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Introduction/backgroundVibrio cholerae is the causative agent of the human intestinal infectious disease cholera, which includes a variety of serogroups. However, there have been very few cases of hepatic space-occupying lesions associated with this infection. Currently, there are various methods for detecting this pathogen, including metagenomic sequencing, which enables quicker and more accurate identification. In this study, metagenomic sequencing is employed to accurately identify non-O1/O139 Vibrio cholerae infections by analyzing the genetic material present in clinical samples.Presentation of caseA 75-year-old man presented with diarrhea and fever after consuming crabs. The initial treatment improved the diarrhea, but a liver abscess developed later. Magnetic resonance imaging (MRI) of the liver revealed a hepatic space-occupying lesion. Upon further investigation, a Gram-negative, rod-shaped bacterium was cultured from the patient’s liver puncture fluid, and Vibrio cholerae was detected in the same fluid using metagenomic next-generation sequencing (mNGS). The pathogen was confirmed to be non-O1/non-O139 Vibrio cholerae (NOVC) using polymerase chain reaction (PCR). Following treatment with piperacillin/tazobactam sodium and moxifloxacin, the patient’s body temperature returned to normal, the liver abscess improved significantly, and he was subsequently discharged from the hospital.DiscussionThis case study describes an elderly male patient with a hepatic space-occupying lesion. Multiple cultures of specimens failed to identify the underlying cause; however, advanced techniques such as mNGS and PCR confirmed an NOVC infection. This indicates that mNGS can serve as a valuable tool in diagnosing cases of unexplained liver infections.ConclusionThe use of mNGS is significant for detecting and clinically diagnosing infectious pathogens in patients with unexplained space-occupying lesions.

BackgroundPrevious observational research has indicated a correlation between Parkinson’s disease (PD) and multiple cancers; but the causality remains unclear. Thus, we utilized Mendelian randomization (MR) analysis to explore the potential causal link between PD and various cancers.MethodsWe conducted a bidirectional two-sample Mendelian randomization (TSMR) of genetic variants associated with PD and 14 types of cancers. Summary statistics on PD and 14 types of cancers were obtained from the International Parkinson’s Disease Genomics Consortium and the study by Sakaue et al. The primary method employed was inverse variance weighted (IVW), complemented by multiple sensitivity analyses to evaluate heterogeneity and pleiotropy. The false discovery rate (FDR) was employed to control the false positive rate of multiple hypothesis testing.ResultsFollowing rigorous sensitivity analyses and corrections, our findings revealed suggestive associations between PD and certain cancers. We observed that PD decreases the risk of gastric cancer and colorectal cancer (OR = 0.936, 95% CI = 0.881–0.995, p = 0.034, P FDR = 0.239; OR = 0.955, 95% CI = 0.912–0.999, p = 0.046, P FDR = 0.215), while increasing the risk of breast cancer (OR = 1.043, 95% CI = 1.004–1.084, p = 0.029, P FDR = 0.402). Notably, we found no evidence supporting a reverse causal relationship. Additionally, in the reverse pathway, skin cancer demonstrated a suggestive causal relationship with PD (OR = 0.913, 95% CI = 0.857–0.973, p = 0.005, P FDR = 0.066).ConclusionOur MR analysis provides evidence supporting unidirectional suggestive causal relationships between PD and certain cancers. These findings enrich our comprehension of the intricate interplay between PD and cancer, warranting further investigation into the underlying biological mechanisms.

Abstract Background Gestational diabetes mellitus (GDM) is a common complication of pregnancy, with significant short-term and long-term implications for both mothers and their offspring. Previous studies have indicated the potential benefits of vitamin D in reducing the risk of GDM, yet little is known about this association in twin pregnancies. This study aimed to investigate maternal vitamin D status in the second trimester and examine its association with the risk of GDM in twin pregnancies. Methods We conducted a prospective cohort study based on data from the Chongqing Longitudinal Twin Study (LoTiS). Peripheral blood serum was collected from the mothers in the second trimester to measure 25(OH)D concentrations. GDM was diagnosed at 23–26 weeks of gestation using a 75-g 2-h oral glucose tolerance test. We used multivariable logistic regression analyses to examine the correlations between vitamin D status and the risk of GDM. Results Of the total participants, 93 (29.9%) women were diagnosed with GDM. The mean serum 25(OH)D concentration in the second trimester was 31.1 ± 11.2 ng/mL, and the rate of vitamin D insufficiency and deficiency were 23.5% and 18.7%, respectively. Compared to women with a 25(OH)D concentration

