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PurposeMagnetic resonance spectroscopy of hyperpolarized substrates allows for the observation of label exchange catalyzed by enzymes providing a powerful tool to investigate tissue metabolism and potentially kinetics in vivo. However, the accuracy of current methods to calculate kinetic parameters has been limited by T1 relaxation effects, extracellular signal contributions, and reduced precision at lower signal-to-noise ratio.Theory and methodsTo address these challenges, we investigated a new modeling technique using metabolic activity decomposition-stimulated echo acquisition mode. The metabolic activity decomposition-stimulated echo acquisition mode technique separates exchanging from nonexchanging metabolites providing twice the information as conventional techniques.ResultsThis allowed for accurate measurements of rates of conversion and of multiple T1 values simultaneously using a single acquisition.ConclusionThe additional measurement of T1 values for the reaction metabolites provides further biological information about the cellular environment of the metabolites. The new technique was investigated through simulations and in vivo studies of transgenic mouse models of cancer demonstrating improved assessments of kinetic rate constants and new T1 relaxation value measurements for hyperpolarized (13) C-pyruvate, (13) C-lactate, and (13) C-alanine.

PurposeTo implement and evaluate combined parallel magnetic resonance imaging (MRI) and partial Fourier acquisition and reconstruction for rapid hyperpolarized carbon-13 ((13) C) spectroscopic imaging. Short acquisition times mitigate hyperpolarized signal losses that occur due to T1 decay, metabolism, and radiofrequency (RF) saturation. Human applications additionally require rapid imaging to permit breath-holding and to minimize the effects of physiologic motion.Materials and methodsNumerical simulations were employed to validate and characterize the reconstruction. In vivo MR spectroscopic images were obtained from a rat following injection of hyperpolarized (13) C pyruvate using an 8-channel array of carbon-tuned receive elements.ResultsFor small spectroscopic matrix sizes, combined parallel imaging and partial Fourier undersampling resulted primarily in decreased spatial resolution, with relatively less visible spatial aliasing. Parallel reconstruction qualitatively restored lost image detail, although some pixel spectra had persistent numerical error. With this technique, a 30 × 10 × 16 matrix of 4800 3D MR spectroscopy imaging voxels from a whole rat with isotropic 8 mm(3) resolution was acquired within 11 seconds.ConclusionParallel MRI and partial Fourier acquisitions can provide the shorter imaging times and wider spatial coverage that will be necessary as hyperpolarized (13) C techniques move toward human clinical applications.

High resolution compressed sensing hyperpolarized (13)C magnetic resonance spectroscopic imaging was applied in orthotopic human glioblastoma xenografts for quantitative assessment of spatial variations in (13)C metabolic profiles and comparison with histopathology. A new compressed sensing sampling design with a factor of 3.72 acceleration was implemented to enable a factor of 4 increase in spatial resolution. Compressed sensing 3D (13)C magnetic resonance spectroscopic imaging data were acquired from a phantom and 10 tumor-bearing rats following injection of hyperpolarized [1-(13)C]-pyruvate using a 3T scanner. The (13)C metabolic profiles were compared with hematoxylin and eosin staining and carbonic anhydrase 9 staining. The high-resolution compressed sensing (13)C magnetic resonance spectroscopic imaging data enabled the differentiation of distinct (13)C metabolite patterns within abnormal tissues with high specificity in similar scan times compared to the fully sampled method. The results from pathology confirmed the different characteristics of (13)C metabolic profiles between viable, non-necrotic, nonhypoxic tumor, and necrotic, hypoxic tissue.

The development of hyperpolarized technology utilizing dynamic nuclear polarization (DNP) has enabled the rapid measurement of (13)C metabolism in vivo with very high SNR. However, with traditional DNP equipment, consecutive injections of a hyperpolarized compound in an animal have been subject to a practical minimum time between injections governed by the polarization build-up time, which is on the order of an hour for [1-(13)C]pyruvate. This has precluded the monitoring of metabolic changes occurring on a faster time scale. In this study, we demonstrated the ability to acquire in vivo dynamic magnetic resonance spectroscopy (MRS) and 3D magnetic resonance spectroscopic imaging (MRSI) data in normal rats with a 5 min interval between injections of hyperpolarized [1-(13)C]pyruvate using a prototype, sub-Kelvin dynamic nuclear polarizer with the capability to simultaneously polarize up to 4 samples and dissolve them in rapid succession. There were minimal perturbations in the hyperpolarized spectra as a result of the multiple injections, suggesting that such an approach would not confound the investigation of metabolism occurring on this time scale. As an initial demonstration of the application of this technology and approach for monitoring rapid changes in metabolism as a result of a physiological intervention, we investigated the pharmacodynamics of the anti-cancer agent dichloroacetate (DCA), collecting hyperpolarized data before administration of DCA, 1 min after administration, and 6 min after administration. Dramatic increases in (13)C-bicarbonate were detected just 1 min (as well as 6 min) after DCA administration.

