Browse Articles

Monitoring drought using explainable statistical machine learning models

Cheung et al. | Oct 28, 2024

Monitoring drought using explainable statistical machine learning models

Droughts have a wide range of effects, from ecosystems failing and crops dying, to increased illness and decreased water quality. Drought prediction is important because it can help communities, businesses, and governments plan and prepare for these detrimental effects. This study predicts drought conditions by using predictable weather patterns in machine learning models.

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Large Language Models are Good Translators

Zeng et al. | Oct 16, 2024

Large Language Models are Good Translators

Machine translation remains a challenging area in artificial intelligence, with neural machine translation (NMT) making significant strides over the past decade but still facing hurdles, particularly in translation quality due to the reliance on expensive bilingual training data. This study explores whether large language models (LLMs), like GPT-4, can be effectively adapted for translation tasks and outperform traditional NMT systems.

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Evaluating the antimicrobial activity of maitake mushroom extract against Staphylococcus epidermidis

Shamsudeen et al. | Oct 06, 2024

Evaluating the antimicrobial activity of maitake mushroom extract against <i>Staphylococcus epidermidis</i>
Image credit: Guido Blokker

Here, seeking to explore new antimicrobial therapies, the authors investigated the antimicrobial activity of Maitake mushroom extract against Staphylococcus epidermidis, a common cause of antibiotic resistant hospital-acquired infections. They found that Maitake extract showed potent antimicrobial activity, with higher concentrations showing inhibition comparable to tetracycline.

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Diagnosing hypertrophic cardiomyopathy using machine learning models on CMRs and EKGs of the heart

Kolluri et al. | Jul 29, 2024

Diagnosing hypertrophic cardiomyopathy using machine learning models on CMRs and EKGs of the heart
Image credit: Jesse Orrico

Here seeking to develop a method to diagnose, hypertrophic cardiomyopathy which can cause sudden cardiac death, the authors investigated the use of a convolutional neural network (CNN) and long short-term memory (LSTM) models to classify cardiac magnetic resonance and heart electrocardiogram scans. They found that the CNN model had a higher accuracy and precision and better other qualities, suggesting that machine learning models could be valuable tools to assist physicians in the diagnosis of hypertrophic cardiomyopathy.

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Transcriptomic profiling identifies differential gene expression associated with childhood abuse

Li et al. | Jul 23, 2024

Transcriptomic profiling identifies differential gene expression associated with childhood abuse
Image credit: The authors

Childhood abuse has severe and lasting effects throughout an individual's life, and may even have long-term biological effects on individuals who suffer it. To learn more about the effects of abuse in childhood, Li and Yearwood analyze gene expression data to look for genes differentially expressed genes in individuals with a history of childhood abuse.

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The effects of age on quality of mental health during the COVID-19 pandemic

Bui et al. | Jul 15, 2024

The effects of age on quality of mental health during the COVID-19 pandemic

The impact of age on mental health is a crucial yet understudied aspect of public health. While mental health is gaining recognition as a vital component of overall well-being, its correlation with age remains largely unexplored. In Canada, where the median age has risen significantly over the past half-century, understanding this relationship becomes increasingly pertinent. Researchers hypothesized that older adults would exhibit lower rates of mental health disorders and report better perceived mental health due to increased emotional stability and maturity.

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