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Background The Vaccine Ontology (VO) is a biomedical ontology that standardizes vaccine annotation. Errors in VO will affect a multitude of applications that it is being used in. Quality assurance of VO is imperative to ensure that it provides accurate domain knowledge to these downstream tasks. Manual review to identify and fix quality issues (such as missing hierarchical is-a relations) is challenging given the complexity of the ontology. Automated approaches are highly desirable to facilitate the quality assurance of VO. Methods We developed an automated lexical approach that identifies potentially missing is-a relations in VO. First, we construct two types of VO concept-pairs: (1) linked; and (2) unlinked. Each concept-pair further derives an Acquired Term Pair (ATP) based on their lexical features. If the same ATP is obtained by a linked concept-pair and an unlinked concept-pair, this is considered to indicate a potentially missing is-a relation between the unlinked pair of concepts. Results Applying this approach on the 1.1.192 version of VO, we were able to identify 232 potentially missing is-a relations. A manual review by a VO domain expert on a random sample of 70 potentially missing is-a relations revealed that 65 of the cases were valid missing is-a relations in VO (a precision of 92.86%). Conclusions The results indicate that our approach is highly effective in identifying missing is-a relation in VO.

Background Model card reports aim to provide informative and transparent description of machine learning models to stakeholders. This report document is of interest to the National Institutes of Health’s Bridge2AI initiative to address the FAIR challenges with artificial intelligence-based machine learning models for biomedical research. We present our early undertaking in developing an ontology for capturing the conceptual-level information embedded in model card reports. Results Sourcing from existing ontologies and developing the core framework, we generated the Model Card Report Ontology. Our development efforts yielded an OWL2-based artifact that represents and formalizes model card report information. The current release of this ontology utilizes standard concepts and properties from OBO Foundry ontologies. Also, the software reasoner indicated no logical inconsistencies with the ontology. With sample model cards of machine learning models for bioinformatics research (HIV social networks and adverse outcome prediction for stent implantation), we showed the coverage and usefulness of our model in transforming static model card reports to a computable format for machine-based processing. Conclusions The benefit of our work is that it utilizes expansive and standard terminologies and scientific rigor promoted by biomedical ontologists, as well as, generating an avenue to make model cards machine-readable using semantic web technology. Our future goal is to assess the veracity of our model and later expand the model to include additional concepts to address terminological gaps. We discuss tools and software that will utilize our ontology for potential application services.

Severe adverse events (AEs) after COVID-19 vaccination are not well studied in randomized controlled trials (RCTs) due to rarity and short follow-up. To monitor the safety of COVID-19 vaccines (“Pfizer” vaccine dose 1 and 2, “Moderna” vaccine dose 1 and 2, and “Janssen” vaccine single dose) in the U.S., especially regarding severe AEs, we compare the relative rankings of these vaccines using both RCT and the Vaccine Adverse Event Reporting System (VAERS) data. The risks of local and systemic AEs were assessed from the three pivotal COVID-19 vaccine trials and also calculated in the VAERS cohort consisting of 559,717 reports between December 14, 2020 and September 17, 2021. AE rankings of the five vaccine groups calculated separately by RCT and VAERS were consistent, especially for systemic AEs. For severe AEs reported in VAERS, the reported risks of thrombosis and GBS after Janssen vaccine were highest. The reported risk of shingles after the first dose of Moderna vaccine was highest, followed by the second dose of the Moderna vaccine. The reported risk of myocarditis was higher after the second dose of Pfizer and Moderna vaccines. The reported risk of anaphylaxis was higher after the first dose of Pfizer vaccine. Limitations of this study are the inherent biases of the spontaneous reporting system data, and only including three pivotal RCTs and no comparison with other active vaccine safety surveillance systems.

