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The Internet of Things (IoT) has promoted emerging applications that require massive device collaboration, heavy computation, and stringent latency. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems can provide flexible services for user devices (UDs) with wide coverage. The optimization of both latency and energy consumption remains a critical yet challenging task due to the inherent trade-off between them. Joint association, offloading, and computing resource allocation are essential to achieving satisfying system performance. However, these processes are difficult due to the highly dynamic environment and the exponentially increasing complexity of large-scale networks. To address these challenges, we introduce a carefully designed cost function to balance the latency and the energy consumption, formulate the joint problem into a partially observable Markov decision process, and propose two multi-agent deep-reinforcement-learning-based schemes to tackle the long-term problem. Specifically, the multi-agent proximal policy optimization (MAPPO)-based scheme uses centralized learning and decentralized execution, while the closed-form enhanced multi-armed bandit (CF-MAB)-based scheme decouples association from offloading and computing resource allocation. In both schemes, UDs act as independent agents that learn from environmental interactions and historic decisions, make decision to maximize its individual reward function, and achieve implicit collaboration through the reward mechanism. The numerical results validate the effectiveness and show the superiority of our proposed schemes. The MAPPO-based scheme enables collaborative agent decisions for high performance in complex dynamic environments, while the CF-MAB-based scheme supports independent rapid response decisions.

In this paper, we present a new two-stage speech enhancement approach, specially conceived to reduce musical and other random noises without requiring their localization in the time–frequency domain. The proposed method is motivated by two observations: (1) the random scattering nature of the energy peaks corresponding to the musical noise in the spectrogram of the processed speech; and (2) the existence of correlation between Wiener filter gains calculated at different frequencies. In the first stage of the proposed method, a preliminary gain function is generated using the nonnegative matrix factorization algorithm. In the second stage, a modified gain function that is more robust to noise artefacts, and referred to as calibrated filter, is estimated by applying a DNN-based nonlinear mapping function to the preliminary gain function. To further decrease the variability of the estimated calibrated filter, we propose to expand the DNN-based extraction of frequency dependencies to a set of preliminary gain functions derived from spectral estimates based on a family of data tapers; the resulting calibrated filter is referred to as multi-filter. The evaluation of the proposed DNN-based calibrated filter models for speech enhancement, under different noise types and input SNR levels, shows substantial improvements in terms of standard speech quality and intelligibility measures when compared to uncalibrated filter.

Vision Transformers (ViTs), inspired by their success in natural language processing, have recently gained attention for heart sound classification (HSC). However, most of the existing studies on HSC rely on single-stream architectures, overlooking the advantages of multi-resolution features. While multi-stream architectures employing early or late fusion strategies have been proposed, they often fall short of effectively capturing cross-modal feature interactions. Additionally, conventional fusion methods, such as concatenation, averaging, or max pooling, frequently result in information loss. To address these limitations, this paper presents a novel attention fusion-based two-stream Vision Transformer (AFTViT) architecture for HSC that leverages two-dimensional mel-cepstral domain features. The proposed method employs a ViT-based encoder to capture long-range dependencies and diverse contextual information at multiple scales. A novel attention block is then used to integrate cross-context features at the feature level, enhancing the overall feature representation. Experiments conducted on the PhysioNet2016 and PhysioNet2022 datasets demonstrate that the AFTViT outperforms state-of-the-art CNN-based methods in terms of accuracy. These results highlight the potential of the AFTViT framework for early diagnosis of cardiovascular diseases, offering a valuable tool for cardiologists and researchers in developing advanced HSC techniques.

Folic acid, served as dietary supplement, is closely linked to one-carbon metabolism and methionine metabolism. Previous clinical evidence indicated that folic acid supplementation displays dual effect on cancer development, promoting or suppressing tumor formation and progression. However, the underlying mechanism remains to be uncovered. Here, we report that high-folate diet significantly promotes cancer development in mice with hepatocellular carcinoma (HCC) induced by DEN/high-fat diet (HFD), simultaneously with increased expression of methionine adenosyltransferase 2A (gene name, MAT2A; protein name, MATIIα), the key enzyme in methionine metabolism, and acceleration of methionine cycle in cancer tissues. In contrast, folate-free diet reduces MATIIα expression and impedes HFD-induced HCC development. Notably, methionine metabolism is dynamically reprogrammed with valosin-containing protein p97/p47 complex-interacting protein (VCIP135) which functions as a deubiquitylating enzyme to bind and stabilize MATIIα in response to folic acid signal. Consistently, upregulation of MATIIα expression is positively correlated with increased VCIP135 protein level in human HCC tissues compared to adjacent tissues. Furthermore, liver-specific knockout of Mat2a remarkably abolishes the advocating effect of folic acid on HFD-induced HCC, demonstrating that the effect of high or free folate-diet on HFD-induced HCC relies on Mat2a . Moreover, folate and multiple intermediate metabolites in one-carbon metabolism are significantly decreased in vivo and in vitro upon Mat2a deletion. Together, folate promotes the integration of methionine and one-carbon metabolism, contributing to HCC development via hijacking MATIIα metabolic pathway. This study provides insight into folate-promoted cancer development, strongly recommending the tailor-made folate supplement guideline for both sub-healthy populations and patients with cancer expressing high level of MATIIα expression.

