Search

Arsenic is a pollutant that can cross the placenta; however, research on the effects of arsenic exposure during pregnancy on the fertility of female offspring is limited. To address this gap, we developed a mouse model to investigate the relationship between arsenic exposure during pregnancy and fertility in female offspring. Our fertility assessment revealed that gestational exposure to 1 mg/kg arsenic or higher (10 mg/kg) resulted in reduction in litter size, ovarian volume, and multistage-follicle number in female offspring. By assessing the in vitro developmental capacity of oocytes and zygotes, we confirmed that the reduced fertility was due not to impaired oocyte quality but rather to a reduction in oocyte quantity. Arsenic exposure impedes synapsis formation in MPI and compromises homologous recombination-mediated repair of double-strand breaks, resulting in fewer crossovers. This disruption activates the pachytene-checkpoint, hindering the progression of the MPI and resulting in the elimination of defective oocytes through p-Chk2 activation. Our study reveals for the first time the detrimental effects of arsenic exposure during pregnancy on the fertility of female offspring, underscoring the urgent need to prevent such exposure to safeguard reproductive health.

3-methyl-4-nitrophenol (PNMC), a degradation product of organophosphorus insecticides and a byproduct of fuel combustion, exerting endocrine-disrupting effects. However, its impact on the meiotic process of oocytes remains unclear. In the present study, we investigated the effects of PNMC on meiotic maturation of mouse oocytes in vitro and related mechanisms. Morphologically, PNMC-exposure affected germinal vesicle breakdown (GVBD) and polar body extrusion (PBE) in mouse oocytes. Proteomic analysis suggested that PNMC-exposure altered oocyte protein expression that are associated with cytoskeleton, mitochondrial function and oxidative stress. Further studies demonstrated that PNMC-exposure disrupted spindle assembly and chromosome alignment, caused sustained activation of spindle assembly checkpoint (SAC), and arrested meiosis in oocytes. Specifically, PNMC-exposure interfered with the function of microtubule organizing centers (MTOCs) by significantly reducing phosphorylated mitogen activated protein kinase (p-MAPK) expression and disrupting the localization of Pericentrin and p-Aurora A, leading to spindle assembly failure. Besides, PNMC-exposure also increased α-tubulin acetylation, decreased microtubule stability. Moreover, PNMC-exposure impaired mitochondrial function, evidenced by abnormal mitochondrial distribution, decreased mitochondrial membrane potential and ATP levels, release of Cytochrome C into the cytoplasm, and elevated ROS levels. As a result, exposure to PNMC caused DNA damage and early apoptosis in oocytes. Fortunately, melatonin was able to promote oocyte maturation by removing the excessive ROS and enhancing mitochondrial function. These results highlight the adverse effects of PNMC on meiotic maturation, and underscore the protective role of melatonin against PNMC-induced damage.

Sirtuin 5 (Sirt5), a member of the Sirtuin family, is involved in various intracellular biological processes. However, the function of Sirt5 in oocyte maturation has not been clearly elucidated. In this study, we observed that Sirt5 was persistently expressed during the meiotic division of mouse oocytes, with a notable decline in expression in aging oocytes. Sirt5 inhibition led to the failure of the first polar body extrusion and induced cell cycle arrest, indicative of unsuccessful oocyte maturation. Furthermore, Sirt5 inhibition was associated with the extrusion of abnormally large polar bodies, suggesting disrupted asymmetric oocyte division. Mechanistically, the inhibition of Sirt5 resulted in aberrant spindle assembly and disordered chromosome alignment in oocytes. Moreover, Sirt5 inhibition caused the spindle to be centrally located in the oocyte without migrating to the cortical region, consequently preventing the formation of the actin cap. Further investigation revealed that Sirt5 inhibition notably diminished the expression of phosphorylated cofilin and profilin1, while increasing cytoplasmic F-actin levels. These findings suggest that Sirt5 inhibition during oocyte maturation adversely affects spindle assembly and chromosome alignment and disrupts actin dynamics impairing spindle migration and contributing to the failure of symmetric oocyte division and maturation.

