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Abstract Background Autism spectrum disorder (ASD) has been associated with intrauterine growth restriction (IUGR), but the underlying mechanisms are unclear. Results We found that the IUGR rat model induced by prenatal caffeine exposure (PCE) showed ASD-like symptoms, accompanied by altered gut microbiota and reduced production of indole 3-propionic acid (IPA), a microbiota-specific metabolite and a ligand of aryl hydrocarbon receptor (AHR). IUGR children also had a reduced serum IPA level consistent with the animal model. We demonstrated that the dysregulated IPA/AHR/NF-κB signaling caused by disturbed gut microbiota mediated the hippocampal microglia hyperactivation and neuronal synapse over-pruning in the PCE-induced IUGR rats. Moreover, postnatal IPA supplementation restored the ASD-like symptoms and the underlying hippocampal lesions in the IUGR rats. Conclusions This study suggests that the microbiota-IPA-brain axis regulates ASD susceptibility in PCE-induced IUGR offspring, and supplementation of microbiota-derived IPA might be a promising interventional strategy for ASD with a fetal origin. Video Abstract

The formation of learning and memory is regulated by synaptic plasticity in hippocampal neurons. Here we explored how gestational exposure to dexamethasone, a synthetic glucocorticoid commonly used in clinical practice, has lasting effects on offspring's learning and memory. Adult offspring rats of prenatal dexamethasone exposure (PDE) displayed significant impairments in novelty recognition and spatial learning memory, with some phenotypes maintained transgenerationally. PDE impaired synaptic transmission of hippocampal excitatory neurons in offspring of F1 to F3 generations, and abnormalities of neurotransmitters and receptors would impair synaptic plasticity and lead to impaired learning and memory, but these changes failed to carry over to offspring of F5 and F7 generations. Mechanistically, altered hippocampal miR-133a-3p-SIRT1-CDK5-NR2B signaling axis in PDE multigeneration caused inhibition of excitatory synaptic transmission, which might be related to oocyte-specific high expression and transmission of miR-133a-3p. Together, PDE affects hippocampal excitatory synaptic transmission, with lasting consequences across generations, and CDK5 in offspring's peripheral blood might be used as an early-warning marker for fetal-originated learning and memory impairment.

Joubert syndrome (JS) is a rare autosomal recessive neurodevelopmental condition characterized by congenital mid-hindbrain abnormalities and a variety of clinical manifestations. This article describes a case of Joubert syndrome type 21 with microcephaly, seizures, developmental delay and language regression, caused by a CSPP1 gene variant and examines the contributing variables. This paper advances the understanding of JS by summarizing the literature and offering detection patterns for practitioners with clinical suspicions of JS.

Abstract Background Tic disorders (TD) are complex neuropsychiatric disorders frequently associated with a variety of comorbid problems, whose negative effects may exceed those of the tics themselves. In this study, we aimed to explore the sociodemographic and clinical characteristics of children with TD and behavioral problems, and develop a prediction model of behavioral problems based on the predictors under real-world conditions. Methods A hospital-based cross-sectional study was conducted on children with TD. Behavioral problems were surveyed using the Achenbach Child Behavior Checklist (CBCL). Sociodemographic information was collected from face-to-face interviews using an electronic questionnaire administered during the initial ambulatory visit. Clinical data were collected from medical records, and quality control was performed. The sociodemographic and clinical characteristics of patients with and without behavioral problems were statistically compared, and a nomogram prediction model was developed based on multivariate logistic regression analysis. The discriminatory ability and clinical utility of the nomogram were assessed by concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve (CIC). Results A total of 343 TD cases were included in the final analysis, of which 30.32% had behavioral problems. The prediction model showed age 12–16 years, abnormal birth history, parenting pattern of indulgence, parent/close relatives with psychiatric disorders, chronic motor or vocal tic disorder (CTD)/Tourette syndrome (TS) and moderate/severe tic severity were associated with behavioral problems in children with TD. The C-index of the prediction model (nomogram) was 0.763 (95% confidence interval, 0.710 ~ 0.816). The nomogram was feasible for making beneficial clinical decisions, according to the satisfactory results of the DCA and CIC. Conclusions A nomogram prediction model for comorbid behavioral problems in children with TD was established. The prediction model demonstrated a good discriminative ability and predictive performance for beneficial clinical decisions. This model further provides a comprehensive understanding of associated sociodemographic and clinical characteristics by visual graphs and allows clinicians to rapidly identify patients with a higher risk of behavioral problems and tailor necessary interventions to improve clinical outcomes.