ABSTRACTBackground Preeclampsia/eclampsia is a severe pregnancy-related disorder associated with hypertension and organ damage. While observational studies have suggested a link between maternal iron status and preeclampsia/eclampsia, the causal relationship remains unclear. The aim of this study was to investigate the genetic causality between iron status and preeclampsia/eclampsia using large-scale genome-wide association study (GWAS) summary data and Mendelian randomization (MR) analysis.Methods Summary data for the GWAS on preeclampsia/eclampsia and genetic markers related to iron status were obtained from the FinnGen Consortium and the IEU genetic databases. The “TwoSampleMR” software package in R was employed to test the genetic causality between these markers and preeclampsia/eclampsia. The inverse variance weighted (IVW) method was primarily used for MR analysis. Heterogeneity, horizontal pleiotropy, and potential outliers were evaluated for the MR analysis results.Results The random-effects IVW results showed that ferritin (OR = 1.11, 95% CI: .89–1.38, p = .341), serum iron (OR = .90, 95% CI: .75–1.09, p = .275), TIBC (OR = .98, 95% CI: .89–1.07, p = .613), and TSAT (OR = .94, 95% CI: .83–1.07, p = .354) have no genetic causal relationship with preeclampsia/eclampsia. There was no evidence of heterogeneity, horizontal pleiotropy, or possible outliers in our MR analysis (p > .05).Conclusions Our study did not detect a genetic causal relationship between iron status and preeclampsia/eclampsia. Nonetheless, this does not rule out a relationship between the two at other mechanistic levels.

Abstract Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3–91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.

Abstract Background This study was conducted to develop and validate an individualized prediction model for spontaneous preterm birth (sPTB) in twin pregnancies. Methods This a retrospective cohort study included 3845 patients who gave birth at the Chongqing Maternal and Child Health Hospital from January 2017 to December 2022. Both univariable and multivariable logistic regression analyses were performed to find factors associated with sPTB. The associations were estimated using the odds ratio (OR) and the 95% confidence interval (CI). Model performance was estimated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results A total of 1313 and 564 cases were included in the training and testing sets, respectively. In the training set, univariate and multivariate logistic regression analysis indicated that age ≥ 35 years (OR, 2.28; 95% CI 1.67–3.13), pre-pregnancy underweight (OR, 2.36; 95% CI 1.60–3.47), pre-pregnancy overweight (OR, 1.67; 95% CI 1.09–2.56), and obesity (OR, 10.45; 95% CI, 3.91–27.87), nulliparity (OR, 0.58; 95% CI 0.41–0.82), pre-pregnancy diabetes (OR, 5.81; 95% CI 3.24–10.39), pre-pregnancy hypertension (OR, 2.79; 95% CI 1.44–5.41), and cervical incompetence (OR, 5.12; 95% CI 3.08–8.48) are independent risk factors for sPTB in twin pregnancies. The AUC of the training and validation set was 0.71 (95% CI 0.68–0.74) and 0.68 (95% CI 0.64–0.73), respectively. And then we integrated those risk factors to construct the nomogram. Conclusions The nomogram developed for predicting the risk of sPTB in pregnant women with twins demonstrated good performance. The prediction nomogram serves as a practical tool by including all necessary predictors that are readily accessible to practitioners.

