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Circular RNAs (circRNAs) are recognized as functional non-coding transcripts in eukaryotic cells. Recent evidence has indicated that even though circRNAs are generally expressed at low levels, they may be involved in many physiological or pathological processes, such as gene regulation, tissue development and carcinogenesis. Although the ‘microRNA sponge’ function is well characterized, most circRNAs do not contain perfect trapping sites for microRNAs, which suggests the possibility that circRNAs have functions that have not yet been defined. In this study, we show that a circRNA containing an open reading frame (ORF) driven by the internal ribosome entry site (IRES) can translate a functional protein. The circular form of the SNF2 histone linker PHD RING helicase (SHPRH) gene encodes a novel protein that we termed SHPRH-146aa. Circular SHPRH (circ-SHPRH) uses overlapping genetic codes to generate a ‘UGA’ stop codon, which results in the translation of the 17 kDa SHPRH-146aa. Both circ-SHPRH and SHPRH-146aa are abundantly expressed in normal human brains and are down-regulated in glioblastoma. The overexpression of SHPRH-146aa in U251 and U373 glioblastoma cells reduces their malignant behavior and tumorigenicity in vitro and in vivo. Mechanistically, SHPRH-146aa protects full-length SHPRH from degradation by the ubiquitin proteasome. Stabilized SHPRH sequentially ubiquitinates proliferating cell nuclear antigen (PCNA) as an E3 ligase, leading to inhibited cell proliferation and tumorigenicity. Our findings provide a novel perspective regarding circRNA function in physiological and pathological processes. Specifically, SHPRH-146aa generated from overlapping genetic codes of circ-SHPRH is a tumor suppressor in human glioblastoma.

The limited availability of labeled ECG data restricts the application of supervised deep learning methods in ECG detection. Although existing self-supervised learning approaches have been applied to ECG analysis, they are predominantly image-based, which limits their effectiveness. To address these limitations and provide novel insights, we propose a Temporal-Spatial Self-Supervised Learning (TSSL) method specifically designed for ECG detection. TSSL leverages the intrinsic temporal and spatial characteristics of ECG signals to enhance feature representation. Temporally, ECG signals retain consistent identity information over time, enabling the model to generate stable representations for the same individual across different time points while isolating representations of different leads to preserve their unique features. Spatially, ECG signals from various leads capture the heart’s activity from different perspectives, revealing both commonalities and distinct patterns. TSSL captures these correlations by maintaining consistency in the relationships between signals and their representations across different leads. Experimental results on the CPSC2018, Chapman, and PTB-XL databases demonstrate that TSSL introduces new capabilities by effectively utilizing temporal and spatial information, achieving superior performance compared to existing methods and approaching the performance of full-label training with only 10% of the labeled data. This highlights TSSL’s ability to provide deeper insights and enhanced feature extraction beyond mere performance improvements. We make our code publicly available on https://github.com/cwp9731/temporal-spatial-self-supervised-learning.

The development of efficient adsorbents is significant for the miniaturization and cost reduction of water purifying facility. In our previous study, a new kind of silica aerogel fibers (SAFs) with hierarchical structure and hollow structure were developed, which shows outstanding mass transport property and adsorption performance. Herein, we further control and study the hierarchical structure and hollow structure of silica aerogel fibers by changing the sulfuric acid concentration of ageing bath and spinning speed. For removing methylene blue, A0W120 SAFs sample, with DI water as ageing bath and a winding speed of 120 mm/s, presents the best mass transport property and adsorption performance, as they possess larger pore sizes in the outer layer, thinner fiber wall and higher degree of hollowness. The removal rates of methylene blue by A0W120 SAFs sample is up to 91.6% and 98.2% in 2 and 5 min, respectively, which are at least 30% higher than other comparable commercial adsorbents, while the adsorption capacity of methylene blue reaches up to 139.1 mg/g.