Objective: To examine the association between family environmental and clinical factors with the whole range of quality of life (QOL) in children with tic disorders (TD). Methods: A hospital-based cross-sectional study was conducted among children with TD. All participants were given a family environmental survey and scale evaluations with Yale Global Tic Severity Scale (YGTSS), Achenbach Child Behavior Checklist (CBCL) and PedsQL-Generic Core Scale of the Chinese Version (PedsQL). Variable selection and data analysis was done by the least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression analysis. Results: A total of 363 TD cases were included in the analysis. YGTSS scores, total CBCL score had significant negative correlations with PedsQL scores (P

Autonomous vehicles (AVs) have been made possible by advances in sensing and computing technologies. However, the high cost of AVs makes privatization take longer. Therefore, companies with autonomous vehicles can develop shared autonomous vehicle (SAV) projects. AVs with a high level of automation require high upgrade and use costs. In order to meet the needs of more customers and reduce the investment cost of the company, SAVs with different levels of automation may coexist for a long time. Faced with multiple travel modes (autonomous cars with different levels of automation, private cars, and buses), travelers’ travel mode choices are worth studying. To further differentiate the types of travelers, this paper defines high-income travelers and low-income travelers. The difference between these two types of travelers is whether they have a private car. The differences in time value and willingness to pay of the two types of travelers are considered. Based on the above considerations, this paper establishes a multi-modal selection model with the goal of maximizing the total utility of all travelers and uses the imperial competition algorithm to solve it. The results show that low-income travelers are more likely to choose buses and autonomous vehicles with lower levels of automation, while high-income travelers tend to choose higher levels of automation due to their high value of travel time.

Type 50 early infantile epileptic encephalopathy, or EIEE-50 for short, is an autosomal recessive genetic disorder resulting from CAD mutations. So far, little has been reported on the disease. In this article, we will discuss the case of a male infant who is 8 years and 5 months old. A whole-exome sequencing of the boy revealed CAD compound heterozygous mutations. He suffered from global developmental delay and regression, refractory epilepsy, and anemia. After his diagnosis, we used uridine treatment and gained encouraging results. In this article, we will analyze our case studies in the context of the literature, so as to improve pediatricians' understanding of the disease.

As urban traffic pollution continues to increase, there is an urgent need to build traffic emission monitoring and forecasting system for the urban traffic construction. The traffic emission monitoring and forecasting system's core is the prediction of traffic emission's evolution. And the traffic flow prediction on the urban road network contributes greatly to the prediction of traffic emission's evolution. Due to the complex non-Euclidean topological structure of traffic networks and dynamic heterogeneous spatial-temporal correlations of traffic conditions, it is difficult to obtain satisfactory prediction results with less computation cost. To figure these issues out, a novel deep learning traffic flow forecasting framework is proposed in this paper, termed as Ensemble Attention based Graph Time Convolutional Networks (EAGTCN). More specifically, each component of our model contains two major blocks: (1) the global spatial patterns are captured by the spatial blocks which are fused by the Graph Convolution Network (GCN) and spatial ensemble attention layer; (2) the temporal patterns are captured by the temporal blocks which are composed by the Time Convolution Net (TCN) and temporal ensemble attention layers. Experiments on two real-world datasets demonstrate that our model obtains more accurate prediction results than the state-of-the-art baselines at less computation expense especially in the long-term prediction situation.

The yard template problem in container ports determines the assignment of space to store containers for the vessels, which could impact container truck paths. Actually, the travel time of container truck paths is uncertain. This paper considers the uncertainty from two perspectives: (1) the yard congestion in the context of yard truck interruptions, (2) the correlation among adjacent road sections (links). A mixed-integer programming model is proposed to minimize the travel time of container trucks. The reliable shortest path, which takes the correlation among links into account is firstly discussed. To settle the problem, a Shuffled Complex Evolution Approach (SCE-UA) algorithm is designed to work out the assignment of yard template, and the A* algorithm is presented to find the reliable shortest path according to the port operator’s attitude. In our case study, one yard in Dalian (China) container port is chosen to test the applicability of the model. The result shows the proposed model can save 9% of the travel time of container trucks, compared with the model without considering the correlation among adjacent links.

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.

The building density of Central Business District (CBD) is usually high. Land for a bus terminal is insufficient. In this situation, passengers in CBD have to walk far to take a bus, or take a long time to wait for a taxi. To solve this problem, this paper proposes an indirect approach: the design of a circle line of shuttle bus as a dynamic bus terminal in CBD. The shuttle bus can deliver people to the bus station through a circle line. This approach not only reduces the traffic pressure in CBD, but also saves travel time of the passenger. A bi-objective model is proposed to design a circle line of a shuttle bus for CBD. The problem is solved by non-dominated sorting genetic algorithm (NSGA-II). Furthermore, the Dalian city in China has been chosen as the case study to test the proposed method. The results indicate that the method is effective for circle line optimization of shuttle bus in central business district without a bus terminal.