Background Fast food with its abundance and availability to consumers may have health consequences due to the high calorie intake which is a major contributor to life threatening diseases. Providing nutritional information has some impact on consumer decisions to self regulate and promote healthier diets, and thus, government regulations have mandated the publishing of nutritional content to assist consumers, including for fast food. However, fast food nutritional information is fragmented, and we realize a benefit to collate nutritional data to synthesize knowledge for individuals. Methods We developed the ontology of fast food facts as an opportunity to standardize knowledge of fast food and link nutritional data that could be analyzed and aggregated for the information needs of consumers and experts. The ontology is based on metadata from 21 fast food establishment nutritional resources and authored in OWL2 using Protégé. Results Three evaluators reviewed the logical structure of the ontology through natural language translation of the axioms. While there is majority agreement (76.1% pairwise agreement) of the veracity of the ontology, we identified 103 out of the 430 statements that were erroneous. We revised the ontology and publicably published the initial release of the ontology. The ontology has 413 classes, 21 object properties, 13 data properties, and 494 logical axioms. Conclusion With the initial release of the ontology of fast food facts we discuss some future visions with the continued evolution of this knowledge base, and the challenges we plan to address, like the management and publication of voluminous amount of semantically linked fast food nutritional data.

Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data size. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pre-training of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. We propose Med-BERT, which adapts the BERT framework for pre-training contextualized embedding models on structured diagnosis data from 28,490,650 patients EHR dataset. Fine-tuning experiments are conducted on two disease-prediction tasks: (1) prediction of heart failure in patients with diabetes and (2) prediction of pancreatic cancer from two clinical databases. Med-BERT substantially improves prediction accuracy, boosting the area under receiver operating characteristics curve (AUC) by 2.02-7.12%. In particular, pre-trained Med-BERT substantially improves the performance of tasks with very small fine-tuning training sets (300-500 samples) boosting the AUC by more than 20% or equivalent to the AUC of 10 times larger training set. We believe that Med-BERT will benefit disease-prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare., L.R., X.Y., and Z.X. share first authorship of this work

Background Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction. Methods We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials. Results Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators. Conclusions Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.

In this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semantic Web Conference (ISWC 2019) in Auckland, New Zealand, and then briefly introduce seven research articles included in this supplement issue, covering the topics on Knowledge Graph, Ontology-Powered Analytics, and Deep Learning.

Temperature and pH are the key factors which affect the stability of blueberry anthocyanin and blueberry deep processed products, but the effect of temperature and pH on individual anthocyanin (IAC) in blueberries is lack of the further discussion. Blueberry extract containing a variety of anthocyanins was used as research material and modern analytical instrument was combined to investigate the effect of temperature and pH on the stability of blueberry total anthocyanin (TAC) and IAC. The half-life of blueberry TAC began to decrease rapidly from 60 °C with a final half-life of only one-third of 50 °C, or even the original quarter. The initial content blueberry TAC significantly reduced and the half-life t₁/₂ was only about 4 h at pH 8.0, 9.0. Ten kinds of anthocyanins carrying malvidin, delphinidin, petunidin, cyanidin four common aglycones from blueberry extract were identified. Malvidin-3-O-galactose and malvidin-3-O-arabinose were found to have the alkali resistance. The content of malvidin-3-O-arabinose increased after heated which was different from that of other blueberry anthocyanins under the condition of pH 5.0–6.0. The effect of pH and temperature on all IAC and TAC content were consistent. The results showed that low temperature and low pH were beneficial to the maximum stability of blueberry anthocyanins. What’s more, the data obtained from the research provided important guiding significance for the rational utilization of the abundant resources of blueberry fruit.

This research aimed to investigate the seasonal variations and antibacterial activities of essential oils (EOs) and their major components from bamboo leaves. The Phyllostachys heterocycla cv. Pubescens leaves have undergone steam distillation to prepare EOs; leaves collected in autumn had the highest amount of EOs (0.33%). Chemical characterization by gas chromatography/mass spectrometry (GC/MS) allowed the identification of 36 compounds. The major compounds (%) were ketones (52.25), alcohols (17.79), carboxylic acids (9.41) and alkane hydrocarbons (7.68), which included 6, 5, 7 and 9 distinct compounds, respectively. Antibacterial activities were registered against Gram-negative bacteria [Escherichia coli (minimum inhibitory concentration [MIC] = 2.25 mg/mL)], Gram-positive bacteria [Bacillus subtilis (MIC = 2.25 mg/mL)] and Saccharomyces cerevisiae. The antibacterial effect of the three major monomeric compounds (tricosane, cedrol and hexadecanoic acid) in bamboo leaf EOs and their mixtures was studied. We showed for the first time that tricosane had antimicrobial activity. We found that cedrol had the strongest antibacterial effect, followed by hexadecanoic acid. Cedrol at 5 mg/mL had the largest inhibition zones of up to 14.93 mm against Flavobacterium SHL45. The antimicrobial effect of the equal mixture ratio of the three monomers was higher than that when tricosane and hexadecanoic acid were tested alone, and was also higher than that of the combination of any two of them.

