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Reliable and precise absolute positioning is necessary in the realm of Connected Automated Vehicles (CAV). Global Navigation Satellite Systems (GNSS) provides the foundation for absolute positioning. Recently enhanced Precise Point Positioning (PPP) technology now offers corrections for GNSS on a global scale, with the potential to achieve accuracy suitable for real-time CAV applications. However, in obstructed sky conditions, GNSS signals are often affected by outliers; therefore, addressing outliers is crucial. In GNSS applications, there are many more measurements available than are required to meet the specification. Therefore, selecting measurements to avoid outliers is of interest. The recently developed Risk-Averse Performance-Specified (RAPS) state estimation optimally selects measurements to minimize outlier risk while meeting a positive semi-definite constraint on performance; at present, the existing solution methods are not suitable for real-time computation and have not been demonstrated using challenging real-world data or in Real-time PPP (RT-PPP) applications. This article makes contributions in a few directions. First, it uses a diagonal performance specification, which reduces computational costs relative to the positive semi-definite constraint. Second, this article considers GNSS RT-PPP applications. Third, the experiments use real-world GNSS data collected in challenging environments. The RT-PPP experimental results show that among the compared methods: all achieve comparable performance in open-sky conditions, and all exceed the Society of Automotive Engineers (SAE) specification; however, in challenging environments, the diagonal RAPS approach shows improvement of 6-19% over traditional methods. Throughout, RAPS achieves the lowest estimation risk., Comment: 7 pages,2 figures, Accepted by 2024 American Control Conference

Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV’s) is near at hand due to advances in sensor and computing technology. These capabilities in conjunction with high-definition maps enable lane determination, per lane queue determination, and enhanced performance in applications. This project investigated, analyzed, and demonstrated these related technologies. Project contributions include: (1) Experimental analysis demonstrating that the USDOT Mapping tool achieves internal horizontal accuracy better than 0.2 meters (standard deviation); (2) Theoretical analysis of lane determination accuracy as a function of both distance from the lane centerline and positioning accuracy; (3) Experimental demonstration and analysis of lane determination along the Riverside Innovation Corridor showing that for a vehicle driven within 0.9 meters of the lane centerline, the correct lane is determined for over 90% of the samples; (4) Development of a VISSIM position error module to enable simulation analysis of lane determination and lane queue estimation as a function of positioning error; (5) Development of a lane-level intersection queue prediction algorithm; Simulation evaluation of lane determination accuracy which matched the theoretical analysis; and (6) Simulation evaluation of lane queue prediction accuracy as a function of both CAV penetration rate and positioning accuracy. Conclusions of the simulation analysis in item (6) are the following: First, when the penetration rate is fixed, higher queue length estimation error occurs as the position error increases. However, the disparity across different position error levels diminishes with the decrease of penetration rate. Second, as the penetration rate decreases, the queue length estimation error significantly increases under the same GNSS error level. The current methods that exist for queue length prediction only utilize vehicle position and a penetration rate estimate. These res

Differential Global Navigation Systems (DGNSS) has been demonstrated to provide reliable, high-quality range correction information enabling real-time navigation with centimeter to sub-meter accuracy, which is required for applications such as connected and autonomous vehicles. However, DGNSS requires a local reference station near each user. For a continental or global scale implementation, this information dissemination approach would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning (PPP) affords more flexibility as a public service for GNSS receivers, but its State Space Representation (SSR) format is not supported by most receivers in the field or on the market.This article proposes a novel Virtual Network (VN)DGNSS approach and an optimization algorithm that is key to its implementation. The approach capitalizes on the existing PPP infrastructure without the need for new physical reference stations. Specifically, no reference station is needed in the local vicinity of any user. By connecting to public GNSS SSR data services, a VNDGNSS server maintains current information about satellite code bias, satellite orbit and clock, and atmospheric models. Construction of the RTCM Observation Space Representation (OSR) messages from this SSR information requires both the signal time-of-transmission and the satellite position at that time which are consistent with the time-of-reception for each client. This article presents an algorithm to determine these quantities. Then the VNDGNSS computes and transmits RTCM OSR messages to user receivers. This approach achieves global dissemination coverage for real-time navigation without the need for additional local base stations near each user.The results of real-time stationary and moving platform evaluations are included, using u-blox M8P and ZED-F9P receivers. The performance surpasses the Society of Automotive Engineering (SAE) specifi

