Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC

Advances in multi-modal non-invasive imaging techniques in the diagnosis and treatment of polypoidal choroidal vasculopathy

Polypoidal choroidal vasculopathy (PCV) is a disease characterized by subretinal pigment epithelium (RPE) orange-red polypoidal lesions and abnormal branching neovascular networks (BNNs). In recent years, various non-invasive imaging technologies have rapidly developed, especially the emergence of optical coherence tomography angiography (OCTA), multi-spectral imaging, and other technologies, which enable the observation of more features of PCV. In addition, these technologies are faster and less invasive compared to indocyanine green angiography (ICGA). Multi-modal imaging, which combined multiple imaging techniques, provides important references for the diagnosis and treatment of PCV with the assistance of regression models, deep learning, and other algorithms. In this study, we reviewed the non-invasive imaging techniques, multi-modal imaging diagnosis, and multi-scene therapeutic applications of PCV, with the aim of providing a reference for non-invasive multi-modal diagnosis and treatment of PCV

Numerics of the Lattice Boltzmann Method: Effects of Collision Models on the Lattice Boltzmann Simulations

We conduct a comparative study to evaluate several lattice Boltzmann (LB) models for solving the near incompressible Navier-Stokes equations, including the lattice Boltzmann equation with the multiple-relaxation-time (MRT), the two-relaxation-time (TRT), the single-relaxation-time (SRT) collision models, and the entropic lattice Boltzmann equation (ELBE). The lid-driven square cavity flow in two dimensions is used as a benchmark test. Our results demonstrate that the ELBE does not improve the numerical stability of the SRT or the lattice Bhatnagar-Gross-Krook (LBGK) model. Our results also show that the MRT and TRT LB models are superior to the ELBE and LBGK models in terms of accuracy, stability, and computational efficiency and that the ELBE scheme is the most inferior among the LB models tested in this study, thus is unfit for carrying out numerical simulations in practice. Our study suggests that, to optimize the accuracy, stability, and efficiency in the MRT model, it requires at least three independently adjustable relaxation rates: one for the shear viscosity ν (or the Reynolds number Re), one for the bulk viscosity ζ, and one to satisfy the criterion imposed by the Dirichlet boundary conditions which are realized by the bounce-back-type boundary conditions. © 2011 American Physical Society

Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks

Abstract Non-Orthogonal Multiplex Access (NOMA) can be deployed in Unmanned Aerial Vehicle (UAV) networks to improve spectrum efficiency. Due to the broadcasting feature of NOMA-UAV networks, it is essential to focus on the security of the wireless system. This paper focuses on maximizing the secrecy sum-rate under the constraint of the achievable rate of the legitimate channels. To tackle the non-convexity optimization problem, a reinforcement learning-based alternative optimization algorithm is proposed. Firstly, with the help of successive convex approximations, the optimal power allocation scheme with a given UAV trajectory is obtained by using convex optimization tools. Afterwards, through plenty of explorations on the wireless environment, the Q-learning networks approach the optimal location transition strategy of the UAV, even without the wireless channel state information

Cryptic diversity in hymenolepidid tapeworms infecting humans

An adult hymenolepidid tapeworm was recovered from a 52-year-old Tibetan woman during a routine epidemiological survey for human taeniasis/cysticercosis in Sichuan, China. Phylogenetic analyses based on sequences of nuclear 28S ribosomal DNA and mitochondrial cytochrome c oxidase subunit 1 showed that the human isolate is distinct from Hymenolepis diminuta and Hymenolepis nana, the common parasites causing human hymenolepiasis. Proglottids of the human isolate were unfortunately unsuitable for morphological identification. However, the resultant phylogeny demonstrated the human isolate to be a sister species to Hymenolepis hibernia from Apodemus mice in Eurasia. The present data clearly indicate that hymenolepidid tapeworms causing human infections are not restricted to only H. diminuta and H. nana.Peer reviewe

Optimal Hybrid Beamforming Design for Millimeter-Wave Massive Multi-User MIMO Relay Systems

As a promising technology in the next generation mobile network, millimeter-wave (mmWave) communication can mitigate the spectrum crunch of improving the network capacity by exploiting the large underutilized spectrum bands of the mmWave frequencies. The hybrid (analog/digital) beamforming of multi-data streams are widely used to further the spectrum efficiency of mmWave relay system when faced with the complex environment or long distance communication. This paper investigates the hybrid beamforming scheme for the decode-and-forward (DF) mmWave massive multiple-input multiple-output (MIMO) relay system with mixed structure and full-connected structure. We optimize hybrid beamforming of relay system by maximizing the sum rate of the overall system as an objective function. To reduce the computational complexity, we reformulate the original problem as two single-hop mmWave MIMO sum-rate maximization subproblems. Then, the piecewise successive approximation method is proposed based on the criterion which jointly designs the analog and digital beamforming stages by trying to avoid the loss of information at each stage. The hybrid beamforming of the two subproblems can be solved by the proposed scheme united with the idea of successive interference cancelation (SIC), the baseband block diagonalization (BD) scheme, and waterfilling power allocation method. Finally, simulation results confirm that the proposed optimal method can achieve good performance in hybrid beamforming design of relay system with both mixed and full-connected structures

An improved double channel long short-term memory model for medical text classification

There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model

Central and peripheral changes in the retina and choroid in patients with diabetes mellitus without clinical diabetic retinopathy assessed by ultra-wide-field optical coherence tomography angiography

BackgroundTo explore the central and peripheral retinal and choroidal changes in diabetic patients without clinical diabetic retinopathy (DM-NoDR) using ultra-wide-field swept-source optical coherence tomography angiography (UWF-SS-OCTA).Methods67 DM-NoDR eyes and 32 age-matched healthy eyes were recruited. Retinal and choroidal parameters, including qualitative retinal microangiopathy, vessel flow (VFD) and linear density (VLD), thickness, and volume, were measured in the central and peripheral areas of the 24 × 20 mm2 UWF-SS-OCTA images.ResultsDM-NoDR eyes had significantly more nonperfusion area and capillary tortuosity than controls in the central and peripheral areas (p < 0.05). The presence of central capillary tortuosity was associated with higher levels of serum creatinine (OR 1.049, 95%CI 1.001–1.098; p = 0.044) and blood urea nitrogen (OR 1.775, 95%CI 1.051–2.998; p = 0.032) in DM-NoDR eyes. For DM-NoDR eyes versus controls, VFD in the 300-μm annulus around the foveal avascular zone, superficial capillary plexus (SCP), and full retina, and SCP-VLD significantly decreased, while VFD in the deep capillary plexus (DCP), retinal thickness, and retinal volume increased (p < 0.05). Analysis in the central and peripheral areas recapitulated all these findings, except for decreased peripheral thickness and volume and no difference in peripheral DCP-VFD. In DM-NoDR eyes, choriocapillaris-VFD, choroidal thickness, and choroidal volume increased in the central area, while VFD in the large and medium choroidal vessel layer decreased in the whole image (p < 0.05).ConclusionRetinal and choroidal changes already existed in the central and/or peripheral areas of DM-NoDR eyes. UWF-SS-OCTA, enabling the visualization of the peripheral fundus area, is a promising image technique for the early detection of fundus changes in DM-NoDR patients