PROTOPANAXADIOL SAPONINS IN THE CAUDEXES AND LEAVES OF PANAX NOTOGINSENG COULD BE THE MAIN CONSTITUENTS THAT CONTRIBUTE TO ITS ANTIDEPRESSANT EFFECTS

Objective: We previously found that total saponins, purified from the caudexes and leaves of Panax notoginseng (SCLPN), had antidepressant effects. In the present study, we investigated saponin monomers of SCLPN that may be the main constituents that contribute to the antidepressant effects of SCLPN. Methods: Three effective fractions of SCLPN, purified using a macroporous resin method, at doses of 50 and 100 mg/kg were tested in four different animal models of stress, including the learned helplessness test, tail suspension test, forced swim test, open field test, and reserpine-induced syndrome model. Using the same models of stress and the same doses, we then evaluated the antidepressant effects of four main and representative saponin monomers (ginsenosides Rd, Rb1 and Rg1 and notoginsenoside R1) in different effective fractions. We also examined the effects of Rd and Rb3 on monoamine neurotransmitter levels. To investigate the biotransformation of Rb1 and Rb3 orally administered in mice, Rb1 and Rb3 metabolites in blood and brain were determined by high-performance liquid chromatography. Results: Effective fraction A and C exerted greater antidepressant effects than fraction B in the behavioral tests and reserpine-induced syndrome model. Among the four saponin monomers, Rd had the strongest antidepressant effects, which improved depressive-like behavior in all four animal models of depression. We then found that Rb3 (50 and 100 mg/kg) and Rd (100 mg/kg) increased the levels of 5-hydroxytryptamine, dopamine, and norepinephrine, whereas 50 mg/kg Rd had no effect on the levels of these three neurotransmitters. Ginsenoside Rh2, C-K, and 20 (S)-protopanaxadiol saponins were detected in blood samples from mice that received Rb1 and Rb3, and protopanaxadiol saponins were found in the brain. Conclusion: The present results indicate that protopanaxadiol saponins in SCLPN have potential antidepressant-like effects

Knowledge-Enhanced Top-K Recommendation in Poincar\'e Ball

Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.Comment: Accepted by the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user. However, most of these methods treat all the training triplets equally, which ignores the subtle difference between different positive or negative items. On the other hand, even though some other works make use of the auxiliary information (e.g., dwell time) of user behaviors to capture this subtle difference, such auxiliary information is hard to obtain. To mitigate the aforementioned problems, we propose a novel training framework named Triplet Importance Learning (TIL), which adaptively learns the importance score of training triplets. We devise two strategies for the importance score generation and formulate the whole procedure as a bilevel optimization, which does not require any rule-based design. We integrate the proposed training procedure with several Matrix Factorization (MF)- and Graph Neural Network (GNN)-based recommendation models, demonstrating the compatibility of our framework. Via a comparison using three real-world datasets with many state-of-the-art methods, we show that our proposed method outperforms the best existing models by 3-21\% in terms of Recall@k for the top-k recommendation

Machine Learning‑Assisted Low‑Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional trial and error method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research

A molecular dynamics analysis of the influence of iron corrosion products on the healing process of bitumen

Corrosion of iron materials in the asphalt concrete pavement occurs commonly when the bitumen film peels off, and the generation of corrosion products would affect the healing performance of bitumen. To identify the affection, this research focuses on the influence of iron corrosion products on the healing process of bitumen by molecular dynamics simulation. Firstly, bitumen model and iron corrosion products model were built. Then the healing systems of sandwich structure were constructed, and the simulated temperature were applied to reach equilibrium in the healing process with NVT ensemble (constant number of atoms, volume, and temperature). Dynamic movements of bitumen were characterized by appearance qualitatively. Healing rate of crack and healing rate of bitumen aggregation were held to evaluate the healing effect. Diffusion behaviors, internal force of motivation and interaction effect were also analyzed. The results indicate the duplicity of iron corrosion products in the healing process including the ease for bitumen climbing and the obstruction of bitumen movement. The comprehensive healing index demonstrated that iron corrosion products would reduce the healing degree, which was mainly caused by the obstruction effect and large internal stress generated by severe aggregation of bitumen in the limited space. From the perspective of crack closure and bitumen aggregation degree in the corrosion area, FeO healing systems were healed best, followed by Fe3O4, Fe2O3 and FeOOH. Furthermore, diffusion period of bitumen molecules on the surface of iron corrosion products during the healing process should be regarded as the important period affecting healing

Wetting of bio-rejuvenator nanodroplets on bitumen: A molecular dynamics investigation

Wetting is the first step during the mix process between rejuvenator and bitumen, which is important for mix efficiency and performance recovery. The wetting of bio-rejuvenator nanodroplets on bitumen was investigated by molecular dynamics (MD) simulations in this research. The bitumen molecule model and bio-rejuvenator nanodroplets were firstly built, then bio-rejuvenator nanodroplets/bitumen interface wetting model were assembled and constructed. Different simulated temperatures were applied to reach equilibrium in the wetting process. Dynamic wetting phenomenon, contact angle of nanodroplets, dynamic movement of nanodroplets, interaction between nanodroplets and bitumen, and hysteresis of contact angle were characterized respectively. The results show that the bio-rejuvenator nanodroplets will first approach the bitumen quickly, and then slow down to an equilibrium state in the wetting process, which delayed 1 ns with energy equilibrium independently. Its contact angle would decrease crossing 90° with time, the equilibrium contact angle of which varies linearly with simulated temperature. The time of nanodroplets reaching partial wetting state decreased with the increments of temperature, but complete wetting state was hard to reach even if the temperature was 433 K. During the nanodroplets movement, contact linear velocity of precursor film and cosine of contact angle was linearly related after nanodroplets and bitumen had caught each other. It was also found that the increasing mix degree was supported by the combination of wetting and infiltration before 373 K and by wetting mainly after 373 K. Finally, the application of external force on bio-rejuvenator nanodroplets will cause hysteresis phenomenon and it can be weakened by higher temperature