Differential Global Navigation Systems (DGNSS) has been demonstrated to provide reliable, high-quality range correction information enabling real-time navigation with centimeter to sub-meter accuracy, which is required for applications such as connected and autonomous vehicles. However, DGNSS requires a local reference station near each user. For a continental or global scale implementation, this information dissemination approach would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning (PPP) affords more flexibility as a public service for GNSS receivers, but its State Space Representation (SSR) format is not supported by most receivers in the field or on the market.This article proposes a novel Virtual Network (VN)DGNSS approach and an optimization algorithm that is key to its implementation. The approach capitalizes on the existing PPP infrastructure without the need for new physical reference stations. Specifically, no reference station is needed in the local vicinity of any user. By connecting to public GNSS SSR data services, a VNDGNSS server maintains current information about satellite code bias, satellite orbit and clock, and atmospheric models. Construction of the RTCM Observation Space Representation (OSR) messages from this SSR information requires both the signal time-of-transmission and the satellite position at that time which are consistent with the time-of-reception for each client. This article presents an algorithm to determine these quantities. Then the VNDGNSS computes and transmits RTCM OSR messages to user receivers. This approach achieves global dissemination coverage for real-time navigation without the need for additional local base stations near each user.The results of real-time stationary and moving platform evaluations are included, using u-blox M8P and ZED-F9P receivers. The performance surpasses the Society of Automotive Engineering (SAE) specifi

High-definition (Hi-Def) digital maps are an indispensable automated driving technology that is developing rapidly. There are various commercial or governmental map products in the market. It is notable that the U.S. Department of Transportation (USDOT) map tool allows the user to create MAP and Signal Phase and Timing (SPaT) messages with free access. However, an analysis of the accuracy of this map tool is currently lacking in the literature. This paper provides such an analysis. The analysis manually selects 39 feature points within about 200 meters of the verified point and 55 feature points over longer distances from the verified point. All feature locations are surveyed using GNSS and mapped using the USDOT tool. Different error sources are evaluated to allow assessment of the USDOT map accuracy. In this investigation, The USDOT map tool is demonstrated to achieve 17 centimeters horizontal accuracy, which meets the lane-level map requirement. The maximum horizontal map error is less than 30 centimeters., Comment: 6 pages, 6 figures

Ying Jin, Shuang Wei, Ting-Ting Liu, Chun-Yu Qiu, Wang-Ping Hu Research Center of Basic Medical Sciences, School of Basic Medical Sciences, Hubei University of Science and Technology, Xianning, Hubei, 437100, People’s Republic of ChinaCorrespondence: Wang-Ping HuResearch Center of Basic Medical Sciences, School of Basic Medical Sciences, Hubei University of Science and Technology, 88 Xianning Road, Xianning, Hubei, 437100, People’s Republic of ChinaEmail wangping_hu@163.comPurpose: Tumor necrosis factor-α (TNF-α) is a pro-inflammatory cytokine and involves in a variety of pain conditions. Some findings suggest that TNF-α may act directly on primary afferent neurons to induce acute pain hypersensitivity through non-transcriptional regulation. This study investigated whether TNF-α had an effect on functional activity of P2X3 receptors in primary sensory neurons. Herein, we report that a brief (5 min) application of TNF-α rapidly enhanced the electrophysiological activity of P2X3 receptors in rat dorsal root ganglia (DRG) neurons.Methods: Electrophysiological recordings were carried out on rat DRG neurons, and nociceptive behavior was quantified in rats.Results: A brief (5 min) exposure of TNF-α rapidly increased P2X3 receptor-mediated and α,β-methylene-ATP (α,β-meATP)-evoked inward currents in a dose-dependent manner. The potentiation of P2X3 receptor-mediated ATP currents by TNF-α was voltage-independent. TNF-α shifted the concentration–response curve for α,β-meATP upwards, with an increase of 31.57 ± 6.81% in the maximal current response to α,β-meATP. This acute potentiation of ATP currents by TNF-α was blocked by p38 mitogen-activated protein kinase (MAPK) inhibitor SB202190, but not by non-selective cyclooxygenase inhibitor indomethacin, suggesting involvement of p38 MAPK, but not cyclooxygenase. Moreover, intraplantar injection of TNF-α and α,β-meATP produced a synergistic effect on mechanical allodynia in rats. TNF-α-induced mechanica

