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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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Using DNA Barcodes to Evaluate Ecosystem Health in the SWRCMS Reserve

Horton et al. | Sep 27, 2018

Using DNA Barcodes to Evaluate Ecosystem Health in the SWRCMS Reserve

Although the United States maintains millions of square kilometers of nature reserves to protect the biodiversity of the specimens living there, little is known about how confining these species within designated protected lands influences the genetic variation required for a healthy population. In this study, the authors sequenced genetic barcodes of insects from a recently established nature reserve, the Southwestern Riverside County Multi-Species Reserve (SWRCMSR), and a non-protected area, the Mt. San Jacinto College (MSJC) Menifee campus, to compare the genetic variation between the two populations. Their results demonstrated that the midge fly population from the SWRCMSR had fewer unique DNA barcode sequence changes than the MSJC population, indicating that the comparatively younger nature reserve's population had likely not yet established its own unique genetic drift changes.

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Wind Resistance and Automobile Shapes

Neelakantan et al. | Jan 25, 2019

Wind Resistance and Automobile Shapes

Energy efficiency is becoming more important as we struggle to find better, more sustainable energy sources to power our planet; the car industry is no exception. In this article, the authors examine the effect of shape on automobile aerodynamics By finding the shape that makes cars less resistant to wind, and therefore more energy efficient, can help the automobile industry make better, more eco-friendly cars that are also cheaper to operate.

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Phages Can Be More Effective and Specific Than Antibiotics in Combating Bacteria

Wu et al. | Feb 17, 2019

Phages Can Be More Effective and Specific Than Antibiotics in Combating Bacteria

Phage therapy has been suggested as an alternative to antibiotics because bacteria resistant to antibiotics may still be susceptible to phages. However, phages may have limited effectiveness in combating bacteria since bacteria possess several antiviral defense mechanisms and can quickly develop resistance to phages. In this study, Wu and Pinta compare the effectiveness and specificity of antibiotics and phages in combating bacteria. They found that T4 phages are more specific and effective in fighting or inhibiting both antibiotic-resistant and sensitive bacteria than antibiotics, suggesting that phage therapy can be developed as an efficient tool to combat antibiotic-resistant bacteria.

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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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Capturing Harmful Air Pollutants Using an Electrospun Mesh Embedded with Zinc-based Nanocrystals

Doppalapudi et al. | May 12, 2020

Capturing Harmful Air Pollutants Using an Electrospun Mesh Embedded with Zinc-based Nanocrystals

Zeolithic imidazolate framework-8 (ZIF-8) is a specific metal-organic framework that has favorable qualities for use in an air filter and is known to be capable of adsorbing particulate matter. Therefore, the objective of this experiment was to determine the effectiveness of ZIF-8 in adsorbing polar, gaseous air pollutants, specifically nitrogen dioxide and hydrogen sulfide. In order to determine effectiveness, the percent change in concentration for various gases after the application of ZIF-8 crystals was measured via Fourier-transform infrared spectroscopy (FTIR). The work highlights crystals as a potentially promising alternative or addition to current filter materials to reduce atmospheric pollution.

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