Application of Supercritical CO Extraction and Identification of Polyphenolic Compounds in Three Species of Wild Rose from Kamchatka: , , and .

A comparative metabolomic study of three varieties of wild Rosa (, , and ) from a Kamchatka expedition (2024) was conducted via extraction with supercritical carbon dioxide modified with ethanol (EtOH), and detection of bioactive compounds was realized via tandem mass spectrometry. Several experimental conditions were investigated in the pressure range 50-350 bar, with the used volume of co-solvent ethanol in the amount of 2% in the liquid phase at a temperature in the range of 31-70 °C. The most effective extraction conditions are the following: pressure 200 Bar and temperature 55 °C for ; pressure 250 Bar and temperature 60 °C for ; pressure 200 Bar and temperature 60 °C for .

Enhancing Multispectral Breast Imaging Quality Through Frame Accumulation and Hybrid GA-CPSO Registration.

Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation.

Preliminary study on unlocking growth and yield potential of USDA foxtail millet (Setaria italica L.) lines with NPK fertilization.

Foxtail millet (Setaria italica L.) is nutritionally superior to other cereals of the family Poaceae, with the potential to perform better in marginal environments. In the present context of climate change, ecologically sound and low-input foxtail millet varieties can be chosen for agricultural sustainability.

Attention-guided CenterNet deep learning approach for lung cancer detection.

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection.

Early allelopathic input and later nutrient addition mediated by litter decomposition of invasive affect native plant and facilitate its invasion.

Litter decomposition is essential for nutrient and chemical cycling in terrestrial ecosystems. Previous research on litter decomposition has often underestimated its impact on soil nutrient dynamics and allelopathy. To address this gap, we conducted a comprehensive study involving both field and greenhouse experiments to examine the decomposition and allelopathic effects of the invasive L.