Remnant cholesterol is correlated with retinal vascular morphology and diabetic retinopathy in type 2 diabetes mellitus: a cross-sectional study.

BACKGROUND: The association between remnant cholesterol (RC) and diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) remains unclear. Morphological changes in retinal vessels have been reported to predict vascular complications of diabetes, including DR. METHODS: This cross-sectional study included 6535 individuals with T2DM. The RC value was calculated using the recognized formula. The retinal vascular parameters were measured using fundus photography. The independent relationship between RC and DR was analyzed using binary logistic regression models. Multiple linear regression and subgroup analyses were employed to investigate the link between RC and vascular parameters, including the retinal arteriolar diameter (CRAE), venular diameter (CRVE), and fractal dimension (Df). Mediation analysis was performed to assess whether the vascular morphology could explain the association between RC and DR. RESULTS: RC was independently associated with DR in patients with a longer duration of T2DM (> 7 years). Patients with the highest quartile RC levels had larger CRAE (5.559 [4.093, 7.025] µm), CRVE (7.620 [5.298, 9.941] µm) and Df (0.013 [0.009, 0.017]) compared with patients with the lowest quartile RC levels. Results were robust across different subgroups. The association between RC and DR was mediated by CRVE (0.020 ± 0.005; 95% confidence interval: 0.012-0.032). CONCLUSIONS: RC may be a risk factor for DR among those who have had T2DM for a longer period of time. Higher RC levels were correlated with wider retinal arterioles and venules as well as higher Df, and it may contribute to DR through the dilation of retinal venules.

Artificial Intelligence-based quantitative evaluation of retinal vascular parameters in thyroid-associated ophthalmopathy.

PURPOSE: Thyroid-associated ophthalmopathy (TAO) may result in increased metabolism and abnormalities in microcirculation. The fractal dimension (Df) of retinal vessels has been shown to be related to the pathology of a number of ophthalmic disorders, but it hasn't been investigated in TAO. METHODS: We analyzed 1078 participants aged 18 to 72 (548 healthy volunteers and 530 TAO). Images were captured using a non-mydriatic 45-degree fundus camera. Baseline retinal characteristics, such as vessel width, tortuosity, and Df were measured using semiautomated software from fundus images. The average retinal parameters were compared between the two groups. The receiver operation curve (ROC) was used to assess the diagnostic efficacy of various retinal vascular parameters for TAO. RESULTS: Despite controlling for potential confounding variables, Df, vessel width, and tortuosity significantly increased in TAO compared to healthy volunteers. Compared to active TAO, patients in the inactive phase had a larger retinal venous caliber (p < 0.05), but there was no difference in Df or arterial caliber. Moderate and severe cases had a higher Df compared with mild cases (EUGOGO guidelines). The area under the ROC for Df, tortuosity, and vascular caliber in the diagnosis of TAO was 0.904 (95% CI: 0.884-0.924), 0.638 (95% CI: 0.598-0.679), and 0.617 (95% CI: 0.576-0.658), respectively. CONCLUSIONS: Due to its accessibility, affordability, and non-invasive nature, retinal vascular Df may serve as a surrogate marker for TAO and might be used to identify severe cases. With relatively high diagnostic performance, the Df is of some utility for the detection of TAO.

A health status estimation method based on interpretable neural network observer for HVs.

Estimating the health status is a crucial step in learning about the health of hypersonic vehicles beforehand. The estimation results can be used to detect abnormal states and provide data reference for fault diagnosis. However, certain conventional neural network-based estimate techniques rely heavily on data and have limited model interpretability, which challenges the accuracy of the estimation results. This research aims to address the problems of data dependency and model interpretability in estimation models. In this study, a block interpretable neural network model with constraints on the trajectory and attitude equations is established. On the basis of the interpretable neural network model, two health status estimation methods are proposed: one that is unsupervised and the other that is supervised. Additionally, in the supervised health status estimate approach, an FC-LN-Mish structure is created to fit the relationship between the fault residual and the fault state parameters. The results indicate that the proposed estimation methods can fit the system mechanism relationship more accurately, improve the model interpretability, reduce data dependency, and ensure high estimation efficiency and precision. The FC-LN-Mish structure can reduce the missed detection rate and false detection rate to some extent, and perform better than other models under the low fault deviation degree. In conclusion, the interpretable neural network model-based observers accurately observe the health status parameters of rudders and RCS, reduce data dependence and data processing costs, and have better performance under high uncertainty interference. It provides effective method for online health estimation.

