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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is erupting and spreading globally. Cardiovascular complications secondary to the infection have caught notice. This study aims to delineate the relationship of cardiac biomarkers and outcomes in severe cases of corona virus disease 2019 (COVID-19). One hundred forty-eight critically ill adult patients with COVID-19 were enrolled. From these patients, the demographic data, symptoms, cardiac biomarkers, treatments, and clinical outcomes were collected. Data were compared between survivors and non-survivors. Four patients in the non-survivor group were selected, and their cardiac biomarkers were collected and analyzed. Among the 148 patients, the incidence of cardiovascular complications was 19 (12.8%). Five of them were survivors (5.2%), and 14 of them were non-survivors (26.9%). Compared with the survivors, the non-survivors had higher levels of high-sensitivity cardiac troponin I, creatine kinase isoenzyme-MB, myoglobin, and N-terminal pro-brain natriuretic peptide (P < 0.05). The occurrence of cardiovascular events began at 11-15 days after the onset of the disease and reached a peak at 14-20 days. COVID-19 not only is a respiratory disease but also causes damage to the cardiovascular system. Cardiac biomarkers have the potential for early warning and prognostic evaluation in patients with COVID-19. It is recommended that cardiac biomarker monitoring in patients with COVID-19 should be initiated at least from the 11th day of the disease course.

Objective: Our study was performed to analyze the interrelationship between breastfeeding for the first 6 months of life and the incidence of febrile seizures (FS). Study Design: A case–control stu...

In this paper, we investigate the inland depot location problem of the inland transportation system. In inland container transportation, empty containers are transported between depots and consignees/shippers, and empty containers should be repositioned after/before inbound/outbound full containers. We build a robust mathematical model that focuses on determining when and where consignees/shippers are assigned to. In addition, inter-depots empty container repositioning is implemented considering demand uncertainty. This paper proposes a branch-and-price algorithm that is based on Lagrangian relaxation and column generation. We show the optimality condition of the pricing subproblem and construct a simpler formulation in this paper. Computational experiments are performed with test instances that mimic real life. Our results also show that considering time compatibility of full and empty container routes is closer to reality and increases the utilization of empty containers in depots. The proposed algorithm yields promising solutions compared with CPLEX.

The layout of net points plays a key role in the operation of an enterprise. Research on the layout of net points and optimisation of express enterprises has important practical significance. In this paper an optimisation model is developed, which takes minimising the total cost of the subordinate net points layout of express enterprises as the objective function on the premise of guaranteed express delivery time. The aim of the optimisation model is to reduce the cost of enterprises; it also takes customer satisfaction into consideration. The shuffled complex evolution method developed at the University of Arizona (SCE-UA) algorithm is applied to solve the optimisation problem. Finally, to test the accuracy of the proposed optimisation model, the current subordinate net points layout of ZTO Express in Dalian city is used as the case study. The result shows that the proposed model provides a good performance in term of obtaining the optimal form of quantity and spatial layout of subordinate net points. The result also indicates that the proposed model can provide practical methods and ideas for express enterprises in terms of planning and distributing subordinate net points in the future.

The spatial distribution of bus garages determines the total vehicular dead mileage of the transit system because buses must travel between bus garages and terminals at the start or conclusion of a day. By contrast, the size of the garages determines the queuing status when buses enter or leave the garages. Thus, a bus garage system with reasonable distribution and size is required to address these problems. In this article, a queuing–location–allocation model for optimizing a bus garage system is developed. Since a nonlinear objective function is involved, a linearization technique is introduced to convert the proposed model into an equivalent linear form. Next, a Lagrangian relaxation algorithm is designed to solve the linear form model. To validate the proposed algorithm, two groups of randomly generated test instances and a real-life case, the Dalian transit system in China, are applied. The results show that the proposed Lagrangian heuristic is efficient and stable.

