Publications

Stress refers to a series of physiological reactions to help the human body overcome threats. In this study, the machine learning method was used to analyze the stress in students' daily life environment. The purpose is to explore the neurophysiological model of sleep under stress, so that they can effectively adjust their personal stress and improve their sleep quality. First, we collected ECG and triple-axis acceleration data of 33 college students in their daily life, calculated the inter-beat intervals from every two consecutive R waves in these data. Secondly, we extracted 39 linear and non-linear RR parameters as stress physiological features, and applied Leave-One-Subject-Out cross test and Sequential Backward Selection algorithm for feature selection while training some traditional classifiers. Finally, a binary classification model of stressed and non-stressed states recognition in the first hour of sleep at night was constructed, and the generalization accuracy of the model was 75.00% on the validation data set independent of model training and feature selection. The results show that it is feasible to monitor students' stress state at night by machine learning.

Medicinal herbs can be used for the development of antioxidant and anticancer platforms. However, the mechanisms by which the bioactive materials show these properties remain largely unclear. Also, a detailed understanding of the interaction between small compounds and carrier proteins like albumin can provide useful information about the regulation of their stability and circulation time properties. In this paper, the interaction of rhamnazin (C17H14O7), an O-methylated flavonol exists in Rhamnus petiolaris, with human serum albumin (HSA) was investigated. Also, the antioxidant and anti-lung cancer effects of rhamnazin were assessed. The results showed that a static rhamnazin-HSA complex was formed by the contribution of hydrophobic forces, which led to an increase in the content of the α-helix structure of HSA. Cellular assays revealed that rhamnazin (10 µM) recovered cell viability in human proximal renal tubular epithelial cells (HK2) reduced by H2O2 (0.5 mM) through reduction of ROS, restoration of CAT and SOD activities and GSH content, regulation of Bax, Bcl-2, and caspase-3. Moreover, the anti-lung cancer data demonstrated that rhamnazin induced apoptosis on lung adenocarcinoma A549 cells associated with a p53-dependent manner via overexpression of apoptotic Bax and caspase-3 and down-regulation of anti-apoptotic Bcl-2. To summarize, the potential binding of rhamnazin and HSA, as well as its antioxidant/anticancer properties, are critical for understanding the solubility and stability of this bioactive compound in plasma, as well as its therapeutic implications.

In recent years, wastewater recycling has gained popularity as an alternative water source to address urban water shortages. Accurate wastewater quantity prediction is crucial for urban river water resource management. Decomposition ensemble models have shown excellent predictive capabilities but are challenged by boundary effects when decomposing the original data sequence. To address this, a rolling forecast decomposition ensemble scheme was developed. It involves using a machine learning (ML) model for prediction and progressively integrating prediction outcomes into the original sequence using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Long short-term memory (LSTM) is then applied for sub-signal prediction and ensemble. The ML-CEEMDAN-LSTM model was introduced for wastewater quantity prediction, compared with non-decomposed ML models, CEEMDAN-based LSTM models, and ML-CEEMDAN-based LSTM models. Three ML algorithms—linear regression (LR), gradient boosting regression (GBR), and LSTM—were examined, using real-time prediction data and historical monitoring data, with historical data selected using the decision tree method. The study used daily water volumes data from two reclaimed water plants, CH and WQ, in Beijing. The results indicate that (1) ML models varied in their selection of real-time factors, with LR performing best among ML models during testing; (2) the ML-CEEMDAN-LSTM model consistently outperformed ML models; (3) the ML-CEEMDAN-LSTM hybrid model performed better than the CEEMDAN-LSTM model across different seasons. This study offers a reliable and accurate approach for reclaimed water volumes forecasting, critical for effective water environment management.

Currently, thermal pasteurisation (TP) remains the most widely applied technique for commercial orange juice preservation; however, a high temperature causes adverse effects on the quality attributes of orange juice. In order to explore a novel non-thermal sterilization method for orange juice, the impacts of thermosonication combined with nisin (TSN) and TP treatments on the quality attributes including microbial and enzyme inactivation and the physicochemical, nutritional, functional, and sensory qualities of orange juice were studied. Both TP and TSN treatments achieved desirable bactericidal and enzyme inactivation effects, and nisin had a significant synergistic lethal effect on aerobic bacteria in orange juice (

As an emerging two-dimensional layered material, black phosphorus nanosheets show unparalleled optical and electronic properties. Although black phosphorus nanosheets have attracted much attention in the photoelectric field, their applications in biomedical field were still limited due to their poor biocompatibility of current synthesis strategies. Herein, we propose a novel synthetic strategy for black phosphorus nanosheets that rely on Tween 20-assisted liquid exfoliation and post-processing in deoxygenated water. Microscopic and spectroscopic analysis suggested that the produced black phosphorus nanosheets dispersions exhibited good stability and higher yield compared with other currently prepared methods. Because of their ultrahigh exfoliation efficiency, the black phosphorus flakes present few-layer and even monolayer, which are thinner than the most dispersions of black phosphorus. Thus, this method enables mass-production of high-quality few-layer black phosphorus with high biocompatibility, and has the potential to be directly used in the biological field.