BACKGROUND: Regulated cell death (RCD) maintains cellular homeostasis and tissue integrity, playing a pivotal role in both health and disease. A variety of RCD subroutines have been identified, each characterized by distinct molecular and morphological features. These cell death modalities do not operate in isolation. Instead, they interact with one another in complex ways. This interaction leads to crosstalk through interconnected and often overlapping signaling pathways. Multiple forms of RCD can coexist within the same disease, influencing various cell types or even the same cell type either synchronously or sequentially. Understanding the intricate dynamics of RCD is of high importance. However, it is challenging to discern the relative contribution of various RCD modalities within the specific biological context. This necessitates the development of advanced methodologies to systematically analyze the priority of RCD pathways in various cellular environments. MAIN: In the present study, we first created a manually curated collection of gene sets for 18 well-characterized RCD modes. We then estimated the significance of each RCD pathway by combining the results of seven gene set enrichment tests via the Tippet p-value combination approach. Afterward, the consensus of enrichment for each RCD pathway across tests was evaluated using robust rank aggregation. The priorities of RCD were subsequently resolved by considering both the combined p value and the consensus score. The reliability of the proposed approach was validated by applying it to explore the RCD modes in intracranial aneurysms. Finally, a user-friendly web application was created for researchers worldwide. CONCLUSION: Our study offers a new way to reveal complicated RCD modalities in specific biological settings. The web server is freely accessible at https://www.zhounan.org/rcdrank.

With the continuous development of integrated satellite ground networks, edge servers are laid out on low orbit earth (LEO) satellites to provide seamless services for some remote areas. In order to further improve service quality for mobile devices, the collaborative work between edge and cloud has received widespread attention. In this article, we consider the scenario of insufficient base station (BS) coverage in remote areas and investigate a hybrid model of computing offloading and resource allocation for edge cloud collaborative computing, in which edge servers with limited resources collaborate with the cloud by purchasing cloud computing resources. In this way, cloud server (CS) and edge servers separately price their computing and storage resources to stimulate each other to participate in cooperation and maximize their respective utility. We jointly optimize the size of offloading tasks, offloading strategies for devices and resource pricing issues for edge servers and CS based on comprehensive consideration of price cost, energy cost and latency limitation. A multi-level Stackelberg game is formulated among the CS (leader), BSs (followers) and satellites (followers) in level I-II, the BSs (leaders), satellites (leaders) and IoT devices (followers) in level II-III. Furthermore, we prove the existence and uniqueness of Stackelberg equilibrium (SE) in Stackelberg game. The SERI algorithm is proposed and simulations is conducted to show that SERI algorithm has good convergence performance and better entity utility.

As an edge cache device, the unmanned aerial vehicle (UAV) auxiliary edge network provides a wider coverage for mobile edge computing (MEC), and its flexibility and low delay bring great convenience to user equipments (UEs). To fully utilize the characteristics of UAVs, we need to consider the deployment trajectory and associated UEs of UAV. For computational intensive tasks, proper caching decision and offloading decision also help reduce delay. In this paper, we jointly optimize UE-UAV association, UAV deployment, caching decision, and offloading decision to minimize service delay. Considering the service rate of UAVs, we use the M/M/1 queues to model the calculation process. The problem of minimizing delay is formulated as a multi-objective optimization problem. We decompose the objective into three sub problems and use corresponding algorithms to solve them, namely the successive convex approximation (SCA) method, binary particle swarm optimization (BPSO) algorithm, and convex function difference (DC) method. The simulation results show that our algorithm is better than the other three baseline algorithms in minimizing service delay, and verify the delay effect of different edge storage capabilities in our optimization.

The paradigm of unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) has emerged as an effective scheme for handling intensive tasks in heterogeneous networks. In this work, we consider a user-equipment-rich city network scenario. Due to the limited user equipments (UEs) resources and base station (BS) coverage, we utilise multiple-UAV-assisted UEs and partial offloading to handle the tasks. Meanwhile, considering the impact of task diversity on the quality of service (QoS) of the system, we design an integrated scheme that combines improved clustering techniques and deep reinforcement learning (DRL) for dynamic resource scheduling. Firstly, a random forest-based clustering algorithm (RFCA) is used to cluster UEs according to the service requirements (SR) of tasks, as a way to reduce the complexity of task processing and user association. Then a DRL-based computational offloading and bandwidth allocation algorithm (DCOBA) is used to improve the QoS by jointly optimising UAV-user associations, offloading ratios, and bandwidth allocation to reduce the system latency and energy consumption. Finally, experimental simulation data shows that our scheme can better optimise the Qos compared to traditional schemes.