Periodic analysis on gas path fault diagnosis of gas turbines.

The gas path fault diagnosis is considered widely to ensure the economy, safety and practicability of gas turbines. Traditional gas path diagnosis methods are vulnerable to various uncertainties, resulting in a deviation between the diagnostic results and the real states, which brings huge potential safety hazard to industrial production. Periodic analysis can suppress the uncertainty interference and extract accurately the features of performance parameters to improve the accuracy of health evaluation. Motivated by these, a novel periodic analysis method is proposed for detecting gas path faults, namely the changing periodicity of performance parameters representing the health state of gas turbine is detected to determine whether gas path fault occurs. It is theoretically analyzed that the relationship between the periodicity of observed performance parameters and that of boundary conditions, system uncertainties, and thermodynamic parameters. The simulation experiments are performed to analyze the effects of gas path faults on periodicity of boundary conditions, system uncertainties and thermodynamic parameters. The results show that most gas path faults break the periodicity of performance parameters, proving that the operating states can be monitored through the periodic analysis of performance parameters. An online diagnosis procedure is further proposed by combining signal decomposition and rolling periodic extraction method to judge whether the gas turbine is in health or not. The validity is verified by comparing the periodicity of performance parameters under healthy and fault states. Periodic analysis suppresses the effects of system and parameter uncertainties and detects sensitively gas path faults, which provides a new idea for the fault diagnosis of gas turbines.

Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application.

The stochastic resonance (SR) in a bistable system driven by nonlinear frequency modulation (NLFM) signal and strong noise is studied. Combined with empirical mode decomposition (EMD) and piecewise idea, an adaptive piecewise re-scaled SR method based on the optimal intrinsic mode function (IMF), is proposed to enhance the weak NLFM signal. At first, considering the advantages of EMD for dealing with non-stationary signals, the segmented NLFM signal is processed by EMD. Meanwhile, the cross-correlation coefficient is used as the measure to select the optimal IMF that contains the NLFM signal feature. Then, the spectral amplification gain indicator is proposed to realize the adaptive SR of the optimal IMF of each sub-segment signal and reconstruct the enhanced NLFM signal. Finally, the effectiveness of the proposed method is highlighted with the analysis of the short-time Fourier transform spectrum of the simulation results. As an application example, the proposed method is verified adaptability in bearing fault diagnosis under the speed-varying condition that represents a typical and complicated NLFM signal in mechanical engineering. The research provides a new way for the enhancement of weak non-stationary signals. This article is part of the theme issue 'Vibrational and stochastic resonance in driven nonlinear systems (part 1)'.

Robust Building Extraction for High Spatial Resolution Remote Sensing Images with Self-Attention Network.

Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is simple, because it contains only two parts: one is the backbone of the network, which is used to learn the local features (short distance context information around the pixel) of buildings from the image; the other part is the pyramid self-attention module, which is used to obtain the global features (long distance context information with other pixels in the image) and the comprehensive features (includes color, texture, geometric and high-level semantic feature) of the building. The network is an end-to-end approach. In the training stage, the input is the remote sensing image and corresponding label, and the output is probability map (the probability that each pixel is or is not building). In the prediction stage, the input is the remote sensing image, and the output is the extraction result of the building. The complexity of the network structure was reduced so that it is easy to implement. The proposed PISANet was tested on two datasets. The result shows that the overall accuracy reached 94.50 and 96.15%, the intersection-over-union reached 77.45 and 87.97%, and F1 index reached 87.27 and 93.55%, respectively. In experiments on different datasets, PISANet obtained high overall accuracy, low error rate and improved integrity of individual buildings.