Development of hyperpolarized technology utilizing dynamic nuclear polarization has enabled the measurement of (13)C metabolism in vivo at very high signal-to-noise ratio (SNR). In vivo mitochondrial metabolism can, in principle, be monitored with pyruvate, which is catalyzed to acetyl-CoA via pyruvate dehydrogenase (PDH). The purpose of this work was to determine whether the compound sodium dichloroacetate (DCA) could aid the study of mitochondrial metabolism with hyperpolarized pyruvate. DCA stimulates PDH by inhibiting its inhibitor, pyruvate dehydrogenase kinase. In this work, hyperpolarized [1-(13)C]pyruvate and [2-(13)C]pyruvate were used to probe mitochondrial metabolism in normal rats. Increased conversion to bicarbonate (+181±69%, P=.025) was measured when [1-(13)C]pyruvate was injected after DCA administration, and increased glutamate (+74±23%, P=.004), acetoacetate (+504±281%, P=.009) and acetylcarnitine (+377±157%, P=.003) were detected when [2-(13)C]pyruvate was used.

The use of in vivo 13C nuclear magnetic resonance spectroscopy in probing metabolic pathways to study normal metabolism and characterize disease physiology has been limited by its low sensitivity. However, recent technological advances have enabled greater than 50,000-fold enhancement of liquid-state polarization of metabolically active 13C substrates, allowing for rapid assessment of 13C metabolism in vivo. The present study applied hyperpolarized 13C magnetic resonance spectroscopy to the investigation of liver metabolism, demonstrating for the first time the feasibility of applying this technology to detect differences in liver metabolic states.[1-13C]pyruvate was hyperpolarized with a dynamic nuclear polarization instrument and injected into normal and fasted rats. The uptake of pyruvate and its conversion to the metabolic products lactate and alanine were observed with slice-localized dynamic magnetic resonance spectroscopy and 3D magnetic resonance spectroscopic imaging (3D-MRSI).Significant differences in lactate to alanine ratio (P

The emerging ride-sourcing service has become an important element of urban mobility. A challenging question underlying the provision of such service is how and to what extent the built environment affects origin-destination (OD) travel flows. This paper employs the geographically weighted regression (GWR) model to analyze the OD-based ride-sourcing travel flow. It makes a comparison with the existing ordinary least square (OLS) model and spatial autocorrelation model (SAM). We have collected ride-sourcing order data in Hangzhou, China, to provide an accurate source for acquiring ride-sourcing travel flow. We investigate the effects of the residential area, points of interest (POIs), and transit stations on ride-sourcing travel flow among traffic analysis zones (TAZs). The results show the following: (a) GWR has better goodness-of-fit than SAM and OLS. (b) Residential area, enterprise, and bus stations have positive correlations with ride-sourcing OD flows, but education and subway stations have negative correlations. We have further investigated the issue and found that it is not a causal relationship between the bus station and OD flow, due to collinearity between the two variables. The bus station builds on locations with high demand, but its capacity is not large enough to reduce the ride-sourcing flow to a low level, which results in a positive coefficient. (c) Based on the estimated coefficients, the prediction of ride-sourcing flows is feasible, supporting the impact analysis for urban land use and transportation planning. This paper contributes to understanding OD-based ride-sourcing travel flow distributions and provides a framework of long-term OD flow prediction for urban land use and transportation planning., Author(s): Chuqiao Chen [1]; Simon Hu [2]; Washington Y. Ochieng [3]; Na Xie [4]; Xiqun (Michael) Chen (corresponding author) [1] 1. Introduction The government agencies and urban planning department attach [...]

Real-time road quality monitoring, involves using technologies to collect data on the conditions of the road, including information on potholes, cracks, and other defects. This information can help to improve safety for drivers and reduce costs associated with road damage. Traditional methods are time-consuming and expensive, leading to limited spatial coverage and delayed responses to road conditions. With the widespread use of smartphones and ubiquitous computing technologies, data can be collected from built-in sensors of mobile phones and in-vehicle video, on a large scale. This has raised the question of how these data can be used for road pothole detection and has significant practical relevance. Current methods either use acceleration sequence classification techniques, or image recognition techniques based on deep learning. However, accelerometer-based detection has limited coverage and is sensitive to the driving speed, while image recognition-based detection is highly affected by ambient light. To address these issues, this study proposes a method that utilizes the fusion of accelerometer data and in-vehicle video data, which is uploaded by the participating users. The preprocessed accelerometer data and intercepted video frames, were then encoded into real-valued vectors, and projected into the public space. A deep learning-based training approach was used to learn from the public space and identify road anomalies. Spatial density-based clustering was implemented in a multi-vehicle scenario, to improve reliability and optimize detection results. The performance of the model is evaluated with confusion matrix-based classification metrics. Real-world vehicle experiments are carried out, and the results demonstrate that the proposed method can improve accuracy by 6% compared to the traditional method. Consequently, the proposed method provides a novel approach for large-scale pavement anomaly detection. [ABSTRACT FROM AUTHOR]

