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The authors access a panel of leaf traits across ten deciduous tree species to explore adaptive strategies between large and small trees.

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.

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.

Antibiotic resistance is a growing global health threat, and one strategy to combat it is using bacteriophages to enhance the effectiveness of existing antibiotics. This study tested whether targeting the TolC protein in E. coli with the TLS bacteriophage would increase bacterial sensitivity to antibiotics.

Here the authors investigate the potential of the bioluminescent fungus Panellus stipticus to serve as a sustainable bioindicator for environmental lead contamination. Their findings demonstrate that higher lead concentrations cause a measurable decrease in fungal bioluminescence intensity over time suggesting that the fungus could be an effective tool for detecting lead in an environment.

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.

The authors used a particle-in-cell simulation to determine the effects on extensive and intensive metrics. They found that preliminary simulations could be run quickly with much lower particle counts before more technically demanding and comprehensive simulations are performed.

The authors developed a quantum inspired model for stock market fluctuations.

Here the authors introduce pressing filtration as a novel, efficient, and low-energy method for extracting dietary fiber from cabbage, which successfully retains heat-sensitive nutrients and achieves a high fiber yield. The study demonstrates the scalability and economic viability of this technique for commercial use, highlighting that the resulting high-fiber cabbage powder can be incorporated into familiar foods like hamburger buns and beef patties without compromising taste or sensory quality.

The authors looked at how the length of an audio clip used of a song impacted the ability to properly classify it by musical genre.