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The purpose of this work is to study the efficient numerical solvers for time-dependent conservative space fractional diffusion equations. Specifically, for the discretized Toeplitz-like linear system, we aim to study efficient preconditioning based on a matrix-splitting iteration method. We propose a scaled tridiagonal and Toeplitz-like splitting iteration method. Its asymptotic convergence property is first established. Further, based on the induced preconditioner, a fast circulant-like preconditioner is developed to accelerate the convergence of the Krylov Subspace iteration methods. Theoretical results suggest that the fast preconditioner can inherit the effectiveness of the original induced preconditioner. Numerical results also demonstrate its efficiency.

The Tibetan Plateau (TP) exerts significant impacts on its surroundings through its thermal and dynamical processes. In recent decades, especially since 2000, the TP has been experiencing a more rapid warming than its surrounding regions. This study uses Moderate Resolution Imaging Spectroradiometer (MODIS) monthly averaged land surface temperature (LST) product to detect the recent warming trend with respect to elevations over the entire TP, because the number of weather stations from China Meteorological Administration (CMA) is rather limited in the western TP and, furthermore, are unavailable for areas higher than 4,800 m above sea level (ASL). The trend of MODIS LST is first validated against the warming trend estimated from near-surface air temperatures measured at CMA stations and the warming rate dependence on elevation is then derived from MODIS LST. The results indicate that the warming rate increases from 3,000 to 4,800 m ASL, and then becomes quite stable with a slight decline near the highest elevations. This altitudinal dependence of the warming rate has a significant implication for TP water resources and environmental changes, since most glaciers and snow surfaces are located above 5,000 m ASL over the TP.

Prenatal valproic acid (VPA) exposure increases the risk of neurodevelopmental disorders, though its synaptic mechanisms remain unclear. Using multi-omics analyses, we identified Dlgap2 as a consistently dysregulated protein in VPA models. Mice with Dlgap2 knockdown exhibited synaptic deficits and autism-like behaviors, including social and cognitive impairments. Proteomics of postsynaptic density following Dlgap2 knockdown revealed disruption of synaptic organization and a specific reduction in Intersectin-1 (Itsn1), which interacts with Dlgap2 and undergoes ubiquitin-mediated degradation upon Dlgap2 deficiency. Our study defines a Dlgap2-Itsn1 regulatory axis that underlies VPA-induced synaptic dysfunction.

The CutMix technique is a sophisticated approach for augmenting data in order to train neural network-based image classifiers. Essentially, it involves cutting out a portion of a random image and pasting it into the same location as another image. However, because of the irregularity of graph data, CutMix cannot be directly applied to graph learning. Our paper introduces G-CutMix, a CutMix-based data augmentation approach that we designed specifically for bot detection in social media networks. G-CutMix involves conducting CutMix operations between the original graph and a shuffled graph, which precedes the graph convolution process. The outputs of the graph convolution are then strategically merged with the user representations from both the original and shuffled graphs. Our proposed G-CutMix not only leverages the power of graph convolutions but also introduces a layer of complexity that mimics real-world scenarios where bot behavior can be subtle and varied, making G-CutMix a formidable tool in the arsenal against bot detection. Our experiments confirm that our approach can consistently enhance the performance of bot detection across various GNN architectures, including Graph Convolutional Networks, GraphSAGE, and Graph Attention Networks.

