Predicting Future Mental Disorders Based on Plasma Proteins and Polygenic Risk Score
Wang Jie;Li Yihan;Abudunaibi Wupuer;Peng Xing;Zhao Jianping;Yang Lei;Traditional psychiatric diagnosis relies on subjective symptom assessment, lacking objective biomarkers that hinder early detection and personalized treatment. Plasma proteins and polygenic risk score(PRS), as potential predictive tools, hold promise for advancing early diagnosis of mental disorders. This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression, schizophrenia, and post-traumatic stress disorder(PTSD)). Using participant data from the UK Biobank-Pharma Proteomics Project, we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO) analysis and construct a Cox regression risk prediction model by integrating the PRS. Additionally, we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves. Our findings reveal distinct predictive patterns across disorders. For depression, integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.632 2). For schizophrenia, adding plasma proteins enhances predictive performance, whereas PRS provides no significant improvement. For PTSD, neither plasma proteins nor PRS add substantial predictive value beyond clinical variables. Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression: HR=2.34, P<0.001; schizophrenia: HR=5.47, P<0.001; PTSD: HR=3.02, P<0.001). Although it shows good performance in short-term prediction, its long-term prediction ability has decreased, and it needs to be further optimized in the future. This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.
A Finite Volume Trigonometric WENO Scheme for Nonlinear Degenerate Parabolic Equation
Gulikayier Haerman;Kaiyishaer Reheman;Muyesaier Aihemaiti;Wei Xunan;In this paper, we present a finite volume trigonometric weighted essentially non-oscillatory(TWENO) scheme to solve nonlinear degenerate parabolic equations that may exhibit non-smooth solutions. The present method is developed using the trigonometric scheme, which is based on zero, first, and second moments, and the direct discontinuous Galerkin(DDG) flux is used to discretize the diffusion term. Moreover, the DDG method directly applies the weak form of the parabolic equation to each computational cell, which can better capture the characteristics of the solution, especially the discontinuous solution. Meanwhile, the third-order TVD-Runge-Kutta method is applied for temporal discretization. Finally, the effectiveness and stability of the method constructed in this paper are evaluated through numerical tests.
The High-Precision Scheme for the Coupled Burgers Equation
Fu Yu;Gao Tian;Hu Yudie;Weng Zhifeng;This paper proposes the finite difference-barycentric interpolation collocation scheme for the nonlinearly coupled Burgers equation. For the coupled Burgers equation, it is firstly transformed into a linear equations via the direct linearization iteration. The spatial direction is discretized by the barycentric interpolation collocation method, and the time direction is discretized by the Crank-Nicolson scheme. The corresponding linear algebraic equations are derived. The consistency analysis is provided for the semi-discrete scheme in space and the fully discrete scheme. Numerical experiments verify the high precision and efficiency of our scheme.
Wind Turbine Gear Fault Diagnosis Method Based on Parameter-Optimized VMD and Improved CNN
Liu Lei;Mutalifu Ahemaide;Mubalaike Dugamaiti;Shao Zengzhi;Wind turbine gears, operating at high speeds under complex environmental conditions, exhibit subtle early-stage fault signals that are easily masked, resulting in low accuracy with conventional diagnostic methods. To address this issue, this paper proposes a fault diagnosis method for wind turbine gears based on an improved sailfish optimizer(ISFO) algorithm optimising variational modal decomposition(VMD) and convolutional neural networks(CNN). Firstly, the ISFO algorithm is enhanced by incorporating initialisation via the Logistic chaotic map, optimisation principles from Lévy flight theory, and genetic algorithm techniques. This yields an ISFO algorithm based on hybrid strategies, effectively resolving the algorithm's local optimum issue. Subsequently, using ISFO algorithm refines VMD parameter decomposition of signals, extracting fault feature information from the modal component with the highest correlation coefficient. A short-time Fourier transform(STFT) is then employed to construct a time-frequency map. Finally, the time-frequency map is input into an optimised CNN for fault diagnosis classification. Experimental comparisons and analyses demonstrate that the proposed method achieves high diagnostic accuracy on both public and self-test datasets, with an average accuracy rate of 98.67%, effectively addressing wind turbine gear fault diagnosis challenges.
Apnea Detection Based on 1D-Res&SENet
Xu Jiahao;Hu Shaowen;Shan Xinying;Liu Jizhong;Addressing the current practice where most apnea detection systems rely on time-frequency features extracted from respiratory signal samples for classification, this paper proposes a 1D-Res&SENet classification model. This approach takes complete respiratory signal waveforms as input, extracts features through a one-dimensional convolutional neural network, and incorporates a residual network structure to mitigate gradient vanishing and grid degradation. Furthermore, recognising the varying importance of features across channels, it introduces an SE attention mechanism to identify and strengthen associative information between feature channels, thereby enhancing the accuracy of apnoea detection. Experimental results show that after adding the residual network and SENet module, the accuracy, recall and specificity of the model are increased by 2.0%, 4.9% and 1.7%, respectively.
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