In order to cope with the increasing data traffic, we try to enable Intelligent Reflecting Surface (IRS) interference elimination in Device-to-Device (D2D) communication networks to improve the Signal Interference Noise Ratio (SINR). We build the system model and divide the original problem into two subproblems: IRS reflection parameter adjustment and IRS allocation. We use the the Cross Entropy Global Optimization Method (CEGOM) to solve the first subproblem. For the second subproblem, in order to ensure the fairness of user rates and avoid user starvation, we propose a heuristic algorithm based on the Max-Min Fairness Method (MMFM) to solve the problem. Simulation results demonstrate the superiority of the proposed algorithms, which improves Jain's fairness index by 70%, 72% and 13% and reduces the blocking probability by 92%, 90% and 82%, respectively, when compared to the random, shortest distance, and traditional MMFM strategies.

With the emergence of the Internet of Things (IoT), mobile edge computing (MEC) effectively reduces task delay of multiple applications. The limitations of computing and storage capabilities, as well as the complex dynamic network environments, make efficient edge service computing stressful. To address this challenge, digital twin (DT) technology is a promising solution that bridges the virtual and physical worlds by creating digital representations of physical objects. DT technology can model the behaviors of physical entities through virtual mirroring and assist physical networks in making optimal network strategies. In this paper, we combine DT technology with MEC networks to develop a utility-based task offloading and resource allocation scheme, aiming to maximize the quality of experience (QoE) of user equipments (UEs) and the utility of base stations (BSs). Specifically, we construct a hierarchical Stackelberg game model, study the interaction between BSs and UEs, and prove that the UE layer is an exact potential game with Nash equilibrium (NE). To achieve Stackelberg equilibrium (SE), we propose a game-based hierarchical interaction algorithm (GHIA) and analyze it. The experimental results show that GHIA has good convergence, and the utility performance of participants is better comparing to other algorithms.

Physical layer authentication (PLA) is a promising solution to address the security issue raised due to malicious jamming or spoofing. However, accurate and diversified channel state information is required to implement the PLA schemes. In this regard, reconfigurable intelligent surface (RIS) has the potential to quickly reshape the communication environment at a cheap cost, and thus has great potential to enhance the PLA. In this paper, we propose a RIS-assisted channel impulse response (CIR)-based dynamic PLA scheme. Specifically, the receiver exploits the geographic location information of the transmitters embedded in CIR to identify the message. In order to reduce the impact of the components representing environmental changes in CIR on the authentication, the method of regularly updating CIR database is adopted. In addition, with RIS enriched CIR information, we can achieve a high authentication rate by constructing a classification neural network. Experiments are conducted based on the communication system with DeepMIMO datasets, and the simulation results demonstrate that the proposed authentication scheme is effective for the identification of both first-attack and non-first-attack spoofers.

With the rise of the Internet industry and the technique of artificial intelligence, personalized services are increasingly important in recent years for improving user experience and increasing corporates' competitiveness and profits. Precise prediction of customers' behaviors has shown great effects in modern business marketing, especially when making personalized decisions. In this paper, we develop a deep learning network to make personalized predictions of their behaviors among a list of potential choices. The architecture of this model combines each user's features and his historical event lists by sequence-to-sequence (Seq2Seq) structure and make predictions based on his recent event lists. We also modify the long-short- term memory (LSTM) cell forget gate's structure to enhance the attention ability. Such design, called the attetioned LSTM, converges quicker and better while still maintain the similar performance in open dataset IMDB. In addition, in dealing with personalized prediction problems in real-world datasets provided by our cooperative company, our attentioned LSTM achieves a 10% higher precision in average than the standard LSTM model. The advantage is confirmed in evaluation of this generic method on a real dataset of users' behaviors sequences and individuals' attribute profiles from Ant Financial. It also achieves a great result working on the real-world business scene. This model can also achieve a great performance working on the real-world business scene.