Anti-DCN (C-terminal) polyclonal antibody [Alexa Fluor® 647]

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.

Lung fibroblasts play a pivotal role in pulmonary fibrosis, a devastating lung disease, by producing extracellular matrix. MicroRNAs (miRNAs) suppress numerous genes post-transcriptionally; however, the roles of miRNAs in activated fibroblasts in fibrotic lungs remain poorly understood. To elucidate these roles, we performed global miRNA-expression profiling of fibroblasts from bleomycin-and silica-induced fibrotic lungs and investigated the functions of miRNAs in activated lung fibroblasts. Clustering analysis of global miRNA-expression data identified miRNA signatures exhibiting increased expression during fibrosis progression. Among these signatures, we found that a miR-19a-19b-20a sub-cluster suppressed TGF-beta-induced activation of fibroblasts in vitro. Moreover, to elucidate whether fibroblast-specific intervention against the sub-cluster modulates pathogenic activation of fibroblasts in fibrotic lungs, we intratracheally transferred the sub-cluster-overexpressing fibroblasts into bleomycin-treated lungs. Global transcriptome analysis of the intratracheally transferred fibroblasts revealed that the sub-cluster not only downregulated expression of TGF-beta-associated pro-fibrotic genes, including Acta2, Col1a1, Ctgf, and Serpine1, but also upregulated expression of the anti-fibrotic genes Dcn, Igfbp5, and Mmp3 in activated lung fibroblasts. Collectively, these findings indicated that upregulation of the miR-19a-19b-20a sub-cluster expression in lung fibroblasts counteracted TGF-beta-associated pathogenic activation of fibroblasts in murine pulmonary fibrosis.