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Cronobacter sakazakii is a major foodborne pathogen that is associated with outbreaks of life-threatening necrotizing enterocolitis, meningitis, and sepsis in neonates and infants. Although the incidence of this pathogen is [...]

Global path planning is one of the key technologies in unmanned underwater vehicle (UUV) intelligent control. At present, research on UUV global path planning technology tends to choose long-distance and large-scale 3D space as the research environment, which leads to a sharp increase in the amount of data and search range for 3D spatial path planning. Therefore, an efficient and relatively small data volume 3D spatial path planning method is an urgent problem that needs to be solved for UUV engineering applications. To solve this problem, a new bilevel path planning algorithm for UUV is proposed. In the upper level of the algorithm, a Max Min Ant System-Elite Genetic (MMAS-EGA) algorithm is put forward, which is a hybrid ant colony optimization/genetic algorithm, in order to improve the convergence speed of the algorithm. In the lower level of the bilevel algorithm, a function optimization algorithm and the MMAS algorithm are used to minimize the number of variables to be optimized. To verify the effectiveness of the algorithm, we conducted simulation experiments in a three dimensional environment. The simulation results in the three-dimensional environment show that, compared with the existing bilevel algorithm, the time to search the global optimal solution is reduced by 9%, and the number of iterations is reduced by 4.4%. Furthermore, the new algorithm we proposed is more efficient and suitable for global path planning for different tasks. [ABSTRACT FROM AUTHOR]

Background and Purpose Methods Results Conclusion The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of this paper is to establish a non‐invasive system for identifying HER2 and HR in breast cancer using dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI).In light of the absence of high‐performance algorithms and external validation in previously published methods, this study utilizes 3D deep features and radiomics features to represent the information of the Region of Interest (ROI). A Siamese Neural Network was employed as the classifier, with 3D deep features and radiomics features serving as the network input. To neutralize manufacturer bias, a batch effect normalization method, ComBat, was introduced. To enhance the reliability of the study, two datasets, Predict Your Therapeutic Response with Imaging and moLecular Analysis (I‐SPY 1) and I‐SPY 2, were incorporated. I‐SPY 2 was utilized for model training and validation, while I‐SPY 1 was exclusively employed for external validation. Additionally, a breast tumor segmentation network was trained to improve radiomic feature extraction.The results indicate that our approach achieved an average Area Under the Curve (AUC) of 0.632, with a Standard Error of the Mean (SEM) of 0.042 for HER2 prediction in the I‐SPY 2 dataset. For HR prediction, our method attained an AUC of 0.635 (SEM 0.041), surpassing other published methods in the AUC metric. Moreover, the proposed method yielded competitive results in other metrics. In external validation using the I‐SPY 1 dataset, our approach achieved an AUC of 0.567 (SEM 0.032) for HR prediction and 0.563 (SEM 0.033) for HER2 prediction.This study proposes a non‐invasive system for identifying HER2 and HR in breast cancer. Although the results do not conclusively demonstrate superiority in both tasks, they indicate that the proposed method achieved good performance and is a competitive classifier compared to other reference methods. Ablation studies demonstrate that both radiomics features and deep features for the Siamese Neural Network are beneficial for the model. The introduced manufacturer bias normalization method has been shown to enhance the method's performance. Furthermore, the external validation of the method enhances the reliability of this research. Source code, pre‐trained segmentation network, Radiomics and deep features, data for statistical analysis, and Supporting Information of this article are online at: https://github.com/FORRESTHUACHEN/Siamese_Neural_Network_based_Brest_cancer_Radiogenomic. [ABSTRACT FROM AUTHOR]