BackgroundUrachal tumors are exceedingly rare, and adenocarcinoma is the most common malignant urachal neoplasm. Here, an especially rare patient of primary urachal leiomyosarcoma from our hospital was reported, and only five patients have been reported thus far since 1981.Case descriptionA 24-year-old man was admitted due to urinary tract symptoms. Both urogenital ultrasonography and contrast-enhanced computed tomography showed a mass at the dome of the urinary bladder. Laparoscopic surgical resection was performed, and histopathologic examination of the mass confirmed the diagnosis of urachal leiomyosarcoma. No recurrence was noted after one and a half years.ConclusionsBecause the leiomyosarcoma located in the extraperitoneal space of Retzius and may manifest with nonspecific abdominal or urinary symptoms, early and definitive preoperative diagnosis is challenging. Partial cystectomy with complete excision of the urachus is recommended. Because only a few patients have been recorded, clinical outcomes and recurrence risks are difficult to assess.

Plain English summary One’s response is not arbitrary when confronted with the coronavirus disease 2019 (COVID-19). Previous research has found that individuals with coronavirus stress still be more prone to overeat. We also know from other research that anxiety is the most salient aspect of overeating. However, no research has investigated whether the coronavirus stress of college students is significantly associated with overeating and examine the potential indirect pathway and moderating mechanisms in this association. With the aid of 2926 participants, we found that, coronavirus stress was linked to college students’ overeating. We also found that this relationship was partially explained by anxiety. In addition, the association between anxiety and overeating was stronger for those with higher COVID-19 burnout. This study is an important step in unpacking how coronavirus stress relates to overeating of Chinese college students. However, they are limited by the cross-sectional nature of the study, meaning we cannot imply causality. We recommend that further research replicate our findings in people with diagnosed feeding and eating disorders using a longitudinal design.

Abstract Background The pathogen of banana Fusarium oxysporum f. sp. cubense race 4(Foc4) infects almost all banana species, and it is the most destructive. The molecular mechanism of the interactions between Fusarium oxysporum and banana still needs to be further investigated. Methods We use both the interolog and domain-domain method to predict the protein–protein interactions (PPIs) between banana and Foc4. The predicted protein interaction sequences are encoded by the conjoint triad and autocovariance method respectively to obtain continuous and discontinuous information of protein sequences. This information is used as the input data of the neural network model. The Long Short-Term Memory (LSTM) neural network five-fold cross-validation and independent test methods are used to verify the predicted protein interaction sequences. To further confirm the PPIs between banana and Foc4, the GO (Gene Ontology) and KEGG (Kyoto Encylopedia of Genes and Genomics) functional annotation and interaction network analysis are carried out. Results The experimental results show that the PPIs for banana and foc4 predicted by our proposed method may interact with each other in terms of sequence structure, GO and KEGG functional annotation, and Foc4 protein plays a more active role in the process of Foc4 infecting banana. Conclusions This study obtained the PPIs between banana and Foc4 by using computing means for the first time, which will provide data support for molecular biology experiments.

Fleet replacement is an important decision-making activity in public transit planning and operations. Recently, with the goal of reducing carbon dioxide emissions and achieving carbon neutrality, diesel buses are being replaced with new electric buses (EBs) in many cities around the world. The study proposes a methodology to analyze and compare the life-cycle cost and passenger waiting time of multi-type EBs, so as to assist transit agencies in determining the optimum EB type to replace existing diesel buses. The proposed methodology is applied to a real-life case study of a bus line in Dandong, China. The case study includes six types of EBs categorized by their charging technologies and passenger capacities, including three slow-charging EBs with capacities of 40, 60, and 76 passengers, as well as three fast-charging EBs with capacities of 50, 65, and 75 passengers. The results show that no matter which charging technology is considered, the life-cycle cost of large-size EBs is the lowest. From the perspective of passengers, small-size EBs resulting in shorter waiting time are more favorable. When passenger demands on all route segments change with the same rate, the total passenger waiting time for each vehicle type will not change. But, with the increase of passenger demand, the average passenger waiting time decreases for all vehicle types. The results also indicate that the bus line length does not have a significant impact on EB vehicle selection. In addition, the phase-out of the vehicle purchase subsidy policy will have no impact on EB type selection due to the current low-level purchase subsides.