We propose a novel dictionary learning technique for compressive sensing of speech signals based on the recurrent neural network. First, we exploit the recurrent neural network to solve an \\[\\ell _{0}\\]-norm optimization problem based on a sequential linear prediction model for estimating the linear prediction coefficients for voiced and unvoiced speech, respectively. Then, the extracted linear prediction coefficient vectors are clustered through an improved Linde–Buzo–Gray algorithm to generate codebooks for voiced and unvoiced speech, respectively. A dictionary is then constructed for each type of speech by concatenating a union of structured matrices derived from the column vectors in the corresponding codebook. Next, a decision module is designed to determine the appropriate dictionary for the recovery algorithm in the compressive sensing system. Finally, based on the sequential linear prediction model and the proposed dictionary, a sequential recovery algorithm is proposed to further improve the quality of the reconstructed speech. Experimental results show that when compared to the selected state-of-the-art approaches, our proposed method can achieve superior performance in terms of several objective measures including segmental signal-to-noise ratio, perceptual evaluation of speech quality and short-time objective intelligibility under both noise-free and noise-aware conditions.

Background/Aims: The purpose of this study is to analyze the expression and biological function of lncRNA ANRIL, microRNA-199a, TLR4, and nuclear factor-kappa B (NF-κB) in acute renal injury (AKI) induced by lipopolysaccharide (LPS). Methods: The levels of ANRIL and microRNA-199a in mouse cells and kidneys were detected by quantitative-polymerase chain reaction. Western blot analysis was used for the NF-κB pathway protein. MTT assay was used for cell viability. Enzyme-linked immunosorbent assay was used for the secretion of inflammatory factors in mouse kidney tissue. Apoptosis was measured by flow cytometry and Western blotting. The potential binding region between ANRIL and miR-199a was verified by luciferase reporter assay. Results: The upregulation of ANRIL can reduce the expression of microRNA-199a and increases the number of apoptotic cells. The expression levels of ANRIL in LPS-induced AKI mice and LPS-treated HK2 cells were upregulated compared with the control group. Overexpression of ANRIL increased apoptosis and promoted TLR4 (Toll-like receptor 4), NF-κB phosphorylation, and downstream transcription factor production. Conclusion: ANRIL/NF-κB pathway in LPS-induced apoptosis provided theoretical guidance for ANRIL in the treatment of AKI.

Sparse arrays can increase the array aperture and degrees of freedom through the construction of either sum or difference co-arrays or both. In order to exploit the advantages of sparse arrays while estimating directions of arrival (DOAs) of a mixture of circular and non-circular signals, in this paper, a gridless DOA estimation method is proposed by employing a recently introduced enhanced nested array, whose virtual arrays have no holes. The virtual signals derived from both sum and difference co-arrays are constructed based on atomic norm minimization. It is shown that the proposed method also works when the circular and non-circular signals come from the same set of directions. Simulation results are provided to demonstrate the performance of the proposed method.

