The RUNX1::RUNX1T1 translocation, also termed AML1-ETO, is one of the most frequent cytogenetic abnormalities in acute myeloid leukemia (AML) and is associated with variable clinical outcomes. The R222G hotspot mutation, located in the RNA helicase gene DHX15, is enriched and predominantly found in AML with this translocation, but its diagnostic significance and underlying mechanism remain largely unclear. In this study, we show that pediatric AML patients carrying DHX15 mutations exhibit an inferior prognosis. Functional analysis demonstrates that DHX15R222G cooperates with AML1-ETO fusion protein to enhance AML leukemia stem cell (LSC) activity and promote resistance to standard chemotherapy. Mechanistically, AML1-ETO transcriptionally upregulates mitochondrial transcription factor A (TFAM), while DHX15R222G promotes TFAM protein stabilization and nuclear translocation, resulting in robust activation of oxidative phosphorylation (OXPHOS) gene expression and mitochondrial respiration. Inhibition of oxidative phosphorylation by the Complex V inhibitor S-Gboxin exerts strong anti-leukemic effects and efficiently circumvents chemotherapy resistance in AML1-ETO+ DHX15R222G leukemia. These findings underscore the pivotal role of oncogenic DHX15 mutations in regulating AML LSC activity and identify DHX15R222G as a potential genetic biomarker for AML risk stratification. Moreover, this mutation may predict therapeutic vulnerability to OXPHOS inhibition.

The Brazilian Legal Amazon, home to the largest tropical forest, faces growing threats from frequent fires, putting countless species at risk. This study is relevant as it enables the detection of air pollutants over vast areas, surpassing the limitations of traditional ground-based stations, which lacked insight into the scale and dispersion of these pollutants. This study utilizes data from the TROPOspheric Monitoring Instrument (TROPOMI) for the detection of atmospheric pollutants - specifically nitrogen dioxide (NO2) and carbon monoxide (CO) - which were analyzed in conjunction with Fire Radiative Power (FRP) data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) to construct a quantitative analysis of wildfires in the study region, correlating them with the atmospheric pollution generated during these events. The atmospheric data were collected from 508 points at 100 km intervals during the flood and dry seasons from 2019 to 2023. Satellite images were corrected with the Discrete Cosine Transform (DCT)-Penalized Least Squares (PLS) method, processed using the Sentinel Application Platform (SNAP) and analyzed with descriptive statistics and K-means clustering model, together with the use of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) in an effort to understand the dispersion of atmospheric contaminants present in air masses. Carbon monoxide concentrations increased during the dry season, possibly associated with the rise in fire activity, although its patterns were less consistent and, in some cases, opposite to those of nitrogen dioxide. Nitrogen dioxide (NO2) emerged as the most sensitive compound, showing the strongest apparent relationship with fire activity, with elevated concentrations observed in areas with a higher number of fire outbreaks, particularly during the dry season. These findings highlight the importance of monitoring and create public policies to protect the Amazon before irreversible damage occurs.

Cuproptosis is characterized by the aggregation of lipoylated enzymes of the tricarboxylic acid cycle and subsequent loss of iron-sulfur cluster proteins as a unique copper-dependent form of regulated cell death. As dysregulation of copper homeostasis can induce cuproptosis, there is emerging interest in exploiting cuproptosis for cancer therapy. However, the molecular drivers of cancer cell evasion of cuproptosis were previously undefined. Here, we found that cuproptosis activates the Wnt/β-catenin pathway. Mechanistically, copper binds PDK1 and promotes its interaction with AKT, resulting in activation of the Wnt/β-catenin pathway and cancer stem cell (CSC) properties. Notably, aberrant activation of Wnt/β-catenin signaling conferred resistance of CSCs to cuproptosis. Further studies showed the β-catenin/TCF4 transcriptional complex directly binds the ATP7B promoter, inducing its expression. ATP7B effluxes copper ions, reducing intracellular copper and inhibiting cuproptosis. Knockdown of TCF4 or pharmacological Wnt/β-catenin blockade increased the sensitivity of CSCs to elesclomol-Cu-induced cuproptosis. These findings reveal a link between copper homeostasis regulated by the Wnt/β-catenin pathway and cuproptosis sensitivity, and suggest a precision medicine strategy for cancer treatment through selective cuproptosis induction.

