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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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Analysis of biofertilization impacts on Pisum sativum

Holden et al. | May 18, 2023

Analysis of biofertilization impacts on <i>Pisum sativum</i>
Image credit: David Boozer

This study explored the various effects of three different produce-based biofertilizers on pea plant growth, using red apple, pear, strawberry, and control treatments. It was hypothesized that the application of fruit biomatter would increase the growth of pea plants, with the application of strawberry biomatter having the most significant effect due to strawberries containing a higher nutrient content compared to pears and apples. Analysis confirmed the hypothesis. The application of strawberry biomatter could prove to be an effective way to increase plant growth in commercial agriculture.

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An efficient approach to automated geometry diagram parsing

Date et al. | Oct 02, 2022

An efficient approach to automated geometry diagram parsing

Here, beginning from an initial interest in the possibility to use a computer to automatically solve a geometry diagram parser, the authors developed their own Fast Geometry Diagram Parser (FastGDP) that uses clustering and corner information. They compared their own methods to a more widely available, method, GeoSolver, finding their own to be an order of magnitude faster in most cases that they considered.

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The Effect of Different Fructose Diets on the Lifespan of C. elegans

Chen et al. | May 10, 2020

The Effect of Different Fructose Diets on the Lifespan of <em>C. elegans</em>

High-fructose diets consumed widely in modern societies predisposes to metabolic diseases such as diabetes. Using the worm C. elegans, the authors of this study investigated the effect of fructose on the worm's survival rates. They found that worms fed 15% fructose had a lower life expectancy than those on a fructose-free diet. These results suggest that, like in humans, fructose has a negative effect on worm survival, which makes them an easy, attractive model to study the effects of fructose on health.

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Don’t Waste the Medical Waste: Reducing Improperly Classified Hazardous Waste in a Medical Facility

Hemani et al. | Jun 20, 2018

Don’t Waste the Medical Waste: Reducing Improperly Classified Hazardous Waste in a Medical Facility

Hemani et al. tackled the problem of rampant hospital waste by implementing staff training to help inform hospital workers about proper waste disposal. The authors observed a significant increase in proper waste disposal after the training, showing that simple strategies, such as in-person classroom training and posters, can have a profound effect on limiting improper waste handling.

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Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation

Gupta et al. | Jan 31, 2023

 Machine learning-based enzyme engineering of PETase for improved efficiency in plastic degradation
Image credit: Markus Spiske

Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.

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