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Author: search.filters.author.Singh SStart date: 2020

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    Mutagenesis treatment of Mortierella alpina for PUFA production enhancement for future food development
    (Elsevier B.V., 2025-06) Alhattab M; Lebeau J; Singh S; Puri M
    Random mutagenesis has been identified as a key tool for improving microbial and fungal strains enabling the development of isolates with improved traits suited for industrial scale metabolite production to enhance the nutritional value of future foods. Presented here, is a random mutagenesis strategy employed to assess the effect of 5-fluorouracil (20-200 µg/ml), alone and in combination with the secondary agents octyl gallate and nocodazole, and diethyl sulfate (0.1 to 1 %) chemical mutagenic agents, on the biomass and lipid production as well as the FAME profile. Interestingly, a correlation was demonstrated between 5-fluorouracil exposure time and the arachidonic acid content, which was also influenced by the concentration used. 5-fluororuracil of 100 µg/ml treatment for 48 h resulted in the highest arachidonic acid (% TFA) content in isolates. Mutant M5F047 isolated with 5-fluororuracil (100 µg/ml) alone, proved to be most superior in terms of polyunsaturated fatty acid (PUFA) and arachidonic acid production, as compared to the Mortierella alpina wild type strain, with enhancements that doubled that of the parent strain. These improvements are more favorable for industrial scale production of arachidonic acid, a precursor of meaty flavour to improve plant-based meats in future food development.
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    Real and synthetic Punjabi speech datasets for automatic speech recognition
    (Elsevier Inc, 2024-02) Singh S; Hou F; Wang R
    Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.