and Retirement Study spanning 2010 through 2016 as our primary data source. We calculated total grassy, tree covered, and non-vegetated park space accessible to participants using a comprehensive national database of US parks and a high resolution (10 m) landcover dataset. To measure depressive symptoms, we used the CESD-8 analyzed as a continuous scale. We used Poisson regression to estimate the percent difference in CESD-8 scores comparing quartiles of accessible park space. To control for confounding, we adjusted for sociodemographic characteristics, geography, and climate. Aggregated accessible park area was not substantively associated with depressive symptoms. However, having grassy park area near the home was associated with as much as 27 % fewer depressive symptoms. In contrast, non-vegetated park area was associated with up to 54 % more depressive symptoms. Our findings were robust to adjustment for air pollution, environmental noise, and artificial light at night. Different types of accessible park space may have disparate effects on mental health among older urban US adults.

We introduce the Open Research Knowledge Graph Agriculture Named Entity Recognition (the ORKG Agri-NER) corpus and service for contribution-centric scientific entity extraction and classification. The ORKG Agri-NER corpus is a seminal benchmark for the evaluation of contribution-centric scientific entity extraction and classification in the agricultural domain. It comprises titles of scholarly papers that are available as Open Access articles on a major publishing platform. We describe the creation of this corpus and highlight the obtained findings in terms of the following features: (1) a generic conceptual formalism focused on capturing scientific entities in agriculture that reflect the direct contribution of a work; (2) a performance benchmark for named entity recognition of scientific entities in the agricultural domain by empirically evaluating various state-of-the-art sequence labeling neural architectures and transformer models; and (3) a delineated 3-step automatic entity resolution procedure for the resolution of the scientific entities to an authoritative ontology, specifically AGROVOC that is released in the Linked Open Vocabularies cloud. With this work we aim to provide a strong foundation for future work on the automatic discovery of scientific entities in the scholarly literature of the agricultural domain.

Abstract Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.

The Open Research Knowledge Graph (ORKG) is a digital library for machine-actionable scholarly knowledge, with a focus on structured research comparisons obtained through expert crowdsourcing. While the ORKG has attracted a community of more than 1,000 users, the curated data has not been subject to an in-depth quality assessment so far. Here, proposed as a first exemplary step, within a team of domain experts, we evaluate the quality of six selected ORKG Comparisons based on three criteria, namely: 1) the quality of semantic modelling, 2) the maturity of the Comparisons in terms of their completeness, syntactic representation, identifier stability, and their linkability mechanisms ensuring the interoperability and discoverability. Finally, 3) the informative usefulness of the Comparisons to expert and lay users. We have found that each criterion addresses a unique and independent aspect of quality. Backed by the observations of our quality evaluations presented in this paper, a fitting model of knowledge graph quality appears one that is indeed multidimensional as ours.

Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhance science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers’ contributions in a structured manner, but this is labor-intensive and inconsistent among human domain-expert curators. We propose using Large Language Models (LLMs) to automatically suggest these properties. However, it is essential to assess the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before their application. Our study performs a comprehensive comparative analysis between the ORKG’s manually curated properties and those generated by the aforementioned state-of-the-art LLMs. We evaluate LLM performance from four unique perspectives: semantic alignment with and deviation from ORKG properties, fine-grained property mapping accuracy, SciNCL embedding-based cosine similarity, and expert surveys comparing manual annotations with LLM outputs. These evaluations occur within a multidisciplinary science setting. Overall, LLMs show potential as recommendation systems for structuring science, but further fine-tuning is recommended to improve their alignment with scientific tasks and mimicry of human expertise.

We are faced with an unprecedented production in scholarly publications worldwide. Stakeholders in the digital libraries posit that the document-based publishing paradigm has reached the limits of adequacy. Instead, structured, machine-interpretable, fine-grained scholarly knowledge publishing as Knowledge Graphs (KG) is strongly advocated. In this work, we develop and analyze a large-scale structured dataset of STEM articles across 10 different disciplines, viz. Agriculture, Astronomy, Biology, Chemistry, Computer Science, Earth Science, Engineering, Material Science, Mathematics, and Medicine. Our analysis is defined over a large-scale corpus comprising 60K abstracts structured as four scientific entities process, method, material, and data. Thus, our study presents, for the first time, an analysis of a large-scale multidisciplinary corpus under the construct of four named entity labels that are specifically defined and selected to be domain-independent as opposed to domain-specific. The work is then inadvertently a feasibility test of characterizing multidisciplinary science with domain-independent concepts. Further, to summarize the distinct facets of scientific knowledge per concept per discipline, a set of word cloud visualizations are offered. The STEM-NER-60k corpus, created in this work, comprises over 1 M extracted entities from 60k STEM articles obtained from a major publishing platform and is publicly released.

