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Abstract Uveal melanoma (UVM) is the most common primary tumor in adult human eyes. Costimulatory molecules (CMs) are important in maintaining T cell biological functions and regulating immune responses. To investigate the role of CMs in UVM and exploit prognostic signature by bioinformatics analysis. This study aimed to identify and validate a CMs associated signature and investigate its role in the progression and prognosis of UVM. The expression profile data of training cohort and validation cohort were downloaded from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) dataset. 60 CM genes were identified, and 34 genes were associated with prognosis by univariate Cox regression. A prognostic signature was established with six CM genes. Further, high- and low-risk groups were divided by the median, and Kaplan–Meier (K-M) curves indicated that high-risk patients presented a poorer prognosis. We analyzed the correlation of gender, age, stage, and risk score on prognosis by univariate and multivariate regression analysis. We found that risk score was the only risk factor for prognosis. Through the integration of the tumor immune microenvironment (TIME), it was found that the high-risk group presented more immune cell infiltration and expression of immune checkpoints and obtained higher immune scores. Enrichment analysis of the biological functions of the two groups revealed that the differential parts were mainly related to cell–cell adhesion, regulation of T-cell activation, and cytokine–cytokine receptor interaction. No differences in tumor mutation burden (TMB) were found between the two groups. GNA11 and BAP1 have higher mutation frequencies in high-risk patients. Finally, based on the Genomics of Drug Sensitivity in Cancer 2 (GDSC2) dataset, drug sensitivity analysis found that high-risk patients may be potential beneficiaries of the treatment of crizotinib or temozolomide. Taken together, our CM-related prognostic signature is a reliable biomarker that may provide ideas for future treatments for the disease.

Driven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloading and content caching services can be provided for remote Internet-of-Things (IoT) devices without proximal servers. In this paper, the joint optimization of computation offloading, radio resource allocation and caching placement in LEO satellite MEC networks are investigated. The problem is formulated to minimize the total delay of all ground IoT devices while ensuring the energy, computing and caching constraints. To solve this mixed-integer and non-convex problem, a Lagrange dual decomposition (LDD)-based algorithm is proposed to obtain the closed-form optimal solution. Then, a heuristic algorithm is proposed to further reduce the computation complexity. Numerical results validate that both algorithms are effective compared to the optimal exhaustive search, the full local computing and the full MEC methods. Besides, the offloading ratio and the average delay of all IoT devices with different numbers and computing capacities of devices and satellites are also demonstrated.

As a critical technology in both civil and military fields, specific emitter identification (SEI) can identify signal sources according to their various features. Existing methods on SEI are mostly based on the prior knowledge of emitters, which are powerless in the non-cooperative scenario. In order to realize the unsupervised identification, the mobile SEI method based on fingerprint set construction and feedback classification algorithm is proposed in this paper. The proposed method first divides signal fingerprints into static features and dynamic features, where the former describe the inherent features of emitters, and the latter represent the moving state features of emitters. Then, the feedback classification algorithm composed of dynamic curve fitting and back propagation (BP) neural network is applied in the classification of signals. The dynamic curve accomplishes the first classification and the results with high credibility are used to train the BP neural network which accomplishes the final classification. Simulation results demonstrate that the proposed method can complete the identification of mobile specific emitter sources in the unsupervised state with more than 95% identification rate.

Abstract Cellular RNA is decorated with over 170 types of chemical modifications. Many modifications in mRNA, including m6A and m5C, have been associated with critical cellular functions under physiological and/or pathological conditions. To understand the biological functions of these modifications, it is vital to identify the regulators that modulate the modification rate. However, a high‐throughput method for unbiased screening of these regulators is so far lacking. Here, we report such a method combining pooled CRISPR screen and reporters with RNA modification readout, termed CRISPR integrated gRNA and reporter sequencing (CIGAR‐seq). Using CIGAR‐seq, we discovered NSUN6 as a novel mRNA m5C methyltransferase. Subsequent mRNA bisulfite sequencing in HAP1 cells without or with NSUN6 and/or NSUN2 knockout showed that NSUN6 and NSUN2 worked on non‐overlapping subsets of mRNA m5C sites and together contributed to almost all the m5C modification in mRNA. Finally, using m1A as an example, we demonstrated that CIGAR‐seq can be easily adapted for identifying regulators of other mRNA modification.

Objective To investigate the differences in amino acid metabolism in aqueous humor of patients with cataract, according to diabetes status, using a 1 H-nuclear magnetic resonance approach. Methods Aqueous humor samples from patients with age-related cataract, with or without diabetes, were collected during cataract surgery. All samples underwent nuclear magnetic resonance spectra analysis to characterize their metabolic function. Potential metabolic pathways were analyzed via MetaboAnalyst 3.0. Results This study included eight aqueous humor samples from patients with cataract and diabetes and eight aqueous humor samples from age- and sex-matched patients with cataract alone. Four metabolites were found to significantly differ in the aqueous humor of patients with cataract and diabetes, relative to patients with cataract alone; these metabolites were glucose (higher in patients with diabetes), valine, lysine, and tyrosine (all lower in patients with diabetes). Aminoacyl-tRNA biosynthesis was presumed to be involved in the metabolic differences observed in patients with cataract, according to diabetes status. Conclusions The amino acid metabolic profile in the aqueous humor differed among patients with cataract, according to diabetes status. Disturbance of amino acid metabolism in the aqueous humor may be related to cataract formation in patients with diabetes.