Depression is one of the most common comorbid psychiatric condition associated with epilepsy. It has a negative impact on the patient's quality of life. However, the underlying molecular mechanisms leading to depression are currently unclear. The aim of this study was to determine the hub genes associated with epilepsy and depression.Gene expression profiles (GSE47752 and GSE20388) were downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) for epilepsy and depression groups were separately searched. Subsequently, network analyses methods were employed to establish protein-protein interaction (PPI) networks, and to perform Gene Ontology (GO) terms and pathway enrichment analyses for co-expressed DEGs.A total of 772 genes were upregulated in patients with epilepsy whereas 91 genes were up-regulated in patients with depression. In addition, 1304 genes were down-regulated in epilepsy whereas 141 genes were down-regulated in patients with depression. Among co-expressed DEGs, 5 DEGs were up-regulated and 19 were down-regulated. Further analysis revealed that the co-expressed DEGs were involved in regulation of vasculature development, regulation of angiogenesis, glutamate receptor signaling pathway, cellular response to interleukin-1 and positive regulation of protein kinase B signaling. The Arc and Homer1 genes were identified as the common candidate genes involved in the pathogenesis of epilepsy and depression.Arc and Homer1 may contribute to the comorbidity of epilepsy and depression.

Epidemiological research suggests that convulsions may have an intrauterine developmental origin related to the application of dexamethasone, an artificially synthesized glucocorticoid. Here, using a rat animal model of prenatal dexamethasone exposure (PDE) we confirm that PDE can cause susceptibility to convulsions in male offspring and explore the epigenetic programming mechanism underlying this effect related to intrauterine type 2 K

Amoxicillin has been widely used to treat infectious diseases during pregnancy. Current studies suggest that amoxicillin exposure during pregnancy could lead to developmental disorders in the offspring and increase the incidence of long-term complications such as asthma and kidney damage in adulthood. However, the adverse effects of prenatal amoxicillin exposure (PAmE) including administration stage, doses and courses on fetal hippocampal neurodevelopment and its function in the offspring have not been elucidated. In this study, we intend to investigate the effects of PAmE on fetal hippocampal development and its possible mechanisms.Pregnant Kunming mice were given intragastric administration with amoxicillin at different administration stage, doses and courses, and GD (gestational day) 18 offspring hippocampus was collected for morphological and development-related functional assays, and the molecular mechanisms were explored.PAmE induced hippocampal hypoplasia in the offspring with suppressed hippocampal neuronal cell proliferation and impaired neuronal synaptic plasticity comparatively; hippocampal astrocyte and microglia were damaged to varying degrees. The developmental toxicity of PAmE in fetal mices varies by time, dose, and course of treatment. The most severe damage was observed in the late gestation, high dose, and multi-course dosing groups. The significant reduction either in SOX2, an essential gene in regulating neural progenitor cell proliferation, and reduction of genes related to the Wnt/β-catenin pathway may suggest that the key role of SOX2/Wnt/β-catenin pathway in impaired hippocampal development in the offspring due to PAmE.In this study, PAmE was found to be developmentally toxic to the hippocampus thus to induce developmental damage to various hippocampal cells; Even with current clinically safe doses, potential hippocampal damage to offspring may still present; This study provides a theoretical and experimental basis for guiding the rational usage of drugs during pregnancy and giving effectively assessment of the risk on fetal hippocampal developmental toxicity.

During the COVID-19 pandemic, public transport in many cities faces dramatic reduction of passenger demand. Various countermeasures such as social distancing and in-vehicle disinfection have been implemented to reduce the potential risks concerning infection, the effectiveness in promoting the use of public transport however remains unclear. Unlike the usual situation where time and cost are the main factors affecting travel decisions, the uncertainty hiding behind the behavior change of public transport users in a pandemic might be greatly affected by the control measures and the perception of people. This paper therefore aims to examine the effects of COVID-19 related countermeasures implemented in public transport on individuals' travel decisions. We explore the extent to which do policy countermeasures influence different groups of people on the use of public transport. An error component latent class choice model was estimated using the data collected in the Netherlands. Results show that the restrictions policy lifted by the Dutch central government have significant effect on individuals' transportation mode choice decision during the pandemic. The related measures adopted by the public transport sector, by contrast, present different effects on different people. The older and highly educated people are more susceptible to enforcement measures, whereas young and single Dutch citizens are more accessible to non-compulsory measures. Moreover, compared with other private modes, public transport is generally identified as a riskier option, and the average willingness to travel descends. Findings of this study are helpful for the authorities in designing and promoting effective policies in the context of pandemics.