Background Patients with lymph node (LN)-positive pancreatic ductal adenocarcinoma (PDAC) have extremely poor survival rates. Circular RNAs (circRNAs), a newly discovered type of endogenous noncoding RNAs, have been proposed to mediate the progression of diverse types of tumors. However, the role and underlying regulatory mechanisms of circRNAs in the LN metastasis of PDAC remain unknown. Methods Next-generation sequencing was used to identify differentially expressed circRNAs between PDAC and normal adjacent tissues. In vitro and in vivo experiments were conducted to evaluate the functional role of circNFIB1. RNA pulldown and luciferase assays were performed to examine the binding of circNFIB1 and miR-486-5p. Results In the present study, we identified that a novel circRNA (circNFIB1, hsa_circ_0086375) was downregulated in PDAC and negatively associated with LN metastasis in PDAC patients. Functionally, circNFIB1 knockdown promoted lymphangiogenesis and LN metastasis of PDAC both in vitro and in vivo. Mechanistically, circNFIB1 functioned as a sponge of miR-486-5p, and partially reversed the effect of miR-486-5p. Moreover, circNFIB1 attenuated the oncogenic effect of miR-486-5p and consequently upregulated PIK3R1 expression, which further downregulated VEGF-C expression through inhibition of the PI3K/Akt pathway, and ultimately suppressed lymphangiogenesis and LN metastasis in PDAC. Conclusions Our findings provide novel insight into the underlying mechanism of circRNA-mediated LN metastasis of PDAC and suggest that circNFIB1 may serve as a potential therapeutic target for LN metastasis in PDAC. Graphical abstract

Solids can be generally categorized by their structures into crystalline and amorphous states with different interactions among atoms dictating their properties. Crystalline-amorphous hybrid structures, combining the advantages of both ordered and disordered components, present a promising opportunity to design materials with emergent collective properties. Hybridization of crystalline and amorphous structures at the sublattice level with long-range periodicity has been rarely observed. Here, we report a nested order-disorder framework (NOF) constructed by a crystalline matrix with self-filled amorphous-like innards that is obtained by using pressure to regulate the bonding hierarchy of Cu 12 Sb 4 S 13 . Combined in situ experimental and computational methods demonstrate the formation of disordered Cu sublattice which is embedded in the retained crystalline Cu framework. Such a NOF structure gives a low thermal conductivity (~0.24 W·m −1 ·K −1 ) and a metallic electrical conductivity (8 × 10 −6  Ω·m), realizing the collaborative improvement of two competing physical properties. These findings demonstrate a category of solid-state materials to link the crystalline and amorphous forms in the sublattice-scale, which will exhibit extraordinary properties. The synthesis and characterization of new crystalline-amorphous hybrid materials is challenging. Here, the authors report the preparation of a nested order-disorder framework by applying high pressure to a nested copper chalcogenide Cu 12 Sb 4 S 13 .

This study explores the intervention effect of Sparassis Latifolia Polysaccharides (SLPs) on abnormal glucose metabolism in C57BL/6J mice, induced by a high-fat diet and streptozotocin (STZ). The results show that high-concentration SLPs significantly improve abnormal glucose metabolism. A mouse facial expression dataset was constructed, covering five glucose metabolic states: Norm, Pre-stage of Abnormal Glucose Metabolism, Abnormal Glucose Metabolism, Early Stage of SLPs Intervention, and Late Stage of SLPs Intervention, based on fasting blood glucose and facial expression features. To achieve non-invasive detection of abnormal glucose metabolism, a lightweight deep learning model, LFPP-YOLO, was proposed. This model incorporates a Partial Self-Attention (PSA) module to enhance global context information extraction and utilizes the L-FFCA structure for multi-level feature enhancement and background suppression, improving its adaptability to complex expressions. The PIoU v2 loss function was employed to optimize facial expression localization accuracy and robustness. Experimental results show that the LFPP-YOLO model achieves an average facial detection accuracy of 95.1% across the five metabolic states, with a compact size of 2.4 MB and a real-time inference speed of 5ms, significantly outperforming existing models. This model provides a novel approach and technical support for non-invasive screening and personalized intervention of abnormal glucose metabolism in mice.