Accurately distinguishing different types of jawbone radiation lesions (RJLs) based on cone beam computed tomography (CBCT) images is crucial for oral surgeons to choose appropriate treatment plans. Currently, only experienced radiologists can distinguish different types of RJLs from CBCT images. Therefore, it is necessary to study computing methods for accurately classifying CBCT images of different JRLs lesions. However, the lesions in CBCT images are very small, and different types of lesions exhibit high similarity on the images, posing challenges for computing methods. In this paper, we propose a Local-Global Features Fusion Network (LGFFNet) that simultaneously extracts local features and global features related to the lesions in CBCT images and fuses them. In order to distinguish different types of high similarity CBCT images, we adopted a loss function by combing focal loss and cross entropy loss to train the model, so that the model focuses more on learning the features of difficult-to-classify images while learning general distinguishing features. The experimental results on our collected dataset show that the classification performance of our method is superior to other comparative methods.

The majority of spatial transcriptomics datasets are characterized by low resolution, wherein each spot generally encompasses multiple cells.This limitation poses challenges for exploring biological insights at the cellular level. Consequently, the development and application of robust deconvolution methods for spatial transcriptomics data are imperative to address this challenge. Addressing the limitations of previous deconvolution methods-such as the lack of consideration cell type labels from single-cell sequencing data and the inability to adaptively capture local relationship among points-we propose a novel spatial transcriptomics data deconvolution model based on label-guided Multi-Head Dynamic Graph Attention Networks with Optimal Transport(MHDGATOT). Our approach leverages an advanced multi-head dynamic graph attention network to adaptively capture inter-data relationships and generate effective low-dimensional embeddings. Subsequently, we employ optimal transport based on fused gromov-wasserstein to derive the transport matrix between spatial transcriptomics data and single-cell sequencing data, facilitating the accurate deconvolution of spatial transcriptomics datasets. Experimental validation substantiates the effectiveness of our model.

Satellite remote sensing of PM2.5 (fine particulate matter) mass concentration has become one of the most popular atmospheric research aspects, resulting in the development of different models. Among them, the semi-empirical physical approach constructs the transformation relationship between the aerosol optical depth (AOD) and PM2.5 based on the optical properties of particles, which has strong physical significance. Also, it performs the PM2.5 retrieval independently of the ground stations. However, due to the complex physical relationship, the physical parameters in the semi-empirical approach are difficult to calculate accurately, resulting in relatively limited accuracy. To achieve the optimization effect, this study proposes a method of embedding machine learning into a semi-physical empirical model (RF-PMRS). Specifically, based on the theory of the physical PM2.5 remote sensing (PMRS) approach, the complex parameter (VEf, a columnar volume-to-extinction ratio of fine particles) is simulated by the random forest (RF) model. Also, a fine-mode fraction product with higher quality is applied to make up for the insufficient coverage of satellite products. Experiments in North China (35 degrees-45 degrees N, 110 degrees-120 degrees E) show that the surface PM2.5 concentration derived by RF-PMRS has an average annual value of 57.92 mu gm(-3) vs. the ground value of 60.23 mu gm(-3). Compared with the original method, RMSE decreases by 39.95 mu gm(-3), and the relative deviation is reduced by 44.87 %. Moreover, validation at two Aerosol Robotic Network (AERONET) sites presents a time series change closer to the true values, with an R of about 0.80. This study is also a preliminary attempt to combine model-driven and data-driven models, laying the foundation for further atmospheric research on optimization methods.

