Association of urinary bisphenol A levels with heart failure risk in U.S. adults from the NHANES (2003–2016)

IntroductionAlthough heart failure (HF) has been linked to bisphenol A (BPA), few studies have investigated the cut-off values for the effects of urinary BPA levels on heart failure risk. The association between urinary BPA levels and HF prognosis has not been investigated.MethodsThis study included 11,849 adults over 20 years old using information from the National Health and Nutrition Examination Survey (NHANES), which was conducted from 2003 to 2016. The relationship between urinary BPA levels and the risk of HF was determined via a multivariable logistic regression model, and restricted cubic spline (RCS) methods were used to determine the cut-off for the effect of BPA levels on HF risk. Based on the available NT-proBNP concentration data from the NHANES (2003–2004), multivariable linear regression was applied to determine the linear association between the NT-proBNP concentration and urinary BPA concentration.ResultsThe results revealed a positive correlation between a urinary BPA concentration in the fourth quartile and the occurrence of heart failure [OR 1.49, 95% CI (1.09, 2.04), p = 0.012]. A one-unit increase (1 ng/mg creatinine) in the ln-transformed BPA concentration was linked to a 15% increase in the incidence of HF [OR 1.15, 95% CI (1.03, 1.29), p = 0.014]. The cut-off urinary BPA concentration for HF risk was 1.51 ng/mg creatinine. There was a positive correlation between urinary BPA and NT-proBNP concentrations [β = 0.093, 95% CI (0.014, 0.171), p = 0.02] in males, but there was no linear association [β = 0.040, 95% CI (−0.033, 0.113), p = 0.283] in females.DiscussionIncreased urinary BPA levels are linked to an increased risk of heart failure and poor prognosis. There is a significant increase in the risk of heart failure if the urinary concentration of BPA exceeds 1.51 ng/mg creatinine

Optimizing the Spectrum and Power Allocation for D2D-Enabled Communication Systems Using DC Programming

Device to device (D2D) communication has recently attracted a lot of attentions since it can significantly improve the system throughput and reduce the energy consumption. Indeed, the devices can communicate with each other in a D2D system, and the base station (BS) can share the spectrum with D2D users, which can efficiently improve the spectrum and energy efficiency. Nevertheless, spectrum sharing also raises the difficulty of resource allocation owing to the serious cochannel interference. To reduce the interference, the transmit power of the D2D pairs and BS to cellular users should be further optimized. In this paper, we consider the resource allocation problem of D2D networks involving the power allocation and subcarrier assignment. The resource allocation problem is formulated as a mixed integer programming problem which is difficult to solve. To reduce the computational complexity, the original problem is decomposed as two subproblems in terms of the subcarrier assignment and power allocation. For the subcarrier assignment problem, the particle swarm optimization (PSO) is adopted to solve it since the subcarrier assignment is an integer optimization problem, and it is difficult to be tackled using the traditional optimization approach. When the subcarrier assignment is fixed, there are only the power allocation variables in the original resource allocation problem. The difference of convex functions (DC) programming is adopted to solve the power allocation problem. Simulation results demonstrate the effectiveness of the proposed resource allocation scheme of D2D networks

Research on immature wheat harvesting behavior of farmers from the perspective of food security: An evolutionary game based analysis

Food security constitutes a foundational cornerstone for social stability, with the achievement of sustainable agricultural production serving as a vital step towards this objective. Currently, the untimely harvesting of unripe wheat by farmers has led to a decline in food production, thereby posing a significant threat to the sustainability of China's food system and exacerbating food insecurity. Although the Chinese government has implemented various measures in response, their effectiveness has been limited. Limited scholarly literature exists on this particular issue. To advance food security in China, this study develops a tripartite evolutionary game model involving farmers, the government, and breeding enterprises. Adopting a systemic perspective, this study examines the interactions and impact mechanisms among these key actors during the wheat harvesting process. The findings indicate that the government should prioritize policies that enforce penalties. By implementing penalties within a reasonable range, the government can mitigate farmers' production costs and enhance the market price of grain. This approach discourages farmers from harvesting immature wheat and contributes to enhancing food security. Based on the research findings, this paper provides practical recommendations to guide the government in addressing food security governance issues

Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior

Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, high-quality patient-based datasets for network training. In this paper, we propose a novel deep learning-based method for undersampled MR image reconstruction that does not require pre-training procedure and pre-training datasets. The proposed reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP) is based on the DIP framework and thereby reduces the dependence on datasets. Moreover, RWS-DIP explores and introduces structure and sparsity priors into network learning to improve the efficiency of learning. By employing a high-resolution reference image as the network input, RWS-DIP incorporates structural information into network. RWS-DIP also uses the wavelet sparsity to further enrich the implicit regularization of traditional DIP by formulating the training of network parameters as a constrained optimization problem, which is solved using the alternating direction method of multipliers (ADMM) algorithm. Experiments on in vivo MR scans have demonstrated that the RWS-DIP method can reconstruct MR images more accurately and preserve features and textures from undersampled k-space measurements