Ultrasound-assisted extraction of polyphenols from pine needles (Pinus elliottii): Comprehensive insights from RSM optimization, antioxidant activity, UHPLC-Q-Exactive Orbitrap MS/MS analysis and kinetic model.

Extracting polyphenolic bioactive compounds from Pinus elliottii needles, a forestry residue, promises economic and environmental benefits, however, relevant experimental data are lacking. Herein, a comprehensive investigation of the polyphenolic composition of pine needles (PNs) was carried out. Ultrasound-Assisted Extraction (UAE) was applied to extract the polyphenolic compounds of pine needles. The optimal conditions of extracts were determined by Response Surface Methodology (RSM). The maximum total phenolic content (TPC) of 40.37 mg GAE/g PNs was achieved with solid-liquid ratio of 1:20, 60 % ethanol, and 350 W for 25 min at 45 °C. Polyphenolic extracts showed antioxidant activity in scavenging free radicals and reducing power (DPPH, IC50 41.05 µg/mL; FRAP 1.09 mM Fe2+/g PNs; ABTS, IC50 214.07 µg/mL). Furthermore, the second-order kinetic model was also constructed to describe the mechanism of the UAE process, with the extraction activation energy estimated at 12.26 kJ/mol. In addition, 37 compounds in PNs were first identified by UHPLC-Q-Exactive Orbitrap MS/MS, including flavonoids and phenolic acids. The results suggest that Ultrasound-Assisted is an effective method for the extraction of natural polyphenolic compounds from pine needles and this study could serve as a foundation for utilizing phenolics derived from PNs in the food and pharmaceutical industries.

Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat.

In this paper, we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning. Specifically, we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions. The novelty of our method lies in using the temporal difference (TD) algorithm to optimize the state decision process, which enables the robots to make informed decisions about their behavior choices. To assess the similarity between robot and rat behavior, we use Pearson correlation. We then use TD-λ to update the state value function and make state decisions based on probability. The robots execute these decisions using our dynamics-based controller. Our results demonstrate that our method can generate rat-like behaviors on both short- and long-term timescales, with interaction information entropy comparable to that between real rats. Overall, our approach shows promise for controlling robots in robot-rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.