Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.

Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have been successfully applied in AD classification. However, these works did not handle the class imbalance issue in classification. Besides, they ignore the heterogeneity of the disease. To this end, we propose a novel cost-sensitive weighted contrastive learning method based on graph convolutional networks (CSWCL-GCNs) for imbalanced AD staging using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed method is developed on a multi-view graph constructed using the functional connectivity (FC) and high-order functional connectivity (HOFC) features of the subjects. A novel cost-sensitive weighted contrastive learning procedure is proposed to capture discriminative information from the minority classes, encouraging the samples in the minority class to provide adequate supervision. Considering the heterogeneity of the disease, the weights of the negative pairs are introduced into contrastive learning and they are computed based on the distance to class prototypes, which are automatically learned from the training data. Meanwhile, the cost-sensitive mechanism is further introduced into contrastive learning to handle the class imbalance issue. The proposed CSWCL-GCN is evaluated on 720 subjects (including 184 NCs, 40 SMC patients, 208 EMCI patients, 172 LMCI patients and 116 AD patients) from the ADNI (Alzheimer's Disease Neuroimaging Initiative). Experimental results show that the proposed CSWCL-GCN outperforms state-of-the-art methods on the ADNI database.

How does blockchain technology affect the development of green finance? Theoretical analysis and empirical verification.

Facing the problem of a lack of endogenous incentive mechanisms for the development of green finance, we regard blockchain technology as an institutional technology and elevate it to the height of governance mechanisms. Using a dynamic stochastic general equilibrium (DSGE) model framework, we compare and analyze its effects with traditional supportive policies such as fiscal subsidies. The modeling simulation results show that the blockchain green finance platform model is conducive to better promoting the development of green finance. Subsequently, we construct a financial technology development index centered on blockchain technology and empirically test the impact of blockchain financial technology on the level of green finance development from both the supply and demand sides. The results show that the development of blockchain financial technology has significantly increased the scale of green credit issuance and effectively eased the financing constraints of green enterprises, reducing financing costs. We conduct an economic analysis of the impact of blockchain financial technology on the development of green finance, providing a feasible path for the integration and development of green finance and financial technology.

Routine chest CT combined with the osteoporosis self-assessment tool for Asians (OSTA): a screening tool for patients with osteoporosis.

INTRODUCTION: The osteoporosis self-assessment tool for Asians (OSTA) is a common screening tool for osteoporosis. The seventh thoracic CT (CT-T7) Hounsfield unit (HU) measured by chest CT correlates with osteoporosis. This study aimed to investigate the diagnostic value of OSTA alone, CT-T7 alone, or the combination of OSTA and CT-T7 in osteoporosis. MATERIALS AND METHODS: In this study, 1268 participants were grouped into 586 men and 682 women. We established multiple linear regression models by combining CT-T7 and OSTA. Receiver operating characteristic (ROC) curves were used to evaluate the ability to diagnose osteoporosis. RESULTS: In the male group, the mean age was 59.02 years, and 108 patients (18.4%) had osteoporosis. In the female group, the mean age was 63.23 years, and 308 patients (45.2%) had osteoporosis. By ROC curve comparison, the CT-T7 (male, AUC = 0.789, 95% CI 0.745-0.832; female, AUC = 0.835, 95% CI 0.805-0.864) in the diagnosis of osteoporosis was greater than the OSTA (male, AUC = 0.673, 95% CI 0.620-0.726; female, AUC = 0.775, 95% CI 0.741-0.810) in both the male and female groups (p < 0.001). When OSTA was combined with CT, the equation of multiple linear regression (MLR) was obtained as follows: female = 3.020-0.028*OSTA-0.004*CT-T7. In the female group, it was found that the AUC of MLR (AUC = 0.853, 95% CI 0.825-0.880) in the diagnosis of osteoporosis was larger than that of CT-T7 (p < 0.01). When the MLR was 2.65, the sensitivity and specificity were 53.9% and 90%, respectively. CONCLUSION: For a patient who has completed chest CT, CT-T7 (HU) combined with OSTA is recommended to identify the high-risk population of osteoporosis, and it has a higher diagnostic value than OSTA alone or CT-T7 alone, especially among females. For a female with MLR greater than 2.65, further DXA examination is needed.

Will Green Finance Contribute to a Green Recovery? Evidence From Green Financial Pilot Zone in China.

In the post-epidemic era, green finance plays a more significant role in supporting the "green recovery" of the economy, so it is necessary to evaluate the implementation effect of previous green financial policies. In 2017, the green finance reform and innovation pilot zone set up in five provinces and autonomous regions made an exploration in the development of green finance. From the perspective of micro-enterprises, can this policy play a beneficial policy effect in the long run? Based on the quasi-natural experiment of green finance pilot, using the data of A-share listed companies, this paper empirically tests the impact of pilot policies on the long-term value of green enterprises in pilot areas. It is found that, compared with non-pilot zones, the green finance pilot enables a significant increase in the Tobin Q-measured value of green enterprises in the pilot zones. Heterogeneity analysis shows that green finance pilot has a more significant impact on non-state-owned enterprises, enterprises in traditional industries, large enterprises, and enterprises in the eastern region of China. Green finance pilot zone can achieve better policy effects in areas with stronger environmental impact regulation and higher financial development levels. The mechanism test shows that the green finance pilot affects the long-term value of green enterprises through the capital market effect improving the stock trading activity of enterprises and through the real effect improving the operational efficiency and profitability of enterprises. From the perspective of micro-enterprises, this paper enriches the research on the development effect of green finance and provides theoretical support for the effect evaluation of green finance pilot policies.

Enhanced electrochemical phosphate recovery from livestock wastewater by adjusting pH with plant ash.

In the field of environmental wastewater treatment, it is a very meaningful topic to recover phosphate from swine wastewater in the form of struvite precipitation. The solution pH is one of the important influencing factors in the process of struvite precipitation. In this paper, an attempt was made to recover the phosphate from swine wastewater by adding plant ash. Experimental results have revealed that aeration can be replaced by optimal plant ash adding mode to increase the phosphate recovery efficiency. With the dosages of plant ash and magnesium metal were respectively 11.66 and 3.33 g/L the phosphate recovery efficiency reached 97.69% in 60 min. The efficiency was still above 95% after repeatedly using magnesium pellet for 3 times. The economic evaluation further revealed that the recovery cost of the proposed method was 0.62 $/kg PO4-P.