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Most human cancers develop from stem and progenitor cell populations through the sequential accumulation of various genetic and epigenetic alterations. Cancer stem cells have been identified from medulloblastoma (MB), but a comprehensive understanding of MB stemness, including the interactions between the tumor immune microenvironment and MB stemness, is lacking. Here, we employed a trained stemness index model based on an existent one‐class logistic regression (OCLR) machine‐learning method to score MB samples; we then obtained two stemness indices, a gene expression‐based stemness index (mRNAsi) and a DNA methylation‐based stemness index (mDNAsi), to perform an integrated analysis of MB stemness in a cohort of primary cancer samples (n = 763). We observed an inverse trend between mRNAsi and mDNAsi for MB subgroup and metastatic status. By applying the univariable Cox regression analysis, we found that mRNAsi significantly correlated with overall survival (OS) for all MB patients, whereas mDNAsi had no significant association with OS for all MB patients. In addition, by combining the Lasso‐penalized Cox regression machine‐learning approach with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature that accurately predicted survival in patients with Sonic hedgehog (SHH) MB. Furthermore, positive correlations between mRNAsi and prognostic copy number aberrations in SHH MB, including MYCN amplifications and GLI2 amplifications, were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature. Our findings based on stemness indices might advance the development of objective diagnostic tools for quantitating MB stemness and lead to novel biomarkers that predict the survival of patients with MB or the efficacy of strategies targeting MB stem cells. Here, we employed a trained stemness index model to perform an integrated analysis of medulloblastoma (MB) stemness. By combining the Lasso‐penalized Cox regression with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature. Furthermore, positive correlations between gene expression‐based stemness index and prognostic copy number aberrations were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature.

Health monitoring and damage detection for important and special infrastructures, especially marine structures, are one of the important challenges in structural engineering because they are subjected to corrosion and hydrodynamic loads. Simulation of marine structures under corrosion and hydraulic loads is complex; thus, a combination of point cloud data sets, validation finite element model, parametric studies, and machine‐learning methods was used in this study to estimate the damaged surface of retaining reinforced concrete walls (RRCWs) and the load‐carrying capacity of RRCWs according to design parameters of RRCWs. After validation of the finite element method (FEM), 144 specimens were simulated using the FEM and the obtained displacement‐control loading. Compressive strength, thickness of RRCWs, strength of reinforcement bars, and ratio of reinforcement bars were considered as the design parameters. The results show that the thickness of RRCWs has the most effect on decreasing the damaged surface and load‐carrying capacity. Furthermore, the results demonstrate that Gene Expression Programming (GEP) performs better than all models and can predict the damaged surface and load‐carrying capacity with 99% and 97% accuracy, respectively. Moreover, by decreasing the thickness of RRCWs, the damaged surface is reduced to 2.5%, and by increasing the thickness, the load‐carrying capacity is increased to 51%–59%.

Aim: Despite small and transient populations, early Māori transformed large areas of New Zealand's forest landscapes. We sought to isolate the biophysical predictors that explain forest loss in the pre-historic (i.e. pre-European) period in New Zealand. Location: New Zealand. Methods: We used resampled boosted regression trees to isolate the key predictors of forest loss from a suite of 19 topographic, climatic, soil-related and archaeological predictors at a 1-km spatial resolution across New Zealand. Results: The key predictors of fire-driven forest loss during New Zealand's prehistory relate to moisture and elevation gradients, with sites characterized by low moisture levels and gentle slopes being most vulnerable. Proxies for human activity were important in the North Island, where Māori population densities were higher, but not the South Island. The predicted pattern of forest loss and its relationship with biophysical variables suggest that early Māori neither deliberately protected fire-prone regions nor systematically burnt less fire-prone ones. Main conclusions: Before Māori settlement of New Zealand fire was naturally rare, despite biophysical conditions being conducive to fire spread. The introduction of an ignition source by humans made widespread forest loss inevitable, even in the absence of sustained and deliberate use of fire. Rapid forest loss at the time of human settlement is recurrent across eastern Polynesia, so understanding this dynamic in New Zealand has implications for the region as a whole.

