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3D imaging reveals changes in the neurovascular architecture of the murine calvarium with aging

Calvarial nerves, along with vasculature, influence skull formation during development and following injury, but it remains unclear how calvarial nerves are spatially distributed during postnatal growth and aging. Studying the spatial distribution of nerves in the skull remains a challenge due to a lack of methods to quantify 3D structures in intact bone. To visualize calvarial 3D neurovascular architecture, we imaged nerves and endothelial cells with lightsheet microscopy. We employed machine-learning-based segmentation to facilitate high-resolution characterization from post-natal day 0 (P0) to 80 weeks. We found that TUBB3+ nerve density decreased with aging with the frontal bone demonstrating earlier onset age-related nerve loss than the parietal bone. In addition, nerves in the periosteum and dura mater exhibited similar yet distinct temporal patterns of nerve growth and loss. While no difference was observed in TUBB3+ nerves during skeletal maturation (P0 → 12 weeks), we did observe an increase in the volume of unmyelinated nerves in the dura mater. Regarding calvarial vasculature, larger CD31hiEmcn vessel fraction increased with aging, while CD31hiEmcnhi vessel fraction was reduced. Throughout all ages, calvarial nerves maintained a preferential spatial association with CD31hiEmcnhi vessels, however, this association decreased with aging. Additionally, we used a model of Apert syndrome to explore the impact of suture-related disease on neurovascular architecture. Collectively, this 3D, spatiotemporal characterization of calvarial nerves throughout the lifespan and provides new insights into age-induced neurovascular architecture.

Why do travelers discontinue using integrated ride-hailing platforms? The role of perceived value and perceived risk

Despite integrated ride-hailing platforms have provided many benefits to travelers, there are also various potential risks. This study aims to examine travelers’ discontinuance behavioral intention toward integrated ride-hailing platforms. The research framework was established by extending the theory of planned behavior (TPB) with perceived value and perceived risk. Perceived value was classified into utilitarian, hedonic, and social values, while perceived risk was classified into privacy, performance, security, and financial risks. Additionally, the factors of switch cost and personal innovativeness were included. An empirical analysis was carried out using partial least-squares structural equation modeling (PLS-SEM) based on a survey conducted in Nanjing, China. Furthermore, a multi-group analysis (MGA) was performed to examine behavioral differences across demographic variables. The findings suggest that discontinuous behavioral intention is influenced by subjective norms, perceived behavioral control, and attitude. Among them, perceived behavioral control shows the strongest impact (−0.190). Perceived value, including utilitarian, hedonic, and social dimensions, negatively influences discontinuance intention, whereas the four variables of risk perception positively affect discontinuance intention. Notably, social value, performance risk, and privacy risk act higher total effects on discontinuance intention. Switch cost is negatively associated with attitude (−0.222), and positively affects discontinuance intention (0.189). Personal innovativeness has positive and stronger effects on perceived value (0.237), negative effects on perceived risk (−0.174), and negative effects on discontinuance intention. Regarding MGA results, older travelers demonstrate a stronger impact of social value on perceived value, higher-income groups exhibit greater sensitivity to security risks, and frequent travelers prioritize utilitarian value.

An artificial market model for the forex market

As financial markets have transitioned toward electronic trading, there has been a corresponding increase in the number of algorithmic strategies and degree of transaction frequency. This move to high-frequency trading at the millisecond level, propelled by algorithmic strategies, has brought to the forefront short-term market reactions, like market impact, which were previously negligible in low-frequency trading scenarios. Such evolution necessitates a new framework for analyzing and developing algorithmic strategies in these rapidly evolving markets. Employing artificial markets stands out as a solution to this problem. This study aims to construct an artificial foreign exchange market referencing market microstructure theory, without relying on the assumption of information or technical traders. Furthermore, it endeavors to validate the model by replicating stylized facts, such as fat tails, which exhibit a higher degree of kurtosis in the return distribution than that predicted by normal distribution models. The validated artificial market model will be used to simulate market dynamics and algorithm strategies; its generated rates could also be applied to pricing and risk management for currency options and other foreign exchange derivatives. Moreover, this work explores the importance of order flow and the underlying factors of stylized facts within the artificial market model.

A robust organic hydrogen sensor for distributed monitoring applications

Hydrogen is an abundant and clean energy source that could help to decarbonize difficult-to-electrify economic sectors. However, its safe deployment relies on the availability of cost-effective hydrogen detection technologies. We describe a hydrogen sensor that uses an organic semiconductor as the active layer. It can operate over a wide temperature and humidity range. Ambient oxygen p-dopes the organic semiconductor, which improves hole transport, and the presence of hydrogen reverses this doping process, leading to a drop in current and enabling reliable and rapid hydrogen detection. The sensor exhibits a high responsivity (more than 10,000), fast response time (less than 1 s), low limit of detection (around 192 ppb) and low power consumption (less than 2 μW). It can operate continuously for more than 646 days in ambient air at room temperature. We show that the sensor outperforms a commercial hydrogen detector in realistic sensing scenarios, illustrating its suitability for application in distributed sensor networks for early warning of hydrogen leaks and preventing explosions or fires.

Effect of regional crosstalk between sympathetic nerves and sensory nerves on temporomandibular joint osteoarthritic pain

Temporomandibular joint osteoarthritis (TMJ-OA) is a common disease often accompanied by pain, seriously affecting physical and mental health of patients. Abnormal innervation at the osteochondral junction has been considered as a predominant origin of arthralgia, while the specific mechanism mediating pain remains unclear. To investigate the underlying mechanism of TMJ-OA pain, an abnormal joint loading model was used to induce TMJ-OA pain. We found that during the development of TMJ-OA, the increased innervation of sympathetic nerve of subchondral bone precedes that of sensory nerves. Furthermore, these two types of nerves are spatially closely associated. Additionally, it was discovered that activation of sympathetic neural signals promotes osteoarthritic pain in mice, whereas blocking these signals effectively alleviates pain. In vitro experiments also confirmed that norepinephrine released by sympathetic neurons promotes the activation and axonal growth of sensory neurons. Moreover, we also discovered that through releasing norepinephrine, regional sympathetic nerves of subchondral bone were found to regulate growth and activation of local sensory nerves synergistically with other pain regulators. This study identified the role of regional sympathetic nerves in mediating pain in TMJ-OA. It sheds light on a new mechanism of abnormal innervation at the osteochondral junction and the regional crosstalk between peripheral nerves, providing a potential target for treating TMJ-OA pain.

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