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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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Machine learning on crowd-sourced data to highlight coral disease

Narayan et al. | Jul 26, 2021

Machine learning on crowd-sourced data to highlight coral disease

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

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Effects of Ocean Acidification on Marine Calcium Carbonate

Prahalad et al. | Jul 31, 2020

Effects of Ocean Acidification on Marine Calcium Carbonate

Industrialization has transformed human life and improved it for many. Nonetheless, a side effect has been an increase in chemical waste, which when not disposed of properly, has detrimental effects on surrounding habitats. An increase in ocean acidification could potentially affect many forms of life, disrupting the ecological balance in unforeseeable ways. In this article the authors explore the effect of acidification on corals and shells, and observe that an increase in ocean acidity has a significant effect on corals, but not shells. This illustrates how acidification could negatively affect marine life, and calls our attention to managing the factors that contribute to increasing the pH of the Earth's water bodies.

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The effects of stress on the bacterial community associated with the sea anemone Diadumene lineata

Cahill et al. | Feb 15, 2021

The effects of stress on the bacterial community associated with the sea anemone Diadumene lineata

In healthy ecosystems, organisms interact in a relationship that helps maintain one another's existence. Stress can disrupt this interaction, compromising the survival of some of the members of such relationships. Here, the authors investigate the effect of stress on the interaction between anemones and their microbiome. Their study suggests that stress changes the composition of the surface microbiome of the anemone D. lineata, which is accompanied by an increase in mucus secretion. Future research into the composition of this stress-induced mucus might reveal useful antimicrobial properties.

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Antibacterial properties of household spices and toothpaste against oral bacteria

Toliver et al. | Apr 24, 2023

Antibacterial properties of household spices and toothpaste against oral bacteria

Bacteria cause tooth decay, plaque, bad breath, and other diseases. Despite being cleaned with water and toothpaste, oral bacteria live on our toothbrushes. Bacterial growth has been shown to be inhibited by different toothpastes and common household spices. This study tested how different toothpastes and common household spices, including cinnamon, cumin, nutmeg, and ground white pepper, can inhibit bacteria from growing on toothbrushes

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QuitPuff: A Simple Method Using Saliva to Assess the Risk of Oral Pre-Cancerous Lesions and Oral Squamous Cell Carcinoma in Chronic Smokers

Shamsher et al. | Mar 27, 2019

QuitPuff: A Simple Method Using Saliva to Assess the Risk of Oral Pre-Cancerous Lesions and Oral Squamous Cell Carcinoma in Chronic Smokers

Smoking generates free radicals and reactive oxygen species which induce cell damage and lipid peroxidation. This is linked to the development of oral cancer in chronic smokers. The authors of this study developed Quitpuff, simple colorimetric test to measure the extent of lipid peroxidation in saliva samples. This test detected salivary lipid peroxidation with 96% accuracy in test subjects and could serve as an inexpensive, non-invasive test for smokers to measure degree of salivary lipid peroxidation and potential risk of oral cancer.

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Covalently Entrapping Catalase into Calcium Alginate Worm Pieces Using EDC Carbodiimide as a Crosslinker.

Suresh et al. | Mar 31, 2019

Covalently Entrapping Catalase into Calcium Alginate Worm Pieces Using EDC Carbodiimide as a Crosslinker.

Catalase is a biocatalyst used to break down toxic hydrogen peroxide into water and oxygen in industries such as cheese and textiles. Improving the efficiency of catalase would help us to make some industrial products, such as cheese, less expensively. The best way to maintain catalase’s conformation, and thus enhance its activity, is to immobilize it. The primary goal of this study was to find a new way of immobilizing catalase.

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Antibacterial effectiveness of turmeric against gram-positive Staphylococcus epidermidis

Cox et al. | Jan 10, 2022

Antibacterial effectiveness of turmeric against gram-positive <i>Staphylococcus epidermidis</i>

Infections caused by antibiotic resistance are a leading issue faced by the medical field. The authors studied the antibacterial effectiveness of turmeric against gram-positive Staphylococcus epidermidis using antibiotic sensitivity disks. They infused blank antibiotic sensitivity disks with a 5% concentrated solution of turmeric and placed them on agar plates inoculated with bacteria. Overall, there was no measurable ZOI surrounding the turmeric disk so the measurements for all trials were 0 cm, suggesting that turmeric at a 5% concentration is not an effective antibacterial against S. epidermidis.

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