With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DASs), it becomes possible in recent to explore the determinants of urban taxi ridership using multi-source heterogeneous data. This paper aims to use floating car data, points-of-interests (POIs) data and housing-price data to assess the influence of the built environment on taxi ridership. Within a scale of 0.5 km grid, critical indicators related to the economic aspect, intermodal connection, and land use factors were obtained using the multi-source data in Shanghai. To capture the spatial and temporal heterogeneity, Semi-parametric Geographically Weighted Poisson Regression (SGWPR) models are built over different time dimensions. It is found that SGWPR models result in higher goodness-of-fit than the generalized linear models. More importantly, the results show the impacts of built environment factors on taxi demand are highly heterogeneous, positive or negative in different city areas, reflected in the significant temporal variations of the effects. Overall, these findings suggest that the built environment factors have significant impacts on urban taxi demand, and the spatial context should not be ignored. Findings in this paper are expected to help better understand the relationship between urban taxi demand and built environment factors, improving the service level of the urban taxi system, and offering valuable insights into future urban and transportation planning.

This paper proposes a composite stability control strategy for tire blowout electric vehicle with explicit consideration of vertical load redistribution, subject to multi-constraints, uncertainty and redundant actuation. To readily implement system state and actuator increment constraints, a model predictive controller is designed to enhance vehicle stability performance and original lane keeping. Meanwhile, a sliding mode control-based longitudinal velocity controller is collaboratively proposed for maintaining the original longitudinal velocity after tire blowout in the freeway, which can reduce considerably the rear-end collision accidents. After that, a constrained weighting least square-based reconfigurable torque distributor is developed to realize the tracking of the virtual resultant yaw-moment and longitudinal tire force signals generated by the above controllers, considering system physical constraints and actuator capability simultaneously. Simulations with different tire blowout scenarios are conducted on the developed dynamic simulation platform to demonstrate the effectiveness of the designed control methods; furthermore, the influence of different tire blowout on the vehicle movement behaviors is discussed. Statistical results based on the system transient performance and control effort evaluating indictors highlight the considerable superiority of the developed model predictive controller compared with the linear quadratic regulator.

In an intelligent transportation system, accurate bus information is vital for passengers to schedule their departure time and make reasonable route choice. In this paper, an improved deep belief network (DBN) is proposed to predict the bus travel time. By using Gaussian–Bernoulli restricted Boltzmann machines to construct a DBN, we update the classical DBN to model continuous data. In addition, a back-propagation (BP) neural network is further applied to improve the performance. Based on the real traffic data collected in Shenyang, China, several experiments are conducted to validate the technique. Comparison with typical forecasting methods such as k-nearest neighbor algorithm (k-NN), artificial neural network (ANN), support vector machine (SVM) and random forests (RFs) shows that the proposed method is applicable to the prediction of bus travel time and works better than traditional methods.

This paper presents a data-driven hybrid control (DDHC) framework that can arrange adaptive control strategies for vehicles to effectively improve the transit performance of the public transport system. The framework depicts a powerful combination of a data-driven control method that is used to imitate the control behaviour of dispatchers and a mathematical optimization method. Three components comprise the DDHC framework: a data-driven control module, a performance module, and an optimization module. The data-driven control module contains a random forest model which is adopted to justify whether to intervene in the operation of a bus line, and if so, which vehicles should be controlled and what type of control strategy should be taken – an acceleration strategy or deceleration strategy. The performance module including vehicle operation state models is used to describe the system evolution. The last component optimizes the specific control actions – which type of acceleration or deceleration strategy should be adopted – by minimizing total passenger travel time. The effectiveness of the proposed DDHC framework is evaluated with the data of a transit route in Urumqi, China. The results show that the DDHC framework with reasonable parameters can suit the needs of real-time control in complex traffic environments.

In order to explore the determinants of air-rail integration service in China, this paper employed a comparative analysis based on historical data and stated preferences. According to the results of a partial least-squares regression model and a binary logit model, the numbers of destination cities and flights are the most important factor influencing the use of the ARIS at Shijiazhuang Zhengding International Airport. The impact of other factors is small. Consequently, policymakers have to find rational target markets to promote air-rail integration service at regional airports to attract passengers from overloaded hub airports. Although the impact of other determinants is rather small, considering the large number of passengers at hub airports, a small increase in the market share of air-rail integration service implies considerable increment in the number of air-rail integration service passengers.

