Browse Articles

The authors assessed the correlation between the stock, commodity, and consumer markets with the housing market.

The authors looked at the potential of different phenolic compounds to reduce oxidative stress (i.e., act as antioxidants).

The authors use computational methods to compare tau acetylation to the better studied tau phosphorylation in Alzheimer's disease and then design and computationally test a new drug to prevent abnormal post-translational modifications of tau.

The authors used RNA-seq datasets to assess the effect of CDK7 inhibition on transcriptional pathways in castration-resistant prostate cancer cells.

Here the authors investigated a combination therapy to target the Kirsten rat sarcoma viral oncogene homolog mutation in lung cancer, by analyzing publicly available data. Their findings indicate that the combination therapy of CA170 and Kvax enhances helper T cell function and improves cytotoxic T lymphocyte infiltration, while Kvax alone drives plasma and memory B cell proliferation.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

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

The authors studied the bioethical values of Generation Z.

The authors test symbiotic relationships among cyanobacteria species to generate more robust cultures for potential biofuel production.

This study investigates how the hyperparameters epochs and batch size affect the classification accuracy of a convolutional neural network (CNN) trained on pulsar candidate data. Our results reveal that accuracy improves with increasing number of epochs and smaller batch sizes, suggesting that with optimized hyperparameters, high accuracy may be achievable with minimal training. These findings offer insights that could help create more efficient machine learning classification models for pulsar signal detection, with the potential of accelerating pulsar discovery and advancing astrophysical research.