Background and Purpose
Atrial metabolic remodelling is critical for the process of atrial fibrillation (AF). The PPAR‐α/sirtuin 1 /PPAR co‐activator α (PGC‐1α) pathway plays an important role in ...maintaining energy metabolism. However, the effect of the PPAR‐α agonist fenofibrate on AF is unclear. Therefore, the aim of this study was to determine the effect of fenofibrate on atrial metabolic remodelling in AF and explore its possible mechanisms of action.
Experimental Approach
The expression of metabolic proteins was examined in the left atria of AF patients. Thirty‐two rabbits were divided into sham, AF (pacing with 600 beats·min−1 for 1 week), fenofibrate treated (pretreated with fenofibrate before pacing) and fenofibrate alone treated (for 2 weeks) groups. HL‐1 cells were subjected to rapid pacing in the presence or absence of fenofibrate, the PPAR‐α antagonist GW6471 or sirtuin 1‐specific inhibitor EX527. Metabolic factors, circulating biochemical metabolites, atrial electrophysiology, adenine nucleotide levels and accumulation of glycogen and lipid droplets were assessed.
Key Results
The PPAR‐α/sirtuin 1/PGC‐1α pathway was significantly inhibited in AF patients and in the rabbit/HL‐1 cell models, resulting in a reduction of key downstream metabolic factors; this effect was significantly restored by fenofibrate. Fenofibrate prevented the alterations in circulating biochemical metabolites, reduced the level of adenine nucleotides and accumulation of glycogen and lipid droplets, reversed the shortened atrial effective refractory period and increased risk of AF.
Conclusion and Implications
Fenofibrate inhibited atrial metabolic remodelling in AF by regulating the PPAR‐α/sirtuin 1/PGC‐1α pathway. The present study may provide a novel therapeutic strategy for AF.
Layilin (LAYN) is a critical gene that regulates T cell function. However, the correlations of LAYN to prognosis and tumor-infiltrating lymphocytes in different cancers remain unclear.
LAYN ...expression was analyzed via the Oncomine database and Tumor Immune Estimation Resource (TIMER) site. We evaluated the influence of LAYN on clinical prognosis using Kaplan-Meier plotter, the PrognoScan database and Gene Expression Profiling Interactive Analysis (GEPIA). The correlations between LAYN and cancer immune infiltrates was investigated via TIMER. In addition, correlations between LAYN expression and gene marker sets of immune infiltrates were analyzed by TIMER and GEPIA.
A cohort (GSE17536) of colorectal cancer patients showed that high LAYN expression was associated with poorer overall survival (OS), disease-specific survival (DSS), and disease-free survival (DFS). In addition, high LAYN expression was significantly correlated with poor OS and progression-free survival (PFS) in gastric cancers (OS HR = 1.97,
= 3.6e-10; PFS HR = 2.12,
= 2.3e-10). Moreover, LAYN significantly impacts the prognosis of diverse cancers via The Cancer Genome Atlas (TCGA). Specifically, high LAYN expression was correlated with worse OS and PFS in stage 2 to 4 but not stage 1 and stage N0 gastric cancer patients (
= 0.28, 0.34;
= 0.073, 0.092). LAYN expression was positively correlated with infiltrating levels of CD4+ T and CD8+ T cells, macrophages, neutrophils, and dendritic cells (DCs) in colon adenocarcinoma (COAD) and stomach adenocarcinoma (STAD). LAYN expression showed strong correlations with diverse immune marker sets in COAD and STAD.
These findings suggest that LAYN is correlated with prognosis and immune infiltrating levels of, including those of CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and DCs in multiple cancers, especially in colon and gastric cancer patients. In addition, LAYN expression potentially contributes to regulation of tumor-associated macrophages (TAMs), DCs, T cell exhaustion and Tregs in colon and gastric cancer. These findings suggest that LAYN can be used as a prognostic biomarker for determining prognosis and immune infiltration in gastric and colon cancers.