Differential GNSS (DGNSS) has been demonstrated to provide reliable, high-quality range correction information enabling real-time navigation with centimeter to sub-meter accuracy, which is required for applications such as connected and autonomous vehicles. However, DGNSS requires a local reference station near each user. For a continental or global scale implementation, this information dissemination approach would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning affords more flexibility as a public service for GNSS receivers, but its State Space Representation format is not supported by most receivers in the field or on the market. This article proposes a novel Virtual Network DGNSS (VN-DGNSS) approach and an optimization algorithm that is key to its implementation. The approach capitalizes on the existing PPP infrastructure without the need for new physical reference stations. By connecting to public GNSS SSR data services, a VN-DGNSS server maintains current information about common-mode errors. Construction of the RTCM Observation Space Representation messages from this SSR information requires both the signal time-of-transmission and the satellite position at that time which are consistent with the time-of-reception for each client. This article presents an algorithm to determine these quantities. The results of real-time stationary and moving platform evaluations are included, using u-blox M8P and ZED-F9P receivers. The performance surpasses the SAE specification (68% of horizontal error <= 1.5 m and vertical error <= 3 m) and shows significantly better horizontal performance than GNSS Open Service. The moving tests also show better horizontal performance than the ZED-F9P receiver with SBAS enabled and achieve the lane-level accuracy (95% of horizontal errors less than 1 meter)., Comment: 14 pages, 8 tables, 4 figures, Code and data are available at https://github.com/Azurehappen/Virtual-Network-DGNSS-Project

Hong-Peng Chen,1,2,* Xiao-Yan Wang,1,* Xiao-Yan Pan,1 Wang-Wang Hu,1 Shu-Ting Cai,1 Kiran Joshi,3 Lie-Hua Deng,1,2 Daqing Ma3 1Department of Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, People’s Republic of China; 2Zhanjiang Key Laboratory of Organ Injury and Protection and Translational Medicine, Zhanjiang, Guangdong, People’s Republic of China; 3Division of Anesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Chelsea and Westminster Hospital, London, UK*These authors contributed equally to this workCorrespondence: Lie-Hua Deng; Daqing MaDepartment of Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, ChinaEmail glinson@126.com; d.ma@imperial.ac.ukIntroduction: Because of its high morbidity and mortality, sepsis remains the leading cause of death in the ICU. Microparticles (MP) have been largely studied as potential diagnostic or prognostic markers in various diseases including sepsis.Objective: The biological and clinical relevance of neutrophil-derived microparticles (NDMPs) within the MP population remains unclear. The objective of this study was to elucidate the relationship between plasma NDMPs and the prognosis of patients with sepsis and/or septic shock.Methods: The study was designed as an observational, noninterventional clinical study. The cohort for this study included 40 sepsis and 40 septic shock patients together with 10 healthy controls admitted to the Intensive Care Unit (ICU) and the Health Surveillance Center in the Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China, from January to November 2018, respectively. The degree of critical disease for sepsis and septic shock was evaluated, with data analyses conducted from 2018 to 2019.Results: On days 1, 3 and 5 post-admission a series of data including plasma NDMP levels, patient

Due to the low adoption rate of mHealth apps, the apps designers need to understand the factors behind adoption. But understanding the determinants of mHealth apps adoption remains unclear. Comparatively less attention has been given to the factors affecting the adoption of mHealth apps among the young generation. This study aims to examine the factors influencing behavioral intention and actual usage behavior of mHealth apps among technology prone young generation. The research model has extracted variables from the widely accepted Unified Theory of Acceptance and Use of Technology (UTAUT2) alongside privacy, lifestyles, self-efficacy and trust. Required data were collected from mHealth apps users in Bangladesh. Firstly, this study confirmed that performance expectancy, social influence, hedonic motivation and privacy exerted a positive influence on behavioral intention whereas facilitating conditions, self-efficacy, trust and lifestyle had an influence on both behavioral intention and actual usage behavior. Secondly, the Neural Network Model was employed to rank relatively significant predictors obtained from structural equation modeling (SEM). This study contributes to the growing literature on the use of mHealth apps in trying to elevate the quality of patients' lives. The new methodology and findings from this study will significantly contribute to the extant literature of technology adoption and mHealth apps adoption intention especially. Therefore, for practitioners concerned with fostering mHealth apps adoption, the findings stress the importance of adopting an integrated approach centered on key findings of this study.