This study aimed to identify critical prognostic molecular markers in Childhood acute myeloid leukemia (AML) and construct nomogram-based model for prognostic prediction. The RNA-sequencing profiles and corresponding clinical information were downloaded from TCGA database. Differential expressed genes (DEG) were screened using limma package, subsequently following by GO and KEGG pathway analysis. Univariate and multivariate cox regression analysis were performed to screen critical DEGs. Nomogram-based prediction model were constructed to identify clinical factors with independent prognostic values, and the accuracy of this model was validated. A total of 214 DEGs were identified from relapse AML samples compared with non-relapse samples. These DEGs were mainly involved in twenty GO terms and three signaling pathways, such as chromatin assembly or disassembly, cytokine-cytokine receptor interaction, and JAK-STAT signaling pathway. Among these genes, Univariate and multivariate cox regression analysis results showed that relapse and risk score were significantly correlated with survival outcomes. Finally, the accuracy ability of nomogram-based prediction model was validated. These six DEGs (ABCA5, CYP7A1, HERC5, etc.) play major roles in AMLs progression. Our nomogram-based prognostic predictive model might be an effective method to estimate survival probability of AML patients with different risk status.

With the development of vehicle automation and sharing economy, a mix of human-driven and autonomous vehicles can be expected in the next few decades, which means the conventional vehicles, carsharing, autonomous vehicles (AV) and shared AVs will coexist, affecting the way of travel. AVs, especially combined with shared use, may contribute to reduce or solve some of the present most intractable urban problems such as traffic congestion, crashes and wasted use of urban spaces. Given the emerging trend of new mobility options, understanding the decision of people in owning a private human-driven vehicle or private AV versus using a shared AV or joining car sharing would have important indications for policy makers to reduce car ownership in general. Therefore, this paper is devoted to gain insight into individuals’ propensity for purchasing an autonomous vehicle or using shared (autonomous) cars. Based on the data collected using a stated choice experiment, we estimated a random parameter error component model to identify the possible heterogeneity between individuals in the preference and possible correlations between various mobility alternatives. Results show substantial heterogeneity exists among individuals in their preferences. People in general prefer to keep their current car or buy an AV rather than to use shared cars, and shared AVs are more favorable than the traditional car sharing. Regarding the shared AVs, people are not only concerned about the operational costs and hourly rate, but also vehicle availability and access time. Moreover, younger people are more willing to accept these new mobility options.

Epilepsy (SE) is a common and serious neurological disease. NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome participates in the pathogenesis of SE, while its underlying mechanism is still unclear. Here, we attempted to explore the mechanism of action of NLRP3 inflammasome in SE. SE mouse model was constructed by administration of kainic acid (KA). Astrocytes were treated with KA to mimic SE cell model. MCC950 (NLRP3 inhibitor) and Z-YVAD-FMK (Caspase-1 inhibitor) were used to treat astrocytes to inhibit the activity of NLRP3 and Caspase-1. Nissl staining was performed to examine the morphology of neuron. Western blot, enzyme-linked immunosorbent assay and immunofluorescence staining were performed to assess protein expression. SE mouse model exhibited an increase of neuronal loss, and an up-regulation of Cleaved-Caspase-1, IL-1β and IL-18 in hippocampus. The levels of GFAP+ADK+ cells were significantly increased in SE mice. MCC950 or Z-YVAD-FMK abolished these impacts conferred by KA in SE mice. Moreover, KA treatment enhanced the expression of NLRP3, Cleaved-Caspase-1, IL-1β and IL-18 in astrocytes, which was rescued by knockdown of NLRP3 or Caspase-1. Additionally, CREB, p-CREB, REST were up-regulated, and SP1 was down-regulated in the KA-treated SE mice and KA-treated astrocytes. Inhibition of NLRP3 or Caspase-1 rescued these proteins expression in KA-treated astrocytes. CREB or REST silencing reduced adenosine kinase (ADK) expression, while SP1 knockdown enhanced ADK expression in KA-treated astrocytes. In conclusion, NLRP3 inflammasome activation enhances ADK expression to accelerate SE in mice through regulating CREB/REST/SP1 signaling pathway. Thus, inhibition of NLRP3 inflammasome may be a treatment for SE.

The requirement for transit reliability grows with the increase of pace of life since unstable bus arrivals can raise the anxiety of waiting passengers. This paper proposes a reliability assessment method to evaluate the reliability of each bus stop on the route and the reliability of bus routes. In reliability prediction, the prediction target is locked by rolling horizon to reduce the interference of other information. In addition, a prediction method of the reliability of further transit service using the accurate online support vector machine is proposed. This prediction can provide more accurate and stable data for the arrival of buses and reduce unnecessary waiting of passengers. Finally, the reliability prediction method proposed is tested with the real data of a bus route in Dalian, China. The results show that the accurate online support vector machine with reasonable parameters can predict the reliability of transit service accurately.