The present study examined a moderated mediation model between life satisfaction and suicidal ideation among Chinese college students during the recurrent outbreak of COVID-19. Seven hundred and ninety college students participated in the study and completed questionnaires on life satisfaction, suicidal ideation, depression, and gratitude. Findings indicated that (1) Depression played a partial mediating role between life satisfaction and suicidal ideation. Life satisfaction was not only directly affected suicidal ideation, but also indirectly affected suicidal ideation through the mediating effect of college students' depression; (2) Gratitude played a moderating role in the effect of life satisfaction on depression, and the link between life satisfaction and suicide ideation was only significant for those with higher gratitude. This study provides practical implications for the prevention of suicidal behavior among Chinese college students during the recurrent outbreak of COVID-19.

Abstract Background Drugs achieve pharmacological functions by acting on target proteins. Identifying interactions between drugs and target proteins is an essential task in old drug repositioning and new drug discovery. To recommend new drug candidates and reposition existing drugs, computational approaches are commonly adopted. Compared with the wet-lab experiments, the computational approaches have lower cost for drug discovery and provides effective guidance in the subsequent experimental verification. How to integrate different types of biological data and handle the sparsity of drug-target interaction data are still great challenges. Results In this paper, we propose a novel drug-target interactions (DTIs) prediction method incorporating marginalized denoising model on heterogeneous networks with association index kernel matrix and latent global association. The experimental results on benchmark datasets and new compiled datasets indicate that compared to other existing methods, our method achieves higher scores of AUC (area under curve of receiver operating characteristic) and larger values of AUPR (area under precision-recall curve). Conclusions The performance improvement in our method depends on the association index kernel matrix and the latent global association. The association index kernel matrix calculates the sharing relationship between drugs and targets. The latent global associations address the false positive issue caused by network link sparsity. Our method can provide a useful approach to recommend new drug candidates and reposition existing drugs.

Limited-stop bus services are a highly efficient way to release more potential of the public transit system to meet travel demand, especially under constraints on vehicle fleet size and transportation infrastructure. This work first proposes a visualized fare table for the design of limited-stop bus services along a public transit corridor, along which many lines of public transit carry a heavy load of demand back and forth every working day. Based on this proposed fare table, a set of fare strategies and desired aims of fare policy, a differentiated fare structure is established to improve social equity and increase revenue. The nature of the structure can help travellers understand how to be charged between their origins and destinations (e.g. flat, time-based, stop-based or quality-based pricing) and then plan their trips efficiently. Secondly, a model is formulated to minimize the total social cost in designing a fixed demand limited-stop bus service system with a differentiated fare structure. Thirdly, numerical results are carried out with sensitivity analysis within three scenarios of differentiated fare structures. It is found that a differentiated fare structure has a great effect on passenger path choice behaviour and resulting optimal design of bus services. An attractive feature of this differentiated fare structure is that it could not only enhance the operator’s revenue and social equity but also reduce passenger transfers and social cost.

A landslide susceptibility model based on a metaheuristic optimization algorithm (germinal center optimization (GCO)) and support vector classification (SVC) is proposed and applied to landslide susceptibility mapping in the Three Gorges Reservoir area in this paper. The proposed GCO-SVC model was constructed via the following steps: First, data on 11 influencing factors and 292 landslide polygons were collected to establish the spatial database. Then, after the influencing factors were subjected to multicollinearity analysis, the data were randomly divided into training and testing sets at a ratio of 7:3. Next, the SVC model with 5-fold cross-validation was optimized by hyperparameter space search using GCO to obtain the optimal hyperparameters, and then the best model was constructed based on the optimal hyperparameters and training set. Finally, the best model acquired by GCO-SVC was applied for landslide susceptibility mapping (LSM), and its performance was compared with that of 6 popular models. The proposed GCO-SVC model achieved better performance (0.9425) than the genetic algorithm support vector classification (GA-SVC; 0.9371), grid search optimized support vector classification (GRID-SVC; 0.9198), random forest (RF; 0.9085), artificial neural network (ANN; 0.9075), K-nearest neighbor (KNN; 0.8976), and decision tree (DT; 0.8914) models in terms of the area under the receiver operating characteristic curve (AUC), and the trends of the other metrics were consistent with that of the AUC. Therefore, the proposed GCO-SVC model has some advantages in LSM and may be worth promoting for wide use.

