Recent Trends in Application of Neural Networks to Speech Recognition

: In this paper, we review the research work that deal with neural network based speech recognition and the various approaches they take to bring in accuracy. Three approaches of speech recognition using neural network learning models are discussed: (1) Deep Neural Network(DNN) - Hidden Markov Model(HMM), (2) Recurrent Neural Networks(RNN) and (3) Long Short Term Memory(LSTM). It also discusses how for a given application one model is better suited than the other and when should one prefer one model over another.A pre-trained Deep Neural Network - Hidden Markov Model hybrid architecture trains the DNN to produce a distribution over tied triphone states as its output. The DNN pre-training algorithm is a robust and often a helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. Combining recurrent neural nets and HMM results in a highly discriminative system with warping capabilities. To evaluate the impact of recurrent connections we compare the train and test characteristic error rates of DNN, Recurrent Dynamic Neural Networks (RDNN), and Bi-Directional Deep Neural Network (BRDNN) models while roughly controlling for the total number of free parameters in the model. Both variants of recurrent models show substantial test set characteristic error rate improvements over the non-recurrent DNN model. Inspired from the discussion about how to construct deep RNNs, several alternative architectures were constructed for deep LSTM networks from three points: (1) input-to-hidden function, (2) hidden-to-hidden transition and (3) hidden-to-output function. Furthermore, some deeper variants of LSTMs were also designed by combining different points

DAMPD: a manually curated antimicrobial peptide database

The demand for antimicrobial peptides (AMPs) is rising because of the increased occurrence of pathogens that are tolerant or resistant to conventional antibiotics. Since naturally occurring AMPs could serve as templates for the development of new anti-infectious agents to which pathogens are not resistant, a resource that contains relevant information on AMP is of great interest. To that extent, we developed the Dragon Antimicrobial Peptide Database (DAMPD, http://apps.sanbi.ac.za/dampd) that contains 1232 manually curated AMPs. DAMPD is an update and a replacement of the ANTIMIC database. In DAMPD an integrated interface allows in a simple fashion querying based on taxonomy, species, AMP family, citation, keywords and a combination of search terms and fields (Advanced Search). A number of tools such as Blast, ClustalW, HMMER, Hydrocalculator, SignalP, AMP predictor, as well as a number of other resources that provide additional information about the results are also provided and integrated into DAMPD to augment biological analysis of AMPs

Injectable Graphene Oxide/Hydrogel-Based Angiogenic Gene Delivery System for Vasculogenesis and Cardiac Repair

The objective of this study was to develop an injectable and biocompatible hydrogel which can efficiently deliver a nanocomplex of graphene oxide (GO) and vascular endothelial growth factor-165 (VEGF) pro-angiogenic gene for myocardial therapy. For the study, an efficient nonviral gene delivery system using polyethylenimine (PEI) functionalized GO nanosheets (fGO) complexed with DNAVEGF was formulated and incorporated in the low-modulus methacrylated gelatin (GelMA) hydrogel to promote controlled and localized gene therapy. It was hypothesized that the fGOVEGF/GelMA nanocomposite hydrogels can efficiently transfect myocardial tissues and induce favorable therapeutic effects without invoking cytotoxic effects. To evaluate this hypothesis, a rat model with acute myocardial infarction was used, and the therapeutic hydrogels were injected intramyocardially in the peri-infarct regions. The secreted VEGF from in vitro transfected cardiomyocytes demonstrated profound mitotic activities on endothelial cells. A significant increase in myocardial capillary density at the injected peri-infarct region and reduction in scar area were noted in the infarcted hearts with fGOVEGF/GelMA treatment compared to infarcted hearts treated with untreated sham, GelMA and DNAVEGF/GelMA groups. Furthermore, the fGOVEGF/GelMA group showed significantly higher (p < 0.05, n = 7) cardiac performance in echocardiography compared to other groups, 14 days postinjection. In addition, no significant differences were noticed between GO/GelMA and non-GO groups in the serum cytokine levels and quantitative PCR based inflammatory microRNA (miRNA) marker expressions at the injected sites. Collectively, the current findings suggest the feasibility of a combined hydrogel-based gene therapy system for ischemic heart diseases using nonviral hybrid complex of fGO and DNA