The authors test potential anti-inflammatory and pro-apoptotic effects of a polyherbal extract formulation on cultured breast cancer cells.
Read More...Anti-inflammatory and pro-apoptotic properties of the polyherbal drug, MAT20, in MCF-7 cells
The authors test potential anti-inflammatory and pro-apoptotic effects of a polyherbal extract formulation on cultured breast cancer cells.
Read More...Investigating the potential of zinc oxide nanoparticles and zinc ions as promising approaches to lung cancer
Here, the authors chose to investigate the efficacy of zinc oxide nanoparticles (ZnO NPs) and cisplatin or zinc ions in inducing cancer apoptosis. While both treatments were found to reduce the proliferation of lung cancer cells, the authors suggest that further studies to identify the mechanism are necessary.
Read More...Analyzing the effects of multiple adhesives on elastic collisions and energy loss in a Newton’s Cradle
The energy conservation in a system of objects in collision depends on the elasticity of the objects and environmental factors such as air resistance. One system that relies heavily on elasticity is the Newton’s Cradle. We aimed to determine the extent to which these adhesives serve to mitigate or worsen the chaotic movements and elastic collisions.
Read More...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.
Read More...Determining degree of dissociation through conductivity
The authors looked at how molarity impacts the degree to which ionic compounds dissociate in solution. They found that lower molarities led to decreased conductivity of solutions in a manner that did not follow the theoretical predictions.
Read More...Contribution of environmental factors to genetic variation in the Pacific white-sided dolphin
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.
Read More...Inhibitory effects of captan on growth of Escherichia coli and Bacillus coagulans
The authors test the effects of the pesticide captan on the growth of gut microbiome bacteria including Bacillus coagulan and Escherichia coli.
Read More...Voltage, power, and energy production of a Shewanella oneidensis biofilm microbial fuel cell in microgravity
The authors looked at the ability of Shewanella oneidensis to generate energy in a microbial fuel cell under varying conditions. They found that the S. Onedensis biofilm was able to produce energy in microgravity and that one of the biggest factors that limited energy production was a decrease in growth medium present.
Read More...Extracellular vesicles derived from oxidatively stressed stromal cells promote cancer progression
This paper hypothesized that the tumor microenvironment mediates cancer’s response to oxidative stress by delivering extracellular vesicles to cancer cells. Breast and lung cancer cells were treated with EVs, reavealing that EVs extracted from oxidatively stressed adipocytes increased the cell proliferation of breast cancer cells. These findings present a novel way that the TME influences cancer progression.
Read More...Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires
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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