Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index's opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model's proficiency in linear trend analysis and the deep learning models' capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index's opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

Integrative Analysis of the Gut Microbiota and Metabolome in Obese Mice with Electroacupuncture by 16S rRNA Gene Sequencing and HPLC-MS-based Metabolic Profiling.

Acupuncture has been used to treat numerous diseases such as obesity in China for thousands of years. Several mechanisms of acupuncture on obesity have been surveyed based on metabolomics, but the effects of acupuncture on the alterations in the gut flora are still unclear. In this study, an integrated approach based on 16S rRNA gene sequencing combined with high-performance liquid chromatography-mass spectrometry (HPLC-MS) metabolic profiling was conducted to investigate the effects of acupuncture on high-fat-diet-induced obesity through the regulation of the relative abundances of gut microbiota and their relationships with biomarker candidates. A total of 10 significantly altered bacterial genera and 11 metabolites were recognized, which recovered to normal levels after electroacupuncture treatment. The relative abundances of the bacterial families Muribaculaceae,Lachnospiraceae,Desulfovibrionaceae,Helicobacteraceae, Prevotellaceae,Ruminococcaceae,Rikenellaceae,Deferribacteraceae,Bacteroidaceae andTannerellaceaewere remarkedly changed among the three groups. Potential biomarkers, including LysoPC(0:0/16:0) ([Formula: see text]1),PC(0:0/18:0) ([Formula: see text]2),Cholic acid([Formula: see text]3),LysoPC(16:0) ([Formula: see text]4), 3[Formula: see text],6[Formula: see text],7[Formula: see text]-Trihydroxy-5[Formula: see text]-cholanoic acid([Formula: see text]5), 5beta-Cyprinolsulfate([Formula: see text]6),PC(18:0/0:0) ([Formula: see text]7), 1-Nitro-5-hydroxy-6-glutathionyl-5,6-dihydronaphthalene([Formula: see text]8),Glycocholic acid([Formula: see text]9),[Formula: see text]-Arginine([Formula: see text]10) andGulonic acid([Formula: see text]11), were involved in several metabolic pathways, such as the glycerophospholipid metabolism and primary bile acid biosynthesis. Interestingly, there was a strong correlation between the perturbed gut flora in Bilophila and Bifidobacterium and the altered intestinal metabolite of 3[Formula: see text],6[Formula: see text],7[Formula: see text]-Trihydroxy-5[Formula: see text]-cholanoic acid and Cholanoic acid and [Formula: see text]-Arginine. This finding suggested that the effects of electroacupuncture might change the proportions of Bilophila and Bifidobacterium by regulating the constituents of the functional metabolite of 3[Formula: see text],6[Formula: see text],7[Formula: see text]-Trihydroxy-5[Formula: see text]-cholanoic acid and Cholanoic acid and [Formula: see text]-Arginine. These results indicated that the effects of electroacupuncture focused on custom metabolic pathways as well as depend on the changes in the gut microbiota in obesity. These findings suggest that the 16S rRNA gene sequencing and HPLC-MS-based metabolomics approach can be applied to comprehensively assess the effects of traditional Chinese medicines.

Regulating Gut Flora Dysbiosis in Obese Mice by Electroacupuncture.

Recently, gut flora has been linked to the onset of obesity and has been shown to influence the host's metabolism. Acupuncture is a well-known agent used for the treatment of numerous diseases such as obesity. This study aimed to explore the impacts of electroacupuncture treatment on gut microbiota composition and function in obese mice. Pyrosequencing of 16S rRNA genes and Metagenomic analysis of the fecal microbiota were used for this purpose. The basic parameters of body weight, Lee's index, serum lipid and epididymal adipose weight were ameliorated significantly after introducing an electroacupuncture intervention. Acidobacteria, Cyanobacteria and Basidiomycota (Normal group) and Fusobacteria, Firmicutes and Spirochmycetes (Model group) were remarkably affluent at the phylum level. Bacteroides sp. CAG: 927 and Prevotella sp. CAG: 1031 (Normal group), Lachnospiraceae bacterium and Helicobacter rodentium (Model group) at the species level were distinctly enriched. The structures and functions of the intestinal flora were significantly different between healthy and obese mice, and animals in the acupuncture group gradually tended towards healthy controls. Moreover, electroacupuncture altered the bacterial diversity and metabolic genes to establish new balance, observed the obvious change from 7[Formula: see text]d and stabilized gradually through 21[Formula: see text]d. These findings suggested gut flora could be a novel target of electroacupuncture treatment against obesity.