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.

In mammals, myocardial cell death due to infarction results in scar formation and little regenerative response. In contrast, zebrafish have a high capacity to regenerate the heart after surgical resection of myocardial tissue. However, whether zebrafish can also regenerate lesions caused by cell death has not been tested. Here, we present a simple method for induction of necrotic lesions in the adult zebrafish heart based on cryoinjury. Despite widespread tissue death and loss of cardiomyocytes caused by these lesions, zebrafish display a robust regenerative response, which results in substantial clearing of the necrotic tissue and little scar formation. The cellular mechanisms underlying regeneration appear to be similar to those activated in response to ventricular resection. In particular, the epicardium activates a developmental gene program, proliferates and covers the lesion. Concomitantly, mature uninjured cardiomyocytes become proliferative and invade the lesion. Our injury model will be a useful tool to study the molecular mechanisms of natural heart regeneration in response to necrotic cell death.

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.

A major argument for incorporating deciduous tree species in coniferous forest stands is their role in the amelioration and stabilisation of biogeochemical cycles. Current forest management strategies in central Europe aim to increase the area of mixed stands. In order to formulate statements about the ecological effects of mixtures, studies at the stand level are necessary. In a mixed stand of Norway spruce (Picea abies (L.) Karst.) and silver birch (Betula pendula Roth) in the Ore Mountains (Saxony, Germany), the effects of these two tree species on chemical and microbial parameters in the topsoil were studied at one site in the form of a case study. Samples were taken from the O layer and A horizon in areas of the stand influenced by either birch, spruce or a mixture of birch and spruce. The microbial biomass, basal respiration, metabolic quotient, pH-value and the C and N contents and stocks were analysed in the horizons Of, Oh and A. Significantly higher contents of microbial N were observed in the Of and Oh horizons in the birch and in the spruce-birch strata than in the stratum containing only spruce. The same was found with respect to pH-values in the Of horizon and basal respiration in the Oh horizon. Compared to the spruce stratum, in the birch and spruce-birch strata, significantly lower values were found for the contents of organic C and total N in the A horizon. The findings of the case study indicated that single birch trees have significant effects on the chemical and microbial topsoil properties in spruce-dominated stands. Therefore, the admixture of birch in spruce stands may distinctly affect nutrient cycling and may also be relevant for soil carbon sequestration. Further studies of these functional aspects are recommended.

Erdheim-Chester disease (ECD) represents a clonal non-Langerhans histiocytosis, which manifests under an extensive variety of clinical symptoms. This creates a challenge for the physician, who is required to recognize and diagnose the disease in the early stages. Despite this considerable challenge, in the last decade there has been a dramatic increase in ECD diagnoses, in most part due to an increasing awareness of this rare disorder. Involvement of the axial skeleton is exclusively uncommon with no official recommendations for the treatment of the bone lesions. Here, we present a case report of a young male patient with isolated lesions of the spine, ribs, and pelvis, who was successfully treated with a combination therapy of alfa-interferon and zoledronic acid.

The development of bio-resorbable implant materials is rapidly going on. Sterilization of those materials is inevitable to assure the hygienic requirements for critical medical devices according to the medical device directive (MDD, 93/42/EG). Biopolymer-containing biomaterials are often highly sensitive towards classical sterilization procedures like steam, ethylene oxide treatment or gamma irradiation. Supercritical CO2 (scCO2) treatment is a promising strategy for the terminal sterilization of sensitive biomaterials at low temperature. In combination with low amounts of additives scCO2 treatment effectively inactivates microorganisms including bacterial spores. We established a scCO2 sterilization procedure under addition of 0.25% water, 0.15% hydrogen peroxide and 0.5% acetic anhydride. The procedure was successfully tested for the inactivation of a wide panel of microorganisms including endospores of different bacterial species, vegetative cells of gram positive and negative bacteria including mycobacteria, fungi including yeast, and bacteriophages. For robust testing of the sterilization effect with regard to later application of implant materials sterilization all microorganisms were embedded in alginate/agarose cylinders that were used as Process Challenge Devices (PCD). These PCD served as surrogate models for bioresorbable 3D scaffolds. Furthermore, the impact of scCO2 sterilization on mechanical properties of polysaccharide-based hydrogels and collagen-based scaffolds was analyzed. The procedure was shown to be less compromising on mechanical and rheological properties compared to established low-temperature sterilization methods like gamma irradiation and ethylene oxide exposure as well as conventional steam sterilization. Cytocompatibility of alginate gels and scaffolds from mineralized collagen was compared after sterilization with ethylene oxide, gamma irradiation, steam sterilization and scCO2 treatment. Human mesenchymal stem cell viability and proliferation were not compromised by scCO2 treatment of these materials and scaffolds. We conclude that scCO2 sterilization under addition of water, hydrogen peroxide and acetic anhydride is a very effective, gentle, non-cytotoxic and thus a promising alternative sterilization method especially for biomaterials.

