Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.

Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data's temporal information. In addition, this study used Bayesian optimization to solve the problem of the model's inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration.

A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting.

Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing's air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.

A NeuroD1 AAV-Based Gene Therapy for Functional Brain Repair after Ischemic Injury through In Vivo Astrocyte-to-Neuron Conversion.

Adult mammalian brains have largely lost neuroregeneration capability except for a few niches. Previous studies have converted glial cells into neurons, but the total number of neurons generated is limited and the therapeutic potential is unclear. Here, we demonstrate that NeuroD1-mediated in situ astrocyte-to-neuron conversion can regenerate a large number of functional new neurons after ischemic injury. Specifically, using NeuroD1 adeno-associated virus (AAV)-based gene therapy, we were able to regenerate one third of the total lost neurons caused by ischemic injury and simultaneously protect another one third of injured neurons, leading to a significant neuronal recovery. RNA sequencing and immunostaining confirmed neuronal recovery after cell conversion at both the mRNA level and protein level. Brain slice recordings found that the astrocyte-converted neurons showed robust action potentials and synaptic responses at 2 months after NeuroD1 expression. Anterograde and retrograde tracing revealed long-range axonal projections from astrocyte-converted neurons to their target regions in a time-dependent manner. Behavioral analyses showed a significant improvement of both motor and cognitive functions after cell conversion. Together, these results demonstrate that in vivo cell conversion technology through NeuroD1-based gene therapy can regenerate a large number of functional new neurons to restore lost neuronal functions after injury.

Hypoxia protects H9c2 cells against Ferroptosis through SENP1-mediated protein DeSUMOylation.

Hypoxia affects proliferation, differentiation, as well as death of cardiomyocyte, and plays an important role in the development of myocardial ischemia. However, the detailed mechanisms through which hypoxia regulates cardiomyocyte ferroptosis have not been explored. In this study, we revealed that hypoxia suppresses the proliferation, migration, and erastin-induced ferroptosis of H9c2 cells. First, we confirmed the upregulation of SENP1 in H9c2 cells cultured under hypoxic conditions. Through adenovirus-mediated SENP1 gene transfection, we demonstrated that SENP1 overexpression could enhance H9c2 cell proliferation and migration while also protecting H9c2 cells from erastin-induced ferroptosis. Furthermore, through immunoprecipitation and western blotting, we confirmed that SENP1 mediated deSUMOylation of HIF-1α and ACSL4 in H9c2 cells. In conclusion, this study describes the underlying mechanism through which hypoxia upregulates SENP1 expression, in turn protecting against ferroptosis via the regulation of HIF-1α and ACSL4 deSUMOylation. Our findings provide a theoretical foundation for the development of novel therapeutics for ischemic heart diseases.

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy.

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement's causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network's over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system's big measurement data to improve prediction performance.

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model.

The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model.

Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

ENPP2 protects cardiomyocytes from erastin-induced ferroptosis.

Ferroptosis is an iron- and oxidative-dependent form of regulated cell death and may play important roles in maintaining myocardium homeostasis and pathology of cardiovascular diseases. Currently, the regulatory roles of lipid signals in regulating cardiomyocytes ferroptosis has not been explored. In this study, we show that ENPP2, as a lipid kinase involved in lipid metabolism, protects against erastin-induced ferroptosis in cardiomyocytes. The classical ferroptosis inducer erastin remarkably inhibits the growth which could be rescued by the small molecule Fer-1 in H9c2 cells. Adenovirus mediated ENPP2 overexpression modestly promotes migration and proliferation and significantly inhibits erastin-induced ferroptosis of H9c2 cells. ENPP2 overexpression leads to increase the LPA level in supernatant of H9c2 cells. H9c2 cells express the LPAR1, LPAR3, LPAR4 and LPAR5 receptors. The supernatant of ENPP2 transduced cardiomyocytes could protects the cells from erastin-induced ferroptosis of H9c2 cells. Furthermore, we observed that ENPP2 overexpression regulates ferroptosis-associated gene GPX4, ACSL4 and NRF2 expression and modulates MAPK and AKT signal in H9c2 cells. Collectively, these findings demonstrated that ENPP2/LPA protects cardiomyocytes from erastin-induced ferroptosis through modulating GPX4, ACSL4 and NRF2 expression and enhancing AKT survival signal.

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information.

Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches' ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method's rationality and feasibility when using different data from different sources.

Lipid droplets' functional protein caveolin-2 is associated with lipid metabolism-related molecule FABP5 and EMT marker E-cadherin in oral epithelial dysplasia.