Supplementary Data from miR-192 Regulates Dihydrofolate Reductase and Cellular Proliferation through the p53-microRNA Circuit

BACKGROUND:The tumor-adipose microenvironment (TAME) is characterized by the enrichment of adipocytes, and is considered a special ecosystem that supports cancer progression. However, the heterogeneity and diversity of adipocytes in TAME remains poorly understood.METHODS:We conducted a single-cell RNA sequencing analysis of adipocytes in mouse and human white adipose tissue (WAT). We analyzed several adipocyte subtypes to evaluate their relationship and potential as prognostic factors for overall survival (OS). The potential drugs are screened by using bioinformatics methods. The tumor-promoting effects of a typical adipocyte subtype in breast cancer are validated by performing in vitro functional assays and immunohistochemistry (IHC) in clinical samples.RESULTS:We profiled a comprehensive single-cell atlas of adipocyte in mouse and human WAT and described their characteristics, origins, development, functions and interactions with immune cells. Several cancer-associated adipocyte subtypes, namely DPP4+ adipocytes in visceral adipose and ADIPOQ+ adipocytes in subcutaneous adipose, are identified. We found that high levels of these subtypes are associated with unfavorable outcomes in four typical adipose-associated cancers. Some potential drugs including Trametinib, Selumetinib and Ulixertinib are discovered. Emphatically, knockdown of adiponectin receptor 1 (AdipoR1) and AdipoR2 impaired the proliferation and invasion of breast cancer cells. Patients with AdipoR2-high breast cancer display significantly shorter relapse-free survival (RFS) than those with AdipoR2-low breast cancer.CONCLUSION:Our results provide a novel understanding of TAME at the single-cell level. Based on our findings, several adipocyte subtypes have negative impact on prognosis. These cancer-associated adipocytes may serve as key prognostic predictor and potential targets for treatment in the future.

Over the past few years, single image super-resolution (SR) has become a hotspot in the remote sensing area, and numerous methods have made remarkable progress in this fundamental task. However, they usually rely on the assumption that images suffer from a fixed known degradation process, e.g., bicubic downsampling. To save us from performance drop when real-world distribution deviates from the naive assumption, blind image super-resolution for multiple and unknown degradations has been explored. Nevertheless, the lack of a real-world dataset and the challenge of reasonable degradation estimation hinder us from moving forward. In this paper, a self-supervised degradation-guided adaptive network is proposed to mitigate the domain gap between simulation and reality. Firstly, the complicated degradations are characterized by robust representations in embedding space, which promote adaptability to the downstream SR network with degradation priors. Specifically, we incorporated contrastive learning to blind remote sensing image SR, which guides the reconstruction process by encouraging the positive representations (relevant information) while punishing the negatives. Besides, an effective dual-wise feature modulation network is proposed for feature adaptation. With the guide of degradation representations, we conduct modulation on feature and channel dimensions to transform the low-resolution features into the desired domain that is suitable for reconstructing high-resolution images. Extensive experiments on three mainstream datasets have demonstrated our superiority against state-of-the-art methods. Our source code can be found at https://github.com/XY-boy/DRSR

Abstract. Precise and continuous monitoring on long-term carbon dioxide (CO2) and methane (CH4) over the globe is of great importance, which can help study global warming and achieve the goal of carbon neutrality. Nevertheless, the available observations of CO2 and CH4 from satellites are generally sparse, and current fusion methods to reconstruct their long-term values on a global scale are few. To address this problem, we propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless XCO2 and XCH4 products from 2010 to 2020 over the globe at grids of 0.25°. A total of three datasets are applied in our study, including GOSAT, OCO-2, and CAMS-EGG4. Attributed to the significant sparsity of data from GOSAT and OCO-2, the spatiotemporal Discrete Cosine Transform is considered for our fusion task. Validation results show that the proposed method achieves a satisfactory accuracy, with the σ (R2) of ~ 1.18 ppm (> 0.9) and 11.3 ppb (0.9) for XCO2 and XCH4 against TCCON measurements, respectively. Overall, the performance of fused results distinctly exceeds that of CAMS-EGG4, which is also superior or close to those of GOSAT and OCO-2. Especially, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission inventories for COVID-19 lockdowns in 2020. Moreover, the fused results present coincident spatial patterns with GOSAT and OCO-2, which accurately display the long-term and seasonal changes of globally distributed XCO2 and XCH4. The daily global seamless gridded (0.25°) XCO2 and XCH4 from 2010 to 2020 can be freely accessed at http://doi.org/10.5281/zenodo.7388893 (Wang et al., 2022b).