Appropriate reactions toward emotional stimuli depend on the distribution of prefrontal attentional resources. In mid-adolescence, prefrontal top-down control systems are less engaged, while subcortical bottom-up emotional systems are more engaged. We used functional magnetic resonance imaging to follow the neural development of attentional distribution, i.e. attending versus ignoring emotional stimuli, in adolescence. 144 healthy adolescents were studied longitudinally at age 14 and 16 while performing a perceptual discrimination task. Participants viewed two pairs of stimuli – one emotional, one abstract – and reported on one pair whether the items were the same or different, while ignoring the other pair. Hence, two experimental conditions were created: 'attending emotion/ignoring abstract' and 'ignoring emotion/attending abstract'. Emotional valence varied between negative, positive, and neutral. Across conditions, reaction times and error rates decreased and activation in the anterior cingulate and inferior frontal gyrus increased from age 14 to 16. In contrast, subcortical regions showed no developmental effect. Activation of the anterior insula increased across ages for attending positive and ignoring negative emotions. Results suggest an ongoing development of prefrontal top-down resources elicited by emotional attention from age 14 to 16 while activity of subcortical regions representing bottom-up processing remains stable.

BACKGROUND: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation. RESULTS: It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely \'has target\', and \'may treat\', are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach. CONCLUSIONS: Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.

The vocal tract shape is crucial to voice production. Its lower part seems particularly relevant for voice timbre. This study analyzes the detailed morphology of parts of the epilaryngeal tube and the hypopharynx for the sustained German vowels /a/, /e/, /i/, /o/, and /u/ by thirteen male singer subjects who were at the beginning of their academic singing studies. Analysis was based on two different phonatory conditions: a natural, speech-like phonation and a singing phonation, like in classical singing. 3D models of the vocal tract were derived from magnetic resonance imaging and compared with long-term average spectrum analysis of audio recordings from the same subjects. Comparison of singing to the speech-like phonation, which served as reference, showed significant adjustments of the lower vocal tract: an average lowering of the larynx by 8 mm and an increase of the hypopharyngeal cross-sectional area (+ 21.9%) and volume (+ 16.8%). Changes in the analyzed epilaryngeal portion of the vocal tract were not significant. Consequently, lower larynx-to-hypopharynx area and volume ratios were found in singing compared to the speech-like phonation. All evaluated measures of the lower vocal tract varied significantly with vowel quality. Acoustically, an increase of high frequency energy in singing correlated with a wider hypopharyngeal area. The findings offer an explanation how classical male singers might succeed in producing a voice timbre with increased high frequency energy, creating a singer‘s formant cluster.

Distant brain metastases from oral squamous cell carcinomas (OSCC) are extremely rare. Here we describe a case of a 53-year-old man with a primary OSCC who referred to the neurosurgical department because of epileptic seizures. MR imaging revealed an enhancing lesion in the right parietal lobe. A craniotomy with tumor removing was performed. Histopathological examination verified an invasive, minimally differentiated metastasis of the primary OSCC. The patient refused whole brain radiation therapy and died from pulmonary metastatic disease 10 months after the neurosurgical intervention without any cerebral recurrence. To the authors’ knowledge, only two similar cases have been previously reported.