Effective bus travel time prediction is essential in transit operation system. An improved support vector machine (SVM) is applied in this paper to predict bus travel time and then the efficiency of the improved SVM is checked. The improved SVM is the combination of traditional SVM, Grubbs’ test method and an adaptive algorithm for bus travel-time prediction. Since error data exists in the collected data, Grubbs’ test method is used for removing outliers from input data before applying the traditional SVM model. Besides, to decrease the influence of the historical data in different stages on the forecast result of the traditional SVM, an adaptive algorithm is adopted to dynamically decrease the forecast error. Finally, the proposed approach is tested with the data of No. 232 bus route in Shenyang. The results show that the improved SVM has good prediction accuracy and practicality.

This paper presents a dynamic extra buses scheduling strategy to improve the transit service of transit routes. In this strategy, in order to decide when to dispatch an extra bus, the service reliability of transit route is assessed firstly. A model aimed at maximizing the benefit of the extra buses scheduling strategy is constructed to determine how many stops extra buses need to skip from the terminal to accommodate passengers at the following stops. A heuristic algorithm is defined and implemented to estimate the service reliability of transit route and to optimize the initial stop of extra buses scheduling strategy. Finally, the strategy is tested on two examples: a simple and a real-life transit route in the Dalian city in China. The results show that the extra buses scheduling strategy based on terminal stops with a reasonable threshold can save 8.01% waiting time of passengers.

This paper deals with a real-life vehicle delivery routing problem, which is a seafood product delivery routing problem. Considering the features of the seafood product delivery routing problem, this paper formulated this problem as a multi-depot open vehicle routing problem. Since the multi-depot open vehicle routing problem is a very complex problem, a method is used to reduce the complexity of the problem by changing the multi-depot open vehicle routing problem into an open vehicle routing problem with a dummy central depot in this paper. Then, ant colony optimization is used to solve the problem. To improve the performance of the algorithm, crossover operation and some adaptive strategies are used. Finally, the computational results for the benchmark problems of the multi-depot vehicle routing problem indicate that the proposed ant colony optimization is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the computation results of the seafood product delivery problem from Dalian, China also suggest that the proposed ant colony optimization is feasible to solve the seafood product delivery routing problem.

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.

The mode of automotive maintenance chain is gaining more and more attention in China because of its advantages such as the lower cost, higher speed, higher availability, and strong adaptability. Since the automobile chain maintenance parts delivery problem is a very complex multi-depot vehicle routing problem with time windows, a virtual center depot is assumed and adopted to transfer multi-depot vehicle routing problem with time windows to multi-depot vehicle routing problem with the virtual central depot, which is similar to a vehicle routing problem with time windows. Then an improved ant colony optimization with saving algorithms, mutation operation, and adaptive ant-weight strategy is presented for the multi-depot vehicle routing problem with the virtual central depot. At last, the computational results for benchmark problems indicate that the proposed algorithm is an effective method to solve the multi-depot vehicle routing problem with time windows. Furthermore, the results of an automobile chain maintenance parts delivery problem also indicate that the improved ant colony algorithm is feasible for solving this kind of delivery problem.

The unbalanced distribution of parking demand is a primary source of parking problems. The autonomous driving system and parking assist system of fully autonomous vehicles provide a possibility to alleviate the uneven distribution of parking demand in the city. Exploring the parking behavior of autonomous vehicle (AV) users is necessary for assessing parking pricing policies. In turn, the distribution of parking demand and people's preference for parking lots may be impacted by the change in the AV parking lot's parking charge. Thus, this paper aims to provide an effective optimization method for AV parking lot price strategy, based on the analysis of the parking behavior of AV users. A stated choice experiment is designed to understand AV users' parking behavior. To estimate the impact of attributes and social-demographic factors, the multinomial logit model is adopted. From the results, we find a farther but cheaper parking lot is more attractive than a closer but more expensive parking lot to AVusers. Based on the estimated results, a bilayer optimization model based on parking behavior is built to optimize AV parking lot prices. The Lagrangian relaxation method is employed to solve the model. By empirical testing, the AV parking price optimization model based on parking behavior is proven to be feasible and effective. According to the case study's findings, the parking lot pricing optimization model and algorithm proposed in this paper can not only meet the benefits of parking lots and the utility of autonomous vehicle users, but also alleviate the demand for parking concentrated in the city center, which means the parking difficulties caused by the distribution of parking demand can be alleviated through the optimization of AV parking lots' parking price. [ABSTRACT FROM AUTHOR]