Advanced NOMA Assisted Semi-Grant-Free Transmission Schemes for Randomly Distributed Users

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users' spectrum, {\color{blue}which can greatly improve the spectrum efficiency and effectively relieve the massive access problem of 5G and beyond networks. In this paper, we investigate the performance of SGF schemes under more practical settings.} Firstly, we study the outage performance of the best user scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading, path loss, and random user locations. Then, a fair SGF scheme is proposed by applying cumulative distribution function (CDF)-based scheduling (CS-SGF), which can also make full use of multi-user diversity. Moreover, by employing the theories of order statistics and stochastic geometry, we analyze the outage performances of both BU-SGF and CS-SGF schemes. Results show that full diversity orders can be achieved only when the served users' data rate is capped, which severely limit the rate performance of SGF schemes. To further address this issue, we propose a distributed power control strategy to relax such data rate constraint, and derive closed-form expressions of the two schemes' outage performances under this strategy. Finally, simulation results validate the fairness performance of the proposed CS-SGF scheme, the effectiveness of the power control strategy, and the accuracy of the theoretical analyses.Comment: 41 pages, 8 figure

Image restoration with group sparse representation and low‐rank group residual learning

Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however often leads to undesirable sparse solutions in practice. In order to improve the quality of image restoration based on GSR, the sparsity residual model expects the representation learned from degraded images to be as close as possible to the true representation. In this article, a group residual learning based on low-rank self-representation is proposed to automatically estimate the true group sparse representation. It makes full use of the relation among patches and explores the subgroup structures within the same group, which makes the sparse residual model have better interpretation furthermore, results in high-quality restored images. Extensive experimental results on two typical image restoration tasks (image denoising and deblocking) demonstrate that the proposed algorithm outperforms many other popular or state-of-the-art image restoration methods

Outage Performance of Uplink Rate Splitting Multiple Access with Randomly Deployed Users

With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate splitting multiple access (RSMA) is proposed as a promising solution to improve spectral efficiency and provide better fairness for the next-generation mobile networks. In this paper, the outage performance of uplink RSMA transmission with randomly deployed users is investigated, taking both user scheduling schemes and power allocation strategies into consideration. Specifically, the greedy user scheduling (GUS) and cumulative distribution function (CDF) based user scheduling (CUS) schemes are considered, which could maximize the rate performance and guarantee scheduling fairness, respectively. Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and propose a new rate fairness-oriented power allocation (FPA) strategy to enhance the scheduled users' rate fairness. By employing order statistics and stochastic geometry, an analytical expression of the outage probability for each scheduling scheme combining power allocation is derived to characterize the performance. To get more insights, the achieved diversity order of each scheme is also derived. Theoretical results demonstrate that both GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity orders, and the application of CPA strategy in RSMA can effectively eliminate the secondary user's diversity order constraint from the primary user. Simulation results corroborate the accuracy of the analytical expressions, and show that the proposed FPA strategy can achieve excellent rate fairness performance in high signal-to-noise ratio region.Comment: 38 pages,8 figure

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Identification of metabolism-related subtypes and feature genes in Alzheimer’s disease

Abstract Background Owing to the heterogeneity of Alzheimer's disease (AD), its pathogenic mechanisms are yet to be fully elucidated. Evidence suggests an important role of metabolism in the pathophysiology of AD. Herein, we identified the metabolism-related AD subtypes and feature genes. Methods The AD datasets were obtained from the Gene Expression Omnibus database and the metabolism-relevant genes were downloaded from a previously published compilation. Consensus clustering was performed to identify the AD subclasses. The clinical characteristics, correlations with metabolic signatures, and immune infiltration of the AD subclasses were evaluated. Feature genes were screened using weighted correlation network analysis (WGCNA) and processed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Furthermore, three machine-learning algorithms were used to narrow down the selection of the feature genes. Finally, we identified the diagnostic value and expression of the feature genes using the AD dataset and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis. Results Three AD subclasses were identified, namely Metabolism Correlated (MC) A (MCA), MCB, and MCC subclasses. MCA contained signatures associated with high AD progression and may represent a high-risk subclass compared with the other two subclasses. MCA exhibited a high expression of genes related to glycolysis, fructose, and galactose metabolism, whereas genes associated with the citrate cycle and pyruvate metabolism were downregulated and associated with high immune infiltration. Conversely, MCB was associated with citrate cycle genes and exhibited elevated expression of immune checkpoint genes. Using WGCNA, 101 metabolic genes were identified to exhibit the strongest association with poor AD progression. Finally, the application of machine-learning algorithms enabled us to successfully identify eight feature genes, which were employed to develop a nomogram model that could bring distinct clinical benefits for patients with AD. As indicated by the AD datasets and qRT-PCR analysis, these genes were intimately associated with AD progression. Conclusion Metabolic dysfunction is associated with AD. Hypothetical molecular subclasses of AD based on metabolic genes may provide new insights for developing individualized therapy for AD. The feature genes highly correlated with AD progression included GFAP, CYB5R3, DARS, KIAA0513, EZR, KCNC1, COLEC12, and TST