The use of bisphenol A (BPA) has been restricted due to its endocrine-disrupting effects. As a widely used alternative to BPA today, environmental levels of bisphenol Z (BPZ) continue to rise and accumulate in humans. Oocyte quality is critical for a successful pregnancy. Nevertheless, the toxic impacts of BPZ on the maturation of mammalian oocytes remain unexplored. Therefore, the impacts of BPZ and BPA on oocyte meiotic maturation were compared in an in vitro mouse oocyte culture model. Exposure to 150 μM of both BPZ and BPA disrupted the assembly of the meiotic spindle and the alignment of chromosomes, and BPZ exerted stronger toxicological effects than BPA. Furthermore, BPZ resulted in aberrant expression of F-actin, preventing the formation of the actin cap. Mechanistically, BPZ exposure disrupted the mitochondrial localization pattern, reduced mitochondrial membrane potential and ATP content, leading to impaired mitochondrial function. Further studies revealed that BPZ exposure resulted in oxidative stress and altered expression of genes associated with anti-oxidative stress. Moreover, BPZ induced severe DNA damage and triggered early apoptosis in oocytes, accompanied by impaired lysosomal function. Overall, the data in this study suggest that BPZ is not a safe alternative to BPA. BPZ can trigger early apoptosis by affecting mitochondrial function and causing oxidative stress and DNA damage in oocytes. These processes disrupt cytoskeletal assembly, arrest the cell cycle, and ultimately inhibit oocyte meiotic maturation.

Unconventional events exacerbate the imbalance between regional transportation demand and limited road network resources. Scientific and efficient path planning serves as the foundation for rapidly restoring equilibrium to the road network. In real large-scale road networks, especially during emergencies, it is usually difficult to obtain or predict accurate dynamic traffic network flows in real-time, which is used to support equilibrium path planning. Moreover, the traditional iterative methods cannot meet the real-time demand of emergency equilibrium path planning decision generation. To solve the above problems, this paper proposes a hybrid control architecture for path planning based on equilibrium traffic assignment theory. The architecture introduces the travelers’ real-time travel data and constructs a spatio-temporal neural network, which captures the evolution of traffic network loads. Adaptive multi-graph fusion technology is used to mix the background traffic flow data and the traveler’s real-time Origin–Destination (OD) data, to mine the dynamic correlation between the traffic state and the travelers’ travel. Based on the real-time prediction of dynamic network states, equilibrium mapping learning is carried out to pre-allocate potential travel demands and construct equilibrium traffic graphs based on system optimization traffic assignment. Finally, individual evacuation path strategies are generated online in a data-driven manner in real time to achieve improved resilience in the transportation system.

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.

Lactation is a unique physiological process to produce and secrete milk. Deoxynivalenol (DON) exposure during lactation has been demonstrated to affect adversely the growth development of offspring. However, the effects and potential mechanism of DON on maternal mammary glands remain largely unknown. In this study, we found the length and area of mammary glands were significantly reduced after DON exposure on lactation day (LD) 7 and LD 21. RNA-seq analysis results showed that the differentially expressed genes (DEGs) were significantly enriched in acute inflammatory response and HIF-1 signaling pathway, which led to an increase of myeloperoxidase activity and inflammatory cytokines. Furthermore, lactational DON exposure increased blood-milk barrier permeability by reducing the expression of ZO-1 and Occludin, promoted cell apoptosis by upregulating the expression of Bax and cleaved Caspase-3 and downregulating the expression of Bcl-2 and PCNA. Additionally, lactational DON exposure significantly decreased serum concentration of prolactin, estrogen, and progesterone. All these alterations eventually resulted in a decrease of β-casein expression on LD 7 and LD 21. In summary, our findings indicated that lactational exposure to DON caused lactation-related hormone disorder and mammary gland injury induced by inflammatory response and blood-milk barrier integrity impairment, ultimately resulting in lower production of β-casein.

