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.

Abstract Image harmonization is a widely used technique in image composition, which aims to adjust the appearance of the composited foreground object according to the style of the background image so that the resulting composited image is visually natural and appears to be photographed. Previous methods are mostly trained in a fully supervised manner, while demonstrating promising results, they do not generalize well to complex unseen cases involving significant style and semantic difference between the composited foreground object and the background image. In this paper, we present a self‐supervised image harmonization framework that enables superior performance on complex cases. To do so, we first synthesize a large amount of data with wide diversity for training. We then develop an attentive harmonization module to adaptively adjust the foreground appearance by querying relevant background features. To allow more effective image harmonization, we develop a region‐aware harmony classifier to explicitly judge whether an image is harmonious or not. Experiments on several datasets show that our method performs favourably against previous methods. Our code will be made publicly available.

We present a photorealistic portrait style transfer approach that allows for producing high-quality results in previously challenging unconstrained conditions, e.g., large facial perspective difference between portraits, faces with complex illumination (e.g., shadow and highlight) and occlusion, and can test without portrait parsing masks. We achieve this by developing a framework to learn robust dense correspondence across portraits for semantically aligned style transfer, where a regional style contrastive learning strategy is devised to boost the effectiveness of semantic-aware style transfer while enhancing the robustness to complex illumination. Extensive experiments demonstrate the superiority of our method.

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.

In millimeter-wave (mmWave) communication systems, hybrid beamforming is regarded as an effective way to increase the spectral efficiency of the massive multiple-input multiple-output (MIMO) system. Assuming perfect channel state information (CSI) is known at the transmitter, we focus on a downlink massive multi-user MIMO system which supports multi-stream per user. In the above scenario, we investigate the hybrid beamforming problem with strong correlation between users' channels, where the existing schemes have performance loss. To tackle this problem, this paper proposes the channel correlation cancelation-based hybrid beamforming (CCCHB) algorithm which considers the correlation between channels and decomposes the optimization of overall spectrum efficiency of the users to a series of sub-rate optimization problems. And the block diagonalization (BD) technique is used in the equivalent channel to eliminate inter-user interference. Simulation results illustrate that the performance of the proposed scheme outperforms the existing algorithm, especially significant when there exists high correlation between users' channels.

Cloud radio access network (CRAN) has been proposed as a potential energy saving architecture and a scalable solution to increase the capacity and performance of radio networks. The original CRAN decouples the digital unit (DU) from radio unit (RU) and centralizes the DUs. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN, i.e. the network segment connecting RUs and DUs. In this study, we propose a modified CRAN architecture, namely hybrid cloud RAN (H-CRAN), where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions, i.e., central cloud level and edge cloud level, connected by a transport layer called “midhaul”. We study the interplay of energy efficiency and midhaul bandwidth consumption when baseband functions are centralized at the edge cloud vs central cloud. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying the network constraints. The addressed problem with the associated constrains are modeled as a mixed integer constraint optimization problem. Numerical results show the compromise between energy and bandwidth consumption, with the optimal placement of baseband processing functions in H-CRAN architecture.

Cloud radio access network (CRAN) has been proposed as a promising evolution of mobile network architecture where baseband processing functions of a base station are split/decoupled from the radio unit (RU) and centralized. However, rigid bandwidth and latency requirements are incurred by fronthaul, i.e., transport link, which connects RU to the central cloud. Therefore, new functional splits are discussed for CRAN with dual-site processing, which we call Hybrid-RAN (H-RAN), where some functions remain distributed while others are centralized. In this work, from a perspective of minimizing the total cost of ownership (TCO) for H-RAN, we present a techno-economic study to find the optimal functional splits for a base station (BS), with a given configuration. A configuration of a BS represents frequency layers, carrier bandwidths, and MIMO schemes, associated with different frequency bands. For each functional split, we present a model to calculate the requirement of computational resources and fronthaul bandwidth. We formulate a TCO minimization model using constraint programming. Numerical results show that the optimal functional split depends on BS configuration, fiber ownership, and data transmission direction. H-RAN with optimal functional split can achieve lower TCO than both classical Distributed RAN and CRAN.

5G Radio Access Networks (RANs) are supposed to increase their capacity by 1000x to handle growing number of connected devices and increasing data rates. The concept of cloud-RAN (CRAN) has been recently proposed to decouple digital units (DUs) and radio units (RUs) of base stations (BSs), and centralize DUs into central offices. CRAN can ease the implementation of advanced radio coordination techniques, e.g., Coordinated Multi-Point (CoMP) Transmission/Reception, to enhance its system throughput. However, separating DUs and RUs, and implementing CoMP in CRAN require low-latency and high-bandwidth connectivity links, called "fronthaul". Today, consensus has not yet been achieved on how BSs, fronthaul, and central offices will be orchestrated to enhance the system throughput. In this study, we present a CRAN over Passive Optical Network (PON) architecture called virtualized-CRAN (V-CRAN). V-CRAN leverages the concept of virtualized PON (VPON) that can dynamically associate any RU to any DU so that several RUs can be coordinated by the same DU, and the concept of virtualized BS (V-BS) that can jointly transmit common signals from multiple RUs to a user. We propose a novel mathematical model based on constraint programming for joint allocation of radio, optical network, and baseband processing resources to enhance RAN throughput, and we solve it by optimally forming VPONs and V-BSs. Comprehensive simulations show that V-CRAN can enhance the system throughput and the efficiency of resource utilization.

