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    Chitin and chitosan from shellfish waste and their applications in agriculture and biotechnology industries
    (2025-01-01)
    Rai, Sampurna
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    Pokhrel, Prashant
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    Udash, Pranaya
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    Chemjong, Menjo
    ;
    Bhattarai, Namita
    A shellfish processing plant generates only 30–40% of edible meat, while 70–60% of portions are considered inedible or by-products. This large amount of byproduct or shellfish processing waste contains 20–40% chitin, that can be extracted using chemical or greener alternative extraction technologies. Chitin and its derivative (chitosan) are natural polysaccharides with nontoxicity, biocompatible, and biodegradable properties. Due to their versatile physicochemical, mechanical, and various bioactivities, these compounds find applications in various industries, including: biomedical, dental, cosmetics, food, textiles, agriculture, and biotechnology. In the agricultural sector, these compounds have been reported to promote: plant growth, plant defense system, slow release of nutrients in fertilizer, plant nutrition, and remediate soil conditions, etc. Whereas, biotechnology applications indicated: enhanced enzyme stability and efficacy, water purification and remediation, application in fuel cells and supercapacitors for energy conversion, acting as a catalyst in chemical synthesis, etc. This review provides a comprehensive discussion on the utilization of these biopolymers in agriculture (fertilizer, seed coating, soil treatment, and bioremediation) and biotechnology (enzyme immobilization, energy conversion, wastewater treatment, and chemical synthesis). Additionally, various extraction techniques including conventional and non-thermal techniques have been reported. Lastly, concluding remarks and future direction have been provided.
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    Machine Learning Models Using Data Mining for Biomass Production from Yarrowia lipolytica Fermentation
    (2023-03-01)
    Pensupa, Nattha
    ;
    Treebuppachartsakul, Treesukon
    ;
    Pechprasarn, Suejit
    In this paper, a database of biomass production from Yarrowia lipolytica fermentation is prepared and constructed using machine learning and data mining approaches. The database is curated from 15 publications and consists of 301 rows of data with 25 predictors and 1 label. The predictors include inoculum size, temperature, pH, and time, while the label is the corresponding biomass production. The database is then divided into training, validation, and test datasets and analyzed as a supervised machine learning task for regression. Twenty-six regression models are employed and compared for their performance in predicting biomass production. The best-performing model is the Matern 5/2 Gaussian process regression model, which has the lowest root-mean-squared error of 0.75 g/L, the highest R squared of 0.90, and the lowest mean absolute error of 0.52 g/L. The t-test is used to identify the most important predictors, and 14 predictors are sufficient for creating an accurate model. These 14 predictors are fermentation time, peptone, temperature, total Kjeldahl nitrogen, shaking rate, total nitrogen, inoculum size, yeast extract, crude glycerol, glucose, oil and grease, media pH, ammonium sulfate, and olive oil. This research demonstrates the application of machine learning and data mining to estimate biomass production and gives insight into which parameters are essential for Yarrowia lipolytica fermentation.
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    New Synthetic Operon Vectors for Expressing Multiple Proteins in the Chlamydomonas reinhardtii Chloroplast
    (2023-02-01)
    Yeon, Jihye
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    Miller, Stephen M.
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    Dejtisakdi, Wipawee
    Microalgae are a promising platform for generating valuable commercial products, including proteins that may not express well in more traditional cell culture systems. In the model green alga Chlamydomonas reinhardtii, transgenic proteins can be expressed from either the nuclear or chloroplast genome. Expression in the chloroplast has several advantages, but technology is not yet well developed for expressing multiple transgenic proteins simultaneously. Here, we developed new synthetic operon vectors to express multiple proteins from a single chloroplast transcription unit. We modified an existing chloroplast expression vector to contain intercistronic elements derived from cyanobacterial and tobacco operons and tested the ability of the resulting operon vectors to express two or three different proteins at a time. All operons containing two of the coding sequences (for C. reinhardtii FBP1 and atpB) expressed the products of those genes, but operons containing the other two coding sequences (C. reinhardtii FBA1 and the synthetic camelid antibody gene VHH) did not. These results expand the repertoire of intercistronic spacers that can function in the C. reinhardtii chloroplast, but they also suggest that some coding sequences do not function well in the context of synthetic operons in this alga.