Coronary artery bypass grafts are a common technique to treat coronary heart disease. The authors compared the efficacy of suturing and stapling techniques using an artificial heart pump and silicone tubing and found that suturing, while more time and skill intensive, held pressure in the tubing better than stapling.
Sesame (Sesamum indicum) and moringa (Moringa oleifera) have natural antioxidants that could prevent cancer growth. Previously, this group found that sesame and moringa individually suppress eye tumor grown in the Drosophila melanogaster model. In the present study, combinations of sesame and moringa at different concentrations were included in the D. melanogaster diet. The impact on eye tumor development was assessed at different stages of growth.
Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.
Attention Deficit Hyperactivity Disorder (ADHD) is characterized by impulsivity, hyperactivity, and inattention. The authors hypothesized that people with ADHD would display more inattentional blindness in perceptually simple tasks and less inattentional blindness in perceptually complex tasks. The results indicate that there is no significant correlation between ADHD and inattentional blindness in either type of task.
Numerous organisms, including the marine bacterium Aliivibrio fischeri, produce light. This bioluminescence is involved in many important symbioses and may one day be an important source of light for humans. In this study, the authors investigated ways to increase bioluminescence production from the model organism E. coli.
Noise pollution negatively impacts the health and behavioral routines of humans and other animals, but the production of synthetic sound-absorbing materials contributes to harmful gas emissions into the atmosphere. The authors of this paper investigated the effectiveness of environmentally-friendly, cheap natural-fiber materials, such as jute, as replacements for synthetic materials, such as gypsum and foam, in soundproofing.
Did the COVID-19 pandemic and travel restrictions improve air quality? The authors investigate this question in New York City using existing pollution data and forecasting trends.
Since cough syrup and mouthwash are commonly used items and often end up flushed down the drain or toilet, they can eventually find their way into into freshwater waterways which can be harmful to many marine organisms, such as planarians (aquatic flatworms). To investigate the effects of these substances on planarians, the authors considered different concentrations of Listerine mouthwash and Robitussin syrup along with their active ingredients. By using a behavioral assay, they identified that the active ingredients of cough syrup detrimentally affect planarian behavior. They suggest that these findings could be used to guide disposal methods to lessen detrimental effects on aquatic life.
The cosmic microwave background (CMB) is faint electromagnetic radiation left over from early stages in the formation of the universe. In order to analyze the CMB, scientists need to remove from electromagnetic data foreground radiation that contaminates CMB datasets. In this study, students utilize extensive updated datasets to analyze the correlation between CMB maps and Faraday RM and WMAP sky maps.
This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.