The glyoxalase system is a ubiquitous enzymatic network which plays important roles in biological life. It consists of glyoxalase 1 (GLO1), glyoxalase 2 (GLO2), and reduced glutathione (GSH), which perform an essential metabolic function in cells by detoxifying methylglyoxal (MG) and other endogenous harmful metabolites into non-toxic d-lactate. MG and MG-derived advanced glycation endproducts (AGEs) are associated with various diseases, such as diabetes, cardiovascular disease, neurodegenerative disorders and cancer, and GLO1 is a key rate-limiting enzyme in the anti-glycation defense. The abnormal activity and expression of GLO1 in various diseases make this enzyme a promising target for drug design and development. This review focuses on the regulatory mechanism of GLO1 in diverse pathogenic conditions with a thorough discussion of GLO1 regulators since their discovery, including GLO1 activators and inhibitors. The different classes, chemical structure and structure-activity relationship are embraced. Moreover, assays for the discovery of small molecule regulators of the glyoxalase system are also introduced in this article. Compared with spectrophotometer-based assay, microplate-based assay is a more simple, rapid and quantitative high-throughput method. This review will be useful to design novel and potent GLO1 regulators and hopefully provide a convenient reference for researchers.

People are connected by various social networks, resulting in the interdependence of consumer choice. Therefore, it is very important and realistic to assume choice interdependence in private car ownership modeling. In this paper, we investigate the interdependence of private car ownership choice using a spatial autoregressive binary probit model estimated by the Bayesian Markov chain Monte Carlo (MCMC) method. Constructing the autoregressive matrix demographically shows that the private car ownership choice of a household is dependent on other household choices. Compared with the pure binary probit model estimated by the MCMC method, the spatial autoregressive model achieves a significant improvement both in loglikelihood value and log marginal density value, which are calculated using the importance sampling method of Newton and Raftery, from approximately -202 to approximately -63 and from -208 to -145, respectively. Moreover, the results indicated by the spatial autoregressive probit model suggest that the number of children, the ownership of an apartment or the availability of a parking lot are positively and significantly associated with the private car ownership level. To test the out-of-sample performance of the model, we estimate the model using 600 data points and test it using another 148 data points. The results indicate that the predictive power is greatly improved. Finally, we analyze the augmented parameter and discover that it is associated with the parking variable in addition to the license variable.

This work presents a methodology for minimizing costs involved in the operation of a single line bus service. The model developed is based on optimal implementation of operational strategies tailored to passenger demand for a bi-directional single bus line. As a result, the commonly used timetable for Full Route Operation (FRO) will have to change to accommodate three types of strategies: short turn, limited stop, and mixed strategy (a combination of short turn and limited stop). The use of operational strategies will better match supply and demand, and will thus improve operation efficiency. The optimization model determines which trips of the given FRO timetable will be implemented with given strategies considering the trade-offs between passenger and operator costs. Moreover, in applying the model, the availability of real time information for passengers is considered in the calculation of waiting times. The proposed model is interpreted in the context of a small example, which serves as an explanatory devise. Then, it is applied to a real life case study in Dalian, China. The results show an indication that a significant saving could be attained by the use of multiple strategies. These savings were especially observed in the reduction of operational costs involved with the saving of travel times and running empty seats.

It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks.

Many congestion charging projects charge traffic only within part of a day with predetermined congestion tolls. Demand peaks have been witnessed just around the time when the charge jumps up or down. Such peaks may not be desirable, in particular (a) when the resulting peaks are much higher than available capacities; (b) traffic speeding up to get into the charging zone causes more incidents just before the toll rises up to a higher level; or (c) traffic slowing down or parking on the roadside decreases road traffic throughput just before the toll falls sharply. We term these types of demand peaks ‘boundary effects’ of congestion charging. This paper investigates these effects in a bottleneck scenario and aims to design charging schemes that reduce undesired demand peaks. For this purpose, we observe and analyse the boundary effects utilising a bottleneck model under three types of toll profiles that are indicative of real charging schemes. The first type maintains a constant toll across the charging period, the second type allows the toll to increase from zero to a given maximum level and then decrease back to zero and the third type allows the toll to rise from zero to a given maximum level, remain at this level for a fixed period and then fall down to zero. This investigation shows that all three types of toll profiles can produce greater boundary peak demands than the bottleneck capacity. A significant contribution of this work is that instead of designing an optimal traffic congestion pricing scheme we analyse how existing sub-optimal congestion pricing schemes could be improved and suggest how observed problems may be overcome. Hence, we propose a set of extra requirements to supplement existing principles or requirements for design and implementation of congestion charging, which aim to reduce the adverse consequences of boundary effects. Concluding remarks are made on implications of this investigation for the improvement of existing congestion charging projects and for future research. First published online 13 July 2015

