Corkscrew Technique for Extraction of Premolars and Molars in Standing Sedated Horses: Cadaveric Study and Clinical Cases.

Several tooth extraction techniques are described in equine literature, and oral extraction techniques in standing sedated horses are popular among equine practitioners. The objectives of this study were to develop the corkscrew technique for cheek tooth extraction (CSET) in equine cadaver heads and evaluate this technique in clinical cases. We hypothesized that the CSET could be performed safely to extract cheek teeth in standing sedated horses. First, the CSET was attempted and developed in eight equine cadaver heads. Second, the CSET was performed in clinical cases between 2016 and 2020, and the following information was recorded: diagnosis, affected tooth, procedure duration, intraoperative difficulties, tooth size, postoperative complications, medication, hospitalization time, and 1-year follow-up. Sixteen CSET procedures were performed in eight equine skulls with a 75% success rate. In 24 clinical cases, 25 CSET procedures were attempted to extract 22 superior and 3 inferior cheek teeth. CSET was successful in 76% of procedures. Fractures of the tooth and stripping of screw threads were the major complications that led to the failure of CSET. CSET is a viable and safe technique to extract cheek teeth in standing sedated horses. Longitudinal drilling is a must for this technique to be successful.

Ruminal responses, digestibility, and blood parameters of beef cattle fed diets without forage with different hybrids and processing of the corn.

The objective of this study was to evaluate the effects of corn hybrid and processing methods on intake and digestibility of nutrients, rumen fermentation and blood metabolites of steers fed no-forage finishing diets. Four ruminally fistulated Nellore castrated steers (502 ± 15 kg initial body weight) were distributed in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement consisting of two corn hybrids (semi-dent and flint) and two processing methods (dry milled and high moisture grain). Interactions of hybrid and processing methods were observed on intake of dry matter (DM), organic matter (OM) and crude protein (CP), as well as on digestibility of DM and CP, rumen pH and ammonia nitrogen (N-NH3 ). There was no interaction between hybrid and processing for the volatile fatty acids (VFA) total, acetate (C2), propionate (C3), isobutyric (iC4) and valeric (nC5) concentrations. VFA total concentration shown an average of 103.4 mmol/L. The C2 and C3 concentrations had no effect of the hybrid or processing with averages of 58.7 mmol/L for C2, and 31.3 mmol/l for C3. There was an effect of the processing method on starch consumption and fecal pH, the highest values were observed in grains with high moisture content. Starch digestibility was 0.89 g/g in dry milled and 0.96 g/g in high moisture corn. The greatest digestibility of starch in high moisture corn, irrespective of the corn hybrid, provided evidence of an increase in the energy supply, which may improve the feed efficiency and growth performance of cattle fed no-roughage finishing diets.

Multi-domain semantic similarity in biomedical research.

BACKGROUND: Given the increasing amount of biomedical resources that are being annotated with concepts from more than one ontology and covering multiple domains of knowledge, it is important to devise mechanisms to compare these resources that take into account the various domains of annotation. For example, metabolic pathways are annotated with their enzymes and their metabolites, and thus similarity measures should compare them with respect to both of those domains simultaneously. RESULTS: In this paper, we propose two approaches to lift existing single-ontology semantic similarity measures into multi-domain measures. The aggregative approach compares domains independently and averages the various similarity values into a final score. The integrative approach integrates all the relevant ontologies into a single one, calculating similarity in the resulting multi-domain ontology using the single-ontology measure. CONCLUSIONS: We evaluated the two approaches in a multidisciplinary epidemiology dataset by evaluating the capacity of the similarity measures to predict new annotations based on the existing ones. The results show a promising increase in performance of the multi-domain measures over the single-ontology ones in the vast majority of the cases. These results show that multi-domain measures outperform single-domain ones, and should be considered by the community as a starting point to study more efficient multi-domain semantic similarity measures.

Generating a Tolerogenic Cell Therapy Knowledge Graph from Literature.

Tolerogenic cell therapies provide an alternative to conventional immunosuppressive treatments of autoimmune disease and address, among other goals, the rejection of organ or stem cell transplants. Since various methodologies can be followed to develop tolerogenic therapies, it is important to be aware and up to date on all available studies that may be relevant to their improvement. Recently, knowledge graphs have been proposed to link various sources of information, using text mining techniques. Knowledge graphs facilitate the automatic retrieval of information about the topics represented in the graph. The objective of this work was to automatically generate a knowledge graph for tolerogenic cell therapy from biomedical literature. We developed a system, ICRel, based on machine learning to extract relations between cells and cytokines from abstracts. Our system retrieves related documents from PubMed, annotates each abstract with cell and cytokine named entities, generates the possible combinations of cell-cytokine pairs cooccurring in the same sentence, and identifies meaningful relations between cells and cytokines. The extracted relations were used to generate a knowledge graph, where each edge was supported by one or more documents. We obtained a graph containing 647 cell-cytokine relations, based on 3,264 abstracts. The modules of ICRel were evaluated with cross-validation and manual evaluation of the relations extracted. The relation extraction module obtained an F-measure of 0.789 in a reference database, while the manual evaluation obtained an accuracy of 0.615. Even though the knowledge graph is based on information that was already published in other articles about immunology, the system we present is more efficient than the laborious task of manually reading all the literature to find indirect or implicit relations. The ICRel graph will help experts identify implicit relations that may not be evident in published studies.

Assessing Public Metabolomics Metadata, Towards Improving Quality.