A novel framework is developed to recover missing data in global TROPOMI TCCO product over land from Jun. 01 2018 to May. 31 2021 by fusing multisource data. Validation results show that the accuracy of recovered results is satisfactory and close to that of TROPOMI, with the R of 0.885 against NDACC and 0.918 against TCCON. Furthermore, the recovered results achieve a small (distinctly) better performance than those of MOPITT (CAMS). The spatial pattern of the recovered TCCO is consistent with that of the MOPITT TCCO and can specify much finer spatial details by comparison with CAMS.

Fine particulate matter (PM 2.5 ) is widely concerned for its harmful impacts on global environment and human health, making air pollution monitoring so crucial and indispensable. As the world’s first open, real-time, and historical air quality platform, OpenAQ collects and provides government measurement and research-level data from various channels. However, despite OpenAQ’s innovation in providing us with ground-measured PM 2.5 worldwide, we find significant data gaps in time series for most of the sites. The incompleteness of the data directly affects the public perception of PM 2.5 concentration levels and hinders the progress of research related to air pollution. To address these issues, a two-step hybrid model named ST-SILM, i.e. spatio-temporal model with single exponential smoothing-inverse distance weighted (SES-IDW) and long short-term memory (LSTM), is proposed to repair the missing data from PM 2.5 sites worldwide collected from OpenAQ from 2017 to 2019. Both spatio-temporal correlation and neighborhood fields are considered and established in the model. To be specific, SES-IDW were firstly used to repair missing values, and secondly, the LSTM network was employed to reconstruct the time series of continuous missing data. After the global ground-measured PM 2.5 was reconstructed, the light gradient boosting machine model was applied to remote sensing estimation of the original ground-measured PM 2.5 and of the reconstructed ground-measured PM 2.5 to further verify the performance of ST-SILM. Experiment results show that the estimation accuracy of the reconstructed dataset is better ( R 2 from 2017 to 2019 increased by 0.02, 0.02, and 0.01 compared with the original dataset). Therefore, it is concluded that the proposed model can effectively reconstruct data from PM 2.5 sites worldwide.

•Modeling ground-level O3 at high spatial and temporal resolutions over China.•A novel Self-adaptive Geospatially Local scheme is proposed.•Achieving a state-of-the-art performance compared to recent related works.•Helping further understand the formation mechanisms of ground-level O3 in China.

e17504 Background: Malignant tumor proliferation is one of the important factors for poor prognosis. CDK4/6 inhibitors can delay tumor progression by inhibiting cell cycle and inducing apoptosis, so they are potentially effective broad-spectrum anti-tumor targeted drugs, but drug resistance limits their clinical applications. Our previous research found that the CDK4/6 inhibitor LEE011 has anti-tumor effects on cervical cancer C33A cells, but no anti-tumor activity against HeLa. The mechanism is not very clear, we speculate that it is related to HPV infection. In this research, our aim is to clarify the correlation between HPV and tumor drug sensitivity and explore the possible mechanisms. Methods: Two HPV positive human cervical cancer cell lines, SiHa and Caski were chosen to verify the sensitivity to LEE011 by cell morphology, cell cycle and apoptosis. The antitumor activity of CDK4/6 inhibitor LEE011 by E7 knockin C33A and E7 knock-down HeLa cell lines was assessed by in vitro clonogenic assay, flow cytometry, and target inhibition verified by immunoblotting. Effects of LEE011 and E7 knock-down combination in vivo were studied by xenograft tumor regrowth delay, and tissue section immunohistochemistry. Results: SiHa and Caski were insensitive to LEE011 antitumor activity. Enhancement of drugsensitivity was lost in cell lines with HPV positive. After down-regulation of E7, LEE011 treatment increased the inhibition cell proliferation and pro-apoptosis of HeLa. Mechanistically, the loss of E7-induced Rb inactivation and LEE011 inhibited Rb phosphorylation, leading to cell-cycle arrest. In vivo, LEE011 inhibited tumor regrowth, with sustained inhibition of cyclin D-CDK4/6-Rb-E2F1 activity in E7 knock-down HeLa. However, up-regulation of E7 can reduce the ability of LEE011 to inhibit cell proliferation and pro-apoptosis of C33A. In summary, our study signifies inhibiting the CDK4/6 pathway by LEE011 in combination with down-regulation of E7 as a promising therapeutic strategy to treat cervical cancer. Conclusions: These findings suggested that HPV can affect Rb function by expressing oncoprotein E7 leading to resistance to targeted drug therapy, which means that HPV may be a biomarkers of drug therapy efficacy.

