Ontobull and BFOConvert: Web-based programs to support automatic ontology conversion

When a widely reused ontology appears in a new version which is not compatible with older versions, the ontologies reusing it need to be updated accordingly. Ontobull has been developed to automatically update ontologies with new term IRI(s) and associated metadata to take account of such version changes. To use the Ontobull web interface a user is required to (i) upload one or more ontology OWL source files; (ii) input an ontology term IRI mapping; and (where needed) (iii) provide update settings for ontology headers and XML namespace IDs. Using this information, the backend Ontobull Java program automatically updates the OWL ontology files with desired term IRIs and ontology metadata. The Ontobull subprogram BFOConvert supports the conversion of an ontology that imports a previous version of BFO. A use case is provided to demonstrate the features of Ontobull and BFOConvert

Bioinformatics analysis of Brucella vaccines and vaccine targets using VIOLIN

Abstract Background Brucella spp. are Gram-negative, facultative intracellular bacteria that cause brucellosis, one of the commonest zoonotic diseases found worldwide in humans and a variety of animal species. While several animal vaccines are available, there is no effective and safe vaccine for prevention of brucellosis in humans. VIOLIN (http://www.violinet.org) is a web-based vaccine database and analysis system that curates, stores, and analyzes published data of commercialized vaccines, and vaccines in clinical trials or in research. VIOLIN contains information for 454 vaccines or vaccine candidates for 73 pathogens. VIOLIN also contains many bioinformatics tools for vaccine data analysis, data integration, and vaccine target prediction. To demonstrate the applicability of VIOLIN for vaccine research, VIOLIN was used for bioinformatics analysis of existing Brucella vaccines and prediction of new Brucella vaccine targets. Results VIOLIN contains many literature mining programs (e.g., Vaxmesh) that provide in-depth analysis of Brucella vaccine literature. As a result of manual literature curation, VIOLIN contains information for 38 Brucella vaccines or vaccine candidates, 14 protective Brucella antigens, and 68 host response studies to Brucella vaccines from 97 peer-reviewed articles. These Brucella vaccines are classified in the Vaccine Ontology (VO) system and used for different ontological applications. The web-based VIOLIN vaccine target prediction program Vaxign was used to predict new Brucella vaccine targets. Vaxign identified 14 outer membrane proteins that are conserved in six virulent strains from B. abortus, B. melitensis, and B. suis that are pathogenic in humans. Of the 14 membrane proteins, two proteins (Omp2b and Omp31-1) are not present in B. ovis, a Brucella species that is not pathogenic in humans. Brucella vaccine data stored in VIOLIN were compared and analyzed using the VIOLIN query system. Conclusions Bioinformatics curation and ontological representation of Brucella vaccines promotes classification and analysis of existing Brucella vaccines and vaccine candidates. Computational prediction of Brucella vaccine targets provides more candidates for rational vaccine development. The use of VIOLIN provides a general approach that can be applied for analyses of vaccines against other pathogens and infection diseases.http://deepblue.lib.umich.edu/bitstream/2027.42/78263/1/1745-7580-6-S1-S5.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78263/2/1745-7580-6-S1-S5.pdfPeer Reviewe

Improvement of PubMed Literature Searching using Biomedical Ontology

PubMed articles are annotated using the Medical Subject Headings (MeSH) to increase search efficiency. However, MeSH contains limited information on many biomedical domains (e.g., vaccine). Biomedical ontologies may be used to improve PubMed searching capability. This study demonstrates that Vaccine Ontology (VO) can be used to significantly improve PubMed searching efficacy in the vaccine domain. The recall and precision of the ontology-based literature mining approach are analyzed and discussed

OntoFox:

OntoFox ("http://ontofox.hegroup.org/":http://ontofox.hegroup.org/) is a web server that facilitates ontology development by automatically fetching ontology terms and their annotations from existing ontologies and saving the results in importable RDF/OWL format. OntoFox is developed based on the MIREOT principle. Currently OntoFox can fetch ontology annotations from >10 existing ontologies. OntoFox provides an efficient approach to promote ontology sharing and interoperability

PHIDIAS: a pathogen-host interaction data integration and analysis system

PHIDIAS is a web-based database system serving as a centralized source to search, compare and analyse integrated genome sequences, conserved domains and transcriptional data related to pathogen-host interactions

Quantification of uf and pf Resins in MDF Fiber with an X-Ray Fluorescence Spectrometer

This article describes methods to quantify urea-formaldehyde (UF) resin and phenol-formaldehyde (PF) resin contents in medium density fiberboard (MDF) using a wavelength dispersive X-ray fluorescence spectrometer (WDXRF). The methods are based on the principle that a specific metallic element shows its characteristic X-ray fluorescence spectrum, the intensity of which is correlated quantitatively to its concentration. In the case of UF-blended MDF fiber, 2.4% copper sulfate pentahydrate CuSO4.5H2O (based on resin solids) was premixed with the resin as a labeling agent. Quantification of copper ion was performed using XRF. Based on calibrations with laboratory-prepared standard fiber samples of known UF resin and copper quantities, the results of XRF measurements were converted to resin loading rates. In the case of PF-blended fiber, the PF resin contents in the MDF fiber samples were successfully quantified by measuring the existing sodium ion Na+ in the resol resin with XRF. Linear calibration curves between fluorescence intensity of copper or sodium and resin content were established respectively for UF and PF resins. Test results show that the methods were precise and reliable

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs

Abstract Background Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs). Results We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems. Conclusions Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.https://deepblue.lib.umich.edu/bitstream/2027.42/144217/1/13326_2018_Article_185.pd

OPTIMIZING PREPARATION CONDITIONS OF ULTRA-LOW-DENSITY FIBERBOARD BY RESPONSE SURFACE METHODOLOGY

Preparation conditions of ultra-low-density fiberboard (ULDF) were optimized using the Box–Behnken design and response surface methodology. The effect and interactions of Si-Al molar ratio, additive amount of Si sol, and additive amount of Si-Al compounds on internal bond strength of ULDF were investigated. The regression model for ULDF preparation was significant ( p < 0.0001), and the Si-Al molar ratio and the additive amount of Si sol had a significant effect on internal bond strength, whereas the additive amount of Si-Al compounds did not. Optimum internal bond strength (12.68 ± 0.35 KPa) was achieved at 500 mL Si-Al compounds with Si-Al molar ratio of 2:1 and 20 mL Si sol. Fourier transform infrared spectra of the ULDF confirmed that some covalent bonds between Si-Al additives and fibers might be formed, and the thermal conductivity, noise reduction coefficient, and contact angle analysis of ULDF further confirmed the validity of the optimal preparation conditions