Distracted driving is one of the leading causes of traffic accidents and has become a bottleneck for improving driver assistance technologies. It is still a challenge to detect distracted driving behavior in real-life scenarios, which have the features of complex backgrounds, different target scales, and resolutions. In this context, a lightweight YOLOv5-GBC model is proposed for real-time distracted driving detection in this work. Firstly, the lightweight network GhostConv is used to perform lightweight operations on the convolutional layers, aiming to reduce a large number of parameters and computations. Secondly, the path aggregation network structure is improved to enhance the model fusion ability for different scale features, and coordinated attention is introduced to enhance the model extraction ability for effective information. The proposed YOLOv5-GBC model can predict different types of distracted driving. Finally, this work conducts extensive experiments; the results show that the proposed model has a mean accuracy (mAP) of 91.8%, which is 3.9% better than the baseline model, with a reduction of 6.5% and 9.1% in the weight file and Floating-point Operations Per Second, respectively. It outperforms the models of Faster-RCNN, SSD, YOLOv3-tiny, and YOLOv4-tiny, which indicates that the proposed model can identify distracted driving behaviors efficiently and rapidly.

The traveling salesman problem (TSP) aims to find the shortest tour that visits each node of a given graph exactly once. TSPs have significant importance as numerous practical problems can be naturally formulated as TSPs. Various algorithms have been developed for solving TSPs, including combinatorial optimization algorithms and deep learning-based approaches. However, these algorithms often face a trade-off between providing exact solutions with long running times and delivering fast but approximate solutions. Therefore, achieving both efficiency and solution quality simultaneously remains a major challenge. In this study, we propose a data-driven algorithm called NeuralGLS to address this challenge. NeuralGLS is a hybrid algorithm that combines deep learning techniques with guided local search (GLS). It incorporates a self-adaptive graph convolutional network (GCN) that takes into account neighborhoods of varying sizes, accommodating TSP instances with different graph sizes. This GCN calculates a regret value for each edge in a given TSP instance. Subsequently, the algorithm utilizes a mixed strategy to construct an initial tour and then employs a GLS module to iteratively improve the tour guided by the acquired regret values until a high-quality tour is obtained. Experimental results on diverse benchmark datasets and real-world TSP instances demonstrate the effectiveness of NeuralGLS in generating high-quality solutions within reasonable computation time. Furthermore, when compared to several state-of-the-art algorithms, our NeuralGLS algorithm exhibits superior generalization performance on both real-world and larger-scale TSP instances. Notably, NeuralGLS also outperforms another hybrid algorithm that also incorporates GLS by reducing the mean optimality gap for real-world TSP instances from 1.318% to 0.958%, with both methods achieving results within the same computation time. This remarkable improvement in solution quality amounts to an impressive relative enhancement of 27.31%.

Bone age assessment is a common diagnostic method used for abnormal growth and development in children. Despite recent significant advancements in convolutional neural network (CNN)-based intelligent bone age assessment in children, there remains room for improvement in assessment accuracy. Studies have indicated that a dual-path network (DPN) can incorporate different features of a CNN and improve the potential of the model to extract critical features compared to a single structural CNN. Attention mechanisms can also contribute to the enhanced ability of the model to extract channel and spatial features. Therefore, we propose a dual attention dual-path network (DADPN) to improve the accuracy of pediatric bone age assessment. DPN serves as a backbone network in DADPN by incorporating residual and dense connections. DPN was modified using two different attention mechanisms while containing gender information to compensate for physiological differences in bone age between males and females. Experiments were performed using this method with the RSNA Pediatric Bone Age Challenge dataset and compared to nine representative bone age assessment methods. This method achieved an optimal assessment accuracy with a mean absolute error (MAE) of 4.76 months. The experimental results suggest that the DADPN can extract the effective features of pediatric hand bone X-ray images and improve bone age assessment accuracy more than other deep learning methods.