Browse Articles

This study explored whether planaria, known for their regenerative abilities, can retain learned memories after regeneration and how stressors like alcohol affect memory.

In the battle against Alzheimer's disease, early detection is critical to mitigating symptoms in patients. Here, the authors use a collection of MRI scans, layering with deep learning computer modeling, to investigate early stages of AD which can be hard to catch by human eye. Their model is successful, able to outperform previous models, and detected regions of interest in the brain for further consideration.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

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.

To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.

This manuscript focuses on improving the mechanical strength and radiopacity of SLA 3D-printed stent materials by incorporating nanoparticles for their application in flow-diverting stents (FDSs) used to treat aneurysms. The authors used biocompatible, bioresorbable resin formulations with varying concentrations of silica and zirconium to enhance tensile strength and radiopacity, respectively. The authors employed SEM to confirm the incorporation of nanoparticles, and CT imaging was used to assess the effect of zirconium-infused FDSs.

The authors looked at the dynamics of tennis serves from professional and amateur athletes.

The authors investigated adoption rates of solar photovoltaic power at K-12 schools in California.

The authors looked at using computer vision to evaluate the degree of exotropia in individuals with strabismus.

How accurate are DNA parentage tests? In this study, the authors hypothesized that current parentage tests are reliable if the analysis involves only one or a few families of yellow perch fish Perca flavescens. Their results suggest that DNA parentage tests are reliable as long as the right methods are used, since these tests involve only one family in most cases, and that the results from parentage analyses of large populations can only be used as a reference.