Housing prices and consumption : the case of China

The rapid soaring housing prices in Chinese residential property market have attracted increasing worldwide attention in recent years. Facing the rising concerns about both the stability and sustainability of Chinese housing market prices dynamics, this study aims at investigating the impacts of changes in housing wealth on consumption in China. Previous studies on this subject usually use country level data with relatively shorter sample period, or individual time series for a single or a few cities. Recent development in literatures suggests that panel data have the more heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow. In this study, in order to identify both long-term and short-term elasticity of consumption with respect to housing wealth, panel framework of ECM is constructed, with quarterly data from 23 cities throughout China, covering the period of 2005Q1-2010Q4. The estimation results confirm large and highly significant positive housing wealth effect on consumption in both long-run and short-run for China. Furthermore, due to the potential endogeneity problem driven by the fact that housing prices are highly correlated with income, instrumental variable estimations are also implemented. The resulting empirical findings confirm that changes in housing values can exert large and positive impacts on household consumption, even after this potential endogeneity bias is controlled for

Pharmacokinetic, acute toxicity, and pharmacodynamic studies of semen strychni total alkaloid microcapsules

Purpose: To investigate the safety and effectiveness of semen strychni total alkaloid microcapsules (SSTAM), compared with semen strychni total alkaloids (SSTA). Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed to assess pharmacokinetics of brucine and strychnine in rats. Acute toxicity was investigated in pre-test and formal experiments in mice. The pharmacodynamics of SSTAM and SSTA were evaluated by their analgesic and anti-inflammatory activities. Results: With respect to brucine, the half-life of SSTA group (1.6 mg/kg), low-dose SSTAM group (6 mg/kg) and high-dose SSTAM group (10 mg/kg) was 5.723, 9.321 and 9.025 h, respectively. With respect to strychnine, the half-life of SSTA group, low-dose SSTAM group and high-dose SSTAM group was 4.065, 8.819 and 8.654 h, respectively. The LD50 values of SSTAM group and SSTA group were 236.59 and 30.27 mg/kg, respectively. The pain inhibition rates of SSTAM groups (25 and 50 mg/kg) were higher than that of SSTA group (p < 0.05) while the pain threshold values of the SSTAM groups (25 and 50 mg/kg) were higher than that of blank control (p < 0.01) and SSTA groups (p < 0.01) at 60 min and 120 min. The inhibition rates of the SSTAM groups (25 and 50 mg/kg) were higher than that of SSTA group based on ear swelling and cotton ball granulation tests. Compared with blank control and SSTA groups, the absorbance values of SSTAM groups (25 and 50 mg/kg) were lower (p < 0.01). Conclusion: SSTAM increases the dosage of administration but reducea the toxicity of the alkaloids in rats, and is thus a potentially safe and effective drug delivery system

Multi-feature fusion learning for Alzheimer's disease prediction using EEG signals in resting state

IntroductionDiagnosing Alzheimer's disease (AD) lesions via visual examination of Electroencephalography (EEG) signals poses a considerable challenge. This has prompted the exploration of deep learning techniques, such as Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), for AD prediction. However, the classification performance of CNN-based methods has often been deemed inadequate. This is primarily attributed to CNNs struggling with extracting meaningful lesion signals from the complex and noisy EEG data.MethodsIn contrast, ViTs have demonstrated proficiency in capturing global signal patterns. In light of these observations, we propose a novel approach to enhance AD risk assessment. Our proposition involves a hybrid architecture, merging the strengths of CNNs and ViTs to compensate for their respective feature extraction limitations. Our proposed Dual-Branch Feature Fusion Network (DBN) leverages both CNN and ViT components to acquire texture features and global semantic information from EEG signals. These elements are pivotal in capturing dynamic electrical signal changes in the cerebral cortex. Additionally, we introduce Spatial Attention (SA) and Channel Attention (CA) blocks within the network architecture. These attention mechanisms bolster the model's capacity to discern abnormal EEG signal patterns from the amalgamated features. To make well-informed predictions, we employ a two-factor decision-making mechanism. Specifically, we conduct correlation analysis on predicted EEG signals from the same subject to establish consistency.ResultsThis is then combined with results from the Clinical Neuropsychological Scale (MMSE) assessment to comprehensively evaluate the subject's susceptibility to AD. Our experimental validation on the publicly available OpenNeuro database underscores the efficacy of our approach. Notably, our proposed method attains an impressive 80.23% classification accuracy in distinguishing between AD, Frontotemporal dementia (FTD), and Normal Control (NC) subjects.DiscussionThis outcome outperforms prevailing state-of-the-art methodologies in EEG-based AD prediction. Furthermore, our methodology enables the visualization of salient regions within pathological images, providing invaluable insights for interpreting and analyzing AD predictions