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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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Evaluating cinnamaldehyde as an antibacterial agent in a produce wash for leafy greens

Drennan et al. | Oct 28, 2021

Evaluating cinnamaldehyde as an antibacterial agent in a produce wash for leafy greens

Recognizing a growing demand for organic produce, the authors sought to investigate plant-based antibiotic solutions to meet growing consumer demand for safe produce and also meet microbial standards of the USDA. The authors investigated the use of cinnamaldehyde as an antibacterial again E. coli, finding that lettuce treated with cinnamaldehyde displayed significantly lower colony-forming units of E. coli when compared to lettuce treated with chlorine bleach.

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The Effect of Various Preparation Methods on the Spoilage Rate of Roma Tomatoes (Solanum lycopersicum)

Cataltepe et al. | Feb 22, 2018

The Effect of Various Preparation Methods on the Spoilage Rate of Roma Tomatoes (Solanum lycopersicum)

As levels of food waste continue to rise, it is essential to find improved techniques of prolonging the shelf life of produce. The authors aimed to find a simple, yet effective, method of slowing down spoilage in tomatoes. Linear regression analysis revealed that the tomatoes soaked salt water and not dried displayed the lowest correlation between time and spoilage, confirming that this preparation was the most effective.

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The Effect of Neem on Common Nosocomial Infection-Causing Organisms

Shah et al. | Jan 27, 2020

The Effect of Neem on Common Nosocomial Infection-Causing Organisms

Nosocomial infections acquired in hospitals pose a risk to patients, a risk compounded by resistant microorganisms. To combat this problem, researchers have turned to bioactive compounds from medicinal plants such as the widely used neem. In the present study, researchers sought to determine the effectiveness of different neem preparations against several hospital acquired human pathogens. Neem powder in water successfully inhibited microorganism growth making it a potential agent to combat these infections.

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LawCrypt: Secret Sharing for Attorney-Client Data in a Multi-Provider Cloud Architecture

Zhang et al. | Jul 19, 2020

LawCrypt: Secret Sharing for Attorney-Client Data in a Multi-Provider Cloud Architecture

In this study, the authors develop an architecture to implement in a cloud-based database used by law firms to ensure confidentiality, availability, and integrity of attorney documents while maintaining greater efficiency than traditional encryption algorithms. They assessed whether the architecture satisfies necessary criteria and tested the overall file sizes the architecture could process. The authors found that their system was able to handle larger file sizes and fit engineering criteria. This study presents a valuable new tool that can be used to ensure law firms have adequate security as they shift to using cloud-based storage systems for their files.

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An analysis of the distribution of microplastics along the South Shore of Long Island, NY

Sanderson et al. | Sep 21, 2020

An analysis of the distribution of microplastics along the South Shore of Long Island, NY

This study is focused on the distribution of microplastics in Long Island, NY. Microplastics are plastic particles that measure less than 5 mm in length and pose an environmental risk due to their size, composition, and ubiquitous location in the marine environment. Focusing on the South Shore of Long Island, the authors investigated the locations and concentrations of microplastics at four locations along the shore line. While they did not find significant differences in the number of microplastics per location, there were microplastics at all four locations. This finding is important to drive future research and environmental policy as well.

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