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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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Assessing grass water use efficiency through smartphone imaging and ImageJ analysis

Shen et al. | Jul 27, 2022

Assessing grass water use efficiency through smartphone imaging and ImageJ analysis

Overwatering and underwatering grass are widespread issues with environmental and financial consequences. This study developed an accessible method to assess grass water use efficiency (WUE) combining smartphone imaging with open access color unmixing analysis. The method can be applied in automated irrigation systems or apps, providing grass WUE assessment for regular consumer use.

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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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The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Dasgupta et al. | Jul 06, 2021

The Role of a Mask - Understanding the Performance of Deep Neural Networks to Detect, Segment, and Extract Cellular Nuclei from Microscopy Images

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

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Characterizing Quorum Sensing-Induced Bioluminescence in Variable Volumes With Vibrio fischeri Using Computer Processing Methods

Abdel-Azim et al. | Jun 22, 2020

Characterizing Quorum Sensing-Induced Bioluminescence in Variable Volumes With <em>Vibrio fischeri</em> Using Computer Processing Methods

Understanding how bacteria respond to other bacteria could facilitate their ability to initiate and maintain their infectiousness. The phenomenon by which bacteria signal to each other via chemical signals is called quorum sensing, which could be targeted to deter bacterial infection in some cases if better understood. In this article, the authors study how a bacterium called V. fischeri uses quorum sensing to change bioluminescence, an easy readout that facilitates studying quorum sensing in this strain.

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Cytokine Treatment for Myocarditis May Directly Impact Cardiomyocytes Negatively

Kasner et al. | Apr 26, 2019

Cytokine Treatment for Myocarditis May Directly Impact Cardiomyocytes Negatively

The purpose of our study was to determine if direct administration of CXCL1/KC to cardiomyocytes causes negative changes to cell density or proliferation. This molecule has been shown to reduce inflammation in certain instances. Homocysteine models the direct effect of an inflammatory agent on cardiomyocytes. Our question was whether these molecules directly impact cell density through an interaction with the cell proliferation process. We hypothesized that cells treated with CXCL1/KC would maintain the same cell density as untreated cells. In contrast, cells treated with Homocysteine or both Homocysteine and CXCL1/KC, were expected to have a higher cell density that than that of untreated cells.

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