This study examined whether adipose tissue from different anatomical regions differentially influences autophagya cellular recycling process-in human pancreatic$\boldsymbol{\beta}$-cells, and whether the IL-8-CXCR2 signaling pathway mediates this effect. To investigate, human pancreatic$\boldsymbol{\beta}$-cells with either suppressed or overexpressed CXCR2 were co-cultured with various adipose tissues using Transwell chambers. Expression levels of CXCR2 and the autophagy-related proteins SQSTM1 and LC3B were measured. We found that adipose tissue consistently upregulated SQSTM1 protein expression in$\boldsymbol{\beta}$-cells, with no significant effect on LC3B. CXCR2 modulation was pivotal: suppression of CXCR2 reduced SQSTM1 levels and significantly increased LC3B expression, suggesting enhanced autophagic activity. Conversely, CXCR2 overexpression in$\beta$-cells co-cultured with visceral adipose tissue (VAT) significantly elevated SQSTM1 levels, while LC3B expression remained unchanged. These findings suggest the IL-8CXCR2 pathway selectively influences components of the autophagic process. Notably, subcutaneous adipose tissue (SAT) was more effective than VAT in increasing SQSTM1 expression in$\beta$-cells. This depot-specific variability, combined with CXCR2dependent effects, highlights a nuanced role for IL-8-CXCR2 signaling in mediating adipose tissue-induced changes in$\beta$-cell autophagy. In conclusion, distinct adipose tissue depots differentially regulate autophagy-related protein expression in human pancreatic$\beta$-cells, with the IL-8-CXCR2 pathway acting as a key modulator. These insights deepen our understanding of the complex crosstalk between adipose tissue and pancreatic$\beta$-cell function, particularly in relation to cellular self-digestion and stress adaptation processes, and may inform future therapeutic strategies targeting metabolic diseases such as diabetes.

Traffic safety risk significantly impacts road users in urban mobility systems, making it important in transportation management decision-making. Current mobility management strategies predominantly focus on macro-level monitoring through traffic sensing infrastructure, and struggle to capture network-wide, real-time safety risks due to the limited spread of vehicle-based sensors. To address this, we propose a Digital Twin-based Driver Risk-aware Predictive Mobility Analytics (DT-DIMA) system. The DT-DIMA system integrates real-time traffic information from pan-tilt-cameras (PTCs), synchronizes this data into a digital twin to accurately replicate the physical world, and predicts network-wide mobility and safety risks in real time. The system’s innovation lies in its integration of spatial-temporal modeling, simulation, and online control modules. Tested and evaluated under normal traffic conditions and incidental situations (e.g., unexpected accidents, pre-planned work zones) in a simulated testbed in Brooklyn, New York, DT-DIMA demonstrated mean absolute percentage errors (MAPEs) ranging from 9.40% to 13.12% in estimating network-level traffic volume and MAPEs from 2.12% to 12.97% in network-level safety risk prediction. In addition, the highly accurate safety risk prediction enables PTCs to preemptively monitor road segments with high driving risks before incidents take place. Such proactive PTC surveillance creates around a 5-minute lead time in capturing traffic incidents. The DT-DIMA system enables transportation managers to understand mobility not only in terms of traffic patterns but also driver-experienced safety risks, allowing for proactive resource allocation in response to various traffic situations.

Currently, there is growing interest in understanding relationships among vitamin D, bone metabolism indicators, and diabetes. Numerous studies have highlighted vitamin D deficiency as a risk factor not only for obesity but also for fluctuations in blood glucose levels and insulin resistance. Human adipose tissue is categorized into visceral fat and subcutaneous fat based on its distribution. Although visceral fat comprises a smaller proportion compared to subcutaneous fat, it is considered to have a much more significant impact on various diseases such as obesity and diabetes. However, precise relationships among different adipose tissues, vitamin D deficiency, and bone metabolism-related indicators remain unclear. Furthermore, it is uncertain whether the effects of vitamin D on bone metabolism differ between individuals with normal visceral fat and those with excessive visceral fat. Moreover, to date, most research examining the relationship between vitamin D and fat distribution has focused specifically on the vitamin D precursor produced by the liver, 25(OH)D. Importantly, determining 25(OH)D levels constitutes an indirect measure of vitamin D enumerating an inactive intermediate in the pathway generating the active form of vitamin D, 1,25(OH)2D3. As such, this study includes 1,25(OH)2D3 in the research indicators, providing a more comprehensive and direct understanding of the relationships among vitamin D, adipose tissue distribution, and bone metabolism. In addition, this study aims to investigate not only the impact of normal visceral fat and excessive visceral fat on bone metabolism indicators but also to compare the differences between subcutaneous fat and visceral fat contributing to bone metabolism.