Background Liver hepatocellular carcinoma (LIHC) is a lethal cancer. This study aimed to identify the N6‐methyladenosine (m6A)‐targeted long non‐coding RNA (lncRNA) related to LIHC prognosis and to develop an m6A‐targeted lncRNA model for prognosis prediction in LIHC. Methods The expression matrix of mRNA and lncRNA was obtained, and differentially expressed (DE) mRNAs and lncRNAs between tumor and normal samples were identified. Univariate Cox and pathway enrichment analyses were performed on the m6A‐targeted lncRNAs and the LIHC prognosis‐related m6A‐targeted lncRNAs. Prognostic analysis, immune infiltration, and gene DE analyses were performed on LIHC subgroups, which were obtained from unsupervised clustering analysis. Additionally, a multi‐factor Cox analysis was used to construct a prognostic risk model based on the lncRNAs from the LASSO Cox model. Univariate and multivariate Cox analyses were used to assess prognostic independence. Results A total of 5031 significant DEmRNAs and 292 significant DElncRNAs were screened, and 72 LIHC‐specific m6A‐targeted binding lncRNAs were screened. Moreover, a total of 29 LIHC prognosis‐related m6A‐targeted lncRNAs were obtained and enriched in cytoskeletal, spliceosome, and cell cycle pathways. An 11‐m6A‐lncRNA prognostic model was constructed and verified; the top 10 lncRNAs included LINC00152, RP6‐65G23.3, RP11‐620J15.3, RP11‐290F5.1, RP11‐147L13.13, RP11‐923I11.6, AC092171.4, KB‐1460A1.5, LINC00339, and RP11‐119D9.1. Additionally, the two LIHC subgroups, Cluster 1 and Cluster 2, showed significant differences in the immune microenvironment, m6A enzyme genes, and prognosis of LIHC. Conclusion The m6A‐lncRNA prognostic model accurately and effectively predicted the prognostic survival of LIHC. Immune cells, immune checkpoints (ICs), and m6A enzyme genes could act as novel therapeutic targets for LIHC. This study constructed a m6A‐lncRNA prognostic model to predict the prognostic survival of LIHC, which might contribute to improving our understanding of the molecular mechanisms of LIHC from the perspective of m6A‐related lncRNAs.

Wireless capsule endoscopy (WCE) is capable of demonstrating the entire gastrointestinal tract at an expense of exhaustive reviewing process for detecting bleeding disorders. The main objective is to develop an automatic method for identifying the bleeding frames and zones from WCE video. Different statistical features are extracted from the overlapping spatial blocks of the preprocessed WCE image in a transformed color plane containing green to red pixel ratio. The unique idea of the proposed method is to first perform unsupervised clustering of different blocks for obtaining two clusters and then extract cluster based features (CBFs). Finally, a global feature consisting of the CBFs and differential CBF is used to detect bleeding frame via supervised classification. In order to handle continuous WCE video, a post-processing scheme is introduced utilizing the feature trends in neighboring frames. The CBF along with some morphological operations is employed to identify bleeding zones. Based on extensive experimentation on several WCE videos, it is found that the proposed method offers significantly better performance in comparison to some existing methods in terms of bleeding detection accuracy, sensitivity, specificity and precision in bleeding zone detection. It is found that the bleeding detection performance obtained by using the proposed CBF based global feature is better than the feature extracted from the non-clustered image. The proposed method can reduce the burden of physicians in investigating WCE video to detect bleeding frame and zone with a high level of accuracy.

Background Our previous study reported that microRNA-26a (miR-26a) inhibited tumor progression by inhibiting tumor angiogenesis and intratumoral macrophage infiltration in hepatocellular carcinoma (HCC). The direct roles of miR-26a on tumor cell invasion remain poorly understood. In this study, we aim to explore the mechanism of miR-26a in modulating epithelial-mesenchymal transition (EMT) in HCC. Methods In vitro cell morphology and cell migration were compared between the hepatoma cell lines HCCLM3 and HepG2, which were established in the previous study. Overexpression and down-regulation of miR-26a were induced in these cell lines, and Western blot and immunofluorescence assays were used to detect the expression of EMT markers. Xenograft nude mouse models were used to observe tumor growth and pulmonary metastasis. Immunohistochemical assays were conducted to study the relationships between miR-26a expression and enhancer of zeste homolog 2 (EZH2) and E-cadherin expression in human HCC samples. Results Down-regulation of miR-26a in HCCLM3 and HepG2 cells resulted in an EMT-like cell morphology and high motility in vitro and increased in tumor growth and pulmonary metastasis in vivo. Through down-regulation of EZH2 expression and up-regulation of E-cadherin expression, miR-26a inhibited the EMT process in vitro and in vivo. Luciferase reporter assay showed that miR-26a directly interacted with EZH2 messenger RNA (mRNA). Furthermore, the expression of miR-26a was positively correlated with E-cadherin expression and inversely correlated with EZH2 expression in human HCC tissue. Conclusions miR-26a inhibited the EMT process in HCC by down-regulating EZH2 expression.