Anti-DCN (aa 147-345) polyclonal antibody

Information Extraction allows machines to decipher natural language through using two tasks: Named Entity Recognition and Relation Extraction. In order to build such a system for Bangla Language, in this work a Named Entity Recognition (NER) System is proposed, which requires a minimum information to deliver a decent performance having less dependency on handcrafted features. The proposed model is based on Deep Learning, which is accomplished through the use of a Densely Connected Network (DCN) in collaboration with a Bidirectional-LSTM (BiLSTM) and word embedding, i.e., DCN-BiLSTM. Such a system, specific to the Bangla language, has never been done before. Furthermore, a unique dataset was made since no Named Entity Recognition dataset exists for Bangla language till date. In the dataset, over 71 thousand Bangla sentences have been collected, annotated, and classified into four different groups using IOB tagging scheme. Those groups are person, location, organization, and object. Due to Bangla's morphological structure, character level feature extraction is also applied so that we can access more features to determine relational structure between different words. This is initially done with the use of a Convolutional Neural Network but is later outperformed by our second approach which is through the use of a Densely Connected Network (DCN). As for the training portion, it has been done for two variations of word embedding which are word2vec and glove, the outcome being the largest vocabulary size known to both models. A detailed discussion in regard to the methodology of the NER system is explained in a comprehensive manner followed by an examination of the various evaluation scores achieved. The proposed model in this work resulted in having a Fl score of 63.37, which is evaluated at Named Entity Level.

Objective: Masticatory muscle tendon-aponeurosis hyperplasia (MMTAH) is a disease which exhibits limiting mouth opening caused by hyperplasia of the tendon and aponeurosis of bilateral masticatory muscles. Although it is unclear whether hyperplasticity of tendons in MMTAH is due to cyclic stretch, we hypothesize that cyclic stretch is the main cause of force on MMTAH. In this study, we examined decorin expression in the masticatory muscle tendon of MMTAH patients. We also examined decorin (Dcn) expression of tenocytes on which stretch forces are loaded to explore whether stretch forces affect yes-associated protein (YAP) signaling and whether Dcn expression is regulated by YAP. Methods: Quantitative reverse-transcription polymerase chain reaction, immunoblot analysis and immunohistochemistry were performed in tendons of patients having MMTAH. Mechanical loading experiment for TT-D6 tendon fibroblastic cells using RNA interference technique was conducted. Results: We found that Dcn expression is increased in tendons of MMTAH patients. We also showed that cyclic stretch force increases decorin expression in TT-D6 tendon fibroblastic cells and that Dcn expression is regulated via YAP in TT-D6 tendon fibroblastic cells acted upon by cyclic stretch. Conclusions: Our results, for the first time, demonstrated that cyclic stretch induces decorin expression via YAP in tenocytes and Dcn is upregulated in tendons of MMTAH, suggesting that tendons of MMTAH were subjected to cyclic stretch conditions.