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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.

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.

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.

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.

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.

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 describes a bi-objective optimization model for the traffic count location problem in stochastic origin–destination (OD) traffic demand estimation. Two measures are defined to capture the maximum possible absolute error of the mean and the covariance of the estimated OD demand. The bounds of these two measures are mathematically deduced, and then the bi-objective optimization model is formulated to minimize the two upper bounds simultaneously. A surrogate-assisted genetic algorithm is proposed to solve this model, and a series of numerical examples are presented to demonstrate the applicability of the proposed model and the efficiency of the proposed algorithm.

Objective Preeclampsia (PE) is a potentially fatal pregnancy-specific complication. Nevertheless, the pathogenesis of PE remains indistinct. Recently, increasing studies emphasized that long noncoding RNAs (lncRNAs) functions as imperative regulators in PE. The aim of this study was to compare the lncRNAs transcript profile of placentae in early onset severe preeclampsia (EOSP) with lncRNAs in normal pregnancy (NP) and to evaluate the role of lncRNA MIR210HG (microRNA 210 host gene) in the PE pathogenesis. Methods Using RNA sequencing, we compared transcriptome profiles of placentae in EOSP (n = 3) and NP (n = 3). Bioinformatic tools were used to predict the function of differentially expressed genes while qRT-PCR was used to verify RNA sequencing data. The role of MIR210HG in HTR8/SVneo migration and invasion were analyzed by in vitro MIR210HG gene overexpression. Results Our results showed that 527 lncRNAs and 600 mRNAs were differentially expressed in placental samples of EOSP, and the analysis identified 63 key EOSP related genes. As indicated by bioinformatics analyses, lncRNA MIR210HG was a potential pathogenic marker of PE. LncRNA-MIR210HG expression was upregulated in placental samples of PE and enriched in the canonical Wnt signalling pathway. MiR210HG overexpression inhibited HTR8/SVneo cell migration and invasion in vitro. Additionally, miR210HG upregulated dickkopf-1 expression via the sponging of microRNA-520a-3p (miR-520a-3p), thus repressing trophoblast migration and invasion. Conclusion Our study showed that MiR210HG is a novel upregulated lncRNA in the placentas of PE and MiR210HG regulates the migration and invasive potential of HTR-8/SVneo cell by targeting the miR-520a-3p/Dickkopf-1 axis.

This paper proposes a bi-level programming model to solve the design problem for bus lane distribution in multi-modal transport networks. The upper level model aims at minimizing the average travel time of travelers, as well as minimizing the difference of passengers’ comfort among all the bus lines by optimizing bus frequencies. The lower level model is a multi-modal transport network equilibrium model for the joint modal split/traffic assignment problem. The column generation algorithm, the branch-and-bound algorithm and the method of successive averages are comprehensively applied in this paper for the solution of the bi-level model. A simple numerical test and an empirical test based on Dalian economic zone are employed to validate the proposed model. The results show that the bi-level model performs well with regard to the objective of reducing travel time costs for all travelers and balancing transit service level among all bus lines.

This paper presents a transit network optimization method, in which travel time reliability on road is considered. A robust optimization model, taking into account the stochastic travel time, is formulated to satisfy the demand of passengers and provide reliable transit service. The optimization model aims to maximize the efficiency of passenger trips in the optimized transit network. Tabu search algorithm is defined and implemented to solve the problem. Then, transit network optimization method proposed in this paper is tested with two numerical examples: a simple route and a medium-size network. The results show the proposed method can effectively improve the reliability of a transit network and reduce the travel time of passengers in general.

This paper proposes an approach to deal with the hub location problem in urban public transit network design, which includes two phases: to determine the candidate nodes and to optimize the distribution of transit hubs. Firstly, an indicator defined as passenger attraction is introduced to describe the attraction level of a transit node to passengers. A selection model based on passenger attraction is built to choose several candidate nodes from all the transit nodes. Secondly, a mathematical model, aiming at maximizing served populations per construction cost, is proposed to optimize multi-hub location based on the selected candidate nodes. At last, the data of Dalian city, China is used for testing the validity of the proposed approach. The results indicate that the candidate node selection model according to passenger attraction can provide an appropriate searching space for hub location optimization and the selected candidate nodes can almost cover all the passenger flow collection and distribution centers in the urban area of Dalian. Furthermore, the optimized hub location in the case study is consistent with the practice situation in Dalian. This indicates that the proposed two-phase optimization approach is a competitive method for transit hub location.

Transit network design is an important part of urban transportation planning. The purpose of this paper is to build on direct traveler density model and extend it to design transit network considering demand density relating to direct demands and transfers, and lengths of routes. The proposed method aiming to maximize demand density of route under some resource constraints divides transit network design problem into three stages, i.e., skeleton route design, main route design and branch route design, based on the objective functions with different transfer coefficients. An ant colony optimization (ACO) is used to solve the model. The model and algorithm are illustrated with data from Dalian city, China and results show that the approach can improve the solution quality if the transfer coefficient is reasonably set.

In this paper, a model for optimizing bus route headway is presented in a given network configuration and demand matrix, which aims to find an acceptable balance between passenger costs and operator costs, namely the maximization of service quality and the minimization of operational costs. An integrated approach is also proposed in the paper to determine the relative weights between passenger costs and operator costs. A parallel genetic algorithm (PGA), in which a coarse-grained strategy and a local search algorithm based on Tabu search are applied to improve the performance of genetic algorithm, is developed to solve the headway optimization model. Data collected in Dalian City, China, is used to verify the feasibility of the model and the algorithm. Results show that the reasonable resource assessment can increase the benefits of transit system.

Vehicle routing problem with time windows (VRPTW) is a well-known combinatorial problem. Many researches have presented meta-heuristics are effective approaches for VRPTW. This paper proposes a hybrid approach, which consists of ant colony optimization (ACO) and Tabu search, to solve the problem. To improve the performance of ACO, a neighborhood search is introduced. Furthermore, when ACO is close to the convergence Tabu search is used to maintain the diversity of ACO and explore new solutions. Computational experiments are reported for a set of the Solomon's 56 VRPTW and the approach is compared with some meta-heuristic published in literature. Results show that considering the tradeoff of quality and computation time, the hybrid algorithm is a competitive approach for VRPTW.

The vehicle routing problem (VRP), a well-known combinatorial optimization problem, holds a central place in logistics management. This paper proposes an improved ant colony optimization (IACO), which possesses a new strategy to update the increased pheromone, called ant-weight strategy, and a mutation operation, to solve VRP. The computational results for fourteen benchmark problems are reported and compared to those of other metaheuristic approaches.