In this paper, we investigate the resource allocation problem for achieving spectral efficiency (SE) and energy efficiency (EE) tradeoff with users' minimum rate requirements in hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) systems which incorporate both NOMA and orthogonal multiple access (OMA) modes into one unified framework. All the degrees of freedom involved in resource allocation, including the choice of multiple access (MA) modes, user clustering, subcarrier assignment, and power allocation, are jointly considered. We first formulate the SE-EE tradeoff as a multiobjective optimization (MOO) problem with minimum rate requirement constraints. Then, considering the non-convexity of the MOO problem, it is converted into a single-objective optimization (SOO) problem by utilizing weighted Tchebycheff method. Lagrangian dual decomposition and sequential convex programming are applied to solve the SOO problem. We propose a joint resource allocation algorithm which is applicable to the general case, where an arbitrary number of users can be multiplexed on the same subcarrier. Simulation results demonstrate that users' minimum rate requirements and channel conditions have great impact on the selection of MA modes. The proposed hybrid MC-NOMA mode significantly outperforms MC-NOMA and OMA in terms of SE-EE tradeoff, and the performance gain brought by four or more users sharing the same subcarrier is minimal. Meanwhile, the hybrid MC-NOMA also shows great potential to improve the tradeoff between fairness and system efficiency.

To achieve more efficient spectrum utilization in the intelligent transportation system, nonorthogonal multiple access-enabled (NOMA-enabled) V2X communications have been emerging as a promising technology. In this paper, the resource allocation problem for NOMA-enabled V2X communications is investigated. For V2I links, in view of the user fairness and the different requirements of cellular users (CUEs), weighted max-min rate fairness for CUEs is applied, where both identical weights and different weights are considered. As for V2V users (VUEs), the minimum signal-to-interference-plusnoise ratio (SINR) requirements are imposed on the problem formulation. To solve the proposed optimization problem, it is decoupled into three stages: 1) power allocation for CUEs; 2) power control and subcarrier (SC) assignment for VUE pairs; 3) SC assignment and user clustering for CUEs. For the three stages, algorithms based on the Perron-Frobenius theorem, the Kuhn-Munkres algorithm, and the matching theory are proposed separately. Finally, an algorithm integrated the three stages is presented to obtain a joint solution. The convergence and the optimality of the proposed algorithms can be verified by theoretical analysis and simulations. Moreover, simulation results also indicate that each cluster of the optimal user clustering scheme is inclined to be occupied by the CUEs with diverse channel gains and the minimum weighted rate of all CUEs, τ, will decrease with the increase of the numbers of V2V links and the minimum SINR requirements of VUE pairs.

Abstract Background Unlike influenza viruses, little is known about the prevalence and seasonality of other respiratory viruses because laboratory surveillance for non-influenza respiratory viruses is not well developed or supported in China and other resource-limited countries. We studied the interference between seasonal epidemics of influenza viruses and five other common viruses that cause respiratory illnesses in Hong Kong from 2014 to 2017. Methods The weekly laboratory-confirmed positive rates of each virus were analyzed from 2014 to 2017 in Hong Kong to describe the epidemiological trends and interference between influenza viruses, respiratory syncytial virus (RSV), parainfluenza virus (PIV), adenovirus, enterovirus and rhinovirus. A sinusoidal model was established to estimate the peak timing of each virus by phase angle parameters. Results Seasonal features of the influenza viruses, PIV, enterovirus and adenovirus were obvious, whereas annual peaks of RSV and rhinovirus were not observed. The incidence of the influenza viruses usually peaked in February and July, and the summer peaks in July were generally caused by the H3 subtype of influenza A alone. When influenza viruses were active, other viruses tended to have a low level of activity. The peaks of the influenza viruses were not synchronized. An epidemic of rhinovirus tended to shift the subsequent epidemics of the other viruses. Conclusion The evidence from recent surveillance data in Hong Kong suggests that viral interference during the epidemics of influenza viruses and other common respiratory viruses might affect the timing and duration of subsequent epidemics of a certain or several viruses.

BACKGROUND:The persistence of Mycobacterium leprae (M. leprae) infection is largely dependent on the types of host immune responses being induced. Macrophage, a crucial modulator of innate and adaptive immune responses, could be directly infected by M. leprae. We therefore postulated that M. leprae-infected macrophages might have altered immune functions. METHODOLOGY/PRINCIPAL FINDINGS:Here, we treated monocyte-derived macrophages with live or killed M. leprae, and examined their activation status and antigen presentation. We found that macrophages treated with live M. leprae showed committed M2-like function, with decreased interleukin 1 beta (IL-1beta), IL-6, tumor necrosis factor alpha (TNF-alpha) and MHC class II molecule expression and elevated IL-10 and CD163 expression. When incubating with naive T cells, macrophages treated with live M. leprae preferentially primed regulatory T (Treg) cell responses with elevated FoxP3 and IL-10 expression, while interferon gamma (IFN-gamma) expression and CD8+ T cell cytotoxicity were reduced. Chromium release assay also found that live M. leprae-treated macrophages were more resistant to CD8+ T cell-mediated cytotoxicity than sonicated M. leprae-treated monocytes. Ex vivo studies showed that the phenotype and function of monocytes and macrophages had clear differences between L-lep and T-lep patients, consistent with the in vitro findings. CONCLUSIONS/SIGNIFICANCE:Together, our data demonstrate that M. leprae could utilize infected macrophages by two mechanisms: firstly, M. leprae-infected macrophages preferentially primed Treg but not Th1 or cytotoxic T cell responses; secondly, M. leprae-infected macrophages were more effective at evading CD8+ T cell-mediated cytotoxicity.