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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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Part of speech distributions for Grimm versus artificially generated fairy tales

Arvind et al. | Nov 16, 2024

Part of speech distributions for Grimm versus artificially generated fairy tales
Image credit: Nayalia Y.

Here, the authors wanted to explore mathematical paradoxes in which there are multiple contradictory interpretations or analyses for a problem. They used ChatGPT to generate a novel dataset of fairy tales. They found statistical differences between the artificially generated text and human produced text based on the distribution of parts of speech elements.

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Public Perception of the Effects of Artificial Sweeteners on Diabetes Based on YouTube Comments

Kim et al. | Jun 25, 2024

Public Perception of the Effects of Artificial Sweeteners on Diabetes Based on YouTube Comments
Image credit: The authors

Artificial sweeteners are rising in popularity, in part due to the influence of social media platforms like YouTube. However, YouTube commenters often repeat information about artificial sweeteners that is not supported by scientific research. To investigate how misinformation about sweeteners spreads through social media, Kim and Yoo conduct a content analysis of YouTube comments to reveal how many comments repeat misinformation about artificial sweeteners' effects.

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Ramifications of natural and artificial sweeteners on the gastrointestinal system

Cowen et al. | Jun 19, 2023

Ramifications of natural and artificial sweeteners on the gastrointestinal system

This study aimed to determine whether artificial sweeteners are harmful to the human microbiome by investigating two different bacteria found to be advantageous to the human gut, Escherichia coli and Bacillus coagulans. Results showed dramatic reduction in bacterial growth for agar plates containing two artificial sweeteners in comparison to two natural sweeteners. This led to the conclusion that both artificial sweeteners inhibit the growth of the two bacteria and warrants further study to determine if zero-sugar sweeteners may be worse for the human gut than natural sugar itself.

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Analyzing breath sounds by using deep learning in diagnosing bronchial blockages with artificial lung

Bae et al. | Jan 22, 2024

Analyzing breath sounds by using deep learning in diagnosing bronchial blockages with artificial lung

Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.

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Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease

Kar et al. | Oct 10, 2020

Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease

Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).

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The Clinical Accuracy of Non-Invasive Glucose Monitoring for ex vivo Artificial Pancreas

Levy et al. | Jul 10, 2016

The Clinical Accuracy of Non-Invasive Glucose Monitoring for <i>ex vivo</i> Artificial Pancreas

Diabetes is a serious worldwide epidemic that affects a growing portion of the population. While the most common method for testing blood glucose levels involves finger pricking, it is painful and inconvenient for patients. The authors test a non-invasive method to measure glucose levels from diabetic patients, and investigate whether the method is clinically accurate and universally applicable.

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