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Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis. To address these problems, we propose a multi-channel spatio-temporal graph attention contrastive network for DBNs analysis. Specifically, we first construct dynamic brain functional networks from fMRI data with sliding windows, and embed the structural connectivity derived from diffusion tensor imaging (DTI) to the dynamic functional connectivity graph representation to construct multi-modal brain network. Second, we develop a multi-channel spatial attention contrastive network to extract topological features from the brain network within each time window. This network incorporates an intra-window graph contrastive constraint to enhance the discriminative ability of the extracted features. Moreover, temporal dependencies across windows are captured by integrating feature embeddings through a self-attention mechanism, and the inter-window recurrent contrastive constraint is devised to extract higher-order spatio-temporal topological features. Finally, a multi-layer perceptron (MLP) is used to classify the brain networks. Experiments on epilepsy and ADNI datasets show that our method outperforms several state-of-the-art approaches in diagnosing performance, and it provides discriminative graph features for related brain diseases. •A deep graph model is introduced for DBNs analysis, enabling higher-order spatio-temporal information propagation.•A multi-modal method integrates structural and functional brain connectivity into a unified graph representation.•A graph attention network with contrastive loss improves spatio-temporal feature discrimination.•Our method outperforms state-of-the-art approaches on epilepsy and ADNI datasets, and demonstrates potential in identifying neurological biomarkers.

Decidualization is a complex process involving cellular proliferation and differentiation of the endometrial stroma and is required to establish and support pregnancy. Dysregulated decidualization has been reported to be a critical cause of recurrent implantation failure (RIF). In this study, we found that Activating transcription factor 3 (ATF3) expression was significantly downregulated in the endometrium of RIF patients. Knockdown of ATF3 in human endometrium stromal cells (hESCs) hampers decidualization, while overexpression could trigger the expression of decidual marker genes, and ameliorate the decidualization of hESCs from RIF patients. Mechanistically, ATF3 promotes decidualization by upregulating FOXO1 via suppressing miR-135b expression. In addition, the endometrium of RIF patients was hyperproliferative, while overexpression of ATF3 inhibited the proliferation of hESCs through CDKN1A. These data demonstrate the critical roles of endometrial ATF3 in regulating decidualization and proliferation, and dysregulation of ATF3 in the endometrium may be a novel cause of RIF and therefore represent a potential therapeutic target for RIF.

Global change is accelerating the chemical weathering of silicate rocks and the associated phosphorus release. However, the effects of phosphorus release on the global patterns of plant phosphorus limitation remain unclear. Here, we show that approximately 47% of the exposed areas in global silicate rocks are subject to phosphorus limitation of vegetation growth, as estimated using the ratio of leaf nitrogen to phosphorus resorption efficiency. Phosphorus-limited areas are projected to expand markedly with global warming, and the proportion may reach 54 − 59% according to two model scenarios (the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5). Nevertheless, phosphorus release from accelerated chemical weathering of silicate rocks mitigates this limitation, with a relative contribution of approximately 15.5%. This work highlights the implications of accelerated chemical weathering of silicate rocks and its resulting phosphorus release for the global patterns of phosphorus limitation, providing a scientific foundation for phosphorus management strategies. Climate warming is projected to expand plant phosphorus limitation from 47% to 59% of global silicate rock areas. However, phosphorus release from accelerated chemical weathering of these rocks offsets about 15.5% of this increase.

Silicate rock weathering maintains a stable and long‐term absorption of CO2. However, the magnitude, spatial pattern, and evolution characteristics of global silicate rock weathering carbon sink (SCS) remain unclear. To solve this problem, based on high‐precision hydrometeorological data (1996–2017) and CMIP5 data (2041–2060), using the Celine model, we calculated the global silicate rock weathering carbon sink flux (SCSF) magnitude and spatio‐temporal distribution for 1996–2017. We also predicted the SCSF under two future greenhouse gas emission scenarios (RCP 4.5 and RCP 8.5). Then, we produced a spatial data set (0.5 × 0.5) of global SCSF from 1996 to 2017 and found that the global average annual SCSF was 1.67 t/km2/yr, and the SCS was 127.11 Tg/yr. In particular, Brazil's silicate rock contribution accounts for nearly a quarter of the global SCS (24.41%). Although the GEM‐CO2 model is now widely used, the SCSF, without considering the temperature, may be overestimated by 5.4%, and the maximum contribution of temperature to it can reach 240 kg/km2/yr. Moreover, the global SCS is now showing a downward trend, but the global emission of greenhouse gases in the future (2041–2060) will continue to increase the carbon sink capacity (23.8%) due to temperature changes. In summary, we have produced a set of high‐resolution spatiotemporal data of the past and the future. The above results fill up the large‐scale data gap of SCSF and provide a scientific basis for quantitatively assessing the impact of climate change on SCS. Key Points Silicate rock carbon sink is expanded to global scale with high spatial resolution There is huge spatial heterogeneity in global silicate rock carbon sink flux From 2041 to 2060, silicate rock carbon sink will rise in response to global warming

