The authors investigated an important parasite-host relationship between the raccoon roundworm and the raccoon to understand how parasite prevalence is affected by location. They found that the parasite infection was more prevalent in raccoons found closer to human dwellings, though the number of roundworm eggs was not significantly different. These results are important human health, since roundworm infection is lethal to humans and can be transmitted from raccoons to humans - the authors suggest that more research into this parasite and awareness of its prevalence is needed to prevent disease.
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The Prevalence of White Guilt Among American High School Students
Racial inequality has been a major issue throughout the history of the United States. In recent years, however, especially with the election of America's first black president, many have claimed that we have made progress and are moving towards a post-racial society. The authors of this study sought to test that claim by evaluating whether high school age students still experience a phenomenon known as white guilt. White guilt is defined as remorse or shame felt by people of Caucasian descent about racial inequality.
Read More...Examining the prevalence of depression in coronary artery disease patients: a cross-sectional analysis
The authors surveyed individuals diagnosed with coronary artery disease about their mental health to study a potential connection between coronary artery disease and depression.
Read More...TGFβ1 Codon 10 Polymorphism and its Association with the Prevalence of Low Myopia
The goal of this project was to assess the relationships among low myopia, behavioral and demographic factors, and a single-nucleotide polymorphism (SNP) in the TGFβ1 gene.
Read More...Machine Learning Algorithm Using Logistic Regression and an Artificial Neural Network (ANN) for Early Stage Detection of Parkinson’s Disease
Despite the prevalence of PD, diagnosing PD is expensive, requires specialized testing, and is often inaccurate. Moreover, diagnosis is often made late in the disease course when treatments are less effective. Using existing voice data from patients with PD and healthy controls, the authors created and trained two different algorithms: one using logistic regression and another employing an artificial neural network (ANN).
Read More...Sex differences in sleep disorders of Parkinson’s disease patients associated with a genetic risk variant
The authors use known Parkinson's disease-associated genetic variants to compare the prevalence of sleep dysfunction between males and females diagnosed with Parkinson's disease.
Read More...Stress and depression among individuals with low socioeconomic status during economic inflation
The authors use the Census Household Pulse Survey issued by the US Census Bureau to examine the prevalence of stress and depression among people across socioeconomic statuses.
Read More...Testing Simarouba amara’s therapeutic effects against weedicide-induced tumor-like morphology in planarians
According to the World Health Organization, cancer is a leading cause of death globally. The disease’s prevalence is rapidly increasing in association with factors including the increased use of pesticides and herbicides, such as glyphosate, which is one of the most widely used herbicide ingredients. Natural antioxidants and phytochemicals are being tested as anti-cancer agents due to their antiproliferative, antioxidative, and pro-apoptotic properties. Thus, we aimed to investigate the potential role of S. amara extract as a therapeutic agent against glyphosate-induced toxicity and tumor-like morphologies in regenerating and homeostatic planaria (Dugesia dorotocephala).
Read More...Similarity Graph-Based Semi-supervised Methods for Multiclass Data Classification
The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.
Read More...A land use regression model to predict emissions from oil and gas production using machine learning
Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO2), volatile organic compounds (VOCs), and ozone (O3) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO2, NOx, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.
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