Runoff prediction plays a crucial role in the scheduling and management of water resources. A novel enhanced long short-term memory (LSTM) model called LN-LSTM-PSO is proposed by integratin...

The peak load of a bus route is essential to service frequency determination. From the supply side, there exist ineffective predicted errors of peak load for the optimal number of trips. Whilst many studies were undertaken to model demand prediction and supply optimization separately, little evidence is provided about how the predicted results of peak load affect supply optimization. We propose a prediction model for the peak load of bus routes built upon the idea of newsvendor model, which explicitly combines demand prediction with supply optimization. A new cost-based indicator is devised built upon the practical implication of peak load on bus schedule. We further devise a scaled Shepard interpolation algorithm to resolve discontinuities in the probability distribution of prediction errors arising from the new indicator, while leveraging the potential efficacy of multi-source data by adding a novel quasi-attention mechanism (i.e., scaling feature space and parameter optimization). The real-world application showed that our method can achieve high stability and accuracy, and is more robust to predicted errors with higher capacity. Our method can also produce a larger number of better trip supply plans as compared to traditional methods, while presenting stronger explanatory power in prioritizing the relative contribution of influential factors to peak load prediction.

A novel charging approach, named mobile charging service, is proposed for battery electric vehicles. The mobile charging service operator receives booking reservations from battery electric vehicle drivers including electricity, locations and required time windows, and based on the information allocated mobile charging vehicles do the charging. This service offers an alternative of fixed charging facilities and also flexibility to battery electric vehicle drivers as they do not need to wait in their vehicles during the charging service. Mobile charging service operators may be equipped with high service-efficiency mobile charging vehicles and low service-efficiency mobile charging vehicles. High service-efficiency mobile charging vehicles can complete the same charging service in a short period of time and charge high service fees, but the cost is high, while low service-efficiency mobile charging vehicles are the opposite. It is urgent to optimize both the number and routes of these two types of mobile charging vehicles at the same time to take advantage of their respective advantages from the operator’s perspective. To address this challenge, we introduce the mobile charging service problem with time windows and multiple mode service, and formulate it as a 0–1 mixed integer linear model. The designed model is tested in 24 small examples and two large instances in Dalian, China, where sensitivity analyses are performed to further evaluate the effectiveness of this model. The result shows, the combination of mobile charging vehicles with different service efficiency can help the operator obtain more profits than all mobile charging vehicles with the same service efficiency regardless of high or low service efficiency.

Ride-sharing enables reduction of private car usage for commuting. This paper proposes a stable matching model for the ride-sharing which aims to minimize the travel cost of all commuters. A payment for the ride-sharing is designed considering the equity and incentive. An algorithm based on the deferred acceptance algorithm is proposed for the model. To verify the model and algorithm, cases with different scales are presented based on Dalian. The results illustrate that the compensation, time window and driver-to-rider ratios can affect the successful matching rate.

Vehicle electrification can help to decrease environmental pollution. But fast electricity consumption and inconvenient recharging service restrict the widespread adoption of electric vehic...

BackgroundmicroRNA-23b (miR-23b) is a multiple functional miRNA. We hypothesize that miR-23b plays a role in the pathogenesis of sepsis. Our study investigated the effect of miR-23b on sepsis-induced immunosuppression.MethodsMice were treated with miR-23b inhibitors by tail vein injection 2 days after cecal ligation puncture (CLP)–induced sepsis. Apoptosis in spleens and apoptotic signals were investigated, and survival was monitored. T-cell immunoreactivities were examined during late sepsis. Nuclear factor κB (NF-κB)–inducing kinase (NIK), tumor necrosis factor (TNF)–receptor associated factor 1 (TRAF1), and X-linked inhibitor of apoptosis protein (XIAP), the putative targets of miR-23b, were identified by a dual-luciferase reporter assay.ResultsmiR-23b expression is upregulated and sustained during sepsis. The activation of the TLR4/TLR9/p38 MAPK/STAT3 signal pathway contributes to the production of miR-23b in CLP-induced sepsis. miR-23b inhibitor decreased the number of spleen cells positive by terminal deoxynucleotidyl transferase dUTP nick-end labeling and improved survival. miR-23b inhibitor restored the immunoreactivity by alleviating the development of T-cell exhaustion and producing smaller amounts of immunosuppressive interleukin 10 and interleukin 4 during late sepsis. We demonstrated that miR-23b mediated immunosuppression during late sepsis by inhibiting the noncanonical NF-κB signal and promoting the proapoptotic signal pathway by targeting NIK, TRAF1, and XIAP.ConclusionsInhibition of miR-23b reduces late-sepsis-induced immunosuppression and improves survival. miR-23b might be a target for immunosuppression.

