The optimized gate recurrent unit based on improved evolutionary algorithm to predict stock market returns

In order to accelerate the learning ability of neural network structure parameters and improve the prediction accuracy of deep learning algorithms, an evolutionary algorithm, based on a prior Gaussian mutation (PGM) operator, is proposed to optimize the structure parameters of a gated recurrent unit (GRU) neural network. In this algorithm, the sensitivity learning process of GRU model parameters into the Gaussian mutation operator, used the variance of the GRU model parameter training results as the Gaussian mutation variance to generate the optimal individual candidate set. Then, the optimal GRU neural network structure is constructed using the evolutionary algorithm of the prior Gaussian mutation operator. Moreover, the PGM-EA-GRU algorithm is applied to the prediction of stock market returns. Experiments show that the prediction model effectively overcomes the GRU neural network, quickly falling into a local optimum and slowly converging. Compared to the RF, SVR, RNN, LSTM, GRU, and EA-GRU benchmark models, the model significantly improves the searchability and prediction accuracy of the optimal network structure parameters. It also validates the effectiveness and the progressive nature of the PGM-EA-GRU model proposed in this paper with stock market return prediction

Macrophages in Glioblastoma Development and Therapy: A Double-Edged Sword

Glioblastoma (GBM) is one of the leading lethal tumors, featuring aggressive malignancy and poor outcome to current standard temozolomide (TMZ) or radio-based therapy. Developing immunotherapies, especially immune checkpoint inhibitors, have improved patient outcomes in other solid tumors but remain fatigued in GBM patients. Emerging evidence has shown that GBM-associated macrophages (GAMs), comprising brain-resident microglia and bone marrow-derived macrophages, act critically in boosting tumor progression, altering drug resistance, and establishing an immunosuppressive environment. Based on its crucial role, evaluations of the safety and efficacy of GAM-targeted therapy are ongoing, with promising (pre)clinical evidence updated. In this review, we summarized updated literature related to GAM nature, the interplay between GAMs and GBM cells, and GAM-targeted therapeutic strategies

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy

Abstract Objectives This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson’s disease (PD) and multiple system atrophy (MSA). Methods A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. Results The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. Conclusions Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences