In this study, the authors investigate the effects that flagella have on E. coli's ability to adhere to glass surfaces.
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An analysis of junior rower performance and how it is affected by rower's features
In this study, with consideration for the increasing participation of high school students in indoor rowing, the authors analyzed World Indoor Rowing Championship data. Statistical analysis revealed two key features that can determine the performance of a rower as well as increasing competitiveness in nearly all categories considered. They conclude by offering a 2000-meter ergometer time distribution that can help junior rowers assess their current performance relative to the world competition.
Read More...Investigating the Role of the Novel ESCRT-III Recruitment Factor CCDC11 in HIV Budding: A Potential Target for Antiviral Therapy
Acquired immunodeficiency syndrome (AIDS) is a life-threatening condition caused by the human immunodeficiency virus (HIV). In this work, Takemaru et al explored the role of Coiled-Coil Domain-Containing 11 (CCDC11) in HIV-1 budding. Their results suggest that CCDC11 is critical for efficient HIV-1 budding, potentially indicating CCDC11 a viable target for antiviral therapeutics without major side effects.
Read More...Majority and Minority Influence in Teenagers for Different Social Dilemmas
Because humans live in societies, they may feel social pressure to conform to majority opinions. This follow-up study explores whether teenagers are likely to change their opinions to match others’, particularly in ambiguous situations.
Read More...Investigating Lymphocytic Involvement in Minimal Change Nephrotic Syndrome
Minimal Change Disease (MCD) is a degenerative kidney disease. Researchers know very little about the cause of this disorder, however some research has suggested that T lymphocytes may be involved. In this study, the authors measure CD4 and CD8 T cell subpopulations in patients with MCD to investigate whether irregular T lymphocyte populations may be involved in MCD pathogenesis.
Read More...Functional Network Connectivity: Possible Biomarker for Autism Spectrum Disorders (ASD)
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and is difficult to diagnose in young children. Here magnetoencephalography was used to compare the brain activity in patients with ASD to patients in a control group. The results show that patients with ASD have a high level of activity in different areas of the brain than those in the control group.
Read More...Assessing the possibility of using entomopathogenic fungi for mosquito control in Hawaii
Fungi that attack and kill insects have promise for targeting mosquitoes without the harmful environmental impacts of chemicals like DDT. To find out whether fungi might be effective in controlling mosquitoes in Hawaii, Jiang and Chan test the effects of Hawaiian fungal isolates on mosquito larvae.
Read More...English learner status in Florida public schools is correlated with significantly lower graduation rates
The authors explore factors affecting graduation rates of students learning English as a second language across Florida counties.
Read More...A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis
Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.
Read More...Diagnosing hypertrophic cardiomyopathy using machine learning models on CMRs and EKGs of the heart
Here seeking to develop a method to diagnose, hypertrophic cardiomyopathy which can cause sudden cardiac death, the authors investigated the use of a convolutional neural network (CNN) and long short-term memory (LSTM) models to classify cardiac magnetic resonance and heart electrocardiogram scans. They found that the CNN model had a higher accuracy and precision and better other qualities, suggesting that machine learning models could be valuable tools to assist physicians in the diagnosis of hypertrophic cardiomyopathy.
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