As the population ages, neurodegenerative diseases such as Parkinson’s disease (PD) and Alzheimer’s disease (AD) impose a heavy burden on society and families. The pathogeneses of PD and AD are complex. There are no radical cures for the diseases, and existing therapeutic agents for PD and AD have diverse side effects. Tea contains many bioactive components such as polyphenols, theanine, caffeine, and theaflavins. Some investigations of epidemiology have demonstrated that drinking tea can decrease the risk of PD and AD. Tea polyphenols can lower the morbidity of PD and AD by reducing oxidative stress and regulating signaling pathways and metal chelation. Theanine can inhibit the glutamate receptors and regulate the extracellular concentration of glutamine, presenting neuroprotective effects. Additionally, the neuroprotective mechanisms of caffeine and theaflavins may contribute to the ability to antagonize the adenosine receptor A2AR and the antioxidant properties, respectively. Thus, tea bioactive components might be useful for neuronal degeneration treatment in the future. In the present paper, the neuro protection and the mechanisms of tea and its bioactive components are reviewed. Moreover, the potential challenges and future work are also discussed.

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Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations. However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities. In this paper, we introduce a Chinese single-and multimodal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis. Furthermore, we propose a multi-task learning framework based on late fusion as the baseline. Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations. The full dataset and codes are available for use at https://github.com/ thuiar/MMSA.