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Determining the Effects of Fibroblast Growth Factor 2 on the Regenerative Abilities of Echinometra lucunter Sea Urchins

Kisling et al. | Feb 12, 2019

Determining the Effects of Fibroblast Growth Factor 2 on the Regenerative Abilities of Echinometra lucunter Sea Urchins

As humans, not all our body organs can adequately regenerate after injury, an ability that declines with age. In some species, however, regeneration is a hallmark response that can occur limitless numbers of time throughout the life of an organism. Understanding how such species can regenerate so efficiently is of central importance to regenerative medicine. Sea urchins, unlike humans, can regenerate their spinal tissue after injury. Here the authors study the effect of a growth factor, FGF2, on sea urchin regeneration but find no conclusive evidence for a pro-regenerative effect after spinal tissue injury.

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The influence of working memory on auditory category learning in the presence of visual stimuli

Vishag et al. | Sep 18, 2022

The influence of working memory on auditory category learning in the presence of visual stimuli

Here in an effort to better understand how our brains process and remember different categories of information, the authors assessed working memory capacity using an operation span task. They found that individuals with higher working memory capacity had higher overall higher task accuracy regardless of the type of category or the type of visual distractors they had to process. They suggest this may play a role in how some students may be less affected by distracting stimuli compared to others.

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Luteolin's positive inhibition of melanoma cell lines.

Su et al. | Nov 17, 2020

Luteolin's positive inhibition of melanoma cell lines.

Luteolin (3′,4′,5,7-tetrahydroxyflavone) is a flavonoid that occurs in fruits, vegetables, and herbs. Research suggests that luteolin is effective against various forms of cancer by triggering apoptosis pathways. This experiment analyzes the effects of luteolin on the cell viability of malignant melanoma cells using an in vitro experiment to research alternative melanoma treatments and hopefully to help further cancer research as a whole.

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Access to public parks, drinking fountains, and clean public drinking water in the Bay Area is not driven by income

Zaroff et al. | Jul 15, 2021

Access to public parks, drinking fountains, and clean public drinking water in the Bay Area is not driven by income

Access to green space—an area of grass, trees, or other vegetation set apart for recreational or aesthetic purposes in an urban environment—and clean drinking water can be unequally distributed in urban spaces, which are often associated with income inequality. Little is known about public drinking water and green space inequities in the Bay Area. For our study, we sought to understand how public park access, drinking fountain access, and the quality of public drinking water differ across income brackets in the Bay Area. Though we observed smaller-scale instances of inequalities, in the park distribution in the Bay Area as a whole, and in the Southern Bay’s water quality and park distribution, our results indicate that other factors could be influencing water quality, and park and fountain access in the Bay Area.

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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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Comparing Virulence of Three T4 Bacteriophage Strains on Ampicillin-Resistant and Sensitive E. coli Bacteria

Hudanich et al. | Dec 09, 2020

Comparing Virulence of Three T4 Bacteriophage Strains on Ampicillin-Resistant and Sensitive <em>E. coli</em> Bacteria

In this study, the authors investigate an alternative way to kill bacteria other than the use of antibiotics, which is useful when considering antibiotic-resistance bacteria. They use bacteriophages, which are are viruses that can infect bacteria, and measure cell lysis. They make some important findings that these bacteriophage can lyse both antibiotic-resistant and non-resistant bacteria.

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Genomic Signature Analysis for the Strategic Bioremediation of Polycyclic Aromatic Hydrocarbons in Mangrove Ecosystems in the Gulf of Tonkin

Dao et al. | Jun 27, 2021

Genomic Signature Analysis for the Strategic Bioremediation of Polycyclic Aromatic Hydrocarbons in Mangrove Ecosystems in the Gulf of Tonkin

Engineered bacteria that degrade oil are currently being considered as a safe option for the treatment of oil spills. For this approach to be successful, the bacteria must effectively express oil-degrading genes they uptake as part of an external genoming vehicle called a "plasmid". Using a computational approach, the authors investigate plasmid-bacterium compatibility to find pairs that ensure high levels of gene expression.

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Optimizing Interplanetary Travel Using a Genetic Algorithm

Murali et al. | Oct 28, 2018

Optimizing Interplanetary Travel Using a Genetic Algorithm

In this work, the authors develop an algorithm that solves the problem of efficient space travel between planets. This is a problem that could soon be of relevance as mankind continues to expand its exploration of outer space, and potentially attempt to inhabit it.

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A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood

Adami et al. | Sep 20, 2023

A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
Image credit: National Cancer Institute

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.

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