Active anti-acetylcholinesterase component of secondary metabolites produced by the endophytic fungi of Huperzia serrata

Background: An endophytic fungus lives within a healthy plant during certain stages of, or throughout, its life cycle. Endophytic fungi do not always cause plant disease, and they include fungi that yield different effects, including mutual benefit, and neutral and pathogenic effects. Endophytic fungi promote plant growth, improve the host plant's resistance to biotic and abiotic stresses, and can produce the same or similar biologically active substances as the host. Thus, endophytic fungal products have important implications in drug development. Result: Among the numerous endophytic fungi, we identified two strains, L10Q37 and LQ2F02, that have anti-acetylcholinesterase activity, but the active compound was not huperzine A. The aim of this study was to investigate the anti-acetylcholinesterase activity of secondary metabolites isolated from the endophytic fungi of Huperzia serrata . Microbial cultivation and fermentation were used to obtain secondary metabolites. Active components were then extracted from the secondary metabolites, and their activities were tracked. Two compounds that were isolated from endophytic fungi of H. serrata were identified and had acetylcholine inhibitory activities. In conclusion, endophytic fungal strains were found in H. serrata that had the same anti-acetylcholinesterase activity. Conclusion: We isolated 4 compounds from the endophytic fungus L10Q37, among them S1 and S3 are new compounds. 6 compounds were isolated from LQ2F02, all 6 compounds are new compounds. After tested anti acetylcholinesterase activity, S5 has the best activity. Other compounds' anti acetylcholinesterase activity was not better compared with huperzine A

The State of the Art in Speaker Adaptation for Automatic Speech Recognition (ASR)

Automatic speech recognition (ASR) incorporates knowledge and research in linguistics, computer science and electrical engineering to develop methodologies and algorithms to translate human speech into text. In ASR, speaker adaptation refers to the technologies that adapt acoustic features to better model the variation for individual speakers. Its goal is to reduce the mismatch between individual speakers and the acoustic model in order to reduce the word error rate (WER). Adaptation strategies include long short-term memory recurrent neural networks (LSTM-RNN), maximum likelihood linear regression (MLLR) for hidden Markov models (HMM), and I-vectors. Recently, deep neural networks (DNN) have become an alternative modeling approach. Combined with older adaptation techniques, DNNs have improved ASR performance significantly. This research presents a review of adaptation techniques used with DNNs, examines existing experimental results, and investigate speaker difference in recognition using a virtual machine (VM) from the Speech Recognition Virtual Kitchen (SRVK). The SRVK toolkit is comprised of Linux-based VMs which allow users at teaching-focused institutions to participate in ASR research. The TI-digits will be used as training datasets, as they have sufficient individual speaker data to separate for adaptation experiments. WER is the main indicator for performance evaluation. The work presented includes discussion and comparison results of each strategy used with DNN, an overview of the SRVK toolkit, results of recognition performance, and potential methods to improve adaptation within the toolkit

A Web Application to Support Research on Epistemic Beliefs

Research into the epistemic beliefs of engineering students – that is, their beliefs about knowledge, its structure and where it comes from – is currently being used to guide and improve the educational process, and to provide insight into how people learn. Data-gathering for this research includes presenting subjects with a set of knowledge-related words, and then measuring how the subjects perceive the importance of and relationships between the words. This project presents a novel web application designed to support this data-gathering task in an intuitive and visual manner. Researchers load the web app with a list of predetermined words, which appear in circles on a blank canvas. A web prototype has been created to structure how the site is going to look. Then, we created a website application dividing different sections into keywords, circles, and colors. The goal is to have the user can move, resize, delete, create and link word-circles to represent how they perceive the importance of and relationship between the words. The digital diagrams created, can be used as prompts in qualitative interviews. The diagrams will also allow research to be extended quantitatively, as comparisons of relationships, size, and placement can be done across multiple subjects, or with the same subject at different times. Within the month of March, the web application would include a database to store students and teachers information; and improvements would be made based on feedback from the researchers about ease of use and quality of data collection

Enhancing an Offline Transcriber for the Speech Recognition Virtual Kitchen

The Speech Recognition Virtual Kitchen (SRVK) is a web resource (http://speechkitchen.org) created to improve community research and education infrastructure for automatic speech recognition (ASR). SRVK has been developed by researchers at Carnegie Mellon University, the Ohio State University and Minnesota State University, Mankato. The resource is comprised of Linux based virtual machines (VMs) and open-source software which can be run on multiple platforms, allowing a wide range of users to participate in ASR research. This project evaluates the Eesen offline transcriber, a Kaldi-based offline transcriber that transcribes audio speech files into text files, that should be easily used by researchers not familiar with ASR software but who would benefit from transcribed data. Kaldi (http://kaldi.sourceforge.net) is an open source ASR toolkit developed at John Hopkins University, typically used for research. The speech data used in this project are interviews with SRVK users about their experiences and provide evidence for toolkit improvements. Here, we investigated changing parameters within the decoding script, improving existing acoustic models and examining ways to improve transcription of non-native speakers and the performance of speaker diarization, or segmentation of the speech signal for individual speakers. We use Sclite to calculate the word error rate (WER) and use it to evaluate our VM performance. Our goal is to reduce WER, thus enhancing the performance of the existing offline transcriber. The work presented includes an overview of the toolkit and its uses, results of transcription performance, and avenues for transcription improvement for non-native speakers of American English

Active anti-acetylcholinesterase component of secondary metabolites produced by the endophytic fungi of Huperzia serrata

Background: An endophytic fungus lives within a healthy plant during certain stages of, or throughout, its life cycle. Endophytic fungi do not always cause plant disease, and they include fungi that yield different effects, including mutual benefit, and neutral and pathogenic effects. Endophytic fungi promote plant growth, improve the host plant's resistance to biotic and abiotic stresses, and can produce the same or similar biologically active substances as the host. Thus, endophytic fungal products have important implications in drug development. Result: Among the numerous endophytic fungi, we identified two strains, L10Q37 and LQ2F02, that have anti-acetylcholinesterase activity, but the active compound was not huperzine A. The aim of this study was to investigate the anti-acetylcholinesterase activity of secondary metabolites isolated from the endophytic fungi of Huperzia serrata. Microbial cultivation and fermentation were used to obtain secondary metabolites. Active components were then extracted from the secondary metabolites, and their activities were tracked. Two compounds that were isolated from endophytic fungi of H. serrata were identified and had acetylcholine inhibitory activities. In conclusion, endophytic fungal strains were found in H. serrata that had the same anti-acetylcholinesterase activity. Conclusion: We isolated 4 compounds from the endophytic fungus L10Q37, among them S1 and S3 are new compounds. 6 compounds were isolated from LQ2F02, all 6 compounds are new compounds. After tested anti acetylcholinesterase activity, S5 has the best activity. Other compounds' anti acetylcholinesterase activity was not better compared with huperzine A