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Vineyard vigilance: Harnessing deep learning for grapevine disease detection

Mandal et al. | Aug 21, 2024

Vineyard vigilance: Harnessing deep learning for grapevine disease detection

Globally, the cultivation of 77.8 million tons of grapes each year underscores their significance in both diets and agriculture. However, grapevines face mounting threats from diseases such as black rot, Esca, and leaf blight. Traditional detection methods often lag, leading to reduced yields and poor fruit quality. To address this, authors used machine learning, specifically deep learning with Convolutional Neural Networks (CNNs), to enhance disease detection.

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Identification of potential therapeutic targets for multiple myeloma by gene expression analysis

Kochenderfer et al. | Apr 26, 2024

Identification of potential therapeutic targets for multiple myeloma by gene expression analysis
Image credit: The authors

A central challenge of cancer therapy is identifying treatments that will effectively target cancer cells while minimizing effects on healthy cells. To identify potential targets for treating a multiple myeloma, a frequently incurable cancer, Kochenderfer and Kochenderfer analyze RNA sequencing data from the Cancer Cell Line Encyclopedia to find genes with high expression in multiple myeloma cells and low expression in normal tissues

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Automated classification of nebulae using deep learning & machine learning for enhanced discovery

Nair et al. | Feb 01, 2024

Automated classification of nebulae using deep learning & machine learning for enhanced discovery

There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.

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Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin

Talwar et al. | Jan 23, 2024

Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin
Image credit: Flavio

Here the authors sought to understand the effects of different variables that may be tied to pollution and climate change on genetic variation of Pacific white-sided dolphins, a species that is currently threatened by water pollution. Based on environmental data collected alongside a genetic distance matrix, they found that ocean currents had the most significant impact on the genetic diversity of Pacific white-sided dolphins along the Japanese coast.

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Therapy dogs effectively reduce stress in college preparatory students

Ikeda et al. | Nov 27, 2023

Therapy dogs effectively reduce stress in college preparatory students
Image credit: Ryan Stone

In this article the authors looked at the effect of spending time with a therapy dog before and after stressful events. They found that interacting with a therapy before a stressful event showed more significant reduction in signs of stress compared to interacting with a therapy dog after stressful events have already occurred.

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