MCPIP1 Down-Regulates IL-2 Expression through an ARE-Independent Pathway

<div><p>IL-2 plays a key role in the survival and proliferation of immune cells, especially T lymphocytes. Its expression is precisely regulated at transcriptional and posttranscriptional level. IL-2 is known to be regulated by RNA binding proteins, such as tristetraprolin (TTP), via an AU-rich element (ARE) in the 3′-untranslated region (3′UTR) to influence the stability of mRNA. MCPIP1, identified as a novel RNase, can degrade IL-6, IL-12 and TNF-α mRNA by an ARE-independent pathway in the activation of macrophages. Here, we reported that MCPIP1 was induced in the activation of T lymphocytes and negatively regulated IL-2 gene expression in both mouse and human primary T lymphocytes through destabilizing its mRNA. A set of Luciferase reporter assay demonstrated that a non-ARE conserved element in IL-2 3′UTR, which formed a stem-loop structure, responded to MCPIP1 activity.RNA immunoprecipitation and Biotin pulldown experiments further suggested that MCPIP1 could modestly bind to IL-2 mRNA. Taken together, these data demonstrate that MCPIP1 down-regulates IL-2 via an ARE-independent pathway.</p> </div

IL-2 mRNA 3'UTR responses to MCPIP1 activity by non-ARE region.

<p>(A).Schematic of the IL-2-3′UTR with the indicated AREs and the luciferase report constructs. (B–D). HEK293 cells were co-transfected with pGL3 containing (B) <i>IL-2</i>-3′UTR, <i>IL-6</i>-3′UTR, <i>IL-12p40</i>-3′UTR or <i>IFN-γ</i>-3′UTR, (C) indicated sequences of <i>IL-2</i>-3′UTR, (D) <i>IL-2</i>-3′UTR (Δ83-166).The luciferase activity was determined after 48 h. Data were presented as mean ± S.D of five independent experiments. *P<0.05; **P<0.01.</p

MCPIP1 negatively regulates IL-2 gene expression in human peripheral CD4⁺T lymphocytes.

<p>(A–B). Overexpression of MCPIP1 inhibits IL-2 expression. Human peripheral mononuclear cells (PBMC) were obtained from healthy subjects by Ficoll-Hypaque density centrifugation. Purified CD4⁺T lymphocytes were transiently transfected with hMCPIP1-flag or control plasmids by electroporation. After incubated for 4 h, cells were challenged with anti-CD3 and anti-CD28 for 12 hours. Cells and cultured media were harvested and IL-2 mRNA level was measured by Q-PCR (A) and protein level was detected by ELISA (B). (C-D).MCPIP1 Knockdown promotes IL-2 expression stimulated by anti-CD3 and anti-CD28 Abs. Purified CD4⁺T lymphocytes were transiently transfected with control-siRNA or MCPIP1-siRNA by electroporation. After incubated for 4 h, cells were challenged with anti-CD3 and anti-CD28 Abs for 12 hours. Cells and cultured media were harvested and IL-2 mRNA level was measured by Q-PCR (C) and protein level was detected by ELISA (D). (E). MCPIP1 destabilizes IL-2 mRNA in human CD4⁺T lymphocytes<b>.</b> Purified CD4⁺T lymphocytes were transiently transfected with MCPIP1-siRNA or control-siRNA by electroporation. After incubated for 4 h, cells were challenged with anti-CD3 and anti-CD28 Abs. 6 h later, actinomycin D (10 µg/ml) was added to stop transcription, and total RNA was harvested after 0, 15, 30, or 45 min. IL-2 mRNA levels were measured by Q-PCR and normalized to β-actin mRNA. The normalized level of IL-2 mRNA at time 0 was set at 100. Data were presented as mean ± S.D of three independent experiments. *P<0.05; **P<0.01.</p