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Detection and Control of Spoilage Fungi in Refrigerated Vegetables and Fruits

Chari et al. | May 16, 2021

Detection and Control of Spoilage Fungi in Refrigerated Vegetables and Fruits

Food spoilage leads to a significant loss in agricultural produce each year. Here, the authors investigate whether certain essential oils can protect against fungus-mediated spoilage of fruits and vegetables. Their results suggest that the compounds they tested might indeed inhibit fungal growth, at various temperatures, a promising result that could reduce food wasting.

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Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste

Singh et al. | Apr 24, 2023

Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste

About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.

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Upregulation of the Ribosomal Pathway as a Potential Blood-Based Genetic Biomarker for Comorbid Major Depressive Disorder (MDD) and PTSD

Ravi et al. | Aug 22, 2018

Upregulation of the Ribosomal Pathway as a Potential  Blood-Based Genetic Biomarker for Comorbid Major Depressive Disorder (MDD) and PTSD

Major Depressive Disorder (MDD), and Post-Traumatic Stress Disorder (PTSD) are two of the fastest growing comorbid diseases in the world. Using publicly available datasets from the National Institute for Biotechnology Information (NCBI), Ravi and Lee conducted a differential gene expression analysis using 184 blood samples from either control individuals or individuals with comorbid MDD and PTSD. As a result, the authors identified 253 highly differentially-expressed genes, with enrichment for proteins in the gene ontology group 'Ribosomal Pathway'. These genes may be used as blood-based biomarkers for susceptibility to MDD or PTSD, and to tailor treatments within a personalized medicine regime.

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In vitro Comparison of Anticancer and Immunomodulatory Activities of Resveratrol and its Oligomers

Zhang et al. | Sep 02, 2020

<em>In vitro</em> Comparison of Anticancer and Immunomodulatory Activities of Resveratrol and its Oligomers

Resveratrol is a type of stillbenoid, a phenolic compound produced in plants, that is known for its anti-inflammatory and anticancer effects. Many oligomers of resveratrol have recently been isolated their bioactivities remain unknown. Here, authors compared the bioactivities of resveratrol with natural dimers (ε-viniferin and gnetin H) and trimers (suffruticosol B and C). Results provide preliminary evidence that resveratrol oligomers could be potential preventive or therapeutic agents for cancers and other immune-related diseases

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Friend or Foe: Investigating the Relationship between a Corn Crop and a Native Ragweed Population

Wainwright et al. | May 07, 2014

Friend or Foe: Investigating the Relationship between a Corn Crop and a Native Ragweed Population

Farmers will need to increase crop yields to feed the world's growing population efficiently. The authors here investigate the effects of growing corn in the presence or absence of ragweed, an invasive weed found in many fields and gardens. Surprisingly, the authors found that corn grown in the presence of weeds grew taller and were more productive than corn that had weeds removed. This may help gardeners rethink the necessity of weeding, and may point a way to improve farm yields in the future.

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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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Carbonated liquids and carbonation level

Irina et al. | Jan 21, 2024

Carbonated liquids and carbonation level

In our work we followed the formation of gas bubbles on the surface of the vessel walls in different carbonated liquids, over different time intervals, at different temperatures and in vessels made of different materials. Our results made it possible to identify patterns affecting the process of formation and disappearance of carbon dioxide bubbles.

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Income mobility and government spending in the United States

Datta et al. | Nov 04, 2023

Income mobility and government spending in the United States
Image credit: CDC via Unsplash

Recent research suggests that the "American Dream" of income mobility may be becoming increasingly hard to obtain. Datta and Schmitz explore the role of government spending in socioeconomic opportunity by determining which state government spending components are associated with increased income mobility.

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Differences in Reliability and Predictability of Harvested Energy from Battery-less Intermittently Powered Systems

Sampath et al. | Apr 29, 2020

Differences in Reliability and Predictability of Harvested Energy from Battery-less Intermittently Powered Systems

Solar and radio frequency harvesters serve as a viable alternative energy source to batteries in many cases where the battery cannot be easily replaced. Using specifically designed circuit models, the authors quantify the reliability of different harvested energy sources to identify the most practical and efficient forms of renewable energy.

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