This document presents an analysis of antenna performance as a function of installation position on a vehicle. Each of three antennas is evaluated at three different installation locations on top of a car: front, middle, and rear along the car center-line (see Fig. 2).The equipment and experimental procedures are described in Section II. GPS measurement models are reviewed in Section III. Performance is analyzed by two methods. The first analysis approach uses differential GPS (DGPS) estimated position relative to a ground truth antenna position. The second analysis approach directly analyzes the statistics of the estimated multipath signal for each satellite. The methodology for each analysis approach is described in Sections IV and V. The experimental results are presented in Section VI.The method for determining ground truth for the DGPS anal- ysis approach is presented in Appendix A. The method for estimating Ionospheric delay for the multipath signal analysis is presented in Appendix B. Example Ionospheric delay calculations are include in Appendix C. The elevation and signal strength thresholds used to select the epochs to be included in this study are discussed in Appendix D.The conclusions related to performance are as follows. Among the antennas, the Sharkfin consistently performed worse than the other two antennas. Between the single and multi-band patch antennas, the performance was similar with the multi- band antenna performing better by some measures (e.g. position estimation histogram and standard deviation) and the single-band antenna performing better by other measures (e.g., multi-path standard deviation). The rear antenna location yielded slightly better performance, but the differences in performance between the three mounting locations was not large and therefore some- what inconclusive.

This document presents an analysis of antenna performance as a function of installation position on a vehicle. Each of three antennas is evaluated at three different installation locations on top of a car: front, middle, and rear along the car center-line (see Fig. 2).The equipment and experimental procedures are described in Section II. GPS measurement models are reviewed in Section III. Performance is analyzed by two methods. The first analysis approach uses differential GPS (DGPS) estimated position relative to a ground truth antenna position. The second analysis approach directly analyzes the statistics of the estimated multipath signal for each satellite. The methodology for each analysis approach is described in Sections IV and V. The experimental results are presented in Section VI.The method for determining ground truth for the DGPS anal- ysis approach is presented in Appendix A. The method for estimating Ionospheric delay for the multipath signal analysis is presented in Appendix B. Example Ionospheric delay calculations are include in Appendix C. The elevation and signal strength thresholds used to select the epochs to be included in this study are discussed in Appendix D.The conclusions related to performance are as follows. Among the antennas, the Sharkfin consistently performed worse than the other two antennas. Between the single and multi-band patch antennas, the performance was similar with the multi- band antenna performing better by some measures (e.g. position estimation histogram and standard deviation) and the single-band antenna performing better by other measures (e.g., multi-path standard deviation). The rear antenna location yielded slightly better performance, but the differences in performance between the three mounting locations was not large and therefore some- what inconclusive.

Efficient global optimization (EGO) uses the surrogate uncertainty estimator called expected improvement (EI) to guide the selection of the next sampling candidates. Theoretically, any modelling methods can be integrated with the EI criterion. To improve the convergence ratio, a multi-surrogate efficient global optimization (MSEGO) was suggested. In practice, the EI-based optimization methods with different surrogates show widely divergent characteristics. Therefore, it is important to choose the most suitable algorithm for a certain problem. For this purpose, four single-surrogate efficient global optimizations (SSEGOs) and an MSEGO involving four surrogates are investigated. According to numerical tests, both the SSEGOs and the MSEGO are feasible for weak nonlinear problems. However, they are not robust for strong nonlinear problems, especially for multimodal and high-dimensional problems. Moreover, to investigate the feasibility of EGO in practice, a material identification benchmark is designed to demonstrate the performance of EGO methods. According to the tests in this study, the kriging EGO is generally the most robust method.

Prokineticins ( PKs), consisting of PK1 and PK2, are a pair of newly identified regulatory peptides. Two closely related G-protein coupled receptors, PKR1 and PKR2, mediate the signaling of PKs. PKs/PKRs participate in the regulation of diverse biological processes, ranging from development to adult physiology. A number of studies have indicated the involvement of PKs/PKRs in nociception. Here we show that PK2 is a sensitizer for nociception. Intraplantar injection of recombinant PK2 resulted in a strong and localized hyperalgesia with reduced thresholds to nociceptive stimuli. PK2 mobilizes calcium in dissociated dorsal root ganglion (DRG) neurons. Mice lacking the PK2 gene displayed strong reduction in nociception induced by thermal and chemical stimuli, including capsaicin. However, PK2 mutant mice showed no difference in inflammatory response to capsaicin. As the majority of PK2-responsive DRG neurons also expressed transient receptor potential vanilloid (TRPV1) and exhibited sensitivity to capsaicin, TRPV1 is likely a significant downstream molecule of PK2 signaling. Taken together, these results reveal that PK2 sensitize nociception without affecting inflammation.

