Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents

We study multi-agent reinforcement learning (MARL) with centralized training and decentralized execution. During the training, new agents may join, and existing agents may unexpectedly leave the training. In such situations, a standard deep MARL model must be trained again from scratch, which is very time-consuming. To tackle this problem, we propose a special network architecture with a few-shot learning algorithm that allows the number of agents to vary during centralized training. In particular, when a new agent joins the centralized training, our few-shot learning algorithm trains its policy network and value network using a small number of samples; when an agent leaves the training, the training process of the remaining agents is not affected. Our experiments show that using the proposed network architecture and algorithm, model adaptation when new agents join can be 100+ times faster than the baseline. Our work is applicable to any setting, including cooperative, competitive, and mixed.Comment: 10 pages, 7 figure

A dual mode privacy-preserving scheme enabled secure and anonymous for edge computing assisted internet of vehicle networks

This paper adopts Named Data Network technology for data delivery/forwarding over the Internet of Vehicles (IoVs) and proposes an NDN-based architecture for IoVs based on mobile edge computing(MEC). Advanced research has demonstrated the considerable benefits of introducing MEC into IoVs, but comes with issues such as insufficient security and privacy protection problems. To address these issues, we propose a dual-mode privacy-preserving framework for the security layer of the proposed network architecture. Specifically, we construct a privacy protection identity-based broadcast proxy re-encryption scheme to provide privacy to a set of vehicles with data requests. Furthermore, we use a federated learning scheme based on local differential privacy in the proposed NDN-based architecture for MEC-empowered IoV to achieve high-speed response and decision making. Simulation results demonstrate that our proposed scheme performs effectively

Reliability-aware joint optimization for cooperative vehicular communication and computing

This paper comprehensively discusses the cooperative communication and computation of vehicular system. Based on the cooperative transmission, an stochastic model of vehicle-to-vehicle (V2V) communication reliability is established using probability theory. Furthermore, the computation reliability is defined as a new metric for computation offloading, and a vehicle computational performance evaluation model is also established. In order to effectively compute the required data, we combine V2V communication and vehicle computing to further characterize the coupling reliability of cooperative communications and computation systems. In addition, we propose a virtual queue model that combines queue length and vehicle privacy entropy to optimize partitioning. Finally, considering the amount of processing data and cut-off time of vehicle applications, we establish the optimal partition model of vehicle computing with the goal of maximizing the coupling reliability, and propose the coupling-oriented reliability calculation for vehicle collaboration using dynamic programming methods. Simulations show that the proposed scheme outperforms traditional approaches in terms of coupling reliability and completion rate. In addition, the allocation between local computing and data offloading is controlled by the server's privacy perception of collaboration events

Genome-Wide Association Study of Susceptibility to Idiopathic Pulmonary Fibrosis

Rationale: Idiopathic pulmonary fibrosis (IPF) is a complex lung disease characterised by scarring of the lung that is believed to result from an atypical response to injury of the epithelium. Genome-wide association studies have reported signals of association implicating multiple pathways including host defence, telomere maintenance, signalling and cell-cell adhesion. Objectives: To improve our understanding of factors that increase IPF susceptibility by identifying previously unreported genetic associations. Methods and measurements: We conducted genome-wide analyses across three independent studies and meta-analysed these results to generate the largest genome-wide association study of IPF to date (2,668 IPF cases and 8,591 controls). We performed replication in two independent studies (1,456 IPF cases and 11,874 controls) and functional analyses (including statistical fine-mapping, investigations into gene expression and testing for enrichment of IPF susceptibility signals in regulatory regions) to determine putatively causal genes. Polygenic risk scores were used to assess the collective effect of variants not reported as associated with IPF. Main results: We identified and replicated three new genome-wide significant (P<5×10−8) signals of association with IPF susceptibility (associated with altered gene expression of KIF15, MAD1L1 and DEPTOR) and confirmed associations at 11 previously reported loci. Polygenic risk score analyses showed that the combined effect of many thousands of as-yet unreported IPF susceptibility variants contribute to IPF susceptibility. Conclusions: The observation that decreased DEPTOR expression associates with increased susceptibility to IPF, supports recent studies demonstrating the importance of mTOR signalling in lung fibrosis. New signals of association implicating KIF15 and MAD1L1 suggest a possible role of mitotic spindle-assembly genes in IPF susceptibility

Beckmann Rearrangement of Ketoxime Catalyzed by N-methyl-imidazolium Hydrosulfate

