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BackgroundAs the manual creation and maintenance of biomedical ontologies are labor-intensive, automatic aids are desirable in the lifecycle of ontology development. ObjectiveProvided with a set of concept names in the Foundational Model of Anatomy (FMA), we propose an innovative method for automatically generating the taxonomy and the partonomy structures among them, respectively. MethodsOur approach comprises 2 main tasks: The first task is predicting the direct relation between 2 given concept names by utilizing word embedding methods and training 2 machine learning models, Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory Networks (Bi-LSTM). The second task is the introduction of an original granularity-based method to identify the semantic structures among a group of given concept names by leveraging these trained models. ResultsResults show that both CNN and Bi-LSTM perform well on the first task, with F1 measures above 0.91. For the second task, our approach achieves an average F1 measure of 0.79 on 100 case studies in the FMA using Bi-LSTM, which outperforms the primitive pairwise-based method. ConclusionsWe have investigated an automatic way of predicting a hierarchical relationship between 2 concept names; based on this, we have further invented a methodology to structure a group of concept names automatically. This study is an initial investigation that will shed light on further work on the automatic creation and enrichment of biomedical ontologies.

We investigated the functional role and mechanism of miR-1-3p and DKK1 in oral squamous cell carcinoma (OSCC) cells. The level of miR-1-3p and DKK1 expression were detected in OSCC tissues and cells using reverse-transcription--quantitative PCR and Western blot. A dual luciferase reporter gene assay was applied to confirm the targeting relationship between miR-1-3p and DKK1. Functional assays, including MTT, Transwell, colony formation, and flow cytometry analysis were conducted to verify their effect on cell progressions. MTT, colony formation, and Transwell assays indicated that the proliferation, migration, and invasion of SCC-4 cells was impaired with high miR-1-3p expression but promoted with high DKK1 expression. The results from cell cycle analysis and annexin-V--PI assays for apoptosis suggested that miR-1-3p suppressed the transit of SCC-4 cells from G0/G1 to S and induced apoptosis. In summary, miR-1-3p suppressed the progression of OSCC by inhibiting DKK1 expression. Key words: OSCC, miR-1-3p, DKK1, SCC-4 cells. Les auteurs visaient a examiner le role fonctionnel et le mecanisme de miR-l-3p et DKK1 chez les cellules de carcinome epidermoide de la cavite orale (CECO). Les niveaux d'expression de miR-1-3p et de DKK1 ont ete detectes dans les tissus et les cellules de CECO par RT-qPCR et par buvardage Western. Un dosage d'un rapporteur luciferase double a ete utilise pour confirmer la relation de cible entre miR-1-3p et DKK1. Des tests fonctionnels, y compris les tests MTT, sur Transwell, de formation de colonies et des analyses par cytometrie en flux ont ete realises pour verifier leur effet sur la progression cellulaire. Les tests MTT, de formation de colonies et sur Transwell indiquaient que la proliferation, la migration et l'invasion des cellules SCC-4 etaient reduites par une expression elevee de miR-1-3p mais etaient favorisees par un niveau eleve d'expression de DKK1. Les resultats d'une analyse du cycle cellulaire et de l'apoptose par le dosage de l'annexine V--PI suggeraient que miR-1-3p supprimait la transition G0/G1 vers S des cellules SCC-4 et induisait l'apoptose. En resume, miR-1-3p supprimait la progression du CECO en inhibant l'expression de DKK1. [Traduit par la Redaction] Mots-cles: CECO, miR-1-3p, DKK1, cellules SCC-4., Introduction Oral cancer had a high prevalence globally, with approximately 300 400 new cases and 145 400 deaths attributing to oral cancer worldwide in the year of 2012 (Torre et [...]

Attitude determination is fundamental for spacecraft missions in aerospace engineering. Kalman filter (KF) is the optimal estimator in least square sense and, using the symmetry properties of matrix Lie groups system, the invariant Kalman filter (IKF) has been developed to boost the filtering performance for attitude estimation. However, due to presence of frequent and severer maneuvers, the Lie groups attitude dynamics is usually corrupted by significant biases and heavy-tailed outliers, which usually leads to decreased precision of IKF. For attitude estimation problem troubled by biased and heavy-tailed process noise, this work proposes a new invariant Kalman filter (VBAIKF) by constructing the hierarchical Gaussian system model: the probability density function of prior estimate state is first described using the student's t distribution, while the unknown scale covariance matrix and degrees of freedom (dof) of the employed student's t distribution are defined as the inverse Wishart distribution (IWD) and Gamma distribution. In VBAIKF, the Lie groups rotation matrix of spacecraft, the biased mean, the parameters of dof and scale covariance matrix are online estimated together by variational Bayesian fixed-point iterations. The simulation results with Lie groups attitude estimation system further verify the superior filtering adaptability and precision of proposed approach VBAIKF than other methods for attitude determination with biased mean and heavy-tailed process noise. [ABSTRACT FROM AUTHOR]