AIMS: To explore the accumulation of lipid droplets (LDs) and its relationship with lipid metabolism, and epithelial-mesenchymal transition (EMT) in the carcinogenesis processes in the oral cavity. METHODS: LDs were stained by oil red O. Forty-eight oral squamous cell carcinomas (OSCC), 78 oral potentially malignant disorders (OPMDs) and 25 normal tissue sections were included to explore the LDs surface protein caveolin-2 and perilipin-3, lipid metabolism-related molecule FABP5 and EMT biomarker E-cadherin expression by immunohistochemical staining. RESULTS: The accumulation of LDs was observed in OPMDs and OSCCs compared with normal tissues (p<0.05). In general, an increasing trend of caveolin-2, perilipin-3 and FABP5 expression was detected from the normal to OPMDs to OSCC groups (p<0.05). Additionally, caveolin-2, perilipin-3 and FABP5 expression were positively correlated with epithelial dysplasia in OPMDs, whereas E-cadherin positivity was negatively correlated with histopathological grade in both OPMDs and OSCC, respectively. A negative correlation of caveolin-2 (p<0.01, r =-0.1739), and FABP5 (p<0.01, r =-0.1880) with E-cadherin expression was detected. The caveolin-2 (p<0.0001, r=0.2641) and perilipin-3 (p<0.05, r=0.1408) staining was positively correlated with FABP5. Increased caveolin-2 expression was related to local recurrence and worse disease-free survival (p<0.05). CONCLUSION: In the oral epithelial carcinogenesis process, LDs begin to accumulate early in the precancerous stage. LDs may be the regulator of FABP5-associated lipid metabolism and may closely related to the process of EMT; caveolin-2 could be the main functional protein.

The Potential of Lipid Droplet-associated Genes as Diagnostic and Prognostic Biomarkers in Head and Neck Squamous Cell Carcinoma.

OBJECTIVE: The role of lipid droplets (LDs) and lipid droplet-associated genes (LD-AGs) remains unclear in head and neck squamous cell carcinoma (HNSCC). This study aimed to investigate LDs in HNSCC and identify LD-AGs essential for the diagnosis and prognosis of HNSCC patients. METHODS: The LDs in the HNSCC and normal cell lines were stained with oil red O. Bioinformatic analysis was used to find LD-AGs in HNSCC that had diagnostic and prognostic significance. RESULTS: LDs accumulation was increased in HNSCC cell lines compared with normal cell lines (P<0.05). Fifty-three differentially expressed genes, including 34 upregulated and 19 downregulated, were found in HNSCC based on the TCGA platform (P<0.05). Then, 53 genes were proved to be functionally enriched in lipid metabolism and LDs. Among them, with an AUC value > 0.7, 34 genes demonstrated a high predictive power. Six genes (AUP1, CAV1, CAV2, CAVIN1, HILPDA, and SQLE) out of 34 diagnostic genes were linked to overall survival in patients with HNSCC (P<0.05). The significant prognostic factors AUP1, CAV1, CAV2, and SQLE were further identified using the univariate and multivariate cox proportional hazard models (P<0.05). The protein expression of CAV2 and SQLE was significantly increased in the HNSCC tissue compared to normal tissues (P<0.05). Finally, the knockdown of the four LD-AGs decreased LDs accumulation, respectively. CONCLUSIONS: Increased LDs accumulation was a hallmark of HNSCC, and AUP1, CAV1, CAV2, and SQLE were discovered as differentially expressed LD-AGs with diagnostic and prognostic potential in HNSCC.

[Increased expressions of leptin and its receptor in the epididymis of varicocele model rats and their implications].

OBJECTIVE: To investigate the expressions of leptin and its receptor in the epididymis of experimental varicocele (EV) rats. METHODS: Forty male Sprague-Dawley rats were randomly divided into four groups: 4-week EV (n = 12), 8-week EV (n = 12), 4-week control (n = 8), and 8-week control (n = 8). EV models were established by partial ligation of the left renal vein. The expressions of leptin and its receptor in the rat epididymis were measured by immunohistochemistry, and their mRNA expressions determined by real-time quantitative PCR. RESULTS: The expressions of leptin and its receptor in the epididymis were significantly higher in the 4- and 8-week EV groups than in the 4- and 8-week control groups (P < 0.01), with no significant difference between the two EV groups (P > 0.05). So were their mRNA expressions in the former two than in the latter two groups (P < 0.01), with no significant difference between the former two (P > 0.05). CONCLUSION: The expressions of leptin and its receptor are markedly increased in the epididymis of varicocele rats. Leptin may be involved in the mechanisms of varicocele inducing male infertility.