Graph contrastive learning has been developed to learn discriminative node representations on homogeneous graphs. However, it is not clear how to augment the heterogeneous graphs without substantially altering the underlying semantics or how to design appropriate pretext tasks to fully capture the rich semantics preserved in heterogeneous information networks (HINs). Moreover, early investigations demonstrate that contrastive learning suffer from sampling bias, whereas conventional debiasing techniques (e.g., hard negative mining) are empirically shown to be inadequate for graph contrastive learning. How to mitigate the sampling bias on heterogeneous graphs is another important yet neglected problem. To address the aforementioned challenges, we propose a novel multi-view heterogeneous graph contrastive learning framework in this paper. We use metapaths, each of which depicts a complementary element of HINs, as the augmentation to generate multiple subgraphs (i.e., multi-views), and propose a novel pretext task to maximize the coherence between each pair of metapath-induced views. Furthermore, we employ a positive sampling strategy to explicitly select hard positives by jointly considering semantics and structures preserved on each metapath view to alleviate the sampling bias. Extensive experiments demonstrate MCL consistently outperforms state-of-the-art baselines on five real-world benchmark datasets and even its supervised counterparts in some settings.

Acute aortic dissection (AAD) is a life-threatening cardiovascular emergency characterized by aortic layer separation and false lumen formation, with high mortality rates. Emerging evidence highlights the critical role of innate immunity in AD pathogenesis. Innate immune activation drives AAD progression through multiple mechanisms, including macrophage polarization (M1/M2 imbalance), neutrophil extracellular trap (NET) formation, and inflammasome activation. These processes amplify vascular inflammation via cytokine storms (IL-1β, IL-6, TNF-α) and oxidative stress, further promoting matrix metalloproteinase activation and smooth muscle cell phenotypic switching. The cGAS-STING pathway, triggered by mitochondrial DNA release, and TLR signaling act as central hubs connecting vascular injury to innate immune responses. This review synthesizes recent advances in the molecular mechanisms of AAD, focusing on aortic wall structural alterations, dysregulated signaling pathway, including TGF-β, Ang II, STING, and TLR cascades, and immune-inflammatory responses mediated by innate immune components. A deeper understanding of these innate immune components may lead to improved diagnostic biomarkers and targeted therapies for AAD management.

In this paper, the nonlinear time-fractional Sine-Gordon equation is investigated via the Fourier spectral method. The time-fractional derivative is approximated by the L1 approximation scheme, and the spatial component is discretized using the Fourier spectral method. The existence and uniqueness of the numerical solution are proven, and the stability and convergence of the proposed method are analyzed, respectively. Theoretical results are validated by numerical experiment.

Knot detection is an important aspect of timber grading. Reducing the false-positive frequency of knot detection will improve the accuracy of the predicted grade, as well as the utilization of the graded timber. In this study, a framework for timber knot detection was proposed. Faster R-CNN, a state-of-the-art defect identification algorithm, was first employed to detect timber knots because of its high true-positive frequency. Then, an overlapping bounding box filter was proposed to lower the false positive frequency achieved by Faster R-CNN, where a single knot is sometimes marked several times. The filter merges the overlapping bounding boxes for one actual knot into one box and ensures that each knot is marked only once. The main advantage of this framework is that it reduces the false positive frequency with a small computational cost and a small impact on the true positive frequency. The experimental results showed that the detection precision improved from 90.9% to 97.5% by filtering the overlapping bounding box. The framework proposed in this study is competitive and has potential applications for detecting timber knots for timber grading.

In this paper, we propose and experimentally demonstrate an integrated optical device that can implement the logical function of priority encoding from a 4-bit electrical signal to a 2-bit optical signal. For the proof of concept, the thermo-optic modulation scheme is adopted to tune each micro-ring resonator (MRR). A monochromatic light with the working wavelength is coupled into the input port of the device through a lensed fiber, and the four input electrical logic signals regarded as pending encode signals are applied to the micro-heaters above four MRRs to control the working states of the optical switches. The encoding results are directed to the output ports in the form of light. At last, the logical function of priority encoding with an operation speed of 10 Kbps is demonstrated successfully.

