In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.
Read More...A novel CNN-based machine learning approach to identify skin cancers
In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.
Read More...Who is at Risk for a Spinal Fracture? – A Comparative Study of National Health and Nutrition Examination Survey Data
One common age-related health problem is the loss of bone mineral density (BMD), which can lead to a variety of negative health outcomes, including increased risk of spinal fracture. In this study, the authors investigate risk factors that may be predictive of an individual's risk of spinal fracture. Their findings provide valuable information that clinicians can use in patient evaluations.
Read More...The relationship between multilingualism and visual imagery: Investigating aphantasia using the VVIQ
The authors looked at the correlation between being able to speak more than one language (multilingualism) and visual imagery. They found multilingual individuals had higher visual imagery as measured by the VVIQ.
Read More...Design and implementation of a cryptographically secure electronic voting infrastructure
In this study, the authors present proposed cryptographic controls for election sites with the hypothesis that this will mitigate risk and remediate vulnerabilities.
Read More...Development of a novel machine learning platform to identify structural trends among NNRTI HIV-1 reverse transcriptase inhibitors
With advancements in machine learning a large data scale, high throughput virtual screening has become a more attractive method for screening drug candidates. This study compared the accuracy of molecular descriptors from two cheminformatics Mordred and PaDEL, software libraries, in characterizing the chemo-structural composition of 53 compounds from the non-nucleoside reverse transcriptase inhibitors (NNRTI) class. The classification model built with the filtered set of descriptors from Mordred was superior to the model using PaDEL descriptors. This approach can accelerate the identification of hit compounds and improve the efficiency of the drug discovery pipeline.
Read More...Optimizing AI-generated image detection using a Convolutional Neural Network model with Fast Fourier Transform
Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.
Read More...Silver nanoparticle-coated orthopedic screws lead to greater calcium precipitation
The authors test whether coating stainless steel orthopedic screws in silver will promote calcium precipitation to improve orthopedic implant integration into bone.
Read More...Deep dive into predicting insurance premiums using machine learning
The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.
Read More...Exploring a new mechanism controlling thermogenesis of adipose tissue
The effect of other neuroendocrine hormones in the regulation of adipose tissue thermogenesis.
Read More...Increased carmine red exposure periods yields a higher number of vacuoles formed in Tetrahymena pyriformis
T. pyriformis can use phagocytosis to create vacuoles of carmine red, a dye which is made using crushed insects and is full of nutrients. Establishing a relationship between vacuole formation and duration of exposure to food can demonstrate how phagocytosis occurs in T. pyriformis. We hypothesized that if T. pyriformis was incubated in a carmine red solution, then more vacuoles would form over time in each cell.
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