In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties and serious economic losses. Meanwhile, disaster detection based on the Internet of Things(IoT) has become a hot spot that benefited from the established development of smart city construction. And the IoT is visibly sensitive to the management and monitoring of disasters, but massive amounts of monitoring data have brought huge challenges to data storage and data analysis. This article develops a new and much more general framework for disaster emergency management under the IoT environment. The framework is a bottom-up integration of highly scalable Raw Data Storages(RD-Stores) technology, hybrid indexing and queries technology, and machine learning technology for emergency disasters. Experimental results show that hybrid index and query technology have better performance under the condition of supporting multi-modal retrieval, and providing a better solution to offer real-time retrieval for the massive sensor sampling data in the IoT. In addition, further works to evaluate the top-level sub-application system in this framework were performed based on the GPS trajectory data of 35,000 Beijing taxis and the volumetric ground truth data of 7,500 images. The results show that the framework has desirable scalability and higher utility.

This study aimed to address the problem of post-disaster emergency material dispatching from multiple supply points to multiple demand points. In large-scale natural disasters, it is very important for multiple emergency material supply points to serve as sources of materials for multiple disaster sites and to determine emergency material scheduling solutions accurately. Furthermore, the quantity of emergency materials required at each disaster site is uncertain. To address this issue, in this study, we developed an emergency material scheduling model with multiple logistics supply points for multiple demand points based on the grey interval numbers. To optimize the proposed multi-supply-point and multi-demand-point emergency material scheduling mode, a multi-objective optimization algorithm based on a genetic algorithm was used. Experimental results demonstrate that the multi-objective optimization method can solve the emergency logistics scheduling problem better than the particle swarm optimization multi-objective solution algorithm. Additionally, the multi-supply point and multi-demand point emergency material dispatch model and optimization algorithm provides robust support for emergency management system decision-makers when they need to respond quickly to disaster relief activities.

Emergency supplies scheduling needs to consider the state of the demanders, and reasonably scheduling and resource allocation are the heart of efficient rescue. Taking rescue time, scheduling cost and demanders’ satisfaction as goals, in this paper, an emergency supplies scheduling model based on multi-objective optimization was proposed to provide a wealth of decision-making information. Then four multi-objective optimization algorithms are employed to obtain the optimal set of scheduling models. In addition, we design the minimum time cost model and the shortest route cost model by considering the change of the road network status. The extensive simulation experiments are conducted on a real urban traffic dataset. The experimental results show that the two cost models can serve different scheduling needs and provide efficient scheduling for emergency supplies.

With the widespread use of the location-based social networks (LBSNs), the next point-of-interest (POI) recommendation has become an essential service, which aims to understand the user’s check-in behavior at the current moment by analyzing and mining the correlations between the user’s check-in behaviors within his/her historical trajectory and then recommending the POI that the user is most likely to visit at the next time step. However, the user’s check-in trajectory presents extremely irregular sequential patterns, such as spatial–temporal patterns, semantic patterns, etc. Intuitively, the user’s visiting behavior is often accompanied by a certain purpose, which makes the check-in data in LBSNs often have rich semantic activity characteristics. However, existing research mainly focuses on exploring the spatial–temporal sequential patterns and lacks the mining of semantic information within the trajectory, so it is difficult to capture the user’s visiting intention. In this paper, we propose a self-attention- and multi-task-based method, called MSAN, to explore spatial–temporal and semantic sequential patterns simultaneously. Specifically, the MSAN proposes to mine the user’s visiting intention from his/her semantic sequence and uses the user’s visiting intention prediction task as the auxiliary task of the next POI recommendation task. The user’s visiting intention prediction uses hierarchical POI category attributes to describe the user’s visiting intention and designs a hierarchical semantic encoder (HSE) to encode the hierarchical intention features. Moreover, a self-attention-based hierarchical intention-aware module (HIAM) is proposed to mine temporal and hierarchical intention features. The next POI recommendation uses the self-attention-based spatial–temporal-aware module (STAM) to mine the spatial–temporal sequential patterns within the user’s check-in trajectory and fuses this with the hierarchical intention patterns to generate the next POI list. Experiments based on two real datasets verified the effectiveness of the model.