Prokineticins ( PKs), consisting of PK1 and PK2, are a pair of newly identified regulatory peptides. Two closely related G-protein coupled receptors, PKR1 and PKR2, mediate the signaling of PKs. PKs/PKRs participate in the regulation of diverse biological processes, ranging from development to adult physiology. A number of studies have indicated the involvement of PKs/PKRs in nociception. Here we show that PK2 is a sensitizer for nociception. Intraplantar injection of recombinant PK2 resulted in a strong and localized hyperalgesia with reduced thresholds to nociceptive stimuli. PK2 mobilizes calcium in dissociated dorsal root ganglion (DRG) neurons. Mice lacking the PK2 gene displayed strong reduction in nociception induced by thermal and chemical stimuli, including capsaicin. However, PK2 mutant mice showed no difference in inflammatory response to capsaicin. As the majority of PK2-responsive DRG neurons also expressed transient receptor potential vanilloid (TRPV1) and exhibited sensitivity to capsaicin, TRPV1 is likely a significant downstream molecule of PK2 signaling. Taken together, these results reveal that PK2 sensitize nociception without affecting inflammation.

The mammalian circadian clock is composed of single-cell oscillators. Neurochemical and electrical signaling among these oscillators is important for the normal expression of circadian rhythms. Prokineticin 2 (PK2), encoding a cysteine-rich secreted protein, has been shown to be a critical signaling molecule for the regulation of circadian rhythms. PK2 expression in the suprachiasmatic nucleus (SCN) is highly rhythmic, peaking during the day and being essentially absent during the night. Mice with disrupted PK2 gene or its receptor PKR2 display greatly reduced rhythmicity of broad circadian parameters such as locomotor activity, body temperature and sleep/wake patterns. PK2 has been shown to increase the firing rate of SCN neurons, with unknown molecular mechanisms. Here we report that TRPV2, an ion channel belonging to the family of TRP, is co-expressed with PKR2 in the SCN neurons. Further, TRPV2 protein, but not TRPV2 mRNA, was shown to oscillate in the SCN in a PK2-dependent manner. Functional studies revealed that TRPV2 enhanced signaling of PKR2 in calcium mobilization or ion current conductance, likely via the increased trafficking of TRPV2 to the cell surface. Taken together, these results indicate that TRPV2 is likely part of the downstream signaling of PK2 in the regulation of the circadian rhythms.

Efficient global optimization (EGO) uses the surrogate uncertainty estimator called expected improvement (EI) to guide the selection of the next sampling candidates. Theoretically, any modelling methods can be integrated with the EI criterion. To improve the convergence ratio, a multi-surrogate efficient global optimization (MSEGO) was suggested. In practice, the EI-based optimization methods with different surrogates show widely divergent characteristics. Therefore, it is important to choose the most suitable algorithm for a certain problem. For this purpose, four single-surrogate efficient global optimizations (SSEGOs) and an MSEGO involving four surrogates are investigated. According to numerical tests, both the SSEGOs and the MSEGO are feasible for weak nonlinear problems. However, they are not robust for strong nonlinear problems, especially for multimodal and high-dimensional problems. Moreover, to investigate the feasibility of EGO in practice, a material identification benchmark is designed to demonstrate the performance of EGO methods. According to the tests in this study, the kriging EGO is generally the most robust method.

