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The effects of different modes of vocalization and food consumption on the level of droplet transmission of bacteria

Wong et al. | May 10, 2021

The effects of different modes of vocalization and food consumption on the level of droplet transmission of bacteria

Microbial agents reposnsible for respiratory infections are often carried in spittle, which means they can be easily transmitted. Here, the authors investigate how likely certain activities are to spread microbes carried in spittle. They also investigate whether eating certain types of food might reduce the spread of spittle-borne bacteria too.

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Impact of Population Density and Elevation on Tuberculosis Spread and Transmission in Maharashtra, India

Rao et al. | Nov 07, 2021

Impact of Population Density and Elevation on Tuberculosis Spread and Transmission in Maharashtra, India

India accounts for over 2.4 million recorded cases of tuberculosis, about 26% of the world’s cases. This research ascertained the bearing of both the population density and the average elevation above mean sea level (MSL) on the number of cases of TB recorded by the districts in Maharashtra, India. The results found a strong positive correlation between the number of TB cases per thousand people and the population density and a strong negative correlation between the number of TB cases per thousand people and the average elevation above MSL.

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Potential Multifunctional Agents for Dual Therapy of Age-Related and Associated Diseases: Alzheimer’s Disease and Type 2 Diabetes Mellitus

Kumar et al. | Nov 13, 2019

Potential Multifunctional Agents for Dual Therapy of Age-Related and Associated Diseases: Alzheimer’s Disease and Type 2 Diabetes Mellitus

Studies show an age-related link between Alzheimer’s Disease and Type 2 Diabetes Mellitus with oxidative stress a characteristic of both. Here, methanolic fractionations and extracts of four Ayurvedic plants were assessed for their protective abilities using a number of in vitro assays. Extracts inhibited oxidative stress and reduced activity of key enzymes involved in the pathogenesis of both diseases in neuroblastoma cells.

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Tomato disease identification with shallow convolutional neural networks

Trinh et al. | Mar 03, 2023

Tomato disease identification with shallow convolutional neural networks

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

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A Retrospective Study of Research Data on End Stage Renal Disease

Ponnaluri et al. | Mar 09, 2016

A Retrospective Study of Research Data on End Stage Renal Disease

End Stage Renal Disease (ESRD) is a growing health concern in the United States. The authors of this study present a study of ESRD incidence over a 32-year period, providing an in-depth look at the contributions of age, race, gender, and underlying medical factors to this disease.

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Molecular Alterations in a High-Fat Mouse Model Before the Onset of Diet–Induced Nonalcoholic Fatty Liver Disease

Lee et al. | Sep 20, 2016

Molecular Alterations in a High-Fat Mouse Model Before the Onset of Diet–Induced Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is one of the most prevalent chronic liver diseases worldwide, but there are few studied warning signs for early detection of the disease. Here, researchers study alterations that occur in a mouse model of NAFLD, which indicate the onset of NAFLD sooner. Earlier detection of diseases can lead to better prevention and treatment.

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Machine learning on crowd-sourced data to highlight coral disease

Narayan et al. | Jul 26, 2021

Machine learning on crowd-sourced data to highlight coral disease

Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.

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Prediction of preclinical Aβ deposit in Alzheimer’s disease mice using EEG and machine learning

Igarashi et al. | Nov 29, 2022

Prediction of preclinical Aβ deposit in Alzheimer’s disease mice using EEG and machine learning

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

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