Near-surface air temperature variability and the reliability of temperature extrapolation within glacierized regions are important issues for hydrological and glaciological studies that remain elusive because of the scarcity of high-elevation observations. Based on air temperature data in 2019 collected from 12 automatic weather stations, 43 temperature loggers, and 6 national meteorological stations in 6 different catchments, this study presents air temperature variability in different glacierized and nonglacierized regions and assesses the robustness of different temperature extrapolations to reduce errors in melt estimation. The results show high spatial variability in temperature lapse rates (LRs) in different climatic contexts, with the steepest LRs located on the cold and dry northwestern Tibetan Plateau and the lowest LRs located on the warm and humid monsoonal-influenced southeastern Tibetan Plateau. Near-surface air temperatures in high-elevation glacierized regions of the western and central Tibetan Plateau are less influenced by katabatic winds and thus can be linearly extrapolated from off-glacier records. In contrast, the local katabatic winds prevailing on the temperate glaciers of the southeastern Tibetan Plateau exert pronounced cooling effects on the ambient air temperature, and thus, on-glacier air temperatures are significantly lower than that in elevation-equivalent nonglacierized regions. Consequently, linear temperature extrapolation from low-elevation nonglacierized stations may lead to as much as 40% overestimation of positive degree-days, particularly with respect to large glaciers with a long-flowline distances and significant cooling effects. These findings provide noteworthy evidence that the different LRs and relevant cooling effects on high-elevation glaciers under distinct climatic regimes should be carefully accounted for when estimating glacier melting on the Tibetan Plateau.

Background Accumulating evidence suggests that circular RNAs (circRNAs) are important participants in cancer progression. However, the biological processes and underlying mechanisms of circRNAs in pancreatic ductal adenocarcinoma (PDAC) are unclear. Method CircRNAs were verified by Sanger sequencing. Colony formation, 5-Ethynyl-2′-deoxyuridine (EdU), and Transwell assays were performed to investigate the effect of circBFAR on the proliferation, invasion, and migration of PDAC cells in vitro. RNA pull-down assays were conducted to verify the binding of circBFAR with microRNA miR-34b-5p. Results In the present study, we identified a novel circRNA (termed as circBFAR, hsa_circ_0009065) that was upregulated in a 208-case cohort of patients with PDAC. The ectopic expression of circBFAR correlated positively with the tumor-node-metastasis (TNM) stage and was related to poorer prognosis of patients with PDAC. Moreover, circBFAR knockdown dramatically inhibited the proliferation and motility of PDAC cells in vitro and their tumor-promoting and metastasis properties in in vivo models. Mechanistically, circBFAR upregulated mesenchymal-epithelial transition factor (MET) expression via sponging miR-34b-5p. Additionally, circBFAR overexpression increased the expression of MET and activated downstream phosphorylation of Akt (Ser 473) and further activated the MET/PI3K/Akt signaling pathway, which ultimately promoted the progression of PDAC cells. Importantly, application of MET inhibitors could significantly attenuate circBFAR-mediated tumorigenesis in vivo. Conclusions Our findings showed that circBFAR plays an important role in the proliferation and metastasis of PDAC, which might be explored as a potential prognostic marker and therapeutic target for PDAC.

Aims Sepsis‐associated encephalopathy (SAE) is a common but serious complication in septic survivors and often causes long‐term cognitive impairments. The role of RIPK3‐participated necroptosis in SAE remains obscured. STING is a key molecule in regulating necroptosis and apoptosis. However, there is uncertainty as to the mechanisms of STING in CLP‐induced SAE. The aim of this study was to investigate whether STING is involved in the underlying mechanism of SAE. Methods The contextual fear conditioning test (CFCT) assesses cognitive impairment. A transmission electron microscope (TEM) was used to notice the necroptosis. Western blotting and immunofluorescence labeling were applied for the observation of related proteins. Results The phosphorylated STING in the hippocampal neuron of SAE mice was significantly elevated. Knocking down STING inhibited necroptosis and attenuated cognitive impairment in SAE mice. Moreover, RIPK3−/− mice had less cognitive deficit in the SAE model. However, STING overexpression did not deteriorate cognitive impairment in RIPK3−/− mice with SAE, indicating that STING is upstream involved in necroptosis. Furthermore, PERK inhibition ameliorated cognitive deficits through a STING‐dependent pathway in SAE mice. Conclusion PERK‐STING‐RIPK3 pathway facilitates cognitive impairment by inducing neuronal necroptosis in the pathology of SAE, which provided a new therapeutic target in SAE treatment. This study is the first to elucidate the role of STING in SAE; additionally, the study demonstrates that the PERK‐STING‐RIPK3 pathway facilitates cognitive impairment by inducing neuronal necroptosis in the pathology of SAE, which provided a new therapeutic target in SAE treatment.