Deoxynivalenol (DON) is one of the most common feed contaminants, and it poses a serious threat to the health of dairy cows. The existing studies of biological toxicity of DON mainly focus on the proliferation, oxidative stress, and inflammation in bovine mammary epithelial cells, while its toxicity on the biosynthesis of milk components has not been well documented. Hence, we investigated the toxic effects and the underlying mechanism of DON on the bovine mammary alveolar cells (MAC-T). Our results showed that exposure to various concentrations of DON significantly inhibited cell proliferation, induced apoptosis, and altered the cell morphology which was manifested by cell distortion and shrinkage. Moreover, the transepithelial electrical resistance (TEER) values of MAC-T cells exposed to DON were gradually decreased in a time- and concentration- dependent manner, but lactate dehydrogenase (LDH) leakage was significantly increased with the maximum increase of 2.4-fold, indicating the cell membrane and tight junctions were damaged by DON. Importantly, DON significantly reduced the synthesis of β-casein and lipid droplets, along with the significantly decreases of phospho-mTOR, phospho-4EBP1, phospho-JAK2, and phospho-STAT5. Gene expression profiles showed that the expressions of several genes related to lipid synthesis and metabolism were changed, including acyl-CoA synthetase short-chain family member 2 (ACSS2), fatty acid binding protein 3 (FABP3), 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1), and insulin-induced gene 1 (INSIG1). GO and KEGG enrichment analyses revealed that the differentially expressed genes (DEGs) were significantly enriched in ribosome, glutathione metabolism, and lipid biosynthetic process, which play important roles in the toxicological process induced by DON. Taken together, DON affects the proliferation and functional differentiation of MAC-T cells, which might be related to the cell junction disruption and morphological alteration. Our data provide new insights into functional differentiation and transcriptomic alterations of MAC-T cells after DON exposure, which contributes to a comprehensive understanding of DON-induced toxicity mechanism.

The optimal path planning is one of the hot spots in the research of intelligence transportation and geographic information systems. There are many productions and applications in path planning and navigation, however due to the complexity of urban road networks, the difficulty of the traffic prediction increases. The optimal path means not only the shortest distance in geography, but also the shortest time, the lowest cost, the maximum road capacity, etc. In fast-paced modern cities, people tend to reach the destination with the shortest time. The corresponding paths are considered as the optimal paths. However, due to the high data sensing speed of GPS devices, it is different to collect or describe real traffic flows. To address this problem, we propose an innovative path planning method in this paper. Specially, we first introduce a crossroad link analysis algorithm to calculate the real-time traffic conditions of crossroads (i.e. the CrossRank values). Then, we adopt a CrossRank value based A-Star for the path planning by considering the real-time traffic conditions. To avoid the high volume update of CrossRank values, a R-Tree structure is proposed to dynamically update local CrossRank values from the multi-level subareas. In the optimization process, to achieve desired navigation results, we establish the traffic congestion coefficient to reflect different traffic congestion conditions. To verify the effectiveness of the proposed method, we use the actual traffic data of Beijing. The experimental results show that our method is able to generate the appropriate path plan in the peak and low dynamic traffic conditions as compared to online applications.

Wireless sensor networks have played an important role in many applications, where sensors in the network can automatically collect and transmit information in an environment without much human maintenance. To enhance the adaptability of sensors, people produce mobile wireless sensors through fixing wireless sensors on mobile vehicles or robots. The same as static wireless sensors, since the energy of mobile wireless sensors is still supplied by the battery, their energy management has a great impact on their lifetime. Different with static wireless sensors, the mobility of mobile wireless sensors gives us a new perspective to achieve the efficient energy management. In this paper, a decentralized self-adaptive approach is proposed for mobile wireless sensors, which enables sensors in a mobile wireless sensor network to adapt their locations according to the information transmission regularity in the network so as to reduce their energy consumption for the information transmission. The experimental results indicate that after employed the proposed approach to adapt locations, the energy consumption for the information transmission of sensors in a mobile wireless sensor network can be greatly reduced and their lifetime is extended.