CO2 supercritical fluid technique with an ethanol modifier was used to extract bamboo (Phyllostachys heterocycla) leave’s polysaccharide (BLPs) regarding various operating conditions. Pressure (30, 40 and 50 MPa), extraction temperature (40, 45 and 50 °C), extraction time (2, 3 and 4 h) and modifier dosage (20, 25 and 30 mL) were important operating factors affecting the yield and total antioxidant activity of each BLPs extraction. The optimal parameters for harvesting the BLPs were an extracting time of 2 h plus 40 MPa pressure with 30 mL of ethanol modifier at 50 °C. In this case, 2.47% BLPs (in wt) was extracted. The BLPs, after purified by D101 macroporous resin and DEAE-cellulose column, with the average molecular weight of 5.05 × 104 Da are composed of rhamnose, fucose, arabinose, xylose, mannose, glucose, and galactose in the molar ratio of 1.318:2.100:1.189:1.016:1:4.776:1.318, respectively. It showed the best antioxidant activity, and its total antioxidant activity of the BLPs extracted was equivalent to 20.70 mg ascorbic acid [(VcE)/g]. In a word, our technique should be potential for producing the best yield and activity of bamboo (Phyllostachys heterocycla) leave’s polysaccharide.

BackgroundIn the United States and parts of the world, the human papillomavirus vaccine uptake is below the prescribed coverage rate for the population. Some research have noted that dialogue that communicates the risks and benefits, as well as patient concerns, can improve the uptake levels. In this paper, we introduce an application ontology for health information dialogue called Patient Health Information Dialogue Ontology for patient-level human papillomavirus vaccine counseling and potentially for any health-related counseling.ResultsThe ontology’s class level hierarchy is segmented into 4 basic levels -Discussion,Goal,Utterance, andSpeech Task. The ontology also defines core low-level utterance interaction for communicating human papillomavirus health information. We discuss the design of the ontology and the execution of the utterance interaction.ConclusionWith an ontology that represents patient-centric dialogue to communicate health information, we have an application-driven model that formalizes the structure for the communication of health information, and a reusable scaffold that can be integrated for software agents. Our next step will to be develop the software engine that will utilize the ontology and automate the dialogue interaction of a software agent.

This paper studies the fast generation of micro structured surface by means of micro polycrystalline diamond (PCD) tools, which are manufactured by applying the wire cut electrical discharge machining (WEDM). Firstly, the fabrication of micro PCD tools is introduced in the WEDM process. Furthermore, by applying the micro cutting tools, the micro structures are directly fabricated on the alumina workpiece with just a simple micro turning process, which verifies the feasibility of the proposed microfabrication method. Following that, the tool wear in the micro turning process is also investigated. Although there is no considerable tool shape deterioration and tool edge damage, the adhesion of the workpiece material to the cutting edge is observed, which may cause the increase of cutting forces and the decrease of the machining accuracy. As following, a series of fundamental micro turning tests are conducted, and the influence of cutting conditions on the machined structure quality is investigated in detail. Finally, it is proposed that the micro/nano structures can also provide the self-cleaning properties due to the low surface energy. The proposed microfabrication process is expected to become a potential method for the fast generation of micro structured surface in the future.