Public transport passenger demand is inevitably made non-uniform because of spatial and temporal land use planning. This non-uniformity warrants the use of public transport operational strategies to attain operating efficiency. The optimization of these strategies is commonly being done from the operator perspective, and indirectly from the user perspective. However, the environmental perspective of these strategies, in terms of vehicle's emission, has not been investigated. This study proposed a methodology to analyze the benefits of using transit operational strategies to reduce operating cost and eventually also to reduce undesirable emissions. First, a strategy-based optimization model is established to minimize the number of transit vehicles required. Four candidate operational strategies are considered in this model, including full route operation (FRO), short turn, limited stop, and a combination of limited stop and short turn. Second, the pollutant emissions of transit vehicles are estimated by the MOVES emission model. The developed methodology is applied to a real life case study in Dalian, China. Results show that the use of operational strategies can not only significantly save the number of vehicles by 12.5%, but also reduce emissions of pollutants (i.e., CO2, HC, CO, NOx, PM2.5) by approximately 13%, compared with applying FRO strategy exclusively. In addition, both benefits can be further enhanced through the use of an efficient payment mode (e.g., off-board or contactless card) or improving bus performance in deceleration/acceleration as well as doors opening and closing at a stop.

An optimization approach for designing a transit service system is proposed. Its objective would be the maximization of total social welfare, by providing a profitable fare structure and tailoring operational strategies to passenger demand. These operational strategies include full route operation (FRO), limited stop, short turn, and a mix of the latter two strategies. The demand function is formulated to reflect the attributes of these strategies, in-vehicle crowding, and fare effects on demand variation. The fare is either a flat fare or a differential fare structure; the latter is based on trip distance and achieved service levels. This proposed methodology is applied to a case study of Dalian, China. The optimal results indicate that an optimal combination of operational strategies integrated with a differential fare structure results in the highest potential for increasing total social welfare, if the value of parameter ε related to additional service fee is low. When this value increases up to more than a threshold, strategies with a flat fare show greater benefits. If this value increases beyond yet another threshold, the use of skipped stop strategies is not recommended.

Many symptoms of sporadic Parkinson's disease (sPD) can’t be completely explained by the lesion of simple typical extrapyramidal circuit between striatum and substantia nigra. Therefore, we investigated the alteration of cortical volume, thickness, surface and density in the intermediate sPD from the Han population of Mainland China in order to find the new pathological brain regions associated with the complex clinical manifestations of sPD. The cortical volume, thickness, surface and density were examined using the voxel-based cortical morphometry and corticometry on magnetic resonance image (MRI) in 67 intermediate sPD and 35 controls, the multiple adjusted comparisons analysis of all MRI data were employed to assess the relationships between the cortical morphometric alteration in the specific brain regions and sPD. Results showed that a significantly shrunk volume, thinned thickness and enlarged or reduced surface of cortex in some specific brain regions were closely associated with sPD, but all cortical densities were not different. The majority of morphometric alteration of hemisphere cortex was symmetric, but that in the left hemisphere was more significant. The cortical morphometric alterations in the frontal, temporal, parietal, occipital and limbic lobe, cerebellum, caudate and thalamus were closely related to the clinical neural dysfunction (Clinical manifestations) of sPD. Our data indicated that the deficits of extensive brain regions involved in the development of sPD, resulted in a series of correspondent complex clinical manifestations in the disease.

"Travel demand management: short review of the special issue" Transport, 29(3), pp. 233-234