New advances of adiponectin in regulating obesity and related metabolic syndromes

Obesity and related metabolic syndromes have been recognized as important disease risks,in which the role of adipokines cannot be ignored.Adiponectin(ADP)is one of the key adipokines with various beneficial effects,including improving glucose and lipid metabolism,enhancing insulin sensitivity,reducing oxidative stress and inflammation,promoting ceramides degradation,and stimulating adipose tissue vascularity.Based on those,it can serve as a positive regulator in many metabolic syndromes,such as type 2 diabetes(T2D),cardiovascular diseases,non-alcoholic fatty liver disease(NAFLD),sarcopenia,neurodegenerative diseases,and certain cancers.Therefore,a promising therapeutic approach for treating various metabolic diseases may involve elevating ADP levels or activating ADP receptors.The modulation of ADP genes,multimerization,and secretion covers the main processes of ADP generation,providing a comprehensive orientation for the development of more appropriate therapeutic strategies.In order to have a deeper understanding of ADP,this paper will provide an all-encompassing review of ADP.

HACAN:a hierarchical answer-aware and context-aware network for question generation

Question Generation(QG)is the task of generating questions according to the given contexts.Most of the existing methods are based on Recurrent Neural Networks(RNNs)for generating questions with passage-level input for providing more details,which seriously suffer from such problems as gradient vanishing and ineffective information utilization.In fact,reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education scenario.To that end,in this paper,we propose a novel Hierarchical Answer-Aware and Context-Aware Network(HACAN)to construct a high-quality passage representation and judge the balance between the sentences and the whole passage.Specifically,a Hierarchical Passage Encoder(HPE)is proposed to construct an answer-aware and context-aware passage representation,with a strategy of utilizing multi-hop reasoning.Then,we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder(HPD)which determines when to utilize the passage information.We conduct extensive experiments on the SQuAD dataset,where the results verify the effectivenesss of our model in comparison with several baselines.