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The effect of molecular weights of chitosan on the synthesis and antifungal effect of copper chitosan

Byakod et al. | Apr 07, 2024

The effect of molecular weights of chitosan on the synthesis and antifungal effect of copper chitosan

Pathogenic fungi such as Alternaria alternata (A. alternata) can decimate crop yields and severely limit food supplies when left untreated. Copper chitosan (CuCts) is a promising alternative fungicide for developing agricultural areas due to being inexpensive and nontoxic. We hypothesized that LMWc CuCts would exhibit greater fungal inhibition due to the beneficial properties of LMWc.

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Methanotrophic bioremediation for the degradation of oceanic methane and chlorinated hydrocarbons

Lee et al. | Oct 08, 2021

Methanotrophic bioremediation for the degradation of oceanic methane and chlorinated hydrocarbons

Seeking an approach to address the increasing levels of methane and chlorinated hydrocarbons that threaten the environment, the authors worked to develop a novel, low-cost biotrickling filter for use as an ex situ method tailored to marine environments. By using methanotrophic bacteria in the filter, they observed methane degradation, suggesting the feasibility of chlorinated hydrocarbon degradation.

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Using the COmplex PAthway SImulator, Stage Analysis, and Chemical Kinetics to Develop a Novel Solution to Lower Tau Concentrations in Alzheimer’s Disease

Carroll et al. | Sep 28, 2020

Using the COmplex PAthway SImulator, Stage Analysis, and Chemical Kinetics to Develop a Novel Solution to Lower Tau Concentrations in Alzheimer’s Disease

In this study, the authors ask whether a Tau immunotherapy treatment, Hsp70 protein treatment, or dual treatment approach of both the Tau imunotherapy treatment and Hsp70 protein treatment leads to a greater reduction in Tau protein concentration in Alzheimer's disease. Overall, they conclude that the effectiveness of the treatment ultimately relies on the stage of Alzheimer’s.

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The Impact of Age on Post-Concussive Symptoms: A Comparative Study of Symptoms Related and Not Related to the Default Mode Network

Wurscher et al. | Mar 05, 2017

The Impact of Age on Post-Concussive Symptoms: A Comparative Study of Symptoms Related and Not Related to the Default Mode Network

The Default Mode Network (DMN) is a network of connected brain regions that are active when the brain is not focused on external tasks. Minor brain injuries, such as concussions, can affect this network and manifest symptoms. In this study, the authors examined correlations between DMN age and post-concussion symptoms in previously concussed individuals and healthy controls.

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The Effect of Varying Training on Neural Network Weights and Visualizations

Fountain et al. | Dec 04, 2019

The Effect of Varying Training on Neural Network Weights and Visualizations

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

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Artificial Intelligence Networks Towards Learning Without Forgetting

Kreiman et al. | Oct 26, 2018

Artificial Intelligence Networks Towards Learning Without Forgetting

In their paper, Kreiman et al. examined what it takes for an artificial neural network to be able to perform well on a new task without forgetting its previous knowledge. By comparing methods that stop task forgetting, they found that longer training times and maintenance of the most important connections in a particular task while training on a new one helped the neural network maintain its performance on both tasks. The authors hope that this proof-of-principle research will someday contribute to artificial intelligence that better mimics natural human intelligence.

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Behavioral Longevity: The Impact of Smoking, Alcohol Consumption, and Obesity on Life Expectancy

Han et al. | Oct 03, 2019

Behavioral Longevity: The Impact of Smoking, Alcohol Consumption, and Obesity on Life Expectancy

In this article, the authors look into what is already known about the factor affecting longevity and determine the importance of behavioral factors including alcohol consumption, smoking, and obesity on longevity. The authors quantify data from over 150 countries and, interestingly, find that the impact each factor has on longevity is at least in part dependent on the country's economic development status. Overall, they conclude that an average person’s life expectancy can increase by more than 3 years if smoking and alcohol consumption is reduced by a half and weight is decreased by 10%.

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Using two-stage deep learning to assist the visually impaired with currency differentiation

Nachnani et al. | Jun 02, 2024

Using two-stage deep learning to assist the visually impaired with currency differentiation
Image credit: Omer Shahzad

Here, recognizing the difficulty that visually impaired people may have differentiating United States currency, the authors sought to use artificial intelligence (AI) models to identify US currencies. With a one-stage AI they reported a test accuracy of 89%, finding that multi-level deep learning models did not provide any significant advantage over a single-level AI.

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A Taste of Sweetness in Bioplastics

Tsai et al. | Apr 05, 2019

A Taste of Sweetness in Bioplastics

Sweet potatoes are one of the most common starches in Taiwan, and sweet potato peels hold significant potential to make biodegradable plastics which can alleviate the environmental impact of conventional petroleum-based plastics. In this paper, Tsai et al created starch-based bioplastics derived from sweet potato peels and manipulated the amount of added glycerol to alter the plastic’s strength and flexibility properties. Their results indicated that higher concentrations of glycerol yield more malleable plastics, providing insights into how recycled agricultural waste material might be used to slow down the rate of pollution caused by widespread production of conventional plastics.

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