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Effects of spices on rice spoilage

Govindaraj et al. | Aug 15, 2022

Effects of spices on rice spoilage

In this work, based on centuries of history where spices have been used and thought to have antimicrobial properties that prolong the shelf life of food, the authors investigated if several spices used in Indian cooking could delay the spoilage of cooked white rice. Based on changed in appearance and smell, as well as growth on agar plates, they found that cinnamon was the most effective in delaying spoilage, followed by cumin, pepper, garlic, and ginger. Their findings suggest the ability to use spices rather than chemical food preservatives to prolong the shelf life of foods.

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Utilizing a Wastewater-Based Medium for Engineered Saccharomyces cerevisiae for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals

Ramesh et al. | Oct 02, 2019

Utilizing a Wastewater-Based Medium for Engineered <em>Saccharomyces cerevisiae</em> for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals

Saccharomyces cerevisiae yeast is used to produce bioethanol, an alternative to fossil fuels. In this study, authors take advantage of this well studied yeast by genetically engineering them to increase fatty acid biosynthesis and culturing in a cost-effective wastewater based medium; potentially providing a sustainable alternative to petrochemicals.

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Using machine learning to develop a global coral bleaching predictor

Madireddy et al. | Feb 21, 2023

Using machine learning to develop a global coral bleaching predictor
Image credit: Madireddy, Bosch, and McCalla

Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

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A land use regression model to predict emissions from oil and gas production using machine learning

Cao et al. | Mar 24, 2023

A land use regression model to predict emissions from oil and gas production using machine learning

Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.

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Banana-based Biofuels for Combating Climate Change: How the Composition of Enzyme Catalyzed Solutions Affects Biofuel Yield

Klein-Hessling Barrientos et al. | May 27, 2020

Banana-based Biofuels for Combating Climate Change: How the Composition of Enzyme Catalyzed Solutions Affects Biofuel Yield

The authors investigate whether amylase or yeast had a more prominent role in determining the bioethanol concentration and bioethanol yield of banana samples. They hypothesized that amylase would have the most significant impact on the bioethanol yield and concentration of the samples. They found that while yeast is an essential component for producing bioethanol, the proportion of amylase supplied through a joint amylase-yeast mixture has a more significant impact on the bioethanol yield. This study provides a greater understanding of the mechanisms and implications involved in enzyme-based biofuel production, specifically of those pertaining to amylase and yeast.

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Buttermilk and baking soda increase pancake fluffiness by liberating carbon dioxide

Rojas et al. | Sep 18, 2022

Buttermilk and baking soda increase pancake fluffiness by liberating carbon dioxide

Here, seeking a better understanding of what determines the fluffiness of a pancake, the authors began by considering a chemical reaction that results in the production of carbon dioxide gas from recipe ingredients, specifically sodium bicarbonate or baking soda. The substitution of homemade buttermilk for milk and adding more baking soda was found to result in significantly fluffier pancakes.

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