Beckmann rearrangement of ketoxime catalyzed by acidic ionic liquid-N-methyl-imidazolium hydrosulfate was studied. Rearrangement of benzophenone oxime gave the desirable product with 45% yield at 90 &deg;C. When co-catalyst P2O5 was added, the yield could be improved to 91%. The catalyst could be reused three cycles with the same efficiency. Finally, reactions of other ketoximes were also investigated

MEC intelligence driven electro-mobility management for battery switch service

As a key enabler in the green transport system, the popularity of Electric Vehicles (EV) has attracted attention from academia and industrial communities. However, the driving range of EVs is inevitably affected by the insufficient battery volume, as such EV drivers may experience trip discomfort due to a long battery charging time (under traditional plug-in charging service). One feasible alternative to accelerate the service time to feed electricity is the battery switch technology, by cycling switchable (fully-recharged) batteries at Battery Switch Stations (BSSs) to replace the depleted batteries from incoming EVs. Along with recent advance of vehicle cooperation through emerging Information Communication Technology (ICT), in this paper we propose a Mobile Edge Computing (MEC) driven architecture to gear the intelligent battery switch service management for EVs. Here, the decision making on where to switch battery is operated by EVs in a distributed manner. Besides, the Vehicle-to-Vehicle (V2V) communication in line with public transportation bus system is applied to operate flexible information exchange between EVs and BSSs. Dedicated MEC functions are positioned for bus system to efficiently disseminate BSSs status and aggregate EVs' reservations, concerning the massive signalling exchange cost. The Global Controller (GC) is positioned as cloud server to gather BSSs (service providers) status and EVs' reservations (clients), and predict the service availability of BSS (e.g., whether/when a battery can be switched). We conduct performance evaluation to show the advantage of MEC system in terms of reduction of communication cost, and BSS service management scheme regarding reduction of service waiting time (e.g., how long to wait for battery switch) and increase of service satisfaction rate (e.g., how many batteries to switch for EVs)

Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis

Lung cancer is one of the most malignant tumors in the world. Early diagnosis and treatment of lung cancer are vitally important to reduce the mortality of lung cancer patients. In the present study, we attempt to identify the candidate biomarkers for lung cancer by weighted gene co-expression network analysis (WGCNA). Gene expression profile of GSE30219 was downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the limma package, and the co-expression modules of genes were built by WGCNA. UALCAN was used to analyze the relative expression of normal group and tumor subgroups based on tumor individual cancer stages. Survival analysis for the hub genes was performed by Kaplan–Meier plotter analysis with the TCGA database. A total of 2176 genes (745 upregulated and 1431 downregulated genes) were obtained from the GSE30219 database. Seven gene co-expression modules were conducted by WGCNA and the blue module might be inferred as the most crucial module in the pathogenesis of lung cancer. In the pathway enrichment analysis of KEGG, the candidate genes were enriched in the “DNA replication,” “Cell cycle,” and “P53 signaling pathway” pathways. Among these, the cell cycle pathway was the most significant pathway in the blue module with four hub genes CCNB1, CCNE2, MCM7, and PCNA which were selected in our study. Kaplan–Meier plotter analysis indicated that the high expressions of four hub genes were correlated with a worse overall survival (OS) and advanced tumors. qRT-PCR showed that mRNA expression levels of MCM7 (p=0.038) and CCNE2 (0.003) were significantly higher in patients with the TNM stage. In summary, the high expression of the MCM7 and CCNE2 were significantly related with advanced tumors and worse OS in lung cancer. Thus, the MCM7 and CCNE2 genes can be good indicators for cellular proliferation and prognosis in lung cancer

The role of tissue and serum carcinoembryonic antigen in stages I to III of colorectal cancer—A retrospective cohort study