The dynamic spatiotemporal characteristics of queues at urban intersections are crucial to traffic operation tasks such as signal performance measure and signal optimization. This paper addresses the high time-resolution estimation of queue profile at urban signalized intersection using Extended Kalman Filtering (EKF) with data of connected vehicles (CVs). The main features of this work are as follows: (i) a machine learning method was applied to construct a dynamic shockwave propagation model based on shockwave theory and historical data of CVs; (ii) a heuristic approach was proposed to measure the shockwave speed for use in EKF; (iii) an urban queue estimator was designed to combine the dynamic shockwave propagation model and real-time shockwave information via EKF to deliver second-by-second queue profile estimates. The queue estimator does not require any priori information about vehicle arrival patterns and the market penetration rate (MPR) of CVs. The performance and robustness of the queue estimator were evaluated using both simulation and real-world CV data. The results show that the method can provide satisfactory queue estimation results at various MPR levels of CVs, with the estimation error of 2.5 vehicles at the MPR of 5%, and of 0.5 vehicle at the MPR of 40%. [ABSTRACT FROM AUTHOR]

This article develops a new platoon control strategy for heterogeneous connected vehicles (CVs) subject to time delays and external disturbances. Specifically, based on the third-order vehicle model, a novel platoon controller is developed by embedding the variable time headway (VTH) spacing policy and the nonlinear motion coupling interactions between CVs. Simultaneously, an integral sliding mode (ISM) controller is developed to resist the disturbances. Then, the condition of asymptotic stability for the CV platoon and the upper bound of communication delay are deduced by using the Lyapunov theorem. Also, the string stability is proved by using the infinity-norm method. Finally, extensive simulations and co-simulations are provided to show the validity of the developed controller. Moreover, experiments with intelligent micro vehicles are conducted further to validate the practical feasibility of the developed controller. [ABSTRACT FROM AUTHOR]

This paper proposes a new car-following (CF) model to capture the realistic behaviors of connected and automated vehicles (CAVs), whose communication topology (CT) among vehicles is characterized by graph theory in the V2V communication environment. By considering the heterogeneous time delays under the fixed and switching CTs, a generalized CF model is proposed. Based on the Lyapunov–Krasovskii method, a convergence analysis has been implemented for this new CF model with multiple time delays to obtain the convergence condition. Meanwhile, provides an estimate of the time delay bound. Finally, numerical experiments are performed under three typical fixed CTs (i.e., PF topology, BDLF topology, and TPLF topology) and the corresponding switching topology. Results support that the proposed CF model is capable of accurately reproducing the velocity, acceleration, position, and space headway profiles of CAVs traffic flow. [ABSTRACT FROM AUTHOR]

Reactionary delays that propagate from a primary source throughout train journeys are an immediate concern for British railway systems. Complex non-linear interactions between various spatiotemporal variables govern the propagation of these delays which can avalanche throughout railway network causing further severe disruptions. This paper introduces several machine learning (ML) techniques alongside data mining processes to create a framework that predicts key performance indicators (KPIs), reactionary arrival delay, reactionary departure delay, dwell time and travel time. The frameworks in this paper provide greater accuracy in predicting KPIs through state-of-the-art ML models compared to existing delay prediction systems. Further discussion on the improvements, applicability and scalability of this framework are also provided in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a longitudinal platoon controller for connected vehicles (CVs) by considering the information of multiple preceding vehicles and the car-following interactions between CVs. The stability of the proposed controller is analyzed using the Routh criterion. For the verification, we develop an integrated platoon control framework for CVs in a V2V/V2I communication environment. The proposed framework consists of two main components: simulation platform and experimental platform. In particular, the simulation platform is developed based on the TransModeler software, and the experimental platform is designed using the self-developed V2X devices. Finally, a scenario of platoon forming is taken as an example and is conducted in simulation platform and experimental platform, respectively. Results demonstrate the effectiveness of the proposed controller with respect to the trajectory and velocity profiles. [ABSTRACT FROM AUTHOR]