Today, with the development of the Semantic Web, Linked Open Data (LOD), expressed using the Resource Description Framework (RDF), has reached the status of "big data" and can be considered as a giant data resource from which knowledge can be discovered. The process of learning knowledge defined in terms of OWL 2 axioms from the RDF datasets can be viewed as a special case of knowledge discovery from data or "data mining", which can be called "RDF mining". The approaches to automated generation of the axioms from recorded RDF facts on the Web may be regarded as a case of inductive reasoning and ontology learning. The instances, represented by RDF triples, play the role of specific observations, from which axioms can be extracted by generalization. Based on the insight that discovering new knowledge is essentially an evolutionary process, whereby hypotheses are generated by some heuristic mechanism and then tested against the available evidence, so that only the best hypotheses survive, we propose the use of Grammatical Evolution, one type of evolutionary algorithm, for mining disjointness OWL 2 axioms from an RDF data repository such as DBpedia. For the evaluation of candidate axioms against the DBpedia dataset, we adopt an approach based on possibility theory.
We have previously reported that silica nanoparticles (SiNPs) of nominal size 50 nm (Si50) induce the pro-inflammatory cytokines CXCL8 and IL-6 in BEAS-2B cells, via mechanisms involving MAPK p38, TACE-mediated TGF-α release and the NF-κB pathway. In this study, we examined whether these findings are cell specific or might be extended to another epithelial lung cell model, HBEC3-KT, and also to SiNPs of a smaller size (nominal size of 10 nm; Si10). The TEM average size of Si10 and Si50 was 10.9 and 34.7 nm, respectively. The surface area (BET) of Si10 was three times higher than for Si50 per mass unit. With respect to hydrodynamic size (DLS), Si10 in exposure medium showed a higher z-average for the main peak than Si50, indicating more excessive agglomeration. Si10 strongly induced CXCL8 and IL-6, as assessed by ELISA and RT-PCR, and was markedly more potent than Si50, even when adjusted to equal surface area. Furthermore, Si10 was far more cytotoxic, measured as lactate dehydrogenase (LDH) release, than Si50 in both epithelial cell cultures. With respect to signalling pathways, Western analysis and experiments with and without inhibition of MAPK, TACE and NF-κB (synthetic inhibitors) revealed that p38-phosphorylation, TACE-mediated TGF-α release and NF-κB activation seem to be important triggering mechanisms for both Si50 and Si10 in the two different lung epithelial cell cultures. In conclusion, the identified signalling pathways are suggested to be important in inducing cytokine responses in different epithelial cell types and also for various sizes of silica nanoparticles.
Axiom learning is an essential task in enhancing the quality of an ontology, a task that sometimes goes under the name of ontology enrichment. To overcome some limitations of recent work and to contribute to the growing library of ontology learning algorithms, we propose an evolutionary approach to automatically discover axioms from the abundant RDF data resource of the Semantic Web. We describe a method applying an instance of an Evolutionary Algorithm, namely Grammatical Evolution, to the acquisition of OWL class disjointness axioms, one important type of OWL axioms which makes it possible to detect logical inconsistencies and infer implicit information from a knowledge base. The proposed method uses an axiom scoring function based on possibility theory and is evaluated against a Gold Standard, manually constructed by knowledge engineers. Experimental results show that the given method possesses high accuracy and good coverage.
Background: Risk communication is necessary to improve the booster vaccination rate, but Vietnam does not have a system to collect and disclose such information. Therefore, the purpose of this study was to clarify adverse reactions and their frequency in the early period after booster vaccination, and to obtain primary data for improving the booster vaccination rate. Methods: A cross-sectional survey was conducted among adults aged ≥18 years. Clinical data were collected 14 days after booster vaccination by using a standard questionnaire. Results: A total of 1322 participants were included with median age = 23 and sex ratio (Male/Female) = 0.53. AstraZeneca was the most commonly used vaccine for the first and second doses, while Pfizer was the most commonly used vaccine for booster shots. Injection site pain, fatigue, and myalgia were the most common side effect reported (71.9%, 28.1%, and 21.8%, respectively). Compared to previous COVID-19 vaccine injections, 81.9% of participants reported that their symptoms were similar or milder after receiving the booster dose. They were more likely to present injection site pain (OR = 1.43, p < 0.0001) and lymphadenopathy (OR = 4.76, p < 0.0001) after receiving the booster shot. Fever (OR = 0.33, p < 0.0001) and fatigue (OR = 0.77, p = 0.002) were less often reported after booster shots compared to the first and second injections. The severity of symptoms occurring after booster dose versus first and second doses increased significantly with each additional year of age and among participants receiving the Pfizer and Moderna vaccines. Conclusion: Adverse reactions to booster vaccination are minor and their incidence is the same as for the first or the second vaccination. Multicenter studies with larger sample sizes on the side effects and safety of COVID-19 vaccine booster shots need to be conducted to make the population less worried, in order to increase the vaccination rate, to protect individuals’ and communities’ health.
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