Provision of effective taxi cruising recommendation strategy is vital to drivers for improving their profit and reducing taxi exhaust emissions. This paper focuses on the problem of mining environmental benefits from the taxi cruising recommendation strategy while maximizing the profit of drivers. With abundant global position system (GPS) trajectories of taxis in Beijing, China, passenger hotspots are identified by density-based spatial clustering of application with noise (DBSCAN) algorithm, including weekdays and weekends. Then, spatio-temporal patterns of passenger hotspots as well as idleness emissions are analyzed and visualized based on the geographic information system (GIS) techniques. Based on spatio-temporal patterns of passenger hotspots and emissions under the idleness state, a profitable taxi cruising recommendation strategy is proposed to quantify and analyze the environmental benefits in terms of empty emissions. The results show that the proposed strategy significantly outperforms the current method by reducing idleness emissions 19.6% and 31.7% on the weekday and weekend, respectively. The results also appear to demonstrate that the proposed strategy is an effective approach in energy conservation and emission reduction.

In this paper, we envision an emerging Mixed Operation Zone (MOZ) where both autonomous vehicles (AVs) and human-driven vehicles (HVs) are present for on-demand ride services. This paper aims to size and operate a fleet of AVs and HVs in the presence of MOZs and to investigate the impact of MOZs on on-demand ride services. Considering the demand uncertainty, we propose a demand-oriented robust minimum fleet problem (RMFP) and employ a two-stage robust optimization (RO) to model the decision-making. Fluctuant demand is bounded by distribution-free uncertainty sets. For the convenience of solving RO models, we reformulate the second-stage recourse problem with an equivalent mathematical programming formulation. A tailored column-and-constraint generation algorithm is developed to solve the RMFP exactly. The algorithm is proved to converge in a finite number of iterations. Extensive experiments are conducted on the instances based on a real-world on-demand ride service in Chengdu. The developed algorithm performs better than the state-of-the-art Benders decomposition approach. Numerical results imply huge potential benefits from MOZs on improving service performance for ride service platforms.

The electric vehicle is seen as an effective way to alleviate the current energy crisis and environmental problems. However, the lack of supporting charging facilities is still a bottleneck in the development of electric vehicles in the Chinese market. In this paper, the cloud model is used to first predict drivers’ charging behavior. An optimization model of charging stations is proposed, which is based on waiting time. The target of this optimization model is to minimize the time cost to electric vehicle drivers. We use the SCE-UA algorithm to solve the optimization model. We apply our method to Dalian, China to optimize charging station locations. We also analyze the optimized result with or without behavior prediction, the optimized result of different numbers of electric vehicles, and the optimized result of different cost constraints. The analysis shows the feasibility and advantages of the charging station location optimization method proposed in this paper.

Depending on the duration and severity, psychological tension and physical stress can enhance or suppress the immune system in both humans and animals. Although it has been established that chronic stress exerts a significant suppressive effect on immune function, the mechanisms by which affects immune responses remain elusive. By employing an in vivo murine system, we revealed that TGF-β1/Smad2/3/Foxp3 axis was remarkably activated following chronic stress. Furthermore, TLR9 and p38 MAPK played a critical role in the activation of TGF-β1/Smad2/3/Foxp3 signaling cascade. Moreover, inhibition of TGF-β1/Smad2/3/Foxp3 or p38 significantly attenuated chronic stress-induced lymphocyte apoptosis and apoptosis-related proteins, as well as the differentiation of T regulatory cells in spleen. Interestingly, disequilibrium of pro-inflammatory and anti-inflammatory cytokines balance caused by chronic stress was also rescued by blocking TGF-β1/Smad2/3/Foxp3 axis. These findings yield insight into a novel mechanism by which chronic stress modulates immune functions and identifies new targets for the development of novel anti-immune suppressant medications.