Air temperature (Ta) is one of the most fundamental and important variables in ecological and environmental science. In this work, we fuse the brightness temperature data of Himawari-8 and auxiliary data such as terrain, vegetation, time, and meteorological elements to develop a model based on the Light Gradient Boosting Machine (LightGBM). The hourly high-resolution Ta is estimated and the spatial distributions are mapped in China. The evaluation results show that the model performs well in the study area. Based on the five-fold cross-validation of all samples, the R 2 is 0.986 and the RMSE is 1.598 K. The spatial distribution patterns can well reflect the Ta changes in different regions of China.

Introduction: Aberrations in cell cycle control is defined as one of the hallmarks of cancer, while cyclin D1 is an essential protein to cell cycle which promote G1 phase into S phase, and frequently overexpressed in many human cancers. However, new functions have been identified in transgenic mice models, including the transcription of genome, the development of chromosome instability and DNA repair. In this research, our aim is to find the function of cyclin D1 in transcription in human cancers. Methods: The correlation of the cyclin D1 expression levels and prognosis of cervical cancer patients were analyzed in tissue microarray (TMA) cohort. We chose C33A as our main research object. Using chromatin immunoprecipitation sequencing (ChIP-seq) coupled with RNA sequencing (RNA-seq), to find out the genes differentially expressed in C33A, cyclin D1 knock-in C33A and cyclin D1 knock-down C33A. Results: We found that upregulation of cyclin D1 was associated with shorter overall survival (OS) and disease-free survival (DFS). Functionally, we identified 422 genes differentially expressed through analysis of the results of ChIP-seq and RNA-seq. These genes are highly enriched in Gene Ontology categories and involve in diverse cellular functions via KEGG classification, including replication and repair, signal transduction, cell growth and death. Conclusion: These findings suggested that the expression of cyclin D1 was associated with the prognosis of patients with cervical cancer. Cyclin D1 can serve both to activate and downregulate gene expression as a transcriptional role directly binding with genome DNA, which means that cyclin D1 may be a key protein during oncogenesis and tumor development.

Background Mitochondrial ribosomal protein L15 (MRPL15), a member of mitochondrial ribosomal proteins whose abnormal expression is related to tumorigenesis. However, the prognostic value and regulatory mechanisms of MRPL15 in non-small-cell lung cancer (NSCLC) remain unclear. Methods GEPIA, ONCOMINE, Gene Expression Omnibus (GEO), UALCAN, Kaplan–Meier plotter, PrognoScan, LinkedOmics and GeneMANIA database were utilized to explore the expression and prognostic value of MRPL15 in NSCLC. Additionally, immune infiltration patterns were evaluated via ESTIMATE algorithm and TISIDB database. Furthermore, the expression and prognostic value of MRPL15 in lung cancer were validated via immunohistochemistry (IHC) assays. Results In NSCLC, multiple cohorts including GEPIA, ONCOMINE and 8 GEO series (GSE8569, GSE101929, GSE33532, GSE27262, GSE21933, GSE19804, GSE19188, GSE18842) described that MRPL15 was up-regulated. Moreover, MRPL15 was notably linked to gender, clinical stage, lymph node status and the TP53 mutation status. And patients with high MRPL15 expression showed poor overall survival (OS), progression-free survival (PFS), disease-free survival (DFS) and relapse-free survival (RFS) in NSCLC. Then, functional network analysis suggested that MRPL15 participated in metabolism-related pathways, DNA replication and cell cycle signaling via pathways involving several kinases, miRNAs and transcription factors. Additionally, it was found that MRPL15 expression was negatively related to immune infiltration, including immune scores, stromal scores and several tumor-infiltrating lymphocytes (TILs). Furthermore, IHC results further confirmed the high MRPL15 expression and its prognostic potential in lung cancer. Conclusions These findings demonstrate that high MRPL15 expression indicates poor prognosis in NSCLC and reveal potential regulatory networks as well as the negative relationship with immune infiltration. Thus, MRPL15 may be an attractive predictor and therapeutic strategy for NSCLC.