In emerging cellular systems, optical fronthaul is expected to play a major role to support many control operations, e.g., Coordinated Multipoint (CoMP). CoMP is a promising technique for interference mitigation as it can transform interfing signals into joint transmission (reception) in which signals from adjacent cell sites are simultaneously transmitted (received) to (from) mobile terminals. But the exchange of information required by CoMP demands high flexibility and capacity. This paper proposes a new architecture for supporting CoMP operations in emerging cellular systems. It is based on a time-and-wavelength-division-multiplexed passive optical network (TWDM-PON) fronthaul, using virtualized base stations and a cloud radio access network (C-RAN) architecture. We also propose techniques to distribute the load on controllers to minimize the coordination delay. Results show that, for a typical setting, our methods can save up to 37% on the time required to distribute channel state information among multiple base stations.

In recent years, the increasing traffic demand in radio access networks (RANs) has led to considerable growth in the number of base stations (BSs), posing a serious scalability issue, including the energy consumption of BSs. Optical-access-enabled Cloud-RAN (CRAN) has been recently proposed as a next-generation access network. In CRAN, the digital unit (DU) of a conventional cell site is separated from the radio unit (RU) and moved to the “cloud” (DU cloud) for centralized signal processing and management. Each DU/RU pair exchanges bandwidth-intensive digitized baseband signals through an optical access network (fronthaul). Time-wavelength division multiplexing (TWDM) passive optical network (PON) is a promising fronthaul solution due to its low energy consumption and high capacity. In this paper, we propose and leverage the concept of a virtual base station (VBS), which is dynamically formed for each cell by assigning virtualized network resources, i.e., a virtualized fronthaul link connecting the DU and RU, and virtualized functional entities performing baseband processing in DU cloud. We formulate and solve the VBS formation (VF) optimization problem using an integer linear program (ILP). We propose novel energy-saving schemes exploiting VF for both the network planning stage and traffic engineering stage. Extensive simulations show that CRAN with our proposed VF schemes achieves significant energy savings compared to traditional RAN and CRAN without VF.

Due to the accelerated evolution of application services, optical network virtualization simplifies optical-layer resource management and provides flexibility in spectrum resource allocation. However, the energy consumption is one of the great challenges in the virtual optical networks (VONs). This paper focuses on the energy efficiency problem in survivable VONs with the sliceable multi-flow transponders and the elastic regenerators. For each VON, all virtual links provide the dedicated-path protection in the flexible bandwidth optical networks. An integer linear program (ILP) and a minimum unit-energy submatrix (MinEnSub) VON mapping approach are developed to improve the energy efficiency, minimize the power consumption, and reduce the spectrum usage under different line rates. For comparison, a baseline VON mapping approach is introduced. Simulation results show that the ILP model and the proposed MinEnSub VON mapping approach can save power consumption, improve the energy efficiency, and reduce the spectrum usage compared with the baseline VON mapping approach in a 6-node network. As expected, in a 14-node network, simulation results also validate that our proposed MinEnSub VON mapping approach can achieve better performance in terms of power consumption, energy efficiency, number of frequencies, and the number of regenerators.

In recent years, the increasing traffic demand in radio access networks (RAN) has led to considerable growth of the number of base stations (BS), posing a serious scalability issue with respect to the energy consumption of BSs. Optical-access-enabled Cloud RAN (CRAN) has been recently proposed as a next-generation access network, where the digital unit (DU) of a conventional cell site is separated from the radio unit (RU), by an optical access network (fronthaul), and moved to the “cloud” (DU pool) for centralized signal processing and management. Time-Wavelength Division Multiplexing (TWDM) Passive Optical Network (PON) is a promising fronthaul solution due to its low energy consumption and high capacity. In this study, we propose the concept of Virtual Base Station (VBS), which is dynamically formed for each cell by assigning virtualized network resources, including i) a virtualized PON link connecting the DU and RU and ii) virtualized functional entities performing baseband processing in DU pool. We propose a novel energy-saving scheme exploiting VBS formation for CRAN and compare its performance with the optimal results of an Integer Linear Program for VBS formation optimization problem. Numerical evaluation shows that CRAN with VBS formation achieves significant energy savings compared to traditional RAN and CRAN without VBS formation.