When an emergency occurs in the city, a large area of road congestion usually occurs. Therefore, it is particularly important to provide an effective emergency path planning strategy for vehicles. However, existing emergency path planning methods do not take into account the connectivity characteristics of the road network and commuting capacity. For this purpose, a Grid Map Emergency Path Planning (GMEPP) framework based on a novel model Grid Road Network (GRN) is designed in this paper. First, the road network data is divided into grids under equal spacing bands, and the roads data divided into different grids and use the commuting capacity of each road as the weight of each edge in the grid. Then a Grid PageRank (GPR) algorithm will be introduced, the output value of this methodology is calculated based on the capacity and number of connected edges of all vertices pointed by the external grid in each grid. The higher value of the grid will be recommended to users first when the path is planning. According to the GRN model, an improved Bidirectional Dijkstra will be applied to query the shortest path between two points, which is called Gird Bidirectional Dijkstra (GBD). At last, GMEPP uses Reverse Contraction Hierarchies (RCH) and Multiple Reverse Contraction Hierarchies (MRCH) originality methodologies based on intersection type to speed up the query algorithm GBD. To compare the efficiency of the proposed method, this paper conducted extensive experiments to verify. The results of the test showed that the Gird Bidirectional Dijkstra Multiple Reverse Contraction Hierarchies (GBD-MRCH) is better than other methods in different grid distributions.

Urban rail transit is an essential part of the urban public transportation system. The reasonable spatial data visualization of urban rail transit stations can provide a more intuitive way for the majority of travelers to arrange travel plans and find destinations. The map service of rail transit stations generated by data visualization has gradually become indispensable information guidance in the rail transit system. The existing map service icons block each other when the scale changes, and new stations cannot be displayed dynamically when users drag the map. This paper uses filtering and sorting methods to dynamically query and visualize the relatively more important transportation stations within the users’ visible range, so as to solve the above problems and provide people with better transportation services. Our method introduces three constraints: spatial diversity, time-sharing passenger flow analysis and whether it is a transit station, and calculates the scores of constraint relationships of feature objects to evaluate stations. On the basis of the skyline query, the scores of feature objects are combined and sorted to obtain an ordered object set of the most interesting k points(top-k POIs), and the rail transit stations are dynamically retrieved and visualized. Before sorting POIs, we filter out POIs that need to be fitted, so that only the k most representative POIs in the currently visible range are displayed. When the map scale changes, the displayed POIs are updated. Finally, through the statistics of efficiency calculation of this method under different scales and centers, combined with users’ evaluations, it was proved that our method could better display critical information and improve user experience.

Many events such as large-scale activities and traffic accidents could cause an increase in vehicle density in an area, which makes the evacuation of vehicles important. However, the existing evacuation methods are not efficient limit to multi-vehicles sequences or destinations. In this paper, we introduce a novel dynamic weighted road network model for route planning. Based on the model, the route planning algorithm can obtain higher search efficiency while avoiding congested roads. For multi-vehicles evacuation, we propose a spatial diversity theory to evaluate the overlaps of routes between vehicles to be evacuated and those already evacuated. To verify the efficiency and effectiveness of our model, we conducted experiments on real road network. The results showed that our methods and algorithms can provide more reasonable paths and manage the process more efficiently.

Taxi travel time estimation based on real-time traffic flow collection in IoT has been well explored; however, it becomes a challenge to use the limited taxi data to estimate the travel time. Most of the existing methods in this scenario rely on shallow feature engineering. Nevertheless, they have limited performance in learning complex moving patterns. Thus, a Latent Semantic Pulse Sequence-based Deep Neural Network (LSPS-DNN) is proposed in this paper to improve the taxi travel time estimation performance by constructing a latent semantic propagation graph representing the latent path sequence. It first extracts the shallow modal features of trips, such as the time period and spatial location at different granularities. The representation of the pulse propagation graph is then extracted from shallow spatial features using a Pulse Coupled Neural Network (PCNN). Further, the propagation graph is encoded with negative sampling to obtain the embedding of deep propagation features between ODs. Meanwhile, we conduct deep network learning based on the Chengdu and NYC taxi datasets; our experimental evaluation results show it has a better performance compared to traditional feature construction methods.