Traffic congestion has a negative impact on our daily life. Predicting the trend of traffic congestion can provide a valuable guideline to address such problems. Most existing approaches focus on the tasks of predicting traffic volume or traffic speed, which do not effectively address the challenges of traffic congestion prediction. First, traffic congestion exhibits daily and weekly temporal patterns, but these patterns are not strictly the same, which indicates complicated long-term periodicity. Second, traffic congestion sparsely distributes over different periods of time, which leads to complex short-term and mid-term temporal dependencies. Third, since traffic congestion will propagate to adjacent road segments over time, it exhibits complex spatio-temporal correlations. To address them, we propose a periodic shift and event-aware spatio-temporal graph convolutional network for traffic congestion prediction. Specifically, we propose to capture the differences and similarities of long-term periodic temporal patterns to handle the complicated long-term periodicity. To effectively capture short-term and mid-term temporal dependencies, we regard a continuous time sequence of the congested condition as a traffic congestion event, and then adopt the widely-used long short-term memory model to learn the sequential dependencies of traffic congestion events. Finally, we integrate the graph convolutional network into the modeling of temporal dependencies to capture the complex spatio-temporal correlations. Extensive experiments demonstrate the superiority of our model. In addition, we deploy our model in production at Amap, and it achieves great performance improvement in terms of the F1-score compared to the production baseline. This confirms that our model is a practical solution for real-world congestion prediction services.

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim at discovering the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: (1) traffic congestion is affected by several factors with complex spatio-temporal relations; (2) there are a few samples of congestion data with known causes due to the limitation of human label; (3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: (1) congestion feature extraction module, which extracts the important features distinguishing between different causes of congestion; and (2) congestion cause discovery module, which designs a deep semi-supervised learning based framework to discover the causes of traffic congestion with limited labeled data. Specifically, in pre-training stage, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, in clustering stage, we propose two different clustering methods to discover the congestion causes. For the first clustering method, we extend the classic deep embedded clustering model to produce clusters via soft assignment. For the second one, we iteratively usek-means to group the latent features extracted from the pre-trained model, and use the cluster results as pseudo-labels to fine-tune the network. Extensive experiments show that the performance of our methods is superior to the state-of-the-art baselines, which demonstrates the effectiveness of the proposed cause discovery system. Additionally, our system is deployed and used in the practical production environment at Amap.

Understanding driver response behavior to Connected Vehicle (CV) warnings is one of the fundamental tasks in improving road safety. However, previous studies often used aggregated safety measures and simulation environments to accomplish this task. Consequently, time-dependent driver response behavior under real-world conditions has rarely been investigated. This study proposes a new functional data analysis (FDA) approach to analyze time-dependent driver response behavior to CV warnings obtained from the New York City CV Pilot Deployment (NYC CVPD) project. Sparse functional design that can account for irregularly spaced functional measurements is adopted to accommodate the potential noise contaminated real-world CV data. Functional principal component analysis and nonparametric functional linear regression are used to smooth raw driver behavior profile and estimate time-dependent effect of driver response behavior, respectively. The speed compliance application implemented in the NYC CVPD project is used as the case study. By modeling time series of speed data after receiving warnings as continuous functions, the proposed FDA approach reveals new patterns of driver response behavior over time, such as the diminishing effect of drivers’ speed reduction and the increase in variability of driver response behavior over time, which have rarely been explored using traditional approaches. Moreover, the proposed FDA approach supports the extraction of certain traditional safety measures and has the potential to be generalized to analyze various CV applications. The findings of this study can support the calibration of detailed driver behavior in CV environments and facilitate better CV application design and further investigation of the benefits of CVs.

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim to discover the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: 1) traffic congestion is affected by several factors with complex spatio-temporal relations; 2) the amount of congestion data with known causes is small due to the limitation of human label; 3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: 1) congestion feature extraction, which extracts the important features influencing congestion; and 2) congestion cause discovery, which utilize a deep semi-supervised learning based method to discover the causes of traffic congestion with limited labeled causes. Specifically, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, the k-means algorithm is performed to produce the clusters. Extensive experiments show that the performance of our proposed method is superior to the baselines. Additionally, our system is deployed and used in the practical production environment at Amap.