Location estimation based on feature mode matching with deep network models.

Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements. Methods: A method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University. Results and discussion: The results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.

Nanomedicines in oral cancer: inspiration comes from extracellular vesicles and biomimetic nanoparticles.

Oral cancer is a common life-threatening malignancy having high mortality and morbidity rates. During the treatment process, individuals unavoidably experience severe side effects. It is essential to develop safer and more effective strategies. Currently, extracellular vesicles (EVs) and biomimetic nanoparticles are nanomedicines with long-term blood circulation and lower off-target toxicity that orchestrate immune responses and accumulate specifically in tumor sites. EVs create a synergetic effect by encapsulating drugs and collaborating with naturally loaded elements in the EVs. Biomimetic nanoparticles retain the characteristic features of the synthetic nanocarriers and inherit the intrinsic cell membrane functionalities. This review outlines the properties, applications, challenges, pros and cons of EVs and biomimetic nanoparticles, providing novel perspectives on oral cancer.

This review explains how extracellular vesicles (EVs) and biomimetic nanoparticles are emerging as nanomedicines applied in oral cancer. EVs are phospholipid bilayer vesicles, mainly including exosomes and microvesicles, responsible for intercellular communication and cargo transport. EVs can carry RNA, metabolites and other molecular payloads. Biomimetic nanomedicines are synthetic nanoparticles coated with the parent or host cell membrane to escape the immune system and elevate targeting ability. Various cell membranes have been used for camouflaging nanoparticles, such as red blood cells, white blood cells, platelets, mesenchymal stem cells and cancer cell membranes. During the treatment process, individuals unavoidably experience severe side effects. It is essential to develop safer and more effective strategies. Currently, EVs and biomimetic nanoparticles are nanomedicines with long-term blood circulation and lower off-target toxicity that orchestrate immune responses and accumulate specifically in tumor sites. EVs create a synergetic effect by encapsulating drugs and collaborating with naturally loaded elements in the EVs. Biomimetic nanoparticles retain the characteristic features of the synthetic nanocarriers and inherit the intrinsic cell membrane functionalities. This review outlines the properties, applications, challenges, pros and cons of EVs and biomimetic nanoparticles, providing novel perspectives on oral cancer.

Glaucocalyxin A impairs tumor growth via amplification of the ATF4/CHOP/CHAC1 cascade in human oral squamous cell carcinoma.

ETHNOPHARMACOLOGICAL RELEVANCE: The natural extract glaucocalyxin A (GLA), purified from the aboveground sections of the Chinese traditional medicinal herb Rabdosia japonica (Burm. f.) Hara var. glaucocalyx (Maxim.) Hara, has various pharmacological benefits, such as anti-bacterial, anti-coagulative, anti-neoplastic, and anti-inflammatory activities. Although GLA has shown anti-tumor activity against various cancers, the therapeutic potential and biological mechanisms of GLA remain to be further explored in oral squamous cell carcinoma (OSCC). AIM OF THE STUDY: This study aimed to elucidate the therapeutic potential and regulatory mechanisms of GLA in OSCC. MATERIALS AND METHODS: The cell proliferation and apoptosis effects of GLA were analyzed by CCK-8, clone formation, Annexin V/PI staining, and apoptotic protein expression in vitro. An OSCC xenograft model was applied to confirm the anti-neoplastic effect in vivo. Furthermore, the changes of reactive oxygen species (ROS) were determined by DCFH-DA probe and GSH/GSSG assay, and inhibited by the pan-caspase inhibitor Z-VAD(OMe)-FMK and the ROS scavenger N-acetylcysteine (NAC). The modulation of GLA on mitochondria and ER-dependent apoptosis pathways was analyzed by JC-1 probe, quantitative real-time PCR, and Western blot. Finally, public databases, clinical samples, and transfection cells were analyzed to explore the importance of GLA's indirect targeting molecule CHAC1 in OSCC. RESULTS: GLA significantly inhibited cell proliferation and induced apoptosis in vitro and in vivo. GLA perturbed the redox homeostasis, and cell apoptosis was totally rescued by Z-VAD(OMe)-FMK and NAC. Furthermore, GLA activated the mitochondrial apoptosis pathway. Simultaneously, the overexpression and knockdown of CHAC1 dramatically affected GLA-mediated apoptosis. The endoplasmic reticulum stress-associated ATF4/CHOP signal was identified to participate in GLA-upregulated CHAC1 expression. Finally, we found that CHAC1 expression was lower in OSCC compared with normal tissues and positively correlated with 4-Hydroxynonenal (4-HNE) level. High CHAC1 expression also indicated better overall survival. Moreover, CHAC1 selectively regulated the viability of oral cancer cells. CONCLUSION: GLA is a promising therapeutic agent that activates the ROS-mediated ATF4/CHOP/CHAC1 axis in OSCC patients.

