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Abstract Pulmonary artery hypertension (PAH) is a chronic vascular disease defined by the elevation of pulmonary vascular resistance and mean pulmonary artery pressure, which arises due to pulmonary vascular remodelling. Prior research has already established a link between the autonomic nervous system (ANS) and PAH. Therefore, the rebalancing of the ANS offers a promising approach for the treatment of PAH. The process of rebalancing involves two key aspects: inhibiting an overactive sympathetic nervous system and fortifying the impaired parasympathetic nervous system through pharmacological or interventional procedures. However, the understanding of the precise mechanisms involved in neuromodulation, whether achieved through medication or intervention, remains insufficient. This limited understanding hinders our ability to determine the appropriate timing and scope of such treatment. This review aims to integrate the findings from clinical and mechanistic studies on ANS rebalancing as a treatment approach for PAH, with the ultimate goal of identifying a path to enhance the safety and efficacy of neuromodulation therapy and improve the prognosis of PAH.

To reduce the emission of fine particulate matter (PM) from gasoline engines, a gasoline particulate filter (GPF) filtration model that considers the fractal characteristics of both particulate matter and filters is designed and its filtration characteristics are analyzed based on several GPF filtration models. Based on the GPF filtration mechanism, a new mathematical model of fiber efficiency filtration is established after correcting the classical filtration formula based on correcting the particle size of particles and considering the relationship between the porosity of the fiber filter and the fractal dimension of the filter. Simulation of the filtration efficiency of the established mathematical filtration model is carried out to obtain visual filtration efficiency curves. The filtration efficiency and pressure drop of the model are analyzed, and the effects of parameters such as particle fractal dimension, filter fractal dimension, filter thickness, flow rate, and fiber diameter on filtration efficiency are also comprehensively analyzed. The results show that when the fractal dimension of particles is in the range of 1.8-2.8, the most penetrating particle size decreases with the increase of the fractal dimension of particles. When the particle size is small, the effect of fractal dimension on filtration efficiency is not obvious. When the particle size is larger than the most penetrating particle range, the larger the fractal dimension, the filtration efficiency increases obviously. Considering the relationship between the fractal dimension of the filter body and the porosity and the influence of each on the filtration efficiency, the optimal fractal dimension of the filter body should be between 1.82 and 1.925, and the thickness of the filter has some influence on the filtration efficiency. The slower the gas flow rate is, the better the trapping effect will be. However, considering the problem of automobile emission, a lower exhaust speed can be chosen. Increasing the fiber diameter can improve the trapping efficiency of the model, but will reduce the porosity of the trapping collective. Therefore, the fiber diameter should not be too large under the combined action.

The MLE of the ATE in the logit model for binary outcomes may have a significant second-order bias if the event has a low probability, which is the case we focus on in this article. We derive the second-order bias of the logit ATE estimator, and we propose a bias-corrected estimator of the ATE. We also propose a variation on the logit model with parameters that are elasticities. Finally, we propose a computational trick that avoids numerical instability in the case of estimation for rare events. [ABSTRACT FROM AUTHOR]

Background: Multiple studies have demonstrated that the gut microbiome is closely related to the onset of Alzheimer's disease, but the causal relationship between the gut microbiome and AD, as well as potential mediating factors, have not been fully explored. Objective: Our aim is to validate the causal relationship between the gut microbiome and the onset of AD and determine the key mechanism by which the gut microbiome mediates AD through blood metabolites using Mendelian randomization (MR) analysis methods. Methods: We first conducted bidirectional and mediating MR analyses using gut microbiota, blood amino acid metabolites, and AD-related single nucleotide polymorphisms as research data. In the analysis process, the inverse variance-weighted average method was mainly used as the primary method, with other methods serving as supplementary evidence. Results: Ultimately, we found that six types of gut bacteria and two blood amino acid metabolites have a causal effect on AD. Subsequent mediation analysis proved that decreased glutamine concentration mediates the negative causal effect of Holdemanella bacteria on AD (mediation ratio of 14.5%), and increased serum alanine concentration mediates the positive causal effect of Parabacteroide bacteria on AD (mediation ratio of 9.4%). Conclusions: Our study demonstrates the causality of Holdemanella and Parabacteroides bacteria in the onset of AD and suggests that the reduced glutamine and increased alanine serums concentration may be key nodes in mediating this effect. [ABSTRACT FROM AUTHOR]

Temperature compensation is the main measure to solve the problem that the detection accuracy of non-dispersive infrared CO2 gas sensor is affected by temperature. As the measurement accuracy of the non-dispersive infrared CO2 gas sensor is easily affected by the ambient temperature, this article analyzes the reasons why the sensor is affected by temperature, and proposes a temperature compensation method that integrates the Whale Algorithm (WOA) and BP neural network. The whale algorithm is used to optimize the weights and thresholds of the BP neural network to build a temperature compensation model for the non-dispersive infrared CO2 gas sensor and compare the superiority with the traditional BP neural network model and particle swarm optimization (PSO) BP neural network model. The experimental results show that the temperature compensation model error of WOA-BP algorithm is lower than 30 ppm, and the average absolute error percentage is 3.86%, which is far better than BP neural network and PSO-BP neural network, and effectively reduces the influence of temperature on the accuracy of the sensor. [ABSTRACT FROM AUTHOR]

Aiming at the problem of wind curtailment caused by the lack of system flexibility, an optimal scheduling strategy for improving the flexibility of the electricity-gas- heat interconnection system by the coordinated operation of the gas network management and storage characteristics and the heat storage characteristics of the heat network is proposed. Firstly, the influence of gas network storage and heat network storage on improving flexibility is analyzed respectively. Then, an electricity-gas-heat interconnected system scheduling model considering the dynamic characteristics of gas network management and t the delay characteristics of heat storage in heat network, which greatly improves the imbalance between supply and demand of flexibility in space and time brought by the anti-peak regulation characteristics of wind power to the system. Finally, the IEEE-24 nodes power system, Belgium's 20-node natural gas network and 6- node thermal network are used, for example, analysis. The results show that the proposed scheduling scheme improves the flexibility of the system while its operating cost and wind curtailment cost are the lowest, which promotes the consumption of wind power. [ABSTRACT FROM AUTHOR]

Byline: Juan. Yang, Xiaohui. Zhao, Haijing. Sui, Xueyuan. Liu Background: The prevalence of mild cognitive impairment (MCI), herein China, was without involving the suburban communities, where the awareness of MCI [...]