Background Prostate cancer (PCa) has previously been established as a cold tumor with highly complex tumor environment. Transforming growth factor (TGF)-β1 plays pro-oncogenic roles in PCa. TGF-β3, another isoform of the TGF-β family, is reported to have different and even opposite regulatory roles to TGF-β1. However, the effect of TGF-β3 in PCa has not been elucidated. Methods TGF-β3 expression and its association with multiple clinicopathological characteristics were analyzed immunohistochemically in human PCa specimens. The antitumor effect of TGF-β3 and its combination with immunochemotherapy was observed by subcutaneous xenograft tumor model. RNA-seq of mouse tumor tissues identified differentially expressed genes (DEGs) that were enriched in vascular biological processes. The angiogenesis effect of TGF-β3 was evaluated using tube formation assay. Hypoxic area, NG2 + pericytes, Col IV + basement membrane, adhesion molecules and immune cells were analyzed by immunofluorescence. Vascular permeability was measured by Evans blue staining. The flow cytometry was conducted to examine the composition of tumor-infiltrating CD8 + T cells. Results Low TGF-β3 expression in prostate cancer (PCa) was correlated with higher Gleason scores and pathological T stage. While intratumoral TGF-β3 injection demonstrated antitumor effects in vivo, it did not directly affect PCa cell proliferation, migration or invasion in vitro. GO analysis revealed significant enrichment of DEGs in vascular-related biological process. TGF-β3 treatment normalized tumor vascular architecture and reduced vascular leakage. This vascular normalization upregulated endothelial adhesion molecules and enhanced CD8 + T cell infiltration, suppressing tumor growth. Critically, TGF-β3-induced vascular normalization synergized with anti-PD-L1 immunotherapy or paclitaxel chemotherapy, enhancing CD8 + T cell or drug infiltration and significantly boosting therapeutic efficacy. Conclusions TGF-β3 potentially acts as a protective factor in PCa by promoting vascular normalization and remodeling of the tumor environment, which facilitates infiltration of CD8 + T cells or drugs, significantly enhancing their antitumor effects.

Background Alveolar epithelial type II (AT2) cells participate in epithelial repair and lung immune defense, while the detailed molecular mechanisms through which AT2 cells regulate inflammatory immune responses in asthma remain unclear. Ubiquitin-specific peptidase 18 (USP18) has been implicated in immune regulation, but its role in asthma pathogenesis is not fully understood. Methods USP18 expression in AT2 cells was analyzed in both mouse models of allergic asthma and asthmatic patients. Functional studies were conducted using USP18 knockout mice, AT2 cell-specific chemokine (C-C motif) ligand 8 (CCL8) knockdown, and exogenous CCL8 treatment. Th2 responses, eosinophil recruitment, and chemokine production were assessed. Mechanistic investigations focused on USP18-mediated regulation of SOCS1 stability and downstream ERK-STAT3 signaling. Results USP18 is increased in AT2 cells from both mouse models of asthma and asthmatic patients, and played a protective role in the progression of allergic asthma by suppressing Th2 responses and reducing CCL8 production. Notably, USP18 deficiency causes increased CCL8 in AT2 cells, which in turn recruits Th2 cells and eosinophils, thereby exacerbating allergic asthma. Consistently, CCL8 treatment induced asthmatic inflammation and knocking down CCL8 in AT2 cells of USP18 knockout mice alleviates asthma symptoms. Mechanistically, USP18 stabilizes SOCS1 by inhibiting its ubiquitination and degradation, leading to reduced CCL8 production through the ERK-STAT3 signaling pathway in a negative feedback loop. Conclusions Overall, these findings reveal a previously unrecognized role for USP18 in regulating CCL8 production during asthma progression, highlighting USP18 and CCL8 as potential therapeutic targets for asthma treatment.