Recently, a large number of experimenters have found that the pathogenesis of Parkinson’s disease may be related to the gut microbiome and proposed the microbiome–gut–brain axis. Studies have shown that Toll-like receptors, especially Toll-like receptor 2 (TLR2) and Toll-like receptor 4 (TLR4), are key mediators of gut homeostasis. In addition to their established role in innate immunity throughout the body, research is increasingly showing that the Toll-like receptor 2 and Toll-like receptor 4 signaling pathways shape the development and function of the gut and enteric nervous system. Notably, Toll-like receptor 2 and Toll-like receptor 4 are dysregulated in Parkinson’s disease patients and may therefore be identified as the core of early gut dysfunction in Parkinson’s disease. To better understand the contribution of Toll-like receptor 2 and Toll-like receptor 4 dysfunction in the gut to early α-synuclein aggregation, we discussed the structural function of Toll-like receptor 2 and Toll-like receptor 4 and signal transduction of Toll-like receptor 2 and Toll-like receptor 4 in Parkinson’s disease by reviewing clinical, animal models, and in vitro studies. We also present a conceptual model of the pathogenesis of Parkinson’s disease, in which microbial dysbiosis alters the gut barrier as well as the Toll-like receptor 2 and Toll-like receptor 4 signaling pathways, ultimately leading to a positive feedback loop for chronic gut dysfunction, promoting α-synuclein aggregation in the gut and vagus nerve.

Compared to conventional private vehicles (CPVs), shared autonomous vehicles (SAVs) provide users the potential for the reduced value of time (VoT), improved mobility experience, and less traffic congestion. In the presence of the SAV system, numerous studies have mainly concentrated on the strategic planning and operational decision problem separately while ignoring the complicated interaction between them and the distinct features of autonomous vehicles. It is imperative to determine the relocation and pricing strategies at the operational level. In this study, in terms of the pricing strategy, we formalize a logit model to capture the mode choice behavior in a multimodal network, where the reduced VoT is considered simultaneously. A time-space network is employed to capture the daily operation problem based on the elastic demand. The minimum customer service rate is regarded as a constraint to ensure the system’s reliability. Moreover, a mixed-integer nonlinear programming (MINLP) model is formulated to jointly determine the number of stations and parking spaces, fleet size, relocation, and pricing strategies to maximize the total profit. Then, we integrate the Particle Swarm Optimization (PSO) algorithm with the optimization solver Gurobi to address the complex problem. Numerical experiments and comparative analyses are conducted to demonstrate the feasibility and efficiency of the proposed model.

A great loss of transportation capacity has been caused in auto parts supply logistics due to the independent transportation from auto parts suppliers (APSs) to the automobile production line (APL). It is believed that establishing distribution centers (DCs) for centralized collection and unified distribution is one effective way to address this problem. This paper proposes a unified framework simultaneously considering the location-inventory-routing problem (LIRP) in auto parts supply logistics. Integrating the idea of sustainable development, a multi-objective MIP model is developed to determine the location and inventory capacity of DCs and routing decisions to minimize the total system cost and carbon emissions while concerning multi-period production demand. In addition, a robust optimization model is developed further in the context of uncertain demand. Numerical experiments and sensitivity analyses are conducted to verify the effectiveness of our proposed deterministic and robust models. The results show that synergistically optimizing the location and capacity of DCs and routing decisions are beneficial in reducing total system cost and carbon emissions. The analysis can provide guidelines to decision-makers for the effective management of auto parts supply logistics.