This study tries to explain the reason why the Jefferson fracture is a burst fracture, using two different biomechanical models: a finite element model (FEM) and a cadaver model used to determine strain distribution in C1 during axial static compressive loading. For the FEM model, a three-dimensional model of C1 was obtained from a 29-year-old healthy human, using axial CT scans with intervals of 1.0 mm. The mesh model was composed of 8200 four-noded isoparametric tetrahedrons and 37,400 solid elements. The material properties of the cortical bone of the vertebra were assessed according to the previous literature and were assumed to be linear isotropic and homogeneous for all elements. Axial static compressive loads were applied at between 200 and 1200 N. The strain and stress (maximum shear and von Mises) analyses were determined on the clinically relevant fracture lines of anterior and posterior arches. The results of the FEM were compared with a cadaver model. The latter comprised the C1 bone of a cadaver placed in a methylmethacrylate foam. Axial static compressive loads between 200 and 1200 N were applied by an electrohydraulic testing machine. Strain values were measured using strain gauges, which were cemented to the bone where the clinically relevant fracture lines of the anterior and posterior arches were located. As a result, compressive strain was observed on the outer surface of the anterior arch and inferior surface of the posterior arch. In addition, there was tensile strain on the inner surface of the anterior arch and superior surface of the posterior arch. The strain values obtained from the two experimental models showed similar trends. The FEM analysis revealed that maximum strain changes occurred where the maximum shear and von Mises stresses were concentrated. The changes in the C1 strain and stress values during static axial loading biomechanically prove that the Jefferson fracture is a burst fracture.

In this paper, studies determining abbreviations and their meanings in job texts are explained. The data used in this study consist of job texts stored in the Kariyer.net database. The applied method consists of two separate steps: first, the words and phrases in all job text documents are vectorised with the Word2Vec model. The phrases and abbreviations that are compatible with each other in the proximity of these word vectors are then checked and matched. In the second step, sentences with abbreviations and their meanings in the dataset are defined by the rules determined by Regex. Then, the appropriate abbreviations are collected and added to the dictionary.   Keywords: Word embeddings, text mining, abbreviation detection.  

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