A Nuclear Magnetic Resonance (NMR) Platform for Real-Time Metabolic Monitoring of Bioprocesses

We present a Nuclear Magnetic Resonance (NMR) compatible platform for the automated real-time monitoring of biochemical reactions using a flow shuttling configuration. This platform requires a working sample volume of ∼11 mL and it can circulate samples with a flow rate of 28 mL/min, which makes it suitable to be used for real-time monitoring of biochemical reactions. Another advantage of the proposed low-cost platform is the high spectral resolution. As a proof of concept, we acquire 1H NMR spectra of waste orange peel, bioprocessed using Trichoderma reesei fungus, and demonstrate the real-time measurement capability of the platform. The measurement is performed over more than 60 h, with a spectrum acquired every 7 min, such that over 510 data points are collected without user intervention. The designed system offers high resolution, automation, low user intervention, and, therefore, time-efficient measurement per sample

Structural, morphological, optical and electrical characterization of gahnite ferroan nano composite derived from fly ash silica and ZnO Mixture

The synthesis of a high value-added product, gahnite ferroan nano composite, from a mixture of fly ash silica and ZnO is a low-cost and non-expensive technique. The XRD pattern clearly reveals the synthesized product from fly ash after leaching is a product of high-purity gahnite ferroan composite. The grains are mostly cubical in shape. The optical band gap of powdered gahnite ferroan nano composite is 3.37 eV, which acts as a UV protector. However, the bulk sample shows that the 500 to 700 nm wavelength of visible light is absorbed, and UV light is allowed to pass through. So, the bulk sample acts as a band pass filter of UV light which can be used in many optical applications for conducting UV-irradiation activity. Dielectric permittivity and dielectric loss increase with a rise in temperature. The increase in the ac conductivity at higher temperatures denotes the negative temperature coefficient resistance (NTCR) behavior of the material

Bioenergy Technologies for a Net Zero Transition:Outcomes of UK-India Bioenergy Research Scoping

The report is part of scoping exercise led by UK Research and Innovation (UKRI)’s Engineering and Physical Sciences Research Council (EPSRC) and Biotechnology and Biological Sciences Research Council (BBSRC) and commissioned to Supergen Bioenergy Hub. The report is for UKRI, funded by UKRI India. UKRI launched in April 2018. UKRI is a non-departmental public body sponsored by the Department for Business, Energy and Industrial Strategy (BEIS). Our organisation brings together the seven disciplinary research councils, Research England, which is responsible for supporting research and knowledge exchange at higher education institutions in England, and the UK’s innovation agency, Innovate UK. Our nine councils work together in innovative ways to deliver an ambitious agenda, drawing on our great depth and breadth of expertise and the enormous diversity of our portfolio. http://www.ukri.org UKRI India plays a key role in enhancing the research and innovation collaboration between the UK and India. Since 2008, the UK and Indian governments, and third parties, have together invested over £330 million in co-funded research and innovation programmes. This investment has brought about more than 258 individual projects. The projects were funded by over 15 funding agencies, bringing together more than 220 lead institutions from the UK and India. These research projects have generated more than £450 million in further funding, mainly from public bodies but also from non-profit organisations and commercial entities, attesting the relevance of these projects. www.ukri.org/india This work was commissioned to inform UKRI/UKRI India priorities and pathways for innovation development in bioenergy with UK-India partnerships

Network flux analysis of central metabolism in plants

The aim of this thesis was to develop stable-isotope steady-state metabolic flux analysis (MFA) based on 13C labeling to quantify intracellular fluxes of central carbon metabolism in plants. The experiments focus on the analysis of a heterotrophic cell suspension culture of Arabidopsis thaliana (L) Heynh. (ecotype Landsberg erecta). The first objective was to develop a robust methodology based on combining high quality steady-state stable labeling data, metabolic modeling and computational analysis. A comprehensive analysis of the factors that influence the outcome of MFA was undertaken and best practice established. This allowed a critical analysis of the subcellular compartmentation of carbohydrate oxidation in the cell culture. The second objective was to apply the methodology to nutritional perturbations of the cell suspension. A comparison of growth on different nitrogen sources revealed that transfer to an ammonium-free medium: (i) increased flux through the oxidative pentose phosphate pathway (oxPPP) by 10% relative to glucose utilisation; (ii) caused a substantial decrease in entry of carbon into the tricarboxylic acid cycle (TCA); and (iii) increased the carbon conversion efficiency from 55% to 69%. Although growth on nitrate alone might be expected to increase the demand for reductant, the cells responded by decreasing the assimilation of inorganic N. Cells were also grown in media containing different levels of inorganic phosphate (Pi). Comparison of the flux maps showed that decreasing Pi availability: (i) decreased flux through the oxPPP; (ii) increased the proportion of substrate fully oxidised by the TCA cycle; and (iii) decreased carbon conversion efficiency. These changes are consistent with redirection of metabolism away from biosynthesis towards cell maintenance as Pi is depleted. Although published genome-wide transcriptomic and metabolomic studies suggest that Pi starvation leads to the restructuring of carbon and nitrogen metabolism, the current analysis suggests that the impact on metabolic organisation is much less extreme

