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A Crossover Study Comparing the Effect of a Processed vs. Unprocessed Diet on the Spatial Learning Ability of Zebrafish

Banga et al. | Sep 18, 2022

A Crossover Study Comparing the Effect of a Processed vs. Unprocessed Diet on the Spatial Learning Ability of Zebrafish

The authors compared the short-term effects of processed versus unprocessed food on spatial learning and survival in zebrafish, given the large public concern regarding processed foods. By randomly assigning zebrafish to a diet of brine shrimp flakes (processed) or live brine shrimp (unprocessed), the authors show while there is no immediate effect on a fish's decision process between the two diets, there are significant correlations between improved learning and stress response with the unprocessed diet.

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An Investigative Analysis of Climate Change Using Historical and Modern Weather Data

Han et al. | Dec 02, 2013

An Investigative Analysis of Climate Change Using Historical and Modern Weather Data

Climate change is an important and contentious issue that has far-reaching implications for our future. The authors here compare primary temperature and precipitation data from almost 200 years ago against the present day. They find that the average annual temperature in Brooklyn, NY has risen significantly over this time, as has the frequency of precipitation, though not the amount of precipitation. These data stress the need for more ecologically-conscious choices in our daily lives.

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Validating DTAPs with large language models: A novel approach to drug repurposing

Curtis et al. | Mar 02, 2025

Validating DTAPs with large language models: A novel approach to drug repurposing
Image credit: Growtika

Here, the authors investigated the integration of large language models (LLMs) with drug target affinity predictors (DTAPs) to improve drug repurposing, demonstrating a significant increase in prediction accuracy, particularly with GPT-4, for psychotropic drugs and the sigma-1 receptor. This novel approach offers to potentially accelerate and reduce the cost of drug discovery by efficiently identifying new therapeutic uses for existing drugs.

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Modelling effects of alkylamines on sea salt aerosols using the Extended Aerosols and Inorganics Model

Chang et al. | Apr 29, 2022

Modelling effects of alkylamines on sea salt aerosols using the Extended Aerosols and Inorganics Model

With monitoring of climate change and the evolving properties of the atmosphere more critical than ever, the authors of this study take sea salt aerosols into consideration. These sea salt aerosols, sourced from the bubbles found at the surface of the sea, serve as cloud condensation nuclei (CCN) and are effective for the formation of clouds, light scattering in the atmosphere, and cooling of the climate. With amines being involved in the process of CCN formation, the authors explore the effects of alkylamines on the properties of sea salt aerosols and their potential relevance to climate change.

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Analysis of the effects of positive ions and boundary layer temperature at various hypersonic speeds on boundary layer density

Selvakumar et al. | Oct 02, 2020

Analysis of the effects of positive ions and boundary layer temperature at various hypersonic speeds on boundary layer density

This study's goal was to identify the Mach numbers for which electrostatic drag and heat transfer manipulation would be most applicable inside the stratosphere. The experiments were conducted using computational fluid dynamics software. The study demonstrated that, on average, higher Mach speeds resulted in a considerably higher potential decrease in density. The study highlights that further research on the surface charge method is warranted to explore higher hypersonic speeds within the stratosphere.

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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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Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Chatterjee et al. | Oct 25, 2021

Predicting asthma-related emergency department visits and hospitalizations with machine learning techniques

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

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Pichia kudriavzevii Yeast Exposure Increases the Asthmatic Behavior of Alveolar Epithelial Cells In Vitro

Ortega et al. | Jun 07, 2019

<em>Pichia kudriavzevii</em> Yeast Exposure Increases the Asthmatic Behavior of Alveolar Epithelial Cells <em>In Vitro</em>

Asthma affects over 334 million people worldwide and is triggered by inhalation of environmental stimuli. The authors of this study characterized the effect of exposure to common spoilage yeast, Pichia kidriavzevii on alveolar epithelial cells. A direct correlation between infection duration and asthmatic status of these cells was found, indicating the potential for this yeast to be an environmental stimulus of asthma and warranting further study.

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