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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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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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Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Suresh et al. | Jan 12, 2024

Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

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Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires

Bilwar et al. | Jan 15, 2024

Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires
Image credit: Pixabay

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.

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