Ni-doped spinel oxides NixCo1-xFe2O4 (x=0, 0.25, 0.5, 0.75) hollow nanospheres electrocatalysts are synthesized with a simple hydrothermal approach. Scanning electron microscopy (SEM) and X-ray ...diffraction (XRD) results reveal that the morphology, hollow and spinel structures of the cobalt ferrites remain unchanged with doping. The electrocatalytic activity of the Ni-doped CoFe2O4 with different doping contents has been studied and compared with the pure CoFe2O4 hollow nanospheres in alkaline solution by using rotating ring-disk electrode (RRDE) technique. For ORR, the Ni0.5Co0.5Fe2O4 (x=0.5) exhibits as the most active catalyst with the highest diffusion limited current density and more positive onset potential. Whereas, the Ni0.75Co0.25Fe2O4 (x=0.75) shows the best catalytic activity for OER with more negative onset potential (0.27V vs. Ag/AgCl) and maximum current density (36.0mA/cm2 at 1.0V). X-ray photoelectron spectra (XPS) measurements reveal that the oxygen vacancy on the oxide surfaces increases, while the cations occupied ratio on octahedral/tetrahedral sites in spinel structures decreases along with the increasing of the Ni doping content. Combining with the charge transfer resistance measured by electrochemical impedance spectroscopy (EIS), these three factors work synergistically on the catalytic activities of the Ni-doped CoFe2O4 hollow nanospheres.
Completing missing entries in multidimensional visual data is a typical ill-posed problem that requires appropriate exploitation of prior information of the underlying data. Commonly used priors can ...be roughly categorized into three classes: global tensor low-rankness, local properties, and nonlocal self-similarity (NSS); most existing works utilize one or two of them to implement completion. Naturally, there arises an interesting question: can one concurrently make use of multiple priors in a unified way, such that they can collaborate with each other to achieve better performance? This work gives a positive answer by formulating a novel tensor completion framework which can simultaneously take advantage of the global-local-nonlocal priors. In the proposed framework, the tensor train (TT) rank is adopted to characterize the global correlation; meanwhile, two Plug-and-Play (PnP) denoisers, including a convolutional neural network (CNN) denoiser and the color block-matching and 3 D filtering (CBM3D) denoiser, are incorporated to preserve local details and exploit NSS, respectively. Then, we design a proximal alternating minimization algorithm to efficiently solve this model under the PnP framework. Under mild conditions, we establish the convergence guarantee of the proposed algorithm. Extensive experiments show that these priors organically benefit from each other to achieve state-of-the-art performance both quantitatively and qualitatively.
We describe a method for intramolecular desulfonylative coupling using bis(cyclooctadiene)nickel as a catalyst. A broad range of aromatic, heteroaromatic and aliphatic sulfones can be utilized as ...substrates in this process. This method provides an atom‐economical route to the synthesis of various biaryls and an efficient tool for the catalytic conversion of sulfonyl groups, representing a significant advancement in organic synthesis
Recently, tensor train rank, defined by a well-balanced matricization scheme, has been shown the powerful capacity to capture the hidden correlations among different modes of a tensor, leading to ...great success in tensor completion problem. Most of the high-dimensional data in the real world are more likely to be grossly corrupted with sparse noise. In this paper, based on tensor train rank, we consider a new model for tensor robust principal component analysis which aims to recover a low-rank tensor corrupted by sparse noise. The alternating direction method of multipliers algorithm is developed to solve the proposed model. A tensor augmentation tool called ket augmentation is used to convert lower-order tensors to higher-order tensors to enhance the performance of our method. Experiments of simulated data show the superiority of the proposed method in terms of PSNR and SSIM values. Moreover, experiments of the real rain streaks removal and the real stripe noise removal also illustrate the effectiveness of the proposed method.