The aim of this study was to investigate the d -dimer for prediction of venous thromboembolism (VTE) events in puerperium and to identify other risk factors associated with the occurrence of VTE. This was a prospective observational cohort study, which included 16 127 women who gave birth after 28 weeks of gestation at Women’s Hospital of Zhejiang University, School of Medicine, from January 2016 to December 2016. Data including basic maternal and fetal characteristics, pregnancy complications, and predictive biomarkers within postpartum 24 hours including d -dimer, platelet, and fibrinogen were collected for analyses. In the cohort study, 19 (0.12%) women were identified as VTE, including 1 pulmonary embolism event and 18 deep venous thrombosis events. The receiver operating characteristic curve analysis suggested the best cutoff point for d -dimer level within postpartum 24 hours was 3.695 mg/L, with a specificity of 75.5% and a sensitivity of 73.7%, and there was no statistical correlation between fibrinogen and VTE, as well as between platelets and VTE. On multivariate analysis, age ≥35 years (odds ratio [OR] = 2.895, 95% confidence interval [CI]: 1.079-7.773), scarred uterus (OR = 3.894, 95% CI: 1.234-12.282), intrauterine infection (OR = 7.214, 95% CI: 1.519-34.262), antiphospholipid syndrome (OR = 199.530, 95% CI: 15.152-2627.529), d -dimer ≥3.70 mg/L (OR = 7.573, 95% CI: 2.699-21.247), and emergency cesarean delivery (OR = 23.357, 95% CI: 2.819-193.508) were independently associated with VTE in puerperium. We concluded that d -dimer ≥3.70 mg/L was an independent predictor of VTE during puerperium and d -dimer testing was necessary for perinatal women.

In order to improve the effect of path planning in emergencies, the missing position imputation and velocity restoration in vehicle trajectory provide data support for emergency path planning and analysis. At present, there are many methods to fill in the missing trajectory information, but they basically restore the missing trajectory after analyzing a large number of datasets. However, the trajectory reduction method with few training sets needs to be further explored. For this purpose, a novel trajectory data cube model (TDC) is designed to store time, position, and velocity information hierarchically in the trajectory data. Based on this model, three trajectory Hierarchical Trace-Back algorithms HTB-p, HTB-v, and HTB-KF are proposed in this paper. Finally, experiments verify that conduct in a different number of sample sets, it has a satisfactory performance on information restoration of individual points of the trajectory and information restoration of trajectory segments.

In recent years, with the development of various types of public transportation, they are also more and more closely connected. Among them, subway transportation has become the first choice of major cities. However, the planning of subway stations is very difficult and there are many factors to consider. Besides, few methods for selecting optimal station locations take other public transport in to consideration. In order to study the relationship between different types of public transportation, the authors collected and analyzed the travel data of subway passengers and the passenger trajectory data of taxis. In this paper, a method based on LeaderRank and Gaussian Mixed Model (GMM) is proposed to conduct subway station locations selection. In this method, the author builds a subway-passenger traffic zone weighted network and a station location prediction model. First, we evaluate the nodes in the network, then use the GPS track data of taxis to predict the location of new stations in future subway construction, and analyze and discuss the land use characteristics in the prediction area. Taking the design of the Beijing subway line as an example, the suitability of this method is illustrated.