The mammalian circadian clock is composed of single-cell oscillators. Neurochemical and electrical signaling among these oscillators is important for the normal expression of circadian rhythms. Prokineticin 2 (PK2), encoding a cysteine-rich secreted protein, has been shown to be a critical signaling molecule for the regulation of circadian rhythms. PK2 expression in the suprachiasmatic nucleus (SCN) is highly rhythmic, peaking during the day and being essentially absent during the night. Mice with disrupted PK2 gene or its receptor PKR2 display greatly reduced rhythmicity of broad circadian parameters such as locomotor activity, body temperature and sleep/wake patterns. PK2 has been shown to increase the firing rate of SCN neurons, with unknown molecular mechanisms. Here we report that TRPV2, an ion channel belonging to the family of TRP, is co-expressed with PKR2 in the SCN neurons. Further, TRPV2 protein, but not TRPV2 mRNA, was shown to oscillate in the SCN in a PK2-dependent manner. Functional studies revealed that TRPV2 enhanced signaling of PKR2 in calcium mobilization or ion current conductance, likely via the increased trafficking of TRPV2 to the cell surface. Taken together, these results indicate that TRPV2 is likely part of the downstream signaling of PK2 in the regulation of the circadian rhythms.

Background and purposeA growing number of studies have demonstrated that oxytocin (OT) plays an analgesic role in modulation of nociception and pain. Most work to date has focused on the central mechanisms of OT analgesia, but little is known about whether peripheral mechanisms are also involved. Acid-sensing ion channels (ASICs) are distributed in peripheral sensory neurons and participate in nociception. Here, we investigated the effects of OT on the activity of ASICs in dorsal root ganglion (DRG) neurons.Experimental approachElectrophysiological experiments were performed on neurons from rat DRG. Nociceptive behaviour was induced by acetic acid in rats and mice lacking vasopressin, V1A receptors.Key resultsOT inhibited the functional activity of native ASICs. Firstly, OT dose-dependently decreased the amplitude of ASIC currents in DRG neurons. Secondly, OT inhibition of ASIC currents was mimicked by arginine vasopressin (AVP) and completely blocked by the V1A receptor antagonist SR49059, but not by the OT receptor antagonist L-368899. Thirdly, OT altered acidosis-evoked membrane excitability of DRG neurons and significantly decreased the amplitude of the depolarization and number of action potentials induced by acid stimuli. Finally, peripherally administered OT or AVP inhibited nociceptive responses to intraplantar injection of acetic acid in rats. Both OT and AVP also induced an analgesic effect on acidosis-evoked pain in wild-type mice, but not in V1A receptor knockout mice.Conclusions and implicationsThese results reveal a novel peripheral mechanism for the analgesic effect of OT involving the modulation of native ASICs in primary sensory neurons mediated by V1A receptors.

BackgroundProkineticin 2 (PK2) is a secreted protein and causes potent hyperalgesia in vivo, and is therefore considered to be a new pronociceptive mediator. However, the molecular targets responsible for the pronociceptive effects of PK2 are still poorly understood. Here, we have found that PK2 potentiates the activity of acid-sensing ion channels in the primary sensory neurons.MethodsIn the present study, experiments were performed on neurons freshly isolated from rat dorsal root ganglion by using whole-cell patch clamp and voltage-clamp recording techniques.ResultsPK2 dose-dependently enhanced proton-gated currents with an EC50 of 0.22 ± 0.06 nM. PK2 shifted the proton concentration-response curve upwards, with a 1.81 ± 0.11 fold increase of the maximal current response. PK2 enhancing effect on proton-gated currents was completely blocked by PK2 receptor antagonist. The potentiation was also abolished by intracellular dialysis of GF109203X, a protein kinase C inhibitor, or FSC-231, a protein interacting with C-kinase 1 inhibitor. Moreover, PK2 enhanced the acid-evoked membrane excitability of rat dorsal root ganglion neurons and caused a significant increase in the amplitude of the depolarization and the number of spikes induced by acid stimuli. Finally, PK2 exacerbated nociceptive responses to the injection of acetic acid in rats.ConclusionThese results suggest that PK2 increases the activity of acid-sensing ion channels via the PK2 receptor and protein kinase C-dependent signal pathways in rat primary sensory neurons. Our findings support that PK2 is a proalgesic factor and its signaling likely contributes to acidosis-evoked pain by sensitizing acid-sensing ion channels.