IntroductionStroke rehabilitation guidelines promoteclinical decision making, enhance quality of healthcare delivery, minimize healthcare costs, and identify gaps in current knowledge to guide future research. However, there are no published reviews that have exclusively evaluated the quality of existing Clinical Practice Guidelines (CPGs) for stroke rehabilitation from Low- and Middle-Income Countries (LMICs) or provided any insights into the cultural variation, adaptations, or gaps in implementation specific to LMICs.ObjectivesTo identify CPGs developed by LMICs for stroke rehabilitation and evaluate their quality using AGREE-II and AGREE-REX tool.MethodsThe review protocol is prepared in accordance with the PRISMA-P guidelines and the review was registered in PROSPERO (CRD42022382486). The search was run in Medline, EMBASE, CINHAL, PEDro for guidelines published between 2000 till July 2022. Additionally, SUMSearch, Google, and other guideline portals and gray literature were searched. The included studies were then subjected to data extraction for the following details: Study ID, title of the CPG, country of origin, characteristics of CPG (Scope-national/regional, level of care, multidisciplinary/uni-disciplinary), and information on stroke rehabilitation relevant recommendations. The quality of the included CPGs will be subsequently evaluated using AGREE-II and AGREE-REX tool.Results & conclusionThis systematic review aims to explore the gaps in existing CPGs specific to LMICs and will aid in development/adaptation/contextualization of CPGs for implementation in LMICs.

Abstract Background Pregnant women with epilepsy frequently experience seizures related to pregnancy complications and are often prescribed anti-epileptic drugs (AEDs) to manage their symptoms. However, less is known about the comparative safety of AED exposure in utero. We aimed to compare the risk of congenital malformations (CMs) and prenatal outcomes of AEDs in infants/children who were exposed to AEDs in utero through a systematic review and Bayesian random-effects network meta-analysis. Methods MEDLINE, EMBASE, and Cochrane CENTRAL were searched from inception to December 15, 2015. Two reviewers independently screened titles/abstracts and full-text papers for experimental and observational studies comparing mono- or poly-therapy AEDs versus control (no AED exposure) or other AEDs, then abstracted data and appraised the risk of bias. The primary outcome was incidence of major CMs, overall and by specific type (cardiac malformations, hypospadias, cleft lip and/or palate, club foot, inguinal hernia, and undescended testes). Results After screening 5305 titles and abstracts, 642 potentially relevant full-text articles, and 17 studies from scanning reference lists, 96 studies were eligible (n = 58,461 patients). Across all major CMs, many AEDs were associated with higher risk compared to control. For major CMs, ethosuximide (OR, 3.04; 95% CrI, 1.23–7.07), valproate (OR, 2.93; 95% CrI, 2.36–3.69), topiramate (OR, 1.90; 95% CrI, 1.17–2.97), phenobarbital (OR, 1.83; 95% CrI, 1.35–2.47), phenytoin (OR, 1.67; 95% CrI, 1.30–2.17), carbamazepine (OR, 1.37; 95% CrI, 1.10–1.71), and 11 polytherapies were significantly more harmful than control, but lamotrigine (OR, 0.96; 95% CrI, 0.72–1.25) and levetiracetam (OR, 0.72; 95% CrI, 0.43–1.16) were not. Conclusion The newer generation AEDs, lamotrigine and levetiracetam, were not associated with significant increased risks of CMs compared to control, and were significantly less likely to be associated with children experiencing cardiac malformations than control. However, this does not mean that these agents are not harmful to infants/children exposed in utero. Counselling is advised concerning teratogenic risks when the prescription is written for a woman of childbearing age and before women continue with these agents when considering pregnancy, such as switching from polytherapy to monotherapy with evidence of lower risk and avoiding AEDs, such as valproate, that are consistently associated with CMs. These decisions must be balanced against the need for seizure control. Systematic Review Registration PROSPERO CRD42014008925