The roles of Rho GTPases in various types of cancer have been extensively studied, but the research of Rho guanine nucleotide exchange factors (GEFs) in cancer is not comprehensive. Rho guanine nucleotide exchange factor 6 (ARHGEF6) is an important member of the Rho GEFs family involved in cytoskeletal rearrangement, and it has not been investigated in acute myeloid leukemia (AML). Our research showed that the expression of ARHGEF6 was mainly higher in AML cell lines, meanwhile, was highest in the samples from patients with AML compared to other cancer types. High ARHGEF6 expression in AML was associated with a good prognosis. ARHGEF6 low cases showed significantly higher overall survival (OS) after autologous or allogeneic HSCT (auto/allo-HSCT). High expression of ARHGEF6 downregulates the negative regulation of myeloid differentiation process and upregulates G protein-coupled receptor signaling pathway-related processes, among which HOXA9, HOXB6, and TRH have significant differential expression and prognostic impact in AML. Therefore, ARHGEF6 can become a prognostic marker in AML; ARHGEF6 low patients can gain from auto/allo-HSCT.

The Carbonate rock weathering Carbon Sink (CCS) and Silicate rock weathering Carbon Sink (SCS) play a significant role in the carbon cycle and global climate change. However, the spatial‐temporal patterns and trends of the CCS and SCS from 1950 to 2099 have not been systematically quantified. Thus, Supported by long‐term hydrometeorological data under the RCP8.5, we use the accepted Suchet and Hartmann models to determine the following. First, we found except for the difference in their weathering rates, the SCS covers 37.2 million km2 more area than the CCS. The CCS Flux (CCSF) and SCS Flux (SCSF) are 5.36 and 1.22 t/km2/yr, respectively. Similarly, the Full CCS (FCCS, 0.3 Pg/yr) is more than the Full SCS (FSCS, 0.08 Pg/yr). Furthermore, the CCS (7.01 kg/km2) and SCS (3.95 kg/km2) are in a state of overall increase. In addition, the mid‐to‐high latitudes of the northern hemisphere are aggravated by warming (0.03°C) and humidity (0.65 mm), while the decrease in runoff in the mid‐latitudes of the southern hemisphere reduces karstification. Specifically, by 2099, the CCSF in the mid‐latitudes of the southern hemisphere will decrease by 5.72%. Instead, the CCSF in the northern hemisphere and lower latitudes of the southern hemisphere will exhibit a gentle upward slope. Particularly, the peak regions of the global FCCS (65.63 Tg/yr) and FSCS (33.01 Tg/yr) are the tropical zone. In conclusion, this study contributes a high‐resolution and long‐time series CS datasets for the CCS and SCS. We provide data and a theory for solving terrestrial carbon sink loss. Plain Language Summary The carbon cycle and global‐climate change cannot ignore the Carbonate and Silicate rocks weathering Carbon Sink (CCS and SCS). However, the spatial‐temporal patterns and trends of CCS and SCS from 1950 to 2099 have not been quantified. We use the accepted Suchet and Hartmann models to determine the following. First, we found except for the difference in their weathering rates, the SCS covers 37.2 million km2 more area than the CCS. The CCS Flux (CCSF) and SCS Flux (SCSF) are 5.36 and 1.22 t/km2/yr, respectively. Similarly, the Full CCS (FCCS, 0.3 Pg/yr) is more than the Full SCS (FSCS, 0.08 Pg/yr). Furthermore, the CCS (7.01 kg/km2) and SCS (3.95 kg/km2) are in a state of overall increase. In addition, the mid‐to‐high latitudes of the northern hemisphere are aggravated by warming (0.03°C) and humidity (0.65 mm), while the decrease in runoff in the mid‐latitudes of the southern hemisphere reduces karstification. Specifically, by 2099, the CCSF in the mid‐latitudes of the southern hemisphere will decrease by 5.72%. Instead, the CCSF in the northern hemisphere and lower latitudes of the southern hemisphere will exhibit a gentle upward slope. In conclusion, this study contributes a high‐resolution data set for solving carbon sink loss. Key Points High‐resolution data set for global carbonate and spatial diversification carbon sinks was established Carbon cycle can't ignore CCS (5.36 t/km2/yr) and SCS (1.22 t/km2/yr). Especially CCS (7.01 kg/km2) and SCS (3.95 kg/km2) increased significantly The peak regions of the global FCCS (65.63 Tg/yr) and FSCS (33.01 Tg/yr) are the tropical zone

