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The authors looked at genes that were differentially expressed in patients who had and had not had febrile seizures to determine what differences in gene expression existed.

The authors looked the ability to predict future stock prices using various machine learning models.

The authors created a 1D model to diagnose hepatomegaly and cirrhosis via ultrasound of the liver.

In this study the authors looked at the ability to navigate planes in space between randomly placed planets. They used machine and reinforcement learning to run simulations and found that they were able to identify optimal paths for travel. In the future these techniques may allow for safer travel in unknown spaces.

This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.

Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.

This study examines how anchoring—providing numerical suggestions like "2 for $4"—can influence consumer purchasing decisions and increase revenue. The researchers tested three types of price anchors on 29 high school students shopping in a mock store.

Social media, especially among adolescents, has become a popular communication tool, but its link to negative mental health outcomes is a growing concern. This study analyzed public comments on Lizzo's social media, focusing on the nature of praise and criticism.

Optical reporters like tetrazolium dyes, exemplified by 5-diphenyl tetrazolium bromide (MTT), are effective tools for quantifying cellular responses under experimental conditions. These dyes assess cell viability by producing brightly-colored formazan dyes when reduced inside active cells. However, certain small molecules, including reducing agents like ascorbic acid, cysteine, and glutathione (GSH), can interfere with MTT assays, potentially compromising accuracy.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.