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Characterization and Manipulation of Microbiomes From Arid Landfills for Improved Methane Production
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This feasibility study explored the use of an evolutionary mismatch narrative in nutritional education intervention aiming to reduce ultra-processed foods in the diets of veterans with type 2 diabetes and improve diabetic outcomes. Ultra-processed foods are foods that are primarily manufactured through industrial processes. These foods are high in calories but low in nutritional content. Diets high in these foods have been linked to increased health risks. One of the major health risks is type 2 diabetes. Type 2 diabetes is a chronic disease that is developed when cells become unable to properly utilize insulin. Over time this may lead to additional health conditions such as nerve damage, cardiovascular disease, and renal disease. Evolutionary mismatch narrative nutritional intervention offers a different approach to nutritional education to help reduce ultra-processed foods in diets. This study was a randomized controlled feasibility study at the Phoenix VA. Eleven participants were enrolled and randomly selected to be given either an evolutionary mismatch narrative education intervention or general nutritional education about ultra-processed foods. 24-hour diet recalls and blood chemistry were collected and analyzed. Blood chemistry provided diabetes related measurements which included glucose, HbA1c, insulin, HOMA-IR, and C-reactive protein. Statistically significant findings in this study included percentage of ultra-processed foods decreasing for both control and experimental groups from week 0 to week 4 (p=0.014), and C-reactive protein levels between the control and experimental groups (p=0.042). However, baseline C-reactive protein concentrations were lower in the experimental group such that normalizing for group differences at baseline revealed no significant difference in C-reactive protein change between interventions (p = 1.000). There were no other statistically significant values regarding diabetes related measurements. The results from this study suggest that nutritional education in general may help decrease ultra-processed food consumption.
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This work begins by defining a working window of light intensity (LI). Wild-type and laurate-excreting Synechocystis required an LI of at least 5 µE/m2-s to sustain themselves, but are photo-inhibited by LI of 346 to 598 µE/m2-s.
Fixing electrons into valuable organic products, e.g., biomass and excreted laurate, is critical to success. Wild-type Synechocystis channeled 75% to 84% of its fixed electrons to biomass; laurate-excreting Synechocystis fixed 64 to 69% as biomass and 6.6% to 10% as laurate. This means that 16 to 30% of the electrons were diverted to non-valuable soluble products, and the trend was accentuated with higher LI.
How the Ci concentration depended on the pH and the nitrogen source was quantified by the proton condition and experimentally validated. Nitrate increased, ammonium decreased, but ammonium nitrate stabilized alkalinity and Ci. This finding provides a mechanistically sound tool to manage Ci and pH independently.
Independent evaluation pH and Ci on the growth kinetics of Synechocystis showed that pH 8.5 supported the fastest maximum specific growth rate (µmax): 2.4/day and 1.7/day, respectively, for the wild type and modified strains with LI of 202 µE/m2-s. Half-maximum-rate concentrations (KCi) were less than 0.1 mM, meaning that Synechocystis should attain its µmax with a modest Ci concentration (≥1.0 mM).
Biomass grown with day-night cycles had a night endogenous decay rate of 0.05-1.0/day, with decay being faster with higher LI and the beginning of dark periods. Supplying light at a fraction of daylight reduced dark decay rate and improved overall biomass productivity.
This dissertation systematically evaluates and synthesizes fundamental growth factors of cyanobacteria: light, inorganic carbon (Ci), and pH. LI remains the most critical growth condition to promote biomass productivity and desired forms of biomass, while Ci and pH now can be managed to support optimal productivity.