The seasonal variations in carbon (C), nitrogen (N), and phosphorus (P) at the organ level of Pinus yunnanenis during different season are poorly understood. In this study, the C, N, P, and their stoichiometric ratios in various organs of P. yunnanensis during the four seasons are discussed. The middle and young aged P. yunnanensis forests in central Yunnan province, China were chosen, and the contents of C, N, and P in fine roots (<2 mm), stems, needles, and branches were analyzed. The results showed that the C, N, P contents and their ratios in P. yunnanensis were significantly influenced by season and organ, less affected by age. The C content of the middle-aged and young forests decreased continuously from spring to winter, whereas N and P first decreased and then increased. No significant allometric growth relationships were observed between P-C of the branches or stems in the young and middle-aged forests, whereas a significant allometric growth relationship existed for N-P of needles in the young stands, indicating that the P-C and N-P nutrient distribution patterns shows different trends in the organ level in different age stands. The pattern of P allocation between organs shows differences in stand age, with more allocation to needles in middle-aged stands and more allocation to fine roots in young stands. The N:P ratio in needles was less than 14, indicating that P. yunnanensis was mainly limited by N and increasing the application of N fertilizer would be beneficial for the productivity of this stand. The results will be helpful to nutrient management in P. yunnanensis plantation.

The tourism festival and special event innovation are the important factors influencing the creation of superior value, the achievement of customer loyalty, and profitable growth. Based on the perspective of product supply and consumer demand integration analysis, this paper constructed an integrated model of tourism festival and special event innovation and its impacts on tourists’ behavioral intentions. The basic data was obtained through the tourist survey on Zhangjiajie International Country Music Festival, and the exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were used to empirically test the relationship between various dimensions of tourism festival and special event innovation and their impacts on tourists’ behavioral intentions. The results show the following: (1) tourism festival and special event innovation includes six dimensions of performance, accessibility, self-service technology, aesthetic environment, tourist community, and loyalty program; (2) performance, self-service technology, and aesthetic environment have a significant positive impact on overall innovation, while accessibility, tourist community, and loyalty program have no significant impact on overall innovation; and (3) overall innovation has a significant positive impact on tourists’ satisfaction, brand equity, and tourists’ behavioral intentions. Moreover, tourists’ satisfaction and brand equity play a partial intermediary role in the impacts of overall innovation on the tourists’ behavioral intentions. The article concludes with research limitations and future research directions.

Glioblastoma (GBM) is the most lethal primary brain tumor with intra-tumoral hierarchy of glioblastoma stem cells (GSCs). The heterogeneity of GSCs within GBM inevitably leads to treatment resistance and tumor recurrence. Molecular mechanisms of different cellular state GSCs remain unclear. Here, we find that classical (CL) and mesenchymal (MES) GSCs are enriched in reactive immune region and high CL-MES signature informs poor prognosis in GBM. Through integrated analyses of GSCs RNA sequencing and single-cell RNA sequencing datasets, we identify specific GSCs targets, including MEOX2 for the CL GSCs and SRGN for the MES GSCs. MEOX2-NOTCH and SRGN-NFκB axes play important roles in promoting proliferation and maintaining stemness and subtype signatures of CL and MES GSCs, respectively. In the tumor microenvironment, MEOX2 and SRGN mediate the resistance of CL and MES GSCs to macrophage phagocytosis. Using genetic and pharmacologic approaches, we identify FDA-approved drugs targeting MEOX2 and SRGN. Combined CL and MES GSCs targeting demonstrates enhanced efficacy, both in vitro and in vivo. Our results highlighted a therapeutic strategy for the elimination of heterogeneous GSCs populations through combinatorial targeting of MEOX2 and SRGN in GSCs. The molecular mechanisms underlying the function of different cellular states in glioblastoma stem cells (GSCs) remain poorly understood. Here, the authors perform integrated single cell and bulk analysis of GSCs and identify potential therapeutic targets.

Protein-protein interactions (PPIs) are critical for understanding the molecular mechanisms underlying various biological processes, particularly in microbes associated with cardiovascular disease. Traditional experimental methods for detecting PPIs are often time-consuming and costly, leading to an urgent need for reliable computational approaches. In this study, we present a novel model, the deep denoising autoencoder for protein-protein interaction (DAEPPI), which leverages the denoising autoencoder and the CatBoost algorithm to predict PPIs from the evolutionary information of protein sequences. Our extensive experiments demonstrate the effectiveness of the DAEPPI model, achieving average prediction accuracies of 97.85% and 98.49% on yeast and human datasets, respectively. Comparative analyses with existing effective methods further validate the robustness and reliability of our model in predicting PPIs. Additionally, we explore the application of DAEPPI in the context of cardiovascular disease, showcasing its potential to uncover significant interactions that could contribute to the understanding of disease mechanisms. Our findings indicate that DAEPPI is a powerful tool for advancing research in proteomics and could play a pivotal role in the identification of novel therapeutic targets in cardiovascular disease.