Effective classification methods and prognostic models enable more accurate classification and treatment of hepatocellular carcinoma (HCC) patients. However, the weak correlation between RNA and protein data has limited the clinical utility of previous RNA-based prognostic models for HCC. In this work, we constructed a novel prognostic framework for HCC patients using seven differentially expressed proteins associated with ferroptosis and iron metabolism. Furthermore, this prognostic model robustly classifies HCC patients into three clinically relevant risk groups. Significant differences in overall survival, age, tumor differentiation, microvascular invasion, distant metastasis, and alpha-fetoprotein levels were observed among the risk groups. Based on the prognostic model and known biological pathways, we explored the potential mechanisms underlying the inconsistent differential expression patterns of FTH1 (Ferritin heavy chain 1) mRNA and protein. Our findings demonstrated that tumor tissues in HCC patients promote liver cancer progression by downregulating FTH1 protein expression, rather than upregulating FTH1 mRNA expression, ultimately leading to poor prognosis. Subsequently, based on risk score and tumor size, we developed a nomogram for predicting the prognosis of HCC patients, which demonstrated superior predictive performance in both the training and validation cohorts (C-index: 0.774; AUC for 1-5 years: 0.783-0.964). Additionally, our findings demonstrated that the adverse prognosis of high-risk HCC patients was closely correlated with ferroptosis in liver cancer tissues, alterations in iron metabolism, and changes in the tumor immune microenvironment. In conclusion, our prognostic model and predictive nomogram offer novel insights and tools for the effective classification of HCC patients, potentially enhancing clinical decision-making and outcomes.

Despite advancements in human-robot teamwork, limited progress was made in developing AI assistants capable of advising teams online during task time, due to the challenges of modeling both individual and collective beliefs of the team members. Dynamic epistemic logic has proved to be a viable tool for representing a machine Theory of Mind and for modeling communication in epistemic planning, with applications to human-robot teamwork. However, this approach has yet to be applied in an online teaming assistance context and fails to account for the real-life probabilities of potential team beliefs. We propose a novel blend of epistemic planning and POMDP techniques to create a risk-bounded AI team assistant, that intervenes only when the team’s expected likelihood of failure exceeds a predefined risk threshold or in the case of potential execution deadlocks. Our experiments and simulated demonstration on the Virtualhome testbed show that the assistant can effectively improve team performance.

The construction of deep neural networks depends on a significant number of parameters and computational complexity, which poses a challenge in the field of image processing. To address the issue of the Transformer network model's large size and inability to effectively capture local features of the image, this paper proposes a lightweight composite Transformer structure that combines a spectral feature refinement module (SFRM) and a parameterless attention augmentation module (PAAM). The SFRM and PAAM work together to improve the quality of the spectral features used in the transformer. The proposed structure aims to enhance the performance of the transformer without adding unnecessary complexity. The SFRM utilises the two-dimensional discrete cosine transform to convert the image from the spatial domain to the frequency domain. This process extracts both the overall image structure and detailed feature information from the high-frequency and low-frequency regions, respectively. The aim is to purify the spatially-insignificant features in the original image. The PAAM introduces a parameter-free channel, spatial, and 3D attention enhancement mechanism to extract correlation features of local information in the spatial domain without increasing the number of parameters. This improves the expression of local features in the image. Additionally, Depth Separable (DConv MLP) is introduced to further reduce the network model's weight. The experimental results show that the proposed algorithm achieves an accuracy of 79.6% on the ImageNet-1K dataset, 91.6% on the Oxford 102 Flower Dataset, and 94.1% on the CIFAR-10 dataset. Compared to ViT-B, Swin-T, and CSwin-T, respectively, the number of covariates decreases by 86.11%, 58.62%, and 47.83%. The number of parameters is also lower than VGG-16 and ResNet-110 by 91.07% and 77.70%, respectively.

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

Solving planning and scheduling problems for multiple tasks with highly coupled state and temporal constraints is notoriously challenging. An appealing approach to effectively decouple the problem is to judiciously order the events such that decisions can be made over sequences of tasks. As many problems encountered in practice are over-constrained, we must instead find relaxed solutions in which certain requirements are dropped. This motivates a formulation of optimality with respect to the costs of relaxing constraints and the problem of finding an optimal ordering under which this relaxing cost is minimum. In this paper, we present Generalized Conflict-directed Ordering (GCDO), a branch-and-bound ordering method that generates an optimal total order of events by leveraging the generalized conflicts of both inconsistency and suboptimality from sub-solvers for cost estimation and solution space pruning. Due to its ability to reason over generalized conflicts, GCDO is much more efficient in finding high-quality total orders than the previous conflict-directed approach CDITO. We demonstrate this by benchmarking on temporal network configuration problems, which involves managing networks over time and makes necessary tradeoffs between network flows against CDITO and Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two orders of magnitude more benchmark problems to optimality and twice the problems compared to CDITO and MILP within a runtime limit, respectively.