The numerous oxygenated functional groups on graphite oxide (GO) make it a promising adsorbent for toxic heavy metals in water. However, the GO prepared from natural graphite is water-soluble after exfoliation, making its recovery for reuse extremely difficult. In this study, porous graphitized carbon (PGC) was oxidized to fabricate a GO-like material, PGCO. The PGCO showed an O/C molar ratio of 0.63, and 8.4% of the surface carbon species were carboxyl, exhibiting enhanced oxidation degree compared to GO. The small PGCO sheets were intensely aggregated chemically, yielding an insoluble solid easily separable from water by sedimentation or filtration. Batch adsorption experiments demonstrated that the PGCO afforded significantly higher removal efficiencies for heavy metals than GO, owing to the former's greater functionalization with oxygenated groups. An isotherm study suggested that the adsorption obeyed the Langmuir model, and the derived maximum adsorption capacities for Cr3+, Pb2+, Cu2+, Cd2+, Zn2+, and Ni2+ were 119.6, 377.1, 99.1, 65.2, 53.0, and 58.1 mg/g, respectively. Furthermore, the spent PGCO was successively regenerated by acid treatment. The results of the study indicate that PGCO could be an alternative adsorbent for remediating toxic metal-contaminated waters.

Triple-negative breast cancer (TNBC) is the most aggressive subtype with the worst prognosis and the highest metastatic and recurrence potential, which represents 15–20% of all breast cancers in Chinese females, and the 5-year overall survival rate is about 80% in Chinese women. Recently, emerging evidence suggested that aberrant alternative splicing (AS) plays a crucial role in tumorigenesis and progression. AS is generally controlled by AS-associated RNA binding proteins (RBPs). Monocyte chemotactic protein induced protein 1 (MCPIP1), a zinc finger RBP, functions as a tumor suppressor in many cancers. Here, we showed that MCPIP1 was downregulated in 80 TNBC tissues and five TNBC cell lines compared to adjacent paracancerous tissues and one human immortalized breast epithelial cell line, while its high expression levels were associated with increased overall survival in TNBC patients. We demonstrated that MCPIP1 overexpression dramatically suppressed cell cycle progression and proliferation of TNBC cells in vitro and repressed tumor growth in vivo. Mechanistically, MCPIP1 was first demonstrated to act as a splicing factor to regulate AS in TNBC cells. Furthermore, we demonstrated that MCPIP1 modulated NFIC AS to promote CTF5 synthesis, which acted as a negative regulator in TNBC cells. Subsequently, we showed that CTF5 participated in MCPIP1-mediated antiproliferative effect by transcriptionally repressing cyclin D1 expression, as well as downregulating its downstream signaling targets p-Rb and E2F1. Conclusively, our findings provided novel insights into the anti-oncogenic mechanism of MCPIP1, suggesting that MCPIP1 could serve as an alternative treatment target in TNBC.

Background Clinical management of triple-negative breast cancer (TNBC) patients remain challenging because of the development of chemo-resistance. Identification of biomarkers for risk stratification of chemo-resistance and therapeutic decision-making to overcome such resistance is thus necessary. Methods Retrospective analysis was performed to identify potential stratification biomarkers. The levels of ceramide kinase (CERK) was determined in breast cancer patients. The roles of CERK and its downstream signaling pathways were analysed using cellular and biochemical assays. Results CERK upregulation was identified as a biomarker for chemotherapeutic response in TNBC. A > 2-fold change in CERK (from tumor)/CERK (from normal counterpart) was significantly associated with chemo-resistance (OR = 2.66, 95% CI 1.18–7.34), P = 0.04. CERK overexpression was sufficient to promote TNBC growth and migration, and confer chemo-resistance in TNBC cell lines, although this resistance could be overcome via CERK inhibition. Mechanistic studies suggest that CERK mediates intrinsic resistance and inferior response to chemotherapy in TNBC by regulating multiple oncogenic pathways such as Ras/ERK, PI3K/Akt/mTOR, and RhoA. Conclusions Our work provides an explanation for the heterogeneity of chemo-response across TNBC patients and demonstrates that CERK inhibition offers a therapeutic strategy to overcome treatment resistance.