TA15 (Ti–6Al–2Zr–1Mo–1V) is a near-α titanium alloy and has wide applications in the aerospace industry because of its high strength to mass ratio, good weldability, and superior creep resistance at ...high temperatures up to 550 °C, compared to other titanium alloys. This study investigates the flow behavior and microstructural evolution as functions of temperatures and strain rates during deformations under the superplastic conditions at 880 °C/0.01s−1, 900 °C/0.01s−1, 880 °C/0.001s−1, and 920 °C/0.0005s−1. Results showed that this alloy exhibit excellent superplastic behavior for all selected temperatures and strain rates. The maximum tensile elongation of 1450% is achieved at 880 °C with a strain rate of 0.001s−1. Flow softening is observed under deformation conditions of 880 °C/0.01s−1 and 900 °C/0.01s−1, while strain hardening is observed at deformation conditions of 880 °C/0.001s−1 and 920 °C/0.0005s−1. These complex flow behaviors are rationalized by characterizing the underlying microstructures on the interrupted tensile samples using electron backscatter diffraction (EBSD) and backscattered electrons (BSE). The geometrically necessary dislocations (GNDs) density, which is caused by lattice rotation and misorientations and plays a vital role in the plastic constitutive behaviors, was for the first time, systematically revealed. Together with other key microstructures, i.e. grain sizes, texture, phase fractions, the results show that the dominant deformation mode changes at initial, intermediate, and final stages of the deformation. The probable deformation mechanisms, such as grain boundary sliding (GBS) under different deformation conditions, are discussed in terms of grain morphology, GNDs, and texture evolution. Also, it is observed that the β-phase transformation is accelerated during deformation and contributes to the enhancement of superplasticity.
Hepatitis B virus (HBV) X protein, HBx, interacts with anti-apoptotic Bcl-2 and Bcl-xL proteins through its BH3-like motif to promote HBV replication and cytotoxicity. Here we report the crystal ...structure of HBx BH3-like motif in complex with Bcl-xL where the BH3-like motif adopts a short α-helix to snuggle into a hydrophobic pocket in Bcl-xL via its noncanonical Trp120 residue and conserved Leu123 residue. This binding pocket is ~2 Å away from the canonical BH3-only binding pocket in structures of Bcl-xL with proapoptotic BH3-only proteins. Mutations altering Trp120 and Leu123 in HBx impair its binding to Bcl-xL in vitro and HBV replication in vivo, confirming the importance of this motif to HBV. A HBx BH3-like peptide, HBx-aa113-135, restores HBV replication from a HBx-null HBV replicon, while a shorter peptide, HBx-aa118-127, inhibits HBV replication. These results provide crucial structural and functional insights into drug designs for inhibiting HBV replication and treating HBV patients.
Recently, transform-based tensor nuclear norm (TNN) methods have received increasing attention as a powerful tool for multi-dimensional visual data (color images, videos, and multispectral images, ...etc.) recovery. Especially, the redundant transform-based TNN achieves satisfactory recovery results, where the redundant transform along spectral mode can remarkably enhance the low-rankness of tensors. However, it suffers from expensive computational cost induced by the redundant transform. In this paper, we propose a learnable spatial-spectral transform-based TNN model for multi-dimensional visual data recovery, which not only enjoys better low-rankness capability but also allows us to design fast algorithms accompanying it. More specifically, we first project the large-scale original tensor to the small-scale intrinsic tensor via the learnable semi-orthogonal transforms along the spatial modes. Here, the semi-orthogonal transforms, serving as the key building block, can boost the spatial low-rankness and lead to a small-scale problem, which paves the way for designing fast algorithms. Secondly, to further boost the low-rankness, we apply the learnable redundant transform along the spectral mode to the small-scale intrinsic tensor. To tackle the proposed model, we apply an efficient proximal alternating minimization-based algorithm, which enjoys a theoretical convergence guarantee. Extensive experimental results on real-world data (color images, videos, and multispectral images) demonstrate that the proposed method outperforms state-of-the-art competitors in terms of evaluation metrics and running time.
In this paper, we propose a novel model for remote sensing images destriping, which includes the Schatten 1∕2-norm and the unidirectional first-order and high-order total variation regularization. ...The main idea is that the stripe layer is low-rank, and the desired image possesses smoothness across stripes. Therefore, we use the Schatten 1∕2-norm regularization to depict the low-rankness of stripes, and use the unidirectional total variation and the unidirectional high-order total variation to guarantee the smoothness of the underlying image. We develop the alternating direction method of multipliers algorithm to solve the proposed model. Extensive experiments on synthetic and real data are reported to show the superiority of the proposed method over state-of-the-art methods in terms of both quantitative and qualitative assessments.
PDF