BackgroundProkineticin 2 (PK2) is a secreted protein and causes potent hyperalgesia in vivo, and is therefore considered to be a new pronociceptive mediator. However, the molecular targets responsible for the pronociceptive effects of PK2 are still poorly understood. Here, we have found that PK2 potentiates the activity of acid-sensing ion channels in the primary sensory neurons.MethodsIn the present study, experiments were performed on neurons freshly isolated from rat dorsal root ganglion by using whole-cell patch clamp and voltage-clamp recording techniques.ResultsPK2 dose-dependently enhanced proton-gated currents with an EC50 of 0.22 ± 0.06 nM. PK2 shifted the proton concentration-response curve upwards, with a 1.81 ± 0.11 fold increase of the maximal current response. PK2 enhancing effect on proton-gated currents was completely blocked by PK2 receptor antagonist. The potentiation was also abolished by intracellular dialysis of GF109203X, a protein kinase C inhibitor, or FSC-231, a protein interacting with C-kinase 1 inhibitor. Moreover, PK2 enhanced the acid-evoked membrane excitability of rat dorsal root ganglion neurons and caused a significant increase in the amplitude of the depolarization and the number of spikes induced by acid stimuli. Finally, PK2 exacerbated nociceptive responses to the injection of acetic acid in rats.ConclusionThese results suggest that PK2 increases the activity of acid-sensing ion channels via the PK2 receptor and protein kinase C-dependent signal pathways in rat primary sensory neurons. Our findings support that PK2 is a proalgesic factor and its signaling likely contributes to acidosis-evoked pain by sensitizing acid-sensing ion channels.

USDOT Grant 69A3551747114, National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2023-07-27. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time., Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV’s) is near at hand due to advances in sensor and computing technology. These capabilities in conjunction with high-definition maps enable lane determination, per lane queue determination, and enhanced performance in applications. This project investigated, analyzed, and demonstrated these related technologies. Project contributions include: (1) Experimental analysis demonstrating that the USDOT Mapping tool achieves internal horizontal accuracy better than 0.2 meters (standard deviation); (2) Theoretical analysis of lane determination accuracy as a function of both distance from the lane centerline and positioning accuracy; (3) Experimental demonstration and analysis of lane determination along the Riverside Innovation Corridor showing that for a vehicle driven within 0.9 meters of the lane centerline, the correct lane is determined for over 90% of the samples; (4) Development of a VISSIM position error module to enable simulation analysis of lane determination and lane queue estimation as a function of positioning error; (5) Development of a lane-level intersection queue prediction algorithm; Simulation evaluation of lane determination accuracy which matched the theoretical analysis; and (6) Simulation evaluation of lane queue prediction accuracy as a function of both CAV penetration rate and positioning accuracy. Conclusions of the simulation analysis in item (6) are the following: First, when the penetration rate is fixed, higher queue length estimation error occurs as the position error increases. However, the disparity across different position error levels diminishes with the decrease of penetration rate. Second, as the penetration rate decreases, the queue length estimation error significantly increases under the same GNSS error level. The current methods that exist for queue length prediction only utilize vehicle position and a penetration rate estimate. These res, The total size of the described zip file is 376 MB. File extension .md is a standard markdown file and can be opened in a basic text editor. 7z files are 7-zip files. They can be opened using the free program 7-zip.

USDOT Grant 69A3551747114, Reliable, lane-level, absolute position determination for connected and automated vehicles (CAV’s) is near at hand due to advances in sensor and computing technology. These capabilities in conjunction with high-definition maps enable lane determination, per lane queue determination, and enhanced performance in applications. This project investigated, analyzed, and demonstrated these related technologies. Project contributions include: (1) Experimental analysis demonstrating that the USDOT Mapping tool achieves internal horizontal accuracy better than 0.2 meters (standard deviation); (2) Theoretical analysis of lane determination accuracy as a function of both distance from the lane centerline and positioning accuracy; (3) Experimental demonstration and analysis of lane determination along the Riverside Innovation Corridor showing that for a vehicle driven within 0.9 meters of the lane centerline, the correct lane is determined for over 90% of the samples; (4) Development of a VISSIM position error module to enable simulation analysis of lane determination and lane queue estimation as a function of positioning error; (5) Development of a lane-level intersection queue prediction algorithm; Simulation evaluation of lane determination accuracy which matched the theoretical analysis; and (6) Simulation evaluation of lane queue prediction accuracy as a function of both CAV penetration rate and positioning accuracy. Conclusions of the simulation analysis in item (6) are the following: First, when the penetration rate is fixed, higher queue length estimation error occurs as the position error increases. However, the disparity across different position error levels diminishes with the decrease of penetration rate. Second, as the penetration rate decreases, the queue length estimation error significantly increases under the same GNSS error level. The current methods that exist for queue length prediction only utilize vehicle position and a penetration rate estimate. These res