This paper proposes a selection model of candidate nodes and an air parcel network hub location model to estimate the efficiency of the parcel network when adding pure freighters. Using the data on real demands among airports in practice, this article analyzes the hub location problem of the parcel network for DHL Express in the Asia-Pacific region. We find that Beijing, Hong Kong, Singapore, and Mascot are potential choices to be hub airports. The four hubs have parcel production of 10.1, 5.36, 2.75 and 0.84 million tons in the planning horizon, respectively. A sensitivity analysis is conducted to analyze the influence of different numbers of pure freighters on the air parcel network and the results indicate that the number of pure freighters can alleviate parcel pressure.

The prediction of bus arrival time is important for passengers who want to determine their departure time and reduce anxiety at bus stops that lack timetables. The random forests based on the near neighbor (RFNN) method is proposed in this article to predict bus travel time, which has been calibrated and validated with real-world data. A case study with two bus routes is conducted, and the proposed RFNN is compared with four methods: linear regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), and classic random forest (RF). The results indicate that the proposed model achieves high accuracy. That is, one bus route has the results of 13.65 mean absolute error (MAE), 6.90% mean absolute percentage error (MAPE), 26.37 root mean squared error (RMSE) and 13.77 (MAE), 7.58% (MAPE), 29.01 (RMSE), respectively. RFNN has a longer computation time of 44,301 seconds for a data set with 14,182 data. The proposed method can be optimized by the technology of parallel computing and can be applied to real-time prediction.

This paper studies the problem of the sailing speed optimization for tramp ships. The transportation time requirements which are associated with shippers’ satisfaction are considered by a fuzzy membership function. A bi-objective model is proposed in which the minimum operation cost and the maximum shippers’ satisfaction are optimized simultaneously. The optimal speed on each leg of a given ship route is determined. A fast elitist non-dominated sorting genetic algorithm (NSGAII) is improved to solve the problem. To test the performance of the proposed model and algorithm, a numerical experiment is designed. The results show that the proposed algorithm has good convergence property and convergence speed.

The location affects the competitiveness and market share of a new entry enterprise, especially for a retail enterprise. This study focuses on the competitive location of new chain stores. In this paper, a bi-level model is proposed to formulate the competitive location problem. And the model also considers the pricing game between the new entry enterprise and the existing competitor. The model optimizes the location by maximizing the benefit on the principle of the Nash equilibrium. A heuristic algorithm is proposed to solve the model. Results show the feasibility of the proposed model and provide managerial insights for decision makers to determine an appropriate location.

Energy cost for keeping fresh seafood in cold condition is a main feature of a fresh seafood delivery routing problem. In the delivery routing problem, energy cost varies during the transportation process and the service process. In addition, there are many fresh seafood product factories whose seafood products should be delivered to a set of customers. Therefore, this paper models the fresh seafood delivery problem as a multi-depot vehicle routing problem, which aims to find the routes with the least cost. Due to the complexity of the problem, a method is used to reduce the complexity by changing the multi-depot vehicle routing problem into a vehicle routing problem with a dummy depot in this paper. Then, ant colony optimization (ACO) is used to solve this problem. Scanning strategy and crossover operation are also adopted to improve the performance of ACO. At last, the computational results of the benchmark problems of the multi-depot vehicle routing problem indicate the effectiveness of the algorithm. Furthermore, the real-life fresh seafood delivery routing problem from Dalian city suggests the proposed model is feasible.

Both the sustainable development of China's economy and the deregulation of the China air transport market have acted as a spur for the halting development of low-cost carriers (LCCs) in China. To analyze the development trends of LCCs' network in China, this paper took Spring Airlines, the only LCC in China as an example. First, the winter flight plans of Spring Airlines from 2005 to 2013 were collected. Secondly, the development trends of air transport network of Spring Airlines were explored with methods of mathematical statistics and social network analysis. Additionally, the development trends were analyzed from the levels of navigable cities, air routes and air transport networks. The results show that although Spring Airlines actively launched air routes between tourist cities with non-class I airports, its network has been transformed from a star structure into a complex one with multi-hubs. The development process of the Spring Airlines network can be separated into three stages. In addition, the problems and evolution trends of its network are discussed further.