Browse Articles

In this study, the authors studied the ability for bacteria to develop antibiotic resistance over successive generations and modeled the trajectory to predict how antibiotic resistance is developed.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

Arsenic contamination in rice, caused by the use of arsenic-laden groundwater for irrigation, is a growing global concern, affecting over 150 million people. To address this, researchers hypothesized that genetically modifying rice plants with arsenic-resistant genes could reduce arsenic uptake and allow the plants to detoxify arsenic, making them safer to consume.

The authors propose and test a method that would allow for the generation of a magnetic field on Mars sufficient to support future colonization.

We know relatively little about how vegan diets and non-vegan diets compare when it comes to the gut microbiome. Gollamudi and Gollamudi tackle this challenge by investigating how changes in a participant's diet affected the diversity of their intestinal microbiome.

In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.

This study investigates the presence of alkaloids in a variety of medicinal plants using the Marquis reagent. They reveal some surprising results and how useful the Marquis reagent is.

In this research, a novel bioplastic inclusive of bamboo tannins and chitosan is selected from more than 60 trial formula variations based on resulting strength, fatigue, and transparency attributes. The biodegradability of the finalized bioplastic is compared to that of conventional polyethylene, in addition to investigating its solubility and water absorbance. This research displays the potential of a legitimate, fully biodegradable plastic alternative to current marketplace bioplastics.

The authors looked at different strategies to extract oil from almonds to determine which gave the best results.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.