Public resources need to be appropriately annotated with metadata in order to make them discoverable, reproducible and traceable, further enabling them to be interoperable or integrated with other datasets. While data-sharing policies exist to promote the annotation process by data owners, these guidelines are still largely ignored. In this manuscript, we analyse automatic measures of metadata quality, and suggest their application as a mean to encourage data owners to increase the metadata quality of their resources and submissions, thereby contributing to higher quality data, improved data sharing, and the overall accountability of scientific publications. We analyse these metadata quality measures in the context of a real-world repository of metabolomics data (i.e. MetaboLights), including a manual validation of the measures, and an analysis of their evolution over time. Our findings suggest that the proposed measures can be used to mimic a manual assessment of metadata quality.

Improving chemical entity recognition through h-index based semantic similarity.

BACKGROUND: Our approach to the BioCreative IV challenge of recognition and classification of drug names (CHEMDNER task) aimed at achieving high levels of precision by applying semantic similarity validation techniques to Chemical Entities of Biological Interest (ChEBI) mappings. Our assumption is that the chemical entities mentioned in the same fragment of text should share some semantic relation. This validation method was further improved by adapting the semantic similarity measure to take into account the h-index of each ancestor. We applied this method in two measures, simUI and simGIC, and validated the results obtained for the competition, comparing each adapted measure to its original version. RESULTS: For the competition, we trained a Random Forest classifier that uses various scores provided by our system, including semantic similarity, which improved the F-measure obtained with the Conditional Random Fields classifiers by 4.6%. Using a notion of concept relevance based on the h-index measure, we were able to enhance our validation process so that for a fixed recall, we increased precision by excluding from the results a higher amount of false positives. We plotted precision and recall values for a range of validation thresholds using different similarity measures, obtaining higher precision values for the same recall with the measures based on the h-index. CONCLUSIONS: The semantic similarity measure we introduced was more efficient at validating text mining results from machine learning classifiers than other measures. We improved the results we obtained for the CHEMDNER task by maintaining high precision values while improving the recall and F-measure.

Identifying interactions between chemical entities in biomedical text.

Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, \"Identifying Interactions between Chemical Entities\" (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to state-of-the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

The epidemiology ontology: an ontology for the semantic annotation of epidemiological resources.

BACKGROUND: Epidemiology is a data-intensive and multi-disciplinary subject, where data integration, curation and sharing are becoming increasingly relevant, given its global context and time constraints. The semantic annotation of epidemiology resources is a cornerstone to effectively support such activities. Although several ontologies cover some of the subdomains of epidemiology, we identified a lack of semantic resources for epidemiology-specific terms. This paper addresses this need by proposing the Epidemiology Ontology (EPO) and by describing its integration with other related ontologies into a semantic enabled platform for sharing epidemiology resources. RESULTS: The EPO follows the OBO Foundry guidelines and uses the Basic Formal Ontology (BFO) as an upper ontology. The first version of EPO models several epidemiology and demography parameters as well as transmission of infection processes, participants and related procedures. It currently has nearly 200 classes and is designed to support the semantic annotation of epidemiology resources and data integration, as well as information retrieval and knowledge discovery activities. CONCLUSIONS: EPO is under active development and is freely available at https://code.google.com/p/epidemiology-ontology/. We believe that the annotation of epidemiology resources with EPO will help researchers to gain a better understanding of global epidemiological events by enhancing data integration and sharing.

Exploiting disjointness axioms to improve semantic similarity measures.

MOTIVATION: Representing domain knowledge in biology has traditionally been accomplished by creating simple hierarchies of classes with textual annotations. Recently, expressive ontology languages, such as Web Ontology Language, have become more widely adopted, supporting axioms that express logical relationships other than class-subclass, e.g. disjointness. This is improving the coverage and validity of the knowledge contained in biological ontologies. However, current semantic tools still need to adapt to this more expressive information. In this article, we propose a method to integrate disjointness axioms, which are being incorporated in real-world ontologies, such as the Gene Ontology and the chemical entities of biological interest ontology, into semantic similarity, the measure that estimates the closeness in meaning between classes. RESULTS: We present a modification of the measure of shared information content, which extends the base measure to allow the incorporation of disjointness information. To evaluate our approach, we applied it to several randomly selected datasets extracted from the chemical entities of biological interest ontology. In 93.8% of these datasets, our measure performed better than the base measure of shared information content. This supports the idea that semantic similarity is more accurate if it extends beyond the hierarchy of classes of the ontology. CONTACT: joao.ferreira@lasige.di.fc.ul.pt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Semantic similarity for automatic classification of chemical compounds.

With the increasing amount of data made available in the chemical field, there is a strong need for systems capable of comparing and classifying chemical compounds in an efficient and effective way. The best approaches existing today are based on the structure-activity relationship premise, which states that biological activity of a molecule is strongly related to its structural or physicochemical properties. This work presents a novel approach to the automatic classification of chemical compounds by integrating semantic similarity with existing structural comparison methods. Our approach was assessed based on the Matthews Correlation Coefficient for the prediction, and achieved values of 0.810 when used as a prediction of blood-brain barrier permeability, 0.694 for P-glycoprotein substrate, and 0.673 for estrogen receptor binding activity. These results expose a significant improvement over the currently existing methods, whose best performances were 0.628, 0.591, and 0.647 respectively. It was demonstrated that the integration of semantic similarity is a feasible and effective way to improve existing chemical compound classification systems. Among other possible uses, this tool helps the study of the evolution of metabolic pathways, the study of the correlation of metabolic networks with properties of those networks, or the improvement of ontologies that represent chemical information.