Abstract Background Noninvasive biomarkers are urgently needed for patients with nonalcoholic steatohepatitis (NASH) to assist in diagnosis, monitoring disease progression and assessing treatment response. Recently several exploratory studies showed that circulating level of microRNA is associated with NASH and correlated with disease severity. Although these data were encouraging, the application of circulating microRNA as biomarkers for patient screening and stratification need to be further assessed under well-controlled conditions. Results The expression of circulating microRNAs were profiled in diet-induced NASH progression and regression models to assess the diagnostic and prognostic values and the translatability between preclinical mouse model and men. Since these mice had same genetic background and were housed in the same conditions, there were minimal confounding factors. Histopathological lesions were analyzed at distinct disease progression stages along with microRNA measurement which allows longitudinal assessment of microRNA as NASH biomarkers. Next, differentially expressed microRNAs were identified and validated in an independent cohorts of animals. Thirdly, these microRNAs were examined in a NASH regression model to assess whether they would respond to NASH treatment. MicroRNA profiling in two independent cohorts of animals validated the up-regulation of 6 microRNAs (miR-122, miR-192, miR-21, miR-29a, miR-34a and miR-505) in NASH mice, which was designated as the circulating microRNA signature for NASH. The microRNA signature could accurately distinguish NASH mice from lean mice, and it responded to chow diet treatment in a NASH regression model. To further improve the performance of microRNA-based biomarker, a new composite biomarker was proposed, which consists of miR-192, miR-21, miR-505 and ALT. The new composite biomarker outperformed the microRNA signature in predicting NASH mice which had NAS > 3, and deserves further validations in large scale studies. Conclusion The present study supported the translation of circulating microRNAs between preclinical models and humans in NASH pathogenesis and progression. The microRNA-based composite biomarker may be used for non-invasive diagnosis, clinical monitoring and assessing treatment response for NASH.

In the context of the carbon neutrality target, carbon reduction in the daily operation of the transportation system is more important than that in productive activities. There are few travel services that can quantify low-carbon travel, with a lack of effective low-carbon travel tools to guide transportation behavior. On-demand access to taxi services can effectively reduce the additional carbon emissions caused by cruising, which in turn increases efficiency in urban mobility with a reduced taxi fleet scale. For individual taxis, they lack macroscopic horizon in their choice of passenger pickup paths. The selected travel path based on personal operational experience or real-time location is limited by local optimization when making path decisions. In this work, we proposed a macro-path recommendation method to assist the taxi pickup path selection to accelerate the transformation of the taxi system towards low-carbon sharing. First, an adaptive learning spatiotemporal neural network was used to predict the coarse-grained distribution of potential trips. Next, the trajectory sharing graph was constructed based on the potential trips distribution to reallocate the taxi orders for the continuous pickup path optimization. As a result, the continuous pickup path balanced the relation between travel demands and taxi supply, improving the economic and environmental benefits of taxi operation and contributing to the goal of carbon neutrality. We conducted experiments on the Chengdu city ride-hailing dataset. Compared with the current status of taxi operations, the solution shows improvements in both the scale of taxi services and order gain.

The urban rail transit stations are an important part of an urban transit system. Scientific and reasonable location of rail transit station can greatly alleviate traffic pressure. The number of people in the surrounding area of a rail transit station is an important factor for site selection. However, it is difficult to obtain the spatial distribution of population, which brings great difficulties in terms of site selection. Due to the large-scale popularization of AP (Access Point) in China, the spatial distribution of AP is used instead of population distribution to assist site selection. Therefore, a density visualization method based on a dynamic grid is proposed, which can help decision-makers intuitively see the AP density of the uncovered grid of rail transit stations, and then cluster the AP density of the uncovered area to predict the location of new rail transit stations. The validity of the proposed method is demonstrated by using the AP dataset and rail transit data of Beijing in 2013. The results show that our method has high accuracy in predicting the location of rail transit stations. It can provide data support for urban traffic development and management.

Emergency path planning technology is one of the research hotspots of intelligent transportation systems. Due to the complexity of urban road networks and congested road conditions, emergency path planning is very difficult. Road congestion caused by urban emergencies directly affects the original road network structure. In this way, the static weight of the original road network is no longer suitable as the basis for path recommendation. To handle the dynamic situational road network, an equidistant grid emergency path planning framework will be designed. A novel situation grid road network model, based on situation information, is proposed and applied to an equidistant grid emergency path planning framework. A situational grid heuristic search will be proposed methodology based on this model, which can be used to detect the vehicles passing around the congestion area grid and the road to the destination in the shortest time. In the path planning methodology, a grid inspired search strategy based on quaternion function is included, which can make the algorithm converge to the target grid quickly. Three graph acceleration algorithms are proposed to improve the search efficiency of path planning algorithm. Finally, this paper will set up three experiments to verify our proposed method.