A critical issue for alcohol-induced liver disease (ALD) therapeutics is the lack of a highly efficient delivery system. In this study, a Puerarin-propylene glycol-liposome system was prepared for the purpose of targeting puerarin, an isoflavon, to the liver. Transmission electron microscope (TEM) results showed the liposomes to be spherical in shape with an average diameter of 182 nm with a polydispersity index of 0.239. The zeta potential of the particles was about −30 mV. The entrapment efficiency of puerarin was above 90%. MTT-based assay in HpeG2 cells showed no significant cytotoxicity in the presence of up to 25% concentration of the system containing 3% puerarin. In vivo performance of this system was studied in mice. Pharmacokinetics and distribution of puerarin-PG-liposome system was studied relative to puerarin solution at the same dose levels. The results show that puerarin-PG-liposome prolonged drug retention time and decreased elimination of puerarin in mice (AUC of liposome system and solution was 9.5 and 4.0 mg h L−1, respectively). Furthermore, propylene glycol (PG)-liposome system enhanced puerarin distribution into liver and spleen, while decreasing puerarin distribution in other tissues. Overall, the puerarin-PG-liposome system showed enhanced therapeutic effect in mice with ALD.

Background In this paper, we discuss the design and development of a formal ontology to describe misinformation about vaccines. Vaccine misinformation is one of the drivers leading to vaccine hesitancy in patients. While there are various levels of vaccine hesitancy to combat and specific interventions to address those levels, it is important to have tools that help researchers understand this problem. With an ontology, not only can we collect and analyze varied misunderstandings about vaccines, but we can also develop tools that can provide informatics solutions. Results We developed the Vaccine Misinformation Ontology (VAXMO) that extends the Misinformation Ontology and links to the nanopublication Resource Description Framework (RDF) model for false assertions of vaccines. Preliminary assessment using semiotic evaluation metrics indicated adequate quality for our ontology. We outlined and demonstrated proposed uses of the ontology to detect and understand anti-vaccine information. Conclusion We surmised that VAXMO and its proposed use cases can support tools and technology that can pave the way for vaccine misinformation detection and analysis. Using an ontology, we can formally structure knowledge for machines and software to better understand the vaccine misinformation domain.

In this editorial, we first summarize the 2nd International Workshop on Semantics-Powered Data Analytics (SEPDA 2017) held on November 13, 2017 in Kansas City, Missouri, U.S.A., and then briefly introduce 13 research articles included in this supplement issue, covering topics such as Semantic Integration, Deep Learning, Knowledge Base Construction, and Natural Language Processing.

Brain tumor lacks effective delivery system for treatment. Focused ultrasound (FUS) can reversibly open BBB without impacts on normal tissues. As a potential drug carrier, cationic liposomes (CLs) have the ability to passively accumulate in tumor tissues for their positive charge. In this study, FUS introduced doxorubicin-loaded cationic liposomes (DOX-CLs) were applied to improve the efficiency of glioma-targeted delivery. Doxorubicin-loaded CLs (DOX-CLs) and quantum dot-loaded cationic liposomes (QD-CLs) were prepared using extrusion technology, and their characterizations were evaluated. With the advantage of QDs in tracing images, the glioma-targeted accumulation of FUS + CLs was evaluated by fluorescence imaging and flow cytometer. Cell survival rate, tumor volume, animal survival time, and brain histology in C6 glioma model were investigated to evaluate the glioma-targeted delivery of FUS + DOX-CLs. DOX-CLs and QD-CLs had suitable nanoscale sizes and high entrapment efficiency. The combined strategy of FUS introduced CLs significantly increased the glioma-targeted accumulation for load drugs. FUS + DOX-CLs showed the strongest inhibition on glioma based on glioma cell in vitro and glioma model in vivo experiments. From MRI and histological analysis, FUS + DOX-CLs group strongly suppressed the glioma progression and extended the animal survival time to 81.2 days. Among all the DOX treatment groups, FUS + DOX-CLs group showed the best cell viability and highest level of tumor apoptosis and necrosis. Combining the advantages of BBB reversible opening by FUS and glioma-targeted binding by CLs, ultrasound introduced cationic liposomes could achieve glioma-targeted delivery, which might be developed as a potential strategy for future brain tumor therapy.