RESUMEN
The synthesis of biocompatible and bioresorbable composite materials, such as a "polymer matrix-mineral constituent," stimulating the natural growth of living tissues and the restoration of damaged parts of the body, is one of the challenging problems in regenerative medicine and materials science. Composite films of bioresorbable polymer of polyvinylpyrrolidone (PVP) and hydroxyapatite (HA) were obtained. HA was synthesized in situ in the polymer solution. We applied electron paramagnetic resonance (EPR) and nuclear magnetic resonance (NMR) approaches to study the composite films' properties. The application of EPR in two frequency ranges allowed us to derive spectroscopic parameters of the nitrogen-based light and radiation-induced paramagnetic centers in HA, PVP and PVP-HA with high accuracy. It was shown that PVP did not significantly affect the EPR spectral and relaxation parameters of the radiation-induced paramagnetic centers in HA, while light-induced centers were detected only in PVP. Magic angle spinning (MAS) 1H NMR showed the presence of two signals at 4.7 ppm and -2.15 ppm, attributed to "free" water and hydroxyl groups, while the single line was attributed to 31P. NMR relaxation measurements for 1H and 31P showed that the relaxation decays were multicomponent processes that can be described by three components of the transverse relaxation times. The obtained results demonstrated that the applied magnetic resonance methods can be used for the quality control of PVP-HA composites and, potentially, for the development of analytical tools to follow the processes of sample treatment, resorption, and degradation.
RESUMEN
BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.
