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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.

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.

A single strategy used in the firefly algorithm (FA) cannot effectively solve the complex optimal scheduling problem. Thus, we propose the FA with division of roles (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy Cauchy mutation; the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

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.

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.

This paper studies the dynamic parameters identification problem of load and linkages of a serial robot in the presence of model uncertainty. The dynamic parameters of load and linkages of a serial robot have been identified through a combination procedure, which is useful for different platforms of serial robot systems. The purpose of this paper is to propose a dynamic parameter identification method for a serial robot based on a dual quaternion. Using the information of the force and torque of the load obtained by the six-dimensional force sensor installed on the end-effector of the robot, the dynamics parameter identification matrix of the load is derived, which also uses the information of motion speed and acceleration of the end-effector. On the other hand, the analysis of the dynamic relationship between adjacent linkages and the joints is based on dual quaternion algebra, and the identification matrix for the dynamic parameters and the difference values of associated linkages are derived, as well. The combination procedure of the method is flexible in the application of dynamic parameters identification for a serial robot using a dual quaternion. Furthermore, the proposed DQ (dual quaternion)-based method in this paper has the advantage of lower cost compared with the RBFNN (radial basis function neural network)-based method. The effectiveness of the proposed dynamic parameter identification method for a serial robot has been verified by relevant experiments.

In this paper, we consider a multi-term variable-order fractional diffusion equation on a finite domain, which involves the Caputo variable-order time fractional derivative of order α(x,t)∈(0,1) and the Riesz variable-order space fractional derivatives of order β(x,t)∈(0,1), γ(x,t)∈(1,2). Approximating the temporal direction derivative by L1-algorithm and the spatial direction derivative by the standard and shifted Grünwald method, respectively, a characteristic finite difference scheme is proposed. The stability and convergence of the difference schemes are analyzed via mathematical induction. Some numerical experiments are provided to show the efficiency of the proposed difference schemes.

Dihydroartemisinin (DHA), a widely used antimalarial agent, has clinical potential for the treatment of hepatic carcinoma. Although chemotherapy is indispensable for tumor therapy, it is generally limited by poor solubility, low efficiency, rapid clearance, and side effects. As an emerging treatment method, photothermal therapy (PTT) has many outstanding properties, but suffers from poor photostability of photosensitizer and incomplete ablation. Multimodal therapies could combine the advantages of different therapy methods to improve antitumor efficiency. Hence, we designed a nano-delivery system (ICG&DHA@ZIF-8) using zeolitic imidazolate framework-8 (ZIF-8) with a high porous rate and pH sensitivity property, to co-load DHA and indocyanine green (ICG). Dynamic light scattering and transmission electron microscopy were used to characterize the prepared nanoparticles. The photothermal conversion and drug release performances of ICG&DHA@ZIF-8 were investigated. In vitro antitumor efficacy and cellular uptake were studied. The mechanism of the combination treatment was studied by reactive oxygen species level detection and western blot assays. In vivo antitumor assays were then studied with the guidance of ex vivo imaging. The results showed that the ICG&DHA@ZIF-8 based combination therapy could efficiently kill hepatic carcinoma cells and suppress tumor growth. This research provides a potential nanodrug for the treatment of hepatic carcinoma.