Novel quorum sensing inhibitor Echinatin as an antibacterial synergist against Escherichia coli.

A new antibacterial strategy based on inhibiting bacterial quorum sensing (QS) has emerged as a promising method of attenuating bacterial pathogenicity and preventing bacterial resistance to antibiotics. In this study, we screened Echinatin (Ech) with high-efficiency anti-QS from 13 flavonoids through the AI-2 bioluminescence assay. Additionally, crystal violet (CV) staining combined with confocal laser scanning microscopy (CLSM) was used to evaluate the effect of anti-biofilm against Escherichia coli (E. coli). Further, the antibacterial synergistic effect of Ech and marketed antibiotics were measured by broth dilution and Alamar Blue Assay. It was found that Ech interfered with the phenotype of QS, including biofilm formation, exopolysaccharide (EPS) production, and motility, without affecting bacterial growth and metabolic activity. Moreover, qRT-PCR exhibited that Ech significantly reduced the expression of QS-regulated genes (luxS, pfs, lsrB, lsrK, lsrR, flhC, flhD, fliC, csgD, and stx2). More important, Ech with currently marketed colistin antibiotics (including colistin B and colistin E) showed significantly synergistically increased antibacterial activity in overcoming antibiotic resistance of E. coli. In summary, these results suggested the potent anti-QS and novel antibacterial synergist candidate of Ech for treating E. coli infections.

Modeling and Analysis of Data-Driven Systems through Computational Neuroscience Wavelet-Deep Optimized Model for Nonlinear Multicomponent Data Forecasting.

Complex time series data exists widely in actual systems, and its forecasting has great practical significance. Simultaneously, the classical linear model cannot obtain satisfactory performance due to nonlinearity and multicomponent characteristics. Based on the data-driven mechanism, this paper proposes a deep learning method coupled with Bayesian optimization based on wavelet decomposition to model the time series data and forecasting its trend. Firstly, the data is decomposed by wavelet transform to reduce the complexity of the time series data. The Gated Recurrent Unit (GRU) network is trained as a submodel for each decomposition component. The hyperparameters of wavelet decomposition and each submodel are optimized with Bayesian sequence model-based optimization (SMBO) to develop the modeling accuracy. Finally, the results of all submodels are added to obtain forecasting results. The PM2.5 data collected by the US Air Quality Monitoring Station is used for experiments. By comparing with other networks, it can be found that the proposed method outperforms well in the multisteps forecasting task for the complex time series.

High glucose enhances angiotensin-II-mediated peroxisome proliferation-activated receptor-gamma inactivation in human coronary artery endothelial cells.

Activation of the renin-angiotensin system plays an important role in the pathogenesis of vascular complications of hyperglycemia. Clinical studies have demonstrated that hypoglycemic effects of peroxisome proliferation-activated receptor-gamma (PPAR-gamma) activation is potentially associated with a significant decrease of cardiovascular disease events in diabetes patients. We assessed the effect of high glucose on the angiotensin II (Ang II), which induced the inactivation of PPAR-gamma and its signal pathways in human coronary artery endothelial cells (HCAECs). The expression of angiotensin II receptor I (AT1R) protein was analyzed by Western blot and knocked down using siRNA. PPAR-gamma activation was examined using a luminometer and a Dual Luciferase Reporter Assay System. Adhesion molecule expressions of HCAECs were measured using ELISA. Both high glucose and Ang II induced a progressive increase in AT1R protein expression on the HCAECs. Troglitazone, a PPAR-gamma activator, significantly increased the transcription activity of PPAR-gamma in HCAECs in vitro. However, activation of PPAR-gamma was significantly inhibited by high glucose and Ang II stimulation. Furthermore, silencing of AT1R expression was able to inhibit the inactivation of PPAR-gamma induced by Ang II and high glucose. Meanwhile, expression of proinflammatory adhesion molecules was increased by high glucose and Ang II in HCAECs, which is blocked by troglitazone and silencing of AT1R expression. These data strongly suggest high glucose enhanced Ang-II-mediated peroxisome proliferation-activated receptor-gamma inactivation and expression of proinflammatory adhesion molecules in human coronary artery endothelial cells.