Metabolic, biochemical, mineral and fatty acid profiles of edible Brassicaceae microgreens establish them as promising functional food

Hidden hunger due to micronutrient deficiencies affecting one in three people is a global concern. Identifying functional foods which provide vital health beneficial components in addition to the nutrients is of immense health relevance. Microgreens are edible seedlings enriched with concentrated minerals and phytochemicals whose dietary potential as functional foods needs evaluation. In this study, comprehensive biochemical, mineral, metabolic, and fatty acid profiles of four Brassicaceae microgreens- mustard, pak choi, radish pink, and radish white has been investigated. The biochemical profiling confirms their promising nutritional and antioxidant nature. Mineral profiling using ICP-MS exhibited promising levels of Fe, Mn, Mg, K, and Ca in microgreens indicating them as excellent sources of minerals. GC–MS based metabolite profiling highlighted a range of phytochemicals- sugars, amino acids, organic acids, amines, fatty acids, phenols, and other molecules. Fatty acid profiling established promising levels of health beneficial oleic acid and linoleic acids. It is estimated that fresh microgreens can meet about 20 % to 50 % recommended dietary allowance of macro/micro-minerals along with providing useful fatty acids and antioxidants. Overall, the study highlighted Brassicaceae microgreens as an excellent nutrient source that can act as functional foods with promising potential to overcome ''hidden hunger''

Network flux analysis of central metabolism in plants

The aim of this thesis was to develop stable-isotope steady-state metabolic flux analysis (MFA) based on 13C labeling to quantify intracellular fluxes of central carbon metabolism in plants. The experiments focus on the analysis of a heterotrophic cell suspension culture of Arabidopsis thaliana (L) Heynh. (ecotype Landsberg erecta). The first objective was to develop a robust methodology based on combining high quality steady-state stable labeling data, metabolic modeling and computational analysis. A comprehensive analysis of the factors that influence the outcome of MFA was undertaken and best practice established. This allowed a critical analysis of the subcellular compartmentation of carbohydrate oxidation in the cell culture. The second objective was to apply the methodology to nutritional perturbations of the cell suspension. A comparison of growth on different nitrogen sources revealed that transfer to an ammonium-free medium: (i) increased flux through the oxidative pentose phosphate pathway (oxPPP) by 10% relative to glucose utilisation; (ii) caused a substantial decrease in entry of carbon into the tricarboxylic acid cycle (TCA); and (iii) increased the carbon conversion efficiency from 55% to 69%. Although growth on nitrate alone might be expected to increase the demand for reductant, the cells responded by decreasing the assimilation of inorganic N. Cells were also grown in media containing different levels of inorganic phosphate (Pi). Comparison of the flux maps showed that decreasing Pi availability: (i) decreased flux through the oxPPP; (ii) increased the proportion of substrate fully oxidised by the TCA cycle; and (iii) decreased carbon conversion efficiency. These changes are consistent with redirection of metabolism away from biosynthesis towards cell maintenance as Pi is depleted. Although published genome-wide transcriptomic and metabolomic studies suggest that Pi starvation leads to the restructuring of carbon and nitrogen metabolism, the current analysis suggests that the impact on metabolic organisation is much less extreme.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce. Manual detection of blight disease can be cumbersome and may require trained experts. To overcome these issues, we present an automated system using the Mask Region-based convolutional neural network (Mask R-CNN) architecture, with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions. The approach uses transfer learning, which can generate good results even with small datasets. The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day. The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf. The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches, which can confound the outcome of binary classification. To improve the detection performance, the original RGB dataset was then converted to HSL, HSV, LAB, XYZ, and YCrCb color spaces. A separate model was created for each color space and tested on 417 field-based test images. This yielded 81.4% mean average precision on the LAB model and 56.9% mean average recall on the HSL model, slightly outperforming the original RGB color space model. Manual analysis of the detection performance indicates an overall precision of 98% on leaf images in a field environment containing complex backgrounds