Abstract Purpose Tissue carcinoembryonic antigen (t‐CEA) and serum carcinoembryonic antigen (s‐CEA) expression profiles are the most useful tumor markers for the diagnosis and evaluation of colorectal cancer (CRC) worldwide; however, their roles in CRC progression remain controversial. This study aimed to compare the prognostic values of both s‐CEA and t‐CEA in CRC. Methods A total of 517 patients from January 2006 to December 2010 with stages I‐III CRC were retrospectively examined, with 5‐year postoperative follow‐up and death as end‐points. T‐CEA expression, s‐CEA expression, and clinical pathological parameters were inputted into the SPSS 21.0 software. The Kaplan‐Meier method was used to analyze the 5‐year disease‐free survival (DFS) rate of patients in different tumor node metastasis (TNM) stages based on t‐CEA and s‐CEA expression. Results Tumor differentiation and the number of positive lymph node harvests were significantly different among the t‐CEA groups (P < 0.001, P = 0.002); however, clinicopathological features showed no significant difference. The groups with high s‐CEA and t‐CEA expression had a significantly poorer prognosis than those with low s‐CEA (P = 0.021) and t‐CEA (P < 0.01) expression, respectively. The multivariate analysis demonstrated that t‐CEA was an independent prognostic factor in CRC (P < 0.001), but s‐CEA was not (P = 0.339). The 5‐year disease‐free survival rates among the t‐CEA groups were significantly different in stages I, II, and III of CRC (P = 0.001, P < 0.001, P < 0.001), whereas in the s‐CEA groups, this difference was observed only in stage III (P = 0.014). Conclusion This study shows that postoperative t‐CEA expression is an independent factor associated with poorer CRC prognosis and has a higher prognostic value than that of preoperative s‐CEA expression

Quantifying High-Temperature and Drought Stress Effects on Soybean Growth and Yield in the Western Guanzhong Plain

High-temperature and drought events significantly impact crop growth and development. In the soybean-producing region of the Guanzhong Plain in China, understanding the dynamics of these climatic phenomena is vital for soybean yield preservation. Through a fixed-position observation experiment that analyzed four growth stages, nine agronomic traits, and soybean yield per unit area from 1998 to 2023, this research evaluated the characteristics of high-temperature and drought processes in various growth stages. It also examined the influence of high-temperature processes, drought processes, and their combined effects on agronomic traits and yield. The results indicate the following: (1) High temperature was a constant factor during the soybean growth period, with temperature-related indices markedly surpassing those related to drought. Notably, the occurrence of high-temperature and drought events was more prevalent during the flowering–podding stage than at the podding or grain-filling stages. (2) High temperature profoundly affected soybean yield components, primarily through a decrease in the number of grains per plant during the flowering–podding stage, subsequently impacting the grain weight per plant and yield. In years with extremely high temperatures, the soybean plant height was reduced by 6.1 to 15 cm, the main stem node number decreased by 0.1 to 2.9, the branch number decreased by 0.2 to 0.6, the number of pods per plant decreased by 4.8 to 13.7, the number of grains per pod decreased by 0.1 to 0.3, the number of grains per plant decreased by 13.5 to 32.6, the grain weight per plant decreased by 3.8 to 6.9 g, and the 100-grain weight decreased by 0.1 to 4.5 g. The common impact of high temperature combined with drought processes in different growth stages was reflected in the reduction in the number of branches by 0.1 to 1.4 and the reduction in the number of grains per pod by 0.02 to 13.7. This study underscores the importance of addressing the quantitative effects of climate change and extreme weather on soybean yield, which could help to develop effective adaptation and mitigation strategies

Association of CDKN2BAS Polymorphism rs4977574 with Coronary Heart Disease: A Case-Control Study and a Meta-Analysis

The goal of our study was to explore the significant association between a non-protein coding single nucleotide polymorphism (SNP) rs4977574 of CDKN2BAS gene and coronary heart disease (CHD). A total of 590 CHD cases and 482 non-CHD controls were involved in the present association study. A strong association of rs4977574 with CHD was observed in females (genotype: p = 0.002; allele: p = 0.002, odd ratio (OR) = 1.57, 95% confidential interval (CI) = 1.18–2.08). Moreover, rs4977574 was more likely to be a risk variant of CHD under the recessive model in females (χ2 = 10.29, p = 0.003, OR = 2.14, 95% CI = 1.31–2.77). A breakdown analysis by age had shown that there was an 87% increased risk of CHD for females younger than 65 years (genotype: χ2 = 14.64, degrees of freedom (df) = 2, p = 0.0002; allele: χ2 = 11.31, df = 1, p = 0.0008, OR = 1.87, 95% CI = 1.30–2.70). Similar observation was also found in males younger than 65 years (genotype: χ2 = 8.63, df = 2, p = 0.04; allele: χ2 = 7.55, df = 1, p = 0.006, OR = 1.45, 95% CI = 1.11–1.90). p values were adjusted by age, sex, smoking, high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C). Meta-analysis of 23 studies among 36,452 cases and 39,781 controls showed a strong association between rs4977574 and the risk of CHD (p &lt; 0.0001, OR = 1.27, 95% CI = 1.22–1.31)