In this article the authors look at sugar consumption and the relationship to productivity in the work/labor force.
Read More...A spatiotemporal analysis of OECD member countries on sugar consumption and labor force participation
In this article the authors look at sugar consumption and the relationship to productivity in the work/labor force.
Read More...Effect of hypervitaminosis A in regenerating planaria: A potential model for teratogenicity testing
This unique research study evaluated the potential use of the flatworm, brown planaria (Dugesia tigrine), as an alternative model for teratogenicity testing. In this study, we exposed amputated planaria to varying concentrations of a known teratogen, vitamin A (retinol), for approximately 2 weeks, and evaluated multiple parameters including the formation of blastema and eyes. The results from this study demonstrated that high concentrations of retinol caused defects in head and eye formation in regenerating planaria, with similarities to vitamin A related teratogenicity findings in mammals. Based on these results, regenerating brown planaria are a promising alternative model for teratogenicity testing, which can potentially be paradigm shifting as it can reduce cost, time, and pregnant animal use in research.
Read More...Fluorescein or Green Fluorescent Protein: Is It Possible to Create a Sensor for Dehydration?
Currently there is no early dehydration detection system using temperature and pH as indicators. A sensor could alert the wearer and others of low hydration levels, which would normally be difficult to catch prior to more serious complications resulting from dehydration. In this study, a protein fluorophore, green fluorescent protein (GFP), and a chemical fluorophore, fluorescein, were tested for a change in fluorescence in response to increased temperature or decreased pH. Reversing the pH change did not restore GFP fluorescence, but that of fluorescein was re-established. This finding suggests that fluorescein could be used as a reusable sensor for a dehydration-related pH change.
Read More...Examining the relationship between screen time and achievement motivation in an adolescent population
In this study, the authors conduct a survey of high school students to evaluate the effects of screen time and habits on motivation.
Read More...Investigating the impact of the COVID-19 pandemic on the cognitive dissonance of adolescents
The authors survey adolescents about aspects of the COVID-19 pandemic to explore perspectives that may give rise to cognitive dissonance.
Read More...Investigating the impact of short-chain fatty acids on myofiber dynamics and insulin sensitivity
The authors looked at the impacts of short-chain fatty acids on muscle fiber formation as well as insulin sensitivity using a model of mouse myoblasts.
Read More...Understanding investors behaviors during the COVID-19 outbreak using Twitter sentiment analysis
The authors examine a relationship between tweet sentiment and stock market behavior during the early weeks of the COVID-19 pandemic.
Read More...Quantifying natural recovery of dopamine deficits induced by chronic stress
Here the authors investigated the natural recovery of stress-induced dopamine-related gene deficits in C. elegans by measuring the expression of cat-2 (dopamine biosynthesis) and sod-2 (oxidative stress) following exposure to starvation or hydrocortisone. They found that the reversibility of sod-2 and the expression of cat-2 were highly dependent on the type and severity of the stressor, suggesting that the body's natural ability to recover from dopamine dysfunction has biological limitations.
Read More...The effect of youth marijuana use on high-risk drug use: Examining gateway and substitution hypothesis
The authors looked at whether youth use of marijuana related to later high-risk drug use. Using survey data from 2010-2019 they found that youth marijuana use did correlate to an increased risk of high-risk drug use.
Read More...The precision of machine learning models at classifying autism spectrum disorder in adults
Autism spectrum disorder (ASD) is hard to correctly diagnose due to the very subjective nature of diagnosing it: behavior analysis. Due to this issue, we sought to find a machine learning-based method that diagnoses ASD without behavior analysis or helps reduce misdiagnosis.
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